From b21fb2a4dca927a764d43d00191cc9f5f2be5410 Mon Sep 17 00:00:00 2001 From: Jack Ye Date: Wed, 2 Sep 2026 21:09:30 -0700 Subject: [PATCH] docs: bring the open-source documentation into this repository MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The pages describing this code lived in lancedb/docs, a repository with ten stars that neither contributing guide mentioned. A user who hit a wrong page while reading the source had to find a separate, near-invisible repository to fix it. These 102 pages now sit beside the code, and a documentation fix is a pull request in the repository the reader already had open. `docs/web` is a complete Mintlify site: `cd docs/web && mint dev` serves it with no other checkout. Enterprise pages stay private and are merged in when the published site is assembled, so links into them do not resolve here — every open-source page does. `docs/web-tests` holds the tests the code examples are extracted from. They ran against a published wheel before; here they can run against this repository's own SDK, so an example that stops compiling fails the pull request that broke it rather than being noticed later. Both directories sit beside `docs/src` and are invisible to it: mkdocs reads `docs_dir: src`, so the SDK reference builds and deploys exactly as before. --- docs/web-tests/camelot.json | 66 + docs/web-tests/mdx_snippets_gen.py | 362 + docs/web-tests/py/conftest.py | 56 + docs/web-tests/py/test_basic_usage.py | 205 + .../web-tests/py/test_build_with_ai_agents.py | 109 + docs/web-tests/py/test_connection.py | 148 + docs/web-tests/py/test_embedding.py | 141 + docs/web-tests/py/test_geneva_defaults.py | 53 + .../py/test_geneva_dependency_verification.py | 107 + .../py/test_geneva_profiling_memory.py | 248 + docs/web-tests/py/test_geneva_scalar_udtfs.py | 311 + docs/web-tests/py/test_geneva_udfs_index.py | 52 + docs/web-tests/py/test_indexing.py | 738 ++ docs/web-tests/py/test_integrations.py | 1767 ++++ docs/web-tests/py/test_multimodal.py | 232 + docs/web-tests/py/test_quickstart.py | 208 + docs/web-tests/py/test_search.py | 730 ++ docs/web-tests/py/test_storage.py | 162 + docs/web-tests/py/test_tables.py | 1546 ++++ docs/web-tests/rs/Cargo.lock | 7395 +++++++++++++++++ docs/web-tests/rs/Cargo.toml | 43 + docs/web-tests/rs/basic_usage.rs | 346 + docs/web-tests/rs/bedrock.rs | 92 + docs/web-tests/rs/connection.rs | 134 + docs/web-tests/rs/embedding.rs | 182 + docs/web-tests/rs/full_text_search.rs | 103 + docs/web-tests/rs/ivf_pq.rs | 154 + docs/web-tests/rs/multimodal.rs | 178 + docs/web-tests/rs/openai.rs | 89 + docs/web-tests/rs/quickstart.rs | 347 + docs/web-tests/rs/search.rs | 311 + docs/web-tests/rs/sentence_transformers.rs | 95 + docs/web-tests/rs/simple.rs | 147 + docs/web-tests/rs/src/main.rs | 3 + docs/web-tests/rs/tables.rs | 1516 ++++ docs/web-tests/ts/ann_indexes.test.ts | 57 + docs/web-tests/ts/basic_usage.test.ts | 161 + docs/web-tests/ts/biome.json | 52 + docs/web-tests/ts/connection.test.ts | 121 + .../ts/custom_embedding_function.test.ts | 79 + docs/web-tests/ts/embedding.test.ts | 160 + docs/web-tests/ts/filtering.test.ts | 42 + 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17 + docs/web/index.mdx | 124 + docs/web/indexing/fts-index.mdx | 165 + docs/web/indexing/index.mdx | 161 + docs/web/indexing/quantization.mdx | 79 + docs/web/indexing/reindexing.mdx | 59 + docs/web/indexing/scalar-index.mdx | 243 + docs/web/indexing/vector-index.mdx | 379 + docs/web/integrations/ai/agno.mdx | 151 + docs/web/integrations/ai/genkit.mdx | 73 + docs/web/integrations/ai/hermes-agent.mdx | 327 + docs/web/integrations/ai/huggingface.mdx | 341 + docs/web/integrations/ai/kiln.mdx | 48 + docs/web/integrations/ai/langchain.mdx | 226 + docs/web/integrations/ai/llamaIndex.mdx | 55 + docs/web/integrations/ai/prompttools.mdx | 15 + .../integrations/ai/synthetic-data-kit.mdx | 81 + docs/web/integrations/data/dlt.mdx | 97 + docs/web/integrations/data/duckdb.mdx | 119 + .../integrations/data/pandas_and_pyarrow.mdx | 46 + docs/web/integrations/data/polars_arrow.mdx | 51 + docs/web/integrations/data/pydantic.mdx | 90 + docs/web/integrations/data/voxel51.mdx | 212 + 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legendary ruler of Camelot, wielder of Excalibur, and leader of the Knights of the Round Table.", + "vector": [0.72, -0.28, 0.60, 0.86], + "stats": { "strength": 2, "courage": 5, "magic": 1, "wisdom": 4 } + }, + { + "id": 2, + "name": "Merlin", + "role": "Wizard and Advisor", + "description": "A powerful wizard and prophet who mentors Arthur and shapes the destiny of Camelot through magic and foresight.", + "vector": [0.05, 0.88, 0.62, 0.85], + "stats": { "strength": 2, "courage": 4, "magic": 5, "wisdom": 5 } + }, + { + "id": 3, + "name": "Queen Guinevere", + "role": "Queen of Camelot", + "description": "Arthur's queen, admired for her grace and diplomacy, whose romances and loyalties influence Camelot's fate.", + "vector": [0.22, -0.22, 0.42, 0.82], + "stats": { "strength": 1, "courage": 3, "magic": 1, "wisdom": 4 } + }, + { + "id": 4, + "name": "Sir Lancelot", + "role": "Knight of the Round Table", + "description": "Arthur's most skilled knight, famed for unmatched combat prowess and his tragic love for Queen Guinevere.", + "vector": [0.86, -0.35, 0.38, 0.55], + "stats": { "strength": 5, "courage": 5, "magic": 1, "wisdom": 3 } + }, + { + "id": 5, + "name": "Sir Gawain", + "role": "Knight of the Round Table", + "description": "A noble and honorable knight known for his courtesy and his encounter with the Green Knight.", + "vector": [0.82, -0.32, 0.52, 0.60], + "stats": { "strength": 4, "courage": 5, "magic": 1, "wisdom": 4 } + }, + { + "id": 6, + "name": "Sir Galahad", + "role": "Knight of the Round Table", + "description": "The purest and most virtuous knight, chosen to achieve the Holy Grail due to his unwavering spiritual purity.", + "vector": [0.80, -0.20, 0.70, 0.78], + "stats": { "strength": 4, "courage": 5, "magic": 2, "wisdom": 5 } + }, + { + "id": 7, + "name": "Sir Percival", + "role": "Knight of the Round Table", + "description": "A loyal and innocent knight whose bravery and sincerity make him one of the key seekers of the Holy Grail.", + "vector": [0.78, -0.36, 0.48, 0.52], + "stats": { "strength": 4, "courage": 4, "magic": 1, "wisdom": 3 } + }, + { + "id": 8, + "name": "Mordred", + "role": "Traitor Knight", + "description": "Arthur's treacherous son or nephew who ultimately rebels against him, leading to Camelot's downfall.", + "vector": [0.68, -0.30, -0.65, 0.20], + "stats": { "strength": 4, "courage": 2, "magic": 1, "wisdom": 2 } + } +] diff --git a/docs/web-tests/mdx_snippets_gen.py b/docs/web-tests/mdx_snippets_gen.py new file mode 100644 index 000000000..1c2930fc0 --- /dev/null +++ b/docs/web-tests/mdx_snippets_gen.py @@ -0,0 +1,362 @@ +""" +Generate MDX-ready snippet modules that export raw string constants. + +The generator groups snippets by the originating test file name rather than by +language. For a source file like `tests/py/test_basic_usage.py`, the snippets are +written under `docs/snippets/basic.mdx`. Each exported constant is prefixed with +a language identifier (Py|Ts|Rs) followed by the snippet name converted to +TitleCase so that docs authors can selectively assemble `` blocks. +""" + +from __future__ import annotations + +import argparse +import json +import os +import re +from dataclasses import dataclass +from pathlib import Path +from typing import Dict, Iterable, Iterator, List, Mapping, MutableMapping, Tuple + +# Supported languages and relevant metadata. +LANG_ORDER = ("py", "ts", "rs") +LANG_PREFIX = {"py": "Py", "ts": "Ts", "rs": "Rs"} +LANG_EXTENSIONS = { + ".py": "py", + ".ts": "ts", + ".tsx": "ts", + ".rs": "rs", +} +LANG_MARKERS = {"py": ["#"], "ts": ["//"], "rs": ["//"]} +# Match each export block; we rely on the blank line we emit after each export to +# avoid accidentally truncating on semicolons inside the JSON string literal. +EXPORT_RE = re.compile(r"export const (\w+)\s*=\s*(.+?);\s*\n(?:\s*\n|$)", re.DOTALL) + +DEFAULT_SOURCE_DIRS = ( + Path("tests") / "py", + Path("tests") / "ts", + Path("tests") / "rs", +) + + +@dataclass(frozen=True) +class SnippetRecord: + lang: str + snippet_name: str + export_name: str + text: str + source_rel: str + + +def build_markers( + comment_markers: Iterable[str], +) -> Tuple[re.Pattern[str], re.Pattern[str]]: + escaped = "|".join(re.escape(c) for c in comment_markers) + start_re = re.compile(rf"^\s*(?:{escaped})\s*--8<--\s*\[start:([^\]]+)\]") + end_re = re.compile(rf"^\s*(?:{escaped})\s*--8<--\s*\[end:([^\]]+)\]") + return start_re, end_re + + +def parse_snippets( + lines: List[str], start_re: re.Pattern[str], end_re: re.Pattern[str] +) -> Dict[str, List[str]]: + snippets: Dict[str, List[str]] = {} + stack: List[Tuple[str, int]] = [] + + for idx, line in enumerate(lines): + m_start = start_re.match(line) + if m_start: + name = m_start.group(1).strip() + stack.append((name, idx)) + continue + + m_end = end_re.match(line) + if m_end: + name = m_end.group(1).strip() + if not stack: + raise ValueError( + f"End marker for '{name}' found at line {idx + 1} without matching start" + ) + start_name, start_idx = stack.pop() + if start_name != name: + raise ValueError( + f"Mismatched markers: start '{start_name}' at line {start_idx + 1}, " + f"end '{name}' at line {idx + 1}" + ) + snippets[name] = lines[start_idx + 1 : idx] + continue + + if stack: + open_names = ", ".join(f"{n}@{i + 1}" for n, i in stack) + raise ValueError(f"Unclosed snippet markers for: {open_names}") + + return snippets + + +def dedent(lines: List[str]) -> List[str]: + min_indent = None + for ln in lines: + if ln.strip() == "": + continue + expanded = ln.expandtabs(4) + indent = len(expanded) - len(expanded.lstrip(" ")) + if min_indent is None or indent < min_indent: + min_indent = indent + + if not lines: + return [] + if min_indent in (None, 0): + return [ln.rstrip("\n") for ln in lines] + + dedented: List[str] = [] + for ln in lines: + expanded = ln.expandtabs(4) + if expanded.strip() == "": + dedented.append(expanded.rstrip("\n")) + else: + dedented.append(expanded[min_indent:].rstrip("\n")) + return dedented + + +def ensure_dir(path: Path) -> None: + path.mkdir(parents=True, exist_ok=True) + + +def detect_language(file_path: Path) -> str: + lang = LANG_EXTENSIONS.get(file_path.suffix.lower()) + if not lang: + raise ValueError(f"Unsupported file extension for {file_path}") + return lang + + +def markers_for_lang(lang: str) -> Tuple[re.Pattern[str], re.Pattern[str]]: + markers = LANG_MARKERS.get(lang) + if not markers: + raise ValueError(f"Unsupported language for markers: {lang}") + return build_markers(markers) + + +def normalize_target_name(file_path: Path) -> str: + stem = file_path.stem + if stem.startswith("test_"): + stem = stem[len("test_") :] + if stem.endswith("_test"): + stem = stem[: -len("_test")] + if stem.endswith(".test"): + stem = stem[: -len(".test")] + if stem.endswith(".spec"): + stem = stem[: -len(".spec")] + if stem == "": + stem = file_path.stem + return stem.replace(" ", "_") + + +def to_title_case(name: str) -> str: + parts = re.split(r"[^0-9a-zA-Z]+", name) + filtered = [p for p in parts if p] + return "".join(p[:1].upper() + p[1:].lower() for p in filtered) or "Snippet" + + +def format_export_name(lang: str, snippet_name: str) -> str: + prefix = LANG_PREFIX[lang] + return f"{prefix}{to_title_case(snippet_name)}" + + +def snippet_text(lines: List[str]) -> str: + if not lines: + return "" + joined = "\n".join(lines).rstrip("\n") + return (joined + "\n") if joined else "" + + +def to_js_literal(text: str) -> str: + return json.dumps(text, ensure_ascii=False) + + +def split_export_prefix(export_name: str) -> Tuple[str | None, str]: + for lang, prefix in LANG_PREFIX.items(): + if export_name.startswith(prefix): + suffix = export_name[len(prefix) :] + return lang, suffix or export_name + return None, export_name + + +def parse_existing_module(path: Path) -> Mapping[str, Mapping[str, SnippetRecord]]: + try: + text = path.read_text(encoding="utf-8") + except FileNotFoundError: + return {} + + existing: Dict[str, Dict[str, SnippetRecord]] = {} + for match in EXPORT_RE.finditer(text): + export_name, literal = match.group(1), match.group(2).strip() + lang, snippet_suffix = split_export_prefix(export_name) + if not lang: + continue + try: + decoded = json.loads(literal) + except Exception: + decoded = literal.strip().strip(";") + + record = SnippetRecord( + lang=lang, + snippet_name=snippet_suffix or export_name, + export_name=export_name, + text=decoded, + source_rel="(existing module)", + ) + existing.setdefault(lang, {})[record.snippet_name] = record + + return existing + + +def iter_source_files(source_dirs: Iterable[Path]) -> Iterator[Path]: + for root in source_dirs: + for path in root.rglob("*"): + if path.is_file() and path.suffix.lower() in LANG_EXTENSIONS: + yield path + + +def write_if_changed(path: Path, content: str) -> bool: + if path.exists(): + try: + current = path.read_text(encoding="utf-8") + if current == content: + return False + except Exception: + pass + ensure_dir(path.parent) + path.write_text(content, encoding="utf-8") + return True + + +def collect_snippets( + source_dirs: Iterable[Path], +) -> Mapping[str, Mapping[str, Mapping[str, SnippetRecord]]]: + result: Dict[str, Dict[str, Dict[str, SnippetRecord]]] = {} + for file_path in iter_source_files(source_dirs): + lang = detect_language(file_path) + start_re, end_re = markers_for_lang(lang) + try: + text = file_path.read_text(encoding="utf-8") + except Exception as exc: # pragma: no cover + raise RuntimeError(f"Failed to read {file_path}: {exc}") from exc + lines = text.splitlines(keepends=True) + snippets = parse_snippets(lines, start_re, end_re) + if not snippets: + continue + + target = normalize_target_name(file_path) + lang_map = result.setdefault(target, {}).setdefault(lang, {}) + for snippet_name, content_lines in snippets.items(): + if snippet_name in lang_map: + raise ValueError( + f"Duplicate snippet '{snippet_name}' in {file_path} and {lang_map[snippet_name].source_rel}" + ) + dedented = dedent(content_lines) + export_name = format_export_name(lang, snippet_name) + source_rel = os.path.relpath(file_path, Path.cwd()) + lang_map[snippet_name] = SnippetRecord( + lang=lang, + snippet_name=snippet_name, + export_name=export_name, + text=snippet_text(dedented), + source_rel=source_rel, + ) + return result + + +def render_module( + target: str, lang_map: Mapping[str, Mapping[str, SnippetRecord]] +) -> str: + parts: List[str] = [] + parts.append( + "{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */}\n\n" + ) + for lang in LANG_ORDER: + snippets = lang_map.get(lang) + if not snippets: + continue + for snippet_name in sorted(snippets.keys()): + record = snippets[snippet_name] + literal = to_js_literal(record.text) + parts.append(f"export const {record.export_name} = {literal};\n\n") + return "".join(parts) + + +def generate_modules( + snippets_by_target: Mapping[str, Mapping[str, Mapping[str, SnippetRecord]]], + output_root: Path, +) -> None: + modules_written = 0 + total_exports = 0 + + for target in sorted(snippets_by_target.keys()): + module_path = output_root / f"{target}.mdx" + lang_map = snippets_by_target[target] + existing_langs = parse_existing_module(module_path) + for lang, snippets in existing_langs.items(): + if lang in lang_map: + continue + print(module_path, " -- ", lang) + merged_lang = lang_map.setdefault(lang, {}) + existing_export_names = {rec.export_name for rec in merged_lang.values()} + for snippet_name, record in snippets.items(): + # If an export with the same name already exists (e.g., regenerated), + # prefer the newly generated snippet and skip the old one to avoid duplicates. + if record.export_name in existing_export_names: + continue + merged_lang.setdefault(snippet_name, record) + + total_exports += sum(len(snippets) for snippets in lang_map.values()) + module_content = render_module(target, lang_map) + if write_if_changed(module_path, module_content): + modules_written += 1 + print( + f"\nGenerated {total_exports} snippets\n---" + ) + + +def resolve_source_dirs(args_dirs: List[str] | None) -> List[Path]: + if args_dirs: + dirs = [Path(p) for p in args_dirs] + else: + dirs = list(DEFAULT_SOURCE_DIRS) + + missing = [str(p) for p in dirs if not p.exists()] + if missing: + raise FileNotFoundError(f"Source directories not found: {', '.join(missing)}") + return dirs + + +def main() -> int: + parser = argparse.ArgumentParser( + description="Generate MDX snippet modules grouped by test filename." + ) + parser.add_argument( + "-s", + "--source-dir", + action="append", + help="Path(s) to language test directories (default: tests/py, ts, rs)", + ) + parser.add_argument( + "-o", + "--output-dir", + default=str(Path("docs") / "snippets"), + help="Directory where snippet modules are written (default: docs/snippets)", + ) + args = parser.parse_args() + + source_dirs = resolve_source_dirs(args.source_dir) + snippets = collect_snippets(source_dirs) + if not snippets: + print("No snippets found.") + return 0 + + output_root = Path(args.output_dir) + generate_modules(snippets, output_root) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/docs/web-tests/py/conftest.py b/docs/web-tests/py/conftest.py new file mode 100644 index 000000000..ebd2c811d --- /dev/null +++ b/docs/web-tests/py/conftest.py @@ -0,0 +1,56 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +import shutil +from pathlib import Path + +import lancedb +import pytest +import pytest_asyncio +from lancedb.db import AsyncConnection, DBConnection + + +class DatabasePathFactory: + """Create per-test database directories and ensure they get removed.""" + + def __init__(self, base_dir: Path) -> None: + self._base_dir = base_dir + self._created: list[Path] = [] + + def __call__(self, name: str) -> Path: + safe_name = name.replace("/", "_") + path = self._base_dir / safe_name + if path.exists(): + shutil.rmtree(path, ignore_errors=True) + path.mkdir(parents=True, exist_ok=True) + self._created.append(path) + return path + + def cleanup(self) -> None: + for path in reversed(self._created): + shutil.rmtree(path, ignore_errors=True) + self._created.clear() + + +@pytest.fixture +def db_path_factory(tmp_path: Path): + factory = DatabasePathFactory(tmp_path) + yield factory + factory.cleanup() + + +@pytest.fixture +def mem_db() -> DBConnection: + return lancedb.connect("memory://") + + +@pytest.fixture +def tmp_db(db_path_factory) -> DBConnection: + """Create a temporary database connection for testing.""" + db_path = db_path_factory("tmp_db") + return lancedb.connect(str(db_path)) + + +@pytest_asyncio.fixture +async def mem_db_async() -> AsyncConnection: + return await lancedb.connect_async("memory://") diff --git a/docs/web-tests/py/test_basic_usage.py b/docs/web-tests/py/test_basic_usage.py new file mode 100644 index 000000000..260ebb6f9 --- /dev/null +++ b/docs/web-tests/py/test_basic_usage.py @@ -0,0 +1,205 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +# --8<-- [start:basic_imports] +import json + +import lancedb +import pandas as pd +import polars as pl +import pyarrow as pa +# --8<-- [end:basic_imports] +import pytest + +data_path = "tests/camelot.json" + +def test_basic_usage(db_path_factory): + uri = "./basic_usage_db" + uri = db_path_factory("basic_usage_db") + db = lancedb.connect(uri) + + # --8<-- [start:data_load] + with open(data_path, "r") as f: + data = json.load(f) + # --8<-- [end:data_load] + + # --8<-- [start:basic_create_table] + table = db.create_table("camelot", data=data, mode="overwrite") + # --8<-- [end:basic_create_table] + assert len(table) == 8 + + # --8<-- [start:basic_open_table] + table = db.open_table("camelot") + # --8<-- [end:basic_open_table] + + # --8<-- [start:basic_create_table_pandas] + pandas_df = pd.DataFrame(data) + table_pd = db.create_table("camelot_pd", data=pandas_df, mode="overwrite") + # --8<-- [end:basic_create_table_pandas] + assert len(table_pd) == 8 + db.drop_table("camelot_pd") + + # --8<-- [start:basic_create_table_polars] + polars_df = pl.DataFrame(data) + table_pl = db.create_table("camelot_pl", data=polars_df, mode="overwrite") + # --8<-- [end:basic_create_table_polars] + assert len(table_pl) == 8 + db.drop_table("camelot_pl") + + # --8<-- [start:basic_create_empty_table] + schema = pa.schema( + [ + pa.field("id", pa.uint16()), + pa.field("name", pa.string()), + pa.field("role", pa.string()), + pa.field("description", pa.string()), + pa.field("vector", pa.list_(pa.float32(), 4)), + pa.field( + "stats", + pa.struct( + [ + pa.field("strength", pa.int8()), + pa.field("courage", pa.int8()), + pa.field("magic", pa.int8()), + pa.field("wisdom", pa.int8()), + ] + ), + ), + ] + ) + db.create_table("camelot_pa", schema=schema, mode="overwrite") + # --8<-- [end:basic_create_empty_table] + assert "camelot_pa" in db.list_tables().tables + db.drop_table("camelot_pa") + + # --8<-- [start:basic_add_data] + magical_characters = [ + { + "id": 9, + "name": "Morgan le Fay", + "role": "Sorceress", + "description": "A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.", + "vector": [0.10, 0.84, 0.25, 0.70], + "stats": { "strength": 2, "courage": 3, "magic": 5, "wisdom": 4 } + }, + { + "id": 10, + "name": "The Lady of the Lake", + "role": "Mystical Guardian", + "description": "A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.", + "vector": [0.00, 0.90, 0.58, 0.88], + "stats": { "strength": 2, "courage": 3, "magic": 5, "wisdom": 5 } + } + ] + table.add(magical_characters) + # --8<-- [end:basic_add_data] + assert len(table) == 10 + + # --8<-- [start:basic_vector_search] + query_vector = [0.03, 0.85, 0.61, 0.90] + result = table.search(query_vector).limit(5).to_polars() + print(result) + # --8<-- [end:basic_vector_search] + + # --8<-- [start:basic_add_columns] + table.add_columns( + { + "power": "cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)" + } + ) + # --8<-- [end:basic_add_columns] + assert "power" in table.schema.names + + # Run examples to illustrate search + # --8<-- [start:basic_vector_search_q1] + # Who are the characters similar to "wizard"? + query_vector_1 = [0.03, 0.85, 0.61, 0.90] + r1 = ( + table.search(query_vector_1) + .limit(5) + .select(["name", "role", "description"]) + .to_polars() + ) + print(r1) + # --8<-- [end:basic_vector_search_q1] + + # --8<-- [start:basic_vector_search_q2] + # Who are the characters with high magic stats? + query_vector_2 = [0.03, 0.85, 0.61, 0.90] + r2 = ( + table.search(query_vector_2) + .where("stats.magic > 3") + .select(["name", "role", "description"]) + .limit(5) + .to_polars() + ) + print(r2) + # --8<-- [end:basic_vector_search_q2] + + # --8<-- [start:basic_vector_search_q3] + # Who are the strongest characters? + r3 = ( + table.search() + .where("stats.strength > 3") + .select(["name", "role", "description"]) + .limit(5) + .to_polars() + ) + print(r3) + # --8<-- [end:basic_vector_search_q3] + + # --8<-- [start:basic_vector_search_q4] + # Who are the strongest characters? + r4 = ( + table.search() + .select(["name", "role", "description", "power"]) + .to_polars() + ) + print(r4) + # --8<-- [end:basic_vector_search_q4] + + # --8<-- [start:basic_sort_polars] + # Sort Polars DataFrame by power in descending order + print(r4.sort("power", descending=True).limit(5)) + # --8<-- [end:basic_sort_polars] + sorted_power = r4.sort("power", descending=True)["power"].to_list() + assert sorted_power == sorted(sorted_power, reverse=True) + assert sorted_power[0] == 4.0 + + # --8<-- [start:basic_drop_columns] + table.drop_columns(["power"]) + # --8<-- [end:basic_drop_columns] + # --8<-- [start:basic_delete_rows] + table.delete('role = "Traitor Knight"') + # --8<-- [end:basic_delete_rows] + assert len(table) == 9 + # --8<-- [start:basic_drop_table] + db.drop_table("camelot") + # --8<-- [end:basic_drop_table] + assert "camelot" not in db.list_tables().tables + + +@pytest.mark.asyncio +async def test_basic_usage_async_api(db_path_factory): + uri = db_path_factory("basic_usage_async_db") + with open(data_path, "r") as f: + data = json.load(f) + + # --8<-- [start:basic_async_api] + import lancedb + + async_db = await lancedb.connect_async(uri) + async_table = await async_db.create_table( + "camelot_async", + data=data, + mode="overwrite", + ) + + query_vector = [0.03, 0.85, 0.61, 0.90] + async_results = await ( + await async_table.search(query_vector) + ).limit(5).select(["name", "role", "description"]).to_polars() + print(async_results) + # --8<-- [end:basic_async_api] + + assert async_results.height >= 1 diff --git a/docs/web-tests/py/test_build_with_ai_agents.py b/docs/web-tests/py/test_build_with_ai_agents.py new file mode 100644 index 000000000..5b9091eaa --- /dev/null +++ b/docs/web-tests/py/test_build_with_ai_agents.py @@ -0,0 +1,109 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +# --8<-- [start:camelot_schema] +from lancedb.pydantic import LanceModel +from pydantic import ConfigDict + + +class Stats(LanceModel): + model_config = ConfigDict(strict=True, extra="forbid") + + strength: int + courage: int + magic: int + wisdom: int + + +class Character(LanceModel): + model_config = ConfigDict(strict=True, extra="forbid") + + id: int + name: str + role: str + description: str + stats: Stats + image_filename: str + image: bytes +# --8<-- [end:camelot_schema] + + +# --8<-- [start:camelot_batches] +import json +from collections.abc import Iterator +from pathlib import Path + + +def validated_batches( + input_path: Path, batch_size: int +) -> Iterator[list[dict]]: + raw_records = json.loads(input_path.read_text()) + asset_root = input_path.parent.parent + batch: list[dict] = [] + + for raw in raw_records: + payload = dict(raw) + image_path = asset_root / payload.pop("img") + payload["image_filename"] = image_path.name + payload["image"] = image_path.read_bytes() + + character = Character.model_validate(payload) + batch.append(character.model_dump(mode="python")) + + if len(batch) == batch_size: + yield batch + batch = [] + + if batch: + yield batch +# --8<-- [end:camelot_batches] + + +# --8<-- [start:camelot_oss_ingestion] +import lancedb + + +def ingest_oss( + input_path: Path, + uri: str = "data/camelot.lancedb", + table_name: str = "camelot_multimodal", + batch_size: int = 64, +): + db = lancedb.connect(uri) + if table_name in db.list_tables(): + raise ValueError( + f"Table {table_name!r} already exists. Choose a fresh table name." + ) + + table = db.create_table(table_name, schema=Character) + for batch in validated_batches(input_path, batch_size): + table.add(batch) + + table.optimize() + return table + + +if __name__ == "__main__": + ingest_oss(Path("data/camelot.json")) +# --8<-- [end:camelot_oss_ingestion] + + +def test_camelot_oss_ingestion(tmp_path): + source = Path( + "docs/static/assets/tutorials/build-with-ai-agents/camelot/data/camelot.json" + ) + table = ingest_oss( + source, + uri=str(tmp_path / "camelot.lancedb"), + table_name="camelot", + ) + + rows = ( + table.search() + .select(["id", "name", "image", "image_filename"]) + .limit(8) + .to_list() + ) + assert len(rows) == 8 + assert all(row["image"] for row in rows) + assert all(row["image_filename"].endswith(".jpg") for row in rows) diff --git a/docs/web-tests/py/test_connection.py b/docs/web-tests/py/test_connection.py new file mode 100644 index 000000000..783ac2d8d --- /dev/null +++ b/docs/web-tests/py/test_connection.py @@ -0,0 +1,148 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +from pathlib import Path +import shutil + + +def test_connection(): + # --8<-- [start:connect] + import lancedb + + uri = "ex_lancedb" + db = lancedb.connect(uri) + # --8<-- [end:connect] + assert db is not None + shutil.rmtree(uri, ignore_errors=True) + assert not Path(uri).exists() + + +async def connect_async_example(): + # --8<-- [start:connect_async] + import lancedb + + uri = "ex_lancedb" + async_db = await lancedb.connect_async(uri) + # --8<-- [end:connect_async] + + return async_db + + +def connect_enterprise_quickstart_config(): + import lancedb + + # --8<-- [start:connect_enterprise_quickstart] + uri = "db://your-database-uri" + api_key = "your-api-key" + region = "us-east-1" + host_override = "https://your-enterprise-endpoint.com" + + db = lancedb.connect( + uri=uri, + api_key=api_key, + region=region, + host_override=host_override, + ) + # --8<-- [end:connect_enterprise_quickstart] + return db + + +def test_connect_enterprise_quickstart(monkeypatch): + import lancedb + + captured = {} + + def fake_connect(**kwargs): + captured.update(kwargs) + return object() + + monkeypatch.setattr(lancedb, "connect", fake_connect) + + db = connect_enterprise_quickstart_config() + assert db is not None + assert captured == { + "uri": "db://your-database-uri", + "api_key": "your-api-key", + "region": "us-east-1", + "host_override": "https://your-enterprise-endpoint.com", + } + + +def connect_object_storage_config(): + # --8<-- [start:connect_object_storage] + import lancedb + + uri = "s3://your-bucket/path" + # You can also use "gs://your-bucket/path" or "az://your-container/path". + db = lancedb.connect(uri) + # --8<-- [end:connect_object_storage] + + return db + + +def namespace_table_ops_example(): + # --8<-- [start:namespace_table_ops] + import lancedb + + db = lancedb.connect_namespace("dir", {"root": "./local_lancedb"}) + + # Create namespace tree: prod/search + db.create_namespace(["prod"], mode="exist_ok") + db.create_namespace(["prod", "search"], mode="exist_ok") + db.create_namespace(["prod", "recommendations"], mode="exist_ok") + + db.create_table( + "user", + data=[{"id": 1, "vector": [0.1, 0.2], "name": "alice"}], + namespace_path=["prod", "search"], + mode="create", # use "overwrite" only if you want to replace existing table + ) + + db.create_table( + "user", + data=[{"id": 2, "vector": [0.3, 0.4], "name": "bob"}], + namespace_path=["prod", "recommendations"], + mode="create", # use "overwrite" only if you want to replace existing table + ) + + # Verify + print(db.list_namespaces()) # ['prod'] + print(db.list_namespaces(namespace_path=["prod"])) # ['recommendations', 'search'] + print(db.list_tables(namespace_path=["prod", "search"])) # ['user'] + print(db.list_tables(namespace_path=["prod", "recommendations"])) # ['user'] + # --8<-- [end:namespace_table_ops] + + +def namespace_admin_ops_example(): + # --8<-- [start:namespace_admin_ops] + import lancedb + + db = lancedb.connect_namespace("dir", {"root": "./local_lancedb"}) + namespace = ["prod", "search"] + + db.create_namespace(["prod"]) + db.create_namespace(["prod", "search"]) + + child_namespaces = db.list_namespaces(namespace_path=["prod"]).namespaces + print(f"Child namespaces under {namespace}: {child_namespaces}") + # Child namespaces under ['prod', 'search']: ['search'] + + metadata = db.describe_namespace(["prod", "search"]) + print(f"Metadata for namespace {namespace}: {metadata}") + # Metadata for namespace ['prod', 'search']: properties=None + + db.drop_namespace(["prod", "search"], mode="skip") + db.drop_namespace(["prod"], mode="skip") + # --8<-- [end:namespace_admin_ops] + return child_namespaces, metadata + +async def connect_object_storage_config_async(): + # --8<-- [start:connect_object_storage_async] + import lancedb + + uri = "s3://your-bucket/path" + # You can also use "gs://your-bucket/path" or "az://your-container/path". + async_db = await lancedb.connect_async(uri) + # --8<-- [end:connect_object_storage_async] + + return async_db diff --git a/docs/web-tests/py/test_embedding.py b/docs/web-tests/py/test_embedding.py new file mode 100644 index 000000000..490be268e --- /dev/null +++ b/docs/web-tests/py/test_embedding.py @@ -0,0 +1,141 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +import lancedb + +# --8<-- [start:imports] +from lancedb.pydantic import LanceModel, Vector +from lancedb.embeddings import get_registry + +# --8<-- [end:imports] +import pytest + + +@pytest.mark.skip(reason="OpenAI is not available in the test environment") +def test_create_embedding_function(): + # --8<-- [start:create_embedding_function] + func = get_registry().get("openai").create( + name="text-embedding-3-small", + max_retries=7, + ) + # --8<-- [end:create_embedding_function] + assert func is not None + + +@pytest.mark.skip(reason="OpenAI is not available in the test environment") +def test_embeddings_openai(): + # --8<-- [start:openai_embeddings] + db = lancedb.connect("/tmp/db") + func = get_registry().get("openai").create(name="text-embedding-ada-002") + + class Words(LanceModel): + text: str = func.SourceField() + vector: Vector(func.ndims()) = func.VectorField() + + table = db.create_table("words", schema=Words, mode="overwrite") + table.add([{"text": "hello world"}, {"text": "goodbye world"}]) + + query = "greetings" + actual = table.search(query).limit(1).to_pydantic(Words)[0] + print(actual.text) + # --8<-- [end:openai_embeddings] + + +@pytest.mark.skip(reason="OpenAI is not available in the test environment") +@pytest.mark.asyncio +async def test_embeddings_openai_async(): + uri = "memory://" + # --8<-- [start:async_openai_embeddings] + db = await lancedb.connect_async(uri) + func = get_registry().get("openai").create(name="text-embedding-ada-002") + + class Words(LanceModel): + text: str = func.SourceField() + vector: Vector(func.ndims()) = func.VectorField() + + table = await db.create_table("words", schema=Words, mode="overwrite") + await table.add([{"text": "hello world"}, {"text": "goodbye world"}]) + + query = "greetings" + actual = await (await table.search(query)).limit(1).to_pydantic(Words)[0] + print(actual.text) + # --8<-- [end:async_openai_embeddings] + + +@pytest.mark.skip(reason="OpenAI is not available in the test environment") +def test_embeddings_manual_query(): + # --8<-- [start:manual_query_embeddings] + db = lancedb.connect("/tmp/db") + func = get_registry().get("openai").create(name="text-embedding-ada-002") + + class Words(LanceModel): + text: str = func.SourceField() + vector: Vector(func.ndims()) = func.VectorField() + + table = db.create_table("words", schema=Words, mode="overwrite") + table.add([{"text": "hello world"}, {"text": "goodbye world"}]) + + query_vector = func.generate_embeddings(["greetings"])[0] + # --8<-- [start:manual_query_search] + # query_vector is assumed to already be generated by your embedding function + actual = table.search(query_vector).limit(1).to_pydantic(Words)[0] + print(actual.text) + # --8<-- [end:manual_query_search] + # --8<-- [end:manual_query_embeddings] + + +def test_custom_embedding_function(): + # --8<-- [start:embedding_function] + from functools import cached_property + + from lancedb.embeddings import TextEmbeddingFunction, register + + class MyEmbeddingModel: + def __init__(self, model_name: str): + self.model_name = model_name + + def encode(self, texts: list[str]) -> list[list[float]]: + return [[1.0, 2.0, 3.0] for _ in texts] + + @register("my-embedder") + class MyTextEmbedder(TextEmbeddingFunction): + model_name: str = "my-model" + + def generate_embeddings(self, texts: list[str]) -> list[list[float]]: + # Your embedding logic here + return self._model.encode(texts) + + def ndims(self) -> int: + # Return the dimensionality of the embeddings + return len(self.generate_embeddings(["test"])[0]) + + @cached_property + def _model(self) -> MyEmbeddingModel: + # Initialize your model once + return MyEmbeddingModel(self.model_name) + # --8<-- [end:embedding_function] + + +def test_embeddings_secret(): + # --8<-- [start:register_secret] + registry = get_registry() + registry.set_var("api_key", "sk-...") + + func = registry.get("openai").create(api_key="$var:api_key") + # --8<-- [end:register_secret] + + try: + import torch + except ImportError: + pytest.skip("torch not installed") + + # --8<-- [start:register_device] + import torch + + registry = get_registry() + if torch.cuda.is_available(): + registry.set_var("device", "cuda") + + func = registry.get("huggingface").create(device="$var:device:cpu") + # --8<-- [end:register_device] + assert func.device == "cuda" if torch.cuda.is_available() else "cpu" diff --git a/docs/web-tests/py/test_geneva_defaults.py b/docs/web-tests/py/test_geneva_defaults.py new file mode 100644 index 000000000..af4a42b00 --- /dev/null +++ b/docs/web-tests/py/test_geneva_defaults.py @@ -0,0 +1,53 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +# These tests assert the default resource values for Geneva KubeRay cluster nodes. +# If any of these values change, update the tables in: +# docs/geneva/jobs/performance.mdx (the "Geneva defaults" section) + +import pytest + + +def test_head_node_defaults(): + # If these change, update the Head Node table in performance.mdx + from geneva.cluster.builder import KubeRayClusterBuilder + + builder = KubeRayClusterBuilder() + assert builder._head_cpus == 4 + assert builder._head_memory == "8Gi" + assert builder._head_gpus == 0 + assert builder._head_node_selector == {"geneva.lancedb.com/ray-head": "true"} + assert builder._head_service_account == "geneva-service-account" + + +def test_cpu_worker_defaults(): + # If these change, update the CPU Workers table in performance.mdx + from geneva.cluster.builder import CpuWorkerBuilder + + worker = CpuWorkerBuilder() + assert worker._num_cpus == 4 + assert worker._memory == "8Gi" + assert worker._node_selector == {"geneva.lancedb.com/ray-worker-cpu": "true"} + assert worker._replicas == 1 + assert worker._min_replicas == 0 + assert worker._max_replicas == 100 + assert worker._idle_timeout_seconds == 60 + + # Confirm build produces 0 GPUs + config = worker.build() + assert config.num_gpus == 0 + + +def test_gpu_worker_defaults(): + # If these change, update the GPU Workers table in performance.mdx + from geneva.cluster.builder import GpuWorkerBuilder + + worker = GpuWorkerBuilder() + assert worker._num_cpus == 8 + assert worker._memory == "16Gi" + assert worker._num_gpus == 1 + assert worker._node_selector == {"geneva.lancedb.com/ray-worker-gpu": "true"} + assert worker._replicas == 1 + assert worker._min_replicas == 0 + assert worker._max_replicas == 100 + assert worker._idle_timeout_seconds == 60 diff --git a/docs/web-tests/py/test_geneva_dependency_verification.py b/docs/web-tests/py/test_geneva_dependency_verification.py new file mode 100644 index 000000000..fc00aba6c --- /dev/null +++ b/docs/web-tests/py/test_geneva_dependency_verification.py @@ -0,0 +1,107 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +import os + +import pytest + + +def test_quick_fix_manifest(): + # --8<-- [start:quick_fix_manifest] + from geneva.manifest.builder import PipManifestBuilder + + manifest = PipManifestBuilder.create("fix").pip(["numpy==1.26.4"]).build() + # --8<-- [end:quick_fix_manifest] + assert manifest.pip == ["numpy==1.26.4"] + + +def test_env_vars_via_cluster(monkeypatch): + monkeypatch.setenv("AWS_ACCESS_KEY_ID", "test-key-id") + monkeypatch.setenv("AWS_SECRET_ACCESS_KEY", "test-secret") + + # --8<-- [start:env_vars_via_cluster] + from geneva.cluster.builder import KubeRayClusterBuilder + import os + + cluster = ( + KubeRayClusterBuilder.create("my-cluster") + .ray_init_kwargs({ + "runtime_env": { + "env_vars": { + "AWS_ACCESS_KEY_ID": os.environ["AWS_ACCESS_KEY_ID"], + "AWS_SECRET_ACCESS_KEY": os.environ["AWS_SECRET_ACCESS_KEY"], + } + } + }) + .build() + ) + # --8<-- [end:env_vars_via_cluster] + assert cluster.kuberay.ray_init_kwargs["runtime_env"]["env_vars"]["AWS_ACCESS_KEY_ID"] == "test-key-id" + + +def test_pip_manifest(monkeypatch): + import geneva + from unittest.mock import MagicMock, create_autospec + mock_conn = create_autospec(geneva.db.Connection, instance=True) + monkeypatch.setattr("geneva.connect", MagicMock(return_value=mock_conn)) + + # --8<-- [start:pip_manifest] + import geneva + from geneva.manifest.builder import PipManifestBuilder + + manifest = ( + PipManifestBuilder.create("my-manifest") + .pip([ + "numpy==1.26.4", + "torch==2.0.1", + "attrs==23.2.0", + ]) + .build() + ) + + conn = geneva.connect("s3://my-bucket/my-db") + conn.define_manifest("my-manifest", manifest) + with conn.context(cluster="my-cluster", manifest="my-manifest"): + conn.open_table("my-table").backfill("my-column") + # --8<-- [end:pip_manifest] + assert "numpy==1.26.4" in manifest.pip + + +def test_conda_cluster_path(): + # --8<-- [start:conda_cluster_path] + from geneva.cluster.builder import KubeRayClusterBuilder + + cluster = ( + KubeRayClusterBuilder.create("my-cluster") + .ray_init_kwargs({ + "runtime_env": {"conda": "environment.yml"} + }) + .build() + ) + # --8<-- [end:conda_cluster_path] + assert cluster.kuberay.ray_init_kwargs["runtime_env"]["conda"] == "environment.yml" + + +def test_conda_cluster_inline(): + # --8<-- [start:conda_cluster_inline] + from geneva.cluster.builder import KubeRayClusterBuilder + + cluster = ( + KubeRayClusterBuilder.create("my-cluster") + .ray_init_kwargs({ + "runtime_env": { + "conda": { + "channels": ["conda-forge"], + "dependencies": [ + "python=3.10", + "ffmpeg<8", + "torchvision=0.22.1", + ], + }, + "config": {"eager_install": True}, + } + }) + .build() + ) + # --8<-- [end:conda_cluster_inline] + assert cluster.kuberay.ray_init_kwargs["runtime_env"]["conda"]["channels"] == ["conda-forge"] diff --git a/docs/web-tests/py/test_geneva_profiling_memory.py b/docs/web-tests/py/test_geneva_profiling_memory.py new file mode 100644 index 000000000..7ca45db04 --- /dev/null +++ b/docs/web-tests/py/test_geneva_profiling_memory.py @@ -0,0 +1,248 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +"""Snippets for docs/geneva/udfs/profiling-memory.mdx.""" + +from unittest.mock import MagicMock + + +def test_stateful_udf_class(): + import geneva + import pyarrow as pa + + load_model = MagicMock(return_value=MagicMock(embed=MagicMock(return_value=[0.1] * 512))) + + # --8<-- [start:stateful_udf_class] + @geneva.udf(data_type=pa.list_(pa.float32(), 512)) + class MyEmbedding: + def __init__(self): + self.model = None + + def setup(self): + self.model = load_model() # allocated once per actor + + def __call__(self, text: str) -> list[float]: + if self.model is None: + self.setup() + return self.model.embed(text) + # --8<-- [end:stateful_udf_class] + + assert MyEmbedding is not None + + +def test_memray_tracker_udf(monkeypatch): + import sys + + load_model = MagicMock(return_value=MagicMock(embed=MagicMock(return_value=[0.1] * 512))) + monkeypatch.setitem(sys.modules, "memray", MagicMock()) + + # --8<-- [start:memray_tracker_udf] + import os, pathlib, uuid + from typing import Any + import memray + import geneva + import pyarrow as pa + + _MEMRAY_OUT_DIR_ENV = "MY_UDF_MEMRAY_OUT_DIR" + + + @geneva.udf(data_type=pa.list_(pa.float32(), 512)) + class MyEmbedding: + def __init__(self): + self.model = None + self._tracker: Any = None # memray.Tracker, when profiling is on + + def setup(self): + # Open a memray tracker per worker process, if requested. Each + # worker writes its own .bin file so traces don't collide. + out_dir = os.environ.get(_MEMRAY_OUT_DIR_ENV) + if out_dir: + pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True) + bin_path = pathlib.Path(out_dir) / ( + f"memray-{os.getpid()}-{uuid.uuid4().hex}.bin" + ) + self._tracker = memray.Tracker( + str(bin_path), native_traces=False, follow_fork=False + ) + self._tracker.__enter__() + self.model = load_model() + + def __call__(self, text: str) -> list[float]: + if self.model is None: + self.setup() + return self.model.embed(text) + # --8<-- [end:memray_tracker_udf] + + assert MyEmbedding is not None + + +def test_ray_cluster_profile(monkeypatch): + from contextlib import contextmanager + + table = MagicMock() + + @contextmanager + def _mock_cluster(*args, **kwargs): + yield + + monkeypatch.setattr("geneva.runners.ray._mgr.ray_cluster", _mock_cluster) + + # --8<-- [start:ray_cluster_profile] + from geneva.runners.ray._mgr import ray_cluster + + with ray_cluster( + local=True, + extra_env={"MY_UDF_MEMRAY_OUT_DIR": "/tmp/my-udf-profile"}, + ): + table.backfill("embedding", concurrency=1) + # --8<-- [end:ray_cluster_profile] + + table.backfill.assert_called_once_with("embedding", concurrency=1) + + +def test_log_memory(capsys): + # --8<-- [start:log_memory] + import resource, pyarrow as pa + + def log_memory(seq: int) -> None: + rss_bytes = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss + # ru_maxrss is bytes on macOS, KiB on Linux: + import sys + if sys.platform != "darwin": + rss_bytes *= 1024 + arrow_live = pa.total_allocated_bytes() + print( + f"seq={seq} " + f"rss_mb={rss_bytes // 1024**2} " + f"arrow_live_mb={arrow_live // 1024**2} " + f"gap_mb={(rss_bytes - arrow_live) // 1024**2}", + flush=True, + ) + # --8<-- [end:log_memory] + + log_memory(0) + captured = capsys.readouterr() + assert "seq=0" in captured.out + assert "rss_mb=" in captured.out + + +def test_leaky_cache(): + # --8<-- [start:leaky_cache] + class BadEmbedding: + def __init__(self): + self.cache: dict[str, list[float]] = {} + + def __call__(self, text: str) -> list[float]: + if text not in self.cache: + self.cache[text] = self.model.embed(text) + return self.cache[text] + # --8<-- [end:leaky_cache] + + obj = BadEmbedding() + obj.model = MagicMock(embed=MagicMock(return_value=[0.1, 0.2])) + obj("hello") + obj("hello") + assert obj.model.embed.call_count == 1 # second call served from cache + assert len(obj.cache) == 1 + + +def test_bounded_cache(): + load_model = MagicMock(return_value=MagicMock(embed=MagicMock(return_value=[0.1, 0.2]))) + + # --8<-- [start:bounded_cache] + from functools import lru_cache + + class GoodEmbedding: + def __init__(self): + self._embed = None + + def setup(self): + model = load_model() + self._embed = lru_cache(maxsize=1024)(model.embed) + + def __call__(self, text: str) -> list[float]: + if self._embed is None: + self.setup() + return self._embed(text) + # --8<-- [end:bounded_cache] + + obj = GoodEmbedding() + assert obj("hello") == [0.1, 0.2] + load_model.assert_called_once() + + +def test_leaky_aggregator(): + import pyarrow as pa + + # --8<-- [start:leaky_aggregator] + class BadAggregator: + def __init__(self): + self.history = [] + + def __call__(self, batch: pa.RecordBatch) -> pa.Array: + self.history.append(batch) # holds every batch ever processed + ... + # --8<-- [end:leaky_aggregator] + + obj = BadAggregator() + assert isinstance(obj.history, list) + + +def test_leaky_closure(): + import pyarrow as pa + + def expensive(x): + return x + + # --8<-- [start:leaky_closure] + class BadDeferred: + def __init__(self): + self.work_queue = [] + + def __call__(self, x: pa.Array) -> pa.Array: + # Lambda captures `x` by reference — the whole Array stays alive + self.work_queue.append(lambda: expensive(x)) + ... + # --8<-- [end:leaky_closure] + + obj = BadDeferred() + assert isinstance(obj.work_queue, list) + + +def test_torch_inference_mode(monkeypatch): + import sys + + mock_torch = MagicMock() + monkeypatch.setitem(sys.modules, "torch", mock_torch) + + class TorchUDF: + def __init__(self): + self.model = MagicMock(encode=MagicMock(return_value=[0.1])) + + # --8<-- [start:torch_inference_mode] + def __call__(self, text: str) -> list[float]: + with torch.inference_mode(): # <-- prevents autograd graph retention + return self.model.encode(text) + # --8<-- [end:torch_inference_mode] + + import torch # resolves to monkeypatched mock above + obj = TorchUDF() + assert obj("hello") == [0.1] + + +def test_confidence_check(): + class DebuggingUDF: + def __init__(self): + self._scratches = [] + + # --8<-- [start:confidence_check] + def __call__(self, x): + scratch = bytearray(8 * 1024 * 1024) # 8 MiB + self._scratches.append(scratch) # <-- deliberate leak + return ... + # --8<-- [end:confidence_check] + + obj = DebuggingUDF() + obj(None) + assert len(obj._scratches) == 1 + assert len(obj._scratches[0]) == 8 * 1024 * 1024 diff --git a/docs/web-tests/py/test_geneva_scalar_udtfs.py b/docs/web-tests/py/test_geneva_scalar_udtfs.py new file mode 100644 index 000000000..30aec525a --- /dev/null +++ b/docs/web-tests/py/test_geneva_scalar_udtfs.py @@ -0,0 +1,311 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +import pytest +from unittest.mock import MagicMock + + +def test_scalar_udtf_iterator(): + def extract_video_segment(path, start, end): + return bytes(f"clip:{start}-{end}", "utf-8") + + # --8<-- [start:scalar_udtf_iterator] + from geneva import chunker + from typing import Iterator, NamedTuple + + class Clip(NamedTuple): + clip_start: float + clip_end: float + clip_bytes: bytes + + @chunker + def extract_clips(video_path: str, duration: float) -> Iterator[Clip]: + """Yields multiple clips per video.""" + clip_length = 10.0 + for start in range(0, int(duration), int(clip_length)): + end = min(start + clip_length, duration) + clip_data = extract_video_segment(video_path, start, end) + yield Clip(clip_start=start, clip_end=end, clip_bytes=clip_data) + # --8<-- [end:scalar_udtf_iterator] + + from geneva.transformer import Chunker + assert isinstance(extract_clips, Chunker) + assert extract_clips.input_columns == ["video_path", "duration"] + assert set(extract_clips.output_schema.names) == {"clip_start", "clip_end", "clip_bytes"} + + clips = list(extract_clips.func("/v/a.mp4", 30.0)) + assert len(clips) == 3 + assert clips[0].clip_start == 0.0 + assert clips[2].clip_end == 30.0 + + +def test_scalar_udtf_list_return(): + from geneva import chunker + from typing import NamedTuple + + class Clip(NamedTuple): + clip_start: float + clip_end: float + clip_bytes: bytes + + # --8<-- [start:scalar_udtf_list] + @chunker + def extract_clips(video_path: str, duration: float) -> list[Clip]: + clips = [] + for start in range(0, int(duration), 10): + end = min(start + 10, duration) + clips.append(Clip(clip_start=start, clip_end=end, clip_bytes=b"...")) + return clips + # --8<-- [end:scalar_udtf_list] + + from geneva.transformer import Chunker + assert isinstance(extract_clips, Chunker) + clips = extract_clips.func("/v/a.mp4", 30.0) + assert len(clips) == 3 + assert all(c.clip_bytes == b"..." for c in clips) + + +def test_scalar_udtf_batch(): + import pyarrow as pa + from geneva import chunker + + clip_schema = pa.schema([ + ("clip_start", pa.float64()), + ("clip_end", pa.float64()), + ]) + + # --8<-- [start:scalar_udtf_batch] + @chunker(batch=True, output_schema=clip_schema) + def extract_clips(batch: pa.RecordBatch) -> pa.RecordBatch: + """Process rows in batches. Same 1:N semantic per row.""" + ... + # --8<-- [end:scalar_udtf_batch] + + from geneva.transformer import Chunker + assert isinstance(extract_clips, Chunker) + assert extract_clips.batch is True + + +def test_create_scalar_udtf_view(monkeypatch): + from typing import Iterator, NamedTuple + from geneva.transformer import chunker + + class Clip(NamedTuple): + clip_start: float + clip_end: float + clip_bytes: bytes + + @chunker + def extract_clips(video_path: str, duration: float) -> Iterator[Clip]: + for start in range(0, int(duration), 10): + yield Clip(clip_start=start, clip_end=min(start + 10.0, duration), clip_bytes=b"") + + import geneva + from unittest.mock import create_autospec + mock_clips = MagicMock() + mock_db = create_autospec(geneva.db.Connection, instance=True) + mock_db.create_udtf_view.return_value = mock_clips + monkeypatch.setattr("geneva.connect", MagicMock(return_value=mock_db)) + + # --8<-- [start:create_scalar_udtf_view] + import geneva + + db = geneva.connect("/data/mydb") + videos = db.open_table("videos") + + # Create the 1:N materialized view + clips = db.create_udtf_view( + "clips", + source=videos.search(None).select(["video_path", "metadata"]), + udtf=extract_clips, + ) + + # Populate — runs the UDTF on every source row + clips.refresh() + # --8<-- [end:create_scalar_udtf_view] + + call_kwargs = mock_db.create_udtf_view.call_args + assert call_kwargs.args[0] == "clips" + assert call_kwargs.kwargs["udtf"] is extract_clips + mock_clips.refresh.assert_called_once() + + +def test_add_columns_scalar_udtf(): + import pyarrow as pa + from geneva.transformer import udf + + clips = MagicMock() + embed_model = MagicMock() + embed_model.encode.return_value = [0.1] * 512 + + # --8<-- [start:add_columns_scalar_udtf] + @udf(data_type=pa.list_(pa.float32(), 512)) + def clip_embedding(clip_bytes: bytes) -> list[float]: + return embed_model.encode(clip_bytes) + + # Add an embedding column to the clips table + clips.add_columns({"embedding": clip_embedding}) + + # Backfill computes embeddings for all existing clips + clips.backfill("embedding") + # --8<-- [end:add_columns_scalar_udtf] + + clips.add_columns.assert_called_once_with({"embedding": clip_embedding}) + clips.backfill.assert_called_once_with("embedding") + + +def test_incremental_refresh(): + videos = MagicMock() + clips = MagicMock() + new_video_data = [{"video_path": "/v/c.mp4", "duration": 45.0}] + + # --8<-- [start:incremental_refresh] + # Add new videos to the source table + videos.add(new_video_data) + + # Incremental refresh — only processes the new videos + clips.refresh() + # --8<-- [end:incremental_refresh] + + videos.add.assert_called_once_with(new_video_data) + clips.refresh.assert_called_once() + + +def test_chaining_udtf_views(monkeypatch): + from typing import Iterator, NamedTuple + from geneva.transformer import chunker + + class Clip(NamedTuple): + clip_start: float + clip_end: float + + class Frame(NamedTuple): + frame_index: int + frame_bytes: bytes + + @chunker + def extract_clips(video_path: str, duration: float) -> Iterator[Clip]: + for start in range(0, int(duration), 10): + yield Clip(clip_start=start, clip_end=min(start + 10.0, duration)) + + @chunker + def extract_frames(clip_start: float, clip_end: float) -> Iterator[Frame]: + yield Frame(frame_index=0, frame_bytes=b"") + + import geneva + from unittest.mock import create_autospec + mock_db = create_autospec(geneva.db.Connection, instance=True) + monkeypatch.setattr("geneva.connect", MagicMock(return_value=mock_db)) + + db = geneva.connect("/data/mydb") + videos = db.open_table("videos") + + # --8<-- [start:chaining_udtf_views] + # videos → clips (1:N) + clips = db.create_udtf_view( + "clips", source=videos.search(None), udtf=extract_clips + ) + + # clips → frames (1:N) + frames = db.create_udtf_view( + "frames", source=clips.search(None), udtf=extract_frames + ) + # --8<-- [end:chaining_udtf_views] + + assert mock_db.create_udtf_view.call_count == 2 + first_call = mock_db.create_udtf_view.call_args_list[0] + second_call = mock_db.create_udtf_view.call_args_list[1] + assert first_call.kwargs["udtf"] is extract_clips + assert second_call.kwargs["udtf"] is extract_frames + + +def test_document_chunking_udtf(): + # --8<-- [start:document_chunking_udtf] + from geneva import chunker + from typing import Iterator, NamedTuple + + class Chunk(NamedTuple): + chunk_index: int + chunk_text: str + + @chunker + def chunk_document(text: str) -> Iterator[Chunk]: + """Split a document into overlapping chunks.""" + words = text.split() + chunk_size = 500 + overlap = 50 + for i, start in enumerate(range(0, len(words), chunk_size - overlap)): + chunk_words = words[start:start + chunk_size] + yield Chunk(chunk_index=i, chunk_text=" ".join(chunk_words)) + # --8<-- [end:document_chunking_udtf] + + sample_text = " ".join(["word"] * 600) + chunks = list(chunk_document.func(sample_text)) + assert len(chunks) == 2 + assert chunks[0].chunk_index == 0 + assert chunks[1].chunk_index == 1 + assert len(chunks[0].chunk_text.split()) == 500 + + +def test_document_chunking_full(monkeypatch): + import pyarrow as pa + from unittest.mock import MagicMock + + import geneva + from unittest.mock import create_autospec + mock_chunks_table = MagicMock() + mock_db = create_autospec(geneva.db.Connection, instance=True) + mock_db.create_udtf_view.return_value = mock_chunks_table + monkeypatch.setattr("geneva.connect", MagicMock(return_value=mock_db)) + + embedding_model = MagicMock() + embedding_model.encode.return_value = [0.1] * 1536 + + # --8<-- [start:document_chunking_full] + from geneva import connect, chunker, udf + from typing import Iterator, NamedTuple + import pyarrow as pa + + class Chunk(NamedTuple): + chunk_index: int + chunk_text: str + + @chunker + def chunk_document(text: str) -> Iterator[Chunk]: + """Split a document into overlapping chunks.""" + words = text.split() + chunk_size = 500 + overlap = 50 + for i, start in enumerate(range(0, len(words), chunk_size - overlap)): + chunk_words = words[start:start + chunk_size] + yield Chunk(chunk_index=i, chunk_text=" ".join(chunk_words)) + + db = connect("/data/mydb") + docs = db.open_table("documents") + + # Create chunked view — inherits doc_id, title, etc. from source + chunks = db.create_udtf_view( + "doc_chunks", + source=docs.search(None).select(["doc_id", "title", "text"]), + udtf=chunk_document, + ) + chunks.refresh() + + # Add embeddings to chunks for semantic search + @udf(data_type=pa.list_(pa.float32(), 1536)) + def embed_text(chunk_text: str) -> list[float]: + return embedding_model.encode(chunk_text) + + chunks.add_columns({"embedding": embed_text}) + chunks.backfill("embedding") # Backfills embeddings on all existing chunks + + # Query — parent columns available alongside chunk columns + chunks.search(None).select(["doc_id", "title", "chunk_text", "embedding"]).to_pandas() + # --8<-- [end:document_chunking_full] + + call_kwargs = mock_db.create_udtf_view.call_args + assert call_kwargs.args[0] == "doc_chunks" + assert call_kwargs.kwargs["udtf"] is chunk_document + mock_chunks_table.refresh.assert_called_once() + mock_chunks_table.add_columns.assert_called_once() + mock_chunks_table.backfill.assert_called_once_with("embedding") diff --git a/docs/web-tests/py/test_geneva_udfs_index.py b/docs/web-tests/py/test_geneva_udfs_index.py new file mode 100644 index 000000000..dffe00d5d --- /dev/null +++ b/docs/web-tests/py/test_geneva_udfs_index.py @@ -0,0 +1,52 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +from unittest.mock import MagicMock, create_autospec + + +def test_registration_udf(monkeypatch): + # Verifies that Table.add_columns exists with the expected signature. + # If this fails, update the Registration row in docs/geneva/udfs/index.mdx. + import geneva + mock_table = create_autospec(geneva.table.Table, instance=True) + my_udf = MagicMock() + + # --8<-- [start:registration_udf] + mock_table.add_columns({"col": my_udf}) + # --8<-- [end:registration_udf] + + mock_table.add_columns.assert_called_once() + + +def test_registration_scalar_udtf(monkeypatch): + # Verifies that Connection.create_udtf_view accepts a chunker. + # If this fails, update the Registration row in docs/geneva/udfs/index.mdx. + import geneva + mock_db = create_autospec(geneva.db.Connection, instance=True) + monkeypatch.setattr("geneva.connect", MagicMock(return_value=mock_db)) + my_source = MagicMock() + my_chunker = MagicMock() + + # --8<-- [start:registration_scalar_udtf] + db = geneva.connect("/data/mydb") + db.create_udtf_view("my_view", source=my_source, udtf=my_chunker) + # --8<-- [end:registration_scalar_udtf] + + mock_db.create_udtf_view.assert_called_once() + + +def test_registration_udtf(monkeypatch): + # Verifies that Connection.create_udtf_view exists with the expected signature. + # If this fails, update the Registration row in docs/geneva/udfs/index.mdx. + import geneva + mock_db = create_autospec(geneva.db.Connection, instance=True) + monkeypatch.setattr("geneva.connect", MagicMock(return_value=mock_db)) + my_source = MagicMock() + my_udtf = MagicMock() + + # --8<-- [start:registration_udtf] + db = geneva.connect("/data/mydb") + db.create_udtf_view("my_view", source=my_source, udtf=my_udtf) + # --8<-- [end:registration_udtf] + + mock_db.create_udtf_view.assert_called_once() diff --git a/docs/web-tests/py/test_indexing.py b/docs/web-tests/py/test_indexing.py new file mode 100644 index 000000000..7f63e8f04 --- /dev/null +++ b/docs/web-tests/py/test_indexing.py @@ -0,0 +1,738 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +import random +import string +import uuid + +import numpy as np +import pyarrow as pa +import pytest + + +def _make_vector_rows(count: int, dim: int, column: str = "vector"): + rows = [ + {column: np.random.random(dim).astype(np.float32).tolist(), "id": i} + for i in range(count) + ] + assert len(rows) == count + assert all(len(row[column]) == dim for row in rows) + return rows + + +def test_vector_index_configure_ivf(tmp_db): + table = tmp_db.create_table( + "vector_index_configure_ivf", + _make_vector_rows(512, 4), + mode="overwrite", + ) + + # --8<-- [start:vector_index_configure_ivf] + table.create_index(metric="l2", num_partitions=16, num_sub_vectors=4) + # --8<-- [end:vector_index_configure_ivf] + + assert table.list_indices() + + +def test_vector_index_setup(tmp_db): + tmp_db.create_table( + "vector-index-tbl", + _make_vector_rows(8, 4), + mode="overwrite", + ) + + db = tmp_db + # --8<-- [start:vector_index_setup] + table_name = "vector-index-tbl" + table = db.open_table(table_name) + # --8<-- [end:vector_index_setup] + + assert table.name == table_name + + +def test_vector_index_build_ivf(tmp_db): + table = tmp_db.create_table( + "vector-index-build-ivf", + _make_vector_rows(512, 4, column="keywords_embeddings"), + mode="overwrite", + ) + db = tmp_db + # --8<-- [start:vector_index_build_ivf] + table_name = "vector-index-build-ivf" + table = db.open_table(table_name) + table.create_index( + metric="cosine", + vector_column_name="keywords_embeddings", + ) + # --8<-- [end:vector_index_build_ivf] + + assert table.list_indices() + + +def test_vector_index_nested_field(tmp_db): + dim = 2 + schema = pa.schema( + [ + pa.field("id", pa.int32()), + pa.field( + "image", + pa.struct([pa.field("embedding", pa.list_(pa.float32(), dim))]), + ), + ] + ) + data = [ + { + "id": i, + "image": {"embedding": np.random.random(dim).astype(np.float32).tolist()}, + } + for i in range(512) + ] + table = tmp_db.create_table( + "vector_index_nested_field", data=data, schema=schema, mode="overwrite" + ) + + # --8<-- [start:vector_index_nested_field] + # The vector column `embedding` is nested inside the `image` struct. + # Pass its full dotted path as `vector_column_name`; the same path is used + # at query time and is what `list_indices()` reports under `columns`. + table.create_index( + vector_column_name="image.embedding", + num_partitions=1, + num_sub_vectors=1, + name="image_embedding_idx", + ) + + results = ( + table.search([0.0, 1.0], vector_column_name="image.embedding") + .limit(1) + .to_list() + ) + # --8<-- [end:vector_index_nested_field] + + assert table.index_stats("image_embedding_idx") + assert len(results) == 1 + + +@pytest.mark.asyncio +async def test_vector_index_async_config(tmp_path, monkeypatch): + monkeypatch.chdir(tmp_path) + + # --8<-- [start:vector_index_async_config] + import lancedb + import numpy as np + from lancedb.index import IvfPq + + async def main(): + data = [ + {"id": i, "vector": np.random.random(8).astype(np.float32).tolist()} + for i in range(512) + ] + + db = await lancedb.connect_async("ex_lancedb") + table = await db.create_table( + "vector_index_async", data=data, mode="overwrite" + ) + + await table.create_index( + "vector", + config=IvfPq( + distance_type="cosine", + num_partitions=16, + num_sub_vectors=4, + ), + ) + return await table.list_indices() + # --8<-- [end:vector_index_async_config] + + assert await main() + + +def test_vector_index_query_ivf(tmp_db): + dim = 1536 + data = [ + {"id": i, "keywords_embeddings": np.random.random(dim).tolist()} + for i in range(512) + ] + table = tmp_db.create_table("vector_index_query_ivf", data, mode="overwrite") + table.create_index( + metric="cosine", + vector_column_name="keywords_embeddings", + ) + + # --8<-- [start:vector_index_query_ivf] + tbl = table + tbl.search(np.random.random((1536))).limit(2).nprobes(20).refine_factor( + 10 + ).to_pandas() + # --8<-- [end:vector_index_query_ivf] + + df = ( + table.search(np.random.random((1536))) + .limit(2) + .nprobes(20) + .refine_factor(10) + .to_pandas() + ) + assert len(df) == 2 + + +def test_vector_index_nprobes(tmp_db): + dim = 128 + data = [ + {"id": i, "keywords_embeddings": np.random.random(dim).tolist()} + for i in range(512) + ] + table = tmp_db.create_table("vector_index_nprobes", data, mode="overwrite") + table.create_index( + metric="cosine", + vector_column_name="keywords_embeddings", + ) + + # --8<-- [start:vector_index_nprobes] + # Always scan 10 partitions; scan up to 50 only if the initial pass + # returns fewer than `limit` results (common with narrow filters). + ( + table.search(np.random.random(128)) + .minimum_nprobes(10) + .maximum_nprobes(50) + .where("id > 100") + .limit(5) + .to_pandas() + ) + # --8<-- [end:vector_index_nprobes] + + +def test_vector_index_distance_range(tmp_db): + dim = 128 + data = [ + {"id": i, "keywords_embeddings": np.random.random(dim).tolist()} + for i in range(256) + ] + table = tmp_db.create_table("vector_index_distance_range", data, mode="overwrite") + table.create_index( + metric="cosine", + vector_column_name="keywords_embeddings", + ) + + # --8<-- [start:vector_index_distance_range] + # Only return results whose distance falls within [0.0, 0.5). + # Useful for near-duplicate detection or thresholded similarity search. + ( + table.search(np.random.random(128)) + .distance_range(lower_bound=0.0, upper_bound=0.5) + .limit(10) + .to_pandas() + ) + # --8<-- [end:vector_index_distance_range] + + +def test_vector_index_bypass_recall(tmp_db): + dim = 128 + data = [ + {"id": i, "keywords_embeddings": np.random.random(dim).tolist()} + for i in range(256) + ] + table = tmp_db.create_table("vector_index_bypass_recall", data, mode="overwrite") + table.create_index( + metric="cosine", + vector_column_name="keywords_embeddings", + ) + + # --8<-- [start:vector_index_bypass_recall] + query = np.random.random(128) + k = 10 + + # Ground truth: flat (exhaustive) scan, ignoring the ANN index. + truth = set(table.search(query).bypass_vector_index().limit(k).to_pandas()["id"]) + + # ANN results with the current nprobes setting. + ann = set(table.search(query).nprobes(20).limit(k).to_pandas()["id"]) + + recall_at_k = len(truth & ann) / k + # --8<-- [end:vector_index_bypass_recall] + assert 0.0 <= recall_at_k <= 1.0 + + +def test_vector_index_custom_name(tmp_db): + table = tmp_db.create_table( + "vector_index_custom_name", + _make_vector_rows(512, 8, column="keywords_embeddings"), + mode="overwrite", + ) + + # --8<-- [start:vector_index_custom_name] + # Override the default `{column}_idx` convention by passing `name=...`. + table.create_index( + metric="cosine", + vector_column_name="keywords_embeddings", + name="my_custom_index", + ) + table.wait_for_index(["my_custom_index"]) + print(table.index_stats("my_custom_index")) + # --8<-- [end:vector_index_custom_name] + + assert table.index_stats("my_custom_index") + + +def test_vector_index_hnsw(tmp_db): + table = tmp_db.create_table( + "vector_index_hnsw", + _make_vector_rows(64, 16), + mode="overwrite", + ) + + # --8<-- [start:vector_index_build_hnsw] + table.create_index(index_type="IVF_HNSW_SQ") + # --8<-- [end:vector_index_build_hnsw] + + # --8<-- [start:vector_index_query_hnsw] + tbl = table + tbl.search(np.random.random((16))).limit(2).to_pandas() + # --8<-- [end:vector_index_query_hnsw] + + df = table.search(np.random.random((16))).limit(2).to_pandas() + assert len(df) == 2 + + +def test_vector_index_binary(tmp_db): + table_name = "hamming-index-tbl" + ndim = 256 + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("vector", pa.list_(pa.uint8(), ndim // 8)), + ] + ) + + # --8<-- [start:vector_index_binary_schema] + table = tmp_db.create_table(table_name, schema=schema, mode="overwrite") + # --8<-- [end:vector_index_binary_schema] + + data = [] + for i in range(64): + vector = np.random.randint(0, 2, size=ndim) + vector = np.packbits(vector) + data.append({"id": i, "vector": vector}) + + # --8<-- [start:vector_index_binary_add_data] + table.add(data) + # --8<-- [end:vector_index_binary_add_data] + + # --8<-- [start:vector_index_binary_build_index] + table.create_index( + metric="hamming", + vector_column_name="vector", + index_type="IVF_FLAT", + ) + # --8<-- [end:vector_index_binary_build_index] + + # --8<-- [start:vector_index_binary_search] + query = np.random.randint(0, 2, size=ndim) + query = np.packbits(query) + df = table.search(query).metric("hamming").limit(10).to_pandas() + df.vector = df.vector.apply(np.unpackbits) + # --8<-- [end:vector_index_binary_search] + + assert not df.empty + + +def test_vector_index_check_status(tmp_db): + table = tmp_db.create_table( + "vector_index_check_status", + _make_vector_rows(512, 8, column="keywords_embeddings"), + mode="overwrite", + ) + table.create_index( + metric="cosine", + vector_column_name="keywords_embeddings", + ) + + # --8<-- [start:vector_index_check_status] + index_name = "keywords_embeddings_idx" + table.wait_for_index([index_name]) + print(table.index_stats(index_name)) + # --8<-- [end:vector_index_check_status] + + assert table.index_stats(index_name) + + +def test_scalar_index_build(tmp_db): + table = tmp_db.create_table( + "scalar_index_build", + [ + {"book_id": 1, "publisher": "A", "vector": [0.1, 0.2]}, + {"book_id": 2, "publisher": "B", "vector": [0.2, 0.3]}, + ], + mode="overwrite", + ) + db = tmp_db + # --8<-- [start:scalar_index_build] + tbl = db.open_table("scalar_index_build") + tbl.create_scalar_index("book_id") + tbl.create_scalar_index("publisher", index_type="BITMAP") + # --8<-- [end:scalar_index_build] + + assert tbl.list_indices() + + +def test_scalar_index_wait(tmp_db): + table = tmp_db.create_table( + "scalar_index_wait", + [{"label": "fiction"}], + mode="overwrite", + ) + table.create_scalar_index("label") + + # --8<-- [start:scalar_index_wait] + index_name = "label_idx" + table.wait_for_index([index_name]) + # --8<-- [end:scalar_index_wait] + + assert table.list_indices() + + +def test_scalar_index_optimize(tmp_db): + table = tmp_db.create_table( + "scalar_index_optimize", + [{"vector": [7.0, 8.0], "book_id": 3}], + mode="overwrite", + ) + + # --8<-- [start:scalar_index_optimize] + table.add([{"vector": [7, 8], "book_id": 4}]) + table.optimize() + # --8<-- [end:scalar_index_optimize] + + result = table.search().where("book_id = 4").limit(10).to_pandas() + assert len(result) == 1 + + +def test_scalar_index_filter(tmp_db): + table = tmp_db.create_table( + "books", + [ + {"vector": [1.1, 1.2], "book_id": 1}, + {"vector": [2.1, 2.2], "book_id": 2}, + ], + mode="overwrite", + ) + db = tmp_db + # --8<-- [start:scalar_index_filter] + table = db.open_table("books") + result = table.search().where("book_id = 2").limit(10).to_pandas() + # --8<-- [end:scalar_index_filter] + + assert len(result) == 1 + + +def test_scalar_index_prefilter(tmp_db): + table = tmp_db.create_table( + "book_with_embeddings", + [ + {"vector": [1.2, 1.3], "book_id": 1}, + {"vector": [4.2, 4.3], "book_id": 2}, + ], + mode="overwrite", + ) + db = tmp_db + # --8<-- [start:scalar_index_prefilter] + table = db.open_table("book_with_embeddings") + table.search([1.2] * 2).where("book_id != 3").limit(10).to_pandas() + # --8<-- [end:scalar_index_prefilter] + + result = table.search([1.2] * 2).where("book_id != 3").limit(10).to_pandas() + assert len(result) == 2 + + +def test_scalar_index_uuid(tmp_db): + # --8<-- [start:scalar_index_uuid_type] + import pyarrow as pa + # --8<-- [end:scalar_index_uuid_type] + + # --8<-- [start:scalar_index_uuid_data] + def generate_random_names(): + base_names = ["Alice", "Bob", "Carla", "David", "Eve", "Frank", "Grace"] + letter = random.choice(string.ascii_uppercase) + return f"{random.choice(base_names)} {letter}." + + def generate_uuids(num_items): + return [uuid.uuid4().bytes for _ in range(num_items)] + + # Generate some UUIDs and random names + n = 7 + uuids = generate_uuids(n) + names = [generate_random_names() for _ in range(n)] + # --8<-- [end:scalar_index_uuid_data] + + db = tmp_db + # --8<-- [start:scalar_index_uuid_table] + table_name = "index-on-uuid" + + uuid_array = pa.array(uuids, pa.uuid()) + name_array = pa.array(names, pa.string()) + schema = pa.schema( + [ + pa.field("id", pa.uuid()), + pa.field("name", pa.string()), + ] + ) + data_table = pa.Table.from_arrays([uuid_array, name_array], schema=schema) + table = db.create_table(table_name, data=data_table, mode="overwrite") + # --8<-- [end:scalar_index_uuid_table] + + # --8<-- [start:scalar_index_uuid_wait] + index_name = "id_idx" + table.create_scalar_index("id") + table.wait_for_index([index_name]) + # --8<-- [end:scalar_index_uuid_wait] + + # --8<-- [start:scalar_index_uuid_upsert] + new_users = [ + {"id": uuid.uuid4().bytes, "name": "Hannah D."}, + {"id": uuid.uuid4().bytes, "name": "Ian B."}, + ] + # Insert or update using the UUID index + table.merge_insert( + "id" + ).when_matched_update_all().when_not_matched_insert_all().execute(new_users) + # --8<-- [end:scalar_index_uuid_upsert] + + assert table.list_indices() + result = table.search().limit(100).to_pandas() + assert len(result) == n + len(new_users) + + +@pytest.mark.asyncio +async def test_scalar_index_nested_fields(mem_db_async): + db = mem_db_async + + # --8<-- [start:scalar_index_nested_fields] + import pyarrow as pa + from lancedb.index import BTree + + metadata_type = pa.struct( + [ + pa.field("user_id", pa.int32()), + pa.field("user.id", pa.int32()), + ] + ) + data = pa.Table.from_arrays( + [ + pa.array([1, 2, 3], type=pa.int32()), + pa.array( + [ + {"user_id": 10, "user.id": 100}, + {"user_id": 20, "user.id": 200}, + {"user_id": 30, "user.id": 300}, + ], + type=metadata_type, + ), + ], + names=["user_id", "metadata"], + ) + table = await db.create_table("nested_scalar_index", data) + + # Index a nested struct field. + await table.create_index( + "metadata.user_id", config=BTree(), name="nested_user_id_idx" + ) + + # Escape literal dots inside a segment with backticks. + await table.create_index( + "metadata.`user.id`", config=BTree(), name="escaped_user_id_idx" + ) + + # `columns` is returned as the canonical path you passed in. + for index in await table.list_indices(): + print(index.name, index.columns) + # nested_user_id_idx ['metadata.user_id'] + # escaped_user_id_idx ['metadata.`user.id`'] + # --8<-- [end:scalar_index_nested_fields] + + index_columns = {index.name: index.columns for index in await table.list_indices()} + assert index_columns["nested_user_id_idx"] == ["metadata.user_id"] + assert index_columns["escaped_user_id_idx"] == ["metadata.`user.id`"] + + +def test_fts_index_create(tmp_db): + table = tmp_db.create_table( + "fts-index-create", + [{"text": "hello world", "vector": [0.1, 0.2]}], + mode="overwrite", + ) + + db = tmp_db + # --8<-- [start:fts_index_create] + table_name = "fts-index-create" + table = db.open_table(table_name) + table.create_fts_index("text") + # --8<-- [end:fts_index_create] + + assert table.list_indices() + + +def test_fts_index_wait(tmp_db): + table = tmp_db.create_table( + "fts-index-wait", + [{"text": "full text search"}], + mode="overwrite", + ) + + db = tmp_db + # --8<-- [start:fts_index_wait] + table_name = "fts-index-wait" + + table = db.open_table(table_name) + table.create_fts_index("text") + + index_name = "text_idx" + table.wait_for_index([index_name]) + # --8<-- [end:fts_index_wait] + + assert table.list_indices() + + +def test_fts_index_nested_field(tmp_db): + nested_schema = pa.struct([ + pa.field("text", pa.string()), + pa.field("count", pa.int32()), + ]) + schema = pa.schema([ + pa.field("id", pa.int64()), + pa.field("payload", nested_schema), + ]) + tmp_db.create_table( + "fts-index-nested", + pa.table( + { + "id": pa.array([1, 2], pa.int64()), + "payload": pa.array( + [ + {"text": "Frodo was a happy puppy", "count": 1}, + {"text": "puppy runs through the meadow", "count": 2}, + ], + type=nested_schema, + ), + }, + schema=schema, + ), + mode="overwrite", + ) + + db = tmp_db + # --8<-- [start:fts_index_nested] + from lancedb.query import MatchQuery, PhraseQuery + + table = db.open_table("fts-index-nested") + + # Index a text leaf inside a struct column using a dotted path. + table.create_fts_index("payload.text", with_position=True) + + # The same dotted path works in MatchQuery and PhraseQuery. + matches = ( + table.search(MatchQuery("puppy", "payload.text")).limit(5).to_list() + ) + phrases = ( + table.search(PhraseQuery("puppy runs", "payload.text")) + .limit(5) + .to_list() + ) + # --8<-- [end:fts_index_nested] + + assert len(matches) > 0 + assert all("puppy" in row["payload"]["text"] for row in matches) + assert len(phrases) > 0 + assert all("puppy runs" in row["payload"]["text"] for row in phrases) + + +@pytest.mark.asyncio +async def test_fts_index_async(tmp_path, monkeypatch): + monkeypatch.chdir(tmp_path) + + # --8<-- [start:fts_index_async] + import asyncio + + import lancedb + import polars as pl + from lancedb.index import FTS + + data = pl.DataFrame( + { + "id": [1, 2], + "text": [ + "His first language is spanish", + "Her first language is english", + ], + } + ) + + async def main(data: pl.DataFrame): + uri = "ex_lancedb" + db = await lancedb.connect_async(uri) + tbl = await db.create_table("my_text", data=data, mode="overwrite") + + await tbl.create_index("text", config=FTS(language="English")) + + response = await tbl.search("spanish", query_type="fts") + result = await response.limit(1).to_polars() + print(result) + return result + + if __name__ == "__main__": + asyncio.run(main(data)) + # --8<-- [end:fts_index_async] + + result = await main(data) + assert result.height == 1 + + +def test_gpu_index_snippets(tmp_db, monkeypatch): + table = tmp_db.create_table( + "gpu_index", + _make_vector_rows(32, 8), + mode="overwrite", + ) + + calls = [] + + def fake_create_index(*args, **kwargs): + calls.append(kwargs) + return None + + monkeypatch.setattr(table, "create_index", fake_create_index) + + # --8<-- [start:gpu_index_cuda] + table.create_index( + num_partitions=256, + num_sub_vectors=96, + accelerator="cuda", + ) + # --8<-- [end:gpu_index_cuda] + + # --8<-- [start:gpu_index_mps] + table.create_index( + num_partitions=256, + num_sub_vectors=96, + accelerator="mps", + ) + # --8<-- [end:gpu_index_mps] + + assert calls[0]["accelerator"] == "cuda" + assert calls[1]["accelerator"] == "mps" + + +def test_reindexing_incremental(tmp_db): + table = tmp_db.create_table( + "reindexing_incremental", + [{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}], + mode="overwrite", + ) + db = tmp_db + # --8<-- [start:reindexing_incremental] + table = db.open_table("reindexing_incremental") + table.add([{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}]) + table.optimize() + # --8<-- [end:reindexing_incremental] + + result = table.search().limit(10).to_pandas() + assert len(result) == 2 diff --git a/docs/web-tests/py/test_integrations.py b/docs/web-tests/py/test_integrations.py new file mode 100644 index 000000000..20c317072 --- /dev/null +++ b/docs/web-tests/py/test_integrations.py @@ -0,0 +1,1767 @@ +import os + +import pytest + + +def require_env(var_name: str) -> str: + """Skip the test unless the required environment variable is provided.""" + value = os.environ.get(var_name) + if not value: + pytest.skip(f"Set {var_name} to run this integration snippet") + return value + + +def require_flag(flag_name: str) -> None: + """Skip unless a generic feature flag is set to a truthy value.""" + value = os.environ.get(flag_name, "").lower() + if value not in {"1", "true", "yes", "on"}: + pytest.skip(f"Enable {flag_name} to run this integration snippet") + + +def test_embedding_openai_basic() -> None: + require_env("OPENAI_API_KEY") + + # --8<-- [start:embedding_openai_basic] + import tempfile + from pathlib import Path + + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + db_path = Path(tempfile.mkdtemp()) / "openai-embeddings" + db = lancedb.connect(str(db_path)) + func = get_registry().get("openai").create(name="text-embedding-ada-002") + + class Words(LanceModel): + text: str = func.SourceField() + vector: Vector(func.ndims()) = func.VectorField() + + table = db.create_table("words", schema=Words, mode="overwrite") + table.add( + [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + ) + + query = "greetings" + actual = table.search(query).limit(1).to_pydantic(Words)[0] + print(actual.text) + # --8<-- [end:embedding_openai_basic] + + +def test_embedding_aws_usage() -> None: + require_flag("RUN_AWS_BEDROCK_SNIPPETS") + + # --8<-- [start:embedding_aws_usage] + import tempfile + from pathlib import Path + + import lancedb + import pandas as pd + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + model = get_registry().get("bedrock-text").create() + + class TextModel(LanceModel): + text: str = model.SourceField() + vector: Vector(model.ndims()) = model.VectorField() + + df = pd.DataFrame({"text": ["hello world", "goodbye world"]}) + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "bedrock-demo")) + tbl = db.create_table("test", schema=TextModel, mode="overwrite") + + tbl.add(df) + rs = tbl.search("hello").limit(1).to_pandas() + print(rs.head()) + # --8<-- [end:embedding_aws_usage] + + +def test_embedding_cohere_usage() -> None: + require_env("COHERE_API_KEY") + + # --8<-- [start:embedding_cohere_usage] + import tempfile + from pathlib import Path + + import lancedb + from lancedb.embeddings import EmbeddingFunctionRegistry + from lancedb.pydantic import LanceModel, Vector + + cohere = ( + EmbeddingFunctionRegistry.get_instance() + .get("cohere") + .create(name="embed-multilingual-v2.0") + ) + + class TextModel(LanceModel): + text: str = cohere.SourceField() + vector: Vector(cohere.ndims()) = cohere.VectorField() + + data = [{"text": "hello world"}, {"text": "goodbye world"}] + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "cohere-demo")) + tbl = db.create_table("test", schema=TextModel, mode="overwrite") + tbl.add(data) + # --8<-- [end:embedding_cohere_usage] + + +def test_embedding_gemini_usage() -> None: + require_flag("RUN_GEMINI_SNIPPETS") + + # --8<-- [start:embedding_gemini_usage] + import tempfile + from pathlib import Path + + import lancedb + import pandas as pd + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + model = get_registry().get("gemini-text").create() + + class TextModel(LanceModel): + text: str = model.SourceField() + vector: Vector(model.ndims()) = model.VectorField() + + df = pd.DataFrame({"text": ["hello world", "goodbye world"]}) + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "gemini-demo")) + tbl = db.create_table("test", schema=TextModel, mode="overwrite") + + tbl.add(df) + rs = tbl.search("hello").limit(1).to_pandas() + print(rs.head()) + # --8<-- [end:embedding_gemini_usage] + + +def test_embedding_huggingface_usage() -> None: + require_flag("RUN_HUGGINGFACE_SNIPPETS") + + # --8<-- [start:embedding_huggingface_usage] + import tempfile + from pathlib import Path + + import lancedb + import pandas as pd + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "huggingface-demo")) + model = get_registry().get("huggingface").create(name="facebook/bart-base") + + class Words(LanceModel): + text: str = model.SourceField() + vector: Vector(model.ndims()) = model.VectorField() + + df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]}) + table = db.create_table("greets", schema=Words) + table.add(df) + query = "old greeting" + actual = table.search(query).limit(1).to_pydantic(Words)[0] + print(actual.text) + # --8<-- [end:embedding_huggingface_usage] + + +def test_frameworks_lerobot_lancedb_usage() -> None: + require_flag("RUN_LEROBOT_LANCEDB_SNIPPETS") + + # --8<-- [start:frameworks_lerobot_lancedb_image_dataset] + from lerobot_lancedb import LeRobotLanceDataset + + dataset = LeRobotLanceDataset( + repo_id="your-org/your-lerobot-lance-images", + delta_timestamps={ + "observation.images.front": [-0.2, -0.1, 0.0], + }, + return_uint8=True, + ) + + sample = dataset[0] + print(sample["observation.state"].shape) + print(sample["action"].shape) + # --8<-- [end:frameworks_lerobot_lancedb_image_dataset] + + # --8<-- [start:frameworks_lerobot_lancedb_video_dataset] + from lerobot_lancedb import LeRobotLanceVideoDataset + + video_dataset = LeRobotLanceVideoDataset( + repo_id="lance-format/lerobot-pusht-lance", + delta_timestamps={ + "observation.images.image": [-0.2, -0.1, 0.0], + }, + return_uint8=True, + ) + + video_sample = video_dataset[0] + print(video_sample["observation.images.image"].shape) + # --8<-- [end:frameworks_lerobot_lancedb_video_dataset] + + # --8<-- [start:frameworks_lerobot_open_lance_tables] + import lancedb + + db = lancedb.connect("hf://datasets/lance-format/lerobot-pusht-lance/data") + frames = db.open_table("frames") + episodes = db.open_table("episodes") + videos = db.open_table("videos") + + print(len(frames), len(episodes), len(videos)) + print(frames.schema) + # --8<-- [end:frameworks_lerobot_open_lance_tables] + + # --8<-- [start:frameworks_lerobot_filter_frames] + frame_rows = ( + frames.search() + .where("episode_index = 0 AND frame_index < 10", prefilter=True) + .select(["episode_index", "frame_index", "timestamp", "action"]) + .limit(10) + .to_list() + ) + + for row in frame_rows: + print(row["episode_index"], row["frame_index"], row["timestamp"]) + # --8<-- [end:frameworks_lerobot_filter_frames] + + +def test_frameworks_stable_worldmodel_usage() -> None: + require_flag("RUN_STABLE_WORLDMODEL_SNIPPETS") + + # --8<-- [start:frameworks_stable_worldmodel_collect_lance] + import stable_worldmodel as swm + + world = swm.World("swm/PushT-v1", num_envs=8) + world.set_policy(your_expert_policy) + world.collect("data/pusht_demo.lance", episodes=100, seed=0) + # --8<-- [end:frameworks_stable_worldmodel_collect_lance] + + # --8<-- [start:frameworks_stable_worldmodel_load_lance] + dataset = swm.data.load_dataset("data/pusht_demo.lance", num_steps=16) + + batch = dataset[0] + print(batch.keys()) + # --8<-- [end:frameworks_stable_worldmodel_load_lance] + + # --8<-- [start:frameworks_stable_worldmodel_convert] + swm.data.convert( + "data/pusht_demo.lance", + "data/pusht_video", + dest_format="video", + fps=30, + ) + # --8<-- [end:frameworks_stable_worldmodel_convert] + + # --8<-- [start:frameworks_stable_worldmodel_evaluate] + from stable_worldmodel.policy import PlanConfig, WorldModelPolicy + from stable_worldmodel.solver import CEMSolver + + solver = CEMSolver(model=world_model, num_samples=300) + policy = WorldModelPolicy(solver=solver, config=PlanConfig(horizon=10)) + + world.set_policy(policy) + results = world.evaluate(episodes=50) + print(f"Success Rate: {results['success_rate']:.1f}%") + # --8<-- [end:frameworks_stable_worldmodel_evaluate] + + +def test_embedding_ibm_usage() -> None: + require_flag("RUN_IBM_WATSONX_SNIPPETS") + + # --8<-- [start:embedding_ibm_usage] + import os + import tempfile + from pathlib import Path + + import lancedb + from lancedb.embeddings import EmbeddingFunctionRegistry + from lancedb.pydantic import LanceModel, Vector + + watsonx_embed = ( + EmbeddingFunctionRegistry.get_instance() + .get("watsonx") + .create( + name="ibm/slate-125m-english-rtrvr", + api_key=os.environ.get("WATSONX_API_KEY"), + project_id=os.environ.get("WATSONX_PROJECT_ID"), + ) + ) + + class TextModel(LanceModel): + text: str = watsonx_embed.SourceField() + vector: Vector(watsonx_embed.ndims()) = watsonx_embed.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "watsonx-demo")) + tbl = db.create_table("watsonx_test", schema=TextModel, mode="overwrite") + tbl.add(data) + + rs = tbl.search("hello").limit(1).to_pandas() + print(rs.head()) + # --8<-- [end:embedding_ibm_usage] + + +def test_embedding_imagebind_examples() -> None: + require_flag("RUN_IMAGEBIND_SNIPPETS") + pytest.importorskip("imagebind") + + # --8<-- [start:embedding_imagebind_setup] + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + db = lancedb.connect("/tmp/imagebind-db") + func = get_registry().get("imagebind").create() + + class ImageBindModel(LanceModel): + text: str + image_uri: str = func.SourceField() + audio_path: str + vector: Vector(func.ndims()) = func.VectorField() + + text_list = ["A dog.", "A car", "A bird"] + image_paths = [ + "./assets/dog_image.jpg", + "./assets/car_image.jpg", + "./assets/bird_image.jpg", + ] + audio_paths = [ + "./assets/dog_audio.wav", + "./assets/car_audio.wav", + "./assets/bird_audio.wav", + ] + + inputs = [ + {"text": a, "audio_path": b, "image_uri": c} + for a, b, c in zip(text_list, audio_paths, image_paths) + ] + + table = db.create_table("img_bind", schema=ImageBindModel) + table.add(inputs) + # --8<-- [end:embedding_imagebind_setup] + + # --8<-- [start:embedding_imagebind_image_search] + query_image = "./assets/dog_image2.jpg" + actual = table.search(query_image).limit(1).to_pydantic(ImageBindModel)[0] + print(actual.text == "dog") + # --8<-- [end:embedding_imagebind_image_search] + + # --8<-- [start:embedding_imagebind_audio_search] + query_audio = "./assets/car_audio2.wav" + actual = table.search(query_audio).limit(1).to_pydantic(ImageBindModel)[0] + print(actual.text == "car") + # --8<-- [end:embedding_imagebind_audio_search] + + # --8<-- [start:embedding_imagebind_text_search] + query = "an animal which flies and tweets" + actual = table.search(query).limit(1).to_pydantic(ImageBindModel)[0] + print(actual.text == "bird") + # --8<-- [end:embedding_imagebind_text_search] + + +def test_embedding_colpali_examples() -> None: + require_flag("RUN_COLPALI_SNIPPETS") + pytest.importorskip("colpali_engine") + + # --8<-- [start:embedding_colpali_setup] + import tempfile + from pathlib import Path + + import lancedb + import pandas as pd + import requests + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, MultiVector + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "colpali-demo")) + func = get_registry().get("colpali").create() + + class Images(LanceModel): + label: str + image_uri: str = func.SourceField() + image_bytes: bytes = func.SourceField() + vector: MultiVector(func.ndims()) = func.VectorField() + vec_from_bytes: MultiVector(func.ndims()) = func.VectorField() + + table = db.create_table("images", schema=Images) + labels = ["cat", "dog", "horse"] + uris = [ + "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg", + "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg", + "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg", + ] + image_bytes = [requests.get(uri).content for uri in uris] + table.add( + pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes}) + ) + # --8<-- [end:embedding_colpali_setup] + + # --8<-- [start:embedding_colpali_text_search] + actual = ( + table.search("a furry pet", vector_column_name="vector") + .limit(1) + .to_pydantic(Images)[0] + ) + print(actual.label) + # --8<-- [end:embedding_colpali_text_search] + + +def test_embedding_instructor_usage() -> None: + require_flag("RUN_INSTRUCTOR_SNIPPETS") + + # --8<-- [start:embedding_instructor_usage] + import tempfile + from pathlib import Path + + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + instructor = ( + get_registry() + .get("instructor") + .create( + source_instruction="represent the document for retrieval", + query_instruction="represent the document for retrieving the most similar documents", + ) + ) + + class Schema(LanceModel): + vector: Vector(instructor.ndims()) = instructor.VectorField() + text: str = instructor.SourceField() + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "instructor-demo")) + tbl = db.create_table("test", schema=Schema, mode="overwrite") + + texts = [ + { + "text": "Capitalism has been dominant in the Western world since the end of feudalism." + }, + { + "text": "The disparate impact theory is especially controversial under the Fair Housing Act." + }, + { + "text": "Disparate impact in United States labor law refers to practices in employment." + }, + ] + + tbl.add(texts) + # --8<-- [end:embedding_instructor_usage] + + +def test_embedding_jina_text() -> None: + require_env("JINA_API_KEY") + + # --8<-- [start:embedding_jina_text] + import os + import tempfile + from pathlib import Path + + import lancedb + from lancedb.embeddings import EmbeddingFunctionRegistry + from lancedb.pydantic import LanceModel, Vector + + os.environ["JINA_API_KEY"] = os.environ["JINA_API_KEY"] + + jina_embed = ( + EmbeddingFunctionRegistry.get_instance() + .get("jina") + .create(name="jina-embeddings-v2-base-en") + ) + + class TextModel(LanceModel): + text: str = jina_embed.SourceField() + vector: Vector(jina_embed.ndims()) = jina_embed.VectorField() + + data = [{"text": "hello world"}, {"text": "goodbye world"}] + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "jina-text")) + tbl = db.create_table("test", schema=TextModel, mode="overwrite") + + tbl.add(data) + # --8<-- [end:embedding_jina_text] + + +def test_embedding_jina_multimodal() -> None: + require_flag("RUN_JINA_MULTIMODAL_SNIPPETS") + + # --8<-- [start:embedding_jina_multimodal] + import os + import tempfile + from pathlib import Path + + import lancedb + import pandas as pd + import requests + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + os.environ["JINA_API_KEY"] = os.environ.get("JINA_API_KEY", "jina_*") + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "jina-images")) + func = get_registry().get("jina").create() + + class Images(LanceModel): + label: str + image_uri: str = func.SourceField() + image_bytes: bytes = func.SourceField() + vector: Vector(func.ndims()) = func.VectorField() + vec_from_bytes: Vector(func.ndims()) = func.VectorField() + + table = db.create_table("images", schema=Images) + labels = ["cat", "cat", "dog", "dog", "horse", "horse"] + uris = [ + "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg", + "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg", + "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg", + "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg", + "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg", + "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg", + ] + image_bytes = [requests.get(uri).content for uri in uris] + table.add( + pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes}) + ) + # --8<-- [end:embedding_jina_multimodal] + + +def test_embedding_ollama_usage() -> None: + require_flag("RUN_OLLAMA_SNIPPETS") + + # --8<-- [start:embedding_ollama_usage] + import tempfile + from pathlib import Path + + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "ollama-demo")) + func = get_registry().get("ollama").create(name="nomic-embed-text") + + class Words(LanceModel): + text: str = func.SourceField() + vector: Vector(func.ndims()) = func.VectorField() + + table = db.create_table("words", schema=Words, mode="overwrite") + table.add( + [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + ) + + query = "greetings" + actual = table.search(query).limit(1).to_pydantic(Words)[0] + print(actual.text) + # --8<-- [end:embedding_ollama_usage] + + +def test_embedding_openclip_examples() -> None: + require_flag("RUN_OPENCLIP_SNIPPETS") + + # --8<-- [start:embedding_openclip_setup] + import tempfile + from pathlib import Path + + import lancedb + import pandas as pd + import requests + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "openclip-demo")) + func = get_registry().get("open-clip").create() + + class Images(LanceModel): + label: str + image_uri: str = func.SourceField() + image_bytes: bytes = func.SourceField() + vector: Vector(func.ndims()) = func.VectorField() + vec_from_bytes: Vector(func.ndims()) = func.VectorField() + + table = db.create_table("images", schema=Images) + labels = ["cat", "cat", "dog", "dog", "horse", "horse"] + uris = [ + "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg", + "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg", + "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg", + "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg", + "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg", + "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg", + ] + image_bytes = [requests.get(uri).content for uri in uris] + table.add( + pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes}) + ) + # --8<-- [end:embedding_openclip_setup] + + # --8<-- [start:embedding_openclip_text_search] + actual = table.search("man's best friend").limit(1).to_pydantic(Images)[0] + print(actual.label) + + frombytes = ( + table.search("man's best friend", vector_column_name="vec_from_bytes") + .limit(1) + .to_pydantic(Images)[0] + ) + print(frombytes.label) + # --8<-- [end:embedding_openclip_text_search] + + # --8<-- [start:embedding_openclip_image_search] + import io + + from PIL import Image + + query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg" + image_bytes = requests.get(query_image_uri).content + query_image = Image.open(io.BytesIO(image_bytes)) + actual = table.search(query_image).limit(1).to_pydantic(Images)[0] + print(actual.label == "dog") + + other = ( + table.search(query_image, vector_column_name="vec_from_bytes") + .limit(1) + .to_pydantic(Images)[0] + ) + print(other.label) + # --8<-- [end:embedding_openclip_image_search] + + +def test_embedding_sentence_transformers_baai() -> None: + require_flag("RUN_SENTENCE_TRANSFORMERS_SNIPPETS") + + # --8<-- [start:embedding_sentence_transformers_baai] + import tempfile + from pathlib import Path + + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "sentence-transformers")) + model = ( + get_registry() + .get("sentence-transformers") + .create(name="BAAI/bge-small-en-v1.5", device="cpu") + ) + + class Words(LanceModel): + text: str = model.SourceField() + vector: Vector(model.ndims()) = model.VectorField() + + table = db.create_table("words", schema=Words) + table.add( + [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + ) + + query = "greetings" + actual = table.search(query).limit(1).to_pydantic(Words)[0] + print(actual.text) + # --8<-- [end:embedding_sentence_transformers_baai] + + +def test_embedding_voyageai_usage() -> None: + require_env("VOYAGE_API_KEY") + + # --8<-- [start:embedding_voyageai_usage] + import tempfile + from pathlib import Path + + import lancedb + from lancedb.embeddings import EmbeddingFunctionRegistry + from lancedb.pydantic import LanceModel, Vector + + voyageai = ( + EmbeddingFunctionRegistry.get_instance().get("voyageai").create(name="voyage-3") + ) + + class TextModel(LanceModel): + text: str = voyageai.SourceField() + vector: Vector(voyageai.ndims()) = voyageai.VectorField() + + data = [{"text": "hello world"}, {"text": "goodbye world"}] + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "voyageai-demo")) + tbl = db.create_table("test", schema=TextModel, mode="overwrite") + + tbl.add(data) + # --8<-- [end:embedding_voyageai_usage] + + +def test_embedding_voyageai_multimodal() -> None: + require_env("VOYAGE_API_KEY") + + # --8<-- [start:embedding_voyageai_multimodal] + import tempfile + from pathlib import Path + + import lancedb + from lancedb.embeddings import EmbeddingFunctionRegistry + from lancedb.pydantic import LanceModel, Vector + + # Create multimodal embedding function with custom dimension + voyageai = ( + EmbeddingFunctionRegistry.get_instance() + .get("voyageai") + .create(name="voyage-multimodal-3.5", output_dimension=512) + ) + + class ImageModel(LanceModel): + image_uri: str = voyageai.SourceField() + vector: Vector(voyageai.ndims()) = voyageai.VectorField() + + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "voyageai-multimodal")) + tbl = db.create_table("images", schema=ImageModel, mode="overwrite") + + # Add images using URLs + tbl.add( + [ + {"image_uri": "https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/300px-PNG_transparency_demonstration_1.png"}, + ] + ) + + # Search with text query + results = tbl.search("dice").limit(1).to_list() + print(results) + # --8<-- [end:embedding_voyageai_multimodal] + + +# Reranking integrations + + +def test_reranking_answerdotai_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + + # --8<-- [start:reranking_answerdotai_usage] + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import AnswerdotaiRerankers + + embedder = get_registry().get("sentence-transformers").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = AnswerdotaiRerankers() + + # Run vector search with a reranker + result = tbl.search("hello").rerank(reranker=reranker).to_list() + + # Run FTS search with a reranker + result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list() + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + # --8<-- [end:reranking_answerdotai_usage] + + +def test_reranking_cohere_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + os.environ["COHERE_API_KEY"] = require_env("COHERE_API_KEY") + + # --8<-- [start:reranking_cohere_usage] + import os + + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import CohereReranker + + embedder = get_registry().get("sentence-transformers").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = CohereReranker(api_key=os.environ["COHERE_API_KEY"]) + + # Run vector search with a reranker + result = tbl.search("hello").rerank(reranker=reranker).to_list() + + # Run FTS search with a reranker + result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list() + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + # --8<-- [end:reranking_cohere_usage] + + +def test_reranking_colbert_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + + # --8<-- [start:reranking_colbert_usage] + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import ColbertReranker + + embedder = get_registry().get("sentence-transformers").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = ColbertReranker() + + # Run vector search with a reranker + result = tbl.search("hello").rerank(reranker=reranker).to_list() + + # Run FTS search with a reranker + result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list() + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + # --8<-- [end:reranking_colbert_usage] + + +def test_reranking_cross_encoder_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + + # --8<-- [start:reranking_cross_encoder_usage] + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import CrossEncoderReranker + + embedder = get_registry().get("sentence-transformers").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = CrossEncoderReranker() + + # Run vector search with a reranker + result = tbl.search("hello").rerank(reranker=reranker).to_list() + + # Run FTS search with a reranker + result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list() + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + # --8<-- [end:reranking_cross_encoder_usage] + + +def test_reranking_jina_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + os.environ["JINA_API_KEY"] = require_env("JINA_API_KEY") + + # --8<-- [start:reranking_jina_usage] + import os + + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import JinaReranker + + embedder = get_registry().get("jina").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = JinaReranker(api_key=os.environ["JINA_API_KEY"]) + + # Run vector search with a reranker + result = tbl.search("hello").rerank(reranker=reranker).to_list() + + # Run FTS search with a reranker + result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list() + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + # --8<-- [end:reranking_jina_usage] + + +def test_reranking_linear_combination_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + + # --8<-- [start:reranking_linear_combination_usage] + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import LinearCombinationReranker + + embedder = get_registry().get("sentence-transformers").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = LinearCombinationReranker() + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + # --8<-- [end:reranking_linear_combination_usage] + + +def test_reranking_openai_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + os.environ["OPENAI_API_KEY"] = require_env("OPENAI_API_KEY") + + # --8<-- [start:reranking_openai_usage] + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import OpenaiReranker + + embedder = get_registry().get("sentence-transformers").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = OpenaiReranker() + + # Run vector search with a reranker + result = tbl.search("hello").rerank(reranker=reranker).to_list() + + # Run FTS search with a reranker + result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list() + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + # --8<-- [end:reranking_openai_usage] + + +def test_reranking_rrf_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + + # --8<-- [start:reranking_rrf_usage] + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import RRFReranker + + embedder = get_registry().get("sentence-transformers").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = RRFReranker() + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + # --8<-- [end:reranking_rrf_usage] + + +def test_reranking_mrr_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + + # --8<-- [start:reranking_mrr_usage] + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import MRRReranker + + embedder = get_registry().get("sentence-transformers").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = MRRReranker(weight_vector=0.7, weight_fts=0.3) + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + + # Run multivector search across multiple vector columns + rs1 = tbl.search("hello").limit(10).with_row_id(True).to_arrow() + rs2 = tbl.search("greeting").limit(10).with_row_id(True).to_arrow() + combined = MRRReranker().rerank_multivector([rs1, rs2]) + # --8<-- [end:reranking_mrr_usage] + + +def test_reranking_voyageai_usage() -> None: + require_flag("RUN_RERANKER_SNIPPETS") + os.environ["VOYAGE_API_KEY"] = require_env("VOYAGE_API_KEY") + + # --8<-- [start:reranking_voyageai_usage] + import os + + import lancedb + from lancedb.embeddings import get_registry + from lancedb.pydantic import LanceModel, Vector + from lancedb.rerankers import VoyageAIReranker + + embedder = get_registry().get("sentence-transformers").create() + db = lancedb.connect("~/.lancedb") + + class Schema(LanceModel): + text: str = embedder.SourceField() + vector: Vector(embedder.ndims()) = embedder.VectorField() + + data = [ + {"text": "hello world"}, + {"text": "goodbye world"}, + ] + tbl = db.create_table("test", schema=Schema, mode="overwrite") + tbl.add(data) + reranker = VoyageAIReranker(model_name="rerank-2") + + # Run vector search with a reranker + result = tbl.search("hello").rerank(reranker=reranker).to_list() + + # Run FTS search with a reranker + result = tbl.search("hello", query_type="fts").rerank(reranker=reranker).to_list() + + # Run hybrid search with a reranker + tbl.create_fts_index("text", replace=True) + result = ( + tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list() + ) + # --8<-- [end:reranking_voyageai_usage] + + +# Framework integrations + + +def test_frameworks_langchain_examples() -> None: + require_flag("RUN_LANGCHAIN_SNIPPETS") + pytest.importorskip("langchain") + pytest.importorskip("langchain_openai") + pytest.importorskip("langchain_text_splitters") + + # --8<-- [start:frameworks_langchain_quick_start] + import os + + from langchain.document_loaders import TextLoader + from langchain.vectorstores import LanceDB + from langchain_openai import OpenAIEmbeddings + from langchain_text_splitters import CharacterTextSplitter + + os.environ["OPENAI_API_KEY"] = "sk-..." + + loader = TextLoader( + "../../modules/state_of_the_union.txt" + ) # Replace with your data path + documents = loader.load() + + documents = CharacterTextSplitter().split_documents(documents) + embeddings = OpenAIEmbeddings() + + docsearch = LanceDB.from_documents(documents, embeddings) + query = "What did the president say about Ketanji Brown Jackson" + docs = docsearch.similarity_search(query) + print(docs[0].page_content) + # --8<-- [end:frameworks_langchain_quick_start] + + # --8<-- [start:frameworks_langchain_vector_store_config] + db_url = "db://lang_test" # url of db you created + api_key = "xxxxx" # your API key + region = "us-east-1-dev" # your selected region + + vector_store = LanceDB( + uri=db_url, + api_key=api_key, # (dont include for local API) + region=region, # (dont include for local API) + embedding=embeddings, + table_name="langchain_test", # Optional + ) + # --8<-- [end:frameworks_langchain_vector_store_config] + + # --8<-- [start:frameworks_langchain_add_texts] + vector_store.add_texts(texts=["test_123"], metadatas=[{"source": "wiki"}]) + + # Additionaly, to explore the table you can load it into a df or save it in a csv file: + + tbl = vector_store.get_table() + print("tbl:", tbl) + pd_df = tbl.to_pandas() + pd_df.to_csv("docsearch.csv", index=False) + + # you can also create a new vector store object using an older connection object: + vector_store = LanceDB(connection=tbl, embedding=embeddings) + # --8<-- [end:frameworks_langchain_add_texts] + + # --8<-- [start:frameworks_langchain_create_index] + # for creating vector index + vector_store.create_index(vector_col="vector", metric="cosine") + + # for creating scalar index(for non-vector columns) + vector_store.create_index(col_name="text") + # --8<-- [end:frameworks_langchain_create_index] + + # --8<-- [start:frameworks_langchain_similarity_search] + docs = docsearch.similarity_search(query) + print(docs[0].page_content) + # --8<-- [end:frameworks_langchain_similarity_search] + + # --8<-- [start:frameworks_langchain_similarity_search_by_vector] + docs = docsearch.similarity_search_by_vector(query) + print(docs[0].page_content) + # --8<-- [end:frameworks_langchain_similarity_search_by_vector] + + # --8<-- [start:frameworks_langchain_similarity_search_with_scores] + docs = docsearch.similarity_search_with_relevance_scores(query) + print("relevance score - ", docs[0][1]) + print("text- ", docs[0][0].page_content[:1000]) + # --8<-- [end:frameworks_langchain_similarity_search_with_scores] + + # --8<-- [start:frameworks_langchain_similarity_search_by_vector_with_scores] + query_embedding = embeddings.embed_query("text") + docs = docsearch.similarity_search_by_vector_with_relevance_scores(query_embedding) + print("relevance score - ", docs[0][1]) + print("text- ", docs[0][0].page_content[:1000]) + # --8<-- [end:frameworks_langchain_similarity_search_by_vector_with_scores] + + # --8<-- [start:frameworks_langchain_max_marginal_relevance] + result = docsearch.max_marginal_relevance_search(query="text") + result_texts = [doc.page_content for doc in result] + print(result_texts) + + # search by vector : + result = docsearch.max_marginal_relevance_search_by_vector( + embeddings.embed_query("text") + ) + result_texts = [doc.page_content for doc in result] + print(result_texts) + # --8<-- [end:frameworks_langchain_max_marginal_relevance] + + # --8<-- [start:frameworks_langchain_add_images] + image_uris = ["./assets/image-1.png", "./assets/image-2.png"] + vector_store.add_images(uris=image_uris) + # here image_uris are local fs paths to the images. + # --8<-- [end:frameworks_langchain_add_images] + + +def test_frameworks_llamaindex_examples() -> None: + require_flag("RUN_LLAMAINDEX_SNIPPETS") + pytest.importorskip("llama_index") + pytest.importorskip("llama_index.vector_stores.lancedb") + + # --8<-- [start:frameworks_llamaindex_quick_start] + import logging + import sys + import textwrap + + import openai + + # Uncomment to see debug logs + # logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) + # logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout)) + from llama_index.core import ( + Document, + SimpleDirectoryReader, + StorageContext, + VectorStoreIndex, + ) + from llama_index.vector_stores.lancedb import LanceDBVectorStore + + openai.api_key = "sk-..." + + documents = SimpleDirectoryReader("./data/your-data-dir/").load_data() + print("Document ID:", documents[0].doc_id, "Document Hash:", documents[0].hash) + + ## For LanceDB Enterprise : + # vector_store = LanceDBVectorStore( + # uri="db://db_name", # your remote DB URI + # api_key="sk_..", # lancedb enterprise api key + # region="your-region" # the region you configured + # host_override="https://your-host.com" # if you have a custom host, otherwise omit this + # ) + + vector_store = LanceDBVectorStore( + uri="./lancedb", mode="overwrite", query_type="vector" + ) + storage_context = StorageContext.from_defaults(vector_store=vector_store) + + index = VectorStoreIndex.from_documents(documents, storage_context=storage_context) + lance_filter = "metadata.file_name = 'paul_graham_essay.txt' " + retriever = index.as_retriever(vector_store_kwargs={"where": lance_filter}) + response = retriever.retrieve("What did the author do growing up?") + # --8<-- [end:frameworks_llamaindex_quick_start] + + # --8<-- [start:frameworks_llamaindex_filtering] + from llama_index.core.vector_stores import ( + FilterCondition, + FilterOperator, + MetadataFilter, + MetadataFilters, + ) + + query_filters = MetadataFilters( + filters=[ + MetadataFilter( + key="creation_date", operator=FilterOperator.EQ, value="2024-05-23" + ), + MetadataFilter(key="file_size", value=75040, operator=FilterOperator.GT), + ], + condition=FilterCondition.AND, + ) + # --8<-- [end:frameworks_llamaindex_filtering] + + # --8<-- [start:frameworks_llamaindex_hybrid_search] + from lancedb.rerankers import ColbertReranker + + reranker = ColbertReranker() + vector_store._add_reranker(reranker) + + query_engine = index.as_query_engine( + filters=query_filters, + vector_store_kwargs={ + "query_type": "hybrid", + }, + ) + + response = query_engine.query("How much did Viaweb charge per month?") + # --8<-- [end:frameworks_llamaindex_hybrid_search] + + # --8<-- [start:frameworks_llamaindex_add_reranker] + from lancedb.rerankers import ColbertReranker + + reranker = ColbertReranker() + vector_store._add_reranker(reranker) + # --8<-- [end:frameworks_llamaindex_add_reranker] + + +def test_frameworks_pydantic_examples() -> None: + require_flag("RUN_PYDANTIC_SNIPPETS") + pytest.importorskip("pyarrow") + + # --8<-- [start:frameworks_pydantic_imports] + import tempfile + from pathlib import Path + + import lancedb + from lancedb.pydantic import LanceModel, Vector + + # --8<-- [end:frameworks_pydantic_imports] + # --8<-- [start:frameworks_pydantic_base_model] + class LanceDocs(LanceModel): + text: str + vector: Vector(2) + + # --8<-- [end:frameworks_pydantic_base_model] + + # --8<-- [start:frameworks_pydantic_set_url] + db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "pydantic-docs")) + # --8<-- [end:frameworks_pydantic_set_url] + + # --8<-- [start:frameworks_pydantic_vector_field] + import pyarrow as pa + import pydantic + from lancedb.pydantic import Vector, pydantic_to_schema + + class MyModel(pydantic.BaseModel): + id: int + url: str + embeddings: Vector(768) + + schema = pydantic_to_schema(MyModel) + assert schema == pa.schema( + [ + pa.field("id", pa.int64(), False), + pa.field("url", pa.utf8(), False), + pa.field("embeddings", pa.list_(pa.float32(), 768)), + ] + ) + # --8<-- [end:frameworks_pydantic_vector_field] + + # --8<-- [start:frameworks_pydantic_type_conversion] + from typing import List, Optional + + import pyarrow as pa + import pydantic + from lancedb.pydantic import Vector, pydantic_to_schema + + class FooModel(pydantic.BaseModel): + id: int + s: str + vec: Vector(1536) # fixed_size_list[1536] + li: List[int] + + schema = pydantic_to_schema(FooModel) + assert schema == pa.schema( + [ + pa.field("id", pa.int64(), False), + pa.field("s", pa.utf8(), False), + pa.field("vec", pa.list_(pa.float32(), 1536)), + pa.field("li", pa.list_(pa.int64()), False), + ] + ) + # --8<-- [end:frameworks_pydantic_type_conversion] + + # --8<-- [start:frameworks_pydantic_base_example] + table = db.create_table("docs", schema=LanceDocs, mode="overwrite") + table.add( + [ + {"text": "hello world", "vector": [1.0, 0.0]}, + {"text": "goodbye world", "vector": [0.0, 1.0]}, + ] + ) + results = table.search("hello world").limit(1).to_pydantic(LanceDocs) + print(results[0].text) + # --8<-- [end:frameworks_pydantic_base_example] + + +# Platform integrations + + +def test_platforms_dlt_examples() -> None: + require_flag("RUN_DLT_SNIPPETS") + pytest.importorskip("dlt") + + # --8<-- [start:platforms_dlt_pipeline] + # Import necessary modules + import dlt + from rest_api import rest_api_source + + # Configure the REST API source + movies_source = rest_api_source( + { + "client": { + "base_url": "https://www.omdbapi.com/", + "auth": { # authentication strategy for the OMDb API + "type": "api_key", + "name": "apikey", + "api_key": dlt.secrets[ + "sources.rest_api.api_token" + ], # read API credentials directly from secrets.toml + "location": "query", + }, + "paginator": { # pagination strategy for the OMDb API + "type": "page_number", + "base_page": 1, + "total_path": "totalResults", + "maximum_page": 5, + }, + }, + "resources": [ # list of API endpoints to request + { + "name": "movie_search", + "endpoint": { + "path": "/", + "params": { + "s": "godzilla", + "type": "movie", + }, + }, + } + ], + } + ) + + if __name__ == "__main__": + # Create a pipeline object + pipeline = dlt.pipeline( + pipeline_name="movies_pipeline", + destination="lancedb", # this tells dlt to load the data into LanceDB + dataset_name="movies_data_pipeline", + ) + + # Run the pipeline + load_info = pipeline.run(movies_source) + + # pretty print the information on data that was loaded + print(load_info) + # --8<-- [end:platforms_dlt_pipeline] + + # --8<-- [start:platforms_dlt_adapter_import] + from dlt.destinations.adapters import lancedb_adapter + + # --8<-- [end:platforms_dlt_adapter_import] + # --8<-- [start:platforms_dlt_adapter_usage] + load_info = pipeline.run( + lancedb_adapter( + movies_source, + embed="Title", + ) + ) + # --8<-- [end:platforms_dlt_adapter_usage] + + +def test_platforms_duckdb_examples() -> None: + require_flag("RUN_DUCKDB_SNIPPETS") + pytest.importorskip("duckdb") + + # --8<-- [start:platforms_duckdb_create_table] + import lancedb + + db = lancedb.connect("data/sample-lancedb") + data = [ + {"vector": [3.1, 4.1], "item": "foo", "price": 10.0}, + {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}, + ] + table = db.create_table("pd_table", data=data) + # --8<-- [end:platforms_duckdb_create_table] + + # --8<-- [start:platforms_duckdb_query_table] + import duckdb + + arrow_table = table.to_lance() + + duckdb.query("SELECT * FROM arrow_table") + # --8<-- [end:platforms_duckdb_query_table] + + # --8<-- [start:platforms_duckdb_mean_price] + duckdb.query("SELECT mean(price) FROM arrow_table") + # --8<-- [end:platforms_duckdb_mean_price] + + +def test_frameworks_agno_openai_examples() -> None: + require_flag("RUN_AGNO_SNIPPETS") + pytest.importorskip("agno") + pytest.importorskip("youtube_transcript_api") + + # --8<-- [start:frameworks_agno_setup] + import os + import re + + from agno.agent import Agent + from agno.knowledge.embedder.openai import OpenAIEmbedder + from agno.knowledge.knowledge import Knowledge + from agno.models.openai import OpenAIResponses + from agno.vectordb.lancedb import LanceDb, SearchType + from youtube_transcript_api import YouTubeTranscriptApi + + if "OPENAI_API_KEY" not in os.environ: + os.environ["OPENAI_API_KEY"] = "sk-..." + + def extract_video_id(youtube_url: str) -> str: + match = re.search(r"(?<=v=)[\w-]+", youtube_url) or re.search( + r"(?<=be/)[\w-]+", youtube_url + ) + if not match: + raise ValueError("Could not parse YouTube video ID from URL") + return match.group(0) + + knowledge = Knowledge( + vector_db=LanceDb( + uri="./tmp/lancedb", + table_name="youtube_transcripts", + search_type=SearchType.hybrid, + embedder=OpenAIEmbedder(id="text-embedding-3-small"), + ), + ) + # --8<-- [end:frameworks_agno_setup] + + # --8<-- [start:frameworks_agno_ingest_youtube] + youtube_url = "https://www.youtube.com/watch?v=wl6mFyXoxos" + video_id = extract_video_id(youtube_url) + ytt = YouTubeTranscriptApi() + transcript_segments = ytt.fetch(video_id, languages=["en", "en-US"]).to_raw_data() + transcript_text = " ".join(segment["text"] for segment in transcript_segments) + + knowledge.insert( + name=f"YouTube Transcript ({video_id})", + text_content=transcript_text, + metadata={"source": "youtube", "video_id": video_id, "video_url": youtube_url}, + ) + # --8<-- [end:frameworks_agno_ingest_youtube] + + # --8<-- [start:frameworks_agno_agent] + agent = Agent( + model=OpenAIResponses(id="gpt-5-mini"), + knowledge=knowledge, + search_knowledge=True, + instructions="Search the transcript and answer only from retrieved context.", + markdown=True, + ) + # --8<-- [end:frameworks_agno_agent] + + # --8<-- [start:frameworks_agno_cli_chat] + agent.print_response( + "Summarize the loaded video transcript in 5 concise bullet points.", + stream=True, + ) + while True: + question = input("You: ").strip() + if question.lower() in {"exit", "quit", "bye"}: + break + agent.print_response(question, stream=True) + # --8<-- [end:frameworks_agno_cli_chat] + + +def test_platforms_voxel51_examples() -> None: + require_flag("RUN_VOXEL51_SNIPPETS") + pytest.importorskip("fiftyone") + + # --8<-- [start:platforms_voxel51_load_dataset] + import fiftyone as fo + import fiftyone.brain as fob + import fiftyone.zoo as foz + + # Step 1: Load your data into FiftyOne + dataset = foz.load_zoo_dataset("quickstart") + # --8<-- [end:platforms_voxel51_load_dataset] + + # --8<-- [start:platforms_voxel51_compute_similarity] + # Steps 2 and 3: Compute embeddings and create a similarity index + lancedb_index = fob.compute_similarity( + dataset, + model="clip-vit-base32-torch", + brain_key="lancedb_index", + backend="lancedb", + ) + # --8<-- [end:platforms_voxel51_compute_similarity] + + # --8<-- [start:platforms_voxel51_sort_by_similarity] + # Step 4: Query your data + query = dataset.first().id # query by sample ID + view = dataset.sort_by_similarity( + query, + brain_key="lancedb_index", + k=10, # limit to 10 most similar samples + ) + # --8<-- [end:platforms_voxel51_sort_by_similarity] + + # --8<-- [start:platforms_voxel51_cleanup] + # Step 5 (optional): Cleanup + + # Delete the LanceDB table + lancedb_index.cleanup() + + # Delete run record from FiftyOne + dataset.delete_brain_run("lancedb_index") + # --8<-- [end:platforms_voxel51_cleanup] + + if False: + # --8<-- [start:platforms_voxel51_backend_flag] + import fiftyone.brain as fob + + # Re-run similarity creation using the LanceDB backend explicitly + fob.compute_similarity( + dataset, + model="clip-vit-base32-torch", + brain_key="lancedb_index", + backend="lancedb", + ) + # --8<-- [end:platforms_voxel51_backend_flag] + + # --8<-- [start:platforms_voxel51_brain_config] + import fiftyone.brain as fob + + # Print your current brain config + print(fob.brain_config) + # --8<-- [end:platforms_voxel51_brain_config] + + if False: + # --8<-- [start:platforms_voxel51_backend_params] + lancedb_index = fob.compute_similarity( + dataset, + model="clip-vit-base32-torch", + backend="lancedb", + brain_key="lancedb_index", + table_name="your-table", + metric="euclidean", + uri="/tmp/lancedb", + ) + # --8<-- [end:platforms_voxel51_backend_params] + + +def test_platforms_pandas_examples() -> None: + require_flag("RUN_PANDAS_SNIPPETS") + pytest.importorskip("pandas") + + # --8<-- [start:platforms_pandas_imports] + import asyncio + import tempfile + from pathlib import Path + + import lancedb + import pandas as pd + + # --8<-- [end:platforms_pandas_imports] + # --8<-- [start:platforms_pandas_create_table] + pandas_df = pd.DataFrame( + [ + {"id": "1", "text": "dragon", "vector": [0.9, 0.1, 0.3]}, + {"id": "2", "text": "griffin", "vector": [0.4, 0.5, 0.2]}, + {"id": "3", "text": "phoenix", "vector": [0.7, 0.3, 0.6]}, + ] + ) + pandas_db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "pandas-demo")) + pandas_table = pandas_db.create_table("creatures", data=pandas_df, mode="overwrite") + # --8<-- [end:platforms_pandas_create_table] + + # --8<-- [start:platforms_pandas_vector_search] + pandas_results = ( + pandas_table.search([0.9, 0.1, 0.3]) + .select(["text", "_distance"]) + .limit(1) + .to_pandas() + ) + print(pandas_results) + # --8<-- [end:platforms_pandas_vector_search] + + # --8<-- [start:platforms_pandas_async_example] + async def run_pandas_async_example() -> None: + async_db = await lancedb.connect_async( + str(Path(tempfile.mkdtemp()) / "pandas-async") + ) + async_df = pd.DataFrame( + [ + {"id": "10", "text": "sage", "vector": [0.6, 0.4, 0.8]}, + {"id": "11", "text": "bard", "vector": [0.2, 0.7, 0.3]}, + ] + ) + async_table = await async_db.create_table( + "creatures_async", data=async_df, mode="overwrite" + ) + async_results = await ( + async_table.search([0.6, 0.4, 0.8]) + .select(["text", "_distance"]) + .limit(1) + .to_pandas() + ) + print(async_results) + + asyncio.run(run_pandas_async_example()) + # --8<-- [end:platforms_pandas_async_example] + + +def test_platforms_polars_examples() -> None: + require_flag("RUN_POLARS_SNIPPETS") + pytest.importorskip("polars") + + # --8<-- [start:platforms_polars_imports] + import tempfile + from pathlib import Path + + import lancedb + import polars as pl + from lancedb.pydantic import LanceModel, Vector + + # --8<-- [end:platforms_polars_imports] + # --8<-- [start:platforms_polars_create_table] + birds = pl.DataFrame( + { + "text": ["phoenix", "sparrow"], + "vector": [ + [0.1, 0.2, 0.3], + [0.8, 0.6, 0.5], + ], + } + ) + polars_db = lancedb.connect(str(Path(tempfile.mkdtemp()) / "polars-demo")) + polars_table = polars_db.create_table( + "birds", data=birds.to_arrow(), mode="overwrite" + ) + # --8<-- [end:platforms_polars_create_table] + + # --8<-- [start:platforms_polars_vector_search] + polars_results = ( + polars_table.search([0.1, 0.2, 0.3]) + .select(["text", "_distance"]) + .limit(1) + .to_polars() + ) + print(polars_results) + # --8<-- [end:platforms_polars_vector_search] + + # --8<-- [start:platforms_polars_lazyframe] + lazy_frame = polars_table.to_polars().lazy() + print(lazy_frame.select(["text"]).collect()) + # --8<-- [end:platforms_polars_lazyframe] + + # --8<-- [start:platforms_polars_pydantic] + class BirdModel(LanceModel): + text: str + vector: Vector(3) + + schema_table = polars_db.create_table( + "birds_schema", schema=BirdModel, mode="overwrite" + ) + schema_table.add(birds.to_dicts()) + # --8<-- [end:platforms_polars_pydantic] diff --git a/docs/web-tests/py/test_multimodal.py b/docs/web-tests/py/test_multimodal.py new file mode 100644 index 000000000..a2656bd01 --- /dev/null +++ b/docs/web-tests/py/test_multimodal.py @@ -0,0 +1,232 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +import pytest +try: + import lancedb + import numpy as np + import pyarrow as pa + import io + from PIL import Image +except ImportError: + pass + +# --8<-- [start:multimodal_imports] +import lancedb +import pyarrow as pa +import pandas as pd +import numpy as np +import io +from PIL import Image +# --8<-- [end:multimodal_imports] + +def test_multimodal_ingestion(db_path_factory): + # Ensure dependencies are available + pytest.importorskip("PIL") + pytest.importorskip("lancedb") + pytest.importorskip("numpy") + + # --8<-- [start:create_dummy_data] + # Create some dummy images + def create_dummy_image(color): + img = Image.new('RGB', (100, 100), color=color) + buf = io.BytesIO() + img.save(buf, format='PNG') + return buf.getvalue() + + # Create dataset with metadata, vectors, and image blobs + data = [ + { + "id": 1, + "filename": "red_square.png", + "vector": np.random.rand(128).astype(np.float32), + "image_blob": create_dummy_image('red'), + "label": "red" + }, + { + "id": 2, + "filename": "blue_square.png", + "vector": np.random.rand(128).astype(np.float32), + "image_blob": create_dummy_image('blue'), + "label": "blue" + } + ] + # --8<-- [end:create_dummy_data] + + # --8<-- [start:define_schema] + # Define schema explictly to ensure image_blob is treated as binary + schema = pa.schema([ + pa.field("id", pa.int32()), + pa.field("filename", pa.string()), + pa.field("vector", pa.list_(pa.float32(), 128)), + pa.field("image_blob", pa.binary()), # Important: Use pa.binary() for blobs + pa.field("label", pa.string()) + ]) + # --8<-- [end:define_schema] + + db_uri = db_path_factory("multimodal_db") + db = lancedb.connect(db_uri) + + # --8<-- [start:ingest_data] + tbl = db.create_table("images", data=data, schema=schema, mode="overwrite") + # --8<-- [end:ingest_data] + + assert len(tbl) == 2 + + # --8<-- [start:search_data] + # Search for similar images + query_vector = np.random.rand(128).astype(np.float32) + results = tbl.search(query_vector).limit(1).to_pandas() + # --8<-- [end:search_data] + + # --8<-- [start:process_results] + # Convert back to PIL Image + for _, row in results.iterrows(): + image_bytes = row['image_blob'] + image = Image.open(io.BytesIO(image_bytes)) + print(f"Retrieved image: {row['filename']}, Size: {image.size}") + # You can now use 'image' with other libraries or display it + # --8<-- [end:process_results] + + assert len(results) == 1 + +def test_blob_api_definition(db_path_factory): + # --8<-- [start:blob_api_schema] + import pyarrow as pa + + # Define schema with Blob API metadata for lazy loading + schema = pa.schema([ + pa.field("id", pa.int64()), + pa.field( + "video", + pa.large_binary(), + metadata={"lance-encoding:blob": "true"} # Enable Blob API + ), + ]) + # --8<-- [end:blob_api_schema] + + # --8<-- [start:blob_api_ingest] + import lancedb + + db = lancedb.connect(db_path_factory("blob_db")) + + # Create sample data + data = [ + {"id": 1, "video": b"fake_video_bytes_1"}, + {"id": 2, "video": b"fake_video_bytes_2"} + ] + + # Create the table + tbl = db.create_table("videos", data=data, schema=schema) + # --8<-- [end:blob_api_ingest] + assert len(tbl) == 2 + + +def test_blob_api_to_pandas(db_path_factory): + db = lancedb.connect(db_path_factory("blob_to_pandas_db")) + schema = pa.schema([ + pa.field("id", pa.int64()), + pa.field( + "video", + pa.large_binary(), + metadata={"lance-encoding:blob": "true"}, + ), + ]) + tbl = db.create_table( + "videos", + data=[ + {"id": 1, "video": b"fake_video_bytes_1"}, + {"id": 2, "video": b"fake_video_bytes_2"}, + ], + schema=schema, + mode="overwrite", + ) + + # --8<-- [start:blob_api_to_pandas] + # Default: blob columns come back lazily + df_lazy = tbl.to_pandas() + + # Materialize blob bytes eagerly + df_bytes = tbl.to_pandas(blob_mode="bytes") + + # Return descriptors instead of payloads + df_desc = tbl.to_pandas(blob_mode="descriptions") + + # Forward extra kwargs to PyArrow's to_pandas + df_typed = tbl.to_pandas(split_blocks=True, self_destruct=True) + # --8<-- [end:blob_api_to_pandas] + + assert len(df_lazy) == 2 + assert isinstance(df_bytes["video"].iloc[0], bytes) + assert df_bytes["video"].tolist() == [ + b"fake_video_bytes_1", + b"fake_video_bytes_2", + ] + assert len(df_desc) == 2 + assert len(df_typed) == 2 + + +@pytest.mark.asyncio +async def test_query_to_pandas_kwargs(db_path_factory): + schema = pa.schema([ + pa.field("id", pa.int64()), + pa.field("vector", pa.list_(pa.float32(), 128)), + pa.field( + "video", + pa.large_binary(), + metadata={"lance-encoding:blob": "true"}, + ), + ]) + data = [ + { + "id": i, + "vector": np.random.rand(128).astype(np.float32), + "video": f"fake_video_bytes_{i}".encode(), + } + for i in range(10) + ] + + db = lancedb.connect(db_path_factory("query_to_pandas_db")) + tbl = db.create_table("search_demo", data=data, schema=schema, mode="overwrite") + + async_db = await lancedb.connect_async( + str(db_path_factory("query_to_pandas_async_db")) + ) + tbl_async = await async_db.create_table( + "search_demo", data=data, schema=schema, mode="overwrite" + ) + + query_vector = np.random.rand(128).astype(np.float32) + + # --8<-- [start:query_to_pandas_kwargs] + # Plain scan query: blob_mode is supported end to end + df_lazy = ( + tbl.search() + .where("id = 1") + .select(["id", "video"]) + .to_pandas(blob_mode="lazy") + ) + + # Same call shape works on async query builders + df_bytes = await ( + tbl_async.query() + .where("id = 1") + .select(["id", "video"]) + .to_pandas(blob_mode="bytes") + ) + + # Vector / FTS / hybrid queries can't materialize blob columns, + # so omit them from the projection + df_vec = ( + tbl.search(query_vector) + .limit(10) + .select(["id", "vector"]) + .to_pandas(split_blocks=True, self_destruct=True) + ) + # --8<-- [end:query_to_pandas_kwargs] + + assert len(df_lazy) == 1 + assert len(df_bytes) == 1 + assert df_bytes["video"].iloc[0] == b"fake_video_bytes_1" + assert len(df_vec) == 10 + assert "video" not in df_vec.columns diff --git a/docs/web-tests/py/test_quickstart.py b/docs/web-tests/py/test_quickstart.py new file mode 100644 index 000000000..207e6540f --- /dev/null +++ b/docs/web-tests/py/test_quickstart.py @@ -0,0 +1,208 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +import lancedb +import pytest + + +def test_quickstart(db_path_factory): + uri = db_path_factory("quickstart_db") + db = lancedb.connect(uri) + + # --8<-- [start:quickstart_data] + data = [ + { + "id": "1", + "name": "King Arthur", + "role": "King", + "description": "Leader of Camelot and wielder of Excalibur.", + "stats": {"strength": 4, "magic": 1, "leadership": 5, "wisdom": 4}, + "vector": [0.7, 0.1, 0.9, 0.7], + }, + { + "id": "2", + "name": "Merlin", + "role": "Wizard", + "description": "Advisor and prophet with deep magical knowledge.", + "stats": {"strength": 2, "magic": 5, "leadership": 4, "wisdom": 5}, + "vector": [0.2, 0.9, 0.4, 0.9], + }, + { + "id": "3", + "name": "Sir Lancelot", + "role": "Knight", + "description": "Legendary knight known for courage and combat skill.", + "stats": {"strength": 5, "magic": 1, "leadership": 3, "wisdom": 3}, + "vector": [0.9, 0.1, 0.5, 0.4], + }, + ] + # --8<-- [end:quickstart_data] + + # --8<-- [start:quickstart_create_table] + table = db.create_table("characters", data=data, mode="overwrite") + # --8<-- [end:quickstart_create_table] + assert len(table) == 3 + # Drop the table to test create without overwrite + db.drop_table("characters") + + # --8<-- [start:quickstart_create_table_no_overwrite] + table = db.create_table("characters", data=data) + # --8<-- [end:quickstart_create_table_no_overwrite] + assert len(table) == 3 + # --8<-- [start:quickstart_vector_search_1] + # Search for examples similar to a "wise magical advisor" + query_vector = [0.2, 0.8, 0.4, 0.9] + + # Ensure you run `pip install polars` beforehand + result = ( + table.search(query_vector) + .select(["name", "role", "description", "_distance"]) + .limit(2) + .to_polars() + ) + print(result) + # --8<-- [end:quickstart_vector_search_1] + assert result.head(1)["name"][0] == "Merlin" + + # --8<-- [start:quickstart_curate_with_metadata] + curated = ( + table.search(query_vector) + .where("stats.magic >= 4") + .select(["name", "role", "description", "_distance"]) + .limit(2) + .to_polars() + ) + print(curated) + # --8<-- [end:quickstart_curate_with_metadata] + assert curated.head(1)["name"][0] == "Merlin" + + # --8<-- [start:quickstart_output_pandas] + # Ensure you run `pip install pandas` beforehand + result = table.search(query_vector).limit(2).to_pandas() + print(result) + # --8<-- [end:quickstart_output_pandas] + assert result.iloc[0]["name"] == "Merlin" + + # --8<-- [start:quickstart_add_feature] + table.add_columns( + { + "power_score": "cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)" + } + ) + # --8<-- [end:quickstart_add_feature] + assert "power_score" in table.schema.names + + # --8<-- [start:quickstart_query_feature] + features = table.search().select(["name", "role", "power_score"]).to_polars() + print(features) + # --8<-- [end:quickstart_query_feature] + assert "power_score" in features.columns + + # --8<-- [start:quickstart_multimodal_bytes] + from pathlib import Path + + image_path = Path("docs/static/assets/images/quickstart/sir-lancelot.jpg") + image_bytes = image_path.read_bytes() + + multimodal_table = db.create_table( + "character_images", + data=[ + { + "id": "lancelot", + "description": "Portrait of Sir Lancelot", + "image": image_bytes, + "vector": [0.9, 0.1, 0.5, 0.4], + } + ], + mode="overwrite", + ) + # --8<-- [end:quickstart_multimodal_bytes] + assert len(multimodal_table) == 1 + + # --8<-- [start:quickstart_open_table] + table = db.open_table("characters") + # --8<-- [end:quickstart_open_table] + + # --8<-- [start:quickstart_add_data] + more_data = [ + { + "id": "4", + "name": "Morgana", + "role": "Sorceress", + "description": "Powerful sorceress of Avalon.", + "stats": {"strength": 2, "magic": 5, "leadership": 4, "wisdom": 4}, + "vector": [0.3, 0.9, 0.6, 0.8], + "power_score": 3.75, + }, + ] + + # Add data to table + table.add(more_data) + # --8<-- [end:quickstart_add_data] + assert len(table) == 4 + + # --8<-- [start:quickstart_vector_search_2] + # Search for examples similar to a "powerful sorceress" + query_vector = [0.3, 0.9, 0.6, 0.8] + + results = table.search(query_vector).limit(2).to_polars() + print(results) + # --8<-- [end:quickstart_vector_search_2] + assert results.head(1)["name"][0] == "Morgana" + + +@pytest.mark.asyncio +async def test_quickstart_async_api(db_path_factory): + db_uri = db_path_factory("quickstart_async_db") + import lancedb + async_db = await lancedb.connect_async(db_uri) + + # --8<-- [start:quickstart_data_async] + data = [ + { + "id": "1", + "name": "King Arthur", + "role": "King", + "description": "Leader of Camelot and wielder of Excalibur.", + "stats": {"strength": 4, "magic": 1, "leadership": 5, "wisdom": 4}, + "vector": [0.7, 0.1, 0.9, 0.7], + }, + { + "id": "2", + "name": "Merlin", + "role": "Wizard", + "description": "Advisor and prophet with deep magical knowledge.", + "stats": {"strength": 2, "magic": 5, "leadership": 4, "wisdom": 5}, + "vector": [0.2, 0.9, 0.4, 0.9], + }, + { + "id": "3", + "name": "Sir Lancelot", + "role": "Knight", + "description": "Legendary knight known for courage and combat skill.", + "stats": {"strength": 5, "magic": 1, "leadership": 3, "wisdom": 3}, + "vector": [0.9, 0.1, 0.5, 0.4], + }, + ] + # --8<-- [end:quickstart_data_async] + + # --8<-- [start:quickstart_create_table_async] + async_table = await async_db.create_table( + "characters", + data=data, + mode="overwrite", + ) + # --8<-- [end:quickstart_create_table_async] + + # --8<-- [start:quickstart_vector_search_1_async] + # Search for examples similar to a "wise magical advisor" + query_vector = [0.2, 0.8, 0.4, 0.9] + + # Ensure you run `pip install polars` beforehand + async_result = await ( + await async_table.search(query_vector) + ).select(["name", "role", "description", "_distance"]).limit(2).to_polars() + print(async_result) + # --8<-- [end:quickstart_vector_search_1_async] + + assert async_result.head(1)["name"][0] == "Merlin" diff --git a/docs/web-tests/py/test_search.py b/docs/web-tests/py/test_search.py new file mode 100644 index 000000000..4b7e8a9c7 --- /dev/null +++ b/docs/web-tests/py/test_search.py @@ -0,0 +1,730 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +# --8<-- [start:import-lancedb] +import lancedb + +# --8<-- [end:import-lancedb] +# --8<-- [start:import-numpy] +from lancedb.query import BoostQuery, MatchQuery +import numpy as np +import pyarrow as pa + +# --8<-- [end:import-numpy] +# --8<-- [start:import-datetime] +from datetime import datetime + +# --8<-- [end:import-datetime] +# --8<-- [start:import-lancedb-pydantic] +from lancedb.pydantic import Vector, LanceModel + +# --8<-- [end:import-lancedb-pydantic] +# --8<-- [start:import-pydantic-base-model] +from pydantic import BaseModel + +# --8<-- [end:import-pydantic-base-model] +# --8<-- [start:import-lancedb-fts] +from lancedb.index import FTS + +# --8<-- [end:import-lancedb-fts] +# --8<-- [start:import-os] +import os + +# --8<-- [end:import-os] +# --8<-- [start:import-embeddings] +from lancedb.embeddings import get_registry + +# --8<-- [end:import-embeddings] +import pytest + + +# --8<-- [start:class-definition] +class Metadata(BaseModel): + source: str + timestamp: datetime + + +class Document(BaseModel): + content: str + meta: Metadata + + +class LanceSchema(LanceModel): + id: str + vector: Vector(1536) + payload: Document + + +# --8<-- [end:class-definition] + + +def test_vector_search(): + # --8<-- [start:exhaustive_search] + uri = "data/sample-lancedb" + db = lancedb.connect(uri) + data = [ + {"vector": row, "item": f"item {i}"} + for i, row in enumerate(np.random.random((10_000, 1536)).astype("float32")) + ] + tbl = db.create_table("vector_search", data=data, mode="overwrite") + tbl.search(np.random.random((1536))).limit(10).to_list() + # --8<-- [end:exhaustive_search] + # --8<-- [start:exhaustive_search_cosine] + tbl.search(np.random.random((1536))).distance_type("cosine").limit(10).to_list() + # --8<-- [end:exhaustive_search_cosine] + # --8<-- [start:create_table_with_nested_schema] + # Let's add 100 sample rows to our dataset + data = [ + LanceSchema( + id=f"id{i}", + vector=np.random.randn(1536), + payload=Document( + content=f"document{i}", + meta=Metadata(source=f"source{i % 10}", timestamp=datetime.now()), + ), + ) + for i in range(100) + ] + + # Synchronous client + tbl = db.create_table("documents", data=data, mode="overwrite") + # --8<-- [end:create_table_with_nested_schema] + # --8<-- [start:search_result_as_pyarrow] + tbl.search(np.random.randn(1536)).to_arrow() + # --8<-- [end:search_result_as_pyarrow] + # --8<-- [start:search_result_as_pandas] + tbl.search(np.random.randn(1536)).to_pandas() + # --8<-- [end:search_result_as_pandas] + # --8<-- [start:search_result_as_pandas_flatten_true] + tbl.search(np.random.randn(1536)).to_pandas(flatten=True) + # --8<-- [end:search_result_as_pandas_flatten_true] + # --8<-- [start:search_result_as_pandas_flatten_1] + tbl.search(np.random.randn(1536)).to_pandas(flatten=1) + # --8<-- [end:search_result_as_pandas_flatten_1] + # --8<-- [start:search_result_as_list] + tbl.search(np.random.randn(1536)).to_list() + # --8<-- [end:search_result_as_list] + # --8<-- [start:search_result_as_pydantic] + tbl.search(np.random.randn(1536)).to_pydantic(LanceSchema) + # --8<-- [end:search_result_as_pydantic] + + +@pytest.mark.asyncio +async def test_vector_search_async(): + # --8<-- [start:exhaustive_search_async] + uri = "data/sample-lancedb" + async_db = await lancedb.connect_async(uri) + data = [ + {"vector": row, "item": f"item {i}"} + for i, row in enumerate(np.random.random((10_000, 1536)).astype("float32")) + ] + async_tbl = await async_db.create_table( + "vector_search_async", data=data, mode="overwrite" + ) + (await (await async_tbl.search(np.random.random((1536)))).limit(10).to_list()) + # --8<-- [end:exhaustive_search_async] + # --8<-- [start:exhaustive_search_async_cosine] + ( + await (await async_tbl.search(np.random.random((1536)))) + .distance_type("cosine") + .limit(10) + .to_list() + ) + # --8<-- [end:exhaustive_search_async_cosine] + # --8<-- [start:create_table_async_with_nested_schema] + # Let's add 100 sample rows to our dataset + data = [ + LanceSchema( + id=f"id{i}", + vector=np.random.randn(1536), + payload=Document( + content=f"document{i}", + meta=Metadata(source=f"source{i % 10}", timestamp=datetime.now()), + ), + ) + for i in range(100) + ] + + async_tbl = await async_db.create_table( + "documents_async", data=data, mode="overwrite" + ) + # --8<-- [end:create_table_async_with_nested_schema] + # --8<-- [start:search_result_async_as_pyarrow] + await (await async_tbl.search(np.random.randn(1536))).to_arrow() + # --8<-- [end:search_result_async_as_pyarrow] + # --8<-- [start:search_result_async_as_pandas] + await (await async_tbl.search(np.random.randn(1536))).to_pandas() + # --8<-- [end:search_result_async_as_pandas] + # --8<-- [start:search_result_async_as_list] + await (await async_tbl.search(np.random.randn(1536))).to_list() + # --8<-- [end:search_result_async_as_list] + + +def test_fts_fuzzy_query(): + uri = "data/fuzzy-example" + db = lancedb.connect(uri) + + table = db.create_table( + "my_table_fts_fuzzy", + data=pa.table( + { + "text": [ + "fa", + "fo", # spellchecker:disable-line + "fob", + "focus", + "foo", + "food", + "foul", + ] + } + ), + mode="overwrite", + ) + table.create_fts_index("text", replace=True) + + results = table.search(MatchQuery("foo", "text", fuzziness=1)).to_pandas() + assert len(results) == 4 + assert set(results["text"].to_list()) == { + "foo", + "fo", # 1 deletion # spellchecker:disable-line + "fob", # 1 substitution + "food", # 1 insertion + } + + +def test_fts_boost_query(): + uri = "data/boost-example" + db = lancedb.connect(uri) + + table = db.create_table( + "my_table_fts_boost", + data=pa.table( + { + "title": [ + "The Hidden Gems of Travel", + "Exploring Nature's Wonders", + "Cultural Treasures Unveiled", + "The Nightlife Chronicles", + "Scenic Escapes and Challenges", + ], + "desc": [ + "A vibrant city with occasional traffic jams.", + "Beautiful landscapes but overpriced tourist spots.", + "Rich cultural heritage but humid summers.", + "Bustling nightlife but noisy streets.", + "Scenic views but limited public transport options.", + ], + } + ), + mode="overwrite", + ) + table.create_fts_index("desc", replace=True) + + results = table.search( + BoostQuery( + MatchQuery("beautiful, cultural, nightlife", "desc"), + MatchQuery("bad traffic jams, overpriced", "desc"), + ), + ).to_pandas() + + # we will hit 3 results because the positive query has 3 hits + assert len(results) == 3 + # the one containing "overpriced" will be negatively boosted, + # so it will be the last one + assert ( + results["desc"].to_list()[2] + == "Beautiful landscapes but overpriced tourist spots." + ) + + +def test_fts_native(): + # --8<-- [start:basic_fts] + uri = "data/sample-lancedb" + db = lancedb.connect(uri) + + table = db.create_table( + "my_table_fts", + data=[ + {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}, + {"vector": [5.9, 26.5], "text": "There are several kittens playing"}, + ], + mode="overwrite", + ) + + table.create_fts_index("text") + table.search("puppy").limit(10).select(["text"]).to_list() + # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}] + # ... + # --8<-- [end:basic_fts] + # --8<-- [start:fts_config_stem] + table.create_fts_index("text", tokenizer_name="en_stem", replace=True) + # --8<-- [end:fts_config_stem] + # --8<-- [start:fts_config_folding] + table.create_fts_index( + "text", + language="French", + stem=True, + ascii_folding=True, + replace=True, + ) + # --8<-- [end:fts_config_folding] + # --8<-- [start:fts_prefiltering] + table.search("puppy").limit(10).where("text='foo'", prefilter=True).to_list() + # --8<-- [end:fts_prefiltering] + # --8<-- [start:fts_postfiltering] + table.search("puppy").limit(10).where("text='foo'", prefilter=False).to_list() + # --8<-- [end:fts_postfiltering] + # --8<-- [start:fts_with_position] + table.create_fts_index("text", with_position=True, replace=True) + # --8<-- [end:fts_with_position] + # --8<-- [start:fts_incremental_index] + table.add([{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}]) + table.optimize() + # --8<-- [end:fts_incremental_index] + + +@pytest.mark.asyncio +async def test_fts_native_async(): + # --8<-- [start:basic_fts_async] + uri = "data/sample-lancedb" + async_db = await lancedb.connect_async(uri) + + async_tbl = await async_db.create_table( + "my_table_fts_async", + data=[ + {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}, + {"vector": [5.9, 26.5], "text": "There are several kittens playing"}, + ], + mode="overwrite", + ) + + # async API uses our native FTS algorithm + await async_tbl.create_index("text", config=FTS()) + await (await async_tbl.search("puppy")).select(["text"]).limit(10).to_list() + # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}] + # ... + # --8<-- [end:basic_fts_async] + # --8<-- [start:fts_config_stem_async] + await async_tbl.create_index( + "text", config=FTS(language="English", stem=True, remove_stop_words=True) + ) + # --8<-- [end:fts_config_stem_async] + # --8<-- [start:fts_config_folding_async] + await async_tbl.create_index( + "text", config=FTS(language="French", stem=True, ascii_folding=True) + ) + # --8<-- [end:fts_config_folding_async] + # --8<-- [start:fts_prefiltering_async] + await (await async_tbl.search("puppy")).limit(10).where("text='foo'").to_list() + # --8<-- [end:fts_prefiltering_async] + # --8<-- [start:fts_postfiltering_async] + await ( + (await async_tbl.search("puppy")) + .limit(10) + .where("text='foo'") + .postfilter() + .to_list() + ) + # --8<-- [end:fts_postfiltering_async] + # --8<-- [start:fts_with_position_async] + await async_tbl.create_index("text", config=FTS(with_position=True)) + # --8<-- [end:fts_with_position_async] + # --8<-- [start:fts_incremental_index_async] + await async_tbl.add([{"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}]) + await async_tbl.optimize() + # --8<-- [end:fts_incremental_index_async] + + +def _vectors(n, dim, seed=0, column="vector"): + rng = np.random.default_rng(seed) + return [ + {column: rng.random(dim).astype("float32"), "id": i} for i in range(n) + ] + + +def _build_indexed_table(db, name, dim=128, n=512): + tbl = db.create_table(name, _vectors(n, dim), mode="overwrite") + tbl.create_index(metric="cosine", num_partitions=4, num_sub_vectors=8) + return tbl + + +def test_vs_distance_metric_and_brute_force(tmp_db): + tbl = tmp_db.create_table("vs_metric", _vectors(64, 1536), mode="overwrite") + + # --8<-- [start:configure_distance_metric] + tbl.search(np.random.random((1536))).distance_type("cosine").limit(10).to_list() + # --8<-- [end:configure_distance_metric] + + # --8<-- [start:brute_force_search] + tbl.search(np.random.random((1536))).limit(3).to_list() + # --8<-- [end:brute_force_search] + + +def test_vs_select_vector_column(tmp_db): + db = tmp_db + + # --8<-- [start:select_vector_column] + import pyarrow as pa + + schema = pa.schema([ + pa.field("id", pa.int32()), + pa.field( + "image", + pa.struct([pa.field("embedding", pa.list_(pa.float32(), 2))]), + ), + ]) + table = db.create_table( + "nested", + data=[{"id": 0, "image": {"embedding": [0.0, 1.0]}}], + schema=schema, + ) + + # Inferred: the only vector leaf is `image.embedding`. + table.search([0.0, 1.0]).limit(1).to_list() + + # Explicit: required when more than one vector column matches. + table.search([0.0, 1.0], vector_column_name="image.embedding").limit(1).to_list() + # --8<-- [end:select_vector_column] + + +def test_vs_index_nested_column(tmp_db): + dim = 16 + schema = pa.schema( + [ + pa.field("id", pa.int32()), + pa.field( + "image", + pa.struct([pa.field("embedding", pa.list_(pa.float32(), dim))]), + ), + ] + ) + rng = np.random.default_rng(0) + data = [ + {"id": i, "image": {"embedding": rng.random(dim).astype("float32").tolist()}} + for i in range(512) + ] + table = tmp_db.create_table( + "nested_index", data=data, schema=schema, mode="overwrite" + ) + + # --8<-- [start:index_nested_column] + table.create_index(vector_column_name="image.embedding") + # --8<-- [end:index_nested_column] + + assert table.list_indices() + + +def test_vs_indexed_queries(tmp_db): + table = _build_indexed_table(tmp_db, "vs_indexed", dim=128, n=512) + embedding = np.random.random(128) + + # --8<-- [start:exact_vs_approximate_distances] + # Indexed ANN search without refinement (fast, approximate `_distance`) + fast_results = ( + table.search(embedding) + .limit(10) + .to_pandas() + ) + + # Recompute distances on full vectors for reranked candidates + exact_distance_results = ( + table.search(embedding) + .limit(10) + .refine_factor(1) + .to_pandas() + ) + + # Rerank a larger candidate set for better recall (higher latency) + higher_recall_results = ( + table.search(embedding) + .limit(10) + .refine_factor(20) + .to_pandas() + ) + # --8<-- [end:exact_vs_approximate_distances] + + # --8<-- [start:bypass_vector_index] + table.search(embedding).bypass_vector_index().limit(5).to_pandas() + # --8<-- [end:bypass_vector_index] + + assert len(fast_results) == 10 + assert len(exact_distance_results) == 10 + assert len(higher_recall_results) == 10 + + +def test_vs_fast_search(tmp_db, monkeypatch): + table = _build_indexed_table(tmp_db, "vs_fast", dim=128, n=512) + embedding = np.random.random(128) + + # `fast_search` is an Enterprise/async query flag; strip it so the snippet + # runs against a local OSS table without changing what readers see. + real_search = table.search + monkeypatch.setattr( + table, + "search", + lambda *a, **k: real_search( + *a, **{key: val for key, val in k.items() if key != "fast_search"} + ), + ) + + # --8<-- [start:fast_search] + table.search(embedding, fast_search=True).limit(5).to_pandas() + # --8<-- [end:fast_search] + + +def test_vs_distance_range(tmp_db): + tbl = tmp_db.create_table("vs_distance_range", _vectors(256, 256), mode="overwrite") + + # --8<-- [start:search_distance_range] + query = np.random.random(256) + + # Search for the vectors within the range of [0.1, 0.5) + tbl.search(query).distance_range(0.1, 0.5).to_arrow() + + # Search for the vectors with the distance less than 0.5 + tbl.search(query).distance_range(upper_bound=0.5).to_arrow() + + # Search for the vectors with the distance greater or equal to 0.1 + tbl.search(query).distance_range(lower_bound=0.1).to_arrow() + # --8<-- [end:search_distance_range] + + +def test_vs_multivector_search(tmp_db): + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("vector", pa.list_(pa.list_(pa.float32(), 256))), + ] + ) + rng = np.random.default_rng(0) + data = [{"id": i, "vector": rng.random(size=(2, 256)).tolist()} for i in range(64)] + tbl = tmp_db.create_table( + "vs_multivector", data=data, schema=schema, mode="overwrite" + ) + + # --8<-- [start:multivector_search] + query_multi = np.random.random(size=(2, 256)) + results_multi = tbl.search(query_multi).limit(5).to_pandas() + # --8<-- [end:multivector_search] + + assert len(results_multi) <= 5 + + +def test_vs_binary_search(tmp_db): + db = tmp_db + + # --8<-- [start:search_binary_vectors] + import numpy as np + import pyarrow as pa + + schema = pa.schema( + [ + pa.field("id", pa.int64()), + # for dim=256, lance stores every 8 bits in a byte + # so the vector field should be a list of 256 / 8 = 32 bytes + pa.field("vector", pa.list_(pa.uint8(), 32)), + ] + ) + tbl = db.create_table("my_binary_vectors", schema=schema) + + data = [] + for i in range(1024): + vector = np.random.randint(0, 2, size=256) + # pack the binary vector into bytes to save space + packed_vector = np.packbits(vector) + data.append( + { + "id": i, + "vector": packed_vector, + } + ) + tbl.add(data) + + query = np.random.randint(0, 2, size=256) + packed_query = np.packbits(query) + tbl.search(packed_query).distance_type("hamming").to_arrow() + # --8<-- [end:search_binary_vectors] + + +def test_vs_enterprise_filtering(tmp_db, monkeypatch): + import sys + import types + + dim = 384 + rng = np.random.default_rng(0) + rows = [ + { + "vector": rng.random(dim).astype("float32"), + "text": f"story {i}", + "keywords": f"kw{i}", + "label": i % 4, + } + for i in range(50) + ] + tmp_db.create_table("lancedb-enterprise-quickstart", data=rows, mode="overwrite") + db = tmp_db + + # Mock the Hugging Face dataset loader to avoid network downloads. + class _FakeDataset: + def __init__(self, n): + self._emb = [rng.random(dim).astype("float32") for _ in range(n)] + self._kw = [f"kw{i}" for i in range(n)] + + def __getitem__(self, key): + if isinstance(key, str): + return {"keywords_embeddings": self._emb, "keywords": self._kw}[key] + return {"keywords": self._kw[key], "keywords_embeddings": self._emb[key]} + + fake_datasets = types.ModuleType("datasets") + fake_datasets.load_dataset = lambda *a, **k: _FakeDataset(10) + monkeypatch.setitem(sys.modules, "datasets", fake_datasets) + + # --8<-- [start:vector_search_prefilter] + from datasets import load_dataset + + # Load query vector from dataset + query_dataset = load_dataset("sunhaozhepy/ag_news_sbert_keywords_embeddings", split="test[5000:5001]") + print(f"Query keywords: {query_dataset[0]['keywords']}") + query_embed = query_dataset["keywords_embeddings"][0] + + # Open table and perform search + table_name = "lancedb-enterprise-quickstart" + table = db.open_table(table_name) + + # Vector search with filters (pre-filtering is the default) + search_results = ( + table.search(query_embed) + .where("label > 2") + .select(["text", "keywords", "label"]) + .limit(5) + .to_pandas() + ) + + print("Search results (with pre-filtering):") + print(search_results) + # --8<-- [end:vector_search_prefilter] + + # --8<-- [start:vector_search_postfilter] + results_post_filtered = ( + table.search(query_embed) + .where("label > 1", prefilter=False) + .select(["text", "keywords", "label"]) + .limit(5) + .to_pandas() + ) + + print("Vector search results with post-filter:") + print(results_post_filtered) + # --8<-- [end:vector_search_postfilter] + + # --8<-- [start:batch_search] + # Load a batch of query embeddings + query_dataset = load_dataset( + "sunhaozhepy/ag_news_sbert_keywords_embeddings", split="test[5000:5005]" + ) + query_embeds = query_dataset["keywords_embeddings"] + batch_results = table.search(query_embeds).limit(5).to_pandas() + print(batch_results) + # --8<-- [end:batch_search] + + +@pytest.mark.skip() +def test_hybrid_search(): + # --8<-- [start:import-openai] + import openai + + # --8<-- [end:import-openai] + # --8<-- [start:openai-embeddings] + # Ingest embedding function in LanceDB table + # Configuring the environment variable OPENAI_API_KEY + if "OPENAI_API_KEY" not in os.environ: + # OR set the key here as a variable + openai.api_key = "sk-..." + embeddings = get_registry().get("openai").create() + + # --8<-- [end:openai-embeddings] + # --8<-- [start:class-Documents] + class Documents(LanceModel): + vector: Vector(embeddings.ndims()) = embeddings.VectorField() + text: str = embeddings.SourceField() + + # --8<-- [end:class-Documents] + # --8<-- [start:basic_hybrid_search] + data = [ + {"text": "rebel spaceships striking from a hidden base"}, + {"text": "have won their first victory against the evil Galactic Empire"}, + {"text": "during the battle rebel spies managed to steal secret plans"}, + {"text": "to the Empire's ultimate weapon the Death Star"}, + ] + uri = "data/sample-lancedb" + db = lancedb.connect(uri) + table = db.create_table("documents", schema=Documents) + # ingest docs with auto-vectorization + table.add(data) + # Create a fts index before the hybrid search + table.create_fts_index("text") + # hybrid search with default re-ranker + table.search("flower moon", query_type="hybrid").to_pandas() + # --8<-- [end:basic_hybrid_search] + # --8<-- [start:hybrid_search_pass_vector_text] + vector_query = [0.1, 0.2, 0.3, 0.4, 0.5] + text_query = "flower moon" + ( + table.search(query_type="hybrid") + .vector(vector_query) + .text(text_query) + .limit(5) + .to_pandas() + ) + # --8<-- [end:hybrid_search_pass_vector_text] + + +@pytest.mark.skip +async def test_hybrid_search_async(): + import openai + + # --8<-- [start:openai-embeddings] + # Ingest embedding function in LanceDB table + # Configuring the environment variable OPENAI_API_KEY + if "OPENAI_API_KEY" not in os.environ: + # OR set the key here as a variable + openai.api_key = "sk-..." + embeddings = get_registry().get("openai").create() + + # --8<-- [end:openai-embeddings] + # --8<-- [start:class-Documents] + class Documents(LanceModel): + vector: Vector(embeddings.ndims()) = embeddings.VectorField() + text: str = embeddings.SourceField() + + # --8<-- [end:class-Documents] + # --8<-- [start:basic_hybrid_search_async] + uri = "data/sample-lancedb" + async_db = await lancedb.connect_async(uri) + data = [ + {"text": "rebel spaceships striking from a hidden base"}, + {"text": "have won their first victory against the evil Galactic Empire"}, + {"text": "during the battle rebel spies managed to steal secret plans"}, + {"text": "to the Empire's ultimate weapon the Death Star"}, + ] + async_tbl = await async_db.create_table("documents_async", schema=Documents) + # ingest docs with auto-vectorization + await async_tbl.add(data) + # Create a fts index before the hybrid search + await async_tbl.create_index("text", config=FTS()) + text_query = "flower moon" + # hybrid search with default re-ranker + await (await async_tbl.search("flower moon", query_type="hybrid")).to_pandas() + # --8<-- [end:basic_hybrid_search_async] + # --8<-- [start:hybrid_search_pass_vector_text_async] + vector_query = [0.1, 0.2, 0.3, 0.4, 0.5] + text_query = "flower moon" + await ( + async_tbl.query() + .nearest_to(vector_query) + .nearest_to_text(text_query) + .limit(5) + .to_pandas() + ) + # --8<-- [end:hybrid_search_pass_vector_text_async] diff --git a/docs/web-tests/py/test_storage.py b/docs/web-tests/py/test_storage.py new file mode 100644 index 000000000..f08ab07cf --- /dev/null +++ b/docs/web-tests/py/test_storage.py @@ -0,0 +1,162 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +import lancedb +import pytest + + +class DummyTable: + def __init__(self, name: str, storage_options: dict | None = None): + self.name = name + self.storage_options = storage_options + + +class DummyConnection: + def __init__(self, uri: str, options: dict): + self.uri = uri + self.options = options + self.created_tables: list[DummyTable] = [] + + def create_table( + self, + name: str, + data, + storage_options: dict | None = None, + ): + table = DummyTable(name, storage_options=storage_options) + self.created_tables.append(table) + return table + + +@pytest.fixture +def fake_connect(monkeypatch): + calls: list[DummyConnection] = [] + + def _fake_connect(uri: str, **kwargs): + conn = DummyConnection(uri, kwargs) + calls.append(conn) + return conn + + monkeypatch.setattr(lancedb, "connect", _fake_connect) + return calls + + +def test_storage_snippets(fake_connect): + # --8<-- [start:storage_connect_s3] + db = lancedb.connect("s3://bucket/path") + # --8<-- [end:storage_connect_s3] + + # --8<-- [start:storage_connect_gcs] + db = lancedb.connect("gs://bucket/path") + # --8<-- [end:storage_connect_gcs] + + # --8<-- [start:storage_connect_azure] + db = lancedb.connect("az://bucket/path") + # --8<-- [end:storage_connect_azure] + + # --8<-- [start:storage_connect_timeout] + db = lancedb.connect( + "s3://bucket/path", + storage_options={"timeout": "60s"}, + ) + # --8<-- [end:storage_connect_timeout] + + # --8<-- [start:storage_table_timeout] + table = db.create_table( + "table", + [{"a": 1, "b": 2}], + storage_options={"timeout": "60s"}, + ) + # --8<-- [end:storage_table_timeout] + + # --8<-- [start:storage_s3_ddb] + db = lancedb.connect( + "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table", + ) + # --8<-- [end:storage_s3_ddb] + + # --8<-- [start:storage_s3_ddb_local] + db = lancedb.connect( + "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table", + storage_options={ + "endpoint": "http://localhost:4566", + "dynamodb_endpoint": "http://localhost:4566", + "allow_http": "true", + }, + ) + # --8<-- [end:storage_s3_ddb_local] + + # --8<-- [start:storage_s3_sse_kms] + db = lancedb.connect( + "s3://bucket/path", + storage_options={ + "aws_server_side_encryption": "aws:kms", + "aws_sse_kms_key_id": "", + }, + ) + # --8<-- [end:storage_s3_sse_kms] + + # --8<-- [start:storage_azure_sas] + db = lancedb.connect( + "az://my-container/my-database", + storage_options={ + "azure_storage_account_name": "some-account", + "azure_storage_sas_token": "", + }, + ) + # --8<-- [end:storage_azure_sas] + + # --8<-- [start:storage_s3_minio] + db = lancedb.connect( + "s3://bucket/path", + storage_options={ + "region": "us-east-1", + "endpoint": "http://minio:9000", + }, + ) + # --8<-- [end:storage_s3_minio] + + # --8<-- [start:storage_s3_express] + db = lancedb.connect( + "s3://my-bucket--use1-az4--x-s3/path", + storage_options={ + "region": "us-east-1", + "s3_express": "true", + }, + ) + # --8<-- [end:storage_s3_express] + + # --8<-- [start:storage_gcs_service_account] + db = lancedb.connect( + "gs://my-bucket/my-database", + storage_options={ + "service_account": "path/to/service-account.json", + }, + ) + # --8<-- [end:storage_gcs_service_account] + + # --8<-- [start:storage_azure_account] + db = lancedb.connect( + "az://my-container/my-database", + storage_options={ + "account_name": "some-account", + "account_key": "some-key", + }, + ) + # --8<-- [end:storage_azure_account] + + # --8<-- [start:storage_tigris_connect] + db = lancedb.connect( + "s3://your-bucket/path", + storage_options={ + "endpoint": "https://t3.storage.dev", + "region": "auto", + }, + ) + # --8<-- [end:storage_tigris_connect] + + assert len(fake_connect) == 13 + assert all( + conn.uri.startswith(("s3://", "gs://", "az://", "s3+ddb://")) + for conn in fake_connect + ) diff --git a/docs/web-tests/py/test_tables.py b/docs/web-tests/py/test_tables.py new file mode 100644 index 000000000..b3a456adc --- /dev/null +++ b/docs/web-tests/py/test_tables.py @@ -0,0 +1,1546 @@ +# SPDX-License-Identifier: Apache-2.0 +# SPDX-FileCopyrightText: Copyright The LanceDB Authors + +# --8<-- [start:tables_imports] +import lancedb +import numpy as np +import pandas as pd +import pyarrow as pa +import pytest +from numpy.random import randint, random + +# --8<-- [end:tables_imports] + + +# ============================================================================ +# Table Creation Examples +# ============================================================================ + + +def test_tables_basic_connect_snippet(monkeypatch): + calls = {} + + class DummyDB: + pass + + def fake_connect(uri): + calls["uri"] = uri + return DummyDB() + + import lancedb as _lancedb + + # Monkey patch is used only in this test. + # Maybe can just actually connect, and disconnect instead? + monkeypatch.setattr(_lancedb, "connect", fake_connect) + + # --8<-- [start:tables_basic_connect] + import lancedb + + uri = "data/sample-lancedb" + db = lancedb.connect(uri) + # --8<-- [end:tables_basic_connect] + + assert calls["uri"] == "data/sample-lancedb" + assert isinstance(db, DummyDB) + + +def test_update_connect_enterprise_snippet(monkeypatch): + calls = {} + + class DummyDB: + pass + + def fake_connect(**kwargs): + calls.update(kwargs) + return DummyDB() + + import lancedb as _lancedb + + monkeypatch.setattr(_lancedb, "connect", fake_connect) + + # --8<-- [start:update_connect_enterprise] + import lancedb + + db = lancedb.connect( + uri="db://your-project-slug", + api_key="your-api-key", + region="us-east-1", + ) + # --8<-- [end:update_connect_enterprise] + + assert calls["uri"] == "db://your-project-slug" + assert calls["api_key"] == "your-api-key" + assert calls["region"] == "us-east-1" + assert isinstance(db, DummyDB) + + +def test_update_connect_local_snippet(monkeypatch): + calls = {} + + class DummyDB: + pass + + def fake_connect(uri): + calls["uri"] = uri + return DummyDB() + + import lancedb as _lancedb + + monkeypatch.setattr(_lancedb, "connect", fake_connect) + + # --8<-- [start:update_connect_local] + import lancedb + + db = lancedb.connect("./data") + # --8<-- [end:update_connect_local] + + assert calls["uri"] == "./data" + assert isinstance(db, DummyDB) + + +def test_table_creation_from_dicts(tmp_db): + # --8<-- [start:create_table_from_dicts] + data = [ + {"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7}, + {"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1}, + ] + db = tmp_db + db.create_table("test_table", data, mode="overwrite") + tbl = db["test_table"] + tbl.head() + # --8<-- [end:create_table_from_dicts] + + +def test_create_table_conflict_handling(tmp_db): + db = tmp_db + data = [ + {"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7}, + {"vector": [0.2, 1.8], "lat": 40.1, "long": -74.1}, + ] + db.create_table("conflict_table", data) + + # --8<-- [start:create_table_conflict_handling] + # Idempotent open: reuse the existing table if it exists. + # The provided data is ignored; the schema is validated against the + # existing table and a mismatch raises an error. + tbl = db.create_table("conflict_table", data, exist_ok=True) + + # Overwrite: drop the existing table and create a new one with the + # provided data. This permanently discards the old table's data. + tbl = db.create_table("conflict_table", data, mode="overwrite") + # --8<-- [end:create_table_conflict_handling] + assert tbl.count_rows() == 2 + + +def test_table_creation_from_pandas(tmp_db): + # --8<-- [start:create_table_from_pandas] + import pandas as pd + + data = pd.DataFrame( + { + "vector": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]], + "lat": [45.5, 40.1], + "long": [-122.7, -74.1], + } + ) + db = tmp_db + db.create_table("my_table_pandas", data, mode="overwrite") + db["my_table_pandas"].head() + # --8<-- [end:create_table_from_pandas] + + +def test_table_creation_with_custom_schema(tmp_db): + # --8<-- [start:create_table_custom_schema] + import pyarrow as pa + + custom_schema = pa.schema( + [ + pa.field("vector", pa.list_(pa.float32(), 4)), + pa.field("lat", pa.float32()), + pa.field("long", pa.float32()), + ] + ) + + data = [ + {"vector": [1.1, 1.2, 1.3, 1.4], "lat": 45.5, "long": -122.7}, + {"vector": [0.2, 1.8, 0.4, 3.6], "lat": 40.1, "long": -74.1}, + ] + db = tmp_db + tbl = db.create_table( + "my_table_custom_schema", data, schema=custom_schema, mode="overwrite" + ) + # --8<-- [end:create_table_custom_schema] + + +def test_table_creation_from_polars(tmp_db): + # --8<-- [start:create_table_from_polars] + import polars as pl + + data = pl.DataFrame( + { + "vector": [[3.1, 4.1], [5.9, 26.5]], + "item": ["foo", "bar"], + "price": [10.0, 20.0], + } + ) + db = tmp_db + tbl = db.create_table("my_table_pl", data, mode="overwrite") + # --8<-- [end:create_table_from_polars] + + +def test_table_creation_from_arrow(tmp_db): + # --8<-- [start:create_table_from_arrow] + import numpy as np + import pyarrow as pa + + dim = 16 + total = 2 + schema = pa.schema( + [pa.field("vector", pa.list_(pa.float16(), dim)), pa.field("text", pa.string())] + ) + data = pa.Table.from_arrays( + [ + pa.array( + [np.random.randn(dim).astype(np.float16) for _ in range(total)], + pa.list_(pa.float16(), dim), + ), + pa.array(["foo", "bar"]), + ], + ["vector", "text"], + ) + db = tmp_db + tbl = db.create_table("f16_tbl", data, schema=schema, mode="overwrite") + # --8<-- [end:create_table_from_arrow] + + +def test_table_creation_from_pydantic(tmp_db): + # --8<-- [start:create_table_from_pydantic] + from lancedb.pydantic import LanceModel, Vector + + class Content(LanceModel): + movie_id: int + vector: Vector(128) + genres: str + title: str + imdb_id: int + + @property + def imdb_url(self) -> str: + return f"https://www.imdb.com/title/tt{self.imdb_id}" + + db = tmp_db + tbl = db.create_table("movielens_small", schema=Content, mode="overwrite") + # --8<-- [end:create_table_from_pydantic] + + +def test_table_creation_nested_schema(tmp_db): + # --8<-- [start:create_table_nested_schema] + from lancedb.pydantic import LanceModel, Vector + + # --8<-- [start:tables_document_model] + from pydantic import BaseModel + + class Document(BaseModel): + content: str + source: str + + # --8<-- [end:tables_document_model] + + class NestedSchema(LanceModel): + id: str + vector: Vector(1536) + document: Document + + db = tmp_db + tbl = db.create_table("nested_table", schema=NestedSchema, mode="overwrite") + # --8<-- [end:create_table_nested_schema] + + +def test_tables_tz_validator_snippet(): + # --8<-- [start:tables_tz_validator] + from datetime import datetime + from zoneinfo import ZoneInfo + + from lancedb.pydantic import LanceModel + from pydantic import Field, ValidationError, ValidationInfo, field_validator + + tzname = "America/New_York" + tz = ZoneInfo(tzname) + + class TestModel(LanceModel): + dt_with_tz: datetime = Field(json_schema_extra={"tz": tzname}) + + @field_validator("dt_with_tz") + @classmethod + def tz_must_match(cls, dt: datetime) -> datetime: + assert dt.tzinfo == tz + return dt + + ok = TestModel(dt_with_tz=datetime.now(tz)) + + try: + TestModel(dt_with_tz=datetime.now(ZoneInfo("Asia/Shanghai"))) + assert 0 == 1, "this should raise ValidationError" + except ValidationError: + print("A ValidationError was raised.") + pass + # --8<-- [end:tables_tz_validator] + + assert ok is not None + + +def test_add_from_dataset(tmp_db, tmp_path): + import pyarrow as pa + import pyarrow.dataset as ds + import pyarrow.parquet as pq + + schema = pa.schema( + [ + pa.field("vector", pa.list_(pa.float32(), 4)), + pa.field("item", pa.utf8()), + pa.field("price", pa.float32()), + ] + ) + for i in range(3): + batch = pa.table( + { + "vector": [[1.0, 2.0, 3.0, 4.0], [5.0, 6.0, 7.0, 8.0]], + "item": [f"item{i * 2}", f"item{i * 2 + 1}"], + "price": [float(i * 2), float(i * 2 + 1)], + }, + schema=schema, + ) + pq.write_table(batch, tmp_path / f"part-{i}.parquet") + + data_path = str(tmp_path) + + # --8<-- [start:add_from_dataset] + import pyarrow.dataset as ds + + dataset = ds.dataset(data_path, format="parquet") + db = tmp_db + table = db.create_table("my_table", schema=dataset.schema, mode="overwrite") + table.add(dataset) + # --8<-- [end:add_from_dataset] + assert table.count_rows() == 6 + + +def test_table_creation_from_iterator(tmp_db): + # --8<-- [start:create_table_from_iterator] + import pyarrow as pa + + schema = pa.schema( + [ + pa.field("vector", pa.list_(pa.float32(), 4)), + pa.field("item", pa.utf8()), + pa.field("price", pa.float32()), + ] + ) + + def make_batches(): + for i in range(5): + yield pa.RecordBatch.from_arrays( + [ + pa.array( + [[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]], + pa.list_(pa.float32(), 4), + ), + pa.array(["foo", "bar"]), + pa.array([10.0, 20.0]), + ], + ["vector", "item", "price"], + ) + + db = tmp_db + db.create_table("batched_table", make_batches(), schema=schema, mode="overwrite") + # --8<-- [end:create_table_from_iterator] + +def test_open_existing_table(tmp_db): + # --8<-- [start:open_existing_table] + db = tmp_db + # Create a table first + data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7}] + db.create_table("test_table", data, mode="overwrite") + + # List table names + print(db.list_tables().tables) + + # Open existing table + tbl = db.open_table("test_table") + # --8<-- [end:open_existing_table] + + +def test_create_empty_table(tmp_db): + # --8<-- [start:create_empty_table] + import pyarrow as pa + + schema = pa.schema( + [ + pa.field("vector", pa.list_(pa.float32(), 2)), + pa.field("item", pa.string()), + pa.field("price", pa.float32()), + ] + ) + db = tmp_db + tbl = db.create_table("test_empty_table", schema=schema, mode="overwrite") + # --8<-- [end:create_empty_table] + + +def test_create_empty_table_pydantic(tmp_db): + # --8<-- [start:create_empty_table_pydantic] + from lancedb.pydantic import LanceModel, Vector + + class Item(LanceModel): + vector: Vector(2) + item: str + price: float + + db = tmp_db + tbl = db.create_table( + "test_empty_table_new", schema=Item.to_arrow_schema(), mode="overwrite" + ) + # --8<-- [end:create_empty_table_pydantic] + + +def test_drop_table(tmp_db): + # --8<-- [start:drop_table] + db = tmp_db + # Create a table first + data = [{"vector": [1.1, 1.2], "lat": 45.5}] + db.create_table("my_table", data, mode="overwrite") + + # Drop the table + db.drop_table("my_table") + # --8<-- [end:drop_table] + + +# ============================================================================ +# Data Update Examples +# ============================================================================ + + +def test_add_data_to_table(tmp_db): + db = tmp_db + + # --8<-- [start:add_data_to_table] + import pyarrow as pa + + # create an empty table with schema + data = [ + {"vector": [3.1, 4.1], "item": "foo", "price": 10.0}, + {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}, + {"vector": [10.2, 100.8], "item": "baz", "price": 30.0}, + {"vector": [1.4, 9.5], "item": "fred", "price": 40.0}, + ] + + schema = pa.schema( + [ + pa.field("vector", pa.list_(pa.float32(), 2)), + pa.field("item", pa.utf8()), + pa.field("price", pa.float32()), + ] + ) + + table_name = "basic_ingestion_example" + table = db.create_table(table_name, schema=schema, mode="overwrite") + # Add data + table.add(data) + # --8<-- [end:add_data_to_table] + assert table.count_rows() == len(data) + + +def test_add_data_pydantic_model(tmp_db): + db = tmp_db + + # --8<-- [start:add_data_pydantic_model] + from lancedb.pydantic import LanceModel, Vector + + # Define a Pydantic model + class Content(LanceModel): + movie_id: int + vector: Vector(128) + genres: str + title: str + imdb_id: int + + @property + def imdb_url(self) -> str: + return f"https://www.imdb.com/title/tt{self.imdb_id}" + + # Create table with Pydantic model schema + table_name = "pydantic_example" + table = db.create_table(table_name, schema=Content, mode="overwrite") + # --8<-- [end:add_data_pydantic_model] + assert table.count_rows() == 0 + + +def test_add_data_nested_model(tmp_db): + db = tmp_db + + # --8<-- [start:add_data_nested_model] + from lancedb.pydantic import LanceModel, Vector + from pydantic import BaseModel + + class Document(BaseModel): + content: str + source: str + + class NestedSchema(LanceModel): + id: str + vector: Vector(128) + document: Document + + # Create table with nested schema + table_name = "nested_model_example" + table = db.create_table(table_name, schema=NestedSchema, mode="overwrite") + # --8<-- [end:add_data_nested_model] + assert table.count_rows() == 0 + + +def test_batch_data_insertion(tmp_db): + db = tmp_db + + # --8<-- [start:batch_data_insertion] + import pyarrow as pa + + def make_batches(): + for i in range(5): # Create 5 batches + yield pa.RecordBatch.from_arrays( + [ + pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)), + pa.array([f"item{i * 2 + 1}", f"item{i * 2 + 2}"]), + pa.array([float((i * 2 + 1) * 10), float((i * 2 + 2) * 10)]), + ], + ["vector", "item", "price"], + ) + + schema = pa.schema( + [ + pa.field("vector", pa.list_(pa.float32(), 2)), + pa.field("item", pa.utf8()), + pa.field("price", pa.float32()), + ] + ) + # Create table with batches + table_name = "batch_ingestion_example" + table = db.create_table(table_name, make_batches(), schema=schema, mode="overwrite") + # --8<-- [end:batch_data_insertion] + assert table.count_rows() == 10 + + +def _create_users_example_table(db, table_name="users_example"): + return db.create_table( + table_name, + data=pa.table( + { + "id": [1, 2, 3], + "name": ["Alice", "Bob", "Charlie"], + "login_count": [10, 20, 5], + } + ), + mode="overwrite", + ) + + +def test_update_example_table_setup(tmp_db): + db = tmp_db + + # --8<-- [start:update_example_table_setup] + import pyarrow as pa + + table = db.create_table( + "users_example", + data=pa.table( + { + "id": [1, 2], + "name": ["Alice", "Bob"], + "login_count": [10, 20], + } + ), + mode="overwrite", + ) + # --8<-- [end:update_example_table_setup] + assert table.count_rows() == 2 + + +def test_update_operation(tmp_db): + db = tmp_db + + # --8<-- [start:update_operation] + import pyarrow as pa + + table = db.create_table( + "users_example", + data=pa.table( + { + "id": [1, 2], + "name": ["Alice", "Bob"], + "login_count": [10, 20], + } + ), + mode="overwrite", + ) + table.update(where="id = 2", values={"name": "Bobby"}) + # --8<-- [end:update_operation] + rows = table.to_arrow().sort_by("id").to_pylist() + assert rows == [ + {"id": 1, "name": "Alice", "login_count": 10}, + {"id": 2, "name": "Bobby", "login_count": 20}, + ] + + +def test_update_using_sql(tmp_db): + db = tmp_db + + # --8<-- [start:update_using_sql] + import pyarrow as pa + + table = db.create_table( + "users_example", + data=pa.table( + { + "id": [1, 2], + "name": ["Alice", "Bob"], + "login_count": [10, 20], + } + ), + mode="overwrite", + ) + table.update(where="id = 2", values_sql={"login_count": "login_count + 1"}) + # --8<-- [end:update_using_sql] + rows = table.to_arrow().sort_by("id").to_pylist() + assert rows == [ + {"id": 1, "name": "Alice", "login_count": 10}, + {"id": 2, "name": "Bob", "login_count": 21}, + ] + + +def test_merge_matched_update_only(tmp_db): + db = tmp_db + + # --8<-- [start:merge_matched_update_only] + import pyarrow as pa + + table = db.create_table( + "users_example", + data=pa.table( + { + "id": [1, 2], + "name": ["Alice", "Bob"], + "login_count": [10, 20], + } + ), + mode="overwrite", + ) + + incoming_users = pa.table( + { + "id": [2, 3], + "name": ["Bobby", "Charlie"], + "login_count": [21, 5], + } + ) + + (table.merge_insert("id").when_matched_update_all().execute(incoming_users)) + # --8<-- [end:merge_matched_update_only] + rows = table.to_arrow().sort_by("id").to_pylist() + assert rows == [ + {"id": 1, "name": "Alice", "login_count": 10}, + {"id": 2, "name": "Bobby", "login_count": 21}, + ] + + +def test_insert_if_not_exists(tmp_db): + db = tmp_db + + # --8<-- [start:insert_if_not_exists] + import pyarrow as pa + + table = db.create_table( + "users_example", + data=pa.table( + { + "id": [1, 2], + "name": ["Alice", "Bob"], + "login_count": [10, 20], + } + ), + mode="overwrite", + ) + + incoming_users = pa.table( + { + "id": [2, 3], + "name": ["Bobby", "Charlie"], + "login_count": [21, 5], + } + ) + + (table.merge_insert("id").when_not_matched_insert_all().execute(incoming_users)) + # --8<-- [end:insert_if_not_exists] + rows = table.to_arrow().sort_by("id").to_pylist() + assert rows == [ + {"id": 1, "name": "Alice", "login_count": 10}, + {"id": 2, "name": "Bob", "login_count": 20}, + {"id": 3, "name": "Charlie", "login_count": 5}, + ] + + +def test_merge_update_insert(tmp_db): + db = tmp_db + + # --8<-- [start:merge_update_insert] + import pyarrow as pa + + table = db.create_table( + "users_example", + data=pa.table( + { + "id": [1, 2], + "name": ["Alice", "Bob"], + "login_count": [10, 20], + } + ), + mode="overwrite", + ) + + incoming_users = pa.table( + { + "id": [2, 3], + "name": ["Bobby", "Charlie"], + "login_count": [21, 5], + } + ) + + ( + table.merge_insert("id") + .when_matched_update_all() + .when_not_matched_insert_all() + .execute(incoming_users) + ) + # --8<-- [end:merge_update_insert] + rows = table.to_arrow().sort_by("id").to_pylist() + assert rows == [ + {"id": 1, "name": "Alice", "login_count": 10}, + {"id": 2, "name": "Bobby", "login_count": 21}, + {"id": 3, "name": "Charlie", "login_count": 5}, + ] + + +def test_merge_delete_missing_by_source(tmp_db): + db = tmp_db + + # --8<-- [start:merge_delete_missing_by_source] + import pyarrow as pa + + table = db.create_table( + "users_example", + data=pa.table( + { + "id": [1, 2, 3], + "name": ["Alice", "Bob", "Charlie"], + "login_count": [10, 20, 5], + } + ), + mode="overwrite", + ) + + incoming_users = pa.table( + { + "id": [2, 3], + "name": ["Bobby", "Charlie"], + "login_count": [21, 5], + } + ) + + ( + table.merge_insert("id") + .when_matched_update_all() + .when_not_matched_insert_all() + .when_not_matched_by_source_delete() + .execute(incoming_users) + ) + # --8<-- [end:merge_delete_missing_by_source] + rows = table.to_arrow().sort_by("id").to_pylist() + assert rows == [ + {"id": 2, "name": "Bobby", "login_count": 21}, + {"id": 3, "name": "Charlie", "login_count": 5}, + ] + + +def test_merge_partial_columns(tmp_db): + db = tmp_db + + # --8<-- [start:merge_partial_columns] + import pyarrow as pa + + table = db.create_table( + "users_example", + data=pa.table( + { + "id": [1, 2], + "name": ["Alice", "Bob"], + "login_count": [10, 20], + } + ), + mode="overwrite", + ) + + incoming_users = pa.table( + { + "id": [2, 3], + "name": ["Bobby", "Charlie"], + } + ) + + ( + table.merge_insert("id") + .when_matched_update_all() + .when_not_matched_insert_all() + .execute(incoming_users) + ) + # --8<-- [end:merge_partial_columns] + rows = table.to_arrow().sort_by("id").to_pylist() + assert rows == [ + {"id": 1, "name": "Alice", "login_count": 10}, + {"id": 2, "name": "Bobby", "login_count": 20}, + {"id": 3, "name": "Charlie", "login_count": None}, + ] + + +def test_delete_operation(tmp_db): + db = tmp_db + table = _create_users_example_table(db) + + # --8<-- [start:delete_operation] + # delete data + predicate = "id = 3" + table.delete(predicate) + # --8<-- [end:delete_operation] + assert table.count_rows() == 2 + + +def test_update_optimize_cleanup_snippet(tmp_db): + table = _create_users_example_table(tmp_db, table_name="users_cleanup_example") + + # --8<-- [start:update_optimize_cleanup] + from datetime import timedelta + + table.optimize(cleanup_older_than=timedelta(days=1)) + # --8<-- [end:update_optimize_cleanup] + + +# ============================================================================ +# Schema Evolution Examples +# ============================================================================ + + +def _setup_schema_add_table(tmp_db, data=None): + # --8<-- [start:schema_add_setup] + table_name = "schema_evolution_add_example" + if data is None: + data = [ + { + "id": 1, + "name": "Laptop", + "price": 1200.00, + "vector": np.random.random(128).tolist(), + }, + { + "id": 2, + "name": "Smartphone", + "price": 800.00, + "vector": np.random.random(128).tolist(), + }, + { + "id": 3, + "name": "Headphones", + "price": 150.00, + "vector": np.random.random(128).tolist(), + }, + ] + table = tmp_db.create_table(table_name, data, mode="overwrite") + # --8<-- [end:schema_add_setup] + return table + + +def _setup_schema_alter_table(tmp_db, data=None): + # --8<-- [start:schema_alter_setup] + table_name = "schema_evolution_alter_example" + if data is None: + data = [ + { + "id": 1, + "name": "Laptop", + "price": 1200, + "discount_price": 1080.0, + "vector": np.random.random(128).tolist(), + }, + { + "id": 2, + "name": "Smartphone", + "price": 800, + "discount_price": 720.0, + "vector": np.random.random(128).tolist(), + }, + ] + schema = pa.schema( + { + "id": pa.int64(), + "name": pa.string(), + "price": pa.int32(), + "discount_price": pa.float64(), + "vector": pa.list_(pa.float32(), 128), + } + ) + table = tmp_db.create_table(table_name, data, schema=schema, mode="overwrite") + # --8<-- [end:schema_alter_setup] + return table + + +def _setup_schema_drop_table(tmp_db, data=None): + # --8<-- [start:schema_drop_setup] + if data is None: + data = [ + { + "id": 1, + "name": "Laptop", + "price": 1200.00, + "temp_col1": "X", + "temp_col2": 100, + "vector": np.random.random(128).tolist(), + }, + { + "id": 2, + "name": "Smartphone", + "price": 800.00, + "temp_col1": "Y", + "temp_col2": 200, + "vector": np.random.random(128).tolist(), + }, + { + "id": 3, + "name": "Headphones", + "price": 150.00, + "temp_col1": "Z", + "temp_col2": 300, + "vector": np.random.random(128).tolist(), + }, + ] + table = tmp_db.create_table("schema_evolution_drop_example", data, mode="overwrite") + # --8<-- [end:schema_drop_setup] + return table + + +def test_add_columns_calculated(tmp_db): + table = _setup_schema_add_table(tmp_db) + + # --8<-- [start:add_columns_calculated] + # Add a discounted price column (10% discount) + table.add_columns({"discounted_price": "cast((price * 0.9) as float)"}) + # --8<-- [end:add_columns_calculated] + assert "discounted_price" in table.schema.names + + +def test_add_columns_default_values(tmp_db): + table = _setup_schema_add_table(tmp_db) + + # --8<-- [start:add_columns_default_values] + # Add a stock status column with default value + table.add_columns({"in_stock": "cast(true as boolean)"}) + # --8<-- [end:add_columns_default_values] + assert "in_stock" in table.schema.names + + +def test_add_columns_nullable(tmp_db): + table = _setup_schema_add_table( + tmp_db, + data=[ + { + "id": 1, + "name": "Laptop", + "price": 1200.00, + "vector": np.random.random(128).tolist(), + } + ], + ) + + # --8<-- [start:add_columns_nullable] + # Add a nullable timestamp column + table.add_columns({"last_ordered": "cast(NULL as timestamp)"}) + # --8<-- [end:add_columns_nullable] + assert "last_ordered" in table.schema.names + + +def test_add_feature_columns_sql(tmp_db): + table = _setup_schema_add_table(tmp_db) + + # --8<-- [start:add_feature_columns_sql] + table.add_columns( + { + "price_per_id": "cast(price / id as float)", + "price_log": "ln(price)", + "price_score": "cast(price / (price + 100.0) as float)", + } + ) + # --8<-- [end:add_feature_columns_sql] + assert {"price_per_id", "price_log", "price_score"}.issubset(table.schema.names) + + +def test_alter_columns_rename(tmp_db): + table = _setup_schema_alter_table(tmp_db) + + # --8<-- [start:alter_columns_rename] + # Rename discount_price to sale_price + table.alter_columns({"path": "discount_price", "rename": "sale_price"}) + # --8<-- [end:alter_columns_rename] + assert "sale_price" in table.schema.names + assert "discount_price" not in table.schema.names + + +def test_alter_columns_data_type(tmp_db): + table = _setup_schema_alter_table( + tmp_db, + data=[ + { + "id": 1, + "name": "Laptop", + "price": 1200, + "discount_price": 1080.0, + "vector": np.random.random(128).tolist(), + } + ], + ) + + # --8<-- [start:alter_columns_data_type] + # Change price from int32 to int64 for larger numbers + table.alter_columns({"path": "price", "data_type": pa.int64()}) + # --8<-- [end:alter_columns_data_type] + assert table.schema.field("price").type == pa.int64() + + +def test_alter_columns_nullable(tmp_db): + table = _setup_schema_alter_table( + tmp_db, + data=[ + { + "id": 1, + "name": "Laptop", + "price": 1200, + "discount_price": 1080.0, + "vector": np.random.random(128).tolist(), + } + ], + ) + + # --8<-- [start:alter_columns_nullable] + # Make the name column nullable + table.alter_columns({"path": "name", "nullable": True}) + # --8<-- [end:alter_columns_nullable] + assert table.schema.field("name").nullable is True + + +def test_alter_columns_multiple(tmp_db): + table = _setup_schema_alter_table( + tmp_db, + data=[ + { + "id": 1, + "name": "Laptop", + "price": 1200, + "discount_price": 1080.0, + "vector": np.random.random(128).tolist(), + } + ], + ) + table.alter_columns({"path": "discount_price", "rename": "sale_price"}) + + # --8<-- [start:alter_columns_multiple] + # Rename, change type, and make nullable in one operation + table.alter_columns( + { + "path": "sale_price", + "rename": "final_price", + "data_type": pa.float64(), + "nullable": True, + } + ) + # --8<-- [end:alter_columns_multiple] + assert "final_price" in table.schema.names + assert table.schema.field("final_price").nullable is True + + +def test_alter_columns_with_expression(tmp_db): + # --8<-- [start:alter_columns_with_expression] + # For custom transforms, create a new column from a SQL expression. + expression_table = tmp_db.create_table( + "schema_evolution_expression_example", + [{"id": 1, "price_text": "$100"}], + mode="overwrite", + ) + + expression_table.add_columns( + {"price_numeric": "cast(replace(price_text, '$', '') as int)"} + ) + expression_table.drop_columns(["price_text"]) + expression_table.alter_columns({"path": "price_numeric", "rename": "price"}) + # --8<-- [end:alter_columns_with_expression] + assert "price" in expression_table.schema.names + + +def test_drop_columns_single(tmp_db): + table = _setup_schema_drop_table(tmp_db) + + # --8<-- [start:drop_columns_single] + # Remove the first temporary column + table.drop_columns(["temp_col1"]) + # --8<-- [end:drop_columns_single] + assert "temp_col1" not in table.schema.names + + +def test_drop_columns_multiple(tmp_db): + table = _setup_schema_drop_table( + tmp_db, + data=[ + { + "id": 1, + "name": "Laptop", + "price": 1200.00, + "temp_col1": "X", + "temp_col2": 100, + "vector": np.random.random(128).tolist(), + }, + ], + ) + + # --8<-- [start:drop_columns_multiple] + # Remove the second temporary column + table.drop_columns(["temp_col2"]) + # --8<-- [end:drop_columns_multiple] + assert "temp_col2" not in table.schema.names + + +def test_alter_vector_column(tmp_db): + # --8<-- [start:alter_vector_column] + vector_dim = 768 # Your embedding dimension + table_name = "vector_alter_example" + db = tmp_db + data = [ + { + "id": 1, + "embedding": np.random.random(vector_dim).tolist(), + }, + ] + table = db.create_table(table_name, data, mode="overwrite") + + table.alter_columns( + dict(path="embedding", data_type=pa.list_(pa.float32(), vector_dim)) + ) + # --8<-- [end:alter_vector_column] + + +def test_schema_field_metadata(tmp_db): + table = tmp_db.create_table( + "schema_field_metadata_example", + pa.table({"id": [0, 1], "category": ["a", "b"]}), + mode="overwrite", + ) + + # --8<-- [start:schema_field_metadata_merge] + # Set two metadata keys on the `category` field. + res = table.update_field_metadata( + {"path": "category", "metadata": {"unit": "label", "pii": "false"}} + ) + print(res.version) + + # Merge: add a new key, delete one with None, keep the rest. + table.update_field_metadata( + {"path": "category", "metadata": {"source": "import", "pii": None}} + ) + + # Arrow stores field metadata as bytes. + assert table.schema.field("category").metadata == { + b"unit": b"label", + b"source": b"import", + } + # --8<-- [end:schema_field_metadata_merge] + + # --8<-- [start:schema_field_metadata_replace] + table.update_field_metadata( + { + "path": "category", + "metadata": {"owner": "search-team"}, + "replace": True, + } + ) + # --8<-- [end:schema_field_metadata_replace] + + assert table.schema.field("category").metadata == {b"owner": b"search-team"} + + +# ============================================================================ +# Versioning Examples +# ============================================================================ + + +def _setup_versioning_table(tmp_db, data=None, table_name="quotes_versioning_example"): + import pyarrow as pa + + if data is None: + data = [ + {"id": 1, "author": "Richard", "quote": "Wubba Lubba Dub Dub!"}, + {"id": 2, "author": "Morty", "quote": "Rick, what's going on?"}, + { + "id": 3, + "author": "Richard", + "quote": "I turned myself into a pickle, Morty!", + }, + ] + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("author", pa.string()), + pa.field("quote", pa.string()), + ] + ) + return tmp_db.create_table(table_name, data, schema=schema, mode="overwrite") + + +def test_versioning_basic_setup(tmp_db): + # --8<-- [start:versioning_basic_setup] + import pyarrow as pa + + db = tmp_db + + table_name = "quotes_versioning_example" + data = [ + {"id": 1, "author": "Richard", "quote": "Wubba Lubba Dub Dub!"}, + {"id": 2, "author": "Morty", "quote": "Rick, what's going on?"}, + { + "id": 3, + "author": "Richard", + "quote": "I turned myself into a pickle, Morty!", + }, + ] + + # Define schema + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("author", pa.string()), + pa.field("quote", pa.string()), + ] + ) + + table = db.create_table(table_name, data, schema=schema, mode="overwrite") + # --8<-- [end:versioning_basic_setup] + assert table.count_rows() == 3 + + +def test_versioning_check_initial_version(tmp_db): + table = _setup_versioning_table(tmp_db) + + # --8<-- [start:versioning_check_initial_version] + versions = table.list_versions() + current_version = table.version + print(f"Number of versions after creation: {len(versions)}") + print(f"Current version: {current_version}") + # --8<-- [end:versioning_check_initial_version] + assert len(versions) == 1 + assert current_version == versions[-1]["version"] + + +def test_versioning_flow(tmp_db): + table = _setup_versioning_table(tmp_db) + + # --8<-- [start:versioning_update_data] + table.update(where="author='Richard'", values={"author": "Richard Daniel Sanchez"}) + rows_after_update = table.count_rows("author = 'Richard Daniel Sanchez'") + print(f"Rows updated to Richard Daniel Sanchez: {rows_after_update}") + # --8<-- [end:versioning_update_data] + assert rows_after_update == 2 + + # --8<-- [start:versioning_add_data] + more_data = [ + { + "id": 4, + "author": "Richard Daniel Sanchez", + "quote": "That's the way the news goes!", + }, + {"id": 5, "author": "Morty", "quote": "Aww geez, Rick!"}, + ] + table.add(more_data) + # --8<-- [end:versioning_add_data] + assert table.count_rows() == 5 + + # --8<-- [start:versioning_check_versions_after_mod] + versions = table.list_versions() + version_count_after_mod = len(versions) + version_after_mod = table.version + print(f"Number of versions after modifications: {version_count_after_mod}") + print(f"Current version: {version_after_mod}") + # --8<-- [end:versioning_check_versions_after_mod] + assert version_count_after_mod >= 2 + assert version_after_mod == versions[-1]["version"] + + # --8<-- [start:versioning_list_all_versions] + versions = table.list_versions() + for v in versions: + print(f"Version {v['version']}, created at {v['timestamp']}") + # --8<-- [end:versioning_list_all_versions] + assert len(versions) >= 1 + + # --8<-- [start:versioning_rollback] + table.restore(version_after_mod) + versions = table.list_versions() + version_count_after_rollback = len(versions) + print(f"Total number of versions after rollback: {version_count_after_rollback}") + # --8<-- [end:versioning_rollback] + assert version_count_after_rollback == version_count_after_mod + 1 + assert table.version == versions[-1]["version"] + assert table.count_rows() == 5 + + # --8<-- [start:versioning_checkout_latest] + table.checkout_latest() + # --8<-- [end:versioning_checkout_latest] + assert table.version == table.list_versions()[-1]["version"] + + # --8<-- [start:versioning_delete_data] + table.delete("author = 'Morty'") + rows_after_deletion = table.count_rows() + print(f"Number of rows after deletion: {rows_after_deletion}") + # --8<-- [end:versioning_delete_data] + assert rows_after_deletion == 3 + + +def test_versioning_tags(tmp_db): + import pyarrow as pa + + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("author", pa.string()), + pa.field("quote", pa.string()), + ] + ) + table = tmp_db.create_table( + "quotes_tags_example", + [{"id": 1, "author": "Richard", "quote": "Wubba Lubba Dub Dub!"}], + schema=schema, + mode="overwrite", + ) # v1 + table.add([{"id": 2, "author": "Morty", "quote": "Aww geez, Rick!"}]) # v2 + table.add([{"id": 3, "author": "Summer", "quote": "Whatever, Grandpa"}]) # v3 + + # --8<-- [start:versioning_tags] + # Create a tag pointing at a specific version + table.tags.create("baseline", 1) + table.tags.create("with-edits", table.version) + + # List all tags on this table + print(table.tags.list()) + + # Look up the version a tag points at + print(table.tags.get_version("baseline")) + + # Move an existing tag to a different version + table.tags.update("baseline", 2) + + # Check out a version by tag name + table.checkout("baseline") + print(table.version) + + # Delete a tag (does not delete the underlying version) + table.tags.delete("with-edits") + + # Return to the latest version + table.checkout_latest() + # --8<-- [end:versioning_tags] + assert table.version == 3 + assert "baseline" in table.tags.list() + assert "with-edits" not in table.tags.list() + + +# ============================================================================ +# Branches Examples +# ============================================================================ + + +def test_branches(tmp_db): + import numpy as np + import pyarrow as pa + from lancedb.index import FTS, IvfPq + + db = tmp_db + schema = pa.schema( + [ + pa.field("id", pa.int64()), + pa.field("author", pa.string()), + pa.field("quote", pa.string()), + ] + ) + table = db.create_table( + "quotes_branches_example", + [ + {"id": 1, "author": "Lancelot", "quote": "My lance never fails."}, + {"id": 2, "author": "Arthur", "quote": "Long live Camelot!"}, + {"id": 3, "author": "Merlin", "quote": "Magic always has a price."}, + ], + schema=schema, + mode="overwrite", + ) + + # --8<-- [start:branch_create] + # Fork an isolated, writable branch from main's latest version. + # `create` returns a table handle scoped to the new branch. + branch = table.branches.create("exp") + # --8<-- [end:branch_create] + + # --8<-- [start:branch_write] + # Writes land on the branch handle only; main is left untouched. + branch.add([{"id": 4, "author": "Lancelot", "quote": "For the realm!"}]) + print(branch.count_rows()) # 4 rows on the branch + print(table.count_rows()) # 3 rows; main is unaffected + + # List every branch, each mapped to its metadata (including its fork point). + print(table.branches.list()) + # --8<-- [end:branch_write] + + # --8<-- [start:branch_reopen] + # Reopen an existing branch by name from the table handle... + checked_out = table.branches.checkout("exp") + # ...or open it directly from the database connection. + branch_handle = db.open_table("quotes_branches_example", branch="exp") + print(checked_out.count_rows(), branch_handle.count_rows()) # both 4 + # --8<-- [end:branch_reopen] + + # --8<-- [start:branch_delete] + # Delete the branch and its branch-local history. Data on main is safe. + table.branches.delete("exp") + # --8<-- [end:branch_delete] + + assert table.count_rows() == 3 + assert "exp" not in table.branches.list() + + # Setup: a branch with row results that we want to apply to main. + candidate = table.branches.create("candidate") + candidate.update(where="id = 1", values={"quote": "Revised on the branch"}) + candidate.add( + [{"id": 4, "author": "Galahad", "quote": "The grail awaits."}] + ) + + # --8<-- [start:branch_upsert_to_main] + # This is a row-level upsert, not a merge of branch histories. + # `merge_insert` updates matching rows and inserts new rows using a stable + # unique key. Filter the branch read if you only want to apply some results. + rows_to_apply = candidate.to_arrow() + ( + table.merge_insert("id") + .when_matched_update_all() # update rows that already exist on main + .when_not_matched_insert_all() # insert rows that are new on the branch + .execute(rows_to_apply) + ) + # --8<-- [end:branch_upsert_to_main] + + main_rows = { + row["id"]: row["quote"] for row in table.to_arrow().to_pylist() + } + assert table.count_rows() == 4 + assert main_rows[1] == "Revised on the branch" + assert 4 in main_rows + table.branches.delete("candidate") + + # Setup: a larger table with a vector and a text column to index. + rng = np.random.default_rng(0) + products = db.create_table( + "products_branch_index", + [ + {"id": i, "vector": rng.random(4).tolist(), "text": f"product number {i}"} + for i in range(512) + ], + mode="overwrite", + ) + + # --8<-- [start:branch_index] + # Build and validate indexes on a branch before using the configuration on + # main. + dev = products.branches.create("index-dev") + + # A vector (ANN) index and a full-text search index, both branch-scoped. + dev.create_index( + "vector", + config=IvfPq(distance_type="cosine", num_partitions=1, num_sub_vectors=2), + ) + dev.create_index("text", config=FTS()) + + # Both indexes live only on the branch; main still has none. + print([ix.name for ix in dev.list_indices()]) # branch: two indexes + print([ix.name for ix in products.list_indices()]) # main: [] (untouched) + # --8<-- [end:branch_index] + + assert len(dev.list_indices()) == 2 + assert len(products.list_indices()) == 0 + products.branches.delete("index-dev") + + +# ============================================================================ +# Consistency Examples +# ============================================================================ + + +def test_consistency_strong(tmp_db): + # --8<-- [start:consistency_strong] + from datetime import timedelta + + uri = str(tmp_db.uri) + writer_db = lancedb.connect(uri) + reader_db = lancedb.connect(uri, read_consistency_interval=timedelta(0)) + writer_table = writer_db.create_table( + "consistency_strong_table", [{"id": 1}], mode="overwrite" + ) + reader_table = reader_db.open_table("consistency_strong_table") + writer_table.add([{"id": 2}]) + rows_after_write = reader_table.count_rows() + print(f"Rows visible with strong consistency: {rows_after_write}") + # --8<-- [end:consistency_strong] + assert rows_after_write == 2 + + +def test_consistency_eventual(tmp_db): + # --8<-- [start:consistency_eventual] + from datetime import timedelta + + uri = str(tmp_db.uri) + writer_db = lancedb.connect(uri) + reader_db = lancedb.connect(uri, read_consistency_interval=timedelta(seconds=3600)) + writer_table = writer_db.create_table( + "consistency_eventual_table", [{"id": 1}], mode="overwrite" + ) + reader_table = reader_db.open_table("consistency_eventual_table") + writer_table.add([{"id": 2}]) + rows_after_write = reader_table.count_rows() + print(f"Rows visible before eventual refresh interval: {rows_after_write}") + # --8<-- [end:consistency_eventual] + assert rows_after_write == 1 + + +def test_consistency_checkout_latest(tmp_db): + # --8<-- [start:consistency_checkout_latest] + uri = str(tmp_db.uri) + writer_db = lancedb.connect(uri) + reader_db = lancedb.connect(uri) + writer_table = writer_db.create_table( + "consistency_checkout_latest_table", [{"id": 1}], mode="overwrite" + ) + reader_table = reader_db.open_table("consistency_checkout_latest_table") + + writer_table.add([{"id": 2}]) + rows_before_refresh = reader_table.count_rows() + print(f"Rows before checkout_latest: {rows_before_refresh}") + + reader_table.checkout_latest() + rows_after_refresh = reader_table.count_rows() + print(f"Rows after checkout_latest: {rows_after_refresh}") + # --8<-- [end:consistency_checkout_latest] + assert rows_before_refresh == 1 + assert rows_after_refresh == 2 diff --git a/docs/web-tests/rs/Cargo.lock b/docs/web-tests/rs/Cargo.lock new file mode 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"1", features = ["macros", "rt-multi-thread"] } + +[[example]] +name = "basic_usage" +path = "basic_usage.rs" + +[[example]] +name = "connection" +path = "connection.rs" + +[[example]] +name = "quickstart" +path = "quickstart.rs" + +[[example]] +name = "tables" +path = "tables.rs" + +[[example]] +name = "multimodal" +path = "multimodal.rs" + +[[example]] +name = "search" +path = "search.rs" diff --git a/docs/web-tests/rs/basic_usage.rs b/docs/web-tests/rs/basic_usage.rs new file mode 100644 index 000000000..0adcef474 --- /dev/null +++ b/docs/web-tests/rs/basic_usage.rs @@ -0,0 +1,346 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +use std::fs; +use std::path::PathBuf; +use std::sync::Arc; + +use serde::Deserialize; + +type BatchIter = Box; + +// --8<-- [start:basic_imports] +use arrow_array::types::Float32Type; +use arrow_array::{ + FixedSizeListArray, Int8Array, Int16Array, RecordBatch, RecordBatchIterator, StringArray, + StructArray, +}; +use arrow_schema::{DataType, Field, FieldRef, Schema}; +use futures_util::TryStreamExt; +use lancedb::database::CreateTableMode; +use lancedb::query::{ExecutableQuery, QueryBase, Select}; +use lancedb::{connect, table::NewColumnTransform}; +// --8<-- [end:basic_imports] + +#[derive(Debug, Clone, Deserialize)] +struct Stats { + strength: i8, + courage: i8, + magic: i8, + wisdom: i8, +} + +#[derive(Debug, Clone, Deserialize)] +struct Character { + id: i16, + name: String, + role: String, + description: String, + vector: [f32; 4], + stats: Stats, +} + +fn camelot_schema() -> Arc { + Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int16, false), + Field::new("name", DataType::Utf8, false), + Field::new("role", DataType::Utf8, false), + Field::new("description", DataType::Utf8, false), + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4), + false, + ), + Field::new( + "stats", + DataType::Struct(arrow_schema::Fields::from(vec![ + Arc::new(Field::new("strength", DataType::Int8, false)), + Arc::new(Field::new("courage", DataType::Int8, false)), + Arc::new(Field::new("magic", DataType::Int8, false)), + Arc::new(Field::new("wisdom", DataType::Int8, false)), + ])), + false, + ), + ])) +} + +fn characters_to_record_batch(schema: Arc, characters: &[Character]) -> RecordBatch { + let ids = Int16Array::from_iter_values(characters.iter().map(|c| c.id)); + let names = StringArray::from_iter_values(characters.iter().map(|c| c.name.as_str())); + let roles = StringArray::from_iter_values(characters.iter().map(|c| c.role.as_str())); + let descriptions = + StringArray::from_iter_values(characters.iter().map(|c| c.description.as_str())); + + let vectors = FixedSizeListArray::from_iter_primitive::( + characters + .iter() + .map(|c| Some(c.vector.iter().copied().map(Some).collect::>())), + 4, + ); + + let strength = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.strength)); + let courage = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.courage)); + let magic = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.magic)); + let wisdom = Int8Array::from_iter_values(characters.iter().map(|c| c.stats.wisdom)); + + let stats_fields: Vec = vec![ + Arc::new(Field::new("strength", DataType::Int8, false)), + Arc::new(Field::new("courage", DataType::Int8, false)), + Arc::new(Field::new("magic", DataType::Int8, false)), + Arc::new(Field::new("wisdom", DataType::Int8, false)), + ]; + let stats = StructArray::new( + stats_fields.into(), + vec![ + Arc::new(strength), + Arc::new(courage), + Arc::new(magic), + Arc::new(wisdom), + ], + None, + ); + + RecordBatch::try_new( + schema, + vec![ + Arc::new(ids), + Arc::new(names), + Arc::new(roles), + Arc::new(descriptions), + Arc::new(vectors), + Arc::new(stats), + ], + ) + .unwrap() +} + +fn characters_to_reader(schema: Arc, characters: &[Character]) -> BatchIter { + let batch = characters_to_record_batch(schema.clone(), characters); + Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema)) +} + +fn camelot_json_path() -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")) + .join("..") + .join("camelot.json") +} + +#[tokio::main] +async fn main() { + let temp_dir = tempfile::tempdir().unwrap(); + let uri = temp_dir.path().to_str().unwrap(); + let db = connect(uri).execute().await.unwrap(); + + // --8<-- [start:data_load] + let data: Vec = + serde_json::from_str(&fs::read_to_string(camelot_json_path()).unwrap()).unwrap(); + // --8<-- [end:data_load] + + let schema = camelot_schema(); + + // --8<-- [start:basic_create_table] + let mut table = db + .create_table("camelot", characters_to_reader(schema.clone(), &data)) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:basic_create_table] + assert_eq!(table.count_rows(None).await.unwrap(), 8); + + // --8<-- [start:basic_open_table] + table = db.open_table("camelot").execute().await.unwrap(); + // --8<-- [end:basic_open_table] + + // --8<-- [start:basic_create_empty_table] + let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int16, false), + Field::new("name", DataType::Utf8, false), + Field::new("role", DataType::Utf8, false), + Field::new("description", DataType::Utf8, false), + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4), + false, + ), + Field::new( + "stats", + DataType::Struct(arrow_schema::Fields::from(vec![ + Arc::new(Field::new("strength", DataType::Int8, false)), + Arc::new(Field::new("courage", DataType::Int8, false)), + Arc::new(Field::new("magic", DataType::Int8, false)), + Arc::new(Field::new("wisdom", DataType::Int8, false)), + ])), + false, + ), + ])); + db.create_empty_table("camelot_empty", schema) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:basic_create_empty_table] + db.drop_table("camelot_empty", &[]).await.unwrap(); + + // --8<-- [start:basic_add_data] + let magical_characters = vec![ + Character { + id: 9, + name: "Morgan le Fay".to_string(), + role: "Sorceress".to_string(), + description: "A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.".to_string(), + vector: [0.10, 0.84, 0.25, 0.70], + stats: Stats { + strength: 2, + courage: 3, + magic: 5, + wisdom: 4, + }, + }, + Character { + id: 10, + name: "The Lady of the Lake".to_string(), + role: "Mystical Guardian".to_string(), + description: "A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.".to_string(), + vector: [0.00, 0.90, 0.58, 0.88], + stats: Stats { + strength: 2, + courage: 3, + magic: 5, + wisdom: 5, + }, + }, + ]; + table + .add(characters_to_reader(camelot_schema(), &magical_characters)) + .execute() + .await + .unwrap(); + // --8<-- [end:basic_add_data] + + // --8<-- [start:basic_vector_search] + let query_vector = [0.03, 0.85, 0.61, 0.90]; + let result = table + .query() + .nearest_to(&query_vector) + .unwrap() + .limit(5) + .execute() + .await + .unwrap() + .try_collect::>() + .await + .unwrap(); + println!("{result:?}"); + // --8<-- [end:basic_vector_search] + + // --8<-- [start:basic_add_columns] + table + .add_columns( + NewColumnTransform::SqlExpressions(vec![( + "power".to_string(), + "cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)" + .to_string(), + )]), + None, + ) + .await + .unwrap(); + // --8<-- [end:basic_add_columns] + + // --8<-- [start:basic_vector_search_q1] + // Who are the characters similar to "wizard"? + let query_vector_1 = [0.03, 0.85, 0.61, 0.90]; + let r1 = table + .query() + .nearest_to(&query_vector_1) + .unwrap() + .limit(5) + .select(Select::Columns(vec![ + "name".to_string(), + "role".to_string(), + "description".to_string(), + ])) + .execute() + .await + .unwrap() + .try_collect::>() + .await + .unwrap(); + println!("{r1:?}"); + // --8<-- [end:basic_vector_search_q1] + + // --8<-- [start:basic_vector_search_q2] + // Who are the characters similar to "wizard" with high magic stats? + let query_vector_2 = [0.03, 0.85, 0.61, 0.90]; + let r2 = table + .query() + .nearest_to(&query_vector_2) + .unwrap() + .only_if("stats.magic > 3") + .select(Select::Columns(vec![ + "name".to_string(), + "role".to_string(), + "description".to_string(), + ])) + .limit(5) + .execute() + .await + .unwrap() + .try_collect::>() + .await + .unwrap(); + println!("{r2:?}"); + // --8<-- [end:basic_vector_search_q2] + + // --8<-- [start:basic_vector_search_q3] + // Who are the strongest characters? + let r3 = table + .query() + .only_if("stats.strength > 3") + .select(Select::Columns(vec![ + "name".to_string(), + "role".to_string(), + "description".to_string(), + ])) + .limit(5) + .execute() + .await + .unwrap() + .try_collect::>() + .await + .unwrap(); + println!("{r3:?}"); + // --8<-- [end:basic_vector_search_q3] + + // --8<-- [start:basic_vector_search_q4] + // Who are the strongest characters? + let r4 = table + .query() + .select(Select::Columns(vec![ + "name".to_string(), + "role".to_string(), + "description".to_string(), + "power".to_string(), + ])) + .execute() + .await + .unwrap() + .try_collect::>() + .await + .unwrap(); + println!("{r4:?}"); + // --8<-- [end:basic_vector_search_q4] + + // --8<-- [start:basic_drop_columns] + table.drop_columns(&["power"]).await.unwrap(); + // --8<-- [end:basic_drop_columns] + + // --8<-- [start:basic_delete_rows] + table.delete("role = 'Traitor Knight'").await.unwrap(); + // --8<-- [end:basic_delete_rows] + + // --8<-- [start:basic_drop_table] + db.drop_table("camelot", &[]).await.unwrap(); + // --8<-- [end:basic_drop_table] +} diff --git a/docs/web-tests/rs/bedrock.rs b/docs/web-tests/rs/bedrock.rs new file mode 100644 index 000000000..5cc7e0cbe --- /dev/null +++ b/docs/web-tests/rs/bedrock.rs @@ -0,0 +1,92 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +use std::{iter::once, sync::Arc}; + +use arrow_array::{Float64Array, Int32Array, RecordBatch, RecordBatchIterator, StringArray}; +use arrow_schema::{DataType, Field, Schema}; +use aws_config::Region; +use aws_sdk_bedrockruntime::Client; +use futures::StreamExt; +use lancedb::{ + arrow::IntoArrow, + connect, + embeddings::{bedrock::BedrockEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction}, + query::{ExecutableQuery, QueryBase}, + Result, +}; + +#[tokio::main] +async fn main() -> Result<()> { + let tempdir = tempfile::tempdir().unwrap(); + let tempdir = tempdir.path().to_str().unwrap(); + + // create Bedrock embedding function + let region: String = "us-east-1".to_string(); + let config = aws_config::defaults(aws_config::BehaviorVersion::latest()) + .region(Region::new(region)) + .load() + .await; + + let embedding = Arc::new(BedrockEmbeddingFunction::new( + Client::new(&config), // AWS Region + )); + + let db = connect(tempdir).execute().await?; + db.embedding_registry() + .register("bedrock", embedding.clone())?; + + let table = db + .create_table("vectors", make_data()) + .add_embedding(EmbeddingDefinition::new( + "text", + "bedrock", + Some("embeddings"), + ))? + .execute() + .await?; + + // execute vector search + let query = Arc::new(StringArray::from_iter_values(once("something warm"))); + let query_vector = embedding.compute_query_embeddings(query)?; + let mut results = table + .vector_search(query_vector)? + .limit(1) + .execute() + .await?; + + let rb = results.next().await.unwrap()?; + let out = rb + .column_by_name("text") + .unwrap() + .as_any() + .downcast_ref::() + .unwrap(); + let text = out.iter().next().unwrap().unwrap(); + println!("Closest match: {}", text); + Ok(()) +} + +fn make_data() -> impl IntoArrow { + let schema = Schema::new(vec![ + Field::new("id", DataType::Int32, true), + Field::new("text", DataType::Utf8, false), + Field::new("price", DataType::Float64, false), + ]); + + let id = Int32Array::from(vec![1, 2, 3, 4]); + let text = StringArray::from_iter_values(vec![ + "Black T-Shirt", + "Leather Jacket", + "Winter Parka", + "Hooded Sweatshirt", + ]); + let price = Float64Array::from(vec![10.0, 50.0, 100.0, 30.0]); + let schema = Arc::new(schema); + let rb = RecordBatch::try_new( + schema.clone(), + vec![Arc::new(id), Arc::new(text), Arc::new(price)], + ) + .unwrap(); + Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema)) +} diff --git a/docs/web-tests/rs/connection.rs b/docs/web-tests/rs/connection.rs new file mode 100644 index 000000000..4f7212ef5 --- /dev/null +++ b/docs/web-tests/rs/connection.rs @@ -0,0 +1,134 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +use std::path::PathBuf; + +use lancedb::connect; + +// --8<-- [start:connect] +async fn connect_example(uri: &str) { + let db = connect(uri).execute().await.unwrap(); + let _ = db; +} +// --8<-- [end:connect] + +#[tokio::main] +async fn main() { + let temp_dir = tempfile::tempdir().unwrap(); + let uri = temp_dir.path().join("ex_lancedb"); + connect_example(uri.to_str().unwrap()).await; + + // Keep the enterprise snippet in this file, but don't run it in CI. + let _ = connect_enterprise_quickstart_config(); + let _ = connect_object_storage_config(); +} + +fn connect_enterprise_quickstart_config() -> (String, String, String, String) { + // --8<-- [start:connect_enterprise_quickstart] + let uri = "db://your-database-uri"; + let api_key = "your-api-key"; + let region = "us-east-1"; + let host_override = "https://your-enterprise-endpoint.com"; + // --8<-- [end:connect_enterprise_quickstart] + ( + uri.to_string(), + api_key.to_string(), + region.to_string(), + host_override.to_string(), + ) +} + +fn connect_object_storage_config() -> &'static str { + // --8<-- [start:connect_object_storage] + let uri = "s3://your-bucket/path"; + // You can also use "gs://your-bucket/path" or "az://your-container/path". + // --8<-- [end:connect_object_storage] + + uri +} + +async fn namespace_table_ops_example(uri: &str) -> lancedb::Result<()> { + // --8<-- [start:namespace_table_ops] + let conn = connect(uri).execute().await?; + let search_namespace = vec!["prod".to_string(), "search".to_string()]; + let recommendations_namespace = vec!["prod".to_string(), "recommendations".to_string()]; + + let schema = std::sync::Arc::new(arrow_schema::Schema::new(vec![ + arrow_schema::Field::new("id", arrow_schema::DataType::Int64, false), + ])); + + conn.create_empty_table("user", schema.clone()) + .namespace(search_namespace.clone()) + .execute() + .await?; + + conn.create_empty_table("user", schema) + .namespace(recommendations_namespace.clone()) + .execute() + .await?; + + let search_table_names = conn + .table_names() + .namespace(search_namespace) + .execute() + .await?; + let recommendation_table_names = conn + .table_names() + .namespace(recommendations_namespace) + .execute() + .await?; + + println!("{search_table_names:?}"); // ["user"] + println!("{recommendation_table_names:?}"); // ["user"] + // --8<-- [end:namespace_table_ops] + Ok(()) +} + +async fn namespace_admin_ops_example() -> lancedb::Result<()> { + // --8<-- [start:namespace_admin_ops] + let mut properties = std::collections::HashMap::new(); + properties.insert("root".to_string(), "./local_lancedb".to_string()); + let db = lancedb::connect_namespace("dir", properties).execute().await?; + let namespace = vec!["prod".to_string(), "search".to_string()]; + + db.create_namespace(lance_namespace::models::CreateNamespaceRequest { + id: Some(vec!["prod".to_string()]), + ..Default::default() + }) + .await?; + db.create_namespace(lance_namespace::models::CreateNamespaceRequest { + id: Some(namespace.clone()), + ..Default::default() + }) + .await?; + + let child_namespaces = db + .list_namespaces(lance_namespace::models::ListNamespacesRequest { + id: Some(vec!["prod".to_string()]), + ..Default::default() + }) + .await?; + println!( + "Child namespaces under {:?}: {:?}", + namespace, child_namespaces + ); + // Child namespaces under ["prod", "search"]: ["search"] + + db.drop_namespace(lance_namespace::models::DropNamespaceRequest { + id: Some(namespace.clone()), + ..Default::default() + }) + .await?; + db.drop_namespace(lance_namespace::models::DropNamespaceRequest { + id: Some(vec!["prod".to_string()]), + ..Default::default() + }) + .await?; + // --8<-- [end:namespace_admin_ops] + Ok(()) +} + +#[allow(dead_code)] +fn repo_root() -> PathBuf { + PathBuf::from(env!("CARGO_MANIFEST_DIR")).join("..") +} diff --git a/docs/web-tests/rs/embedding.rs b/docs/web-tests/rs/embedding.rs new file mode 100644 index 000000000..33bdec997 --- /dev/null +++ b/docs/web-tests/rs/embedding.rs @@ -0,0 +1,182 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +// --8<-- [start:openai_embeddings] +use std::{iter::once, sync::Arc}; + +use arrow_array::{record_batch, StringArray}; +use arrow_schema::{DataType, Field, Schema}; +use futures::StreamExt; +use lancedb::{ + connect, + embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction}, + query::{ExecutableQuery, QueryBase}, + Result, +}; + +#[tokio::main] +async fn main() -> Result<()> { + let db = connect("./mydb").execute().await?; + let api_key = std::env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY is not set"); + let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model( + api_key, + "text-embedding-3-large", + )?); + + db.embedding_registry().register("openai", embedding.clone())?; + + let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)])); + let table = db + .create_empty_table("mytable", schema) + .add_embedding(EmbeddingDefinition::new("text", "openai", Some("vector")))? + .execute() + .await?; + + table + .add(record_batch!(("text", Utf8, ["This is a test.", "Another example."]))?) + .execute() + .await?; + + let query = Arc::new(StringArray::from_iter_values(once("test example"))); + let query_vector = embedding.compute_query_embeddings(query)?; + let mut results = table.vector_search(query_vector)?.limit(5).execute().await?; + + while let Some(batch) = results.next().await { + println!("{:?}", batch?); + } + + Ok(()) +} +// --8<-- [end:openai_embeddings] + +// --8<-- [start:create_embedding_function] +use std::sync::Arc; + +use lancedb::embeddings::openai::OpenAIEmbeddingFunction; + +let api_key = std::env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY is not set"); +let embedding = Arc::new( + OpenAIEmbeddingFunction::new_with_model(api_key, "text-embedding-3-small") + .expect("failed to create OpenAI embedding function"), +); +// --8<-- [end:create_embedding_function] + +// --8<-- [start:manual_query_embeddings] +use std::{iter::once, sync::Arc}; + +use arrow_array::{record_batch, StringArray}; +use arrow_schema::{DataType, Field, Schema}; +use futures::StreamExt; +use lancedb::{ + connect, + embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction}, + query::{ExecutableQuery, QueryBase}, + Result, +}; + +#[tokio::main] +async fn main() -> Result<()> { + let db = connect("./mydb").execute().await?; + let api_key = std::env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY is not set"); + let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model( + api_key, + "text-embedding-3-large", + )?); + db.embedding_registry().register("openai", embedding.clone())?; + + let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)])); + let table = db + .create_empty_table("mytable", schema) + .add_embedding(EmbeddingDefinition::new("text", "openai", Some("vector")))? + .execute() + .await?; + + table + .add(record_batch!(("text", Utf8, ["This is a test.", "Another example."]))?) + .execute() + .await?; + + // Manually generate embeddings for the query (Enterprise path) + let query = Arc::new(StringArray::from_iter_values(once("test example"))); + let query_vector = embedding.compute_query_embeddings(query)?; + // --8<-- [start:manual_query_search] + // query_vector is assumed to already be generated by your embedding function + let mut results = table.vector_search(query_vector)?.limit(5).execute().await?; + + while let Some(batch) = results.next().await { + println!("{:?}", batch?); + } + // --8<-- [end:manual_query_search] + + Ok(()) +} +// --8<-- [end:manual_query_embeddings] + +// --8<-- [start:embedding_function] +use std::{borrow::Cow, sync::Arc}; + +use arrow_array::{Array, FixedSizeListArray, Float32Array}; +use arrow_schema::{DataType, Field, Schema}; +use lancedb::{ + connect, + embeddings::{EmbeddingDefinition, EmbeddingFunction}, + Result, +}; + +#[derive(Debug, Clone)] +struct MyTextEmbedder { + dim: usize, +} + +impl EmbeddingFunction for MyTextEmbedder { + fn name(&self) -> &str { + "my-embedder" + } + + fn source_type(&self) -> Result> { + Ok(Cow::Owned(DataType::Utf8)) + } + + fn dest_type(&self) -> Result> { + Ok(Cow::Owned(DataType::new_fixed_size_list( + DataType::Float32, + self.dim as i32, + true, + ))) + } + + fn compute_source_embeddings(&self, source: Arc) -> Result> { + let values = Arc::new(Float32Array::from(vec![1.0f32; source.len() * self.dim])); + let field = Arc::new(Field::new("item", DataType::Float32, true)); + Ok(Arc::new(FixedSizeListArray::new( + field, + self.dim as i32, + values, + None, + ))) + } + + fn compute_query_embeddings(&self, _input: Arc) -> Result> { + unimplemented!() + } +} + +#[tokio::main] +async fn main() -> Result<()> { + let db = connect("./mydb").execute().await?; + db.embedding_registry() + .register("my-embedder", Arc::new(MyTextEmbedder { dim: 3 }))?; + + let schema = Arc::new(Schema::new(vec![Field::new("text", DataType::Utf8, false)])); + db.create_empty_table("mytable", schema) + .add_embedding(EmbeddingDefinition::new( + "text", + "my-embedder", + Some("vector"), + ))? + .execute() + .await?; + + Ok(()) +} +// --8<-- [end:embedding_function] diff --git a/docs/web-tests/rs/full_text_search.rs b/docs/web-tests/rs/full_text_search.rs new file mode 100644 index 000000000..b79400a57 --- /dev/null +++ b/docs/web-tests/rs/full_text_search.rs @@ -0,0 +1,103 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +use std::sync::Arc; + +use arrow_array::{Int32Array, RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray}; +use arrow_schema::{DataType, Field, Schema}; + +use futures::TryStreamExt; +use lance_index::scalar::FullTextSearchQuery; +use lancedb::connection::Connection; +use lancedb::index::scalar::FtsIndexBuilder; +use lancedb::index::Index; +use lancedb::query::{ExecutableQuery, QueryBase}; +use lancedb::{connect, Result, Table}; +use rand::random; + +#[tokio::main] +async fn main() -> Result<()> { + if std::path::Path::new("data").exists() { + std::fs::remove_dir_all("data").unwrap(); + } + let uri = "data/sample-lancedb"; + let db = connect(uri).execute().await?; + let tbl = create_table(&db).await?; + + create_index(&tbl).await?; + search_index(&tbl).await?; + Ok(()) +} + +fn create_some_records() -> Result> { + const TOTAL: usize = 1000; + + let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int32, false), + Field::new("doc", DataType::Utf8, true), + ])); + + let words = random_word::all(random_word::Lang::En) + .iter() + .step_by(1024) + .take(500) + .copied() + .collect::>(); + let n_terms = 3; + let batches = RecordBatchIterator::new( + vec![RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)), + Arc::new(StringArray::from_iter_values((0..TOTAL).map(|_| { + (0..n_terms) + .map(|_| words[random::() % words.len()]) + .collect::>() + .join(" ") + }))), + ], + ) + .unwrap()] + .into_iter() + .map(Ok), + schema.clone(), + ); + Ok(Box::new(batches)) +} + +async fn create_table(db: &Connection) -> Result { + let initial_data: Box = create_some_records()?; + let tbl = db.create_table("my_table", initial_data).execute().await?; + Ok(tbl) +} + +async fn create_index(table: &Table) -> Result<()> { + table + .create_index(&["doc"], Index::FTS(FtsIndexBuilder::default())) + .execute() + .await?; + Ok(()) +} + +async fn search_index(table: &Table) -> Result<()> { + let words = random_word::all(random_word::Lang::En) + .iter() + .step_by(1024) + .take(500) + .copied() + .collect::>(); + let query = words[0].to_owned(); + println!("Searching for: {}", query); + + let mut results = table + .query() + .full_text_search(FullTextSearchQuery::new(words[0].to_owned())) + .select(lancedb::query::Select::Columns(vec!["doc".to_owned()])) + .limit(10) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + Ok(()) +} diff --git a/docs/web-tests/rs/ivf_pq.rs b/docs/web-tests/rs/ivf_pq.rs new file mode 100644 index 000000000..ccecc22f0 --- /dev/null +++ b/docs/web-tests/rs/ivf_pq.rs @@ -0,0 +1,154 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +//! This example demonstrates setting advanced parameters when building an IVF PQ index +//! +//! Snippets from this example are used in the documentation on ANN indices. + +use std::sync::Arc; + +use arrow_array::types::Float32Type; +use arrow_array::{ + FixedSizeListArray, Int32Array, RecordBatch, RecordBatchIterator, RecordBatchReader, +}; +use arrow_schema::{DataType, Field, Schema}; + +use futures::TryStreamExt; +use lancedb::connection::Connection; +use lancedb::index::vector::IvfPqIndexBuilder; +use lancedb::index::Index; +use lancedb::query::{ExecutableQuery, QueryBase}; +use lancedb::{connect, DistanceType, Result, Table}; + +#[tokio::main] +async fn main() -> Result<()> { + if std::path::Path::new("data").exists() { + std::fs::remove_dir_all("data").unwrap(); + } + let uri = "data/sample-lancedb"; + let db = connect(uri).execute().await?; + let tbl = create_table(&db).await?; + + create_index(&tbl).await?; + search_index(&tbl).await?; + Ok(()) +} + +fn create_some_records() -> Result> { + const TOTAL: usize = 1000; + const DIM: usize = 128; + + let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int32, false), + Field::new( + "vector", + DataType::FixedSizeList( + Arc::new(Field::new("item", DataType::Float32, true)), + DIM as i32, + ), + true, + ), + ])); + + // Create a RecordBatch stream. + let batches = RecordBatchIterator::new( + vec![RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)), + Arc::new( + FixedSizeListArray::from_iter_primitive::( + (0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])), + DIM as i32, + ), + ), + ], + ) + .unwrap()] + .into_iter() + .map(Ok), + schema.clone(), + ); + Ok(Box::new(batches)) +} + +async fn create_table(db: &Connection) -> Result
{ + let initial_data: Box = create_some_records()?; + let tbl = db + .create_table("my_table", initial_data) + .execute() + .await + .unwrap(); + Ok(tbl) +} + +async fn create_index(table: &Table) -> Result<()> { + // --8<-- [start:create_index] + // For this example, `table` is a lancedb::Table with a column named + // "vector" that is a vector column with dimension 128. + + // By default, if the column "vector" appears to be a vector column, + // then an IVF_PQ index with reasonable defaults is created. + table + .create_index(&["vector"], Index::Auto) + .execute() + .await?; + // For advanced cases, it is also possible to specifically request an + // IVF_PQ index and provide custom parameters. + table + .create_index( + &["vector"], + Index::IvfPq( + // Here we specify advanced indexing parameters. In this case + // we are creating an index that my have better recall than the + // default but is also larger and slower. + IvfPqIndexBuilder::default() + // This overrides the default distance type of l2 + .distance_type(DistanceType::Cosine) + // With 1000 rows this have been ~31 by default + .num_partitions(50) + // With dimension 128 this would have been 8 by default + .num_sub_vectors(16), + ), + ) + .execute() + .await?; + // --8<-- [end:create_index] + Ok(()) +} + +async fn search_index(table: &Table) -> Result<()> { + // --8<-- [start:search1] + let query_vector = [1.0; 128]; + // By default the index will find the 10 closest results using default + // search parameters that give a reasonable tradeoff between accuracy + // and search latency + let mut results = table + .vector_search(&query_vector)? + // Note: you should always set the distance_type to match the value used + // to train the index + .distance_type(DistanceType::Cosine) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // We can also provide custom search parameters. Here we perform a + // slower but more accurate search + let mut results = table + .vector_search(&query_vector)? + .distance_type(DistanceType::Cosine) + // Override the default of 10 to get more rows + .limit(15) + // Override the default of 20 to search more partitions + .nprobes(30) + // Override the default of None to apply a refine step + .refine_factor(1) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + Ok(()) + // --8<-- [end:search1] +} diff --git a/docs/web-tests/rs/multimodal.rs b/docs/web-tests/rs/multimodal.rs new file mode 100644 index 000000000..8cae02eee --- /dev/null +++ b/docs/web-tests/rs/multimodal.rs @@ -0,0 +1,178 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +// --8<-- [start:multimodal_imports] +use std::collections::HashMap; +use std::sync::Arc; + +use arrow_array::types::Float32Type; +use arrow_array::{ + BinaryArray, FixedSizeListArray, Int32Array, Int64Array, LargeBinaryArray, RecordBatch, + RecordBatchIterator, StringArray, +}; +use arrow_schema::{DataType, Field, Schema}; +use futures_util::TryStreamExt; +use lancedb::connect; +use lancedb::database::CreateTableMode; +use lancedb::query::{ExecutableQuery, QueryBase}; +// --8<-- [end:multimodal_imports] + +#[tokio::main] +async fn main() { + let temp_dir = tempfile::tempdir().unwrap(); + let db_uri = temp_dir.path().to_str().unwrap().to_string(); + let db = connect(&db_uri).execute().await.unwrap(); + + // --8<-- [start:create_dummy_data] + let create_dummy_image = |color: u8| -> Vec { + let mut png_like = vec![137, 80, 78, 71, 13, 10, 26, 10]; + png_like.push(color); + png_like + }; + + let data = vec![ + ( + 1_i32, + "red_square.png", + vec![0.1_f32; 128], + create_dummy_image(1), + "red", + ), + ( + 2_i32, + "blue_square.png", + vec![0.2_f32; 128], + create_dummy_image(2), + "blue", + ), + ]; + // --8<-- [end:create_dummy_data] + + // --8<-- [start:define_schema] + let schema = Schema::new(vec![ + Field::new("id", DataType::Int32, false), + Field::new("filename", DataType::Utf8, false), + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 128), + false, + ), + Field::new("image_blob", DataType::Binary, false), + Field::new("label", DataType::Utf8, false), + ]); + // --8<-- [end:define_schema] + + // --8<-- [start:ingest_data] + let schema = Arc::new(schema); + let image_batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(Int32Array::from_iter_values(data.iter().map(|row| row.0))), + Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.1))), + Arc::new( + FixedSizeListArray::from_iter_primitive::( + data.iter() + .map(|row| Some(row.2.iter().copied().map(Some).collect::>())), + 128, + ), + ), + Arc::new(BinaryArray::from_iter_values( + data.iter().map(|row| row.3.as_slice()), + )), + Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.4))), + ], + ) + .unwrap(); + let image_reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(image_batch)].into_iter(), schema.clone())); + let table = db + .create_table("images", image_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:ingest_data] + assert_eq!(table.count_rows(None).await.unwrap(), 2); + + // --8<-- [start:search_data] + let query_vector = vec![0.1_f32; 128]; + let results = table + .query() + .nearest_to(query_vector) + .unwrap() + .limit(1) + .execute() + .await + .unwrap() + .try_collect::>() + .await + .unwrap(); + // --8<-- [end:search_data] + + // --8<-- [start:process_results] + for batch in &results { + let filenames = batch + .column_by_name("filename") + .unwrap() + .as_any() + .downcast_ref::() + .unwrap(); + let images = batch + .column_by_name("image_blob") + .unwrap() + .as_any() + .downcast_ref::() + .unwrap(); + + for row in 0..batch.num_rows() { + let image_bytes = images.value(row); + println!( + "Retrieved image: {}, Byte length: {}", + filenames.value(row), + image_bytes.len() + ); + } + } + // --8<-- [end:process_results] + let search_rows: usize = results.iter().map(|batch| batch.num_rows()).sum(); + assert_eq!(search_rows, 1); + + // --8<-- [start:blob_api_schema] + let blob_metadata = HashMap::from([( + "lance-encoding:blob".to_string(), + "true".to_string(), + )]); + let blob_schema = Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("video", DataType::LargeBinary, true).with_metadata(blob_metadata), + ]); + // --8<-- [end:blob_api_schema] + + // --8<-- [start:blob_api_ingest] + let blob_rows = vec![ + (1_i64, b"fake_video_bytes_1".to_vec()), + (2_i64, b"fake_video_bytes_2".to_vec()), + ]; + + let blob_schema = Arc::new(blob_schema); + let blob_batch = RecordBatch::try_new( + blob_schema.clone(), + vec![ + Arc::new(Int64Array::from_iter_values(blob_rows.iter().map(|row| row.0))), + Arc::new(LargeBinaryArray::from_iter_values( + blob_rows.iter().map(|row| row.1.as_slice()), + )), + ], + ) + .unwrap(); + let blob_reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(blob_batch)].into_iter(), blob_schema)); + let blob_table = db + .create_table("videos", blob_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:blob_api_ingest] + assert_eq!(blob_table.count_rows(None).await.unwrap(), 2); +} diff --git a/docs/web-tests/rs/openai.rs b/docs/web-tests/rs/openai.rs new file mode 100644 index 000000000..73954f34a --- /dev/null +++ b/docs/web-tests/rs/openai.rs @@ -0,0 +1,89 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +// --8<-- [start:imports] + +use std::{iter::once, sync::Arc}; + +use arrow_array::{Float64Array, Int32Array, RecordBatch, RecordBatchIterator, StringArray}; +use arrow_schema::{DataType, Field, Schema}; +use futures::StreamExt; +use lancedb::{ + arrow::IntoArrow, + connect, + embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction}, + query::{ExecutableQuery, QueryBase}, + Result, +}; + +// --8<-- [end:imports] + +// --8<-- [start:openai_embeddings] +#[tokio::main] +async fn main() -> Result<()> { + let tempdir = tempfile::tempdir().unwrap(); + let tempdir = tempdir.path().to_str().unwrap(); + let api_key = std::env::var("OPENAI_API_KEY").expect("OPENAI_API_KEY is not set"); + let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model( + api_key, + "text-embedding-3-large", + )?); + + let db = connect(tempdir).execute().await?; + db.embedding_registry() + .register("openai", embedding.clone())?; + + let table = db + .create_table("vectors", make_data()) + .add_embedding(EmbeddingDefinition::new( + "text", + "openai", + Some("embeddings"), + ))? + .execute() + .await?; + + let query = Arc::new(StringArray::from_iter_values(once("something warm"))); + let query_vector = embedding.compute_query_embeddings(query)?; + let mut results = table + .vector_search(query_vector)? + .limit(1) + .execute() + .await?; + + let rb = results.next().await.unwrap()?; + let out = rb + .column_by_name("text") + .unwrap() + .as_any() + .downcast_ref::() + .unwrap(); + let text = out.iter().next().unwrap().unwrap(); + println!("Closest match: {}", text); + Ok(()) +} +// --8<-- [end:openai_embeddings] + +fn make_data() -> impl IntoArrow { + let schema = Schema::new(vec![ + Field::new("id", DataType::Int32, true), + Field::new("text", DataType::Utf8, false), + Field::new("price", DataType::Float64, false), + ]); + + let id = Int32Array::from(vec![1, 2, 3, 4]); + let text = StringArray::from_iter_values(vec![ + "Black T-Shirt", + "Leather Jacket", + "Winter Parka", + "Hooded Sweatshirt", + ]); + let price = Float64Array::from(vec![10.0, 50.0, 100.0, 30.0]); + let schema = Arc::new(schema); + let rb = RecordBatch::try_new( + schema.clone(), + vec![Arc::new(id), Arc::new(text), Arc::new(price)], + ) + .unwrap(); + Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema)) +} diff --git a/docs/web-tests/rs/quickstart.rs b/docs/web-tests/rs/quickstart.rs new file mode 100644 index 000000000..bc966aab5 --- /dev/null +++ b/docs/web-tests/rs/quickstart.rs @@ -0,0 +1,347 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +use std::sync::Arc; + +use arrow_array::types::Float32Type; +use arrow_array::{ + FixedSizeListArray, Int8Array, LargeStringArray, RecordBatch, RecordBatchIterator, StructArray, +}; +use arrow_schema::{DataType, Field, FieldRef, Schema}; +use lancedb::arrow::IntoPolars; +use lancedb::database::CreateTableMode; +use lancedb::query::{ExecutableQuery, QueryBase, Select}; +use lancedb::{connect, table::NewColumnTransform}; +use polars::prelude::DataFrame; +use serde::{Deserialize, Serialize}; + +// --8<-- [start:quickstart_define_struct] +// Define structs representing the data schema +#[derive(Debug, Clone, Serialize, Deserialize)] +struct Stats { + strength: i8, + magic: i8, + leadership: i8, + wisdom: i8, +} + +#[derive(Debug, Clone, Serialize, Deserialize)] +struct Character { + id: String, + name: String, + role: String, + description: String, + stats: Stats, + vector: [f32; 4], +} + +fn characters_schema() -> Arc { + Arc::new(Schema::new(vec![ + Field::new("id", DataType::LargeUtf8, false), + Field::new("name", DataType::LargeUtf8, false), + Field::new("role", DataType::LargeUtf8, false), + Field::new("description", DataType::LargeUtf8, false), + Field::new( + "stats", + DataType::Struct(arrow_schema::Fields::from(vec![ + Arc::new(Field::new("strength", DataType::Int8, false)), + Arc::new(Field::new("magic", DataType::Int8, false)), + Arc::new(Field::new("leadership", DataType::Int8, false)), + Arc::new(Field::new("wisdom", DataType::Int8, false)), + ])), + false, + ), + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4), + false, + ), + ])) +} +// --8<-- [end:quickstart_define_struct] + +type BatchIter = Box; + +fn characters_to_reader(schema: Arc, rows: &[Character]) -> BatchIter { + let ids = LargeStringArray::from_iter_values(rows.iter().map(|row| row.id.as_str())); + let names = LargeStringArray::from_iter_values(rows.iter().map(|row| row.name.as_str())); + let roles = LargeStringArray::from_iter_values(rows.iter().map(|row| row.role.as_str())); + let descriptions = + LargeStringArray::from_iter_values(rows.iter().map(|row| row.description.as_str())); + + let strength = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.strength)); + let magic = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.magic)); + let leadership = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.leadership)); + let wisdom = Int8Array::from_iter_values(rows.iter().map(|row| row.stats.wisdom)); + let stats_fields: Vec = vec![ + Arc::new(Field::new("strength", DataType::Int8, false)), + Arc::new(Field::new("magic", DataType::Int8, false)), + Arc::new(Field::new("leadership", DataType::Int8, false)), + Arc::new(Field::new("wisdom", DataType::Int8, false)), + ]; + let stats = StructArray::new( + stats_fields.into(), + vec![ + Arc::new(strength), + Arc::new(magic), + Arc::new(leadership), + Arc::new(wisdom), + ], + None, + ); + + let vectors = FixedSizeListArray::from_iter_primitive::( + rows.iter() + .map(|row| Some(row.vector.iter().copied().map(Some).collect::>())), + 4, + ); + + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(ids), + Arc::new(names), + Arc::new(roles), + Arc::new(descriptions), + Arc::new(stats), + Arc::new(vectors), + ], + ) + .unwrap(); + + Box::new(RecordBatchIterator::new( + vec![Ok(batch)].into_iter(), + schema, + )) +} + +#[tokio::main] +async fn main() { + let temp_dir = tempfile::tempdir().unwrap(); + let uri = temp_dir.path().to_str().unwrap(); + let db = connect(uri).execute().await.unwrap(); + + // --8<-- [start:quickstart_data] + let data = vec![ + Character { + id: "1".to_string(), + name: "King Arthur".to_string(), + role: "King".to_string(), + description: "Leader of Camelot and wielder of Excalibur.".to_string(), + stats: Stats { + strength: 4, + magic: 1, + leadership: 5, + wisdom: 4, + }, + vector: [0.7, 0.1, 0.9, 0.7], + }, + Character { + id: "2".to_string(), + name: "Merlin".to_string(), + role: "Wizard".to_string(), + description: "Advisor and prophet with deep magical knowledge.".to_string(), + stats: Stats { + strength: 2, + magic: 5, + leadership: 4, + wisdom: 5, + }, + vector: [0.2, 0.9, 0.4, 0.9], + }, + Character { + id: "3".to_string(), + name: "Sir Lancelot".to_string(), + role: "Knight".to_string(), + description: "Legendary knight known for courage and combat skill.".to_string(), + stats: Stats { + strength: 5, + magic: 1, + leadership: 3, + wisdom: 3, + }, + vector: [0.9, 0.1, 0.5, 0.4], + }, + ]; + // --8<-- [end:quickstart_data] + + // --8<-- [start:quickstart_create_table] + let schema = characters_schema(); + let table = db + .create_table("characters", characters_to_reader(schema.clone(), &data)) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:quickstart_create_table] + assert_eq!(table.count_rows(None).await.unwrap(), 3); + db.drop_table("characters", &[]).await.unwrap(); + + // --8<-- [start:quickstart_create_table_no_overwrite] + let table = db + .create_table("characters", characters_to_reader(schema.clone(), &data)) + .execute() + .await + .unwrap(); + // --8<-- [end:quickstart_create_table_no_overwrite] + assert_eq!(table.count_rows(None).await.unwrap(), 3); + + // --8<-- [start:quickstart_vector_search_1] + // Search for examples similar to a "wise magical advisor" + let query_vector = [0.2, 0.8, 0.4, 0.9]; + + let result: DataFrame = table + .query() + .nearest_to(&query_vector) + .unwrap() + .select(Select::Columns(vec![ + "name".to_string(), + "role".to_string(), + "description".to_string(), + "_distance".to_string(), + ])) + .limit(2) + .execute() + .await + .unwrap() + .into_polars() + .await + .unwrap(); + println!("{result:?}"); + // --8<-- [end:quickstart_vector_search_1] + let name_col = result.column("name").unwrap().str().unwrap(); + assert_eq!(name_col.get(0).unwrap(), "Merlin"); + + // --8<-- [start:quickstart_curate_with_metadata] + let curated: DataFrame = table + .query() + .nearest_to(&query_vector) + .unwrap() + .only_if("stats.magic >= 4") + .select(Select::Columns(vec![ + "name".to_string(), + "role".to_string(), + "description".to_string(), + "_distance".to_string(), + ])) + .limit(2) + .execute() + .await + .unwrap() + .into_polars() + .await + .unwrap(); + println!("{curated:?}"); + // --8<-- [end:quickstart_curate_with_metadata] + let curated_name_col = curated.column("name").unwrap().str().unwrap(); + assert_eq!(curated_name_col.get(0).unwrap(), "Merlin"); + + // --8<-- [start:quickstart_output_array] + let result: DataFrame = table + .query() + .nearest_to(&query_vector) + .unwrap() + .select(Select::Columns(vec![ + "name".to_string(), + "role".to_string(), + "description".to_string(), + "_distance".to_string(), + ])) + .limit(2) + .execute() + .await + .unwrap() + .into_polars() + .await + .unwrap(); + println!("{result:?}"); + // --8<-- [end:quickstart_output_array] + let name_col = result.column("name").unwrap().str().unwrap(); + assert_eq!(name_col.get(0).unwrap(), "Merlin"); + + // --8<-- [start:quickstart_add_feature] + table + .add_columns( + NewColumnTransform::SqlExpressions(vec![( + "power_score".to_string(), + "cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)" + .to_string(), + )]), + None, + ) + .await + .unwrap(); + // --8<-- [end:quickstart_add_feature] + + // --8<-- [start:quickstart_query_feature] + let features: DataFrame = table + .query() + .select(Select::Columns(vec![ + "name".to_string(), + "role".to_string(), + "power_score".to_string(), + ])) + .execute() + .await + .unwrap() + .into_polars() + .await + .unwrap(); + println!("{features:?}"); + // --8<-- [end:quickstart_query_feature] + assert!(features.column("power_score").is_ok()); + + // --8<-- [start:quickstart_multimodal_bytes] + use std::sync::Arc; + + use arrow_array::{ + BinaryArray, FixedSizeListArray, LargeStringArray, RecordBatch, RecordBatchIterator, + }; + use arrow_schema::{DataType, Field, Schema}; + + let image_path = std::path::Path::new(env!("CARGO_MANIFEST_DIR")) + .join("../../docs/static/assets/images/quickstart/sir-lancelot.jpg"); + let image_bytes = std::fs::read(image_path).unwrap(); + + let image_schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::LargeUtf8, false), + Field::new("description", DataType::LargeUtf8, false), + Field::new("image", DataType::Binary, false), + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4), + false, + ), + ])); + let image_vectors = [[0.9_f32, 0.1, 0.5, 0.4]]; + let image_batch = RecordBatch::try_new( + image_schema.clone(), + vec![ + Arc::new(LargeStringArray::from_iter_values(["lancelot"])), + Arc::new(LargeStringArray::from_iter_values([ + "Portrait of Sir Lancelot", + ])), + Arc::new(BinaryArray::from_iter_values([image_bytes.as_slice()])), + Arc::new( + FixedSizeListArray::from_iter_primitive::( + image_vectors + .iter() + .map(|vector| Some(vector.iter().copied().map(Some).collect::>())), + 4, + ), + ), + ], + ) + .unwrap(); + let image_reader: Box = Box::new( + RecordBatchIterator::new(vec![Ok(image_batch)].into_iter(), image_schema), + ); + let multimodal_table = db + .create_table("character_images", image_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:quickstart_multimodal_bytes] + assert_eq!(multimodal_table.count_rows(None).await.unwrap(), 1); +} diff --git a/docs/web-tests/rs/search.rs b/docs/web-tests/rs/search.rs new file mode 100644 index 000000000..bf510f08b --- /dev/null +++ b/docs/web-tests/rs/search.rs @@ -0,0 +1,311 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +//! This example demonstrates vector search query options. +//! +//! Snippets from this example are used in the documentation on vector search. + +use std::sync::Arc; + +use arrow_array::types::Float32Type; +use arrow_array::{ + Array, FixedSizeListArray, Int32Array, Int64Array, RecordBatch, RecordBatchIterator, + RecordBatchReader, UInt8Array, +}; +use arrow_schema::{DataType, Field, Schema}; + +use futures::TryStreamExt; +use lancedb::connection::Connection; +use lancedb::index::vector::IvfFlatIndexBuilder; +use lancedb::index::Index; +use lancedb::query::{ExecutableQuery, QueryBase, Select}; +use lancedb::{connect, DistanceType, Result, Table}; + +const DIM: usize = 128; + +#[tokio::main] +async fn main() -> Result<()> { + if std::path::Path::new("data").exists() { + std::fs::remove_dir_all("data").unwrap(); + } + let uri = "data/sample-lancedb"; + let db = connect(uri).execute().await?; + let tbl = create_table(&db).await?; + tbl.create_index(&["vector"], Index::Auto).execute().await?; + + configure_distance_metric(&tbl).await?; + exact_vs_approximate(&tbl).await?; + search_distance_range(&tbl).await?; + vector_search_prefilter(&tbl).await?; + vector_search_postfilter(&tbl).await?; + fast_search(&tbl).await?; + brute_force_search(&tbl).await?; + bypass_vector_index(&tbl).await?; + batch_search(&tbl).await?; + binary_search(&db).await?; + Ok(()) +} + +fn create_some_records() -> Result> { + const TOTAL: usize = 1000; + + let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int32, false), + Field::new( + "vector", + DataType::FixedSizeList( + Arc::new(Field::new("item", DataType::Float32, true)), + DIM as i32, + ), + true, + ), + ])); + + let batches = RecordBatchIterator::new( + vec![RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)), + Arc::new( + FixedSizeListArray::from_iter_primitive::( + (0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])), + DIM as i32, + ), + ), + ], + ) + .unwrap()] + .into_iter() + .map(Ok), + schema.clone(), + ); + Ok(Box::new(batches)) +} + +async fn create_table(db: &Connection) -> Result
{ + let initial_data: Box = create_some_records()?; + let tbl = db + .create_table("my_vectors", initial_data) + .execute() + .await + .unwrap(); + Ok(tbl) +} + +async fn configure_distance_metric(table: &Table) -> Result<()> { + let query_vector = [1.0; DIM]; + // --8<-- [start:configure_distance_metric] + // Use the same distance metric the index was trained with. + let mut results = table + .vector_search(&query_vector)? + .distance_type(DistanceType::Cosine) + .limit(10) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:configure_distance_metric] + Ok(()) +} + +async fn exact_vs_approximate(table: &Table) -> Result<()> { + let query_vector = [1.0; DIM]; + // --8<-- [start:exact_vs_approximate] + // Approximate ANN search (fast, distances may come from the index representation) + let mut fast_results = table.vector_search(&query_vector)?.limit(10).execute().await?; + while let Some(batch) = fast_results.try_next().await? { + println!("{:?}", batch); + } + + // Rerank a larger candidate set on full vectors for better recall + let mut refined_results = table + .vector_search(&query_vector)? + .limit(10) + .refine_factor(20) + .execute() + .await?; + while let Some(batch) = refined_results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:exact_vs_approximate] + Ok(()) +} + +async fn search_distance_range(table: &Table) -> Result<()> { + let query_vector = [1.0; DIM]; + // --8<-- [start:search_distance_range] + // Only return rows whose distance falls within [0.1, 0.5). + let mut results = table + .vector_search(&query_vector)? + .distance_range(Some(0.1), Some(0.5)) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:search_distance_range] + Ok(()) +} + +async fn vector_search_prefilter(table: &Table) -> Result<()> { + let query_vector = [1.0; DIM]; + // --8<-- [start:vector_search_prefilter] + // Prefiltering is the default: the filter is applied before vector search. + let mut results = table + .vector_search(&query_vector)? + .only_if("id > 100") + .select(Select::columns(&["id"])) + .limit(5) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:vector_search_prefilter] + Ok(()) +} + +async fn vector_search_postfilter(table: &Table) -> Result<()> { + let query_vector = [1.0; DIM]; + // --8<-- [start:vector_search_postfilter] + // Apply the filter after vector search by calling postfilter(). + let mut results = table + .vector_search(&query_vector)? + .only_if("id > 100") + .postfilter() + .select(Select::columns(&["id"])) + .limit(5) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:vector_search_postfilter] + Ok(()) +} + +async fn fast_search(table: &Table) -> Result<()> { + let query_vector = [1.0; DIM]; + // --8<-- [start:fast_search] + // Skip unindexed data for lower latency. + let mut results = table + .vector_search(&query_vector)? + .fast_search() + .limit(5) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:fast_search] + Ok(()) +} + +async fn brute_force_search(table: &Table) -> Result<()> { + let query_vector = [1.0; DIM]; + // --8<-- [start:brute_force_search] + // A plain vector search returns the top-k closest rows. + let mut results = table.vector_search(&query_vector)?.limit(3).execute().await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:brute_force_search] + Ok(()) +} + +async fn bypass_vector_index(table: &Table) -> Result<()> { + let query_vector = [1.0; DIM]; + // --8<-- [start:bypass_vector_index] + // Force an exhaustive (flat) scan for exact, ground-truth results. + let mut results = table + .vector_search(&query_vector)? + .bypass_vector_index() + .limit(5) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:bypass_vector_index] + Ok(()) +} + +async fn batch_search(table: &Table) -> Result<()> { + let query_1 = [1.0; DIM]; + let query_2 = [0.5; DIM]; + // --8<-- [start:batch_search] + // Search multiple query vectors in one call. Each result row carries a + // `query_index` mapping it back to the query it matched. + let mut results = table + .vector_search(&query_1)? + .add_query_vector(&query_2)? + .limit(5) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:batch_search] + Ok(()) +} + +async fn binary_search(db: &Connection) -> Result<()> { + // A 256-bit binary vector is stored as 256 / 8 = 32 packed uint8 bytes. + const NUM_BYTES: i32 = 32; + const TOTAL: usize = 1024; + + let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new( + "vector", + DataType::FixedSizeList( + Arc::new(Field::new("item", DataType::UInt8, true)), + NUM_BYTES, + ), + true, + ), + ])); + + let values = + UInt8Array::from_iter_values((0..TOTAL * NUM_BYTES as usize).map(|i| (i % 256) as u8)); + let vectors = FixedSizeListArray::try_new( + Arc::new(Field::new("item", DataType::UInt8, true)), + NUM_BYTES, + Arc::new(values), + None, + ) + .unwrap(); + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(Int64Array::from_iter_values(0..TOTAL as i64)), + Arc::new(vectors), + ], + ) + .unwrap(); + let reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema.clone())); + let tbl = db.create_table("binary_vectors", reader).execute().await?; + tbl.create_index( + &["vector"], + Index::IvfFlat(IvfFlatIndexBuilder::default().distance_type(DistanceType::Hamming)), + ) + .execute() + .await?; + + // --8<-- [start:binary_search] + // Binary vectors use `hamming` distance over the packed uint8 bytes. + let query: Arc = Arc::new(UInt8Array::from(vec![1u8; NUM_BYTES as usize])); + let mut results = tbl + .vector_search(query)? + .distance_type(DistanceType::Hamming) + .limit(10) + .execute() + .await?; + while let Some(batch) = results.try_next().await? { + println!("{:?}", batch); + } + // --8<-- [end:binary_search] + Ok(()) +} diff --git a/docs/web-tests/rs/sentence_transformers.rs b/docs/web-tests/rs/sentence_transformers.rs new file mode 100644 index 000000000..9250430d4 --- /dev/null +++ b/docs/web-tests/rs/sentence_transformers.rs @@ -0,0 +1,95 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +use std::{iter::once, sync::Arc}; + +use arrow_array::{RecordBatch, RecordBatchIterator, StringArray}; +use arrow_schema::{DataType, Field, Schema}; +use futures::StreamExt; +use lancedb::{ + arrow::IntoArrow, + connect, + embeddings::{ + sentence_transformers::SentenceTransformersEmbeddings, EmbeddingDefinition, + EmbeddingFunction, + }, + query::{ExecutableQuery, QueryBase}, + Result, +}; + +#[tokio::main] +async fn main() -> Result<()> { + let tempdir = tempfile::tempdir().unwrap(); + let tempdir = tempdir.path().to_str().unwrap(); + let embedding = SentenceTransformersEmbeddings::builder().build()?; + let embedding = Arc::new(embedding); + let db = connect(tempdir).execute().await?; + db.embedding_registry() + .register("sentence-transformers", embedding.clone())?; + + let table = db + .create_table("vectors", make_data()) + .add_embedding(EmbeddingDefinition::new( + "facts", + "sentence-transformers", + Some("embeddings"), + ))? + .execute() + .await?; + + let query = Arc::new(StringArray::from_iter_values(once( + "How many bones are in the human body?", + ))); + let query_vector = embedding.compute_query_embeddings(query)?; + let mut results = table + .vector_search(query_vector)? + .limit(1) + .execute() + .await?; + + let rb = results.next().await.unwrap()?; + let out = rb + .column_by_name("facts") + .unwrap() + .as_any() + .downcast_ref::() + .unwrap(); + let text = out.iter().next().unwrap().unwrap(); + println!("Answer: {}", text); + Ok(()) +} + +fn make_data() -> impl IntoArrow { + let schema = Schema::new(vec![Field::new("facts", DataType::Utf8, false)]); + + let facts = StringArray::from_iter_values(vec![ + "Albert Einstein was a theoretical physicist.", + "The capital of France is Paris.", + "The Great Wall of China is one of the Seven Wonders of the World.", + "Python is a popular programming language.", + "Mount Everest is the highest mountain in the world.", + "Leonardo da Vinci painted the Mona Lisa.", + "Shakespeare wrote Hamlet.", + "The human body has 206 bones.", + "The speed of light is approximately 299,792 kilometers per second.", + "Water boils at 100 degrees Celsius.", + "The Earth orbits the Sun.", + "The Pyramids of Giza are located in Egypt.", + "Coffee is one of the most popular beverages in the world.", + "Tokyo is the capital city of Japan.", + "Photosynthesis is the process by which plants make their food.", + "The Pacific Ocean is the largest ocean on Earth.", + "Mozart was a prolific composer of classical music.", + "The Internet is a global network of computers.", + "Basketball is a sport played with a ball and a hoop.", + "The first computer virus was created in 1983.", + "Artificial neural networks are inspired by the human brain.", + "Deep learning is a subset of machine learning.", + "IBM's Watson won Jeopardy! in 2011.", + "The first computer programmer was Ada Lovelace.", + "The first chatbot was ELIZA, created in the 1960s.", + ]); + let schema = Arc::new(schema); + let rb = RecordBatch::try_new(schema.clone(), vec![Arc::new(facts)]).unwrap(); + Box::new(RecordBatchIterator::new(vec![Ok(rb)], schema)) +} diff --git a/docs/web-tests/rs/simple.rs b/docs/web-tests/rs/simple.rs new file mode 100644 index 000000000..ac98957ae --- /dev/null +++ b/docs/web-tests/rs/simple.rs @@ -0,0 +1,147 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +//! This example demonstrates basic usage of LanceDb. +//! +//! Snippets from this example are used in the quickstart documentation. + +use std::sync::Arc; + +use arrow_array::types::Float32Type; +use arrow_array::{FixedSizeListArray, Int32Array, RecordBatch, RecordBatchIterator}; +use arrow_schema::{DataType, Field, Schema}; +use futures::TryStreamExt; + +use lancedb::arrow::IntoArrow; +use lancedb::connection::Connection; +use lancedb::index::Index; +use lancedb::query::{ExecutableQuery, QueryBase}; +use lancedb::{connect, Result, Table as LanceDbTable}; + +// --8<-- [start:connect] +#[tokio::main] +async fn main() -> Result<()> { + if std::path::Path::new("data").exists() { + std::fs::remove_dir_all("data").unwrap(); + } + // --8<-- [start:connect_uri] + let uri = "data/sample-lancedb"; + let db = connect(uri).execute().await?; + // --8<-- [end:connect_uri] + + // --8<-- [start:list_names] + println!("{:?}", db.table_names().execute().await?); + // --8<-- [end:list_names] + let tbl = create_table(&db).await?; + create_index(&tbl).await?; + let batches = search(&tbl).await?; + println!("{:?}", batches); + + create_empty_table(&db).await.unwrap(); + + // --8<-- [start:delete] + tbl.delete("id > 24").await.unwrap(); + // --8<-- [end:delete] + + // --8<-- [start:drop_table] + db.drop_table("my_table").await.unwrap(); + // --8<-- [end:drop_table] + Ok(()) +} +// --8<-- [end:connect] + +#[allow(dead_code)] +async fn open_with_existing_tbl() -> Result<()> { + let uri = "data/sample-lancedb"; + let db = connect(uri).execute().await?; + #[allow(unused_variables)] + // --8<-- [start:open_existing_tbl] + let table = db.open_table("my_table").execute().await.unwrap(); + // --8<-- [end:open_existing_tbl] + Ok(()) +} + +fn create_some_records() -> Result { + const TOTAL: usize = 1000; + const DIM: usize = 128; + + let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int32, false), + Field::new( + "vector", + DataType::FixedSizeList( + Arc::new(Field::new("item", DataType::Float32, true)), + DIM as i32, + ), + true, + ), + ])); + + // Create a RecordBatch stream. + let batches = RecordBatchIterator::new( + vec![RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(Int32Array::from_iter_values(0..TOTAL as i32)), + Arc::new( + FixedSizeListArray::from_iter_primitive::( + (0..TOTAL).map(|_| Some(vec![Some(1.0); DIM])), + DIM as i32, + ), + ), + ], + ) + .unwrap()] + .into_iter() + .map(Ok), + schema.clone(), + ); + Ok(Box::new(batches)) +} + +async fn create_table(db: &Connection) -> Result { + // --8<-- [start:create_table] + let initial_data = create_some_records()?; + let tbl = db + .create_table("my_table", initial_data) + .execute() + .await + .unwrap(); + // --8<-- [end:create_table] + + // --8<-- [start:add] + let new_data = create_some_records()?; + tbl.add(new_data).execute().await.unwrap(); + // --8<-- [end:add] + + Ok(tbl) +} + +async fn create_empty_table(db: &Connection) -> Result { + // --8<-- [start:create_empty_table] + let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int32, false), + Field::new("item", DataType::Utf8, true), + ])); + db.create_empty_table("empty_table", schema).execute().await + // --8<-- [end:create_empty_table] +} + +async fn create_index(table: &LanceDbTable) -> Result<()> { + // --8<-- [start:create_index] + table.create_index(&["vector"], Index::Auto).execute().await + // --8<-- [end:create_index] +} + +async fn search(table: &LanceDbTable) -> Result> { + // --8<-- [start:search] + table + .query() + .limit(2) + .nearest_to(&[1.0; 128])? + .execute() + .await? + .try_collect::>() + .await + // --8<-- [end:search] +} diff --git a/docs/web-tests/rs/src/main.rs b/docs/web-tests/rs/src/main.rs new file mode 100644 index 000000000..e7a11a969 --- /dev/null +++ b/docs/web-tests/rs/src/main.rs @@ -0,0 +1,3 @@ +fn main() { + println!("Hello, world!"); +} diff --git a/docs/web-tests/rs/tables.rs b/docs/web-tests/rs/tables.rs new file mode 100644 index 000000000..0d568530d --- /dev/null +++ b/docs/web-tests/rs/tables.rs @@ -0,0 +1,1516 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +use std::sync::Arc; +use std::time::Duration as StdDuration; + +use arrow_array::types::Float32Type; +use arrow_array::{ + Array, FixedSizeListArray, Float32Array, Float64Array, Int32Array, Int64Array, RecordBatch, + RecordBatchIterator, RecordBatchReader, StringArray, +}; +use arrow_schema::{DataType, Field, Schema}; +use futures::TryStreamExt; +use lancedb::connect; +use lancedb::query::ExecutableQuery; +use lancedb::database::CreateTableMode; +use lancedb::table::{ + ColumnAlteration, Duration, FieldMetadataUpdate, NewColumnTransform, OptimizeAction, +}; + +// --8<-- [start:update_make_users_reader] +fn make_users_reader( + ids: Vec, + names: Vec<&str>, + login_counts: Option>, +) -> Box { + let mut fields = vec![ + Field::new("id", DataType::Int64, false), + Field::new("name", DataType::Utf8, false), + ]; + let mut columns: Vec> = + vec![Arc::new(Int64Array::from(ids)), Arc::new(StringArray::from(names))]; + + if let Some(login_counts) = login_counts { + fields.push(Field::new("login_count", DataType::Int64, true)); + columns.push(Arc::new(Int64Array::from(login_counts))); + } + + let schema = Arc::new(Schema::new(fields)); + let batch = RecordBatch::try_new(schema.clone(), columns).unwrap(); + let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema); + Box::new(reader) +} +// --8<-- [end:update_make_users_reader] + +// --8<-- [start:versioning_make_quotes_reader] +fn make_quotes_reader(rows: Vec<(i64, &str, &str)>) -> Box { + let ids: Vec = rows.iter().map(|(id, _, _)| *id).collect(); + let authors: Vec<&str> = rows.iter().map(|(_, author, _)| *author).collect(); + let quotes: Vec<&str> = rows.iter().map(|(_, _, quote)| *quote).collect(); + + let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("author", DataType::Utf8, false), + Field::new("quote", DataType::Utf8, false), + ])); + + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new(Int64Array::from(ids)), + Arc::new(StringArray::from(authors)), + Arc::new(StringArray::from(quotes)), + ], + ) + .unwrap(); + let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema); + Box::new(reader) +} +// --8<-- [end:versioning_make_quotes_reader] + +// Helper: a table with a `vector` and a `text` column, used to demonstrate +// building indexes on a branch. +fn make_products_reader(n: i32) -> Box { + const DIM: usize = 4; + let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int32, false), + Field::new( + "vector", + DataType::FixedSizeList( + Arc::new(Field::new("item", DataType::Float32, true)), + DIM as i32, + ), + true, + ), + Field::new("text", DataType::Utf8, false), + ])); + + let ids = Int32Array::from_iter_values(0..n); + let vectors = FixedSizeListArray::from_iter_primitive::( + (0..n).map(|i| { + Some( + (0..DIM) + .map(|d| Some((((i as usize * 31 + d * 7) % 97) as f32) / 97.0)) + .collect::>>(), + ) + }), + DIM as i32, + ); + let texts = StringArray::from_iter_values((0..n).map(|i| format!("product number {i}"))); + + let batch = RecordBatch::try_new( + schema.clone(), + vec![Arc::new(ids), Arc::new(vectors), Arc::new(texts)], + ) + .unwrap(); + let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema); + Box::new(reader) +} + +#[allow(dead_code)] +async fn update_connect_enterprise_example() { + // --8<-- [start:update_connect_enterprise] + let uri = "db://your-project-slug"; + let api_key = "your-api-key"; + let region = "us-east-1"; + // --8<-- [end:update_connect_enterprise] + let _ = (uri, api_key, region); +} + +#[allow(dead_code)] +async fn update_connect_local_example() { + // --8<-- [start:update_connect_local] + let db = connect("./data").execute().await.unwrap(); + // --8<-- [end:update_connect_local] + let _ = db; +} + +#[tokio::main] +async fn main() { + let temp_dir = tempfile::tempdir().unwrap(); + let db_uri = temp_dir.path().to_str().unwrap().to_string(); + let db = connect(&db_uri).execute().await.unwrap(); + + // --8<-- [start:create_table_from_dicts] + struct Location { + vector: [f32; 2], + lat: f32, + long: f32, + } + + let data = vec![ + Location { + vector: [1.1, 1.2], + lat: 45.5, + long: -122.7, + }, + Location { + vector: [0.2, 1.8], + lat: 40.1, + long: -74.1, + }, + ]; + + let schema = Arc::new(Schema::new(vec![ + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2), + false, + ), + Field::new("lat", DataType::Float32, false), + Field::new("long", DataType::Float32, false), + ])); + + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new( + FixedSizeListArray::from_iter_primitive::( + data.iter() + .map(|row| Some(row.vector.iter().copied().map(Some).collect::>())), + 2, + ), + ), + Arc::new(Float32Array::from_iter_values( + data.iter().map(|row| row.lat), + )), + Arc::new(Float32Array::from_iter_values( + data.iter().map(|row| row.long), + )), + ], + ) + .unwrap(); + let reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema.clone())); + let table = db + .create_table("test_table", reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:create_table_from_dicts] + assert_eq!(table.count_rows(None).await.unwrap(), 2); + + // Seed an existing table so the conflict-handling examples have something to act on. + let conflict_seed = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new( + FixedSizeListArray::from_iter_primitive::( + data.iter() + .map(|row| Some(row.vector.iter().copied().map(Some).collect::>())), + 2, + ), + ), + Arc::new(Float32Array::from_iter_values( + data.iter().map(|row| row.lat), + )), + Arc::new(Float32Array::from_iter_values( + data.iter().map(|row| row.long), + )), + ], + ) + .unwrap(); + let conflict_seed_reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(conflict_seed)].into_iter(), schema.clone())); + db.create_table("conflict_table", conflict_seed_reader) + .execute() + .await + .unwrap(); + + // Build readers for the rows we want to ingest. Outside the snippet markers + // because the docs only need to highlight the `.mode(...)` differences. + let make_reader = || { + let batch = RecordBatch::try_new( + schema.clone(), + vec![ + Arc::new( + FixedSizeListArray::from_iter_primitive::( + data.iter().map(|row| { + Some(row.vector.iter().copied().map(Some).collect::>()) + }), + 2, + ), + ), + Arc::new(Float32Array::from_iter_values( + data.iter().map(|row| row.lat), + )), + Arc::new(Float32Array::from_iter_values( + data.iter().map(|row| row.long), + )), + ], + ) + .unwrap(); + let reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema.clone())); + reader + }; + let exist_ok_reader = make_reader(); + let overwrite_reader = make_reader(); + + // --8<-- [start:create_table_conflict_handling] + // Idempotent open: reuse the existing table if it exists. + // The provided data is ignored; the schema is validated against the + // existing table and a mismatch raises an error. + let _conflict_table = db + .create_table("conflict_table", exist_ok_reader) + .mode(CreateTableMode::exist_ok(|req| req)) + .execute() + .await + .unwrap(); + + // Overwrite: drop the existing table and create a new one with the + // provided data. This permanently discards the old table's data. + let conflict_table = db + .create_table("conflict_table", overwrite_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:create_table_conflict_handling] + assert_eq!(conflict_table.count_rows(None).await.unwrap(), 2); + + // --8<-- [start:create_table_custom_schema] + let custom_schema = Arc::new(Schema::new(vec![ + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4), + false, + ), + Field::new("lat", DataType::Float32, false), + Field::new("long", DataType::Float32, false), + ])); + + let custom_batch = RecordBatch::try_new( + custom_schema.clone(), + vec![ + Arc::new( + FixedSizeListArray::from_iter_primitive::( + vec![ + Some(vec![Some(1.1), Some(1.2), Some(1.3), Some(1.4)]), + Some(vec![Some(0.2), Some(1.8), Some(0.4), Some(3.6)]), + ], + 4, + ), + ), + Arc::new(Float32Array::from(vec![45.5, 40.1])), + Arc::new(Float32Array::from(vec![-122.7, -74.1])), + ], + ) + .unwrap(); + let custom_reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(custom_batch)].into_iter(), custom_schema.clone())); + let custom_table = db + .create_table("my_table_custom_schema", custom_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:create_table_custom_schema] + assert_eq!(custom_table.count_rows(None).await.unwrap(), 2); + + // --8<-- [start:create_table_from_arrow] + let arrow_schema = Arc::new(Schema::new(vec![ + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 16), + false, + ), + Field::new("text", DataType::Utf8, false), + ])); + + let arrow_batch = RecordBatch::try_new( + arrow_schema.clone(), + vec![ + Arc::new( + FixedSizeListArray::from_iter_primitive::( + vec![Some(vec![Some(0.1); 16]), Some(vec![Some(0.2); 16])], + 16, + ), + ), + Arc::new(StringArray::from(vec!["foo", "bar"])), + ], + ) + .unwrap(); + let arrow_reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(arrow_batch)].into_iter(), arrow_schema.clone())); + let arrow_table = db + .create_table("arrow_table_example", arrow_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:create_table_from_arrow] + assert_eq!(arrow_table.count_rows(None).await.unwrap(), 2); + + // --8<-- [start:create_table_from_iterator] + let batch_schema = Arc::new(Schema::new(vec![ + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 4), + false, + ), + Field::new("item", DataType::Utf8, false), + Field::new("price", DataType::Float32, false), + ])); + + let batches = (0..5) + .map(|i| { + RecordBatch::try_new( + batch_schema.clone(), + vec![ + Arc::new( + FixedSizeListArray::from_iter_primitive::( + vec![ + Some(vec![Some(3.1 + i as f32), Some(4.1), Some(5.1), Some(6.1)]), + Some(vec![ + Some(5.9), + Some(26.5 + i as f32), + Some(4.7), + Some(32.8), + ]), + ], + 4, + ), + ), + Arc::new(StringArray::from(vec![ + format!("item{}", i * 2 + 1), + format!("item{}", i * 2 + 2), + ])), + Arc::new(Float32Array::from(vec![ + ((i * 2 + 1) * 10) as f32, + ((i * 2 + 2) * 10) as f32, + ])), + ], + ) + .unwrap() + }) + .collect::>(); + + let batch_reader: Box = + Box::new(RecordBatchIterator::new(batches.into_iter().map(Ok), batch_schema.clone())); + let batch_table = db + .create_table("batched_table", batch_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:create_table_from_iterator] + assert_eq!(batch_table.count_rows(None).await.unwrap(), 10); + + // --8<-- [start:open_existing_table] + let open_schema = Arc::new(Schema::new(vec![ + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2), + false, + ), + Field::new("lat", DataType::Float32, false), + Field::new("long", DataType::Float32, false), + ])); + let open_batch = RecordBatch::try_new( + open_schema.clone(), + vec![ + Arc::new( + FixedSizeListArray::from_iter_primitive::( + vec![Some(vec![Some(1.1), Some(1.2)])], + 2, + ), + ), + Arc::new(Float32Array::from(vec![45.5])), + Arc::new(Float32Array::from(vec![-122.7])), + ], + ) + .unwrap(); + let open_reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(open_batch)].into_iter(), open_schema.clone())); + db.create_table("test_table", open_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + println!("{:?}", db.table_names().execute().await.unwrap()); + + let opened_table = db.open_table("test_table").execute().await.unwrap(); + // --8<-- [end:open_existing_table] + assert_eq!(opened_table.count_rows(None).await.unwrap(), 1); + + // --8<-- [start:create_empty_table] + let empty_schema = Arc::new(Schema::new(vec![ + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2), + false, + ), + Field::new("item", DataType::Utf8, false), + Field::new("price", DataType::Float32, false), + ])); + let empty_table = db + .create_empty_table("test_empty_table", empty_schema) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:create_empty_table] + assert_eq!(empty_table.count_rows(None).await.unwrap(), 0); + + // --8<-- [start:drop_table] + let drop_schema = Arc::new(Schema::new(vec![ + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 2), + false, + ), + Field::new("lat", DataType::Float32, false), + ])); + let drop_batch = RecordBatch::try_new( + drop_schema.clone(), + vec![ + Arc::new( + FixedSizeListArray::from_iter_primitive::( + vec![Some(vec![Some(1.1), Some(1.2)])], + 2, + ), + ), + Arc::new(Float32Array::from(vec![45.5])), + ], + ) + .unwrap(); + let drop_reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(drop_batch)].into_iter(), drop_schema.clone())); + db.create_table("my_table", drop_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + db.drop_table("my_table", &[]).await.unwrap(); + // --8<-- [end:drop_table] + assert!( + !db.table_names() + .execute() + .await + .unwrap() + .contains(&"my_table".to_string()) + ); + + // --8<-- [start:schema_add_setup] + let schema_add_schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("name", DataType::Utf8, false), + Field::new("price", DataType::Float64, false), + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 128), + false, + ), + ])); + let schema_add_batch = RecordBatch::try_new( + schema_add_schema.clone(), + vec![ + Arc::new(Int64Array::from(vec![1, 2, 3])), + Arc::new(StringArray::from(vec!["Laptop", "Smartphone", "Headphones"])), + Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])), + Arc::new( + FixedSizeListArray::from_iter_primitive::( + vec![ + Some(vec![Some(0.1_f32); 128]), + Some(vec![Some(0.2_f32); 128]), + Some(vec![Some(0.3_f32); 128]), + ], + 128, + ), + ), + ], + ) + .unwrap(); + let schema_add_reader: Box = Box::new(RecordBatchIterator::new( + vec![Ok(schema_add_batch)].into_iter(), + schema_add_schema.clone(), + )); + let schema_add_table = db + .create_table("schema_evolution_add_example", schema_add_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:schema_add_setup] + assert_eq!(schema_add_table.count_rows(None).await.unwrap(), 3); + + // --8<-- [start:add_columns_calculated] + // Add a discounted price column (10% discount) + schema_add_table + .add_columns( + NewColumnTransform::SqlExpressions(vec![( + "discounted_price".to_string(), + "cast((price * 0.9) as float)".to_string(), + )]), + None, + ) + .await + .unwrap(); + // --8<-- [end:add_columns_calculated] + + // --8<-- [start:add_columns_default_values] + // Add a stock status column with default value + schema_add_table + .add_columns( + NewColumnTransform::SqlExpressions(vec![( + "in_stock".to_string(), + "cast(true as boolean)".to_string(), + )]), + None, + ) + .await + .unwrap(); + // --8<-- [end:add_columns_default_values] + + // --8<-- [start:add_columns_nullable] + // Add a nullable timestamp column + schema_add_table + .add_columns( + NewColumnTransform::SqlExpressions(vec![( + "last_ordered".to_string(), + "cast(NULL as timestamp)".to_string(), + )]), + None, + ) + .await + .unwrap(); + // --8<-- [end:add_columns_nullable] + + // --8<-- [start:add_feature_columns_sql] + schema_add_table + .add_columns( + NewColumnTransform::SqlExpressions(vec![ + ( + "price_per_id".to_string(), + "cast(price / id as float)".to_string(), + ), + ("price_log".to_string(), "ln(price)".to_string()), + ( + "price_score".to_string(), + "cast(price / (price + 100.0) as float)".to_string(), + ), + ]), + None, + ) + .await + .unwrap(); + // --8<-- [end:add_feature_columns_sql] + + // --8<-- [start:schema_alter_setup] + let schema_alter_schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("name", DataType::Utf8, false), + Field::new("price", DataType::Int32, false), + Field::new("discount_price", DataType::Float64, false), + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 128), + false, + ), + ])); + let schema_alter_batch = RecordBatch::try_new( + schema_alter_schema.clone(), + vec![ + Arc::new(Int64Array::from(vec![1, 2])), + Arc::new(StringArray::from(vec!["Laptop", "Smartphone"])), + Arc::new(Int32Array::from(vec![1200, 800])), + Arc::new(Float64Array::from(vec![1080.0, 720.0])), + Arc::new( + FixedSizeListArray::from_iter_primitive::( + vec![Some(vec![Some(0.1_f32); 128]), Some(vec![Some(0.2_f32); 128])], + 128, + ), + ), + ], + ) + .unwrap(); + let schema_alter_reader: Box = Box::new(RecordBatchIterator::new( + vec![Ok(schema_alter_batch)].into_iter(), + schema_alter_schema.clone(), + )); + let schema_alter_table = db + .create_table("schema_evolution_alter_example", schema_alter_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:schema_alter_setup] + assert_eq!(schema_alter_table.count_rows(None).await.unwrap(), 2); + + // --8<-- [start:alter_columns_rename] + // Rename discount_price to sale_price + schema_alter_table + .alter_columns(&[ColumnAlteration::new("discount_price".to_string()) + .rename("sale_price".to_string())]) + .await + .unwrap(); + // --8<-- [end:alter_columns_rename] + + // --8<-- [start:alter_columns_data_type] + // Change price from int32 to int64 for larger numbers + schema_alter_table + .alter_columns(&[ColumnAlteration::new("price".to_string()).cast_to(DataType::Int64)]) + .await + .unwrap(); + // --8<-- [end:alter_columns_data_type] + + // --8<-- [start:alter_columns_nullable] + // Make the name column nullable + schema_alter_table + .alter_columns(&[ColumnAlteration::new("name".to_string()).set_nullable(true)]) + .await + .unwrap(); + // --8<-- [end:alter_columns_nullable] + + // --8<-- [start:alter_columns_multiple] + // Rename, change type, and make nullable in one operation + schema_alter_table + .alter_columns(&[ColumnAlteration::new("sale_price".to_string()) + .rename("final_price".to_string()) + .cast_to(DataType::Float64) + .set_nullable(true)]) + .await + .unwrap(); + // --8<-- [end:alter_columns_multiple] + + // --8<-- [start:alter_columns_with_expression] + // For custom transforms, create a new column from a SQL expression. + let expression_schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("price_text", DataType::Utf8, false), + ])); + let expression_batch = RecordBatch::try_new( + expression_schema.clone(), + vec![ + Arc::new(Int64Array::from(vec![1])), + Arc::new(StringArray::from(vec!["$100"])), + ], + ) + .unwrap(); + let expression_reader: Box = Box::new(RecordBatchIterator::new( + vec![Ok(expression_batch)].into_iter(), + expression_schema.clone(), + )); + let expression_table = db + .create_table("schema_evolution_expression_example", expression_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + expression_table + .add_columns( + NewColumnTransform::SqlExpressions(vec![( + "price_numeric".to_string(), + "cast(replace(price_text, '$', '') as int)".to_string(), + )]), + None, + ) + .await + .unwrap(); + expression_table.drop_columns(&["price_text"]).await.unwrap(); + expression_table + .alter_columns(&[ColumnAlteration::new("price_numeric".to_string()) + .rename("price".to_string())]) + .await + .unwrap(); + // --8<-- [end:alter_columns_with_expression] + assert_eq!(expression_table.count_rows(None).await.unwrap(), 1); + + // --8<-- [start:schema_drop_setup] + let schema_drop_schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("name", DataType::Utf8, false), + Field::new("price", DataType::Float64, false), + Field::new("temp_col1", DataType::Utf8, false), + Field::new("temp_col2", DataType::Int32, false), + Field::new( + "vector", + DataType::FixedSizeList(Arc::new(Field::new("item", DataType::Float32, true)), 128), + false, + ), + ])); + let schema_drop_batch = RecordBatch::try_new( + schema_drop_schema.clone(), + vec![ + Arc::new(Int64Array::from(vec![1, 2, 3])), + Arc::new(StringArray::from(vec!["Laptop", "Smartphone", "Headphones"])), + Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])), + Arc::new(StringArray::from(vec!["X", "Y", "Z"])), + Arc::new(Int32Array::from(vec![100, 200, 300])), + Arc::new( + FixedSizeListArray::from_iter_primitive::( + vec![ + Some(vec![Some(0.1_f32); 128]), + Some(vec![Some(0.2_f32); 128]), + Some(vec![Some(0.3_f32); 128]), + ], + 128, + ), + ), + ], + ) + .unwrap(); + let schema_drop_reader: Box = Box::new(RecordBatchIterator::new( + vec![Ok(schema_drop_batch)].into_iter(), + schema_drop_schema.clone(), + )); + let schema_drop_table = db + .create_table("schema_evolution_drop_example", schema_drop_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:schema_drop_setup] + assert_eq!(schema_drop_table.count_rows(None).await.unwrap(), 3); + + // --8<-- [start:drop_columns_single] + // Remove the first temporary column + schema_drop_table.drop_columns(&["temp_col1"]).await.unwrap(); + // --8<-- [end:drop_columns_single] + + // --8<-- [start:drop_columns_multiple] + // Remove the second temporary column + schema_drop_table.drop_columns(&["temp_col2"]).await.unwrap(); + // --8<-- [end:drop_columns_multiple] + + // --8<-- [start:alter_vector_column] + let old_dim = 384; + let new_dim = 1024; + let vector_schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new( + "embedding", + DataType::FixedSizeList( + Arc::new(Field::new("item", DataType::Float32, true)), + old_dim, + ), + true, + ), + ])); + let vector_batch = RecordBatch::try_new( + vector_schema.clone(), + vec![ + Arc::new(Int64Array::from(vec![1])), + Arc::new( + FixedSizeListArray::from_iter_primitive::( + vec![Some(vec![Some(0.1_f32); old_dim as usize])], + old_dim, + ), + ), + ], + ) + .unwrap(); + let vector_reader: Box = + Box::new(RecordBatchIterator::new(vec![Ok(vector_batch)].into_iter(), vector_schema.clone())); + let vector_table = db + .create_table("vector_alter_example", vector_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + // Changing FixedSizeList dimensions (384 -> 1024) is not supported via alter_columns. + // Use add_columns + drop_columns + alter_columns(rename) to replace the column. + vector_table + .add_columns( + NewColumnTransform::SqlExpressions(vec![( + "embedding_v2".to_string(), + format!("arrow_cast(NULL, 'FixedSizeList({}, Float32)')", new_dim), + )]), + None, + ) + .await + .unwrap(); + vector_table.drop_columns(&["embedding"]).await.unwrap(); + vector_table + .alter_columns(&[ColumnAlteration::new("embedding_v2".to_string()) + .rename("embedding".to_string())]) + .await + .unwrap(); + // --8<-- [end:alter_vector_column] + assert_eq!(vector_table.count_rows(None).await.unwrap(), 1); + + let field_metadata_schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), + Field::new("category", DataType::Utf8, false), + ])); + let field_metadata_batch = RecordBatch::try_new( + field_metadata_schema.clone(), + vec![ + Arc::new(Int64Array::from(vec![0, 1])), + Arc::new(StringArray::from(vec!["a", "b"])), + ], + ) + .unwrap(); + let field_metadata_reader: Box = Box::new( + RecordBatchIterator::new(vec![Ok(field_metadata_batch)].into_iter(), field_metadata_schema), + ); + let field_metadata_table = db + .create_table("schema_field_metadata_example", field_metadata_reader) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + // --8<-- [start:schema_field_metadata_merge] + // Set two metadata keys on the `category` field. + let res = field_metadata_table + .update_field_metadata(&[FieldMetadataUpdate::new("category") + .set("unit", "label") + .set("pii", "false")]) + .await + .unwrap(); + println!("version: {}", res.version); + + // Merge: add a new key, delete one with `.remove`, keep the rest. + field_metadata_table + .update_field_metadata(&[FieldMetadataUpdate::new("category") + .set("source", "import") + .remove("pii")]) + .await + .unwrap(); + // --8<-- [end:schema_field_metadata_merge] + + // --8<-- [start:schema_field_metadata_replace] + field_metadata_table + .update_field_metadata(&[FieldMetadataUpdate::new("category") + .set("owner", "search-team") + .replace()]) + .await + .unwrap(); + // --8<-- [end:schema_field_metadata_replace] + + // --8<-- [start:update_example_table_setup] + let table = db + .create_table( + "users_example", + make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:update_example_table_setup] + let _ = table; + + // --8<-- [start:update_operation] + let table = db + .create_table( + "users_example", + make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + table + .update() + .only_if("id = 2") + .column("name", "'Bobby'") + .execute() + .await + .unwrap(); + // --8<-- [end:update_operation] + + // --8<-- [start:update_using_sql] + let table = db + .create_table( + "users_example", + make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + table + .update() + .only_if("id = 2") + .column("login_count", "login_count + 1") + .execute() + .await + .unwrap(); + // --8<-- [end:update_using_sql] + + // --8<-- [start:merge_matched_update_only] + let table = db + .create_table( + "users_example", + make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + let mut merge_insert = table.merge_insert(&["id"]); + merge_insert.when_matched_update_all(None); + merge_insert + .execute(make_users_reader( + vec![2, 3], + vec!["Bobby", "Charlie"], + Some(vec![21, 5]), + )) + .await + .unwrap(); + // --8<-- [end:merge_matched_update_only] + + // --8<-- [start:insert_if_not_exists] + let table = db + .create_table( + "users_example", + make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + let mut merge_insert = table.merge_insert(&["id"]); + merge_insert.when_not_matched_insert_all(); + merge_insert + .execute(make_users_reader( + vec![2, 3], + vec!["Bobby", "Charlie"], + Some(vec![21, 5]), + )) + .await + .unwrap(); + // --8<-- [end:insert_if_not_exists] + + // --8<-- [start:merge_update_insert] + let table = db + .create_table( + "users_example", + make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + let mut merge_insert = table.merge_insert(&["id"]); + merge_insert + .when_matched_update_all(None) + .when_not_matched_insert_all(); + merge_insert + .execute(make_users_reader( + vec![2, 3], + vec!["Bobby", "Charlie"], + Some(vec![21, 5]), + )) + .await + .unwrap(); + // --8<-- [end:merge_update_insert] + + // --8<-- [start:merge_delete_missing_by_source] + let table = db + .create_table( + "users_example", + make_users_reader( + vec![1, 2, 3], + vec!["Alice", "Bob", "Charlie"], + Some(vec![10, 20, 5]), + ), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + let mut merge_insert = table.merge_insert(&["id"]); + merge_insert + .when_matched_update_all(None) + .when_not_matched_insert_all() + .when_not_matched_by_source_delete(None); + merge_insert + .execute(make_users_reader( + vec![2, 3], + vec!["Bobby", "Charlie"], + Some(vec![21, 5]), + )) + .await + .unwrap(); + // --8<-- [end:merge_delete_missing_by_source] + + // --8<-- [start:merge_partial_columns] + let table = db + .create_table( + "users_example", + make_users_reader(vec![1, 2], vec!["Alice", "Bob"], Some(vec![10, 20])), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + let mut merge_insert = table.merge_insert(&["id"]); + merge_insert + .when_matched_update_all(None) + .when_not_matched_insert_all(); + merge_insert + .execute(make_users_reader(vec![2, 3], vec!["Bobby", "Charlie"], None)) + .await + .unwrap(); + // --8<-- [end:merge_partial_columns] + + let table = db + .create_table( + "users_example", + make_users_reader( + vec![1, 2, 3], + vec!["Alice", "Bob", "Charlie"], + Some(vec![10, 20, 5]), + ), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + // --8<-- [start:delete_operation] + // delete data + let predicate = "id = 3"; + table.delete(predicate).await.unwrap(); + // --8<-- [end:delete_operation] + + let table = db + .create_table( + "users_cleanup_example", + make_users_reader( + vec![1, 2, 3], + vec!["Alice", "Bob", "Charlie"], + Some(vec![10, 20, 5]), + ), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + // --8<-- [start:update_optimize_cleanup] + table + .optimize(OptimizeAction::Prune { + older_than: Some(Duration::days(1)), + delete_unverified: None, + error_if_tagged_old_versions: None, + }) + .await + .unwrap(); + // --8<-- [end:update_optimize_cleanup] + + // --8<-- [start:consistency_strong] + let strong_writer_db = connect(&db_uri).execute().await.unwrap(); + let strong_reader_db = connect(&db_uri) + .read_consistency_interval(StdDuration::from_secs(0)) + .execute() + .await + .unwrap(); + let strong_writer_table = strong_writer_db + .create_table( + "consistency_strong_table", + make_users_reader(vec![1], vec!["Alice"], None), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + let strong_reader_table = strong_reader_db + .open_table("consistency_strong_table") + .execute() + .await + .unwrap(); + strong_writer_table + .add(make_users_reader(vec![2], vec!["Bob"], None)) + .execute() + .await + .unwrap(); + let strong_rows_after_write = strong_reader_table.count_rows(None).await.unwrap(); + println!( + "Rows visible with strong consistency: {}", + strong_rows_after_write + ); + // --8<-- [end:consistency_strong] + assert_eq!(strong_rows_after_write, 2); + + // --8<-- [start:consistency_eventual] + let eventual_writer_db = connect(&db_uri).execute().await.unwrap(); + let eventual_reader_db = connect(&db_uri) + .read_consistency_interval(StdDuration::from_secs(3600)) + .execute() + .await + .unwrap(); + let eventual_writer_table = eventual_writer_db + .create_table( + "consistency_eventual_table", + make_users_reader(vec![1], vec!["Alice"], None), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + let eventual_reader_table = eventual_reader_db + .open_table("consistency_eventual_table") + .execute() + .await + .unwrap(); + eventual_writer_table + .add(make_users_reader(vec![2], vec!["Bob"], None)) + .execute() + .await + .unwrap(); + let eventual_rows_after_write = eventual_reader_table.count_rows(None).await.unwrap(); + println!( + "Rows visible before eventual refresh interval: {}", + eventual_rows_after_write + ); + // --8<-- [end:consistency_eventual] + assert_eq!(eventual_rows_after_write, 1); + + // --8<-- [start:consistency_checkout_latest] + let checkout_writer_db = connect(&db_uri).execute().await.unwrap(); + let checkout_reader_db = connect(&db_uri).execute().await.unwrap(); + let checkout_writer_table = checkout_writer_db + .create_table( + "consistency_checkout_latest_table", + make_users_reader(vec![1], vec!["Alice"], None), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + let checkout_reader_table = checkout_reader_db + .open_table("consistency_checkout_latest_table") + .execute() + .await + .unwrap(); + checkout_writer_table + .add(make_users_reader(vec![2], vec!["Bob"], None)) + .execute() + .await + .unwrap(); + let rows_before_refresh = checkout_reader_table.count_rows(None).await.unwrap(); + println!("Rows before checkout_latest: {}", rows_before_refresh); + checkout_reader_table.checkout_latest().await.unwrap(); + let rows_after_refresh = checkout_reader_table.count_rows(None).await.unwrap(); + println!("Rows after checkout_latest: {}", rows_after_refresh); + // --8<-- [end:consistency_checkout_latest] + assert_eq!(rows_before_refresh, 1); + assert_eq!(rows_after_refresh, 2); + + // --8<-- [start:versioning_basic_setup] + let table_name = "quotes_versioning_example"; + let data = vec![ + (1, "Richard", "Wubba Lubba Dub Dub!"), + (2, "Morty", "Rick, what's going on?"), + (3, "Richard", "I turned myself into a pickle, Morty!"), + ]; + + let table = db + .create_table(table_name, make_quotes_reader(data)) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + // --8<-- [end:versioning_basic_setup] + assert_eq!(table.count_rows(None).await.unwrap(), 3); + + // --8<-- [start:versioning_check_initial_version] + let versions = table.list_versions().await.unwrap(); + let current_version = table.version().await.unwrap(); + println!("Number of versions after creation: {}", versions.len()); + println!("Current version: {}", current_version); + // --8<-- [end:versioning_check_initial_version] + assert_eq!(versions.len(), 1); + assert_eq!(current_version, versions.last().unwrap().version); + + // --8<-- [start:versioning_update_data] + table + .update() + .only_if("author = 'Richard'") + .column("author", "'Richard Daniel Sanchez'") + .execute() + .await + .unwrap(); + let rows_after_update = table + .count_rows(Some("author = 'Richard Daniel Sanchez'".to_string())) + .await + .unwrap(); + println!( + "Rows updated to Richard Daniel Sanchez: {}", + rows_after_update + ); + // --8<-- [end:versioning_update_data] + assert_eq!(rows_after_update, 2); + + // --8<-- [start:versioning_add_data] + let more_data = vec![ + (4, "Richard Daniel Sanchez", "That's the way the news goes!"), + (5, "Morty", "Aww geez, Rick!"), + ]; + table + .add(make_quotes_reader(more_data)) + .execute() + .await + .unwrap(); + // --8<-- [end:versioning_add_data] + assert_eq!(table.count_rows(None).await.unwrap(), 5); + + // --8<-- [start:versioning_check_versions_after_mod] + let versions_after_mod = table.list_versions().await.unwrap(); + let version_count_after_mod = versions_after_mod.len(); + let version_after_mod = table.version().await.unwrap(); + println!( + "Number of versions after modifications: {}", + version_count_after_mod + ); + println!("Current version: {}", version_after_mod); + // --8<-- [end:versioning_check_versions_after_mod] + assert!(version_count_after_mod >= 2); + assert_eq!(version_after_mod, versions_after_mod.last().unwrap().version); + + // --8<-- [start:versioning_list_all_versions] + let all_versions = table.list_versions().await.unwrap(); + for v in &all_versions { + println!("Version {}, created at {}", v.version, v.timestamp); + } + // --8<-- [end:versioning_list_all_versions] + assert!(!all_versions.is_empty()); + + // --8<-- [start:versioning_rollback] + table.checkout(version_after_mod).await.unwrap(); + table.restore().await.unwrap(); + let versions_after_rollback = table.list_versions().await.unwrap(); + let version_count_after_rollback = versions_after_rollback.len(); + println!( + "Total number of versions after rollback: {}", + version_count_after_rollback + ); + // --8<-- [end:versioning_rollback] + assert_eq!(version_count_after_rollback, version_count_after_mod + 1); + assert_eq!(table.count_rows(None).await.unwrap(), 5); + + // --8<-- [start:versioning_checkout_latest] + table.checkout_latest().await.unwrap(); + // --8<-- [end:versioning_checkout_latest] + let latest_version = table.version().await.unwrap(); + let versions_after_checkout = table.list_versions().await.unwrap(); + assert_eq!(latest_version, versions_after_checkout.last().unwrap().version); + + // --8<-- [start:versioning_delete_data] + table.delete("author = 'Morty'").await.unwrap(); + let rows_after_deletion = table.count_rows(None).await.unwrap(); + println!("Number of rows after deletion: {}", rows_after_deletion); + // --8<-- [end:versioning_delete_data] + assert_eq!(rows_after_deletion, 3); + + // Setup: build a table with three versions to operate on with tags. + let tags_table = db + .create_table( + "quotes_tags_example", + make_quotes_reader(vec![(1, "Richard", "Wubba Lubba Dub Dub!")]), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); // v1 + tags_table + .add(make_quotes_reader(vec![(2, "Morty", "Aww geez, Rick!")])) + .execute() + .await + .unwrap(); // v2 + tags_table + .add(make_quotes_reader(vec![(3, "Summer", "Whatever, Grandpa")])) + .execute() + .await + .unwrap(); // v3 + + // --8<-- [start:versioning_tags] + let mut tags = tags_table.tags().await.unwrap(); + + // Create a tag pointing at a specific version + tags.create("baseline", 1).await.unwrap(); + let current_version = tags_table.version().await.unwrap(); + tags.create("with-edits", current_version).await.unwrap(); + + // List all tags on this table + let all_tags = tags.list().await.unwrap(); + println!("Tags: {:?}", all_tags); + + // Look up the version a tag points at + let baseline_version = tags.get_version("baseline").await.unwrap(); + println!("baseline -> v{}", baseline_version); + + // Move an existing tag to a different version + tags.update("baseline", 2).await.unwrap(); + + // Check out a version by tag name (separate method in Rust) + tags_table.checkout_tag("baseline").await.unwrap(); + println!("Current version: {}", tags_table.version().await.unwrap()); + + // Delete a tag (does not delete the underlying version) + tags.delete("with-edits").await.unwrap(); + + // Return to the latest version + tags_table.checkout_latest().await.unwrap(); + // --8<-- [end:versioning_tags] + assert_eq!(tags_table.version().await.unwrap(), 3); + let remaining = tags.list().await.unwrap(); + assert!(remaining.contains_key("baseline")); + assert!(!remaining.contains_key("with-edits")); + + // Setup: a fresh quotes table to demonstrate branches on. + let branches_table = db + .create_table( + "quotes_branches_example", + make_quotes_reader(vec![ + (1, "Lancelot", "My lance never fails."), + (2, "Arthur", "Long live Camelot!"), + (3, "Merlin", "Magic always has a price."), + ]), + ) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + use lancedb::table::Ref; + + // --8<-- [start:branch_create] + // Fork an isolated, writable branch from main's latest version. + // `create_branch` returns a table handle scoped to the new branch. + let branch = branches_table + .create_branch("exp", Ref::Version(None, None)) + .await + .unwrap(); + // --8<-- [end:branch_create] + + // --8<-- [start:branch_write] + // Writes land on the branch handle only; main is left untouched. + branch + .add(make_quotes_reader(vec![(4, "Lancelot", "For the realm!")])) + .execute() + .await + .unwrap(); + println!("Branch rows: {}", branch.count_rows(None).await.unwrap()); // 4 + println!("Main rows: {}", branches_table.count_rows(None).await.unwrap()); // 3 + + // List every branch, each mapped to its metadata (including its fork point). + println!("Branches: {:?}", branches_table.list_branches().await.unwrap()); + // --8<-- [end:branch_write] + + // --8<-- [start:branch_reopen] + // Reopen an existing branch by name from the table handle... + let checked_out = branches_table.checkout_branch("exp", None).await.unwrap(); + // ...or open it directly via the connection's builder. + let opened = db + .open_table("quotes_branches_example") + .branch("exp") + .execute() + .await + .unwrap(); + println!( + "Reopened rows: {}, {}", + checked_out.count_rows(None).await.unwrap(), + opened.count_rows(None).await.unwrap() + ); // both 4 + // --8<-- [end:branch_reopen] + + // --8<-- [start:branch_delete] + // Delete the branch and its branch-local history. Data on main is safe. + branches_table.delete_branch("exp").await.unwrap(); + // --8<-- [end:branch_delete] + + assert_eq!(branches_table.count_rows(None).await.unwrap(), 3); + assert!(!branches_table.list_branches().await.unwrap().contains_key("exp")); + + // Setup: a branch with row results that we want to apply to main. + let candidate = branches_table + .create_branch("candidate", Ref::Version(None, None)) + .await + .unwrap(); + candidate + .update() + .only_if("id = 1") + .column("quote", "'Revised on the branch'") + .execute() + .await + .unwrap(); + candidate + .add(make_quotes_reader(vec![( + 4, + "Galahad", + "The grail awaits.", + )])) + .execute() + .await + .unwrap(); + + // --8<-- [start:branch_upsert_to_main] + // This is a row-level upsert, not a merge of branch histories. + // `merge_insert` updates matching rows and inserts new rows using a stable + // unique key. Filter the branch read if you only want to apply some results. + let schema = candidate.schema().await.unwrap(); + let batches = candidate + .query() + .execute() + .await + .unwrap() + .try_collect::>() + .await + .unwrap(); + let rows_to_apply = RecordBatchIterator::new(batches.into_iter().map(Ok), schema); + + let mut merge = branches_table.merge_insert(&["id"]); + merge + .when_matched_update_all(None) // update rows that already exist on main + .when_not_matched_insert_all(); // insert rows that are new on the branch + merge.execute(Box::new(rows_to_apply)).await.unwrap(); + // --8<-- [end:branch_upsert_to_main] + + assert_eq!(branches_table.count_rows(None).await.unwrap(), 4); + branches_table.delete_branch("candidate").await.unwrap(); + + // Setup: a larger table with a vector and a text column to index. + let products = db + .create_table("products_branch_index", make_products_reader(512)) + .mode(CreateTableMode::Overwrite) + .execute() + .await + .unwrap(); + + // --8<-- [start:branch_index] + use lancedb::index::scalar::FtsIndexBuilder; + use lancedb::index::Index; + + // Build and validate indexes on a branch before using the configuration on + // main. + let dev = products + .create_branch("index-dev", Ref::Version(None, None)) + .await + .unwrap(); + + // A vector (ANN) index and a full-text search index, both branch-scoped. + dev.create_index(&["vector"], Index::Auto) + .execute() + .await + .unwrap(); + dev.create_index(&["text"], Index::FTS(FtsIndexBuilder::default())) + .execute() + .await + .unwrap(); + + // Both indexes live only on the branch; main still has none. + println!("Branch indexes: {}", dev.list_indices().await.unwrap().len()); // 2 + println!("Main indexes: {}", products.list_indices().await.unwrap().len()); // 0 + // --8<-- [end:branch_index] + + assert_eq!(dev.list_indices().await.unwrap().len(), 2); + assert_eq!(products.list_indices().await.unwrap().len(), 0); + products.delete_branch("index-dev").await.unwrap(); +} diff --git a/docs/web-tests/ts/ann_indexes.test.ts b/docs/web-tests/ts/ann_indexes.test.ts new file mode 100644 index 000000000..a37872c9f --- /dev/null +++ b/docs/web-tests/ts/ann_indexes.test.ts @@ -0,0 +1,57 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +// --8<-- [start:import] +import * as lancedb from "@lancedb/lancedb"; +import type { VectorQuery } from "@lancedb/lancedb"; +// --8<-- [end:import] +import { withTempDirectory } from "./util.ts"; + +test("ann index examples", async () => { + await withTempDirectory(async (databaseDir) => { + // --8<-- [start:ingest] + const db = await lancedb.connect(databaseDir); + + const data = Array.from({ length: 5_000 }, (_, i) => ({ + vector: Array(128).fill(i), + id: `${i}`, + content: "", + longId: `${i}`, + })); + + const table = await db.createTable("my_vectors", data, { + mode: "overwrite", + }); + await table.createIndex("vector", { + config: lancedb.Index.ivfPq({ + numPartitions: 10, + numSubVectors: 16, + }), + }); + // --8<-- [end:ingest] + + // --8<-- [start:search1] + const search = table.search(Array(128).fill(1.2)).limit(2) as VectorQuery; + const results1 = await search.nprobes(20).refineFactor(10).toArray(); + // --8<-- [end:search1] + expect(results1.length).toBe(2); + + // --8<-- [start:search2] + const results2 = await table + .search(Array(128).fill(1.2)) + .where("id != '1141'") + .limit(2) + .toArray(); + // --8<-- [end:search2] + expect(results2.length).toBe(2); + + // --8<-- [start:search3] + const results3 = await table + .search(Array(128).fill(1.2)) + .select(["id"]) + .limit(2) + .toArray(); + // --8<-- [end:search3] + expect(results3.length).toBe(2); + }); +}, 100_000); diff --git a/docs/web-tests/ts/basic_usage.test.ts b/docs/web-tests/ts/basic_usage.test.ts new file mode 100644 index 000000000..f00e69ba4 --- /dev/null +++ b/docs/web-tests/ts/basic_usage.test.ts @@ -0,0 +1,161 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +import * as fs from "node:fs"; +import * as path from "node:path"; +// --8<-- [start:basic_imports] +import * as lancedb from "@lancedb/lancedb"; +import * as arrow from "apache-arrow"; +// --8<-- [end:basic_imports] +import { withTempDirectory } from "./util.ts"; + +const dataPath = path.join(__dirname, "..", "camelot.json"); + +test("basic usage examples (async)", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + // --8<-- [start:data_load] + const data = JSON.parse(fs.readFileSync(dataPath, "utf-8")); + // --8<-- [end:data_load] + + // --8<-- [start:basic_create_table] + let table = await db.createTable("camelot", data, { + mode: "overwrite", + }); + // --8<-- [end:basic_create_table] + expect(await table.countRows()).toBe(8); + + // --8<-- [start:basic_open_table] + table = await db.openTable("camelot"); + // --8<-- [end:basic_open_table] + + // --8<-- [start:basic_create_empty_table] + const schema = new arrow.Schema([ + new arrow.Field("id", new arrow.Int16()), + new arrow.Field("name", new arrow.Utf8()), + new arrow.Field("role", new arrow.Utf8()), + new arrow.Field("description", new arrow.Utf8()), + new arrow.Field( + "vector", + new arrow.FixedSizeList( + 4, + new arrow.Field("item", new arrow.Float32(), true), + ), + ), + new arrow.Field( + "stats", + new arrow.Struct([ + new arrow.Field("strength", new arrow.Int8()), + new arrow.Field("courage", new arrow.Int8()), + new arrow.Field("magic", new arrow.Int8()), + new arrow.Field("wisdom", new arrow.Int8()), + ]), + ), + ]); + await db.createEmptyTable("camelot_empty", schema, { mode: "overwrite" }); + // --8<-- [end:basic_create_empty_table] + expect(await db.tableNames()).toContain("camelot_empty"); + await db.dropTable("camelot_empty"); + + // --8<-- [start:basic_add_data] + const magicalCharacters = [ + { + id: 9, + name: "Morgan le Fay", + role: "Sorceress", + description: + "A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.", + vector: [0.1, 0.84, 0.25, 0.7], + stats: { strength: 2, courage: 3, magic: 5, wisdom: 4 }, + }, + { + id: 10, + name: "The Lady of the Lake", + role: "Mystical Guardian", + description: + "A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.", + vector: [0.0, 0.9, 0.58, 0.88], + stats: { strength: 2, courage: 3, magic: 5, wisdom: 5 }, + }, + ]; + await table.add(magicalCharacters); + // --8<-- [end:basic_add_data] + expect(await table.countRows()).toBe(10); + + // --8<-- [start:basic_vector_search] + const queryVector = [0.03, 0.85, 0.61, 0.9]; + const result = await table.search(queryVector).limit(5).toArray(); + console.log(result); + // --8<-- [end:basic_vector_search] + + // --8<-- [start:basic_add_columns] + await table.addColumns([ + { + name: "power", + valueSql: + "cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)", + }, + ]); + // --8<-- [end:basic_add_columns] + const schemaWithPower = await table.schema(); + expect(schemaWithPower.fields.some((f) => f.name === "power")).toBe(true); + + // --8<-- [start:basic_vector_search_q1] + // Who are the characters similar to "wizard"? + const queryVector1 = [0.03, 0.85, 0.61, 0.9]; + const r1 = await table + .search(queryVector1) + .limit(5) + .select(["name", "role", "description"]) + .toArray(); + console.log(r1); + // --8<-- [end:basic_vector_search_q1] + + // --8<-- [start:basic_vector_search_q2] + // Who are the characters similar to "wizard" with high magic stats? + const queryVector2 = [0.03, 0.85, 0.61, 0.9]; + const r2 = await table + .search(queryVector2) + .where("stats.magic > 3") + .select(["name", "role", "description"]) + .limit(5) + .toArray(); + console.log(r2); + // --8<-- [end:basic_vector_search_q2] + + // --8<-- [start:basic_vector_search_q3] + // Who are the strongest characters? + const r3 = await table + .query() + .where("stats.strength > 3") + .select(["name", "role", "description"]) + .limit(5) + .toArray(); + console.log(r3); + // --8<-- [end:basic_vector_search_q3] + + // --8<-- [start:basic_vector_search_q4] + // Who are the strongest characters? + const r4 = await table + .query() + .select(["name", "role", "description", "power"]) + .toArray(); + console.log(r4); + // --8<-- [end:basic_vector_search_q4] + + // --8<-- [start:basic_drop_columns] + await table.dropColumns(["power"]); + // --8<-- [end:basic_drop_columns] + + // --8<-- [start:basic_delete_rows] + await table.delete('role = "Traitor Knight"'); + // --8<-- [end:basic_delete_rows] + expect(await table.countRows()).toBe(9); + + // --8<-- [start:basic_drop_table] + await db.dropTable("camelot"); + // --8<-- [end:basic_drop_table] + expect(await db.tableNames()).not.toContain("camelot"); + }); +}); diff --git a/docs/web-tests/ts/biome.json b/docs/web-tests/ts/biome.json new file mode 100644 index 000000000..f8fb5b200 --- /dev/null +++ b/docs/web-tests/ts/biome.json @@ -0,0 +1,52 @@ +{ + "$schema": "https://biomejs.dev/schemas/1.9.4/schema.json", + "vcs": { + "enabled": false, + "clientKind": "git", + "useIgnoreFile": false + }, + "files": { + "ignoreUnknown": false, + "ignore": [] + }, + "formatter": { + "enabled": true, + "indentStyle": "space" + }, + "organizeImports": { + "enabled": true + }, + "linter": { + "enabled": true, + "rules": { + "recommended": true + } + }, + "javascript": { + "formatter": { + "quoteStyle": "double" + } + }, + "overrides": [ + { + "include": ["*"], + "linter": { + "rules": { + "style": { + "noNonNullAssertion": "off" + } + } + } + }, + { + "include": ["merge_insert.test.ts"], + "linter": { + "rules": { + "style": { + "useNamingConvention": "off" + } + } + } + } + ] +} diff --git a/docs/web-tests/ts/connection.test.ts b/docs/web-tests/ts/connection.test.ts new file mode 100644 index 000000000..f522654ec --- /dev/null +++ b/docs/web-tests/ts/connection.test.ts @@ -0,0 +1,121 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, jest, test } from "@jest/globals"; +import * as path from "node:path"; +import { withTempDirectory } from "./util.ts"; +// --8<-- [start:connect] +import * as lancedb from "@lancedb/lancedb"; + +async function connectExample(uri: string) { + const db = await lancedb.connect(uri); + return db; +} +// --8<-- [end:connect] + +test("connect to a local database", async () => { + await withTempDirectory(async (tempDir) => { + const uri = path.join(tempDir, "ex_lancedb"); + const db = await connectExample(uri); + expect(db).toBeDefined(); + }); +}); + +async function connectEnterpriseQuickstart() { + // --8<-- [start:connect_enterprise_quickstart] + const uri = "db://your-database-uri"; + const apiKey = "your-api-key"; + const region = "us-east-1"; + const hostOverride = "https://your-enterprise-endpoint.com"; + + const db = await lancedb.connect(uri, { + apiKey, + region, + hostOverride, + }); + // --8<-- [end:connect_enterprise_quickstart] + return db; +} + +test("enterprise quickstart connect uses placeholder config", async () => { + const mockDb = { __mock: true } as unknown as Awaited< + ReturnType + >; + const spy = jest.spyOn(lancedb, "connect").mockResolvedValue(mockDb); + + const db = await connectEnterpriseQuickstart(); + expect(db).toBe(mockDb); + expect(spy).toHaveBeenCalledWith("db://your-database-uri", { + apiKey: "your-api-key", + region: "us-east-1", + hostOverride: "https://your-enterprise-endpoint.com", + }); + spy.mockRestore(); +}); + +// --8<-- [start:connect_object_storage] +async function connectObjectStorageExample() { + const uri = "s3://your-bucket/path"; + // You can also use "gs://your-bucket/path" or "az://your-container/path". + const db = await lancedb.connect(uri); + return db; +} +// --8<-- [end:connect_object_storage] + +async function namespaceTableOpsExample() { + // --8<-- [start:namespace_table_ops] + const db = await lancedb.connectNamespace("dir", { root: "./local_lancedb" }); + + // Create namespace tree: prod/search and prod/recommendations + await db.createNamespace(["prod"], { mode: "exist_ok" }); + await db.createNamespace(["prod", "search"], { mode: "exist_ok" }); + await db.createNamespace(["prod", "recommendations"], { mode: "exist_ok" }); + + await db.createTable( + "user", + [{ id: 1, vector: [0.1, 0.2], name: "alice" }], + ["prod", "search"], + { mode: "create" }, // use "overwrite" only if you want to replace existing table + ); + + await db.createTable( + "user", + [{ id: 2, vector: [0.3, 0.4], name: "bob" }], + ["prod", "recommendations"], + { mode: "create" }, + ); + + // Verify + console.log((await db.listNamespaces()).namespaces); // ["prod"] + console.log((await db.listNamespaces(["prod"])).namespaces); // ["recommendations", "search"] + console.log(await db.tableNames(["prod", "search"])); // ["user"] + console.log(await db.tableNames(["prod", "recommendations"])); // ["user"] + // --8<-- [end:namespace_table_ops] +} + +async function namespaceAdminOpsExample() { + // --8<-- [start:namespace_admin_ops] + const db = await lancedb.connectNamespace("dir", { root: "./local_lancedb" }); + const namespace = ["prod", "search"]; + + await db.createNamespace(["prod"]); + await db.createNamespace(["prod", "search"]); + + const childNamespaces = (await db.listNamespaces(["prod"])).namespaces; + console.log(`Child namespaces under ${JSON.stringify(namespace)}:`, childNamespaces); + // Child namespaces under ["prod","search"]: [ 'search' ] + + const metadata = await db.describeNamespace(["prod", "search"]); + console.log(`Metadata for namespace ${JSON.stringify(namespace)}:`, metadata); + + await db.dropNamespace(["prod", "search"], { mode: "skip" }); + await db.dropNamespace(["prod"], { mode: "skip" }); + // --8<-- [end:namespace_admin_ops] + return { childNamespaces, metadata }; +} + +void [ + connectObjectStorageExample, + connectEnterpriseQuickstart, + namespaceTableOpsExample, + namespaceAdminOpsExample, +]; diff --git a/docs/web-tests/ts/custom_embedding_function.test.ts b/docs/web-tests/ts/custom_embedding_function.test.ts new file mode 100644 index 000000000..528928a9d --- /dev/null +++ b/docs/web-tests/ts/custom_embedding_function.test.ts @@ -0,0 +1,79 @@ +import { + type FeatureExtractionPipeline, + pipeline, +} from "@huggingface/transformers"; +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +// --8<-- [start:imports] +import * as lancedb from "@lancedb/lancedb"; +import { + LanceSchema, + TextEmbeddingFunction, + getRegistry, + register, +} from "@lancedb/lancedb/embedding"; +// --8<-- [end:imports] +import { withTempDirectory } from "./util.ts"; + +// --8<-- [start:embedding_impl] +@register("sentence-transformers") +class SentenceTransformersEmbeddings extends TextEmbeddingFunction { + name = "Xenova/all-miniLM-L6-v2"; + #ndims!: number; + extractor!: FeatureExtractionPipeline; + + async init() { + this.extractor = await pipeline("feature-extraction", this.name, { + dtype: "fp32", + }); + this.#ndims = await this.generateEmbeddings(["hello"]).then( + (e) => e[0].length, + ); + } + + ndims() { + return this.#ndims; + } + + toJSON() { + return { + name: this.name, + }; + } + async generateEmbeddings(texts: string[]) { + const output = await this.extractor(texts, { + pooling: "mean", + normalize: true, + }); + return output.tolist(); + } +} +// --8<-- [end:embedding_impl] + +test("Registry examples", async () => { + await withTempDirectory(async (databaseDir) => { + // --8<-- [start:call_custom_function] + const registry = getRegistry(); + + const sentenceTransformer = await registry + .get("sentence-transformers")! + .create(); + + const schema = LanceSchema({ + vector: sentenceTransformer.vectorField(), + text: sentenceTransformer.sourceField(), + }); + + const db = await lancedb.connect(databaseDir); + const table = await db.createEmptyTable("table", schema, { + mode: "overwrite", + }); + + await table.add([{ text: "hello" }, { text: "world" }]); + + const results = await table.search("greeting").limit(1).toArray(); + // --8<-- [end:call_custom_function] + expect(results.length).toBe(1); + }); +}, 100_000); diff --git a/docs/web-tests/ts/embedding.test.ts b/docs/web-tests/ts/embedding.test.ts new file mode 100644 index 000000000..a73b808ca --- /dev/null +++ b/docs/web-tests/ts/embedding.test.ts @@ -0,0 +1,160 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +// --8<-- [start:imports] +import * as lancedb from "@lancedb/lancedb"; +import "@lancedb/lancedb/embedding/openai"; +import { LanceSchema, getRegistry, register } from "@lancedb/lancedb/embedding"; +import { EmbeddingFunction } from "@lancedb/lancedb/embedding"; +import { type Float, Float32, Utf8 } from "apache-arrow"; +// --8<-- [end:imports] +import { withTempDirectory } from "./util.ts"; + +const openAiTest = process.env.OPENAI_API_KEY == null ? test.skip : test; + +openAiTest("create embedding function", () => { + // --8<-- [start:create_embedding_function] + const func = getRegistry().get("openai")!.create({ + model: "text-embedding-3-small", + }); + // --8<-- [end:create_embedding_function] + expect(func).toBeDefined(); +}); + +openAiTest("openai embeddings", async () => { + await withTempDirectory(async (databaseDir) => { + // --8<-- [start:openai_embeddings] + const db = await lancedb.connect(databaseDir); + const func = getRegistry() + .get("openai") + ?.create({ model: "text-embedding-ada-002" }) as EmbeddingFunction; + + const wordsSchema = LanceSchema({ + text: func.sourceField(new Utf8()), + vector: func.vectorField(), + }); + const tbl = await db.createEmptyTable("words", wordsSchema, { + mode: "overwrite", + }); + await tbl.add([{ text: "hello world" }, { text: "goodbye world" }]); + + const query = "greetings"; + const actual = (await tbl.search(query).limit(1).toArray())[0]; + // --8<-- [end:openai_embeddings] + expect(actual).toHaveProperty("text"); + }); +}); + +openAiTest("manual query embeddings", async () => { + await withTempDirectory(async (databaseDir) => { + // --8<-- [start:manual_query_embeddings] + const db = await lancedb.connect(databaseDir); + const func = getRegistry() + .get("openai") + ?.create({ model: "text-embedding-ada-002" }) as EmbeddingFunction; + + const wordsSchema = LanceSchema({ + text: func.sourceField(new Utf8()), + vector: func.vectorField(), + }); + const tbl = await db.createEmptyTable("words", wordsSchema, { + mode: "overwrite", + }); + await tbl.add([{ text: "hello world" }, { text: "goodbye world" }]); + + const queryVector = await func.computeQueryEmbeddings("greetings"); + // --8<-- [start:manual_query_search] + // queryVector is assumed to already be generated by your embedding function + const actual = (await tbl.search(queryVector).limit(1).toArray())[0]; + // --8<-- [end:manual_query_search] + // --8<-- [end:manual_query_embeddings] + expect(actual).toHaveProperty("text"); + }); +}); + +test("custom embedding function", async () => { + await withTempDirectory(async (databaseDir) => { + // --8<-- [start:embedding_function] + const db = await lancedb.connect(databaseDir); + + @register("my_embedding") + class MyEmbeddingFunction extends EmbeddingFunction { + constructor(optionsRaw = {}) { + super(); + const options = this.resolveVariables(optionsRaw); + // Initialize using options + } + ndims() { + return 3; + } + protected getSensitiveKeys(): string[] { + return []; + } + embeddingDataType(): Float { + return new Float32(); + } + async computeQueryEmbeddings(_data: string) { + // This is a placeholder for a real embedding function + return [1, 2, 3]; + } + async computeSourceEmbeddings(data: string[]) { + // This is a placeholder for a real embedding function + return Array.from({ length: data.length }).fill([ + 1, 2, 3, + ]) as number[][]; + } + } + + const func = new MyEmbeddingFunction(); + + const data = [{ text: "pepperoni" }, { text: "pineapple" }]; + + // Option 1: manually specify the embedding function + const table = await db.createTable("vectors", data, { + embeddingFunction: { + function: func, + sourceColumn: "text", + vectorColumn: "vector", + }, + mode: "overwrite", + }); + + // Option 2: provide the embedding function through a schema + + const schema = LanceSchema({ + text: func.sourceField(new Utf8()), + vector: func.vectorField(), + }); + + const table2 = await db.createTable("vectors2", data, { + schema, + mode: "overwrite", + }); + // --8<-- [end:embedding_function] + expect(await table.countRows()).toBe(2); + expect(await table2.countRows()).toBe(2); + }); +}); + +test("embedding function api_key", async () => { + // --8<-- [start:register_secret] + const registry = getRegistry(); + registry.setVar("api_key", "sk-..."); + + const func = registry.get("openai")!.create({ + apiKey: "$var:api_key", + }); + // --8<-- [end:register_secret] +}); + +openAiTest("embedding function variable fallback", () => { + // --8<-- [start:register_model_fallback] + const registry = getRegistry(); + registry.setVar("openai_model", "text-embedding-3-large"); + + const func = registry.get("openai")!.create({ + model: "$var:openai_model:text-embedding-3-small", + }); + // --8<-- [end:register_model_fallback] + expect(func).toBeDefined(); +}); diff --git a/docs/web-tests/ts/filtering.test.ts b/docs/web-tests/ts/filtering.test.ts new file mode 100644 index 000000000..1c35d5120 --- /dev/null +++ b/docs/web-tests/ts/filtering.test.ts @@ -0,0 +1,42 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +import * as lancedb from "@lancedb/lancedb"; +import { withTempDirectory } from "./util.ts"; + +test("filtering examples", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + const data = Array.from({ length: 10_000 }, (_, i) => ({ + vector: Array(1536).fill(i), + id: i, + item: `item ${i}`, + strId: `${i}`, + })); + + const tbl = await db.createTable("myVectors", data, { mode: "overwrite" }); + + // --8<-- [start:search] + const _result = await tbl + .search(Array(1536).fill(0.5)) + .limit(1) + .where("id = 10") + .toArray(); + // --8<-- [end:search] + + // --8<-- [start:vec_search] + const result = await ( + tbl.search(Array(1536).fill(0)) as lancedb.VectorQuery + ) + .where("(item IN ('item 0', 'item 2')) AND (id > 10)") + .postfilter() + .toArray(); + // --8<-- [end:vec_search] + expect(result.length).toBe(0); + + // --8<-- [start:sql_search] + await tbl.query().where("id = 10").limit(10).toArray(); + // --8<-- [end:sql_search] + }); +}); diff --git a/docs/web-tests/ts/full_text_search.test.ts b/docs/web-tests/ts/full_text_search.test.ts new file mode 100644 index 000000000..9777bda7f --- /dev/null +++ b/docs/web-tests/ts/full_text_search.test.ts @@ -0,0 +1,45 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +import * as lancedb from "@lancedb/lancedb"; +import { withTempDirectory } from "./util.ts"; + +test("full text search", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + const words = [ + "apple", + "banana", + "cherry", + "date", + "elderberry", + "fig", + "grape", + ]; + + const data = Array.from({ length: 10_000 }, (_, i) => ({ + vector: Array(1536).fill(i), + id: i, + item: `item ${i}`, + strId: `${i}`, + doc: words[i % words.length], + })); + + const tbl = await db.createTable("myVectors", data, { mode: "overwrite" }); + + await tbl.createIndex("doc", { + config: lancedb.Index.fts(), + }); + + // --8<-- [start:full_text_search] + const result = await tbl + .query() + .nearestToText("apple") + .select(["id", "doc"]) + .limit(10) + .toArray(); + expect(result.length).toBe(10); + // --8<-- [end:full_text_search] + }); +}, 10_000); diff --git a/docs/web-tests/ts/integrations.ts b/docs/web-tests/ts/integrations.ts new file mode 100644 index 000000000..3ae7967e9 --- /dev/null +++ b/docs/web-tests/ts/integrations.ts @@ -0,0 +1,134 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +// --8<-- [start:frameworks_genkit_usage] +import { lancedbIndexerRef, lancedb, lancedbRetrieverRef, WriteMode } from "genkitx-lancedb"; +import { textEmbedding004, vertexAI } from "@genkit-ai/vertexai"; +import { gemini } from "@genkit-ai/vertexai"; +import { z, genkit } from "genkit"; +import { Document } from "genkit/retriever"; +import { chunk } from "llm-chunk"; +import { readFile } from "fs/promises"; +import path from "path"; +import pdf from "pdf-parse/lib/pdf-parse"; + +const ai = genkit({ + plugins: [ + // vertexAI provides the textEmbedding004 embedder + vertexAI(), + + // the local vector store requires an embedder to translate from text to vector + lancedb([ + { + dbUri: ".db", // optional lancedb uri, default to .db + tableName: "table", // optional table name, default to table + embedder: textEmbedding004, + }, + ]), + ], +}); +// --8<-- [end:frameworks_genkit_usage] + +// --8<-- [start:frameworks_genkit_custom_indexer] +export const menuPdfIndexer = lancedbIndexerRef({ + // Using all defaults, for dbUri, tableName, and embedder, etc +}); + +const chunkingConfig = { + minLength: 1000, + maxLength: 2000, + splitter: "sentence", + overlap: 100, + delimiters: "", +} as any; + +async function extractTextFromPdf(filePath: string) { + const pdfFile = path.resolve(filePath); + const dataBuffer = await readFile(pdfFile); + const data = await pdf(dataBuffer); + return data.text; +} + +export const indexMenu = ai.defineFlow( + { + name: "indexMenu", + inputSchema: z.string().describe("PDF file path"), + outputSchema: z.void(), + }, + async (filePath: string) => { + filePath = path.resolve(filePath); + + // Read the pdf. + const pdfTxt = await ai.run("extract-text", () => extractTextFromPdf(filePath)); + + // Divide the pdf text into segments. + const chunks = await ai.run("chunk-it", async () => chunk(pdfTxt, chunkingConfig)); + + // Convert chunks of text into documents to store in the index. + const documents = chunks.map((text) => { + return Document.fromText(text, { filePath }); + }); + + // Add documents to the index. + await ai.index({ + indexer: menuPdfIndexer, + documents, + options: { + writeMode: WriteMode.Overwrite, + } as any, + }); + }, +); +// --8<-- [end:frameworks_genkit_custom_indexer] + +// --8<-- [start:frameworks_genkit_custom_retriever] +export const menuRetriever = lancedbRetrieverRef({ + tableName: "table", // Use the same table name as the indexer. + displayName: "Menu", // Use a custom display name. +}); + +export const menuQAFlow = ai.defineFlow( + { name: "Menu", inputSchema: z.string(), outputSchema: z.string() }, + async (input: string) => { + // retrieve relevant documents + const docs = await ai.retrieve({ + retriever: menuRetriever, + query: input, + options: { + k: 3, + }, + }); + + const extractedContent = docs.map((doc) => { + if (doc.content && Array.isArray(doc.content) && doc.content.length > 0) { + if (doc.content[0].media && doc.content[0].media.url) { + return doc.content[0].media.url; + } + } + return "No content found"; + }); + + console.log("Extracted content:", extractedContent); + + const { text } = await ai.generate({ + model: gemini("gemini-2.0-flash"), + prompt: ` +You are acting as a helpful AI assistant that can answer +questions about the food available on the menu at Genkit Grub Pub. + +Use only the context provided to answer the question. +If you don't know, do not make up an answer. +Do not add or change items on the menu. + +Context: +${extractedContent.join("\n\n")} + +Question: ${input}`, + docs, + }); + + return text; + }, +); +// --8<-- [end:frameworks_genkit_custom_retriever] + diff --git a/docs/web-tests/ts/jest.config.cjs b/docs/web-tests/ts/jest.config.cjs new file mode 100644 index 000000000..db83d639c --- /dev/null +++ b/docs/web-tests/ts/jest.config.cjs @@ -0,0 +1,6 @@ +/** @type {import('ts-jest').JestConfigWithTsJest} */ +module.exports = { + preset: "ts-jest", + testEnvironment: "node", + testPathIgnorePatterns: ["./dist"], +}; diff --git a/docs/web-tests/ts/merge_insert.test.ts b/docs/web-tests/ts/merge_insert.test.ts new file mode 100644 index 000000000..1b33dab6a --- /dev/null +++ b/docs/web-tests/ts/merge_insert.test.ts @@ -0,0 +1,68 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors + +import { expect, test } from "@jest/globals"; +import * as lancedb from "@lancedb/lancedb"; + +test("basic upsert", async () => { + const db = await lancedb.connect("memory://"); + + // --8<-- [start:upsert_basic] + const table = await db.createTable("users", [ + { id: 0, name: "Alice" }, + { id: 1, name: "Bob" }, + ]); + + const newUsers = [ + { id: 1, name: "Bobby" }, + { id: 2, name: "Charlie" }, + ]; + await table + .mergeInsert("id") + .whenMatchedUpdateAll() + .whenNotMatchedInsertAll() + .execute(newUsers); + + await table.countRows(); // 3 + // --8<-- [end:upsert_basic] + expect(await table.countRows()).toBe(3); + + // --8<-- [start:insert_if_not_exists] + const table2 = await db.createTable("domains", [ + { domain: "google.com", name: "Google" }, + { domain: "github.com", name: "GitHub" }, + ]); + + const newDomains = [ + { domain: "google.com", name: "Google" }, + { domain: "facebook.com", name: "Facebook" }, + ]; + await table2 + .mergeInsert("domain") + .whenNotMatchedInsertAll() + .execute(newDomains); + await table2.countRows(); // 3 + // --8<-- [end:insert_if_not_exists] + expect(await table2.countRows()).toBe(3); + + // --8<-- [start:replace_range] + const table3 = await db.createTable("chunks", [ + { doc_id: 0, chunk_id: 0, text: "Hello" }, + { doc_id: 0, chunk_id: 1, text: "World" }, + { doc_id: 1, chunk_id: 0, text: "Foo" }, + { doc_id: 1, chunk_id: 1, text: "Bar" }, + ]); + + const newChunks = [{ doc_id: 1, chunk_id: 0, text: "Baz" }]; + + await table3 + .mergeInsert(["doc_id", "chunk_id"]) + .whenMatchedUpdateAll() + .whenNotMatchedInsertAll() + .whenNotMatchedBySourceDelete({ where: "doc_id = 1" }) + .execute(newChunks); + + await table3.countRows("doc_id = 1"); // 1 + // --8<-- [end:replace_range] + expect(await table3.countRows("doc_id = 1")).toBe(1); +}); diff --git a/docs/web-tests/ts/multimodal.test.ts b/docs/web-tests/ts/multimodal.test.ts new file mode 100644 index 000000000..ac9a8201e --- /dev/null +++ b/docs/web-tests/ts/multimodal.test.ts @@ -0,0 +1,104 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +// --8<-- [start:multimodal_imports] +import * as arrow from "apache-arrow"; +import { Buffer } from "node:buffer"; +import * as lancedb from "@lancedb/lancedb"; +// --8<-- [end:multimodal_imports] +import { withTempDirectory } from "./util.ts"; + +test("multimodal snippets (async)", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + // --8<-- [start:create_dummy_data] + const createDummyImage = (color: string): Uint8Array => { + const pngHeader = Uint8Array.from([137, 80, 78, 71, 13, 10, 26, 10]); + return Buffer.concat([Buffer.from(pngHeader), Buffer.from(color, "utf8")]); + }; + + const data = [ + { + id: 1, + filename: "red_square.png", + vector: Array.from({ length: 128 }, (_, i) => (i % 16) / 16), + image_blob: createDummyImage("red"), + label: "red", + }, + { + id: 2, + filename: "blue_square.png", + vector: Array.from({ length: 128 }, (_, i) => ((i + 8) % 16) / 16), + image_blob: createDummyImage("blue"), + label: "blue", + }, + ]; + // --8<-- [end:create_dummy_data] + + // --8<-- [start:define_schema] + const schema = new arrow.Schema([ + new arrow.Field("id", new arrow.Int32()), + new arrow.Field("filename", new arrow.Utf8()), + new arrow.Field( + "vector", + new arrow.FixedSizeList( + 128, + new arrow.Field("item", new arrow.Float32(), true), + ), + ), + new arrow.Field("image_blob", new arrow.Binary()), + new arrow.Field("label", new arrow.Utf8()), + ]); + // --8<-- [end:define_schema] + + // --8<-- [start:ingest_data] + const multimodalData = lancedb.makeArrowTable(data, { schema }); + const tbl = await db.createTable("images", multimodalData, { + mode: "overwrite", + }); + // --8<-- [end:ingest_data] + expect(await tbl.countRows()).toBe(2); + + // --8<-- [start:search_data] + const queryVector = Array.from({ length: 128 }, (_, i) => (i % 16) / 16); + const results = await tbl.search(queryVector).limit(1).toArray(); + // --8<-- [end:search_data] + + // --8<-- [start:process_results] + for (const row of results) { + const imageBytes = row.image_blob as Uint8Array; + console.log( + `Retrieved image: ${row.filename}, Byte length: ${imageBytes.length}`, + ); + } + // --8<-- [end:process_results] + expect(results).toHaveLength(1); + + // --8<-- [start:blob_api_schema] + const blobSchema = new arrow.Schema([ + new arrow.Field("id", new arrow.Int64()), + new arrow.Field( + "video", + new arrow.LargeBinary(), + true, + new Map([["lance-encoding:blob", "true"]]), + ), + ]); + // --8<-- [end:blob_api_schema] + + // --8<-- [start:blob_api_ingest] + const blobData = lancedb.makeArrowTable( + [ + { id: 1, video: Buffer.from("fake_video_bytes_1") }, + { id: 2, video: Buffer.from("fake_video_bytes_2") }, + ], + { schema: blobSchema }, + ); + const blobTable = await db.createTable("videos", blobData, { + mode: "overwrite", + }); + // --8<-- [end:blob_api_ingest] + expect(await blobTable.countRows()).toBe(2); + }); +}); diff --git a/docs/web-tests/ts/package-lock.json b/docs/web-tests/ts/package-lock.json new file mode 100644 index 000000000..8713b65a2 --- /dev/null +++ b/docs/web-tests/ts/package-lock.json @@ -0,0 +1,5501 @@ +{ + "name": "examples", + "version": "1.0.0", + "lockfileVersion": 3, + "requires": true, + "packages": { + "": { + "name": "examples", + "version": "1.0.0", + "license": "Apache-2.0", + "dependencies": { + "@huggingface/transformers": "^3.0.2", + "@lancedb/lancedb": "^0.31.0", + "openai": "^4.29.2", + "sharp": "^0.33.5" + }, + "devDependencies": { + "@biomejs/biome": "^1.7.3", + "@jest/globals": "^29.7.0", + "jest": "^29.7.0", + "jest-environment-node-single-context": "^29.4.0", + "ts-jest": "^29.2.5", + "typescript": "^5.5.4" + } + }, + "..": { + "name": "@lancedb/lancedb", + "version": "0.12.0", + "cpu": 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LanceDB", + "main": "index.js", + "type": "module", + "scripts": { + "//1": "--experimental-vm-modules is needed to run jest with sentence-transformers", + "//2": "--testEnvironment is needed to run jest with sentence-transformers", + "//3": "See: https://github.com/huggingface/transformers.js/issues/57", + "test": "node --experimental-vm-modules node_modules/.bin/jest --testEnvironment jest-environment-node-single-context --verbose", + "lint": "biome check *.ts && biome format *.ts", + "lint-ci": "biome ci .", + "lint-fix": "biome check --write *.ts && npm run format", + "format": "biome format --write *.ts" + }, + "author": "Lance Devs", + "license": "Apache-2.0", + "dependencies": { + "@huggingface/transformers": "^3.0.2", + "@lancedb/lancedb": "^0.31.0", + "openai": "^4.29.2", + "sharp": "^0.33.5" + }, + "devDependencies": { + "@biomejs/biome": "^1.7.3", + "@jest/globals": "^29.7.0", + "jest": "^29.7.0", + "jest-environment-node-single-context": "^29.4.0", + "ts-jest": "^29.2.5", + "typescript": "^5.5.4" + } +} diff --git a/docs/web-tests/ts/quickstart.test.ts b/docs/web-tests/ts/quickstart.test.ts new file mode 100644 index 000000000..ecd5c0999 --- /dev/null +++ b/docs/web-tests/ts/quickstart.test.ts @@ -0,0 +1,175 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +import * as lancedb from "@lancedb/lancedb"; +import { withTempDirectory } from "./util.ts"; + +test("quickstart example (async)", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + // --8<-- [start:quickstart_data] + const data = [ + { + id: "1", + name: "King Arthur", + role: "King", + description: "Leader of Camelot and wielder of Excalibur.", + stats: { strength: 4, magic: 1, leadership: 5, wisdom: 4 }, + vector: [0.7, 0.1, 0.9, 0.7], + }, + { + id: "2", + name: "Merlin", + role: "Wizard", + description: "Advisor and prophet with deep magical knowledge.", + stats: { strength: 2, magic: 5, leadership: 4, wisdom: 5 }, + vector: [0.2, 0.9, 0.4, 0.9], + }, + { + id: "3", + name: "Sir Lancelot", + role: "Knight", + description: "Legendary knight known for courage and combat skill.", + stats: { strength: 5, magic: 1, leadership: 3, wisdom: 3 }, + vector: [0.9, 0.1, 0.5, 0.4], + }, + ]; + // --8<-- [end:quickstart_data] + + // --8<-- [start:quickstart_create_table] + let table = await db.createTable("characters", data, { mode: "overwrite" }); + // --8<-- [end:quickstart_create_table] + expect(await table.countRows()).toBe(3); + await db.dropTable("characters"); + + // --8<-- [start:quickstart_create_table_no_overwrite] + table = await db.createTable("characters", data); + // --8<-- [end:quickstart_create_table_no_overwrite] + expect(await table.countRows()).toBe(3); + + // --8<-- [start:quickstart_vector_search_1] + // Search for examples similar to a "wise magical advisor" + let queryVector = [0.2, 0.8, 0.4, 0.9]; + + let result = await table + .search(queryVector) + .select(["name", "role", "description", "_distance"]) + .limit(2) + .toArray(); + console.table(result); + // --8<-- [end:quickstart_vector_search_1] + expect(result[0].name).toBe("Merlin"); + + // --8<-- [start:quickstart_curate_with_metadata] + const curated = await table + .search(queryVector) + .where("stats.magic >= 4") + .select(["name", "role", "description", "_distance"]) + .limit(2) + .toArray(); + console.table(curated); + // --8<-- [end:quickstart_curate_with_metadata] + expect(curated[0].name).toBe("Merlin"); + + // --8<-- [start:quickstart_output_array] + result = await table.search(queryVector).limit(2).toArray(); + console.table(result); + // --8<-- [end:quickstart_output_array] + expect(result[0].name).toBe("Merlin"); + + // --8<-- [start:quickstart_add_feature] + await table.addColumns([ + { + name: "power_score", + valueSql: + "cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)", + }, + ]); + // --8<-- [end:quickstart_add_feature] + const schemaWithFeature = await table.schema(); + expect(schemaWithFeature.fields.some((f) => f.name === "power_score")).toBe( + true, + ); + + // --8<-- [start:quickstart_query_feature] + const features = await table + .query() + .select(["name", "role", "power_score"]) + .toArray(); + console.table(features); + // --8<-- [end:quickstart_query_feature] + expect(features[0]).toHaveProperty("power_score"); + + // --8<-- [start:quickstart_multimodal_bytes] + const arrow = await import("apache-arrow"); + const path = await import("node:path"); + const { readFile } = await import("node:fs/promises"); + + const imagePath = path.resolve( + "../../docs/static/assets/images/quickstart/sir-lancelot.jpg", + ); + const imageBytes = await readFile(imagePath); + const imageSchema = new arrow.Schema([ + new arrow.Field("id", new arrow.Utf8()), + new arrow.Field("description", new arrow.Utf8()), + new arrow.Field("image", new arrow.Binary()), + new arrow.Field( + "vector", + new arrow.FixedSizeList( + 4, + new arrow.Field("item", new arrow.Float32(), true), + ), + ), + ]); + const imageData = lancedb.makeArrowTable( + [ + { + id: "lancelot", + description: "Portrait of Sir Lancelot", + image: imageBytes, + vector: [0.9, 0.1, 0.5, 0.4], + }, + ], + { schema: imageSchema }, + ); + const multimodalTable = await db.createTable( + "character_images", + imageData, + { mode: "overwrite" }, + ); + // --8<-- [end:quickstart_multimodal_bytes] + expect(await multimodalTable.countRows()).toBe(1); + + // --8<-- [start:quickstart_open_table] + table = await db.openTable("characters"); + // --8<-- [end:quickstart_open_table] + + // --8<-- [start:quickstart_add_data] + const moreData = [ + { + id: "4", + name: "Morgana", + role: "Sorceress", + description: "Powerful sorceress of Avalon.", + stats: { strength: 2, magic: 5, leadership: 4, wisdom: 4 }, + vector: [0.3, 0.9, 0.6, 0.8], + power_score: 3.75, + }, + ]; + + // Add data to table + await table.add(moreData); + // --8<-- [end:quickstart_add_data] + expect(await table.countRows()).toBe(4); + + // --8<-- [start:quickstart_vector_search_2] + // Search for examples similar to a "powerful sorceress" + queryVector = [0.3, 0.9, 0.6, 0.8]; + + const results = await table.search(queryVector).limit(2).toArray(); + console.table(results); + // --8<-- [end:quickstart_vector_search_2] + expect(results[0].name).toBe("Morgana"); + }); +}); diff --git a/docs/web-tests/ts/search.test.ts b/docs/web-tests/ts/search.test.ts new file mode 100644 index 000000000..bc50968cc --- /dev/null +++ b/docs/web-tests/ts/search.test.ts @@ -0,0 +1,355 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +// --8<-- [start:import] +import * as lancedb from "@lancedb/lancedb"; +// --8<-- [end:import] +// --8<-- [start:import_bin_util] +import { Field, FixedSizeList, Int32, Schema, Uint8 } from "apache-arrow"; +// --8<-- [end:import_bin_util] +import { Float32, Struct } from "apache-arrow"; +import { withTempDirectory } from "./util.ts"; + +async function buildIndexedTable(db: lancedb.Connection, name: string) { + const data = Array.from({ length: 512 }, (_, i) => ({ + vector: Array.from({ length: 128 }, () => Math.random()), + id: i, + })); + const tbl = await db.createTable(name, data, { mode: "overwrite" }); + await tbl.createIndex("vector", { + config: lancedb.Index.ivfPq({ numPartitions: 4, numSubVectors: 8 }), + }); + return tbl; +} + +test("vector search", async () => { + await withTempDirectory(async (databaseDir) => { + { + const db = await lancedb.connect(databaseDir); + + const data = Array.from({ length: 10_000 }, (_, i) => ({ + vector: Array(128).fill(i), + id: `${i}`, + })); + + await db.createTable("my_vectors", data); + } + + // --8<-- [start:search1] + const db = await lancedb.connect(databaseDir); + const tbl = await db.openTable("my_vectors"); + + const results1 = await tbl.search(Array(128).fill(1.2)).limit(10).toArray(); + // --8<-- [end:search1] + expect(results1.length).toBe(10); + + // --8<-- [start:search2] + const results2 = await ( + tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery + ) + .distanceType("cosine") + .limit(10) + .toArray(); + // --8<-- [end:search2] + expect(results2.length).toBe(10); + + // --8<-- [start:distance_range] + const results3 = await ( + tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery + ) + .distanceType("cosine") + .distanceRange(0.1, 0.2) + .limit(10) + .toArray(); + // --8<-- [end:distance_range] + for (const r of results3) { + expect(r.distance).toBeGreaterThanOrEqual(0.1); + expect(r.distance).toBeLessThan(0.2); + } + + { + // --8<-- [start:ingest_binary_data] + const schema = new Schema([ + new Field("id", new Int32(), true), + new Field("vec", new FixedSizeList(32, new Field("item", new Uint8()))), + ]); + const data = lancedb.makeArrowTable( + Array(1_000) + .fill(0) + .map((_, i) => ({ + // the 256 bits would be store in 32 bytes, + // if your data is already in this format, you can skip the packBits step + id: i, + vec: lancedb.packBits(Array(256).fill(i % 2)), + })), + { schema: schema }, + ); + + const tbl = await db.createTable("binary_table", data); + await tbl.createIndex("vec", { + config: lancedb.Index.ivfFlat({ + numPartitions: 10, + distanceType: "hamming", + }), + }); + // --8<-- [end:ingest_binary_data] + + // --8<-- [start:search_binary_data] + const query = Array(32) + .fill(1) + .map(() => Math.floor(Math.random() * 255)); + const results = await tbl.query().nearestTo(query).limit(10).toArrow(); + // --8<-- [end:search_binary_data] + expect(results.numRows).toBe(10); + } + }); +}); + +test("vector search docs snippets", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + // ---- Nested vector column: inference and explicit selection ---- + { + const nestedSchema = new Schema([ + new Field("id", new Int32()), + new Field( + "image", + new Struct([ + new Field( + "embedding", + new FixedSizeList(2, new Field("item", new Float32(), true)), + ), + ]), + ), + ]); + const arrowTbl = lancedb.makeArrowTable( + [{ id: 0, image: { embedding: [0.0, 1.0] } }], + { schema: nestedSchema }, + ); + await db.createTable("nested", arrowTbl, { mode: "overwrite" }); + + // --8<-- [start:select_vector_column] + const table = await db.openTable("nested"); + + // Inferred: LanceDB finds the single nested vector leaf automatically. + await table.query().nearestTo([0.0, 1.0]).limit(1).toArray(); + + // Explicit: required when more than one vector column matches. + await table + .query() + .nearestTo([0.0, 1.0]) + .column("image.embedding") + .limit(1) + .toArray(); + // --8<-- [end:select_vector_column] + } + + // ---- Index a nested vector column ---- + { + const dim = 16; + const nestedSchema = new Schema([ + new Field("id", new Int32()), + new Field( + "image", + new Struct([ + new Field( + "embedding", + new FixedSizeList(dim, new Field("item", new Float32(), true)), + ), + ]), + ), + ]); + const rows = Array.from({ length: 512 }, (_, i) => ({ + id: i, + image: { embedding: Array.from({ length: dim }, () => Math.random()) }, + })); + const arrowTbl = lancedb.makeArrowTable(rows, { schema: nestedSchema }); + const table = await db.createTable("nested_index", arrowTbl, { + mode: "overwrite", + }); + + // --8<-- [start:index_nested_column] + await table.createIndex("image.embedding"); + // --8<-- [end:index_nested_column] + } + + // ---- Indexed queries: refine, fast search, bypass index ---- + { + const table = await buildIndexedTable(db, "ann_table"); + const embedding = Array.from({ length: 128 }, () => Math.random()); + + // --8<-- [start:exact_vs_approximate] + // Indexed ANN search without refinement (fast, approximate `_distance`) + const fastResults = await (table.search(embedding) as lancedb.VectorQuery) + .limit(10) + .toArray(); + + // Recompute distances on full vectors for reranked candidates + const exactDistanceResults = await ( + table.search(embedding) as lancedb.VectorQuery + ) + .limit(10) + .refineFactor(1) + .toArray(); + + // Rerank a larger candidate set for better recall (higher latency) + const higherRecallResults = await ( + table.search(embedding) as lancedb.VectorQuery + ) + .limit(10) + .refineFactor(20) + .toArray(); + // --8<-- [end:exact_vs_approximate] + expect(fastResults.length).toBe(10); + expect(exactDistanceResults.length).toBe(10); + expect(higherRecallResults.length).toBe(10); + + // --8<-- [start:fast_search] + await table + .query() + .nearestTo(embedding) + .fastSearch() + .limit(5) + .toArray(); + // --8<-- [end:fast_search] + + // --8<-- [start:bypass_vector_index] + await table + .query() + .nearestTo(embedding) + .bypassVectorIndex() + .limit(5) + .toArray(); + // --8<-- [end:bypass_vector_index] + } + + // ---- Brute force search (no index) ---- + { + const data = Array.from({ length: 64 }, (_, i) => ({ + vector: Array(128).fill(i), + id: `${i}`, + })); + await db.createTable("my_vectors", data, { mode: "overwrite" }); + + // --8<-- [start:brute_force_search] + const tbl = await db.openTable("my_vectors"); + + const results1 = await tbl.search(Array(128).fill(1.2)).limit(3).toArray(); + // --8<-- [end:brute_force_search] + expect(results1.length).toBe(3); + } + + // ---- Binary (hamming) vector search ---- + { + const schema = new Schema([ + new Field("id", new Int32(), true), + new Field("vector", new FixedSizeList(32, new Field("item", new Uint8()))), + ]); + const data = lancedb.makeArrowTable( + Array(1000) + .fill(0) + .map((_, i) => ({ + // the 256 bits are stored in 32 bytes; if your data is already in + // this format, you can skip the packBits step + id: i, + vector: lancedb.packBits(Array(256).fill(i % 2)), + })), + { schema }, + ); + + // --8<-- [start:binary_search] + const tbl = await db.createTable("binary_vectors", data, { + mode: "overwrite", + }); + await tbl.createIndex("vector", { + config: lancedb.Index.ivfFlat({ + numPartitions: 10, + distanceType: "hamming", + }), + }); + + const query = Array(32) + .fill(1) + .map(() => Math.floor(Math.random() * 255)); + const results = await tbl.query().nearestTo(query).limit(10).toArray(); + // --8<-- [end:binary_search] + expect(results.length).toBeLessThanOrEqual(10); + } + + // ---- Enterprise-style prefilter / postfilter / batch search ---- + { + const dimensions = 768; + const rows = Array.from({ length: 50 }, (_, i) => ({ + vector: Array.from({ length: dimensions }, () => Math.random() * 2 - 1), + text: `story ${i}`, + keywords: `kw${i}`, + label: i % 4, + })); + await db.createTable("lancedb-enterprise-quickstart", rows, { + mode: "overwrite", + }); + + { + // --8<-- [start:vector_search_prefilter] + // Generate a sample 768-dimension embedding vector (typical for BERT-based models) + // In real applications, you would get this from an embedding model + const dimensions = 768; + const queryEmbed = Array.from( + { length: dimensions }, + () => Math.random() * 2 - 1, + ); + + // Open table and perform search + const tableName = "lancedb-enterprise-quickstart"; + const table = await db.openTable(tableName); + + // Vector search with filters (pre-filtering is the default) + const vectorResults = await table + .search(queryEmbed) + .where("label > 2") + .select(["text", "keywords", "label"]) + .limit(5) + .toArray(); + + console.log("Search results (with pre-filtering):"); + console.log(vectorResults); + // --8<-- [end:vector_search_prefilter] + + // --8<-- [start:vector_search_postfilter] + const vectorResultsWithPostFilter = await ( + table.search(queryEmbed) as lancedb.VectorQuery + ) + .where("label > 2") + .postfilter() + .select(["text", "keywords", "label"]) + .limit(5) + .toArray(); + + console.log("Vector search results with post-filter:"); + console.log(vectorResultsWithPostFilter); + // --8<-- [end:vector_search_postfilter] + + // --8<-- [start:batch_search] + // Batch query + console.log("Performing batch vector search..."); + const batchSize = 5; + const queryVectors = Array.from({ length: batchSize }, () => + Array.from({ length: dimensions }, () => Math.random() * 2 - 1), + ); + let batchQuery = table.search(queryVectors[0]) as lancedb.VectorQuery; + for (let i = 1; i < batchSize; i++) { + batchQuery = batchQuery.addQueryVector(queryVectors[i]); + } + const batchResults = await batchQuery + .select(["text", "keywords", "label"]) + .limit(5) + .toArray(); + console.log("Batch vector search results:"); + console.log(batchResults); + // --8<-- [end:batch_search] + expect(batchResults.length).toBeGreaterThan(0); + } + } + }); +}); diff --git a/docs/web-tests/ts/sentence-transformers.test.ts b/docs/web-tests/ts/sentence-transformers.test.ts new file mode 100644 index 000000000..a407e192b --- /dev/null +++ b/docs/web-tests/ts/sentence-transformers.test.ts @@ -0,0 +1,96 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +import { withTempDirectory } from "./util.ts"; + +// --8<-- [start:quickstart_imports] +import * as lancedb from "@lancedb/lancedb"; +import "@lancedb/lancedb/embedding/transformers"; +import { Utf8 } from "apache-arrow"; +// --8<-- [end:quickstart_imports] + +test("full text search", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + const func = (await lancedb.embedding + .getRegistry() + .get("huggingface") + ?.create()) as lancedb.embedding.EmbeddingFunction; + + const facts = [ + "Albert Einstein was a theoretical physicist.", + "The capital of France is Paris.", + "The Great Wall of China is one of the Seven Wonders of the World.", + "Python is a popular programming language.", + "Mount Everest is the highest mountain in the world.", + "Leonardo da Vinci painted the Mona Lisa.", + "Shakespeare wrote Hamlet.", + "The human body has 206 bones.", + "The speed of light is approximately 299,792 kilometers per second.", + "Water boils at 100 degrees Celsius.", + "The Earth orbits the Sun.", + "The Pyramids of Giza are located in Egypt.", + "Coffee is one of the most popular beverages in the world.", + "Tokyo is the capital city of Japan.", + "Photosynthesis is the process by which plants make their food.", + "The Pacific Ocean is the largest ocean on Earth.", + "Mozart was a prolific composer of classical music.", + "The Internet is a global network of computers.", + "Basketball is a sport played with a ball and a hoop.", + "The first computer virus was created in 1983.", + "Artificial neural networks are inspired by the human brain.", + "Deep learning is a subset of machine learning.", + "IBM's Watson won Jeopardy! in 2011.", + "The first computer programmer was Ada Lovelace.", + "The first chatbot was ELIZA, created in the 1960s.", + ].map((text) => ({ text })); + + const factsSchema = lancedb.embedding.LanceSchema({ + text: func.sourceField(new Utf8()), + vector: func.vectorField(), + }); + + const tbl = await db.createTable("facts", facts, { + mode: "overwrite", + schema: factsSchema, + }); + + const query = "How many bones are in the human body?"; + const actual = await tbl.search(query).limit(1).toArray(); + + expect(actual[0].text).toBe("The human body has 206 bones."); + }); +}, 100_000); + +test.skip("embedding quickstart snippets", async () => { + // --8<-- [start:quickstart_connect] + const db = await lancedb.connect("data/sample-lancedb"); + // --8<-- [end:quickstart_connect] + + // --8<-- [start:quickstart_init_model] + const model = (await lancedb.embedding + .getRegistry() + .get("huggingface") + ?.create()) as lancedb.embedding.EmbeddingFunction; + // --8<-- [end:quickstart_init_model] + + // --8<-- [start:quickstart_schema] + const wordsSchema = lancedb.embedding.LanceSchema({ + text: model.sourceField(new Utf8()), + vector: model.vectorField(), + }); + // --8<-- [end:quickstart_schema] + + // --8<-- [start:quickstart_create_table] + const table = await db.createEmptyTable("words", wordsSchema, { + mode: "overwrite", + }); + await table.add([{ text: "hello world" }, { text: "goodbye world" }]); + // --8<-- [end:quickstart_create_table] + + // --8<-- [start:quickstart_query] + const query = "greetings"; + const actual = (await table.search(query).limit(1).toArray())[0]; + console.log(actual.text); + // --8<-- [end:quickstart_query] +}); diff --git a/docs/web-tests/ts/storage.test.ts b/docs/web-tests/ts/storage.test.ts new file mode 100644 index 000000000..38c27cf27 --- /dev/null +++ b/docs/web-tests/ts/storage.test.ts @@ -0,0 +1,187 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +import * as lancedb from "@lancedb/lancedb"; + +// --8<-- [start:storage_connect_s3] +async function storageConnectS3() { + const db = await lancedb.connect("s3://bucket/path"); + return db; +} +// --8<-- [end:storage_connect_s3] + +// --8<-- [start:storage_connect_gcs] +async function storageConnectGcs() { + const db = await lancedb.connect("gs://bucket/path"); + return db; +} +// --8<-- [end:storage_connect_gcs] + +// --8<-- [start:storage_connect_azure] +async function storageConnectAzure() { + const db = await lancedb.connect("az://bucket/path"); + return db; +} +// --8<-- [end:storage_connect_azure] + +// --8<-- [start:storage_connect_timeout] +async function storageConnectTimeout() { + const db = await lancedb.connect("s3://bucket/path", { + storageOptions: { timeout: "60s" }, + }); + return db; +} +// --8<-- [end:storage_connect_timeout] + +// --8<-- [start:storage_table_timeout] +async function storageTableTimeout() { + const db = await lancedb.connect("s3://bucket/path"); + const table = await db.createTable( + "table", + [{ a: 1, b: 2 }], + { storageOptions: { timeout: "60s" } }, + ); + return table; +} +// --8<-- [end:storage_table_timeout] + +// --8<-- [start:storage_s3_ddb] +async function storageS3Ddb() { + const db = await lancedb.connect( + "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table", + ); + return db; +} +// --8<-- [end:storage_s3_ddb] + +// --8<-- [start:storage_s3_ddb_local] +async function storageS3DdbLocal() { + const db = await lancedb.connect( + "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table", + { + storageOptions: { + endpoint: "http://localhost:4566", + dynamodbEndpoint: "http://localhost:4566", + allowHttp: "true", + }, + }, + ); + return db; +} +// --8<-- [end:storage_s3_ddb_local] + +// --8<-- [start:storage_s3_sse_kms] +async function storageS3SseKms() { + const db = await lancedb.connect("s3://bucket/path", { + storageOptions: { + awsServerSideEncryption: "aws:kms", + awsSseKmsKeyId: "", + }, + }); + return db; +} +// --8<-- [end:storage_s3_sse_kms] + +// --8<-- [start:storage_azure_sas] +async function storageAzureSas() { + const db = await lancedb.connect( + "az://my-container/my-database", + { + storageOptions: { + azureStorageAccountName: "some-account", + azureStorageSasToken: "", + }, + }, + ); + return db; +} +// --8<-- [end:storage_azure_sas] + +// --8<-- [start:storage_s3_minio] +async function storageS3Minio() { + const db = await lancedb.connect("s3://bucket/path", { + storageOptions: { + region: "us-east-1", + endpoint: "http://minio:9000", + }, + }); + return db; +} +// --8<-- [end:storage_s3_minio] + +// --8<-- [start:storage_s3_express] +async function storageS3Express() { + const db = await lancedb.connect( + "s3://my-bucket--use1-az4--x-s3/path", + { + storageOptions: { + region: "us-east-1", + s3Express: "true", + }, + }, + ); + return db; +} +// --8<-- [end:storage_s3_express] + +// --8<-- [start:storage_gcs_service_account] +async function storageGcsServiceAccount() { + const db = await lancedb.connect( + "gs://my-bucket/my-database", + { + storageOptions: { + serviceAccount: "path/to/service-account.json", + }, + }, + ); + return db; +} +// --8<-- [end:storage_gcs_service_account] + +// --8<-- [start:storage_azure_account] +async function storageAzureAccount() { + const db = await lancedb.connect( + "az://my-container/my-database", + { + storageOptions: { + accountName: "some-account", + accountKey: "some-key", + }, + }, + ); + return db; +} +// --8<-- [end:storage_azure_account] + +// --8<-- [start:storage_tigris_connect] +async function storageTigrisConnect() { + const db = await lancedb.connect( + "s3://your-bucket/path", + { + storageOptions: { + endpoint: "https://t3.storage.dev", + region: "auto", + }, + }, + ); + return db; +} +// --8<-- [end:storage_tigris_connect] + +test("storage ts snippets compile", async () => { + expect(storageConnectS3).toBeDefined(); + expect(storageConnectGcs).toBeDefined(); + expect(storageConnectAzure).toBeDefined(); + expect(storageConnectTimeout).toBeDefined(); + expect(storageTableTimeout).toBeDefined(); + expect(storageS3Ddb).toBeDefined(); + expect(storageS3DdbLocal).toBeDefined(); + expect(storageS3Minio).toBeDefined(); + expect(storageS3Express).toBeDefined(); + expect(storageS3SseKms).toBeDefined(); + expect(storageGcsServiceAccount).toBeDefined(); + expect(storageAzureAccount).toBeDefined(); + expect(storageAzureSas).toBeDefined(); + expect(storageTigrisConnect).toBeDefined(); +}); + diff --git a/docs/web-tests/ts/tables.test.ts b/docs/web-tests/ts/tables.test.ts new file mode 100644 index 000000000..b2a49b6b2 --- /dev/null +++ b/docs/web-tests/ts/tables.test.ts @@ -0,0 +1,1029 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import { expect, test } from "@jest/globals"; +import * as arrow from "apache-arrow"; +import * as lancedb from "@lancedb/lancedb"; +import { withTempDirectory } from "./util.ts"; + +async function updateConnectEnterpriseExample() { + // --8<-- [start:update_connect_enterprise] + const db = await lancedb.connect("db://your-project-slug", { + apiKey: "your-api-key", + region: "us-east-1", + }); + // --8<-- [end:update_connect_enterprise] + return db; +} + +async function updateConnectLocalExample() { + // --8<-- [start:update_connect_local] + const db = await lancedb.connect("./data"); + // --8<-- [end:update_connect_local] + return db; +} + +test("table creation snippets (async)", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + // --8<-- [start:create_table_from_dicts] + type Location = { + vector: number[]; + lat: number; + long: number; + }; + + const data: Location[] = [ + { vector: [1.1, 1.2], lat: 45.5, long: -122.7 }, + { vector: [0.2, 1.8], lat: 40.1, long: -74.1 }, + ]; + const table = await db.createTable("test_table", data, { + mode: "overwrite", + }); + // --8<-- [end:create_table_from_dicts] + expect(await table.countRows()).toBe(2); + + await db.createTable("conflict_table", data); + // --8<-- [start:create_table_conflict_handling] + // Idempotent open: reuse the existing table if it exists. + // The provided data is ignored; the schema is validated against the + // existing table and a mismatch raises an error. + let conflictTable = await db.createTable("conflict_table", data, { + existOk: true, + }); + + // Overwrite: drop the existing table and create a new one with the + // provided data. This permanently discards the old table's data. + conflictTable = await db.createTable("conflict_table", data, { + mode: "overwrite", + }); + // --8<-- [end:create_table_conflict_handling] + expect(await conflictTable.countRows()).toBe(2); + + // --8<-- [start:create_table_custom_schema] + const customSchema = new arrow.Schema([ + new arrow.Field( + "vector", + new arrow.FixedSizeList( + 4, + new arrow.Field("item", new arrow.Float32(), true), + ), + ), + new arrow.Field("lat", new arrow.Float32()), + new arrow.Field("long", new arrow.Float32()), + ]); + + const customSchemaData = lancedb.makeArrowTable( + [ + { vector: [1.1, 1.2, 1.3, 1.4], lat: 45.5, long: -122.7 }, + { vector: [0.2, 1.8, 0.4, 3.6], lat: 40.1, long: -74.1 }, + ], + { schema: customSchema }, + ); + const customSchemaTable = await db.createTable( + "my_table_custom_schema", + customSchemaData, + { mode: "overwrite" }, + ); + // --8<-- [end:create_table_custom_schema] + expect(await customSchemaTable.countRows()).toBe(2); + + // --8<-- [start:create_table_from_arrow] + const arrowSchema = new arrow.Schema([ + new arrow.Field( + "vector", + new arrow.FixedSizeList( + 16, + new arrow.Field("item", new arrow.Float32(), true), + ), + ), + new arrow.Field("text", new arrow.Utf8()), + ]); + const arrowData = lancedb.makeArrowTable( + [ + { vector: Array(16).fill(0.1), text: "foo" }, + { vector: Array(16).fill(0.2), text: "bar" }, + ], + { schema: arrowSchema }, + ); + const arrowTable = await db.createTable("f32_tbl", arrowData, { + mode: "overwrite", + }); + // --8<-- [end:create_table_from_arrow] + expect(await arrowTable.countRows()).toBe(2); + + // --8<-- [start:create_table_from_iterator] + const batchSchema = new arrow.Schema([ + new arrow.Field( + "vector", + new arrow.FixedSizeList( + 4, + new arrow.Field("item", new arrow.Float32(), true), + ), + ), + new arrow.Field("item", new arrow.Utf8()), + new arrow.Field("price", new arrow.Float32()), + ]); + + const tableForBatches = await db.createEmptyTable( + "batched_table", + batchSchema, + { + mode: "overwrite", + }, + ); + + const rows = Array.from({ length: 10 }, (_, i) => ({ + vector: [i + 0.1, i + 0.2, i + 0.3, i + 0.4], + item: `item-${i + 1}`, + price: (i + 1) * 10, + })); + + const chunkSize = 2; + for (let i = 0; i < rows.length; i += chunkSize) { + const batch = lancedb.makeArrowTable(rows.slice(i, i + chunkSize), { + schema: batchSchema, + }); + await tableForBatches.add(batch); + } + // --8<-- [end:create_table_from_iterator] + expect(await tableForBatches.countRows()).toBe(10); + + // --8<-- [start:open_existing_table] + const openTableData = [{ vector: [1.1, 1.2], lat: 45.5, long: -122.7 }]; + await db.createTable("test_table_open", openTableData, { + mode: "overwrite", + }); + + console.log(await db.tableNames()); + + const openedTable = await db.openTable("test_table_open"); + // --8<-- [end:open_existing_table] + expect(await openedTable.countRows()).toBe(1); + + // --8<-- [start:create_empty_table] + const emptySchema = new arrow.Schema([ + new arrow.Field( + "vector", + new arrow.FixedSizeList( + 2, + new arrow.Field("item", new arrow.Float32(), true), + ), + ), + new arrow.Field("item", new arrow.Utf8()), + new arrow.Field("price", new arrow.Float32()), + ]); + const emptyTable = await db.createEmptyTable( + "test_empty_table", + emptySchema, + { + mode: "overwrite", + }, + ); + // --8<-- [end:create_empty_table] + expect(await emptyTable.countRows()).toBe(0); + + // --8<-- [start:drop_table] + await db.createTable("my_table", [{ vector: [1.1, 1.2], lat: 45.5 }], { + mode: "overwrite", + }); + + await db.dropTable("my_table"); + // --8<-- [end:drop_table] + expect(await db.tableNames()).not.toContain("my_table"); + }); +}); + +test("schema evolution snippets (async)", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + // --8<-- [start:schema_add_setup] + const schemaAddData = [ + { + id: 1, + name: "Laptop", + price: 1200.0, + vector: Array.from({ length: 128 }, () => Math.random()), + }, + { + id: 2, + name: "Smartphone", + price: 800.0, + vector: Array.from({ length: 128 }, () => Math.random()), + }, + { + id: 3, + name: "Headphones", + price: 150.0, + vector: Array.from({ length: 128 }, () => Math.random()), + }, + ]; + const schemaAddTable = await db.createTable( + "schema_evolution_add_example", + schemaAddData, + { mode: "overwrite" }, + ); + // --8<-- [end:schema_add_setup] + expect(await schemaAddTable.countRows()).toBe(3); + + // --8<-- [start:add_columns_calculated] + // Add a discounted price column (10% discount) + await schemaAddTable.addColumns([ + { + name: "discounted_price", + valueSql: "cast((price * 0.9) as float)", + }, + ]); + // --8<-- [end:add_columns_calculated] + + // --8<-- [start:add_columns_default_values] + // Add a stock status column with default value + await schemaAddTable.addColumns([ + { + name: "in_stock", + valueSql: "cast(true as boolean)", + }, + ]); + // --8<-- [end:add_columns_default_values] + + // --8<-- [start:add_columns_nullable] + // Add a nullable timestamp column + await schemaAddTable.addColumns([ + { + name: "last_ordered", + valueSql: "cast(NULL as timestamp)", + }, + ]); + // --8<-- [end:add_columns_nullable] + + // --8<-- [start:add_feature_columns_sql] + await schemaAddTable.addColumns([ + { + name: "price_per_id", + valueSql: "cast(price / id as float)", + }, + { + name: "price_log", + valueSql: "ln(price)", + }, + { + name: "price_score", + valueSql: "cast(price / (price + 100.0) as float)", + }, + ]); + // --8<-- [end:add_feature_columns_sql] + expect((await schemaAddTable.schema()).fields.map((field) => field.name)).toEqual( + expect.arrayContaining(["price_per_id", "price_log", "price_score"]), + ); + + // --8<-- [start:schema_alter_setup] + const schemaAlter = new arrow.Schema([ + new arrow.Field("id", new arrow.Int64()), + new arrow.Field("name", new arrow.Utf8()), + new arrow.Field("price", new arrow.Int32()), + new arrow.Field("discount_price", new arrow.Float64()), + new arrow.Field( + "vector", + new arrow.FixedSizeList( + 128, + new arrow.Field("item", new arrow.Float32(), true), + ), + ), + ]); + const schemaAlterData = lancedb.makeArrowTable( + [ + { + id: 1, + name: "Laptop", + price: 1200, + discount_price: 1080.0, + vector: Array.from({ length: 128 }, () => Math.random()), + }, + { + id: 2, + name: "Smartphone", + price: 800, + discount_price: 720.0, + vector: Array.from({ length: 128 }, () => Math.random()), + }, + ], + { schema: schemaAlter }, + ); + const schemaAlterTable = await db.createTable( + "schema_evolution_alter_example", + schemaAlterData, + { mode: "overwrite" }, + ); + // --8<-- [end:schema_alter_setup] + expect(await schemaAlterTable.countRows()).toBe(2); + + // --8<-- [start:alter_columns_rename] + // Rename discount_price to sale_price + await schemaAlterTable.alterColumns([ + { path: "discount_price", rename: "sale_price" }, + ]); + // --8<-- [end:alter_columns_rename] + + // --8<-- [start:alter_columns_data_type] + // Change price from int32 to int64 for larger numbers + await schemaAlterTable.alterColumns([ + { path: "price", dataType: new arrow.Int64() }, + ]); + // --8<-- [end:alter_columns_data_type] + + // --8<-- [start:alter_columns_nullable] + // Make the name column nullable + await schemaAlterTable.alterColumns([{ path: "name", nullable: true }]); + // --8<-- [end:alter_columns_nullable] + + // --8<-- [start:alter_columns_multiple] + // Rename, change type, and make nullable in one operation + await schemaAlterTable.alterColumns([ + { + path: "sale_price", + rename: "final_price", + dataType: new arrow.Float64(), + nullable: true, + }, + ]); + // --8<-- [end:alter_columns_multiple] + + // --8<-- [start:alter_columns_with_expression] + // For custom transforms, create a new column from a SQL expression. + const expressionTable = await db.createTable( + "schema_evolution_expression_example", + [{ id: 1, price_text: "$100" }], + { mode: "overwrite" }, + ); + + await expressionTable.addColumns([ + { + name: "price_numeric", + valueSql: "cast(replace(price_text, '$', '') as int)", + }, + ]); + await expressionTable.dropColumns(["price_text"]); + await expressionTable.alterColumns([ + { path: "price_numeric", rename: "price" }, + ]); + // --8<-- [end:alter_columns_with_expression] + expect(await expressionTable.countRows()).toBe(1); + + // --8<-- [start:schema_drop_setup] + const schemaDropData = [ + { + id: 1, + name: "Laptop", + price: 1200.0, + temp_col1: "X", + temp_col2: 100, + vector: Array.from({ length: 128 }, () => Math.random()), + }, + { + id: 2, + name: "Smartphone", + price: 800.0, + temp_col1: "Y", + temp_col2: 200, + vector: Array.from({ length: 128 }, () => Math.random()), + }, + { + id: 3, + name: "Headphones", + price: 150.0, + temp_col1: "Z", + temp_col2: 300, + vector: Array.from({ length: 128 }, () => Math.random()), + }, + ]; + const schemaDropTable = await db.createTable( + "schema_evolution_drop_example", + schemaDropData, + { mode: "overwrite" }, + ); + // --8<-- [end:schema_drop_setup] + expect(await schemaDropTable.countRows()).toBe(3); + + // --8<-- [start:drop_columns_single] + // Remove the first temporary column + await schemaDropTable.dropColumns(["temp_col1"]); + // --8<-- [end:drop_columns_single] + + // --8<-- [start:drop_columns_multiple] + // Remove the second temporary column + await schemaDropTable.dropColumns(["temp_col2"]); + // --8<-- [end:drop_columns_multiple] + + // --8<-- [start:alter_vector_column] + const oldDim = 384; + const newDim = 1024; + const vectorSchema = new arrow.Schema([ + new arrow.Field("id", new arrow.Int64()), + new arrow.Field( + "embedding", + new arrow.FixedSizeList( + oldDim, + new arrow.Field("item", new arrow.Float16(), true), + ), + true, + ), + ]); + const vectorData = lancedb.makeArrowTable( + [{ id: 1, embedding: Array.from({ length: oldDim }, () => Math.random()) }], + { schema: vectorSchema }, + ); + const vectorTable = await db.createTable("vector_alter_example", vectorData, { + mode: "overwrite", + }); + + // Changing FixedSizeList dimensions (384 -> 1024) is not supported via alterColumns. + // Use addColumns + dropColumns + alterColumns(rename) to replace the column. + await vectorTable.addColumns([ + { + name: "embedding_v2", + valueSql: `arrow_cast(NULL, 'FixedSizeList(${newDim}, Float16)')`, + }, + ]); + await vectorTable.dropColumns(["embedding"]); + await vectorTable.alterColumns([{ path: "embedding_v2", rename: "embedding" }]); + // --8<-- [end:alter_vector_column] + expect(await vectorTable.countRows()).toBe(1); + + const fieldMetadataTable = await db.createTable( + "schema_field_metadata_example", + [ + { id: 0, category: "a" }, + { id: 1, category: "b" }, + ], + { mode: "overwrite" }, + ); + + // --8<-- [start:schema_field_metadata_merge] + // Set two metadata keys on the `category` field. + const res = await fieldMetadataTable.updateFieldMetadata([ + { path: "category", metadata: { unit: "label", pii: "false" } }, + ]); + console.log(res.version); + + // Merge: add a new key, delete one via null, keep the rest. + await fieldMetadataTable.updateFieldMetadata([ + { path: "category", metadata: { source: "import", pii: null } }, + ]); + // --8<-- [end:schema_field_metadata_merge] + + // --8<-- [start:schema_field_metadata_replace] + await fieldMetadataTable.updateFieldMetadata([ + { + path: "category", + metadata: { owner: "search-team" }, + replace: true, + }, + ]); + // --8<-- [end:schema_field_metadata_replace] + }); +}); + +test("update snippets (async)", async () => { + // Keep connection snippets in this file, but do not run enterprise/local examples in CI. + void updateConnectEnterpriseExample; + void updateConnectLocalExample; + + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + { + // --8<-- [start:update_example_table_setup] + const table = await db.createTable( + "users_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + ], + { mode: "overwrite" }, + ); + // --8<-- [end:update_example_table_setup] + await table.countRows(); + } + + { + // --8<-- [start:update_operation] + const table = await db.createTable( + "users_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + ], + { mode: "overwrite" }, + ); + await table.update({ where: "id = 2", values: { name: "Bobby" } }); + // --8<-- [end:update_operation] + await table.countRows(); + } + + { + // --8<-- [start:update_using_sql] + const table = await db.createTable( + "users_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + ], + { mode: "overwrite" }, + ); + await table.update({ + where: "id = 2", + valuesSql: { login_count: "login_count + 1" }, + }); + // --8<-- [end:update_using_sql] + await table.countRows(); + } + + { + // --8<-- [start:merge_matched_update_only] + const table = await db.createTable( + "users_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + ], + { mode: "overwrite" }, + ); + + const incomingUsers = [ + { id: 2, name: "Bobby", login_count: 21 }, + { id: 3, name: "Charlie", login_count: 5 }, + ]; + + await table + .mergeInsert("id") + .whenMatchedUpdateAll() + .execute(incomingUsers); + // --8<-- [end:merge_matched_update_only] + await table.countRows(); + } + + { + // --8<-- [start:insert_if_not_exists] + const table = await db.createTable( + "users_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + ], + { mode: "overwrite" }, + ); + + const incomingUsers = [ + { id: 2, name: "Bobby", login_count: 21 }, + { id: 3, name: "Charlie", login_count: 5 }, + ]; + + await table + .mergeInsert("id") + .whenNotMatchedInsertAll() + .execute(incomingUsers); + // --8<-- [end:insert_if_not_exists] + await table.countRows(); + } + + { + // --8<-- [start:merge_update_insert] + const table = await db.createTable( + "users_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + ], + { mode: "overwrite" }, + ); + + const incomingUsers = [ + { id: 2, name: "Bobby", login_count: 21 }, + { id: 3, name: "Charlie", login_count: 5 }, + ]; + + await table + .mergeInsert("id") + .whenMatchedUpdateAll() + .whenNotMatchedInsertAll() + .execute(incomingUsers); + // --8<-- [end:merge_update_insert] + await table.countRows(); + } + + { + // --8<-- [start:merge_delete_missing_by_source] + const table = await db.createTable( + "users_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + { id: 3, name: "Charlie", login_count: 5 }, + ], + { mode: "overwrite" }, + ); + + const incomingUsers = [ + { id: 2, name: "Bobby", login_count: 21 }, + { id: 3, name: "Charlie", login_count: 5 }, + ]; + + await table + .mergeInsert("id") + .whenMatchedUpdateAll() + .whenNotMatchedInsertAll() + .whenNotMatchedBySourceDelete() + .execute(incomingUsers); + // --8<-- [end:merge_delete_missing_by_source] + await table.countRows(); + } + + { + // --8<-- [start:merge_partial_columns] + const table = await db.createTable( + "users_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + ], + { mode: "overwrite" }, + ); + + const incomingUsers = [ + { id: 2, name: "Bobby" }, + { id: 3, name: "Charlie" }, + ]; + + await table + .mergeInsert("id") + .whenMatchedUpdateAll() + .whenNotMatchedInsertAll() + .execute(incomingUsers); + // --8<-- [end:merge_partial_columns] + await table.countRows(); + } + + { + const table = await db.createTable( + "users_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + { id: 3, name: "Charlie", login_count: 5 }, + ], + { mode: "overwrite" }, + ); + + // --8<-- [start:delete_operation] + // delete data + const predicate = "id = 3"; + await table.delete(predicate); + // --8<-- [end:delete_operation] + await table.countRows(); + } + + { + const table = await db.createTable( + "users_cleanup_example", + [ + { id: 1, name: "Alice", login_count: 10 }, + { id: 2, name: "Bob", login_count: 20 }, + { id: 3, name: "Charlie", login_count: 5 }, + ], + { mode: "overwrite" }, + ); + + // --8<-- [start:update_optimize_cleanup] + const olderThan = new Date(); + olderThan.setDate(olderThan.getDate() - 1); + await table.optimize({ cleanupOlderThan: olderThan }); + // --8<-- [end:update_optimize_cleanup] + } + }); +}); + +test("versioning snippets (async)", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + + // --8<-- [start:versioning_basic_setup] + const tableName = "quotes_versioning_example"; + const data = [ + { id: 1, author: "Richard", quote: "Wubba Lubba Dub Dub!" }, + { id: 2, author: "Morty", quote: "Rick, what's going on?" }, + { + id: 3, + author: "Richard", + quote: "I turned myself into a pickle, Morty!", + }, + ]; + const table = await db.createTable(tableName, data, { mode: "overwrite" }); + // --8<-- [end:versioning_basic_setup] + expect(await table.countRows()).toBe(3); + + // --8<-- [start:versioning_check_initial_version] + const versions = await table.listVersions(); + const currentVersion = await table.version(); + console.log(`Number of versions after creation: ${versions.length}`); + console.log(`Current version: ${currentVersion}`); + // --8<-- [end:versioning_check_initial_version] + expect(versions.length).toBe(1); + expect(currentVersion).toBe(versions[versions.length - 1].version); + + // --8<-- [start:versioning_update_data] + await table.update({ + where: "author = 'Richard'", + values: { author: "Richard Daniel Sanchez" }, + }); + const rowsAfterUpdate = await table.countRows( + "author = 'Richard Daniel Sanchez'", + ); + console.log(`Rows updated to Richard Daniel Sanchez: ${rowsAfterUpdate}`); + // --8<-- [end:versioning_update_data] + expect(rowsAfterUpdate).toBe(2); + + // --8<-- [start:versioning_add_data] + const moreData = [ + { + id: 4, + author: "Richard Daniel Sanchez", + quote: "That's the way the news goes!", + }, + { id: 5, author: "Morty", quote: "Aww geez, Rick!" }, + ]; + await table.add(moreData); + // --8<-- [end:versioning_add_data] + expect(await table.countRows()).toBe(5); + + // --8<-- [start:versioning_check_versions_after_mod] + const versionsAfterMod = await table.listVersions(); + const versionCountAfterMod = versionsAfterMod.length; + const versionAfterMod = await table.version(); + console.log( + `Number of versions after modifications: ${versionCountAfterMod}`, + ); + console.log(`Current version: ${versionAfterMod}`); + // --8<-- [end:versioning_check_versions_after_mod] + expect(versionCountAfterMod).toBeGreaterThanOrEqual(2); + expect(versionAfterMod).toBe(versionsAfterMod[versionsAfterMod.length - 1].version); + + // --8<-- [start:versioning_list_all_versions] + const allVersions = await table.listVersions(); + for (const v of allVersions) { + console.log(`Version ${v.version}, created at ${v.timestamp}`); + } + // --8<-- [end:versioning_list_all_versions] + expect(allVersions.length).toBeGreaterThanOrEqual(1); + + // --8<-- [start:versioning_rollback] + await table.checkout(versionAfterMod); + await table.restore(); + const versionsAfterRollback = await table.listVersions(); + const versionCountAfterRollback = versionsAfterRollback.length; + console.log( + `Total number of versions after rollback: ${versionCountAfterRollback}`, + ); + // --8<-- [end:versioning_rollback] + expect(versionCountAfterRollback).toBe(versionCountAfterMod + 1); + expect(await table.countRows()).toBe(5); + + // --8<-- [start:versioning_checkout_latest] + await table.checkoutLatest(); + // --8<-- [end:versioning_checkout_latest] + const latestVersion = await table.version(); + const versionsAfterCheckout = await table.listVersions(); + expect(latestVersion).toBe( + versionsAfterCheckout[versionsAfterCheckout.length - 1].version, + ); + + // --8<-- [start:versioning_delete_data] + await table.delete("author = 'Morty'"); + const rowsAfterDeletion = await table.countRows(); + console.log(`Number of rows after deletion: ${rowsAfterDeletion}`); + // --8<-- [end:versioning_delete_data] + expect(rowsAfterDeletion).toBe(3); + + const tagsTable = await db.createTable( + "quotes_tags_example", + [{ id: 1, author: "Richard", quote: "Wubba Lubba Dub Dub!" }], + { mode: "overwrite" }, + ); // v1 + await tagsTable.add([ + { id: 2, author: "Morty", quote: "Aww geez, Rick!" }, + ]); // v2 + await tagsTable.add([ + { id: 3, author: "Summer", quote: "Whatever, Grandpa" }, + ]); // v3 + + // --8<-- [start:versioning_tags] + const tags = await tagsTable.tags(); + + // Create a tag pointing at a specific version + await tags.create("baseline", 1); + await tags.create("with-edits", await tagsTable.version()); + + // List all tags on this table + console.log(await tags.list()); + + // Look up the version a tag points at + console.log(await tags.getVersion("baseline")); + + // Move an existing tag to a different version + await tags.update("baseline", 2); + + // Check out a version by tag name + await tagsTable.checkout("baseline"); + console.log(await tagsTable.version()); + + // Delete a tag (does not delete the underlying version) + await tags.delete("with-edits"); + + // Return to the latest version + await tagsTable.checkoutLatest(); + // --8<-- [end:versioning_tags] + expect(await tagsTable.version()).toBe(3); + const remainingTags = await tags.list(); + expect(remainingTags).toHaveProperty("baseline"); + expect(remainingTags).not.toHaveProperty("with-edits"); + }); +}); + +test("branch snippets (async)", async () => { + await withTempDirectory(async (databaseDir) => { + const db = await lancedb.connect(databaseDir); + const table = await db.createTable( + "quotes_branches_example", + [ + { id: 1, author: "Lancelot", quote: "My lance never fails." }, + { id: 2, author: "Arthur", quote: "Long live Camelot!" }, + { id: 3, author: "Merlin", quote: "Magic always has a price." }, + ], + { mode: "overwrite" }, + ); + + const branches = await table.branches(); + + // --8<-- [start:branch_create] + // Fork an isolated, writable branch from main's latest version. + // `create` returns a table handle scoped to the new branch. + const branch = await branches.create("exp"); + // --8<-- [end:branch_create] + + // --8<-- [start:branch_write] + // Writes land on the branch handle only; main is left untouched. + await branch.add([{ id: 4, author: "Lancelot", quote: "For the realm!" }]); + console.log(await branch.countRows()); // 4 rows on the branch + console.log(await table.countRows()); // 3 rows; main is unaffected + + // List every branch, each mapped to its metadata (including its fork point). + console.log(await branches.list()); + // --8<-- [end:branch_write] + + // --8<-- [start:branch_reopen] + // Reopen an existing branch by name from the table handle... + const checkedOut = await branches.checkout("exp"); + // ...or open it directly from the database connection. + const branchHandle = await db.openTable( + "quotes_branches_example", + undefined, + { branch: "exp" }, + ); + console.log(await checkedOut.countRows(), await branchHandle.countRows()); // both 4 + // --8<-- [end:branch_reopen] + + // --8<-- [start:branch_delete] + // Delete the branch and its branch-local history. Data on main is safe. + await branches.delete("exp"); + // --8<-- [end:branch_delete] + + expect(await table.countRows()).toBe(3); + expect(await branches.list()).not.toHaveProperty("exp"); + + // Setup: a branch with row results that we want to apply to main. + const candidate = await branches.create("candidate"); + await candidate.update({ + where: "id = 1", + values: { quote: "Revised on the branch" }, + }); + await candidate.add([ + { id: 4, author: "Galahad", quote: "The grail awaits." }, + ]); + + // --8<-- [start:branch_upsert_to_main] + // This is a row-level upsert, not a merge of branch histories. + // `mergeInsert` updates matching rows and inserts new rows using a stable + // unique key. Filter the branch read if you only want to apply some results. + const rowsToApply = await candidate.toArrow(); + await table + .mergeInsert("id") + .whenMatchedUpdateAll() // update rows that already exist on main + .whenNotMatchedInsertAll() // insert rows that are new on the branch + .execute(rowsToApply); + // --8<-- [end:branch_upsert_to_main] + + expect(await table.countRows()).toBe(4); + await branches.delete("candidate"); + + // Setup: a larger table with a vector and a text column to index. + const products = await db.createTable( + "products_branch_index", + Array.from({ length: 512 }, (_, i) => ({ + id: i, + vector: Array.from({ length: 4 }, () => Math.random()), + text: `product number ${i}`, + })), + { mode: "overwrite" }, + ); + const productBranches = await products.branches(); + + // --8<-- [start:branch_index] + // Build and validate indexes on a branch before using the configuration on + // main. + const dev = await productBranches.create("index-dev"); + + // A vector (ANN) index and a full-text search index, both branch-scoped. + await dev.createIndex("vector", { + config: lancedb.Index.ivfPq({ + distanceType: "cosine", + numPartitions: 1, + numSubVectors: 2, + }), + }); + await dev.createIndex("text", { config: lancedb.Index.fts() }); + + // Both indexes live only on the branch; main still has none. + console.log((await dev.listIndices()).map((ix) => ix.name)); // branch: two indexes + console.log((await products.listIndices()).map((ix) => ix.name)); // main: [] (untouched) + // --8<-- [end:branch_index] + + expect(await dev.listIndices()).toHaveLength(2); + expect(await products.listIndices()).toHaveLength(0); + await productBranches.delete("index-dev"); + }); +}); + +test("consistency snippets (async)", async () => { + await withTempDirectory(async (databaseDir) => { + // --8<-- [start:consistency_strong] + const strongWriterDb = await lancedb.connect(databaseDir); + const strongReaderDb = await lancedb.connect(databaseDir, { + readConsistencyInterval: 0, + }); + const strongWriterTable = await strongWriterDb.createTable( + "consistency_strong_table", + [{ id: 1 }], + { mode: "overwrite" }, + ); + const strongReaderTable = await strongReaderDb.openTable( + "consistency_strong_table", + ); + await strongWriterTable.add([{ id: 2 }]); + const strongRowsAfterWrite = await strongReaderTable.countRows(); + console.log(`Rows visible with strong consistency: ${strongRowsAfterWrite}`); + // --8<-- [end:consistency_strong] + expect(strongRowsAfterWrite).toBe(2); + + // --8<-- [start:consistency_eventual] + const eventualWriterDb = await lancedb.connect(databaseDir); + const eventualReaderDb = await lancedb.connect(databaseDir, { + readConsistencyInterval: 3600, + }); + const eventualWriterTable = await eventualWriterDb.createTable( + "consistency_eventual_table", + [{ id: 1 }], + { mode: "overwrite" }, + ); + const eventualReaderTable = await eventualReaderDb.openTable( + "consistency_eventual_table", + ); + await eventualWriterTable.add([{ id: 2 }]); + const eventualRowsAfterWrite = await eventualReaderTable.countRows(); + console.log( + `Rows visible before eventual refresh interval: ${eventualRowsAfterWrite}`, + ); + // --8<-- [end:consistency_eventual] + expect(eventualRowsAfterWrite).toBe(1); + + // --8<-- [start:consistency_checkout_latest] + const checkoutWriterDb = await lancedb.connect(databaseDir); + const checkoutReaderDb = await lancedb.connect(databaseDir); + const checkoutWriterTable = await checkoutWriterDb.createTable( + "consistency_checkout_latest_table", + [{ id: 1 }], + { mode: "overwrite" }, + ); + const checkoutReaderTable = await checkoutReaderDb.openTable( + "consistency_checkout_latest_table", + ); + await checkoutWriterTable.add([{ id: 2 }]); + const rowsBeforeRefresh = await checkoutReaderTable.countRows(); + console.log(`Rows before checkoutLatest: ${rowsBeforeRefresh}`); + await checkoutReaderTable.checkoutLatest(); + const rowsAfterRefresh = await checkoutReaderTable.countRows(); + console.log(`Rows after checkoutLatest: ${rowsAfterRefresh}`); + // --8<-- [end:consistency_checkout_latest] + expect(rowsBeforeRefresh).toBe(1); + expect(rowsAfterRefresh).toBe(2); + }); +}); diff --git a/docs/web-tests/ts/tsconfig.json b/docs/web-tests/ts/tsconfig.json new file mode 100644 index 000000000..49a04ac10 --- /dev/null +++ b/docs/web-tests/ts/tsconfig.json @@ -0,0 +1,17 @@ +{ + "include": ["*.test.ts"], + "compilerOptions": { + "target": "es2022", + "module": "NodeNext", + "declaration": true, + "outDir": "./dist", + "strict": true, + "allowJs": true, + "resolveJsonModule": true, + "emitDecoratorMetadata": true, + "experimentalDecorators": true, + "moduleResolution": "NodeNext", + "allowImportingTsExtensions": true, + "emitDeclarationOnly": true + } +} diff --git a/docs/web-tests/ts/util.ts b/docs/web-tests/ts/util.ts new file mode 100644 index 000000000..404abedf7 --- /dev/null +++ b/docs/web-tests/ts/util.ts @@ -0,0 +1,16 @@ +// SPDX-License-Identifier: Apache-2.0 +// SPDX-FileCopyrightText: Copyright The LanceDB Authors +import * as fs from "node:fs"; +import { tmpdir } from "node:os"; +import * as path from "node:path"; + +export async function withTempDirectory( + fn: (tempDir: string) => Promise, +) { + const tmpDirPath = fs.mkdtempSync(path.join(tmpdir(), "temp-dir-")); + try { + await fn(tmpDirPath); + } finally { + fs.rmSync(tmpDirPath, { recursive: true }); + } +} diff --git a/docs/web/README.md b/docs/web/README.md new file mode 100644 index 000000000..ddd8a93c8 --- /dev/null +++ b/docs/web/README.md @@ -0,0 +1,35 @@ +# LanceDB documentation + +The open-source pages of [docs.lancedb.com](https://docs.lancedb.com), beside the +code they describe. + +## Preview locally + +```bash +npm i -g mint # https://mintlify.com/docs/installation +cd docs/web && mint dev +``` + +That serves this directory on its own. Enterprise pages live in a private +repository and are merged in when the published site is assembled, so links to +`/enterprise/...` and the SQL and Geneva sections will not resolve here. Every +open-source page does. + +## Code examples + +Examples are not written into the pages. They live in real tests under +`docs/web-tests/{py,ts,rs}`, are extracted into `docs/web/snippets/`, and are +imported by the pages. Edit the test, then regenerate: + +```bash +uv run docs/web-tests/mdx_snippets_gen.py -s docs/web-tests/py +``` + +An example that does not compile fails the pull request that broke it. + +## Section anchors + +Every heading carries an explicit `{#anchor}`. Those anchors are how the +Enterprise pages attach their additions to the right section, so they are +assigned once and not regenerated — leave an existing one alone even if the +heading it sits on is reworded. diff --git a/docs/web/agent-branch-experiments.mdx b/docs/web/agent-branch-experiments.mdx new file mode 100644 index 000000000..59bcf689b --- /dev/null +++ b/docs/web/agent-branch-experiments.mdx @@ -0,0 +1,100 @@ +--- +title: "Run experiments on branches" +sidebarTitle: "Run experiments on branches" +description: "Use LanceDB branches to isolate agent-driven experiments from main, evaluate them on a fixed test set, and promote only the winner." +icon: "flask" +keywords: ["AI agents", "branches", "experiments", "embeddings", "evaluation"] +--- + +LanceDB supports [branching](/tables/branching) to isolate experiments from the main table when working with agents. +Branches are useful when you instruct an agent to try several approaches without +affecting the production data on the `main` branch. +Each experiment gets its own writable table history. + +For example, you can generate and compare embeddings from two text embedding +models on the given table: + +| Branch | Model | Added column | +| --- | --- | --- | +| `embed-minilm` | `sentence-transformers/all-MiniLM-L6-v2` | `vector_minilm` | +| `embed-nomic` | `nomic-embed-text` from Ollama | `vector_nomic` | + +The branches share a logical table, but each has its own schema and history. +The original table handle still points to `main`. LanceDB does not have a +process-wide current branch. + +Install the model packages, then download the `nomic-embed-text` model from Ollama +(or any other model you prefer): + +```bash +uv add sentence-transformers ollama +ollama pull nomic-embed-text +``` + +It's always recommended to measure the results of an experiment on a fixed evaluation set. +Here's an example of how you could ask the agent to run both experiments and compare the results: + +```text Agent prompt +Use the lancedb plugin and the LanceDB branching documentation to compare two +embedding experiments on the `camelot_multimodal` table. + +1. Fork `embed-minilm` from `main`. Build normalized embeddings from + `"{role}. {description}"` with + `sentence-transformers/all-MiniLM-L6-v2` and add them as `vector_minilm`. +2. Fork `embed-nomic` from `main`. Embed the identical text with Ollama + `nomic-embed-text` and add the results as `vector_nomic`. +3. Process rows in batches and write only through each branch-scoped table + handle. Verify that neither new column appears on `main`. +4. Evaluate both branches with the queries "wise magical advisor", + "treacherous rebel", and "virtuous Grail knight". Report the top three names, + latency, and whether the expected character ranks first. +5. Do not modify `main` and do not delete either branch. Recommend a winner + based on the results. +``` + + +The vector dimensions may differ because the columns live on separate branches. +Use a different column name for each model. Within an experiment, use the same +model for the stored text and the search queries. + + +## Apply the winning experiment to `main` {#apply-the-winning-experiment-to-main} + +The branch experiments leave you with the results side by side, and they +deliberately never touch `main`. Once you've picked a winner, apply it to +`main` yourself by rerunning that experiment's validated transformation +directly against the `main` table. Rerunning the reviewed operation is the +reliable path on both OSS and Enterprise: it replays exactly the transformation +you validated on the branch, and it works the same regardless of how the branch +evolved. + +Suppose `nomic-embed-text` wins. Rerun the same Nomic embedding transformation +you validated on `embed-nomic` against `main` in batches, then call +`table.optimize()` on OSS. Verify a bounded vector search on `main`, and keep +the losing branch around until you're confident in the result. + +See [Branches](/tables/branching) for more on how branches, versions, and tags +relate. + +## More experiments you can run {#more-experiments-you-can-run} + +Swapping embedding models on a branch while working with agents is only one example +of what you can do with the LanceDB plugin. The table below shows other experiments +you can run with the plugin. + +| Hypothesis | Change on the branch | What to evaluate | +| --- | --- | --- | +| A different embedding model improves retrieval | Add a new vector column | Recall, ranking quality, latency, and cost | +| A new parser or OCR model improves source data | Reprocess files and backfill metadata | Validation failures and a reviewed sample | +| A new search setup works better | Build an index or change hybrid-search and reranking settings | Relevance and latency on a fixed query set | +| A curation rule improves the corpus | Deduplicate, classify, or filter records | False positives, false negatives, and row counts | +| A migration is safe to deploy | Add columns or run a backfill | Schema compatibility and application checks | + +For each experiment, ask the agent to state the hypothesis, use a fixed +evaluation set, report the comparison, and, if you want, work on an alternate +branch that's isolated from `main` until you decide you want the new derived +column in `main`. + +Once you've chosen a winner, rerun that validated transformation to ingest it into `main` +yourself, on either LanceDB OSS or Enterprise. See our documentation on [branches](/tables/branching) +for more information. diff --git a/docs/web/api-reference/index.mdx b/docs/web/api-reference/index.mdx new file mode 100644 index 000000000..50756023a --- /dev/null +++ b/docs/web/api-reference/index.mdx @@ -0,0 +1,48 @@ +--- +title: SDKs and REST API Reference +sidebarTitle: "SDKs" +description: "SDK and REST API reference for LanceDB Enterprise and OSS." + +--- + +For detailed information of the available functions and methods in your preferred language's SDKs, +refer to the API documentation linked below. + +If you're looking for a REST API reference, visit the [REST API](/api-reference/rest) page. + +If you're looking for conceptual and practical namespace guidance before diving into method signatures, see +[Namespaces and Catalog Model](/namespaces) and [Using Namespaces in SDKs](/namespaces/usage). + +## Supported SDKs {#supported-sdks} + +Python, Typescript and Rust SDKs are officially supported by LanceDB. You can use these SDKs to interact with both LanceDB OSS and Enterprise deployments. + +| Reference | Description | +|:--------------|-------------------| +| [Python](https://lancedb.github.io/lancedb/python/python/) | Full-featured Python client with pandas & numpy integration | +| [Typescript](https://lancedb.github.io/lancedb/js/) | A TypeScript wrapper around the Rust library, built with `napi-rs` +| [Rust](https://docs.rs/lancedb/latest/lancedb/index.html) | Native Rust library with persistent-storage and high performance | + +## REST API SDKs {#rest-api-sdks} + +Enterprise + +REST API-based SDKs provide a convenient way to interact with LanceDB Enterprise deployments using the Lance REST Namespace API. + +| Reference | Description | +|:--------------|-------------------| +| [Java](https://lancedb.github.io/lancedb/java/java/)| REST API Enterprise SDK in Java | + +## Community-driven SDKs {#community-driven-sdks} + +In addition to the officially supported SDKs, the LanceDB community may contribute SDKs in other languages. +These SDKs may not have the same level of support or feature parity as the official ones supported by LanceDB, but they can be an option +for users working in languages other than those listed above. + +| Reference | Description | +|:--------------|-------------------| +| [Go](https://pkg.go.dev/github.com/lancedb/lancedb-go/pkg/lancedb) | Community-contributed Go SDK for LanceDB | +| [Ruby](https://github.com/scientist-labs/lancelot) | Community-contributed Ruby bindings for LanceDB | +| [Swift](https://github.com/RyanLisse/LanceDbSwiftKit) | Community-contributed Swift SDK for LanceDB | +| [R](https://github.com/CathalByrneGit/lancedb) | Community-contributed R package for LanceDB | +| [Flutter](https://github.com/Alexcn/flutter_lancedb) | Community-contributed Flutter bindings for LanceDB | diff --git a/docs/web/api-reference/rest/index.mdx b/docs/web/api-reference/rest/index.mdx new file mode 100644 index 000000000..1442225fc --- /dev/null +++ b/docs/web/api-reference/rest/index.mdx @@ -0,0 +1,84 @@ +--- +title: REST API Reference +sidebarTitle: "Overview" +description: "API reference for LanceDB" + +--- + +[Lance REST Namespace](https://lance.org/format/namespace/) spec +is an OpenAPI protocol that enables reading, writing and managing Lance tables by connecting +those metadata services or building a custom metadata server in a standardized way. + +LanceDB OSS allows you to interface with Lance tables via the REST Namespace. +LanceDB Enterprise provides an extended REST API with +additional endpoints for managing tables and data. +If you have specific needs or questions about the Enterprise REST API Namespace, +please [contact us](mailto:support@lancedb.com). + +## Authentication {#authentication} + +Enterprise + +All HTTP requests to LanceDB APIs must contain an x-api-key header that specifies a valid API key and +must be encoded as JSON or Arrow RPC. + +To authenticate to the Enterprise REST API, you need the endpoint for your deployment and a valid API key for that deployment. + +### Get your Enterprise credentials {#get-your-enterprise-credentials} + +1. Obtain the following values from your LanceDB administrator or the LanceDB team that provisioned your Enterprise deployment: + - an API key + - your Enterprise REST endpoint + - your database name, if your deployment uses a private endpoint or `host_override` + +2. Export those values in your terminal: + +```bash +export LANCEDB_API_KEY="" +export LANCEDB_URI="https://your-enterprise-endpoint.com" +export LANCEDB_DATABASE="your-database-name" +``` + +3. If your Enterprise deployment is private, connect through the private network endpoint provided for your deployment. For example, Azure Private Link deployments commonly use a private IP or an internal DNS name as the endpoint. + +### Verify authentication {#verify-authentication} + +4. Check that you can reach the deployment and list tables: + +```bash +curl -X GET "$LANCEDB_URI/v1/tables" \ + -H "Content-Type: application/json" \ + -H "x-api-key: $LANCEDB_API_KEY" \ + -H "x-lancedb-database: $LANCEDB_DATABASE" +``` + +If your deployment endpoint already includes the database host name, you can omit the `x-lancedb-database` header. + +5. Create a table to confirm write access. Let's call it `words`. + +```bash +curl -X POST "$LANCEDB_URI/v1/tables/words" \ + -H "Content-Type: application/vnd.apache.arrow.stream" \ + -H "x-api-key: $LANCEDB_API_KEY" \ + -H "x-lancedb-database: $LANCEDB_DATABASE" +``` + +6. Check that the table has been created: + +```bash +curl -X GET "$LANCEDB_URI/v1/tables" \ + -H "Content-Type: application/json" \ + -H "x-api-key: $LANCEDB_API_KEY" \ + -H "x-lancedb-database: $LANCEDB_DATABASE" +``` + +That's it -- you're connected! Now, you can start adding data and querying it. +You can visit the tutorial section to build your own applications with LanceDB. + + + Check out our tutorials on building various applications with LanceDB. + diff --git a/docs/web/build-with-ai-agents.mdx b/docs/web/build-with-ai-agents.mdx new file mode 100644 index 000000000..82964009a --- /dev/null +++ b/docs/web/build-with-ai-agents.mdx @@ -0,0 +1,270 @@ +--- +title: "Tutorial: Use the LanceDB agent plugin" +sidebarTitle: "LanceDB agent plugin" +description: "Install the LanceDB plugin and use an AI coding agent to quickly build a multimodal ingestion pipeline." +icon: "robot" +keywords: ["AI agents", "agent plugins", "multimodal", "Pydantic", "branches", "embeddings"] +--- + +import { + PyCamelotSchema, + PyCamelotBatches, + PyCamelotOssIngestion, +} from '/snippets/build_with_ai_agents.mdx'; + +The LanceDB agent plugin gives coding agents a maintained reference for the +Python and TypeScript APIs. It also covers portable OSS and Enterprise code, +ingestion performance, and branch operations. It ships as the `lancedb` plugin in +the [lancedb-agent-plugins](https://github.com/lancedb/lancedb-agent-plugins) +repository, which is also a plugin marketplace. Install it with your agent's +plugin manager: + + +```text Claude Code +/plugin marketplace add lancedb/lancedb-agent-plugins +/plugin install lancedb@lancedb +``` + +```bash Codex icon="terminal" +codex plugin marketplace add lancedb/lancedb-agent-plugins +codex plugin add lancedb@lancedb +``` + +```bash Other agents icon="terminal" +npx plugins add lancedb/lancedb-agent-plugins +``` + + +The [`plugins`](https://www.npmjs.com/package/plugins) installer shown under +**Other agents** is a cross-tool option. It detects which agent CLIs are on your +`PATH` — Cursor, GitHub Copilot CLI, VS Code, Grok Build, and Kimi Code, as well +as Claude Code and Codex — and installs through each one's native plugin system. + +The plugin supplements the agent's training data with current LanceDB +instructions. To pick up later revisions, refresh the marketplace: + + +```text Claude Code +/plugin marketplace update lancedb +``` + +```bash Codex icon="terminal" +codex plugin marketplace upgrade +``` + +```bash Other agents icon="terminal" +npx plugins add lancedb/lancedb-agent-plugins +``` + + + +Each plugin manager clones the repository, reads its marketplace manifest to find +the `lancedb` plugin, and hands the plugin to the agent's own plugin store rather +than copying files into your project. Claude Code and Codex keep it under +`~/.claude/plugins` and `~/.codex/plugins` respectively, so the plugin is available +in every project on the machine. + +The `npx plugins` installer defaults to the same user-wide scope. Pass +`--scope project` to record the plugin in the current repository instead, so that +everyone working in it gets the same plugin, or `-t ` to install for a +single agent rather than every one it detects: + +```bash +npx plugins add lancedb/lancedb-agent-plugins --scope project -t cursor +``` + +Not every agent supports project scope; `npx plugins targets` lists what it +found and what each target supports. + + +## Get started with the LanceDB agent plugin {#get-started-with-the-lancedb-agent-plugin} + +This tutorial uses the Camelot dataset from the [quickstart](/quickstart), with +a portrait added for each character. Each LanceDB row contains validated +metadata and raw JPEG bytes. Text, images, and any embeddings you add later +remain in the same table. + +### 1\. Download the multimodal dataset {#1-download-the-multimodal-dataset} + +From a new project directory, download the JSON file and portraits: + +```bash +mkdir -p data/img + +BASE_URL="https://docs.lancedb.com/static/assets/tutorials/build-with-ai-agents/camelot/data" +curl -fsSL "$BASE_URL/camelot.json" -o data/camelot.json + +for image in \ + arthur.jpg \ + guinevere.jpg \ + merlin.jpg \ + mordred.jpg \ + sir_galahad.jpg \ + sir_gawain.jpg \ + sir_lancelot.jpg \ + sir_percival.jpg +do + curl -fsSL "$BASE_URL/img/$image" -o "data/img/$image" +done +``` + +Each JSON record has this shape: + +```json +{ + "id": 2, + "name": "Merlin", + "role": "Wizard and Advisor", + "description": "A powerful wizard and prophet who mentors Arthur.", + "stats": { + "strength": 2, + "courage": 4, + "magic": 5, + "wisdom": 5 + }, + "img": "data/img/merlin.jpg" +} +``` + +JSON input may have missing fields, unexpected fields, or values of the wrong +type. The LanceDB plugin tells the agent to validate each record with strict +Pydantic models before writing it. + +After the agent writes the pipeline, inspect the schema, batching, and write +path rather than assuming it followed the plugin's guidance correctly. + +### 2\. Prompt your agent to build the pipeline {#2-prompt-your-agent-to-build-the-pipeline} + +Install the Python packages used by the example: + +```bash icon="terminal" +uv init +uv add lancedb pyarrow pydantic +``` + +If you're using LanceDB Enterprise, ask the agent to ingest into an Enterprise table, +provide the relevant environment variables for connecting to your Enterprise deployment +in a local `.env` file, and point the agent to it. + +```text .env +LANCEDB_URI=db://your_project_name +LANCEDB_API_KEY=your_api_key_here +LANCEDB_REGION=us-east-1 +LANCEDB_HOST_OVERRIDE=https://your-enterprise-endpoint.com +``` + +If you're using LanceDB OSS, no connection settings are required, as it runs as an +embedded retrieval library. A simple prompt like this should work: + +```text Agent prompt +# If using OSS +Use the lancedb plugin to ingest the dataset in `data/` into a LanceDB +OSS table. + +# If using enterprise +Use the lancedb plugin to ingest the dataset in `data/` into a LanceDB +Enterprise table using the connection information in `.env`. +``` + +Because the plugin is registered with the agent's own plugin system, the agent +should pick it up on its own once you restart the session. If it does not, +simply ask it to use the `lancedb` plugin in the prompt, as shown above. + +That should be enough! The agent will create `ingest_multimodal.py`, or similar. +The following sections inspect the script to verify that it follows the plugin's guidance. + +#### Data validation {#data-validation} + +The plugin encourages the agent to validate each record with Pydantic before writing it. +The agent should ideally define a schema for the table and a nested schema for the +`stats` field. + + + {PyCamelotSchema} + + +In this case, our agent correctly defined `Character` and `Stats` Pydantic models +and validated the JSON before adding it to the table. + +#### Batched ingestion {#batched-ingestion} + +Naively calling `table.add()` once per row is slow, and is considered an anti-pattern +in LanceDB. The plugin encourages the agent to collect incoming rows into batches and +write them with a single `table.add()` call. When you use the plugin, the agent should +produce something like this: + + + {PyCamelotBatches} + + +The script calls `Character.model_validate(...)` before adding a record to the +batch. If validation fails, that batch is never written. The function yields up +to `batch_size` rows at a time, providing an iterable of batches for the ingestion step, +shown next. + +#### Table maintenance {#table-maintenance} +For LanceDB OSS, the plugin instructs the agent to call +`table.optimize()` after the ingestion loop. This compacts small fragments, cleans up +old versions according to the retention policy, and incorporates new data into indexes. + + + {PyCamelotOssIngestion} + + +If you're using LanceDB Enterprise, the plugin mentions that this step is not needed +because LanceDB Enterprise handles maintenance automatically. + + + +This example dataset has only eight rows, so the default batch size writes it in +one call. Larger inputs should still avoid single-row write commits. + + +### 3\. Run the OSS pipeline {#3-run-the-oss-pipeline} + +```bash +uv run python ingest_multimodal.py +``` + +The table now contains the validated character records and their JPEG bytes. +Here are the first three rows: + +| Image | Character | Role | +| --- | --- | --- | +| King Arthur | King Arthur | King of Camelot | +| Merlin | Merlin | Wizard and Advisor | +| Queen Guinevere | Queen Guinevere | Queen of Camelot | + +Once your pipeline works, you can [run experiments on branches](/agent-branch-experiments) +to try new embedding models, parsers, or search settings without touching `main`. + +## Takeaways {#takeaways} + +The example in this tutorial was small, but similar ideas apply to other workflows, too. +Give the agent the data source, the constraints it must respect, and the +artifacts it should return. + +The plugin supplies LanceDB-specific guidance, but it's the user's responsibility to +ensure the output makes sense for the application. + +### Try the plugin with your own dataset {#try-the-plugin-with-your-own-dataset} + +The plugin shown in this tutorial should generalize reasonably well to other use cases. +If you find any issues, open [an issue](https://github.com/lancedb/lancedb-agent-plugins/issues) +on GitHub, clearly describing the intended behavior. + +You can choose OSS or Enterprise based on how the work will run: + +Start with [LanceDB OSS](/quickstart) during the early stages of a project +when an agent is helping you prototype, +explore a dataset, or run small workflows on a subset of the data. The application owns +the storage and lifecycle work, so ask the agent to validate inputs, write in +batches, and it will include table maintenance operations such as `optimize()` +where appropriate. + +Choose [LanceDB Enterprise](/enterprise) when the resulting table becomes +shared production infrastructure, and the workload needs distributed capacity, +private deployment, or platform-managed operations. The underlying data format and table +API stay the same, so the pipeline does not need to be redesigned. The agent +instead connects to a remote `db://` table and lets the cluster handle +maintenance and background work. diff --git a/docs/web/demos/index.mdx b/docs/web/demos/index.mdx new file mode 100644 index 000000000..95f1ab365 --- /dev/null +++ b/docs/web/demos/index.mdx @@ -0,0 +1,78 @@ +--- +title: "Demo Application Gallery" +sidebarTitle: "App Gallery" +description: "Demo apps showcasing end-to-end applications built with LanceDB for production use cases." +--- + +Explore the demo applications built with LanceDB below. + +App | Description +--- | --- +[Semantic.Art](#semantic-art) | A multimodal art discovery platform using feelings, phrases, and images. +[Wikipedia 41M Hybrid Search](#wikipedia-41m-hybrid-search) | An interactive hybrid search demo combining full-text search and vector search. +[Video Search](#video-search) | A video search application that allows searching through a library of videos using natural language queries. + + +## Semantic.art {#semantic-art} + +multimodal +hybrid-search +vector-search +semantic-routing + + +Semantic.art turns real, human-made art discovery into a multimodal search experience using +feelings, phrases, and images. It's built with LanceDB hybrid search and semantic routing. + + +Read in detail about how Semantic.art is built in this blog post. + + + + +## Wikipedia 41M Hybrid Search {#wikipedia-41m-hybrid-search} + +multimodal +hybrid-search +vector-search +fts + + +Interactive hybrid search, full-text search (FTS) and vector search demo with 41M+ Wikipedia entries. +Explore the power of combining FTS with vector search for more relevant results. + + +Read in detail about how the Wikipedia 41M hybrid search demo is built in this blog post. + + + + +## Video Search {#video-search} + +multimodal +video-search +vector-search + + +Search through a library of videos using natural language queries. This demo showcases how to use LanceDB +to perform semantic search on video content. + + \ No newline at end of file diff --git a/docs/web/docs.json b/docs/web/docs.json new file mode 100644 index 000000000..93c607886 --- /dev/null +++ b/docs/web/docs.json @@ -0,0 +1,338 @@ +{ + "$schema": "https://mintlify.com/docs.json", + "appearance": { + "strict": false + }, + "theme": "mint", + "name": "LanceDB", + "banner": { + "content": "[Why Multimodal Data Needs a Better Lakehouse? — Download the Research Study](https://lancedb.com/download/)", + "dismissible": true + }, + "colors": { + "primary": "#FF6B35", + "light": "#FF8A5C", + "dark": "#E55A2B" + }, + "fonts": { + "family": "Inter" + }, + "styling": { + "codeblocks": { + "theme": { + "light": "vitesse-light", + "dark": "catppuccin-mocha" + } + } + }, + "favicon": "/static/favicon.ico", + "navigation": { + "tabs": [ + { + "tab": "Documentation", + "groups": [ + { + "group": "Get started", + "pages": [ + "quickstart", + { + "group": "What 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"tutorials/agents/multimodal-agent/index" + ] + }, + "tutorials/feature-engineering/index" + ] + } + ] + }, + { + "tab": "Demos", + "groups": [ + { + "group": "Demos", + "pages": [ + "demos/index" + ] + } + ] + }, + { + "tab": "Use Cases", + "groups": [ + { + "group": "Robotics", + "pages": [ + "integrations/lerobotdataset" + ] + }, + { + "group": "World Models", + "pages": [ + "integrations/stable-worldmodel" + ] + } + ] + }, + { + "tab": "API Reference", + "groups": [ + { + "group": "API Reference", + "pages": [ + "api-reference/index", + { + "group": "REST API", + "expanded": true, + "pages": [ + "api-reference/rest/index" + ] + } + ] + } + ] + } + ] + }, + "logo": { + "light": "/static/assets/logo/dark-lancedb-logo.svg", + "dark": "/static/assets/logo/light-lancedb-logo.svg", + "href": "https://lancedb.com" + }, + "background": { + "color": { + "light": "#FAF5F0", + "dark": "#000000" + } + }, + "navbar": { + "links": [ + { + "label": "Support", + "href": "mailto:contact@lancedb.com" + } + ] + }, + "footer": { + "socials": { + "github": "https://github.com/lancedb", + "linkedin": "https://www.linkedin.com/company/lancedb", + "x": "https://x.com/lancedb", + "discord": "https://discord.gg/AUEWnJ7Txb" + } + }, + "integrations": { + "ga4": { + "measurementId": "G-1TMC2PR69E" + } + } +} diff --git a/docs/web/embedding/index.mdx b/docs/web/embedding/index.mdx new file mode 100644 index 000000000..30ad26003 --- /dev/null +++ b/docs/web/embedding/index.mdx @@ -0,0 +1,239 @@ +--- +title: "Managing Embeddings" +sidebarTitle: Overview +description: "Use the embedding API in LanceDB -- registry, functions, schemas, and multi-language SDK support." +icon: "bars" +--- + +import { + PyOpenaiEmbeddings, + PyCreateEmbeddingFunction, + PyManualQuerySearch, + PyEmbeddingFunction, + PyRegisterDevice, + PyRegisterSecret, + TsOpenaiEmbeddings, + TsCreateEmbeddingFunction, + TsManualQuerySearch, + TsEmbeddingFunction, + TsRegisterModelFallback, + TsRegisterSecret, + RsOpenaiEmbeddings, + RsCreateEmbeddingFunction, + RsManualQuerySearch, + RsEmbeddingFunction, +} from '/snippets/embedding.mdx'; + +Modern machine learning models can be trained to convert raw data into embeddings, which are vectors +of floating point numbers. The position of an embedding in vector space captures the semantics of +the data, so vectors that are close to each other are considered similar. + +LanceDB provides an embedding function registry in OSS as well as its Enterprise versions +([see below](#embeddings-in-lancedb-enterprise)) +that automatically generates vector embeddings during data ingestion. Automatic query-time embedding +generation is available in LanceDB OSS, with SDK-specific query ergonomics. The API abstracts +embedding generation, allowing you to focus on your application logic. + +## Embedding Registry {#embedding-registry} + +You can get a supported embedding function from the registry, and then use it in your table schema. +Once configured, the embedding function will automatically generate embeddings when you insert data +into the table. Query-time behavior depends on SDK: Python/TypeScript can query with text directly, +while Rust examples typically compute query embeddings explicitly before vector search. + + + + {PyOpenaiEmbeddings} + + + + {TsOpenaiEmbeddings} + + + + {RsOpenaiEmbeddings} + + + +### Using an embedding function {#using-an-embedding-function} + +Create an embedding function before you attach it to table or schema metadata. Python and TypeScript fetch +provider implementations from the embedding registry, while Rust constructs the provider embedding function +directly and registers it on the connection before using it in an `EmbeddingDefinition`. + + + + {PyCreateEmbeddingFunction} + + + + {TsCreateEmbeddingFunction} + + + + {RsCreateEmbeddingFunction} + + + +Provider configuration is SDK-specific, so copy the option names from the provider page for the SDK you use. +For example, the OpenAI model is selected with `name` in Python, `model` in TypeScript, and the model argument +to `OpenAIEmbeddingFunction::new_with_model` in Rust. + +| Concept | Python | TypeScript | Rust | +| --- | --- | --- | --- | +| Model | `name="text-embedding-3-small"` | `{ model: "text-embedding-3-small" }` | `new_with_model(api_key, "text-embedding-3-small")` | +| Retry count | `max_retries=7` | Provider/client-specific | Provider/client-specific | +| API key | `api_key="..."`, environment variables, or `$var:` | `apiKey: "..."`, environment variables, or `$var:` | Constructor argument or environment variable | +| Device | Provider-specific, for example `device="cuda"` | Provider-specific | Provider-specific | + +When ingesting data with an embedding definition, LanceDB only computes the vector column if that +column is missing from the incoming batch or present but entirely null. If you provide any non-null +values in the vector column, LanceDB treats the column as user-supplied and does not backfill the +remaining rows in that batch. + +For reusable runtime configuration, the registry also supports `$var:` placeholders in embedding-function config. +This is useful for provider secrets and environment-specific settings in Python and TypeScript. + +- Python uses `registry.set_var(...)`. +- TypeScript uses `registry.setVar(...)`. +- You can provide a fallback with `$var:name:default`. +- Sensitive values such as API keys should be passed through registry variables instead of hardcoding them in config. + + + + {PyRegisterSecret} + + + + {TsRegisterSecret} + + + +For non-sensitive settings such as inference device selection, you can also use a default fallback: + + + + {PyRegisterDevice} + + + + {TsRegisterModelFallback} + + + +Find the full list of arguments for each provider in the [integrations](/integrations/embedding) section. + +## Multiple embedding columns {#multiple-embedding-columns} + +A single table can include more than one embedding definition when you want to store multiple semantic views +of the same data, or generate embeddings from different source columns. In practice, each embedding definition +maps one source column to one vector column, and the table schema can contain multiple such pairs. + +The exact setup differs by SDK, but the underlying pattern is the same: define a distinct source/vector pair +for each embedding function you want applied during ingest. + +In TypeScript, automatic query embedding currently uses the first embedding function stored in the +table metadata. If a table has multiple embedding definitions and you need to query a specific vector +column, compute the query embedding explicitly and pass the vector to the search builder. + +## Embedding model providers {#embedding-model-providers} + +LanceDB supports most popular embedding providers. + +### Text embeddings {#text-embeddings} + +| Provider | Model ID | Default Model | +|----------|----------|---------------| +| OpenAI | `openai` | `text-embedding-ada-002` | +| Sentence Transformers | `sentence-transformers` | `all-MiniLM-L6-v2` | +| Hugging Face | `huggingface` | `colbert-ir/colbertv2.0` | +| Cohere | `cohere` | `embed-english-v3.0` | +| ... | ... | ... | + +### Multimodal embedding {#multimodal-embedding} + +| Provider | Model ID | Supported Inputs | +|----------|----------|------------------| +| OpenCLIP | `open-clip` | Text, Images | +| ImageBind | `imagebind` | Text, Images, Audio, Video | +| ... | ... | ... | + +You can find all supported embedding models in the [integrations](/integrations/embedding) section. + +## Embeddings in LanceDB Enterprise {#embeddings-in-lancedb-enterprise} +Enterprise + +In LanceDB Enterprise, embedding generation during data ingestion is client-side and the resulting vectors are +stored on the remote table. + + +The Enterprise server does not currently generate embeddings from query text on its own. Any automatic +query-time embedding happens on the client side. + + +### How string queries are interpreted {#how-string-queries-are-interpreted} + +For the Python remote client, `table.search("hello")` can take two different paths: + +- If the selected vector column has embedding metadata + (i.e., the table schema stores the source-column, vector-column, and + embedding-function mapping created from fields like `SourceField()` and + `VectorField()` during table creation), then the embeddings are computed in the Python client process. + The client uses the same local LanceDB embedding registry used by OSS tables to + reconstruct the embedding function from schema metadata, compute the query vector in + the client process, and send that vector to Enterprise for search. + ```python + result = table.search("hello").limit(1).to_list() + # The Python client computes the query embedding locally, then sends a vector search. + ``` +- If the table does not have embedding metadata for that search, `table.search("hello")` in `auto` mode is + treated as an FTS query instead. + ```python + result = table.search("hello").limit(5).to_list() + # In auto mode this is treated as an FTS query, not a vector query. + ``` + +If you want explicit vector or hybrid behavior and the client cannot resolve an embedding function from the +table metadata, generate the query embedding yourself and pass the vector directly. + +TypeScript has a similar string-query distinction in `auto` mode: if no embedding providers have been +registered in the process, `search("text")` falls back to FTS. Once an embedding provider has been +imported and registered, the client expects table embedding metadata for automatic vector search and +raises an error if the table has none. + +The manual query-embedding flow below works across Enterprise SDKs and is an explicit path you can use when you +want full control over query-time behavior. + + + + {PyManualQuerySearch} + + + + {TsManualQuerySearch} + + + + {RsManualQuerySearch} + + + +## Custom Embedding Functions {#custom-embedding-functions} + +You can always implement your own embedding function: +- Python/TypeScript: subclass `TextEmbeddingFunction` (text) or `EmbeddingFunction` (multimodal). +- Rust: implement the `EmbeddingFunction` trait. + + + + {PyEmbeddingFunction} + + + + {TsEmbeddingFunction} + + + + {RsEmbeddingFunction} + + diff --git a/docs/web/embedding/quickstart.mdx b/docs/web/embedding/quickstart.mdx new file mode 100644 index 000000000..bcaf90f6f --- /dev/null +++ b/docs/web/embedding/quickstart.mdx @@ -0,0 +1,155 @@ +--- +title: "Embeddings: Quickstart" +sidebarTitle: "Quickstart" +description: "Quickstart guide for generating and working with embeddings." +icon: "rocket" +--- + +import { + TsQuickstartConnect, + TsQuickstartCreateTable, + TsQuickstartImports, + TsQuickstartInitModel, + TsQuickstartQuery, + TsQuickstartSchema, +} from '/snippets/sentence-transformers.mdx'; + +LanceDB will automatically vectorize the data both at ingestion and query time. All you need to do is specify which model to use. +Popular embedding models like OpenAI, Hugging Face, Sentence Transformers, CLIP, and more, are supported. + +## Step 1: Import Required Libraries {#step-1-import-required-libraries} + +First, import the necessary LanceDB components: + + +```python Python icon="python" +import lancedb +from lancedb.pydantic import LanceModel, Vector +from lancedb.embeddings import get_registry +``` + + + {TsQuickstartImports} + + + +- `lancedb`: The main database connection and operations +- `LanceModel`: Pydantic model for defining table schemas +- `Vector`: Field type for storing vector embeddings +- `get_registry()`: Access to the embedding function registry. It has all the supported as well as custom embedding functions registered by the user +- TypeScript uses `lancedb.embedding.getRegistry()` and `lancedb.embedding.LanceSchema()` for the same registry/schema workflow +- In TypeScript, import the provider module before calling `getRegistry().get(...)`; the provider import is what registers names such as `"huggingface"` or `"openai"` + +## Step 2: Connect to LanceDB {#step-2-connect-to-lancedb} + +Establish a connection to your LanceDB OSS directory or Enterprise cluster: + + +```python Python icon="python" +# Enter your LanceDB connection URI for OSS or Enterprise here +db = lancedb.connect(...) +``` + + + {TsQuickstartConnect} + + + +## Step 3: Initialize the Embedding Function {#step-3-initialize-the-embedding-function} + +Choose and configure your embedding model: + + +```python Python icon="python" +model = get_registry().get("sentence-transformers").create(name="BAAI/bge-small-en-v1.5", ) +``` + + + {TsQuickstartInitModel} + + + +This creates an embedding function from the local embedding registry. The Python snippet uses the +`sentence-transformers` provider with the BGE model; the TypeScript snippet uses the Transformers-backed +`huggingface` provider. You can: +- Change `"sentence-transformers"` to other providers like `"openai"`, `"cohere"`, etc. +- Modify the model name for different embedding models +- Set `device="cuda"` for GPU acceleration if available + +## Step 4: Define Your Schema {#step-4-define-your-schema} + +Create a Pydantic model that defines your table structure: + + +```python Python icon="python" +class Words(LanceModel): + text: str = model.SourceField() + vector: Vector(model.ndims()) = model.VectorField() +``` + + + {TsQuickstartSchema} + + + +- `SourceField()`: This field will be embedded +- `VectorField()`: This stores the embeddings +- `model.ndims()`: Sets vector dimensions for your model +- In TypeScript, use `model.sourceField(...)` and `model.vectorField()` inside `LanceSchema(...)` + +## Step 5: Create Table and Ingest Data {#step-5-create-table-and-ingest-data} + +Create a table with your schema and add data: + + +```python Python icon="python" +table = db.create_table("words", schema=Words) +table.add([ + {"text": "hello world"}, + {"text": "goodbye world"} +]) +``` + + + {TsQuickstartCreateTable} + + + +The `table.add()` call automatically: +- Takes the text from each document +- Generates embeddings using your chosen model +- Stores both the original text and the vector embeddings + +If your input already includes the vector column, automatic embedding only runs when that column is +absent or entirely null for the batch. Partially supplied vectors are treated as manual data, so +LanceDB preserves them instead of filling only the missing rows. + +## Step 6: Query with Automatic Embedding {#step-6-query-with-automatic-embedding} + +Note: On LanceDB Enterprise, the server does not generate embeddings from query text. In the Python remote +client, `table.search("greetings")` can still work when the table schema includes embedding metadata, because +the client computes the query embedding before sending the vector search. If there is no embedding metadata for +that search, `search("greetings")` in `auto` mode is treated as FTS instead. + +Search your data using natural language queries: + + +```python Python icon="python" +query = "greetings" +actual = table.search(query).limit(1).to_pydantic(Words)[0] +print(actual.text) +``` + + + {TsQuickstartQuery} + + + +The search process: +1. Automatically converts your query text to embeddings +2. Finds the most similar vectors in your table +3. Returns the matching documents + +Automatic text search depends on the table's embedding metadata. If the client cannot reconstruct an +embedding function from that metadata, compute the query embedding yourself and search with the +vector directly. diff --git a/docs/web/faq/faq-oss.mdx b/docs/web/faq/faq-oss.mdx new file mode 100644 index 000000000..7c1b4ceaf --- /dev/null +++ b/docs/web/faq/faq-oss.mdx @@ -0,0 +1,90 @@ +--- +title: "LanceDB: Frequently Asked Questions" +sidebarTitle: "LanceDB OSS" +description: "Commonly asked questions about LanceDB OSS." +icon: "code" +mode: wide +--- + +This section covers some common questions and issues that you may encounter when using LanceDB. + +### Is LanceDB open source? {#is-lancedb-open-source} + +LanceDB OSS is a permissively licensed embedded retrieval library available under an Apache 2.0 license. We also have a LanceDB Enterprise, a commercial product that can be deployed on a private cloud or a bring-your-own-cloud (BYOC) solution. LanceDB Enterprise transforms your data lake into a high-performance multimodal lakehouse. + +### What is the difference between Lance and LanceDB? {#what-is-the-difference-between-lance-and-lancedb} + +[Lance](https://github.com/lancedb/lance) is a modern lakehouse format for multimodal AI. It's perfect for building search engines, feature stores and being the foundation of large-scale ML training jobs requiring high performance IO and shuffles. It also has native support for storing, querying, and inspecting deeply nested data for robotics or large blobs like images, point clouds, and more. + +LanceDB is the multimodal lakehouse that's built on top of Lance, and utilizes the underlying optimized storage format to build efficient disk-based indexes that power semantic search & retrieval applications, from RAGs to QA bots to recommender systems. + +### Why invent another data format instead of using Parquet? {#why-invent-another-data-format-instead-of-using-parquet} + +As we mention in our talk titled "[Lance, a modern columnar data format](https://www.youtube.com/watch?v=ixpbVyrsuL8)", Parquet and other tabular formats that derive from it are rather dated (Parquet is over 10 years old), especially when it comes to random access on vectors. We needed a format that's able to handle the complex trade-offs involved in shuffling, scanning, OLAP and filtering large datasets involving vectors, and our extensive experiments with Parquet didn't yield sufficient levels of performance for modern ML. [Our benchmarks](https://lancedb.com/blog/benchmarking-random-access-in-lance/) show that Lance is up to 1000x faster than Parquet for random access, which we believe justifies our decision to create a new data format for AI. + +### Why build in Rust? {#why-build-in-rust} + +We believe that the Rust ecosystem has attained mainstream maturity and that Rust will form the underpinnings of large parts of the data and ML landscape in a few years. Performance, latency and reliability are paramount to a vector DB, and building in Rust allows us to iterate and release updates more rapidly due to Rust's safety guarantees. Both Lance (the data format) and LanceDB (the database) are written entirely in Rust. We also provide Python, JavaScript, and Rust client libraries to interact with the database. + +### What makes LanceDB different? {#what-makes-lancedb-different} + +LanceDB is among the few embedded vector DBs out there that we believe can unlock a whole new class of LLM-powered applications in the browser or via edge functions. Lance's multimodal nature allows you to store the raw data, metadata and the embeddings all at once, unlike other solutions that typically store just the embeddings and metadata. + +The Lance data format that powers our storage system also provides true zero-copy access and seamless interoperability with numerous other data formats (like Pandas, Polars, Pydantic) via Apache Arrow, as well as automatic data versioning and data management without needing extra infrastructure. + +### How large of a dataset can LanceDB handle? {#how-large-of-a-dataset-can-lancedb-handle} + +LanceDB and its underlying data format, Lance, are built to scale to really large amounts of data. LanceDB OSS can comfortably handle millions of vectors on a single node, making it a great fit for most applications. Its disk-based indexes keep performance strong without requiring expensive infrastructure. + +If you need to scale to hundreds of millions of vectors or work with terabytes of data, we recommend [LanceDB Enterprise](/enterprise). Enterprise customers regularly operate on billions of rows, backed by distributed infrastructure designed for large-scale production workloads. + +### Do I need to build a vector index to run vector search? {#do-i-need-to-build-a-vector-index-to-run-vector-search} + +No. LanceDB is blazing fast (due to its disk-based index) for even brute force kNN search, within reason. In our benchmarks, computing 100K pairs of 1000-dimension vectors takes less than 20ms. For small datasets of ~100K records or applications that can accept ~100ms latency, a vector index is usually not necessary. + +For large-scale (>1M) or higher dimension vectors, it is beneficial to create a vector index. See the [Vector Indexes](/indexing/vector-index/) section for more details. + +### How can I speed up data inserts? {#how-can-i-speed-up-data-inserts} + +LanceDB auto-parallelizes large writes when you call `table.add()` with materialized +data such as `pa.Table`, `pd.DataFrame`, or `pa.dataset()`. No extra configuration +is needed — writes are automatically split into partitions of ~1M rows or 2GB. + +For best results: + +- **Create an empty table first**, then call `table.add()`. The `add()` path enables + automatic write parallelism, while passing data directly to `create_table()` does not. +- **For file-based data**, use `pyarrow.dataset.dataset("path/to/data/", format="parquet")` + so LanceDB can stream from disk without loading everything into memory. +- **Avoid inserting one row at a time.** Each insert creates a new data fragment on + disk. Batch your data into Arrow tables, DataFrames, or use iterators. + +See [Loading Large Datasets](/tables/create#loading-large-datasets) for full examples. + +### Do I need to set a refine factor when using an index? {#do-i-need-to-set-a-refine-factor-when-using-an-index} + +Yes. LanceDB uses PQ, or Product Quantization, to compress vectors and speed up search when using an ANN index. However, because PQ is a lossy compression algorithm, it tends to reduce recall while also reducing the index size. To address this trade-off, we introduce a process called **refinement**. The normal process computes distances by operating on the compressed PQ vectors. The refinement factor (*rf*) is a multiplier that takes the top-k similar PQ vectors to a given query, fetches `rf * k` *full* vectors and computes the raw vector distances between them and the query vector, reordering the top-k results based on these scores instead. + +For example, if you're retrieving the top 10 results and set `refine_factor` to 25, LanceDB will fetch the 250 most similar vectors (according to PQ), compute the distances again based on the full vectors for those 250 and then re-rank based on their scores. This can significantly improve recall, with a small added latency cost (typically a few milliseconds), so it's recommended you set a `refine_factor` of anywhere between 5-50 and measure its impact on latency prior to deploying your solution. + +### How can I improve IVF-PQ recall while keeping latency low? {#how-can-i-improve-ivf-pq-recall-while-keeping-latency-low} + +When using an IVF-PQ index, there's a trade-off between recall and latency at query time. You can improve recall by increasing the number of probes and the `refine_factor`. In our benchmark on the GIST-1M dataset, we show that it's possible to achieve >0.95 recall with a latency of under 10 ms on most systems, using ~50 probes and a `refine_factor` of 50. This is, of course, subject to the dataset at hand and a quick sensitivity study can be performed on your own data. You can find more details on the benchmark in a past [blog post](https://medium.com/etoai/benchmarking-lancedb-92b01032874a). + +![](/static/assets/images/faq/recall-vs-latency.webp) + +### How much data can LanceDB practically manage without affecting performance? {#how-much-data-can-lancedb-practically-manage-without-affecting-performance} + +We target good performance on ~10-50 billion rows and ~10-30 TB of data. For the best performance and +scalability guarantees, check out [LanceDB Enterprise](/enterprise). + +### Does LanceDB support concurrent operations? {#does-lancedb-support-concurrent-operations} + +LanceDB can handle concurrent reads very well, and can scale horizontally. The main constraint is how well the storage layer you've chosen, scales. For writes, we support concurrent writing, though too many concurrent writers can lead to failing writes as there is a limited number of times a writer retries a commit. + + +If you use Python's multiprocessing, you should probably not use `fork` as Lance is multi-threaded +internally and `fork` and multi-threaded Python do not work well together. +[Refer to this discussion](https://discuss.python.org/t/concerns-regarding-deprecation-of-fork-with-alive-threads/33555) +for more information. + diff --git a/docs/web/faq/index.mdx b/docs/web/faq/index.mdx new file mode 100644 index 000000000..c4bc36a71 --- /dev/null +++ b/docs/web/faq/index.mdx @@ -0,0 +1,17 @@ +--- +title: Frequently Asked Questions +sidebarTitle: FAQ +description: "Common questions about LanceDB" +icon: "question-circle" +--- + +Find answers to common questions about LanceDB across different deployment options and use cases. + + +Reach out on [Discord](https://discord.gg/AUEWnJ7Txb) for community support or [contact us](mailto:support@lancedb.com) for Enterprise assistance. + + +| Category | Description | +|:---------|:------------| +| [LanceDB OSS](/faq/faq-oss) | Questions about LanceDB open source deployment, installation, and community support | +| [LanceDB Enterprise](/faq/faq-enterprise) | Questions about LanceDB Enterprise features, security, compliance, and support | \ No newline at end of file diff --git a/docs/web/index.mdx b/docs/web/index.mdx new file mode 100644 index 000000000..e59402a6d --- /dev/null +++ b/docs/web/index.mdx @@ -0,0 +1,124 @@ +--- +title: LanceDB +sidebarTitle: "LanceDB" +description: "Multimodal lakehouse for AI." +icon: "/static/assets/logo/lancedb-icon-gray.svg" +keywords: ["multimodal lakehouse", "training", "feature engineering", "search", "open source", "oss"] +--- + +**LanceDB** is a [multimodal lakehouse](https://lancedb.com/blog/multimodal-lakehouse/) for AI teams that need +one data layer for curation, feature engineering, search and retrieval, and model training. +It is built on top of [Lance](/lance), an open-source lakehouse format designed for multimodal AI data. + +Move from data exploration to model training on one, unified platform without needing to manage a +fragmented stack of storage, feature, retrieval, and training systems. + +## Build better models, faster {#build-better-models-faster} + +Training data and experimentation slow down when raw data, metadata, embeddings, features, and governance +artifacts live in separate systems. LanceDB keeps them together in one versioned multimodal table, so AI teams spend less +time stitching infrastructure together and more time improving datasets, testing features, and keeping GPUs fed. + +![Training data lifecycle: Curation, Feature Engineering, Search and Retrieval, Training](/static/assets/images/overview/training-data-lifecycle.svg) + +Use the same table to curate training data, add derived features, retrieve examples, and feed training jobs that rely on expensive GPUs. +Training workloads can sample, shuffle, and scan projected columns from local storage or object storage, then assemble +GPU-ready batches from a tagged dataset version. + +For a deeper look at how this works in training pipelines, start with [Why LanceDB for training](/training/why-lancedb). + +## LanceDB suite {#lancedb-suite} + +The LanceDB suite includes LanceDB OSS, an open-source embedded retrieval library, and LanceDB Enterprise, +a multimodal lakehouse platform for the full AI data lifecycle. +OSS is easy to set up on a local machine for search and regular-scale workflows. LanceDB Enterprise is built +for teams that need scale without building bespoke infrastructure for curation, +feature engineering, search and retrieval, and efficient training data access. + +![LanceDB suite: OSS search and Enterprise multimodal lakehouse on Lance format](/static/assets/images/overview/lancedb-suite.svg) + +## Why teams use LanceDB {#why-teams-use-lancedb} + + + + Store images, video, audio, text, annotations, embeddings, and model-generated features together in one schema-enforced table. + The same table can support dataset curation, feature backfills, experiment splits, retrieval, and training. + + + Training workloads mix fast random access with high-throughput sequential scans. LanceDB is designed for both, so + teams can shuffle data into GPU-ready batches more efficiently, improve input throughput, and iterate on experiments faster. + + + Whether the end user is a human or an agent, LanceDB powers production retrieval workloads such as semantic search, + hybrid search, RAG, agent memory, and recommendation systems. Retrieval runs against the same LanceDB tables used + for curation, feature engineering, and training workflows. + + + +## Start with your workload {#start-with-your-workload} + + + + Learn why LanceDB works well as the data layer for training workloads. + + + Use LanceDB tables and permutations for projected, shuffled, random-access training reads. + + + Explore Lance-formatted multimodal datasets with raw bytes, metadata, embeddings, and indices. + + + Use vector search, full-text search, hybrid search, reranking, filtering, and SQL. + + + +## From local development to production scale {#from-local-development-to-production-scale} + +LanceDB OSS and LanceDB Enterprise share the same Lance format and table model. Start locally with the embedded OSS +library, then move to Enterprise when your team needs distributed scale, managed infrastructure, private deployment, +or higher-throughput curation, feature engineering, search and retrieval, and training workflows. + +### 1\. LanceDB OSS {#1-lancedb-oss} +The fastest way to get started is the open-source embedded library, with client SDKs in Python, TypeScript +and Rust. Run it locally in just a few steps, which lets you explore datasets, curate data, and run search and retrieval workloads +for agents. Start here: + + + + Get started with LanceDB in minutes. + + + Create tables, evolve schemas, version data, and modify rows in LanceDB. + + + +### 2\. LanceDB Enterprise {#2-lancedb-enterprise} + +[LanceDB Enterprise](/enterprise) is a petabyte-scale (and beyond), distributed **multimodal lakehouse** platform built for +search, curation, feature engineering, and high-throughput training data access workflows on top of the same core table +abstraction. This eliminates the need for teams to build bespoke infrastructure to manage large multimodal datasets. +To set up LanceDB Enterprise in your organization, reach out to us at +[contact@lancedb.com](mailto:contact@lancedb.com). + + +**Built with scale, performance, and security in mind.** + +LanceDB Enterprise is designed for very large-scale, high-performance, distributed workloads in +private deployments, and can operate under strict [security requirements](/enterprise/security). + + + + Get started with LanceDB in minutes, including Enterprise `db://` connections. + diff --git a/docs/web/indexing/fts-index.mdx b/docs/web/indexing/fts-index.mdx new file mode 100644 index 000000000..2d62ad893 --- /dev/null +++ b/docs/web/indexing/fts-index.mdx @@ -0,0 +1,165 @@ +--- +title: "Full-Text Search (FTS) Index" +sidebarTitle: "FTS index" +description: "Create and tune BM25-based full-text search indexes in LanceDB." +icon: "book" +--- +import { PyFtsIndexAsync as FtsIndexAsync, PyFtsIndexCreate as FtsIndexCreate, PyFtsIndexNested as FtsIndexNested, PyFtsIndexWait as FtsIndexWait } from '/snippets/indexing.mdx'; + +LanceDB provides performant full-text search based on BM25, allowing you to incorporate keyword-based search in your retrieval solutions. This page shows +examples on how to create and configure FTS indexes in LanceDB OSS and Enterprise, using the synchronous and asynchronous APIs. + + +In LanceDB Enterprise, `create_fts_index` API returns immediately, but index building happens asynchronously. + + +## Creating FTS Indexes {#creating-fts-indexes} + +### Synchronous API {#synchronous-api} + +Use `create_fts_index` with synchronous LanceDB connections: + + + + {FtsIndexCreate} + + + +Check FTS index status using the API: + + + + {FtsIndexWait} + + + +`wait_for_index(...)` waits until the named FTS index exists and `index_stats(...)` reports `num_unindexed_rows == 0`. It can time out if writes keep adding rows faster than the index catches up. If a table has multiple FTS indexes, specify the target text column when querying instead of relying on implicit selection. + +### Asynchronous API {#asynchronous-api} + +When using async connections (`connect_async`), use `create_index` with the `FTS` configuration: + + + + {FtsIndexAsync} + + + + +The `create_fts_index` method is not available on `AsyncTable`. Use `create_index` with `FTS` config instead. + + +The current FTS implementation is Lance-native. Legacy Tantivy-only options, including +`use_tantivy`, are no longer accepted by the index creation APIs. + +## Nested field paths {#nested-field-paths} + +FTS indexes can target text leaves inside struct columns by passing a dotted path (for example, `payload.text`). The same path works for [`MatchQuery`](/search/full-text-search) and [`PhraseQuery`](/search/full-text-search), and for the `columns` argument on async `nearest_to_text` queries. + +You can point an index at any string leaf nested in a struct, regardless of depth. The struct container itself isn't indexable: you have to name a specific text field. + + + + {FtsIndexNested} + + + +LanceDB rejects paths that don't resolve to a text leaf: + +- A struct container (for example, `payload`): raises `ValueError: FTS index cannot be created ...`. +- A non-text leaf such as an integer or float (for example, `payload.count`): raises the same error. +- A path that doesn't exist in the schema (for example, `payload.missing`): raises `ValueError: Field path ... not found`. + +The async API accepts the same dotted paths through `create_index`: + +```python Python icon="python" +from lancedb.index import FTS + +await async_table.create_index("payload.text", config=FTS(with_position=True)) +``` + +## Configuration Options {#configuration-options} + +### FTS Parameters {#fts-parameters} + +| Parameter | Type | Default | Description | +|:----------|:-----|:--------|:------------| +| `with_position` | bool | `False` | Store token positions (required for phrase queries) | +| `base_tokenizer` | str | `"simple"` | Text splitting method (`simple`, `whitespace`, `raw`, `ngram`, `icu`, `jieba/*`, or `lindera/*`) | +| `language` | str | `"English"` | Language for stemming and stop-word filters. Choose CJK and mixed-language segmentation with `base_tokenizer`. | +| `max_token_length` | int | `40` | Maximum token size; longer tokens are omitted | +| `lower_case` | bool | `True` | Lowercase tokens | +| `stem` | bool | `True` | Apply stemming (`running` → `run`) | +| `remove_stop_words` | bool | `True` | Drop common stop words | +| `ascii_folding` | bool | `True` | Normalize accented characters | +| `custom_stop_words` | list[str] | `None` | Extra stop words to drop in addition to the language defaults. Requires `remove_stop_words=True`. | +| `ngram_min_length` | int | `3` | Minimum n-gram length. Applies only when `base_tokenizer="ngram"`. | +| `ngram_max_length` | int | `3` | Maximum n-gram length. Applies only when `base_tokenizer="ngram"`. | +| `prefix_only` | bool | `False` | Index only prefix n-grams rather than all substrings. Applies only when `base_tokenizer="ngram"`. | +| `block_size` | int | `128` | Number of documents per compressed posting block. Supported values are `128` and `256`. Setting this to `256` opts in to the experimental FTS V3 layout. | + + +- `max_token_length` can filter out base64 blobs or long URLs. +- Disabling `with_position` reduces index size but disables phrase queries. +- `ascii_folding` helps with international text (e.g., “café” → “cafe”). + + +### Tokenizer choices {#tokenizer-choices} + +`base_tokenizer` controls segmentation before token filters run: + +- `simple`, `whitespace`, and `raw` cover common tokenization strategies for space-delimited text. +- `ngram` indexes overlapping character spans for substring-style matching. +- `icu` uses bundled ICU4X word segmentation for mixed-language text and scripts where whitespace splitting is not enough. ICU stands for [International Components for Unicode](https://icu.unicode.org/), and this tokenizer does not need external model files. +- `jieba/*` is for Chinese word segmentation with Jieba. +- `lindera/*` loads a compiled Lindera dictionary, such as `lindera/ipadic` for Japanese or `lindera/ko-dic` for Korean. + +Model-backed tokenizers such as `jieba/default`, `lindera/ipadic`, and `lindera/ko-dic` require tokenizer model files in Lance's language model home. Lance looks under the default platform data directory for `lance/language_models`, or you can set `LANCE_LANGUAGE_MODEL_HOME` to point to another model root. For example, `jieba/default` is resolved under `/jieba/default/...`. + +`language` is used by token filters, not by the base tokenizer. Stemming supports Arabic, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil, and Turkish. Built-in stop-word removal supports Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Russian, Spanish, and Swedish. For other stemming languages, set `remove_stop_words=False` or pass `custom_stop_words`. + +### Posting block size {#posting-block-size} + +`block_size` controls the number of documents packed into each compressed posting block on disk. The default of `128` matches the current FTS layout and is the right choice for most workloads. Setting it to `256` opts in to the experimental FTS V3 format, which changes how postings are encoded and may introduce breaking changes in future releases. Any other value is rejected at index creation time. + +You can set the option through either the synchronous or asynchronous API. In the async Python API, pass it on the `FTS` config, and in the TypeScript API use the camelCase `blockSize` field on `FtsOptions`: + +```python Python icon="python" +from lancedb.index import FTS + +await async_table.create_index("text", config=FTS(block_size=256)) +``` + +```typescript TypeScript icon="square-js" +await table.createIndex("text", { + config: lancedb.Index.fts({ blockSize: 256 }), +}); +``` + +### Phrase Query Configuration {#phrase-query-configuration} + +Enable phrase queries by setting: + +| Parameter | Required Value | Purpose | +|:----------|:---------------|:--------| +| `with_position` | `True` | Track token positions for phrase matching | +| `remove_stop_words` | `False` | Preserve stop words for exact phrase matching | + +## Indexing nested string fields {#indexing-nested-string-fields} + +You can build an FTS index on a string field inside a struct by passing its full dotted path, like `nested.text`. The same path is used when you query the index through `fts_columns`, and the indexed column is reported back as the full path from `list_indices()`. + +```python +# Schema: pa.struct([pa.field("text", pa.string())]) stored under the `nested` column. +table.create_fts_index("nested.text") + +results = ( + table.search("puppy", query_type="fts", fts_columns="nested.text") + .limit(5) + .to_list() +) +``` + + +Use the canonical Lance path: dot-separate each struct field from root to leaf (for example, `metadata.author.name`). The same convention applies to scalar and vector indexes. + diff --git a/docs/web/indexing/index.mdx b/docs/web/indexing/index.mdx new file mode 100644 index 000000000..d3b123f9c --- /dev/null +++ b/docs/web/indexing/index.mdx @@ -0,0 +1,161 @@ +--- +title: "Indexing Data" +sidebarTitle: "Overview" +description: "Optimize search performance in LanceDB using vector indexes, full-text search, and scalar indexes. Understand IVF-PQ indexing for efficient vector similarity search." +icon: "list" +--- + +An **index** is a data structure that facilitates efficient scans and lookups on the embeddings of a given dataset. LanceDB provides a comprehensive suite of indexes to optimize query performance across diverse workloads: + +- **Vector Index**: Optimized for searching high-dimensional data (like images, audio, or text embeddings) by efficiently finding the most similar vectors +- **Full-Text Search Index**: Enables fast keyword-based searches by indexing words and phrases +- **Scalar Index**: Accelerates filtering and sorting of structured numeric or categorical data (e.g., timestamps, prices) + + +Scalar indices serve as a foundational optimization layer, accelerating filtering across diverse search workloads. They can be combined with: + +- Vector search (prefilter or post-filter results using metadata) +- Full-text search (combining keyword matching with structured filters) +- SQL scans (optimizing WHERE clauses on scalar columns) +- Key-value lookups (enabling rapid primary key-based retrievals) + + +## Supported Index Types {#supported-index-types} + +LanceDB provides a comprehensive suite of indexing strategies for different data types and use cases: + +| Index | Use Case | Description | +| :--------- | :------- | :---------- | +| `IVF` (Vector) | Large-scale vector search with configurable accuracy/speed trade-offs. Supports binary vectors with hamming distance. | Inverted File Index—a partition-based approximate nearest neighbor algorithm that groups similar vectors into partitions for efficient search.
Distance metrics: `l2` `cosine` `dot` `hamming`
Quantizations: `None/Flat` `PQ` `SQ` `RQ`| +| `IVF_HNSW` (Vector) | Large-scale vector search requiring both high recall and efficient partitioning. Combines the scalability of IVF with the search quality of HNSW. | Hybrid index combining IVF partitioning with HNSW graphs built within each partition. Provides improved search quality over pure IVF while maintaining scalability.
Distance metrics: `l2` `cosine` `dot`
Quantizations: `None/Flat` `SQ` `PQ`| +| `FTS` (Full-text search) | String columns (e.g., title, description, content) requiring keyword-based search with BM25 ranking. | Full-text search index using BM25 ranking algorithm. Tokenizes text with configurable tokenization, stemming, stop word removal, and language-specific processing. | +| `BTree` (Scalar) | Numeric, temporal, and string columns with mostly distinct values. Best for selective equality, inequality, and range predicates. | Sorted index storing sorted copies of scalar columns with block headers in a btree cache. Header entries map to blocks of rows (4096 rows per block) for efficient disk reads. | +| `Bitmap` (Scalar) | Low-cardinality columns with few thousand or fewer distinct values. Accelerates equality and range filters. | Stores a bitmap for each distinct value in the column, with one bit per row indicating presence. Memory-efficient for low-cardinality data. | +| `LabelList` (Scalar) | List columns (e.g., tags, categories, keywords) requiring `array_contains_all` or `array_contains_any` filters. | Scalar index for `List` and `LargeList` columns of primitive values, using an underlying bitmap index structure to enable fast array membership lookups. | +| `FM` (Scalar) | String or binary columns that need raw substring search. | FM-Index over `Utf8`, `LargeUtf8`, `Binary`, or `LargeBinary` data for filters such as `contains(path, 'needle')`. Use FTS instead for tokenized word search and BM25 ranking. | + + +TypeScript currently doesn't support `IvfSq` (IVF with Scalar Quantization). + + + +**Operational checks** + +For vector indexes, use the same distance metric when creating the index and searching it. After appends or other writes, use `optimize()` to fold new rows into existing indexes, then check `index_stats(...)` or `wait_for_index(...)` if you need to confirm the index has caught up. `wait_for_index(...)` waits until the named indexes exist and report `num_unindexed_rows == 0`; it can time out if writes keep adding unindexed rows. + +By default, automatic vector indexing creates `IVF_PQ`, and scalar index creation defaults to +`BTree` unless you pass another scalar index config. `BTree` and `Bitmap` indexes target scalar +columns, not list columns; use `LabelList` for list containment filters. + + +### Quantization Types {#quantization-types} + +Vector indexes can use different quantization methods to compress vectors and improve search performance: + +| Quantization | Use Case | Description | +| :----------- | :------- | :---------- | +| `PQ` (Product Quantization) | Default choice for most vector search scenarios. Use when you need to balance index size and recall. | Divides vectors into subvectors and quantizes each subvector independently. Provides a good balance between compression ratio and search accuracy. | +| `SQ` (Scalar Quantization) | Use when you need faster indexing or when vector dimensions have consistent value ranges. | Quantizes each dimension independently. Simpler than PQ but typically provides less compression. | +| `RQ` (RabitQ Quantization) | Use when you need maximum compression or have specific per-dimension requirements. | Per-dimension quantization using a RabitQ codebook. Provides fine-grained control over compression per dimension. For `IVF_RQ`, vector dimensions must be divisible by `8`. | +| `None/Flat` | Use for binary vectors (with `hamming` distance) or when you need maximum recall and have sufficient storage. | No quantization—stores raw vectors. Provides the highest accuracy but requires more storage and memory. | + +## Understanding the IVF-PQ Index {#understanding-the-ivf-pq-index} + +An ANN (Approximate Nearest Neighbors) index is a data structure that quickly produces an approximate solution to the **k-nearest neighbors (kNN)** problem. +It greatly improves upon the runtime of a brute-force kNN search, while admitting a slight decrease in accuracy. LanceDB uses the disk-based indexing technique IVF-PQ, discussed below. + +LanceDB differs from other vector databases in that it is built on top of [Lance](https://github.com/lancedb/lance), an open-source columnar data format designed for performant ML workloads and fast random access. Due to the design of Lance, LanceDB's indexing philosophy adopts a primarily *disk-based* indexing philosophy. + +## IVF-PQ {#ivf-pq} + +LanceDB uses **IVF-PQ** indexing, which combines the clustering-based **Inverted File Index (IVF)** with **Product Quantization (PQ)** to efficiently compress embeddings. +The implementation provides several parameters to fine-tune the index's size, query throughput, latency, and recall. + +### Product Quantization {#product-quantization} + +Quantization is a compression technique used to reduce the dimensionality of an embedding to speed up search. + +Product quantization (PQ) works by dividing a large, high-dimensional vector of size into equally sized subvectors. Each subvector is assigned a "reproduction value" that maps to the nearest centroid of points for that subvector. The reproduction values are then assigned to a codebook using unique IDs, which can be used to reconstruct the original vector. + +![](/static/assets/images/indexing/ivfpq_pq_desc.png) + +It's important to remember that quantization is a *lossy process*, i.e., the reconstructed vector is not identical to the original vector. This results in a trade-off between the size of the index and the accuracy of the search results. + +As an example, consider starting with 128-dimensional vector consisting of 32-bit floats. Quantizing it to an 8-bit integer vector with 4 dimensions as in the image above, we can significantly reduce memory requirements. + + +Original: `128 × 32 = 4096` bits +Quantized: `4 × 8 = 32` bits + +Quantization results in a **128x** reduction in memory requirements for each vector in the index, which is substantial. + + +### Inverted File Index (IVF) Implementation {#inverted-file-index-ivf-implementation} + +While PQ helps with reducing the size of the index, IVF primarily addresses search performance. The primary purpose of an inverted file index is to facilitate rapid and effective nearest neighbor search by narrowing down the search space. + +In IVF, the PQ vector space is divided into *Voronoi cells*, which are essentially partitions that consist of all the points in the space that are within a threshold distance of the given region's seed point. These seed points are initialized by running K-means over the stored vectors. The centroids of K-means turn into the seed points which then each define a region. These regions are then are used to create an inverted index that correlates each centroid with a list of vectors in the space, allowing a search to be restricted to just a subset of vectors in the index. + +![](/static/assets/images/indexing/ivfpq_ivf_desc.webp) + +During query time, depending on where the query lands in vector space, it may be close to the border of multiple Voronoi cells, which could make the top-k results ambiguous and span across multiple cells. To address this, the IVF-PQ introduces the `nprobe` parameter, which controls the number of Voronoi cells to search during a query. The higher the `nprobe`, the more accurate the results, but the slower the query. +![](/static/assets/images/indexing/ivfpq_query_vector.webp) + +## HNSW Index Implementation {#hnsw-index-implementation} + +Approximate Nearest Neighbor (ANN) search is a method for finding data points near a given point in a dataset, though not always the exact nearest one. HNSW is one of the most accurate and fastest Approximate Nearest Neighbour search algorithms, It's beneficial in high-dimensional spaces where finding the same nearest neighbor would be too slow and costly. + +### Types of ANN Search Algorithms {#types-of-ann-search-algorithms} + +Approximate Nearest Neighbor (ANN) search is a method for finding data points near a given point in a dataset, though not always the exact nearest one. +For example, HNSW is an ANN index that performs well in high-dimensional spaces where other techniques prove too slow and costly. + +There are three main types of ANN search algorithms: + +* **Tree-based search algorithms**: Use a tree structure to organize and store data points. +* **Hash-based search algorithms**: Use a specialized geometric hash table to store and manage data points. These algorithms typically focus on theoretical guarantees, and don't usually perform as well as the other approaches in practice. +* **Graph-based search algorithms**: Use a graph structure to store data points, which can be a bit complex. + +HNSW is a graph-based algorithm. All graph-based search algorithms rely on the idea of a k-nearest neighbor (or k-approximate nearest neighbor) graph, which we outline below. +HNSW also combines this with the ideas behind a classic 1-dimensional search data structure: the skip list. + +### Understanding k-Nearest Neighbor Graphs {#understanding-k-nearest-neighbor-graphs} + +The k-nearest neighbor graph actually predates its use for ANN search. Its construction is quite simple: + +* Each vector in the dataset is given an associated vertex. +* Each vertex has outgoing edges to its k nearest neighbors. That is, the k closest other vertices by Euclidean distance between the two corresponding vectors. This can be thought of as a "friend list" for the vertex. +* For some applications (including nearest-neighbor search), the incoming edges are also added. + +Eventually, it was realized that the following greedy search method over such a graph typically results in good approximate nearest neighbors: + +* Given a query vector, start at some fixed "entry point" vertex (e.g. the approximate center node). +* Look at that vertex's neighbors. If any of them are closer to the query vector than the current vertex, then move to that vertex. +* Repeat until a local optimum is found. + +The above algorithm also generalizes to e.g. top 10 approximate nearest neighbors. + +Computing a k-nearest neighbor graph is actually quite slow, taking quadratic time in the dataset size. It was quickly realized that near-identical performance can be achieved using a k-approximate nearest neighbor graph. That is, instead of obtaining the k-nearest neighbors for each vertex, an approximate nearest neighbor search data structure is used to build much faster. +In fact, another data structure is not needed: This can be done "incrementally". +That is, if you start with a k-ANN graph for n-1 vertices, you can extend it to a k-ANN graph for n vertices as well by using the graph to obtain the k-ANN for the new vertex. + +One downside of k-NN and k-ANN graphs alone is that one must typically build them with a large value of k to get decent results, resulting in a large index. + +### Hierarchical Navigable Small Worlds (HNSW) {#hierarchical-navigable-small-worlds-hnsw} + +HNSW builds on k-ANN in two main ways: + +* Instead of getting the k-approximate nearest neighbors for a large value of k, it sparsifies the k-ANN graph using a carefully chosen "edge pruning" heuristic, allowing for the number of edges per vertex to be limited to a relatively small constant. +* The "entry point" vertex is chosen dynamically using a recursively constructed data structure on a subset of the data, similarly to a skip list. + +This recursive structure can be thought of as separating into layers: + +* At the bottom-most layer, a k-ANN graph on the whole dataset is present. +* At the second layer, a k-ANN graph on a fraction of the dataset (e.g. 10%) is present. +* At the Lth layer, a k-ANN graph is present. It is over a (constant) fraction (e.g. 10%) of the vectors/vertices present in the L-1th layer. + +Then the greedy search routine operates as follows: + +* At the top layer (using an arbitrary vertex as an entry point), use the greedy local search routine on the k-ANN graph to get an approximate nearest neighbor at that layer. +* Using the approximate nearest neighbor found in the previous layer as an entry point, find an approximate nearest neighbor in the next layer with the same method. +* Repeat until the bottom-most layer is reached. Then use the entry point to find multiple nearest neighbors (e.g. top 10). diff --git a/docs/web/indexing/quantization.mdx b/docs/web/indexing/quantization.mdx new file mode 100644 index 000000000..8acc1fc71 --- /dev/null +++ b/docs/web/indexing/quantization.mdx @@ -0,0 +1,79 @@ +--- +title: "Quantization" +sidebarTitle: "Quantization" +description: "Learn about quantization when creating an index in LanceDB." +icon: "compress" +keywords: ["quantization", "quantize", "rabitq"] +--- + +Quantization compresses high-dimensional float vectors into a smaller, approximate representation, where instead of storing every vector as a float32 or float64, it's stored in compressed form, without too much of a compromise in search quality. + +Use quantization when: + +- You have a large dataset with relatively high-dimensional vectors (512, 768, 1024+) +- Index build time and query latency matter + +LanceDB currently exposes multiple quantized vector index types, including: +- `IVF_PQ` -- Inverted File index with Product Quantization (default). See the [vector indexing guide](/indexing/vector-index) for `IVF_PQ` examples. +- `IVF_SQ` -- Inverted File index with Scalar Quantization. This is available in Python and Rust; TypeScript does not currently expose `IvfSq`. +- `IVF_RQ` -- Inverted File index with **RaBitQ** quantization (binary, 1 bit per dimension). Requires vector dimensions divisible by `8`. See [below](#rabitq-quantization) for details. +- `IVF_HNSW_SQ` -- IVF partitions with an **HNSW graph per partition** plus **Scalar Quantization**. Strong recall/latency/size trade-off for most workloads. +- `IVF_HNSW_PQ` -- IVF partitions with an **HNSW graph per partition** plus **Product Quantization**. Prefer when PQ-level compression matters and you still want HNSW-style in-partition search. + +Two axes are being combined here: whether partitions are searched flatly or via an HNSW graph (`IVF_*` vs. `IVF_HNSW_*`), and which quantizer compresses the vectors (`PQ`, `RQ`, or `SQ`). `IVF_PQ` is the default and works well in many cases. For more drastic compression, RaBitQ (`IVF_RQ`) is a reasonable option. For higher recall at low latency, the HNSW-backed variants are usually the right pick. The ["Choose the Right Index"](/indexing/vector-index#choose-the-right-index) table on the vector indexing page is the canonical decision tool. + +Use the same distance metric when training the index and running queries against it. For IVF-based indexes, `num_partitions` controls the number of groups and `sample_rate` controls how many training vectors are sampled per partition, so the training sample is roughly `sample_rate * num_partitions`. + +## RaBitQ quantization {#rabitq-quantization} + +RaBitQ is a binary quantization method that represents each normalized embedding using **1 bit per dimension**, plus a couple of small corrective scalars. In practice, a 1,024-dimensional `float32` vector that would normally take 4 KB can be compressed to roughly a few hundred bytes with RaBitQ, while still maintaining reasonable recall. + +### How RaBitQ works {#how-rabitq-works} + +- Embeddings are grouped around centroids (as in other IVF indexes). +- Each residual vector is normalized and mapped to the nearest vertex of a randomly rotated hypercube on the unit sphere. +- The sign pattern of that vector is stored as bits (1 bit per dimension). +- Two small corrective factors are stored: + 1. The distance from the original vector to its centroid + 2. The dot product between the normalized vector and its quantized version + +Compared to `IVF_PQ`, RaBitQ: +- Avoids training expensive PQ codebooks +- Builds indexes faster and handles updates more easily +- Maintains or improves recall at high dimensionality under the same storage budget + +For a deeper dive into the theory and some benchmark results, see the blog post: [LanceDB's RaBitQ Quantization for Blazing Fast Vector Search](https://lancedb.com/blog/feature-rabitq-quantization/). + +### Using RaBitQ {#using-rabitq} + +You can create an RaBitQ-backed vector index by setting `index_type="IVF_RQ"` when calling `create_index`. + + +When using `IVF_RQ`, vector dimensions must be divisible by `8`. + + +`num_bits` controls how many bits per dimension are used: + +1 bit is the classic RaBitQ setting. You can set it to 2, 4, or 8 bits to improve fidelity for better precision or recall — the main trade-off is additional storage for the extra bits per dimension, with only a modest increase in query-time compute. +It's also possible to tune the number of IVF partitions in `IVF_RQ`, similar to how you would do in `IVF_PQ`. + + +Indexes built with `num_bits >= 2` use an updated on-disk layout. Older LanceDB versions cannot read them and will fail with a clear missing-column error rather than returning incorrect results. Existing indexes keep working and upgrade automatically when they are rewritten (for example, during compaction, optimize, or remap). `num_bits=1` indexes are unaffected in both directions. + + +## API Reference {#api-reference} + +The full list of parameters to the algorithm are listed below. + +- `distance_type`: Literal["l2", "cosine", "dot"], defaults to "l2" + The distance metric to use for similarity comparison. Choose "l2" for Euclidean, "cosine" for cosine similarity, or "dot" for dot product. +- `num_partitions`: Optional[int], defaults to None + Number of IVF partitions (affects index build time and query accuracy). More partitions can improve recall but may increase build time. When unset, LanceDB chooses roughly the square root of the row count. +- `num_bits`: int, defaults to 1 + Bits per dimension for quantization (1 is standard RaBitQ). Higher values improve fidelity, mainly at the cost of additional storage. +- `max_iterations`: int, defaults to 50 + Maximum number of iterations for training the quantizer. Increase for larger datasets or to improve quantization quality. +- `sample_rate`: int, defaults to 256 + Number of samples per partition during training. Higher values may improve accuracy but increase training time. +- `target_partition_size`: Optional[int], defaults to None + Target number of vectors per partition. Adjust to control partition granularity and memory usage. If `num_partitions` is also set, `num_partitions` takes precedence. diff --git a/docs/web/indexing/reindexing.mdx b/docs/web/indexing/reindexing.mdx new file mode 100644 index 000000000..2a1cabc9e --- /dev/null +++ b/docs/web/indexing/reindexing.mdx @@ -0,0 +1,59 @@ +--- +title: "Keeping Indexes Up-to-Date with Reindexing" +sidebarTitle: "Reindexing" +description: "Learn how to keep your indexes up-to-date in LanceDB using incremental indexing, including best practices for adding new records without full reindexing." +icon: "refresh" +keywords: ["reindexing", "re-index", "incremental indexing", "reindex"] +--- +import { PyReindexingIncremental as ReindexingIncremental } from '/snippets/indexing.mdx'; + +As you add new data to your LanceDB tables, your indexes may become outdated. +Reindexing is the process of updating the index to account for new data -- this applies to either a full-text search (FTS) index or a vector index. Reindexing is an important operation to run periodically as your data grows, as it has performance implications. + +As data is being added and a reindex operation is running, LanceDB will combine results from the existing index with exhaustive/flat search on the new data. This is done to ensure that you're still retrieving results over all your data, but it does come at a performance cost. The more data that you add without reindexing, the impact on latency (due to exhaustive search) can be noticeable. + +Rather than dropping an existing index entirely and reindexing from scratch, LanceDB supports **incremental indexing**. + +## Incremental Reindexing {#incremental-reindexing} + +You can manually trigger an incremental indexing operation on updated data +using the `optimize()` method on a table. + +Table optimization performs three maintenance operations: + +1. **Compaction**: merges small fragments into larger ones to improve read performance +2. **Pruning/Cleanup**: removes files from versions older than a retention window (7 days by default) +3. **Index update**: adds newly-ingested data to existing vector, scalar, and FTS indexes + + + + {ReindexingIncremental} + + + +Enterprise + +LanceDB Enterprise support incremental reindexing through an automated background process. When new data is added to a table, the system automatically triggers a new index build. As the dataset grows, indexes are asynchronously updated in the background. + +- While indexes are being rebuilt, queries use brute force methods on unindexed rows, which may temporarily increase latency. To avoid this, set `fast_search=True` to search only indexed data. +- Use `index_stats()` to view the number of unindexed rows. This will be zero when indexes are fully up-to-date. If you call `wait_for_index(...)`, it polls the same status and can time out while continuous writes keep adding unindexed rows. + +The benefit of using LanceDB Enterprise is that it automates the reindexing process +and operates continuously in the background, minimizing the impact on latency under high loads. +In OSS, you must manually manage the reindexing cadence based on your data growth and performance needs. + +## Disk utilization {#disk-utilization} + +Compaction by itself does not immediately free disk space, and can temporarily increase it because new +compacted files are written before old-version files are deleted. Disk space is reclaimed when old versions +are pruned during cleanup. Set retention only as low as your rollback and time-travel requirements allow. + +If you need to reclaim space more aggressively in OSS, use a shorter retention window: + + ```python Python icon=Python + from datetime import timedelta + + table.optimize(cleanup_older_than=timedelta(days=1)) + ``` + + diff --git a/docs/web/indexing/scalar-index.mdx b/docs/web/indexing/scalar-index.mdx new file mode 100644 index 000000000..68bffdda5 --- /dev/null +++ b/docs/web/indexing/scalar-index.mdx @@ -0,0 +1,243 @@ +--- +title: "Scalar Indexes" +sidebarTitle: "Scalar Index" +description: "Learn how to use scalar indexes in LanceDB for efficient metadata filtering and query optimization." +icon: "tree" +--- +import { + PyScalarIndexBuild as ScalarIndexBuild, + PyScalarIndexWait as ScalarIndexWait, + PyScalarIndexOptimize as ScalarIndexOptimize, + PyScalarIndexFilter as ScalarIndexFilter, + PyScalarIndexPrefilter as ScalarIndexPrefilter, + PyScalarIndexUuidType as ScalarIndexUuidType, + PyScalarIndexUuidData as ScalarIndexUuidData, + PyScalarIndexUuidTable as ScalarIndexUuidTable, + PyScalarIndexUuidWait as ScalarIndexUuidWait, + PyScalarIndexUuidUpsert as ScalarIndexUuidUpsert, + PyScalarIndexNestedFields as ScalarIndexNestedFields, +} from '/snippets/indexing.mdx'; + +Scalar indexes organize data by scalar attributes (e.g., numbers, categories) and enable fast filtering of vector data. They accelerate retrieval of scalar data associated with vectors, thus enhancing query performance. + +LanceDB supports four types of scalar indexes: + +- `BTREE`: Stores column data in sorted order for binary search. Best for columns with many unique values. +- `BITMAP`: Uses bitmaps to track value presence. Ideal for columns with few unique values (e.g., categories, tags). +- `LABEL_LIST`: Special index for `List` and `LargeList` columns of primitive values supporting `array_contains_all` and `array_contains_any` queries. +- `FM`: FM-Index over string or binary columns that accelerates substring search via `contains(col, 'needle')`. + +## Choosing the Right Index Type {#choosing-the-right-index-type} + +| Data Type | Filter | Index Type | +|:----------------------------------------------------------------|:------------------------------------------|:-------------| +| Numeric, String, Temporal | `<`, `=`, `>`, `in`, `between`, `is null` | `BTREE` | +| Boolean, numbers or strings with fewer than 1,000 unique values | `<`, `=`, `>`, `in`, `between`, `is null` | `BITMAP` | +| List of low cardinality of numbers or strings | `array_has_any`, `array_has_all` | `LABEL_LIST` | +| String or binary (`Utf8`, `LargeUtf8`, `Binary`, `LargeBinary`) | `contains(col, 'needle')` | `FM` | + +## Scalar Index Operations {#scalar-index-operations} + +### 1\. Build the Index {#1-build-the-index} + +You can create multiple scalar indexes within a table. By default, the index will be `BTREE`, but you can always configure another type like `BITMAP` + + + + {ScalarIndexBuild} + + + + +If you are using LanceDB Enterprise, the `create_scalar_index` API returns immediately, but the building of the scalar index is asynchronous. To wait until all data is fully indexed, you can specify the `wait_timeout` parameter on `create_scalar_index()` or call `wait_for_index()` on the table. + + +### 2\. Check Index Status {#2-check-index-status} + + + + {ScalarIndexWait} + + + +`wait_for_index(...)` waits until the named scalar indexes exist and `index_stats(...)` reports `num_unindexed_rows == 0`. If a table is receiving steady writes, that fully indexed state may not stabilize before the timeout. + +### 3\. Update the Index {#3-update-the-index} + +Updating the table data (adding, deleting, or modifying records) requires that you also update the scalar index. This can be done by calling `optimize`, which will trigger an update to the existing scalar index. + + + + {ScalarIndexOptimize} + + + + +New data added after creating the scalar index will still appear in search results if optimize is not used, but with increased latency due to a flat search on the unindexed portion. LanceDB Enterprise automates the optimize process, minimizing the impact on search speed. + + +### 4\. Run Indexed Searches {#4-run-indexed-searches} + +The following scan will be faster if the column `book_id` has a scalar index: + + + + {ScalarIndexFilter} + + + +Scalar indexes can also speed up scans containing a vector search or full text search, and a prefilter: + + + + {ScalarIndexPrefilter} + + + +## Indexing nested fields {#indexing-nested-fields} + +Scalar indexes can target a scalar field inside a struct by passing its full dotted path. The path is preserved end to end: it's the value you pass to `create_scalar_index`, it's what `list_indices()` reports under `columns`, and it's the column reference you use in filter predicates. + +```python +# Schema: pa.struct([pa.field("user_id", pa.int32())]) stored under the `metadata` column. +table.create_scalar_index("metadata.user_id", name="metadata_user_id_idx") + +# The same dotted path works in WHERE clauses. +table.search().where("metadata.user_id = 42").limit(1).to_list() +``` + + +Nested paths follow Lance field-path semantics: dot-separate each struct field from root to leaf (for example, `metadata.author.name`). The same convention applies to FTS and vector indexes. + + +## FM-Index for substring search {#fm-index-for-substring-search} + +The `FM` index is a scalar index built over string or binary columns that +accelerates substring lookups expressed as `contains(col, 'needle')`. Unlike the +tokenized [FTS index](/indexing/fts-index), which matches whole words after +tokenization, the FM-Index matches arbitrary substrings of the raw bytes — so it +works well for URLs, file paths, identifiers, log lines, or any column where you +search for a fragment rather than a word. + +Use the FM-Index when: + +- Filters use `contains(col, 'needle')` (substring), not equality or word search. +- The column is `Utf8`, `LargeUtf8`, `Binary`, or `LargeBinary`. +- You want substring matches without paying for tokenization, language analysis, + or BM25 scoring. + +Pick `FTS` instead when you need word-level relevance ranking, phrase queries, +or language-aware tokenization. + +### Create an FM-Index {#create-an-fm-index} + +Build an FM-Index with the async `create_index` API by passing the `Fm` config in +Python or `Index.fm()` in TypeScript. In Rust, use `Index::Fm(FmIndexBuilder::default())`. + + +```python Python icon="python" +from lancedb.index import Fm + +await tbl.create_index("text", config=Fm()) +``` + +```typescript TypeScript icon="square-js" +import * as lancedb from "@lancedb/lancedb"; +import { Index } from "@lancedb/lancedb"; + +await tbl.createIndex("text", { config: Index.fm() }); +``` + +```rust Rust icon="rust" +use lancedb::index::Index; +use lancedb::index::scalar::FmIndexBuilder; + +tbl.create_index(&["text"], Index::Fm(FmIndexBuilder::default())) + .execute() + .await?; +``` + + +After the index is built, substring filters use it automatically: + +```python +table.search().where("contains(text, 'needle')").limit(10).to_pandas() +``` + +`list_indices()` reports the index type as `"Fm"`. + +## Index UUID Columns {#index-uuid-columns} + +LanceDB supports scalar indexes on UUID columns (stored as `FixedSizeBinary(16)`), enabling efficient lookups and filtering on UUID-based primary keys. + + +**To use `FixedSizeBinary`, ensure you have:** + +- Python SDK version `0.22.0` or later +- TypeScript SDK version `0.19.0` or later + + +### 1\. Define UUID Type {#1-define-uuid-type} + + + + {ScalarIndexUuidType} + + + +### 2\. Generate UUID Data {#2-generate-uuid-data} + + + + {ScalarIndexUuidData} + + + +### 3\. Create Table with UUID Column {#3-create-table-with-uuid-column} + + + + {ScalarIndexUuidTable} + + + +### 4\. Create and Wait for the Index {#4-create-and-wait-for-the-index} + + + + {ScalarIndexUuidWait} + + + +### 5\. Perform Operations with the UUID Index {#5-perform-operations-with-the-uuid-index} + + + + {ScalarIndexUuidUpsert} + + + +## Index nested fields {#index-nested-fields} + +You can build a scalar index on a field inside a struct column by passing the +canonical dot-separated path to `create_index`. This is useful when filters +target attributes nested under a `metadata`-style column, for example +`metadata.user_id` or `metadata.event.type`. + +If a literal segment of the path itself contains a dot (for example a column +named `user.id` nested inside `metadata`), wrap that segment in backticks so +LanceDB can tell the dot apart from the path separator: `` metadata.`user.id` ``. + +`list_indices()` echoes the same canonical path back, so the column you pass in +round-trips through index metadata regardless of nesting depth or escaping. + + + + {ScalarIndexNestedFields} + + + + +Composite indexes that cover multiple columns aren't supported yet. Each +`create_index` call must target a single (possibly nested) field path. + diff --git a/docs/web/indexing/vector-index.mdx b/docs/web/indexing/vector-index.mdx new file mode 100644 index 000000000..febe76435 --- /dev/null +++ b/docs/web/indexing/vector-index.mdx @@ -0,0 +1,379 @@ +--- +title: "Vector Indexes" +sidebarTitle: "Vector Index" +description: "Build and optimize LanceDB vector indexes, including IVF, HNSW and binary quantized indexes." +icon: "arrow-up-right-dots" +--- +import { + PyVectorIndexConfigureIvf as VectorIndexConfigureIvf, + PyVectorIndexSetup as VectorIndexSetup, + PyVectorIndexBuildIvf as VectorIndexBuildIvf, + PyVectorIndexNestedField as VectorIndexNestedField, + PyVectorIndexAsyncConfig as VectorIndexAsyncConfig, + PyVectorIndexQueryIvf as VectorIndexQueryIvf, + PyVectorIndexBuildHnsw as VectorIndexBuildHnsw, + PyVectorIndexQueryHnsw as VectorIndexQueryHnsw, + PyVectorIndexBinarySchema as VectorIndexBinarySchema, + PyVectorIndexBinaryAddData as VectorIndexBinaryAddData, + PyVectorIndexBinaryBuildIndex as VectorIndexBinaryBuildIndex, + PyVectorIndexBinarySearch as VectorIndexBinarySearch, + PyVectorIndexCheckStatus as VectorIndexCheckStatus, + PyVectorIndexNprobes as VectorIndexNprobes, + PyVectorIndexDistanceRange as VectorIndexDistanceRange, + PyVectorIndexBypassRecall as VectorIndexBypassRecall, + PyVectorIndexCustomName as VectorIndexCustomName, +} from '/snippets/indexing.mdx'; + +You can create and manage multiple vector indexes on any Lance dataset. LanceDB offers two kinds of vector indexing algorithms: **Inverted File (IVF)** and **Hierarchical Navigable Small World (HNSW)**. + + +**IVF + HNSW** + +In LanceDB, HNSW is not exposed as a top-level vector index. Instead, it's available as a sub-index inside IVF partitions. What this means in practice is that vectors are first partitioned by IVF, then each selected partition is searched using an HNSW graph. LanceDB supports the unquantized variant `IVF_HNSW_FLAT`, along with quantized variants such as `IVF_HNSW_PQ` and `IVF_HNSW_SQ`. This combines IVF's scalability with HNSW's higher-recall ANN search within partitions. + + +### Manual Indexing {#manual-indexing} + +If using LanceDB OSS, you will have to create the vector index manually, by calling `table.create_index()`, and updating the index as new data arrives and tuning its parameters is also a manual process. + +### Automatic Indexing {#automatic-indexing} + + Enterprise-only +Vector indexing is managed **automatically** in LanceDB Enterprise. As soon as data is updated, the system updates the index and optimizates it. *This is done asynchronously as a background process*. + +When you create a table in LanceDB Enterprise, LanceDB automatically: + +- Infers the vector columns from the schema +- Create an optimized `IVF_PQ` index without manual configuration +- Automatically configure indexing parameters + +The default distance is `l2` (Euclidean). + + +You can call `create_index()` with different parameters to create a new index -- this replaces any existing index. +Although the `create_index` API returns immediately, the building of the vector index is asynchronous. To wait until all data is fully indexed, you can specify the `wait_timeout` parameter. + + +Use the same distance metric for index creation and search. Once a vector index exists, queries use the metric stored with that index. If you need to confirm an async build or refresh is finished, `wait_for_index(...)` waits for the named index to exist and for `index_stats(...)` to report `num_unindexed_rows == 0`; it can time out if new writes keep arriving. + +Rows appended after an index build remain outside that index until optimization refreshes it. Normal +search still checks those unindexed rows with a slower fallback path; `fast_search()` skips that +fallback and searches only indexed rows. + +## Choose the Right Index {#choose-the-right-index} + +Use this table as a quick starting point for choosing the right index type and quantization method for your use case: + +| If your top priority is... | Use this index | Why | Typical compressed size vs. raw vectors | +| :--- | :--- | :--- | :--- | +| Highest recall / no quantization | `IVF_HNSW_FLAT` | Uses raw vectors inside the IVF+HNSW structure, avoiding quantization loss. | Around raw vector size plus HNSW graph overhead | +| Best recall/latency trade-off | `IVF_HNSW_SQ` | Combines IVF partitioning with HNSW graph search for strong quality at low latency. | Typically a little larger than `1/4` of raw size | +| Maximum compression | `IVF_RQ` | RaBitQ-style quantization with very strong compression. | Around `1/32` of raw size | +| Higher accuracy at small dimensions (`dimension <= 256`) | `IVF_PQ` | On small-dimensional vectors, `IVF_PQ` often provides higher accuracy with similar performance compared to `IVF_RQ`. | Usually `1/64` to `1/16` of raw size (depends on `num_sub_vectors`) | + + +If your vector search frequently includes metadata filters (`where(...)`), prefer `IVF_RQ` or `IVF_PQ`. In filtered workloads, HNSW-backed IVF indexes such as `IVF_HNSW_FLAT` and `IVF_HNSW_SQ` can show higher latency variance. + + +Compression ratios are practical rules of thumb and can vary with vector distribution, metric, and configuration. +For small dimensions, choose `IVF_PQ` for accuracy, not for guaranteed higher compression than `IVF_RQ`. + +### Index Tuning {#index-tuning} + +Start with these values, then tune for your workload: + +- HNSW-backed IVF indexes (`IVF_HNSW_FLAT`, `IVF_HNSW_SQ`, `IVF_HNSW_PQ`) + - `num_partitions`: start at `num_rows // 1,048,576` (rounded to an integer) + - Lower `num_partitions` can reduce search latency, but index build may become slower because partitions are larger. + - `ef_construction`: start at `150`; increase for better recall, decrease for faster indexing. +- `IVF_RQ` + - `num_partitions`: start at `num_rows // 4096` (rounded to an integer). This is a strong default for most datasets. +- `IVF_PQ` + - `num_partitions`: start at `num_rows // 4096` (rounded to an integer). + - `num_sub_vectors`: start at `dimension // 8`. Increase for better recall, decrease for faster search and smaller indexes. + - For small dimensions (`dimension <= 256`), `IVF_PQ` is often preferred over `IVF_RQ` for better accuracy at similar query performance. + +## Example: Construct an IVF Index {#example-construct-an-ivf-index} + +In this example, we will create an index for a table containing 1536-dimensional vectors. The index will use IVF_PQ with L2 distance, which is well-suited for high-dimensional vector search. + +Make sure you have enough data in your table (at least a few thousand rows) for effective index training. + +### Index Configuration {#index-configuration} + +Sometimes you need to configure the index beyond default parameters: + +- Index Types: + - `IVF_HNSW_FLAT`: highest recall, with no vector quantization + - `IVF_HNSW_SQ`: best recall/latency trade-off + - `IVF_RQ`: best compression for large, high-dimensional datasets + - `IVF_PQ`: often higher accuracy than `IVF_RQ` for small dimensions (`<= 256`) at similar query performance +- `metrics`: default is `l2`, other available are `cosine` or `dot` + - When using `cosine` similarity, distances range from 0 (identical vectors) to 2 (maximally dissimilar) +- `num_partitions`: use index-specific starting points from the section above: + - HNSW-backed IVF indexes (`IVF_HNSW_FLAT`, `IVF_HNSW_SQ`, `IVF_HNSW_PQ`): `num_rows // 1,048,576` + - `IVF_RQ` and `IVF_PQ`: `num_rows // 4096` +- `target_partition_size`: alternative IVF sizing knob that asks LanceDB to derive the partition + count from a target number of rows per partition. If you set both `num_partitions` and + `target_partition_size`, `num_partitions` takes precedence. +- `num_sub_vectors`: applies to `IVF_PQ`; start with `dimension // 8`. Larger values often improve recall but can slow search. + +Let's take a look at a sample request for an IVF index: + + + + + {VectorIndexConfigureIvf} + + + +### 1\. Setup {#1-setup} + +Connect to LanceDB and open the table you want to index. + + + + {VectorIndexSetup} + + + +### 2\. Construct an IVF Index {#2-construct-an-ivf-index} + +Create an `IVF_PQ` index with `cosine` similarity. Specify `vector_column_name` if you use multiple vector columns or non-default names. For a vector field nested inside a struct, use dot notation (e.g. `image.embedding`); see [Selecting the vector column](/search/vector-search#selecting-the-vector-column) for the full syntax. You can switch `index_type` to `IVF_RQ`, `IVF_HNSW_SQ`, or `IVF_HNSW_FLAT` depending on your recall/latency/compression target. + + + + {VectorIndexBuildIvf} + + + +#### Indexing nested vector fields {#indexing-nested-vector-fields} + +If your vector column lives inside a struct, pass its full dotted path as `vector_column_name`. The same path is used at query time and is what `list_indices()` reports under `columns`: + + + + {VectorIndexNestedField} + + + + +Nested paths follow Lance field-path semantics: dot-separate each struct field from root to leaf (for example, `image.thumbnail.embedding`). The same convention applies to FTS and scalar indexes. + + +### Async API and Config Objects {#async-api-and-config-objects} + +With asynchronous Python connections, create vector indexes with `await table.create_index("vector", config=...)`. The `config` object carries the same index choices you configure in the synchronous API, such as distance metric, partition count, and quantization settings: + + + + {VectorIndexAsyncConfig} + + + +Use these Python config classes for the index types shown on this page: + +| Index type | Python config class | +| :--- | :--- | +| `IVF_FLAT` | `IvfFlat` | +| `IVF_PQ` | `IvfPq` | +| `IVF_RQ` | `IvfRq` | +| `IVF_SQ` | `IvfSq` | +| `IVF_HNSW_FLAT` | `IvfHnswFlat` | +| `IVF_HNSW_PQ` | `IvfHnswPq` | +| `IVF_HNSW_SQ` | `IvfHnswSq` | + +### 3\. Query the IVF Index {#3-query-the-ivf-index} + +Search using a random 1,536-dimensional embedding. + + + + {VectorIndexQueryIvf} + + + +#### Search Configuration {#search-configuration} + +Core knobs available on a vector search call: + +| Parameter | Description | +| :--- | :--- | +| `limit` | Number of results to return (`k`). | +| `nprobes` | Shorthand that sets both `minimum_nprobes` and `maximum_nprobes` to the same value. LanceDB auto-tunes this by default. | +| `minimum_nprobes` | Partitions that are *always* scanned. Higher values raise recall at the cost of latency. | +| `maximum_nprobes` | Upper bound on partitions scanned. The partitions above `minimum_nprobes` are only searched if the initial pass does not return enough results — useful for narrow filters. Set to `0` to remove the cap. | +| `ef` | HNSW search-time exploration factor. Relevant for `IVF_HNSW_FLAT` and `IVF_HNSW_SQ`; start around `1.5 * k` and increase up to `10 * k` for higher recall. | +| `refine_factor` | Reads additional candidates and reranks them in memory to recover recall lost to quantization. | + + +**Filtered queries and adaptive nprobes.** When a `where(...)` filter is active, LanceDB starts by scanning `minimum_nprobes` partitions and only extends toward `maximum_nprobes` if fewer than `limit` rows survive the filter. Setting `minimum_nprobes == maximum_nprobes` (or calling `nprobes(n)`) disables this adaptive behavior and fixes the partition count. + + + + + {VectorIndexNprobes} + + + +Recommended `nprobes` behavior by index type: + +| Index type | Guidance | +| :--- | :--- | +| `IVF_HNSW_FLAT`, `IVF_HNSW_SQ` | Keep the auto-tuned `nprobes`, then tune `ef` first. Expect higher latency variance under filtered search. | +| `IVF_RQ` | Keep auto-tuned `nprobes`; raise only when recall is insufficient. | +| `IVF_PQ` | Keep auto-tuned `nprobes`; raise when recall is insufficient. Often preferred over `IVF_RQ` when `dimension <= 256`. | + +#### Advanced Search Controls {#advanced-search-controls} + +These controls are useful for thresholded retrieval, recall measurement, and working around index-level metric constraints. + +| Method | Description | +| :--- | :--- | +| `distance_range(lower_bound, upper_bound)` | Return only rows whose distance falls within `[lower_bound, upper_bound)`. Either bound is optional. Useful for near-duplicate detection or "close-enough" matching. | +| `bypass_vector_index()` | Skip the ANN index and perform an exhaustive (flat) scan. Primary uses: (1) compute ground-truth results to measure ANN recall@k, and (2) query with a metric the index was not built for (e.g., a non-cosine query on a multivector column). | + +**Thresholding with `distance_range`:** + + + + {VectorIndexDistanceRange} + + + +**Measuring recall with `bypass_vector_index`:** + +Compare ANN results against a flat-scan ground truth to compute recall@k. This is the standard way to pick `nprobes` for your workload. + + + + {VectorIndexBypassRecall} + + + + +Flat search is $O(n)$ — reserve `bypass_vector_index()` for sampled recall measurements or small tables, not production queries. + + + +Multivector indexing currently requires `distance_type="cosine"` — `l2` is rejected at index-creation time. That restriction is why `bypass_vector_index()` is the escape hatch for non-cosine queries on a multivector column: the metric you want at query time cannot be served by the index, so you fall back to a flat scan. See [Multivector Search](/search/multivector-search) for the full rules. + + +## Example: Construct an HNSW Index {#example-construct-an-hnsw-index} + +### Index Configuration {#index-configuration-2} + +There are four key parameters to set when constructing an HNSW index: + +- `index_type`: choose `IVF_HNSW_SQ` for a strong recall/latency/size trade-off, or `IVF_HNSW_FLAT` when you want the IVF+HNSW structure without vector quantization. +- `metric`: The default is `l2` euclidean distance metric. Other available are `dot` and `cosine`. +- `m`: The number of neighbors to select for each vector in the HNSW graph. +- `ef_construction`: The number of candidates to evaluate during the construction of the HNSW graph. + +### 1\. Construct an HNSW Index {#1-construct-an-hnsw-index} + +The snippet below uses `IVF_HNSW_SQ`. If you want the unquantized variant, change `index_type` to `IVF_HNSW_FLAT`. + + + + {VectorIndexBuildHnsw} + + + +### 2\. Query the HNSW Index {#2-query-the-hnsw-index} + + + + {VectorIndexQueryHnsw} + + + +## Example: Construct a Binary Vector Index {#example-construct-a-binary-vector-index} + +Binary vectors are useful for hash-based retrieval, fingerprinting, or any scenario where data can be represented as bits. + +### Index Configuration {#index-configuration-3} + +- Store binary vectors as fixed-size binary data (uint8 arrays, with 8 bits per byte). For storage, pack binary vectors into bytes to save space. +- Index Type: `IVF_FLAT` is used for indexing binary vectors +- `metric`: the `hamming` distance is used for similarity search +- The dimension of binary vectors must be a multiple of 8. For example, a 128-dimensional vector is stored as a uint8 array of size 16. + + +**`IVF_FLAT` + `hamming` is the only supported path for binary vectors.** + +- `hamming` distance is only valid on packed binary (uint8) data; it is rejected on float vector columns. +- Quantized index types (`IVF_PQ`, `IVF_RQ`, `IVF_SQ`, `IVF_HNSW_PQ`, `IVF_HNSW_SQ`) do not accept binary inputs — their `distance_type` is restricted to `l2`, `cosine`, or `dot`. + + +### 1\. Create Table and Schema {#1-create-table-and-schema} + + + + {VectorIndexBinarySchema} + + + +### 2\. Generate and Add Data {#2-generate-and-add-data} + + + + {VectorIndexBinaryAddData} + + + +### 3\. Construct the Binary Index {#3-construct-the-binary-index} + + + + {VectorIndexBinaryBuildIndex} + + + +### 4\. Vector Search {#4-vector-search} + + + + {VectorIndexBinarySearch} + + + +## Check Index Status {#check-index-status} + +Vector index creation runs in the background and may take some time to complete. While it is ongoing, you can check its status either programmatically through the API or from the **LanceDB Enterprise UI**. + +In the LanceDB Enterprise UI, navigate to your table page - the "Index" column reflects each column's index status: it is blank when no index exists, shows an "in progress" label while the index is being built, and shows the index type once the build completes. + +Programmatically, use `list_indices()` and `index_stats()`. **By default**, the index name is formed by appending `_idx` to the column name (e.g., a `keywords_embeddings` column produces `keywords_embeddings_idx`). Note that `list_indices()` only returns information after the index is fully built. +To wait until all data is fully indexed, you can specify the `wait_timeout` parameter on `create_index()` or call `wait_for_index()` on the table. + +Each entry returned by `list_indices()` also carries detailed per-index metadata, so you can inspect an index without a follow-up `index_stats()` call. Node.js exposes the same fields in camelCase (`num_indexed_rows` → `numIndexedRows`): + +| Field | What it tells you | +| :--- | :--- | +| `num_indexed_rows`, `num_unindexed_rows` | Index coverage over the table | +| `size_bytes` | Total size of the index files on disk | +| `num_segments`, `index_version` | On-disk layout and format version | +| `created_at` | Creation time (ms since the Unix epoch in Node.js) | +| `index_uuid`, `type_url` | Internal identifiers for the index segment | +| `index_details` | Type-specific details (e.g. IVF partition counts or quantization settings) | + + +These fields are populated for local and embedded tables. On LanceDB Enterprise remote tables they are returned as `None` / `undefined` until the server response surfaces them. + + + + + {VectorIndexCheckStatus} + + + +## Custom Index Names {#custom-index-names} + +The `{column}_idx` suffix is a default convention, not the only supported naming path. Pass `name=...` to `create_index()` to override it — useful when you want to manage multiple indexes on the same column (for example, side-by-side `IVF_PQ` and `IVF_HNSW_SQ` builds) or when you script index replacement by name. Once set, `list_indices()`, `index_stats(name)`, and `wait_for_index([name])` all reference the custom name. + + + + {VectorIndexCustomName} + + diff --git a/docs/web/integrations/ai/agno.mdx b/docs/web/integrations/ai/agno.mdx new file mode 100644 index 000000000..b5edef031 --- /dev/null +++ b/docs/web/integrations/ai/agno.mdx @@ -0,0 +1,151 @@ +--- +title: "Agno" +sidebarTitle: "Agno" +description: "Build a search assistant using the Agno agent framework with LanceDB as the knowledge backend." +--- + +import { + PyFrameworksAgnoAgent, + PyFrameworksAgnoCliChat, + PyFrameworksAgnoIngestYoutube, + PyFrameworksAgnoSetup, +} from '/snippets/integrations.mdx'; + +[Agno](https://docs.agno.com/introduction) is a framework for building agentic AI applications. +It supports LanceDB as a knowledge backend, allowing you to easily ingest and retrieve external content for your agents. + +When you pair Agno's `Knowledge` system with LanceDB, you get a clean Agentic RAG setup. +We'll walk through the steps below to build a YouTube transcript-aware Agno assistant that can: +- Ingest a transcript from a YouTube video via the YouTube API +- Store embeddings and metadata in LanceDB +- Retrieve context during responses with hybrid search +- Ask questions about the video content in a CLI chat loop + +## Prerequisites {#prerequisites} + +Install dependencies: + + +```bash pip icon="terminal" +pip install -U agno openai lancedb youtube-transcript-api beautifulsoup4 +``` + +```bash uv icon="terminal" +uv add agno openai lancedb youtube-transcript-api beautifulsoup4 +``` + + +## Step 1: Configure LanceDB-backed knowledge {#step-1-configure-lancedb-backed-knowledge} + +First, you can initialize the core `Knowledge` object that your agent will use for retrieval. +It configures LanceDB as the vector store, enables hybrid search with native LanceDB FTS, and sets the embedding model. + + + {PyFrameworksAgnoSetup} + + +## Step 2: Fetch and ingest the YouTube transcript {#step-2-fetch-and-ingest-the-youtube-transcript} + +Next, extract a YouTube video ID, fetch the full transcript, and flatten it into text for indexing. +The snippet shown below then inserts that transcript text into the Agno knowledge base, which writes vectors and metadata to LanceDB. + + + {PyFrameworksAgnoIngestYoutube} + + + +This path explicitly fetches the transcript first, then inserts transcript text into LanceDB through Agno. + + +## Step 3: Create an Agno agent with knowledge search {#step-3-create-an-agno-agent-with-knowledge-search} + +The next step is to construct an Agno `Agent` and attach the knowledge base you just populated. +With `search_knowledge=True`, the agent performs retrieval before answering, so responses stay grounded in transcript context. + +In Agno, retrieval is exposed as a tool call that the model can invoke at runtime. +When `search_knowledge=True`, Agno makes a knowledge-search tool (shown in output as `search_knowledge_base(...)`) available to the model; the model decides when to call it, Agno executes the tool, and the returned context is fed back into the final answer. + + + {PyFrameworksAgnoAgent} + + +## Step 4: Start a CLI chat loop {#step-4-start-a-cli-chat-loop} + +You can now ask an initial question and then start an interactive loop for follow-up queries. +Each prompt runs through the same retrieval pipeline, so you can iteratively inspect what the transcript contains. + + + {PyFrameworksAgnoCliChat} + + + +Want local-first inference? Replace OpenAI model/embedder classes with Agno's Ollama providers. See Agno's Ollama knowledge examples: [docs.agno.com/examples/models/ollama/chat/knowledge](https://docs.agno.com/examples/models/ollama/chat/knowledge). + + +### Question 1 {#question-1} + +The following question is asked in the CLI chat loop: +``` +┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ ┃ +┃ Q: What kinds of data can LanceDB handle? ┃ +┃ ┃ +┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ +┏━ Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ ┃ +┃ • search_knowledge_base(query=What kinds of data can LanceDB handle?) ┃ +┃ • search_knowledge_base(query=LanceDB images audio video handle kinds of data ┃ +┃ can handle 'LanceDB can handle' 'kinds of data' 'images audio video' transcript) ┃ +┃ ┃ +┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ +┏━ Response (19.1s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ ┃ +┃ ┃ +┃ • Images, audio, video — i.e., multimodal AI data and “all manners of things ┃ +┃ you don't put into traditional databases” (per the transcript). ┃ +┃ ┃ +┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ +``` + +We get the response based on the transcript's contents as expected. + +### Question 2 {#question-2} + +Let's ask a more specific question about the CEO of LanceDB, which is also in the transcript: + +``` +You: What is the name of the CEO of LanceDB? +INFO Found 10 documents +┏━ Message ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ ┃ +┃ What is the name of the CEO of LanceDB? ┃ +┃ ┃ +┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ +┏━ Tool Calls ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ ┃ +┃ • search_knowledge_base(query=CEO of LanceDB) ┃ +┃ ┃ +┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ +┏━ Response (16.7s) ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓ +┃ ┃ +┃ ┃ +┃ • According to the retrieved YouTube transcript/title, the CEO of LanceDB is ┃ +┃ Chang She. ┃ +┃ ┃ +┗━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┛ +``` + +We get the response based on the transcript's contents and title as expected. + +## Why this works well {#why-this-works-well} + +To start, LanceDB OSS can run from a local directory, so transcript data can stay on your machine when you are using the OSS stack. + +- You do not need to maintain a separate transcript parser in your application code. +- You do not need to hand-roll chunking and retrieval orchestration across multiple modules. +- One explicit Agno `Knowledge` object, backed by LanceDB, defines both ingestion and search behavior in one place. +- Fewer moving parts means the tutorial stays readable and the same pattern is easier to carry into production code. + +As your application needs grow, you can migrate to LanceDB [Enterprise](/enterprise) for +convenience features like automatic compaction and reindexing and the ability to scale to +really large datasets. diff --git a/docs/web/integrations/ai/genkit.mdx b/docs/web/integrations/ai/genkit.mdx new file mode 100644 index 000000000..6314842b8 --- /dev/null +++ b/docs/web/integrations/ai/genkit.mdx @@ -0,0 +1,73 @@ +--- +title: "GenKit" +sidebarTitle: "GenKit" + +--- + +import { + TsFrameworksGenkitCustomIndexer, + TsFrameworksGenkitCustomRetriever, + TsFrameworksGenkitUsage, +} from '/snippets/integrations.mdx'; + +### genkitx-lancedb {#genkitx-lancedb} +Genkit is an open-source framework for building end-to-end AI and RAG pipelines with a clean, TypeScript-first +developer experience. The genkitx-lancedb plugin lets you use LanceDB as a high-performance vector store +inside your Genkit flows, so you can index, search, and retrieve data efficiently as part of your AI +applications. + +### Installation {#installation} +```bash +pnpm install genkitx-lancedb +``` + +### Usage {#usage} + +Adding LanceDB plugin to your genkit instance. + + + {TsFrameworksGenkitUsage} + + +You can run this app with the following command: +```bash +genkit start -- tsx --watch src/index.ts +``` + +This'll add LanceDB as a retriever and indexer to the genkit instance. You can see it in the GUI view +Screenshot 2025-05-11 at 7 21 05 PM + +**Testing retrieval on a sample table** +Let's see the raw retrieval results + +Screenshot 2025-05-11 at 7 21 05 PM +On running this query, you'll get 5 results fetched from the lancedb table, where each result looks something like this: +Screenshot 2025-05-11 at 7 21 18 PM + + + +## Creating a custom RAG flow {#creating-a-custom-rag-flow} + +Now that we've seen how you can use LanceDB in a Genkit pipeline, let's refine the flow and create a RAG. A RAG flow will consist of an index and a retriever with its outputs postprocessed and fed into an LLM for final response + +### Creating custom indexer flows {#creating-custom-indexer-flows} +You can also create custom indexer flows, utilizing more options and features provided by LanceDB. + + + {TsFrameworksGenkitCustomIndexer} + + +Screenshot 2025-05-11 at 8 35 56 PM + +In your console, you can see the logs + +Screenshot 2025-05-11 at 7 19 14 PM + +### Creating custom retriever flows {#creating-custom-retriever-flows} +You can also create custom retriever flows, utilizing more options and features provided by LanceDB. + + {TsFrameworksGenkitCustomRetriever} + +Now using our retrieval flow, we can ask a question about the ingested PDF +Screenshot 2025-05-11 at 7 18 45 PM + diff --git a/docs/web/integrations/ai/hermes-agent.mdx b/docs/web/integrations/ai/hermes-agent.mdx new file mode 100644 index 000000000..a5521a9d8 --- /dev/null +++ b/docs/web/integrations/ai/hermes-agent.mdx @@ -0,0 +1,327 @@ +--- +title: "Hermes Agent" +sidebarTitle: "Hermes Agent" +description: "Use LanceDB as a persistent, semantic memory backend for Hermes Agent. Get durable recall across sessions with vector and hybrid search." +--- + +[Hermes Agent](https://github.com/NousResearch/hermes-agent) is a self-hosted, open-source +personal agent from [Nous Research](https://nousresearch.com). You can talk to it from a +terminal UI or reach the same agent from Telegram, Discord, and Slack, and it exposes a +dedicated slot for external *memory providers* that run alongside its built-in notes. + +The [LanceDB memory plugin](https://github.com/lancedb/hermes-agent-memory) fills that slot. +It gives Hermes durable, semantic recall across sessions: state a preference or a project +convention once, and the agent can retrieve it weeks later in a brand-new session — even when +you ask for it in completely different words. Everything runs inside Hermes' own Python +process, storing a single LanceDB table on local disk. There's no memory server to operate. + + +**The mental model is clean** + +- Hermes owns the agent loop +- LanceDB manages the durable long-term memory and offers semantic recall. + + +## Why LanceDB fits agent memory {#why-lancedb-fits-agent-memory} + +Out of the box, Hermes remembers with a small curated notes file frozen into the system +prompt, plus lexical (keyword) search over past sessions. Both are useful, but keyword search +misses paraphrases of what you originally typed — the exact thing you need when recalling a +fact you phrased differently months ago. + +LanceDB is an embedded retrieval library, which makes it a natural fit here: + +- **No server to stand up** — it reads and writes a table on local disk, so the plugin ships + as a dependency rather than a service to operate. +- **One table holds everything** — content, metadata, and embeddings live together. A memory + becomes a structured row with a category, tags, timestamps, and provenance, not just a text + blob. +- **Query it any way you need** — vector similarity for meaning, BM25 full-text for exact + names and jargon, a hybrid of the two, or plain metadata filters to keep recall scoped to + the right workspace. +- **It scales up** — the same table abstraction carries over to larger LanceDB deployments + later, so the local setup is never a dead end. + +## Install and activate {#install-and-activate} + + +Want to try this without touching your existing Hermes setup? Run everything in an isolated +profile: `hermes profile create demo`, then add `-p demo` to the commands below. When you're +done, `rm -rf ~/.hermes/profiles/demo` removes all trace. + + + + +Skip this if you already have Hermes installed. + +```bash +curl -fsSL https://raw.githubusercontent.com/NousResearch/hermes-agent/main/scripts/install.sh | bash +``` + + + +This shallow-clones the plugin into `~/.hermes/plugins/lancedb/`. + +```bash +hermes plugins install lancedb/hermes-agent-memory +``` + + + +Hermes loads plugins inside its own Python interpreter, so the dependencies go *there* — not +into a separate virtualenv. (This interpreter is shared across profiles, so you only install +once.) + +```bash +uv pip install --python ~/.hermes/hermes-agent/venv/bin/python3 lancedb openai pyyaml +``` + + + +The plugin turns conversations into embeddings, so it needs an embeddings key. By default that +is OpenAI, so set `OPENAI_API_KEY` in your environment or in `~/.hermes/.env`. + + +Prefer a local or non-OpenAI model? The plugin uses an OpenAI-compatible client, so you can +point it at any compatible endpoint (OpenRouter, Ollama, vLLM, …) in your config — no code +change needed. See [Configuration](#configuration) below. + + + + +Switch memory on and pick this plugin: + +```bash +hermes memory setup # choose "lancedb" +``` + +Then confirm it's actually active before you start chatting — this is the one step worth not +skipping, because Hermes quietly falls back to its built-in notes if the provider isn't set: + +```bash +hermes memory status +``` + +```text +Memory status +──────────────────────────────────────── + Built-in: always active + Provider: lancedb + + Plugin: installed ✓ + Status: available ✓ +``` + +You want to see `Provider: lancedb` with both `installed ✓` and `available ✓`. + + + +## The memory tools {#the-memory-tools} + +Once activated, the agent has four tools for working with long-term memory: + +| Tool | What it does | +|:--|:--| +| `lancedb_recall` | Semantic (vector, the default) or hybrid search over your workspace memory. Returns matching facts with scores and provenance. | +| `lancedb_remember` | Stores a durable fact when you explicitly ask. Deduplicated by content hash, so remembering the same thing twice doesn't pile up rows. | +| `lancedb_read` | Fetches a single memory by ID, optionally with the original conversation messages it was distilled from. | +| `lancedb_forget` | Deletes safely: previews candidates first, then deletes by exact ID, so nothing disappears by accident. | + +Beyond these tools, the plugin also captures durable facts from your conversations +automatically — an auxiliary model distills them before context is compressed and again when a +session ends, so insights survive even when the raw messages are summarized away. + +## Walkthrough {#walkthrough} + +"_Teach it your project preferences_" + +Let's make this concrete with the pain we opened on: re-explaining your setup to the agent every session. +We'll save a convention once and then prove a brand-new session can recall it. This example will touch all four +tools along the way. + +### Remember {#remember} + +Ask Hermes to commit a convention to long-term memory. Saying "remember in long-term memory" +makes sure it lands in the LanceDB store, which shows up as the `⚡ lancedb_r` (`lancedb_remember`) +line below: + +```text +● Remember in long-term memory: for this project I only use uv, never pip, and I always add type hints to Python functions. + + ┊ 🧠 memory +memory: "For this project, the user only uses uv for Python package management, never pip, and always adds type hints to Python functions." + ┊ ⚡ lancedb_r 0.0s + ─ ⚕ Hermes ──────────────────────────────────────────────────────────────── + + Remembered. I've stored that project convention: use uv only, never pip, and always add type hints to Python functions. +``` + +### Recall {#recall} + +First, take Hermes' built-in notes out of the picture so recall can *only* come from LanceDB — +the two layers run side by side otherwise, and either could answer: + +```bash +hermes config set memory.memory_enabled false +``` + +Now start a fresh session (`/new`) and ask for the convention back, worded completely +differently from how you saved it: + +```text +● /new +● Look through your long-term memory for my Python project preferences. + + ─ ⚕ Hermes ──────────────────────────────────────────────────────────────── + + I found one durable memory about Python project preferences: + + - Project Python tooling preference is uv; pip is not used; type hints are required. +``` + +This is the whole point in one exchange. You asked about your *preferences* using none of the +words you stored the fact with — no *uv*, no *pip*, no *type hints* — yet a fresh session, with +none of the earlier conversation in context, pulls it straight back. Semantic search matches on +meaning rather than exact wording, which is precisely where keyword search falls short. + + +Turn the built-in layer back on for everyday use with `hermes config set memory.memory_enabled true` +— the two memory layers happily coexist. We only disabled it here to prove the recall came from +LanceDB. + + +### Read {#read} + +You can also ask where a fact came from. Hermes attributes the answer to its stored memory +rather than guessing from a file in the repo (under the hood, `lancedb_read` can also return +the exact source messages a fact was distilled from): + +```text +● Where did that uv preference come from? + + ─ ⚕ Hermes ──────────────────────────────────────────────────────────────── + + It came from the project's persistent memory, not from a codebase file I inspected. + + Specifically, the recalled memory says: + - "For this project, the user only uses uv for Python package management, never pip, and always adds type hints to Python functions." +``` + +### Forget {#forget} + +When a preference changes, ask Hermes to drop the old fact. The tool calls tell the whole +story: the two `⚡ lancedb_f` (`lancedb_forget`) lines are it previewing matches and then +deleting, and the trailing `⚡ lancedb_r` is it saving the replacement in the same breath: + +```text +● I've switched from uv to pixi. Forget the uv preference. + + ┊ ⚡ lancedb_f uv preference python package management never pip type hints 0.3s + ┊ ⚡ lancedb_f 0.0s + ┊ ⚡ lancedb_r 0.2s + ─ ⚕ Hermes ──────────────────────────────────────────────────────────────── + + Done — I removed the uv preference from memory and replaced it with: + + - This project uses pixi for Python package management + - Type hints on Python functions are still preferred +``` + +Remember, recall, read, forget: four small operations that between them cover the entire +lifecycle of a durable memory. + +## Retrieval modes {#retrieval-modes} + +Recall ships in `vector` mode by default — pure semantic search, which is what survives the +paraphrasing you saw above. If you also need exact name or jargon matching, switch to `hybrid` +(vector + BM25) and choose how the two legs are fused: RRF, a vector-biased linear blend, or a +cross-encoder reranker. Mode is set per call; fusion is a config setting. + +```yaml +# ~/.hermes/config.yaml +plugins: + lancedb: + retrieval: + mode: hybrid # vector (default) | hybrid + reranker: + type: rrf # how the vector + BM25 legs are fused + # Swap RRF for a reranking pass (pulls in sentence-transformers + torch): + # type: cross-encoder + # model: cross-encoder/ettin-reranker-17m-v1 + # rerank_top_n: 50 +``` + +The cross-encoder is the one path that pulls in a local ML stack, so it stays opt-in. It +defaults to the compact 17M-parameter [ettin reranker](https://huggingface.co/cross-encoder/ettin-reranker-17m-v1). + +## Inspect the store {#inspect-the-store} + +Everything lives in one table named `memories` at `~/.hermes/lancedb/memories.lance`. Because +it's a plain LanceDB table, you can open it directly and see exactly what the agent has stored +— a `kind` column separates extracted `fact` rows from the raw `turn` rows they were drawn +from: + +```python +import lancedb + +db = lancedb.connect("~/.hermes/lancedb") +tbl = db.open_table("memories") +print(tbl.to_pandas()[["kind", "category", "content"]].head()) +``` + +## Configuration {#configuration} + +The plugin runs on sensible defaults once activated — you don't have to configure anything. +`~/.hermes/config.yaml` is purely for overrides. Two common ones: + +Use a cheaper model for the auxiliary fact-extraction calls: + +```yaml +# ~/.hermes/config.yaml +auxiliary: + lancedb_extraction: + provider: openrouter + model: google/gemini-3-flash +``` + +Point embeddings at a fully local endpoint (for example, Ollama) so nothing leaves your +machine: + +```yaml +# ~/.hermes/config.yaml +plugins: + lancedb: + embedding: + model: nomic-embed-text + base_url: http://localhost:11434/v1 + api_key_env: OLLAMA_API_KEY # any value works for local Ollama +``` + + +Changing the embedding model (or its dimension) against an existing store requires recreating +the table — the plugin fails loudly on a dimension mismatch rather than silently returning +nothing. Every option is documented in the plugin's [`default_config.yaml`](https://github.com/lancedb/hermes-agent-memory/blob/main/src/default_config.yaml). + + +## Benchmark {#benchmark} + +On [LongMemEval-S](https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned), a +long-conversation QA benchmark, LanceDB's semantic recall clearly beat Hermes' built-in lexical +search (0.66 vs. 0.53 answer accuracy) by finding the right messages even when the question was +worded differently from the original conversation. For the full methodology, the +per-question-type breakdown, and a reproducible harness, see the +[blog post](https://www.lancedb.com/blog/semantic-memory-for-hermes-agent-with-lancedb) and the +[benchmark harness](https://github.com/lancedb/hermes-agent-memory/tree/main/benchmarks). + +## Why this works well {#why-this-works-well} + +- **It's local-first and embedded.** The LanceDB memory table lives on your disk with no server to run; + the plugin installs as a dependency of Hermes' own environment. +- **Recall survives paraphrasing.** Semantic search matches meaning, not spelling, which is the + failure mode that sinks keyword-only session search. +- **Memories are structured and traceable.** Each fact is a row with metadata and a link back + to the messages it came from, and `forget` always previews before it deletes. +- **Nothing about it is a dead end.** As your needs grow, the same table abstraction carries + over to LanceDB [Enterprise](/enterprise) for automatic compaction, reindexing, and scale. + +To try it, install the plugin, enable it with `hermes memory setup`, and run the kind of +workflow we walked through above. diff --git a/docs/web/integrations/ai/huggingface.mdx b/docs/web/integrations/ai/huggingface.mdx new file mode 100644 index 000000000..62ac7e8cd --- /dev/null +++ b/docs/web/integrations/ai/huggingface.mdx @@ -0,0 +1,341 @@ +--- +title: "Hugging Face Hub" +sidebarTitle: "Hugging Face" +description: "Use LanceDB directly on Lance datasets hosted on the Hugging Face Hub for multimodal search and retrieval." +--- + +[Hugging Face Hub](https://huggingface.co/datasets?format=format:lance&sort=trending) is a popular platform for sharing machine learning datasets, models, and other resources. + +LanceDB can directly scan Lance datasets hosted on the [Hugging Face Hub](https://huggingface.co/datasets?format=format:lance) with `hf://` URIs. +This is enabled under the hood by the [lance-huggingface](https://lance.org/integrations/huggingface/) +integration that allows users to stream Lance datasets directly from Hugging Face without needing to +download them first. + +For ML and AI engineers working in LanceDB, this capability is incredibly useful for quickly exploring +multimodal datasets and reusing Lance datasets shared by others, without writing custom data loaders +or preprocessing pipelines. + +The snippets below use the [`lance-format/laion-1m`](https://huggingface.co/datasets/lance-format/laion-1m) +dataset published in Lance format. The dataset includes a million image-caption pairs, and the +Lance dataset can package image embeddings alongside the metadata. This makes it useful for +demonstrating LanceDB's multimodal search capabilities in combination with easy sharing via the +Hugging Face Hub. + +The LAION table includes multimodal columns such as: + +- `image` (inline JPEG bytes) +- `caption` (text) +- `img_emb` (image embedding vector) +- metadata fields such as `url` and `similarity` + +## Install dependencies {#install-dependencies} + +```bash +pip install lancedb pillow +``` + +## Open the dataset with LanceDB {#open-the-dataset-with-lancedb} + +LanceDB can open the dataset directly from the Hub, without needing to download it first. +Note that in LanceDB, you need to specify the table name when opening a Lance dataset, +and the Hugging Face convention is to use `train` and `test` splits for datasets. +The LAION dataset is uploaded as a single split named `train`, so we specify the table name +that contains the `*.lance` files when opening the dataset. + +```python +import lancedb + +db = lancedb.connect("hf://datasets/lance-format/laion-1m/data") +table = db.open_table("train") + +print(f"Opened table: {table.name}") +print(f"Rows: {len(table)}") +``` + +## Inspect schema and available indexes {#inspect-schema-and-available-indexes} + +```python +print(table.schema) +``` +This prints the schema of the LAION table. Note that there's an image embedding column that's +a fixed-size list of 768-dimensional floats, and a binary column containing the raw JPEG bytes of the image. +``` +image_path: string +caption: string +NSFW: string +similarity: double +LICENSE: string +url: string +key: string +status: string +error_message: null +width: int64 +height: int64 +original_width: int64 +original_height: int64 +exif: string +md5: string +img_emb: fixed_size_list[768] + child 0, item: float +image: binary +``` + +When inspecting Lance datasets from Hugging Face, it's also a good idea to check whether the dataset author included +any pre-built indexes that you can use for search. You can check the available indexes with: + +```python +print(table.list_indices()) +``` +``` +[ + Index(IvfPq, columns=["img_emb"], name="img_emb_idx"), + Index(FTS, columns=["caption"], name="caption_idx") +] +``` + +In this case, we see that we have an IVF_PQ vector index on the `img_emb` column, and an FTS index on the `caption` +column, which means we can directly do vector search on the image embeddings and keyword search on the captions +without needing to build the indexes ourselves! + + +If you see an empty list, it may be because the dataset author did not include the index files when uploading +to Hugging Face. You can download the dataset locally, and build the indexes yourself. See the [indexing guide](/indexing/) +for instructions on building different types of indexes with LanceDB. + + +## Projection scan {#projection-scan} + +Run a simple scan by projecting relevant columns to get a feel for the dataset. For example, we +can run a search without any filters or input parameters to get a small subset of the data: + +```python +rows = ( + table.search() + .select(["caption", "url", "similarity"]) + .limit(3) + .to_list() +) + +for i, row in enumerate(rows, start=1): + print(f"{i}. {row['caption']}") + print(f" url={row['url']}") + print(f" similarity={row['similarity']}") +``` + +We get the first three rows and their metadata printed out, which look like this: +``` +1. Cordelia and Dudley on their wedding day last year + url=https://i.dailymail.co.uk/i/pix/2012/01/05/article-2082728-0EF8956600000578-53_233x315.jpg + similarity=0.2926466464996338 +2. Statistics on challenges for automation in 2021 + url=https://verloop.io/wp-content/uploads/2021/02/Challenges.jpg + similarity=0.30174341797828674 +3. Teacher Gifts / Great gifts for your child's teacher. Don't know what to get? Take a look at these gifts that the teacher in your life will love! + url=https://i.pinimg.com/custom_covers/216x146/550494823141083777_1487893945.jpg + similarity=0.3362061381340027 +``` + +## Scan and filter data {#scan-and-filter-data} + +Filtered search is a common pattern to narrow down interesting subsets of the data during early +exploration. Here's an example: + +```python +filtered = ( + table.search() + .where("height > 600") + .select(["caption", "url", "width", "height"]) + .limit(3) + .to_list() +) + +for row in filtered: + print(row["caption"], row["url"], row["width"], row["height"]) +``` + +This prints out the metadata for large images with height greater than 600 pixels: +``` +Luca Trousers, mustard stripe https://cdn.shopify.com/s/files/1/0151/5333/products/IMG_0791_1024x1024.jpg?v=1585142190 384 766 +Baby Blue Fitted Short Sleeve T Shirt 3 https://cdn-img.prettylittlething.com/a/d/d/1/add198cab3ec30a61102437275573f4963642528_cmf6022_3.jpg 384 612 +pattern cutting made easy pdf https://i.pinimg.com/736x/7c/6c/a7/7c6ca7361815a8929b3dd6ad34a03ab9.jpg 384 1045 +``` + +## Export image bytes to local files {#export-image-bytes-to-local-files} + +To work with a subset of the data locally, you can export the image bytes from the table and save them as JPEG files. +```python +from pathlib import Path + +sample = ( + table.search() + .select(["image", "caption"]) + .limit(3) + .to_list() +) + +out_dir = Path("samples") +out_dir.mkdir(exist_ok=True) + +for i, row in enumerate(sample): + out_path = out_dir / f"laion_{i}.jpg" + with open(out_path, "wb") as f: + f.write(row["image"]) + print(f"Saved {out_path} | caption={row['caption']}") +``` + +You can now preview the images you just exported on your local machine to get a better sense of the data. + +## Vector search {#vector-search} + +You can use LanceDB to run vector search directly on the data on the Hub, **without needing to download the dataset +or build your own vector index**. This makes it incredibly easy to explore the dataset and iterate on your search queries +before you decide to download a local copy for further experimentation on your end. + +```python +# Pick an arbitrary image embedding from the dataset +query_embedding = ( + table.search() + .select(["img_emb"]) + .limit(1) + .to_list()[0]["img_emb"] +) + +results = ( + table.search(query_embedding, vector_column_name="img_emb") + .select(["caption", "url", "_distance"]) + .limit(3) + .to_list() +) + +for row in results: + print(row["_distance"], row["caption"]) +``` + +| distance | caption | +| --- | --- | +| 0.17765313386917114 | Cordelia and Dudley on their wedding day last year | +| 0.17765313386917114 | Cordelia and Dudley on their wedding day last year | +| 0.17765313386917114 | Cordelia and Dudley on their wedding day last year | + + +Note that the LAION dataset is known to contain a lot of duplicate images, so you may see the same image +showing up multiple times in the search results. + + +## Full-text search {#full-text-search} + +Run an FTS search query that uses BM25 ranking on the `caption` column (on which we already have an FTS index): + +```python +fts_results = ( + table.search("dog running on beach", query_type="fts") + .select(["caption", "url", "_score"]) + .limit(3) + .to_list() +) +``` + +| caption | url | _score | +| --- | --- | --- | +| running with dog | https://www.doggytastic.com/wp… | 15.73168 | +| Dog Running in Water | https://static.wixstatic.com/m… | 14.756516 | +| Dogs on the run by heidiannemo… | http://ih2.redbubble.net/image… | 14.756516 | + +## Download the full dataset {#download-the-full-dataset} + + +You may hit Hugging Face rate limits when streaming large samples from `hf://`, despite using a Hugging Face token. +For repeated queries or queries that operate on the full dataset, it's recommended to +download the dataset locally and query from disk. + + +Here's how to download the entire dataset via the [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli): + +```bash +huggingface-cli download lance-format/laion-1m --repo-type dataset --local-dir ./laion-1m +``` + +## Upload your own datasets to Hugging Face in Lance format {#upload-your-own-datasets-to-hugging-face-in-lance-format} + +This section shows how you can upload your own Lance datasets to the Hugging Face Hub to share with the community. + +First, install the [Hugging Face CLI](https://huggingface.co/docs/huggingface_hub/en/guides/cli) and export both `OPENAI_API_KEY` and `HF_TOKEN`. +Then, create a Lance dataset using LanceDB on a local machine, and then proceed to upload it to the Hub via a CLI command. + +```bash +export OPENAI_API_KEY=... +export HF_TOKEN=hf_... +hf auth login --token "$HF_TOKEN" +``` + +A typical sequence of steps is given below. + +### 1\. Upload your local directory to the Hub {#1-upload-your-local-directory-to-the-hub} + +Upload the full local directory to a specified repository on the Hugging Face Hub. The command below uploads the contents of your local LanceDB directory at `/path/to/your_local_dir` to a new repository named `your_hf_org/repo_name` under your Hugging Face account. + +```bash bash icon="code" +hf upload-large-folder /path/to/your_local_dir your_hf_org/repo_name \ + --repo-type dataset \ + --revision main +``` + + +The `upload-large-folder` command is designed for [uploading large datasets](https://huggingface.co/docs/huggingface_hub/en/guides/upload) (potentially terabytes in size) and will handle multipart uploads, retries, and resuming interrupted uploads. + + +### 2\. Inspect dataset versions {#2-inspect-dataset-versions} + +Because you can query your remote dataset directly from Hugging Face with `hf://` URIs in LanceDB, you can easily inspect the dataset versions and updates on the Hub without needing to download the data locally. This is very useful to keep track of changes to the dataset and iterate on your data collection and curation process. + +```python Python icon="python" +import lancedb + +db = lancedb.connect("hf://datasets/your_hf_org/repo_name") +table = db.open_table("table_name") + +versions = table.list_versions() +print(versions) +``` +This will print out the list of versions available for the dataset on the Hub, along with their metadata such as creation date and description. + +### 3\. Add a dataset card {#3-add-a-dataset-card} + +The Hub dataset card allows you to communicate the schema and usage of the dataset to other developers. It sits at the repo's root in a file named `README.md` on the Hub. +This project keeps the source card text in `HF_DATASET_CARD.md`, so you can publish updates +to the dataset there and upload it as `README.md` using the following command on the HF CLI: +this requires a regular `hf upload` because it is a single-file upload to a specific target path (a custom commit message can be added if you wish). + +```bash +hf upload lancedb/magical_kingdom HF_DATASET_CARD.md README.md \ + --repo-type dataset \ + --commit-message "Update dataset card" +``` + +### 4\. Update the dataset {#4-update-the-dataset} + +Over time, you may want to add new rows (append) or columns (backfill) to your dataset as your needs evolve. You can make the necessary updates to your local dataset using LanceDB, and then upload the updated version back to the Hub with the same `hf upload-large-folder` command. + +```bash bash icon="code" +hf upload-large-folder /path/to/your_local_dir your_hf_org/repo_name \ + --repo-type dataset \ + --revision main +``` +The CLI will only upload the new data that has changed since the last upload, avoiding wasted I/O while making it easy to keep your dataset up-to-date on the Hub. + +That's it! Your dataset is now updated on the Hub with the new data and schema changes, and other users can query the latest version of the dataset directly from Hugging Face with `hf://` URIs in LanceDB. + +## Explore more Lance datasets on Hugging Face {#explore-more-lance-datasets-on-hugging-face} + +The LanceDB team is actively uploading useful and interesting datasets in Lance format to the Hugging Face Hub +under the [lance-format](https://huggingface.co/lance-format) organization. We actively encourage the Hugging Face +and LanceDB communities to upload their own Lance datasets to the Hub to share with others! + +In the meantime, feel free to check out the Hugging Face Hub to discover more Lance datasets uploaded by the community. + + +Click here to explore the latest trending Lance datasets on 🤗 Hugging Face! + diff --git a/docs/web/integrations/ai/kiln.mdx b/docs/web/integrations/ai/kiln.mdx new file mode 100644 index 000000000..c93f31e17 --- /dev/null +++ b/docs/web/integrations/ai/kiln.mdx @@ -0,0 +1,48 @@ +--- +title: "Kiln AI" +sidebarTitle: "Kiln" + +--- + +[**Kiln**](https://kiln.tech) is a free tool for building production-ready AI systems, combining an intuitive desktop application and an open-source Python library. It supports RAG pipelines, evaluations, agents, MCP tool-calling, synthetic data generation, and fine-tuning. Kiln provides deep integration with LanceDB for vector search, full-text search (BM25), and hybrid search. + +## Quick Start: Build a RAG Pipeline in 5 Minutes with Kiln and LanceDB {#quick-start-build-a-rag-pipeline-in-5-minutes-with-kiln-and-lancedb} + +Watch the [quick start overview on Vimeo](https://vimeo.com/1119945690). + +Kiln's [app](https://kiln.tech/download) makes it easy to: + + - Build a RAG pipeline with a simple drag-and-drop interface + - [Compare](#find-the-best-rag-pipeline-for-your-use-case) search index options (powered by LanceDB), document extractors, embedding models, and chunking strategies + - Create end-to-end [evaluations](https://docs.kiln.tech/docs/evaluations) to determine which search configuration works best for your use case + - Load your data from Kiln into [LanceDB Enterprise](/enterprise) for production use + - Iterate with confidence by evaluating new content, prompts, models, and embeddings in minutes instead of weeks + +## Find the Best RAG Pipeline for Your Use Case {#find-the-best-rag-pipeline-for-your-use-case} + +There is no universal best RAG solution—only the best solution for your specific use case. Kiln makes it easy to compare state-of-the-art configurations and find which works best for you. + +Start with pre-configured templates for state-of-the-art RAG at various performance/quality/cost levels, or experiment with any combination of options: + +|Area|Technologies|Description| +|:----|:----|:----| +|Search Index|LanceDB|Compare LanceDB's vector search, full-text search (BM25), and hybrid search to find the best approach for your use case.| +|Content|Kiln Document Library|Collaborate on a document library with your team to find the best content for your RAG. Track every revision and tag document sets.| +|Document Extraction|Gemini, OpenAI GPT, Qwen VL, and more|Find the most accurate document extraction models for converting PDFs, images, audio, video, and other formats into textual data for RAG.| +|Embeddings|Embedding models from Gemini, OpenAI, Nomic, Qwen, and more|Find the embedding model best suited to your use case.| +|Chunking|LlamaIndex|Find the ideal chunk size and method.| + +## Get Started {#get-started} + +To get started, download the [Kiln App](https://kiln.tech/download), create a project, and navigate to "Docs & Search". + +See the [Kiln documentation for creating a RAG system](https://docs.kiln.tech/docs/documents-and-search-rag) for details on each step of the process. + +## More Information {#more-information} + + - [Kiln Homepage](https://kiln.tech) + - [Download the Kiln App](https://kiln.tech/download) + - [Kiln GitHub Repository](https://github.com/Kiln-AI/Kiln) + - [Building RAG Systems - Kiln Documentation](https://docs.kiln.tech/docs/documents-and-search-rag) + - [Python Library](https://pypi.org/project/kiln-ai/) or `pip install kiln_ai` + diff --git a/docs/web/integrations/ai/langchain.mdx b/docs/web/integrations/ai/langchain.mdx new file mode 100644 index 000000000..363331093 --- /dev/null +++ b/docs/web/integrations/ai/langchain.mdx @@ -0,0 +1,226 @@ +--- +title: "LangChain" +sidebarTitle: "LangChain" + +--- + +import { + PyFrameworksLangchainAddImages, + PyFrameworksLangchainAddTexts, + PyFrameworksLangchainCreateIndex, + PyFrameworksLangchainMaxMarginalRelevance, + PyFrameworksLangchainQuickStart, + PyFrameworksLangchainSimilaritySearch, + PyFrameworksLangchainSimilaritySearchByVector, + PyFrameworksLangchainSimilaritySearchByVectorWithScores, + PyFrameworksLangchainSimilaritySearchWithScores, + PyFrameworksLangchainVectorStoreConfig, +} from '/snippets/integrations.mdx'; + +**LangChain** is a framework designed for building applications with large language models (LLMs) by chaining together various components. It supports a range of functionalities including memory, agents, and chat models, enabling developers to create context-aware applications. + +![Illustration](https://raw.githubusercontent.com/lancedb/assets/refs/heads/main/docs/assets/integration/langchain_rag.png) + +LangChain streamlines these stages (in figure above) by providing pre-built components and tools for integration, memory management, and deployment, allowing developers to focus on application logic rather than underlying complexities. + +Integration of **Langchain** with **LanceDB** enables applications to retrieve the most relevant data by comparing query vectors against stored vectors, facilitating effective information retrieval. It results in better and context aware replies and actions by the LLMs. + +## Quick Start {#quick-start} +You can load your document data using langchain's loaders, for this example we are using `TextLoader` and `OpenAIEmbeddings` as the embedding model. + + + {PyFrameworksLangchainQuickStart} + + +## Documentation {#documentation} +In the above example `LanceDB` vector store class object is created using `from_documents()` method which is a `classmethod` and returns the initialized class object. + +You can also use `LanceDB.from_texts(texts: List[str],embedding: Embeddings)` class method. + +The exhaustive list of parameters for `LanceDB` vector store are : + +|Name|type|Purpose|default| +|:----|:----|:----|:----| +|`connection`| (Optional) `Any` |`lancedb.db.LanceDBConnection` connection object to use. If not provided, a new connection will be created.|`None`| +|`embedding`| (Optional) `Embeddings` | Langchain embedding model.|Provided by user.| +|`uri`| (Optional) `str` |It specifies the directory location of **LanceDB database** and establishes a connection that can be used to interact with the database. |`/tmp/lancedb`| +|`vector_key` |(Optional) `str`| Column name to use for vector's in the table.|`'vector'`| +|`id_key` |(Optional) `str`| Column name to use for id's in the table.|`'id'`| +|`text_key` |(Optional) `str` |Column name to use for text in the table.|`'text'`| +|`table_name` |(Optional) `str`| Name of your table in the database.|`'vectorstore'`| +|`api_key` |(Optional `str`) |API key to use for LanceDB Enterprise deployment.|`None`| +|`region` |(Optional) `str`| Region to use for LanceDB Enterprise deployment.| Only for LanceDB Enterprise : `None`.| +|`mode` |(Optional) `str` | Mode to use for adding data to the table. Valid values are "append" and "overwrite".|`'overwrite'`| +|`table`| (Optional) `Any`| You can connect to an existing table of LanceDB, created outside of langchain, and utilize it.|`None`| +|`distance`|(Optional) `str`| The choice of distance metric used to calculate the similarity between vectors.|`'l2'`| +|`reranker` |(Optional) `Any`| The reranker to use for LanceDB.|`None`| +|`relevance_score_fn` |(Optional) `Callable[[float], float]` | Langchain relevance score function to be used.|`None`| +|`limit`|`int`|Set the maximum number of results to return.| `DEFAULT_K` (it is 4)| + + + {PyFrameworksLangchainVectorStoreConfig} + + +### Methods {#methods} + +##### `add_texts()` + +This method turn texts into embedding and add it to the database. + +|Name|Purpose|defaults| +|:---|:---|:---| +|`texts`|`Iterable` of strings to add to the vectorstore.|Provided by user| +|`metadatas`|Optional `list[dict()]` of metadatas associated with the texts.|`None`| +|`ids`|Optional `list` of ids to associate with the texts.|`None`| +|`kwargs`| Other keyworded arguments provided by the user. |-| + +It returns list of ids of the added texts. + + + {PyFrameworksLangchainAddTexts} + + +------ + + +##### create_index() + +This method creates a scalar(for non-vector cols) or a vector index on a table. + +|Name|type|Purpose|defaults| +|:---|:---|:---|:---| +|`vector_col`|`Optional[str]`| Provide if you want to create index on a vector column. |`None`| +|`col_name`|`Optional[str]`| Provide if you want to create index on a non-vector column. |`None`| +|`metric`|`Optional[str]` |Provide the metric to use for vector index. choice of metrics: 'l2', 'dot', 'cosine'. |`l2`| +|`num_partitions`|`Optional[int]`|Number of partitions to use for the index.|`256`| +|`num_sub_vectors`|`Optional[int]` |Number of sub-vectors to use for the index.|`96`| +|`index_cache_size`|`Optional[int]` |Size of the index cache.|`None`| +|`name`|`Optional[str]` |Name of the table to create index on.|`None`| + +For index creation make sure your table has enough data in it. An ANN index is usually not needed for datasets ~100K vectors. For large-scale (>1M) or higher dimension vectors, it is beneficial to create an ANN index. + + + {PyFrameworksLangchainCreateIndex} + + +------ + +##### similarity_search() + +This method performs similarity search based on **text query**. + +| Name | Type | Purpose | Default | +|---------|----------------------|---------|---------| +| `query` | `str` | A `str` representing the text query that you want to search for in the vector store. | N/A | +| `k` | `Optional[int]` | It specifies the number of documents to return. | `None` | +| `filter` | `Optional[Dict[str, str]]`| It is used to filter the search results by specific metadata criteria. | `None` | +| `fts` | `Optional[bool]` | It indicates whether to perform a full-text search (FTS). | `False` | +| `name` | `Optional[str]` | It is used for specifying the name of the table to query. If not provided, it uses the default table set during the initialization of the LanceDB instance. | `None` | +| `kwargs` | `Any` | Other keyworded arguments provided by the user. | N/A | + +Return documents most similar to the query **without relevance scores**. + + + {PyFrameworksLangchainSimilaritySearch} + + +------ + +##### similarity_search_by_vector() + +The method returns documents that are most similar to the specified **embedding (query) vector**. + +| Name | Type | Purpose | Default | +|-------------|---------------------------|---------|---------| +| `embedding` | `List[float]` | The embedding vector you want to use to search for similar documents in the vector store. | N/A | +| `k` | `Optional[int]` | It specifies the number of documents to return. | `None` | +| `filter` | `Optional[Dict[str, str]]`| It is used to filter the search results by specific metadata criteria. | `None` | +| `name` | `Optional[str]` | It is used for specifying the name of the table to query. If not provided, it uses the default table set during the initialization of the LanceDB instance. | `None` | +| `kwargs` | `Any` | Other keyworded arguments provided by the user. | N/A | + +**It does not provide relevance scores.** + + + {PyFrameworksLangchainSimilaritySearchByVector} + + +------ + +##### similarity_search_with_score() + +Returns documents most similar to the **query string** along with their relevance scores. + +| Name | Type | Purpose | Default | +|----------|---------------------------|---------|---------| +| `query` | `str` |A `str` representing the text query you want to search for in the vector store. This query will be converted into an embedding using the specified embedding function. | N/A | +| `k` | `Optional[int]` | It specifies the number of documents to return. | `None` | +| `filter` | `Optional[Dict[str, str]]`| It is used to filter the search results by specific metadata criteria. This allows you to narrow down the search results based on certain metadata attributes associated with the documents. | `None` | +| `kwargs` | `Any` | Other keyworded arguments provided by the user. | N/A | + +It gets called by base class's `similarity_search_with_relevance_scores` which selects relevance score based on our `_select_relevance_score_fn`. + + + {PyFrameworksLangchainSimilaritySearchWithScores} + + +------ + +##### similarity_search_by_vector_with_relevance_scores() + +Similarity search using **query vector**. + +| Name | Type | Purpose | Default | +|-------------|---------------------------|---------|---------| +| `embedding` | `List[float]` | The embedding vector you want to use to search for similar documents in the vector store. | N/A | +| `k` | `Optional[int]` | It specifies the number of documents to return. | `None` | +| `filter` | `Optional[Dict[str, str]]`| It is used to filter the search results by specific metadata criteria. | `None` | +| `name` | `Optional[str]` | It is used for specifying the name of the table to query. | `None` | +| `kwargs` | `Any` | Other keyworded arguments provided by the user. | N/A | + +The method returns documents most similar to the specified embedding (query) vector, along with their relevance scores. + + + {PyFrameworksLangchainSimilaritySearchByVectorWithScores} + + +------ + +##### max_marginal_relevance_search() + +This method returns docs selected using the maximal marginal relevance(MMR). +Maximal marginal relevance optimizes for similarity to query AND diversity among selected documents. + +| Name | Type | Purpose | Default | +|---------------|-----------------|-----------|---------| +| `query` | `str` | Text to look up documents similar to. | N/A | +| `k` | `Optional[int]` | Number of Documents to return.| `4` | +| `fetch_k`| `Optional[int]`| Number of Documents to fetch to pass to MMR algorithm.| `None` | +| `lambda_mult` | `float` | Number between 0 and 1 that determines the degree of diversity among the results with 0 corresponding to maximum diversity and 1 to minimum diversity. | `0.5` | +| `filter`| `Optional[Dict[str, str]]`| Filter by metadata. | `None` | +|`kwargs`| Other keyworded arguments provided by the user. |-| + +Similarly, `max_marginal_relevance_search_by_vector()` function returns docs most similar to the embedding passed to the function using MMR. instead of a string query you need to pass the embedding to be searched for. + + + {PyFrameworksLangchainMaxMarginalRelevance} + + +------ + +##### add_images() + +This method adds images by automatically creating their embeddings and adds them to the vectorstore. + +| Name | Type | Purpose | Default | +|------------|-------------------------------|--------------------------------|---------| +| `uris` | `List[str]` | File path to the image | N/A | +| `metadatas`| `Optional[List[dict]]` | Optional list of metadatas | `None` | +| `ids` | `Optional[List[str]]` | Optional list of IDs | `None` | + +It returns list of IDs of the added images. + + + {PyFrameworksLangchainAddImages} + + + diff --git a/docs/web/integrations/ai/llamaIndex.mdx b/docs/web/integrations/ai/llamaIndex.mdx new file mode 100644 index 000000000..a38e2c61e --- /dev/null +++ b/docs/web/integrations/ai/llamaIndex.mdx @@ -0,0 +1,55 @@ +--- +title: "LlamaIndex" +sidebarTitle: "LlamaIndex" + +--- + +import { + PyFrameworksLlamaindexAddReranker, + PyFrameworksLlamaindexFiltering, + PyFrameworksLlamaindexHybridSearch, + PyFrameworksLlamaindexQuickStart, +} from '/snippets/integrations.mdx'; + +## Quickstart {#quickstart} + +LlamaIndex is a well-known framework for building LLM-powered agents over your data with LLMs and workflows. +You can build your LlamaIndex pipeline and persist your metadata and embeddings in LanceDB via the `LanceDBVectorStore` class. + +First, install the LlamaIndex-LanceDB integration. + + +pip install llama-index-vector-stores-LanceDB + + +Run the below script as an example. + + + {PyFrameworksLlamaindexQuickStart} + + +The vector store connector will open an existing LanceDB directory or create the directory if it does not exist. + +### Filtering {#filtering} +For metadata filtering, you can use a Lance SQL-like string filter as demonstrated in the example above. Additionally, you can also filter using the `MetadataFilters` class from LlamaIndex: + + {PyFrameworksLlamaindexFiltering} + + +### Hybrid Search {#hybrid-search} +For complete documentation, refer [here](https://docs.lancedb.com/search/hybrid-search). This example uses the `colbert` reranker. Make sure to install necessary dependencies for the reranker you choose. + + {PyFrameworksLlamaindexHybridSearch} + + +In the snippet above, you can change/specify `query_type` when creating the engine/retriever +to use different search strategies, such as vector search or FTS. + +## API reference {#api-reference} + + +See the official LlamaIndex Vector Stores API reference for more details. + \ No newline at end of file diff --git a/docs/web/integrations/ai/prompttools.mdx b/docs/web/integrations/ai/prompttools.mdx new file mode 100644 index 000000000..b0edd575a --- /dev/null +++ b/docs/web/integrations/ai/prompttools.mdx @@ -0,0 +1,15 @@ +--- +title: "PromptTools" +sidebarTitle: "PromptTools" + +--- + +[PromptTools](https://github.com/hegelai/prompttools) offers a set of free, open-source tools for testing and experimenting with models, prompts, and configurations. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. You can use it to experiment with different configurations of LanceDB, and test how LanceDB integrates with the LLM of your choice. +{/* +[Evaluating Prompts with PromptTools](./examples/prompttools-eval-prompts/) | Open In Colab +*/} + +Open In Colab + +![Alt text](https://prompttools.readthedocs.io/en/latest/_images/demo.gif "a title") + diff --git a/docs/web/integrations/ai/synthetic-data-kit.mdx b/docs/web/integrations/ai/synthetic-data-kit.mdx new file mode 100644 index 000000000..18b90674a --- /dev/null +++ b/docs/web/integrations/ai/synthetic-data-kit.mdx @@ -0,0 +1,81 @@ +--- +title: "Meta Llama Synthetic Data Kit" +sidebarTitle: "Synthetic Data Kit" +description: "Use Meta Llama's Synthetic Data Kit with LanceDB to generate high-quality synthetic datasets for LLM fine-tuning and training." + +--- + + + +[Synthetic Data Kit](https://github.com/meta-llama/synthetic-data-kit) is a tool from Meta LLAMA that helps you generate high-quality synthetic datasets for fine-tuning large language models (LLMs). It simplifies the process of preparing data for fine-tuning by providing a command-line interface (CLI) with a modular four-command flow. + +One of the key features of the `synthetic-data-kit` is its use of the Lance format for storing and ingesting datasets. This allows for efficient storage and retrieval of data, which is crucial when working with large datasets. + +### Key Features: {#key-features} + +* **Data Ingestion:** The toolkit can ingest various file formats, including PDF, HTML, YouTube transcripts, DOCX, PPT, and TXT. +* **Fine-tuning Format Creation:** It can create different fine-tuning formats, such as question-answer (QA) pairs, QA pairs with Chain-of-Thought (CoT), and summarization formats. +* **Data Curation:** The tool uses Llama as a judge to curate high-quality examples, ensuring the quality of the generated dataset. +* **Flexible Saving Options:** You can save the generated datasets in various formats compatible with your fine-tuning workflow, including Hugging Face, JSONL, and JSON. + +### How it Works: {#how-it-works} + +The synthetic-data-kit follows a simple four-step process: + +1. **Ingest:** Import your input files into the toolkit. The data is stored in the Lance format for efficient processing. +2. **Create:** Generate diverse fine-tuning datasets, such as reasoning, summarization, and QA pairs, from the ingested documents. +3. **Curate:** Use Llama to filter and select high-quality examples from the generated dataset. +4. **Save-as:** Export the curated dataset in your preferred format. + +### Usage {#usage} + +The `synthetic-data-kit` uses Lance format to store and manage the data that you ingest. The workflow is a series of commands that build on each other, starting with the `ingest` command. + +Here is an example of the end-to-end workflow: + +1. **Ingest Data into a LanceDB dataset** + + This command takes a directory of source files and creates a LanceDB dataset from them. + + ```bash + synthetic-data-kit ingest docs/report.pdf --multimodal + # This will create a Lance dataset at data/parsed/report.lance + # with 'text' and 'image' columns. + + #Generate multimodal-qa pairs from the ingested data + synthetic-data-kit create data/parsed/report.lance --type multimodal-qa + ``` + +2. **Create fine-tuning data** + + This command uses the LanceDB dataset created in the previous step to generate synthetic data in the desired format. + + ```bash + synthetic_data create data/parsed/report.lance + ``` + +3. **Curate the data** + + This step uses a language model to curate the generated data and ensure its quality. + + ```bash + synthetic_data curate report.json + ``` + +4. **Save the final dataset** + + Finally, save the curated data to a file in the desired format. + + ```bash + synthetic_data save-as report.json --save_path ./my_finetuning_data.jsonl + ``` + +This workflow allows you to go from a collection of documents to a high-quality, fine-tuning dataset with just a few commands. The use of LanceDB in the background makes the process efficient and scalable. + +### Getting Started: {#getting-started} + +To get started with the synthetic-data-kit, you can clone the [GitHub Repository](https://github.com/meta-llama/synthetic-data-kit) and install the necessary dependencies. + +> **Note:** You will also need access to a Llama model, either running locally or via a hosted API. + + diff --git a/docs/web/integrations/data/dlt.mdx b/docs/web/integrations/data/dlt.mdx new file mode 100644 index 000000000..f15a4b776 --- /dev/null +++ b/docs/web/integrations/data/dlt.mdx @@ -0,0 +1,97 @@ +--- +title: "dlt" +sidebarTitle: "DLT" + +--- + +import { + PyPlatformsDltAdapterImport, + PyPlatformsDltAdapterUsage, + PyPlatformsDltPipeline, +} from '/snippets/integrations.mdx'; + +[dlt](https://dlthub.com/docs/intro) is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets. dlt's [integration with LanceDB](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb) lets you ingest data from any source (databases, APIs, CSVs, dataframes, JSONs, and more) into LanceDB with a few lines of simple python code. The integration enables automatic normalization of nested data, schema inference, incremental loading and embedding the data. dlt also has integrations with several other tools like dbt, airflow, dagster etc. that can be inserted into your LanceDB workflow. + +## How to ingest data into LanceDB {#how-to-ingest-data-into-lancedb} + +In this example, we will be fetching movie information from the [Open Movie Database (OMDb) API](https://www.omdbapi.com/) and loading it into a local LanceDB instance. To implement it, you will need an API key for the OMDb API (which can be created freely [here](https://www.omdbapi.com/apikey.aspx)). + +1. **Install `dlt` with LanceDB extras:** + ```sh + pip install dlt[lancedb] + ``` + +2. **Inside an empty directory, initialize a `dlt` project with:** + ```sh + dlt init rest_api lancedb + ``` + This will add all the files necessary to create a `dlt` pipeline that can ingest data from any REST API (ex: OMDb API) and load into LanceDB. + ```text + ├── .dlt + │ ├── config.toml + │ └── secrets.toml + ├── rest_api + ├── rest_api_pipeline.py + └── requirements.txt + ``` + + dlt has a list of pre-built [sources](https://dlthub.com/docs/dlt-ecosystem/verified-sources/) like [SQL databases](https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database), [REST APIs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api), [Google Sheets](https://dlthub.com/docs/dlt-ecosystem/verified-sources/google_sheets), [Notion](https://dlthub.com/docs/dlt-ecosystem/verified-sources/notion) etc., that can be used out-of-the-box by running `dlt init lancedb`. Since dlt is a python library, it is also very easy to modify these pre-built sources or to write your own custom source from scratch. + + +3. **Specify necessary credentials and/or embedding model details:** + + In order to fetch data from the OMDb API, you will need to pass a valid API key into your pipeline. Depending on whether you're using LanceDB OSS or LanceDB Enterprise, you also may need to provide the necessary credentials to connect to the LanceDB instance. These can be pasted inside `.dlt/secrets.toml`. + + dlt's LanceDB integration also allows you to automatically embed the data during ingestion. Depending on the embedding model chosen, you may need to paste the necessary credentials inside `.dlt/secrets.toml`: + ```toml + [sources.rest_api] + api_key = "api_key" # Enter the API key for the OMDb API + + [destination.lancedb] + embedding_model_provider = "sentence-transformers" + embedding_model = "all-MiniLM-L6-v2" + [destination.lancedb.credentials] + uri = ".lancedb" + api_key = "api_key" # API key to connect to LanceDB Enterprise. Leave out if you are using LanceDB OSS. + embedding_model_provider_api_key = "embedding_model_provider_api_key" # Not needed for providers that don't need authentication (ollama, sentence-transformers). + ``` + See [here](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb#configure-the-destination) for more information and for a list of available models and model providers. + + +4. **Write the pipeline code inside `rest_api_pipeline.py`:** + + The following code shows how you can configure dlt's REST API source to connect to the [OMDb API](https://www.omdbapi.com/), fetch all movies with the word "godzilla" in the title, and load it into a LanceDB table. The REST API source allows you to pull data from any API with minimal code, to learn more read the [dlt docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api). + + + {PyPlatformsDltPipeline} + + + The script above will ingest the data into LanceDB as it is, i.e. without creating any embeddings. If we want to embed one of the fields (for example, `"Title"` that contains the movie titles), then we will use dlt's `lancedb_adapter` and modify the script as follows: + + - Add the following import statement: + + {PyPlatformsDltAdapterImport} + + - Modify the pipeline run like this: + + {PyPlatformsDltAdapterUsage} + + This will use the embedding model specified inside `.dlt/secrets.toml` to embed the field `"Title"`. + +5. **Install necessary dependencies:** + ```sh + pip install -r requirements.txt + ``` + + Note: You may need to install the dependencies for your embedding models separately. + ```sh + pip install sentence-transformers + ``` + +6. **Run the pipeline:** + Finally, running the following command will ingest the data into your LanceDB instance. + ```sh + python custom_source.py + ``` + +For more information and advanced usage of dlt's LanceDB integration, read [the dlt documentation](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb). diff --git a/docs/web/integrations/data/duckdb.mdx b/docs/web/integrations/data/duckdb.mdx new file mode 100644 index 000000000..f5e311452 --- /dev/null +++ b/docs/web/integrations/data/duckdb.mdx @@ -0,0 +1,119 @@ +--- +title: "DuckDB" +sidebarTitle: "DuckDB" +description: "Learn how to use the DuckDB-Lance extension to query Lance tables with SQL." +--- + +LanceDB integrates with [DuckDB](https://duckdb.org/) through the [Lance extension](https://github.com/lance-format/lance-duckdb) for DuckDB. In this page, we'll show how LanceDB manages table lifecycle, and DuckDB provides SQL analytics (including joins) and search over those tables. + +Note that earlier versions of LanceDB used to recommend converting Lance tables to Arrow tables via `table.to_arrow()`. Although this method is still available (because DuckDB [natively scans Arrow tables](https://duckdb.org/2021/12/03/duck-arrow)), it is no longer the recommended workflow for working with Lance tables in DuckDB. This page shows how to use the Lance extension with namespace-attached LanceDB tables, allowing you to pushdown SQL queries directly to the Lance layer. + + +## Install {#install} + +Install the DuckDB CLI as per [their docs](https://duckdb.org/install) and alternatively, their Python package with `pip install duckdb`. + +Then, open the DuckDB CLI and install and load the Lance extension as follows: + +```sql SQL icon="database" +INSTALL lance; +LOAD lance; +``` + +## Attach the directory namespace in DuckDB {#attach-the-directory-namespace-in-duckdb} + +Attach the LanceDB root directory as a Lance namespace: + +```sql SQL icon="database" +ATTACH './local_lancedb' AS lance_ns (TYPE LANCE); +``` + +In this page, tables are referenced using `lance_ns.main.`, so the table path is `lance_ns.main.lance_duck`. + +## Write Lance table {#write-lance-table} + +Create the `lance_duck` table using SQL and populate it with sample data: + +```sql SQL icon="database" +CREATE OR REPLACE TABLE lance_ns.main.lance_duck AS +SELECT * +FROM ( + VALUES + ('duck', 'quack', [0.9, 0.7, 0.1]::FLOAT[]), + ('horse', 'neigh', [0.3, 0.1, 0.5]::FLOAT[]), + ('dragon', 'roar', [0.5, 0.2, 0.7]::FLOAT[]) +) AS t(animal, noise, vector); +``` + +This table is the source of truth for all DuckDB queries below. + + +The examples below show SQL entered in the DuckDB CLI. You can run the same SQL from +Python as well, using LanceDB and DuckDB's Python clients in your application code. + + +## Query the table with SQL {#query-the-table-with-sql} + +```sql SQL icon="database" +SELECT * + FROM lance_ns.main.lance_duck + LIMIT 5; +``` + +## Vector search {#vector-search} + +```sql SQL icon="database" +SELECT animal, noise, vector, _distance + FROM lance_vector_search( + 'lance_ns.main.lance_duck', + 'vector', + [0.8, 0.7, 0.2]::FLOAT[], + k = 1, + prefilter = true + ) + ORDER BY _distance ASC; +``` + +## Full-text search {#full-text-search} + +```sql SQL icon="database" +SELECT animal, noise, vector, _score + FROM lance_fts( + 'lance_ns.main.lance_duck', + 'animal', + 'the brave knight faced the dragon', + k = 1, + prefilter = true + ) + ORDER BY _score DESC; +``` + +## Hybrid search {#hybrid-search} + +```sql SQL icon="database" +SELECT animal, noise, vector, _hybrid_score, _distance, _score + FROM lance_hybrid_search( + 'lance_ns.main.lance_duck', + 'vector', + [0.8, 0.7, 0.2]::FLOAT[], + 'animal', + 'the duck surprised the dragon', + k = 2, + prefilter = false, + alpha = 0.5, + oversample_factor = 4 + ) + ORDER BY _hybrid_score DESC; +``` + +## Directory namespace model {#directory-namespace-model} + +A directory namespace maps a LanceDB catalog root to namespace-qualified table identifiers in DuckDB. This keeps table discovery and table naming stable as your project grows. + +To learn more about the catalog and namespace model, see [Namespaces and the Catalog Model](/namespaces). + + +## Advanced usage {#advanced-usage} + +See the [docs](https://github.com/lance-format/lance-duckdb) directory in the Lance-DuckDB extension repo +for more advanced usage on SQL and REST API clients. diff --git a/docs/web/integrations/data/pandas_and_pyarrow.mdx b/docs/web/integrations/data/pandas_and_pyarrow.mdx new file mode 100644 index 000000000..170ccf772 --- /dev/null +++ b/docs/web/integrations/data/pandas_and_pyarrow.mdx @@ -0,0 +1,46 @@ +--- +title: "Pandas and PyArrow" +sidebarTitle: "Pandas & PyArrow" + +--- + +import { + PyPlatformsPandasAsyncExample, + PyPlatformsPandasCreateTable, + PyPlatformsPandasImports, + PyPlatformsPandasVectorSearch, +} from '/snippets/integrations.mdx'; + +Because Lance is built on top of [Apache Arrow](https://arrow.apache.org/), +LanceDB fits naturally into Pandas-first workflows. You can ingest a `DataFrame`, +query it with LanceDB's vector operators, and keep working in Pandas without any glue code. + +## Create a dataset {#create-a-dataset} + +Start by importing LanceDB alongside your usual Pandas utilities and connect to a temporary database. + + + {PyPlatformsPandasImports} + + +Use the familiar `pd.DataFrame` API to prepare your rows, then pass the entire frame to `db.create_table`. + + + {PyPlatformsPandasCreateTable} + + +## Vector search {#vector-search} + +Queries can return Pandas frames as well, so you can immediately inspect the results or pipe them into downstream analytics. + + + {PyPlatformsPandasVectorSearch} + + +## Async API {#async-api} + +For web services or background jobs that already rely on `asyncio`, use the asynchronous helpers to keep everything non-blocking. + + + {PyPlatformsPandasAsyncExample} + diff --git a/docs/web/integrations/data/polars_arrow.mdx b/docs/web/integrations/data/polars_arrow.mdx new file mode 100644 index 000000000..4cb85737e --- /dev/null +++ b/docs/web/integrations/data/polars_arrow.mdx @@ -0,0 +1,51 @@ +--- +title: "Polars" +sidebarTitle: "Polars & Arrow" + +--- + +import { + PyPlatformsPolarsCreateTable, + PyPlatformsPolarsImports, + PyPlatformsPolarsLazyframe, + PyPlatformsPolarsPydantic, + PyPlatformsPolarsVectorSearch, +} from '/snippets/integrations.mdx'; + +LanceDB supports [Polars](https://github.com/pola-rs/polars), a blazingly fast DataFrame library for Python written in Rust. Under the hood, both Lance and Polars speak Arrow, so passing data back and forth stays zero-copy and ergonomic. + +## Create and Query a Table {#create-and-query-a-table} + +Import the required libraries, including the optional Pydantic helpers if you plan to define schemas. + + + {PyPlatformsPolarsImports} + + +Build a Polars `DataFrame`, convert it to Arrow, and use it directly when creating a LanceDB table. + + + {PyPlatformsPolarsCreateTable} + + +Run vector search and keep the results as a Polars `DataFrame` for further processing or visualization. + + + {PyPlatformsPolarsVectorSearch} + + +## Work with LazyFrames {#work-with-lazyframes} + +When you want to operate on the entire table (potentially larger than RAM), convert to a Polars `LazyFrame` so you can chain transformations without loading everything at once. + + + {PyPlatformsPolarsLazyframe} + + +## Define Schemas with Pydantic {#define-schemas-with-pydantic} + +You can also describe your table via `LanceModel` and continue ingesting data from Polars. This is useful when multiple teams share a schema or when you want validation. + + + {PyPlatformsPolarsPydantic} + diff --git a/docs/web/integrations/data/pydantic.mdx b/docs/web/integrations/data/pydantic.mdx new file mode 100644 index 000000000..b6f80aca3 --- /dev/null +++ b/docs/web/integrations/data/pydantic.mdx @@ -0,0 +1,90 @@ +--- +title: "Pydantic" +sidebarTitle: "Pydantic" + +--- + +import { + PyFrameworksPydanticBaseExample, + PyFrameworksPydanticBaseModel, + PyFrameworksPydanticImports, + PyFrameworksPydanticSetUrl, + PyFrameworksPydanticTypeConversion, + PyFrameworksPydanticVectorField, +} from '/snippets/integrations.mdx'; + +[Pydantic](https://docs.pydantic.dev/latest/) is a data validation library in Python. +LanceDB integrates with Pydantic for schema inference, data ingestion, and query result casting. +Using `lancedb.pydantic.LanceModel`, users can seamlessly +integrate Pydantic with the rest of the LanceDB APIs. + +First, import the necessary LanceDB and Pydantic modules: + + + {PyFrameworksPydanticImports} + + +Next, define your Pydantic model by inheriting from `LanceModel` and specifying your fields including a vector field: + + + {PyFrameworksPydanticBaseModel} + + +Set the database connection URL: + + + {PyFrameworksPydanticSetUrl} + + +Now you can create a table, add data, and perform vector search operations: + + + {PyFrameworksPydanticBaseExample} + + + +## Vector Field {#vector-field} + +LanceDB provides a `lancedb.pydantic.Vector` method to define a +vector Field in a Pydantic Model. + + + {PyFrameworksPydanticVectorField} + + +This example demonstrates how LanceDB automatically converts Pydantic field types to their corresponding Apache Arrow data types. The `pydantic_to_schema()` function takes a Pydantic model and generates an Arrow schema where: +- `int` fields become `pa.int64()` (64-bit integers) +- `str` fields become `pa.utf8()` (UTF-8 encoded strings) +- `Vector(768)` becomes `pa.list_(pa.float32(), 768)` (fixed-size list of 768 float32 values) +- The `False` parameter indicates that the fields are not nullable + +## Type Conversion {#type-conversion} + +LanceDB automatically convert Pydantic fields to +[Apache Arrow DataType](https://arrow.apache.org/docs/python/generated/pyarrow.DataType.html#pyarrow.DataType). + +Current supported type conversions: + +| Pydantic Field Type | PyArrow Data Type | +| ------------------- | ----------------- | +| `int` | `pyarrow.int64` | +| `float` | `pyarrow.float64` | +| `bool` | `pyarrow.bool` | +| `str` | `pyarrow.utf8()` | +| `list` | `pyarrow.List` | +| `BaseModel` | `pyarrow.Struct` | +| `Vector(n)` | `pyarrow.FixedSizeList(float32, n)` | + +LanceDB supports to create Apache Arrow Schema from a +`pydantic.BaseModel` +via `lancedb.pydantic.pydantic_to_schema` method. + + + {PyFrameworksPydanticTypeConversion} + + +This example shows a more complex Pydantic model with various field types and demonstrates how LanceDB handles: +- Basic types: `int` and `str` fields +- Vector fields: `Vector(1536)` creates a fixed-size list of 1536 float32 values +- List fields: `List[int]` becomes a variable-length list of int64 values +- Schema generation: The `pydantic_to_schema()` function automatically converts all these types to their Arrow equivalents \ No newline at end of file diff --git a/docs/web/integrations/data/voxel51.mdx b/docs/web/integrations/data/voxel51.mdx new file mode 100644 index 000000000..1ee782353 --- /dev/null +++ b/docs/web/integrations/data/voxel51.mdx @@ -0,0 +1,212 @@ +--- +title: "Voxel51" +sidebarTitle: "Voxel51" +--- + +import { + PyPlatformsVoxel51BackendFlag, + PyPlatformsVoxel51BackendParams, + PyPlatformsVoxel51BrainConfig, + PyPlatformsVoxel51Cleanup, + PyPlatformsVoxel51ComputeSimilarity, + PyPlatformsVoxel51LoadDataset, + PyPlatformsVoxel51SortBySimilarity, +} from '/snippets/integrations.mdx'; + +# FiftyOne + +[FiftyOne](https://docs.voxel51.com/) is an open source toolkit that enables users to curate better data and build better models. It includes tools for data exploration, visualization, and management, as well as features for collaboration and sharing. + +Any developers, data scientists, and researchers who work with computer vision and machine learning can use FiftyOne to improve the quality of their datasets and deliver insights about their models. + +![example](/static/assets/images/integrations/voxel.gif) + +**FiftyOne** provides an API to create LanceDB tables and run similarity queries, both **programmatically in Python** and via **point-and-click in the App**. + +Let's get started and see how to use **LanceDB** to create a **similarity index** on your FiftyOne datasets. + +## Overview {#overview} + +[Embeddings](/embedding/) are foundational to all of the **vector search** features. In FiftyOne, embeddings are managed by the [**FiftyOne Brain**](https://docs.voxel51.com/user_guide/brain.html) that provides powerful machine learning techniques designed to transform how you curate your data from an art into a measurable science. + +> _Have you ever wanted to find the images most similar to an image in your dataset?_ + +The **FiftyOne Brain** makes computing **visual similarity** really easy. You can compute the similarity of samples in your dataset using an embedding model and store the results in the **brain key**. +You can then sort your samples by similarity or use this information to find potential duplicate images. + + +We'll be doing the following : + +1. **Create Index** - In order to run similarity queries against our media, we need to **index** the data. We can do this via the `compute_similarity()` function. + - In the function, specify the **model** you want to use to generate the embedding vectors, and what **vector search engine** you want to use on the **backend** (here LanceDB). + + You can also give the similarity index a name(`brain_key`), which is useful if you want to run vector searches against multiple indexes. + + +2. **Query** - Once you have generated your similarity index, you can query your dataset with `sort_by_similarity()`. The query can be any of the following: + + - An ID (sample or patch) + - A query vector of same dimension as the index + - A list of IDs (samples or patches) + - A text prompt (search semantically) + +## Prerequisites: install necessary dependencies {#prerequisites-install-necessary-dependencies} + +1. **Create and activate a virtual environment** + +Install virtualenv package and run the following command in your project directory. + +python -m venv fiftyone_ + +From inside the project directory run the following to activate the virtual environment. + + + + source fiftyone_/Scripts/activate + + + fiftyone_/Scripts/activate + + + +2. **Install the following packages in the virtual environment** + + To install FiftyOne, ensure you have activated any virtual environment that you are using, then run + +pip install fiftyone + + + +## Understand basic workflow {#understand-basic-workflow} + +The basic workflow shown below uses LanceDB to create a similarity index on your FiftyOne datasets: + +1. Load a dataset into FiftyOne. + +2. Compute embedding vectors for samples or patches in your dataset, or select a model to use to generate embeddings. + +3. Use the `compute_similarity()` method to generate a LanceDB table for the samples or object patches embeddings in a dataset by setting the parameter `backend="lancedb"` and specifying a `brain_key` of your choice. + +4. Use this LanceDB table to query your data with `sort_by_similarity()`. + +5. If desired, delete the table. + +## Quick Example {#quick-example} + +Let's jump on a quick example that demonstrates this workflow. + + + + {PyPlatformsVoxel51LoadDataset} + +Make sure you install torch ([guide here](https://pytorch.org/get-started/locally/)) before proceeding. + + + {PyPlatformsVoxel51ComputeSimilarity} + + +!!! note + Running the code above will download the clip model (2.6Gb) + +Once the similarity index has been generated, we can query our data in FiftyOne by specifying the `brain_key`: + + + {PyPlatformsVoxel51SortBySimilarity} + + +The returned result are of type - `DatasetView`. + + +`DatasetView` does not hold its contents in-memory. Views simply store the rule(s) that are applied to extract the content of interest from the underlying Dataset when the view is iterated/aggregated on. + +This means, for example, that the contents of a `DatasetView` may change as the underlying Dataset is modified. + + +> _Can you query a view instead of dataset?_ + +Yes, you can also query a view. + +Performing a similarity search on a `DatasetView` will only return results from the view; if the view contains samples that were not included in the index, they will never be included in the result. + +This means that you can index an entire Dataset once and then perform searches on subsets of the dataset by constructing views that contain the images of interest. + + + {PyPlatformsVoxel51Cleanup} + + + +## Using LanceDB backend {#using-lancedb-backend} +By default, calling `compute_similarity()` or `sort_by_similarity()` will use an sklearn backend. + +To use the LanceDB backend, simply set the optional `backend` parameter of `compute_similarity()` to `"lancedb"`: + + + {PyPlatformsVoxel51BackendFlag} + + +Alternatively, you can configure FiftyOne to use the LanceDB backend by setting the following environment variable. + +In your terminal, set the environment variable using: + + + + export FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND=lancedb + + + + $Env:FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND="lancedb" //powershell + + set FIFTYONE_BRAIN_DEFAULT_SIMILARITY_BACKEND=lancedb //cmd + + + + +This will only run during the terminal session. Once terminal is closed, environment variable is deleted. + + +Alternatively, you can **permanently** configure FiftyOne to use the LanceDB backend creating a `brain_config.json` at `~/.fiftyone/brain_config.json`. The JSON file may contain any desired subset of config fields that you wish to customize. + +```json +{ + "default_similarity_backend": "lancedb" +} +``` +This will override the default `brain_config` and will set it according to your customization. You can check the configuration by running the following code : + + + {PyPlatformsVoxel51BrainConfig} + + +## LanceDB config parameters {#lancedb-config-parameters} + +The LanceDB backend supports query parameters that can be used to customize your similarity queries. These parameters include: + +| Name | Purpose | Default | +| :------------- | :--------------------------------------------------------------------------------------------------------------- | :--------------- | +| **table_name** | The name of the LanceDB table to use. If none is provided, a new table will be created | `None` | +| **metric** | The embedding distance metric to use when creating a new table. The supported values are ("cosine", "euclidean") | `"cosine"` | +| **uri** | The database URI to use. In this Database URI, tables will be created. | `"/tmp/lancedb"` | + +There are two ways to specify/customize the parameters: + +1. **Using `brain_config.json` file** + +```json +{ + "similarity_backends": { + "lancedb": { + "table_name": "your-table", + "metric": "euclidean", + "uri": "/tmp/lancedb" + } + } +} +``` + +2. **Directly passing to `compute_similarity()` to configure a specific new index** : + + + {PyPlatformsVoxel51BackendParams} + + +For a much more in depth walkthrough of the integration, visit the LanceDB x Voxel51 [docs page](https://docs.voxel51.com/integrations/lancedb.html). diff --git a/docs/web/integrations/embedding/aws.mdx b/docs/web/integrations/embedding/aws.mdx new file mode 100644 index 000000000..4e1c4fb90 --- /dev/null +++ b/docs/web/integrations/embedding/aws.mdx @@ -0,0 +1,42 @@ +--- +title: AWS Bedrock +sidebarTitle: AWS +--- + +import { PyEmbeddingAwsUsage } from '/snippets/integrations.mdx'; + +AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function. +You can do so by using `awscli` and also add your session_token: +```shell +aws configure +aws configure set aws_session_token "" +``` +to ensure that the credentials are set up correctly, you can run the following command: +```shell +aws sts get-caller-identity +``` + +Supported Embedding modelIDs are: +* `amazon.titan-embed-text-v1` +* `cohere.embed-english-v3` +* `cohere.embed-multilingual-v3` + +Supported parameters (to be passed in `create` method) are: + +| Parameter | Type | Default Value | Description | +|---|---|---|---| +| **name** | str | "amazon.titan-embed-text-v1" | The model ID of the bedrock model to use. Supported base models for Text Embeddings: amazon.titan-embed-text-v1, cohere.embed-english-v3, cohere.embed-multilingual-v3 | +| **region** | str | "us-east-1" | Optional name of the AWS Region in which the service should be called (e.g., "us-east-1"). | +| **profile_name** | str | None | Optional name of the AWS profile to use for calling the Bedrock service. If not specified, the default profile will be used. | +| **assumed_role** | str | None | Optional ARN of an AWS IAM role to assume for calling the Bedrock service. If not specified, the current active credentials will be used. | +| **role_session_name** | str | "lancedb-embeddings" | Optional name of the AWS IAM role session to use for calling the Bedrock service. If not specified, a "lancedb-embeddings" name will be used. | +| **runtime** | bool | True | Optional choice of getting different client to perform operations with the Amazon Bedrock service. | +| **max_retries** | int | 7 | Optional number of retries to perform when a request fails. | + +Usage Example: + + + + {PyEmbeddingAwsUsage} + + diff --git a/docs/web/integrations/embedding/cohere.mdx b/docs/web/integrations/embedding/cohere.mdx new file mode 100644 index 000000000..8051462a9 --- /dev/null +++ b/docs/web/integrations/embedding/cohere.mdx @@ -0,0 +1,51 @@ +--- +title: Cohere +sidebarTitle: Cohere +--- + +import { PyEmbeddingCohereUsage } from '/snippets/integrations.mdx'; + +Using cohere API requires cohere package, which can be installed using `pip install cohere`. Cohere embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification. +You also need to set the `COHERE_API_KEY` environment variable to use the Cohere API. + +Supported models are: + +- embed-english-v3.0 +- embed-multilingual-v3.0 +- embed-english-light-v3.0 +- embed-multilingual-light-v3.0 +- embed-english-v2.0 +- embed-english-light-v2.0 +- embed-multilingual-v2.0 + + +Supported parameters (to be passed in `create` method) are: + +| Parameter | Type | Default Value | Description | +|---|---|--------|---------| +| `name` | `str` | `"embed-english-v2.0"` | The model ID of the cohere model to use. Supported base models for Text Embeddings: embed-english-v3.0, embed-multilingual-v3.0, embed-english-light-v3.0, embed-multilingual-light-v3.0, embed-english-v2.0, embed-english-light-v2.0, embed-multilingual-v2.0 | +| `source_input_type` | `str` | `"search_document"` | The type of input data to be used for the source column. | +| `query_input_type` | `str` | `"search_query"` | The type of input data to be used for the query. | + +Cohere supports following input types: + +| Input Type | Description | +|-------------------------|---------------------------------------| +| "`search_document`" | Used for embeddings stored in a vector| +| | database for search use-cases. | +| "`search_query`" | Used for embeddings of search queries | +| | run against a vector DB | +| "`semantic_similarity`" | Specifies the given text will be used | +| | for Semantic Textual Similarity (STS) | +| "`classification`" | Used for embeddings passed through a | +| | text classifier. | +| "`clustering`" | Used for the embeddings run through a | +| | clustering algorithm | + +Usage Example: + + + + {PyEmbeddingCohereUsage} + + \ No newline at end of file diff --git a/docs/web/integrations/embedding/colpali.mdx b/docs/web/integrations/embedding/colpali.mdx new file mode 100644 index 000000000..dcd65a017 --- /dev/null +++ b/docs/web/integrations/embedding/colpali.mdx @@ -0,0 +1,53 @@ +--- +title: ColPali +sidebarTitle: ColPali +--- + +import { + PyEmbeddingColpaliSetup, + PyEmbeddingColpaliTextSearch, +} from '/snippets/integrations.mdx'; + +We support [ColPali](https://github.com/illuin-tech/colpali) model embeddings for multimodal multi-vector retrieval. ColPali produces multiple embedding vectors per input (multi-vector), enabling more nuanced similarity matching between text queries and image documents. + +Using ColPali requires the colpali-engine package, which can be installed using `pip install colpali-engine`. + + +ColPali produces **multi-vector** embeddings, meaning each input generates multiple embedding vectors rather than a single vector. Use `MultiVector(func.ndims())` instead of `Vector(func.ndims())` when defining your schema. + + +Supported models are: + +- Metric-AI/ColQwen2.5-3b-multilingual-v1.0 (default) +- vidore/colpali-v1.3 +- vidore/colqwen2-v1.0 +- vidore/colSmol-256M + +Supported parameters (to be passed in `create` method) are: + +| Parameter | Type | Default Value | Description | +|---|---|---|---| +| `model_name` | `str` | `"Metric-AI/ColQwen2.5-3b-multilingual-v1.0"` | The name of the model to use. | +| `device` | `str` | `"auto"` | The device for inference. Can be `"auto"`, `"cpu"`, `"cuda"`, or `"mps"`. | +| `dtype` | `str` | `"bfloat16"` | Data type for model weights (bfloat16, float16, float32, float64). | +| `pooling_strategy` | `str` | `"hierarchical"` | Token pooling strategy: `"hierarchical"`, `"lambda"`, or `None`. | +| `pool_factor` | `int` | `2` | Factor to reduce sequence length when pooling is enabled. | +| `batch_size` | `int` | `2` | Batch size for processing inputs. | +| `quantization_config` | `Optional[BitsAndBytesConfig]` | `None` | Quantization configuration for the model (requires bitsandbytes). | + +This embedding function supports ingesting images as both bytes and URLs. You can query them using text. + + + + {PyEmbeddingColpaliSetup} + + + +Now we can search using text queries: + + + + {PyEmbeddingColpaliTextSearch} + + + diff --git a/docs/web/integrations/embedding/gemini.mdx b/docs/web/integrations/embedding/gemini.mdx new file mode 100644 index 000000000..e3359ec0d --- /dev/null +++ b/docs/web/integrations/embedding/gemini.mdx @@ -0,0 +1,26 @@ +--- +title: Gemini +sidebarTitle: Gemini +--- + +import { PyEmbeddingGeminiUsage } from '/snippets/integrations.mdx'; + +With Google's Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity. For more on how and why you should use embeddings, refer to the Embeddings guide. +The Gemini Embedding Model API supports various task types: + +| Task Type | Description | +|-------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------| +| "`retrieval_query`" | Specifies the given text is a query in a search/retrieval setting. | +| "`retrieval_document`" | Specifies the given text is a document in a search/retrieval setting. Using this task type requires a title but is automatically provided by Embeddings API | +| "`semantic_similarity`" | Specifies the given text will be used for Semantic Textual Similarity (STS). | +| "`classification`" | Specifies that the embeddings will be used for classification. | +| "`clustering`" | Specifies that the embeddings will be used for clustering. | + + +Usage Example: + + + + {PyEmbeddingGeminiUsage} + + \ No newline at end of file diff --git a/docs/web/integrations/embedding/huggingface.mdx b/docs/web/integrations/embedding/huggingface.mdx new file mode 100644 index 000000000..69cf85599 --- /dev/null +++ b/docs/web/integrations/embedding/huggingface.mdx @@ -0,0 +1,16 @@ +--- +title: Hugging Face +sidebarTitle: Hugging Face +--- + +import { PyEmbeddingHuggingfaceUsage } from '/snippets/integrations.mdx'; + +We offer support for all Hugging Face models (which can be loaded via [transformers](https://huggingface.co/docs/transformers/en/index) library). The default model is `colbert-ir/colbertv2.0` which also has its own special callout - `registry.get("colbert")`. Some Hugging Face models might require custom models defined on the HuggingFace Hub in their own modeling files. You may enable this by setting `trust_remote_code=True`. This option should only be set to True for repositories you trust and in which you have read the code, as it will execute code present on the Hub on your local machine. + +Example usage: + + + + {PyEmbeddingHuggingfaceUsage} + + \ No newline at end of file diff --git a/docs/web/integrations/embedding/ibm.mdx b/docs/web/integrations/embedding/ibm.mdx new file mode 100644 index 000000000..cec6c32bf --- /dev/null +++ b/docs/web/integrations/embedding/ibm.mdx @@ -0,0 +1,64 @@ +--- +title: IBM watsonx +sidebarTitle: IBM WatsonX +--- + +import { PyEmbeddingIbmUsage } from '/snippets/integrations.mdx'; + +Generate text embeddings using IBM's watsonx.ai platform. + +## Supported Models {#supported-models} + +You can find a list of supported models at [IBM watsonx.ai Documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). The currently supported model names are: + +| Model ID | Dimensions | +|----------------------------------------------|------------| +| `ibm/granite-embedding-278m-multilingual` | 768 | +| `ibm/slate-125m-english-rtrvr-v2` | 768 | +| `ibm/slate-30m-english-rtrvr-v2` | 384 | +| `intfloat/multilingual-e5-large` | 1024 | +| `sentence-transformers/all-minilm-l6-v2` | 384 | + + +For new tables, `ibm/granite-embedding-278m-multilingual` is the recommended default. Older model IDs (such as `ibm/slate-125m-english-rtrvr` and `sentence-transformers/all-minilm-l12-v2`) remain resolvable for tables whose stored metadata references them, but they are no longer advertised for new use. + + +## Parameters {#parameters} + +The following parameters can be passed to the `create` method: + +| Parameter | Type | Default Value | Description | +|------------|----------|----------------------------------|-----------------------------------------------------------| +| name | str | `"ibm/slate-125m-english-rtrvr"` | The model ID of the watsonx.ai model to use. Pass one of the current supported IDs above (e.g. `"ibm/granite-embedding-278m-multilingual"`) when creating new tables. | +| api_key | str | None | Optional IBM Cloud API key (or set `WATSONX_API_KEY`) | +| project_id | str | None | Optional watsonx project ID (or set `WATSONX_PROJECT_ID`). Mutually exclusive with `space_id`. | +| space_id | str | None | Optional watsonx deployment space ID (or set `WATSONX_SPACE_ID`). Mutually exclusive with `project_id`. | +| url | str | None | Optional custom URL for the watsonx.ai instance | +| params | dict | None | Optional additional parameters for the embedding model (e.g. `{"truncate_input_tokens": 512}`) | + + +You must supply exactly one of `project_id` or `space_id` (either as an argument or via its environment variable). Setting both, or neither, raises a `ValueError`. + + +## Usage Example {#usage-example} + +First, the watsonx.ai library is an optional dependency, so must be installed separately: + +``` +pip install ibm-watsonx-ai +``` + +Optionally set environment variables (if not passing credentials to `create` directly): + +```sh +export WATSONX_API_KEY="YOUR_WATSONX_API_KEY" +# Provide exactly one of the following: +export WATSONX_PROJECT_ID="YOUR_WATSONX_PROJECT_ID" +export WATSONX_SPACE_ID="YOUR_WATSONX_SPACE_ID" +``` + + + + {PyEmbeddingIbmUsage} + + diff --git a/docs/web/integrations/embedding/imagebind.mdx b/docs/web/integrations/embedding/imagebind.mdx new file mode 100644 index 000000000..4010de634 --- /dev/null +++ b/docs/web/integrations/embedding/imagebind.mdx @@ -0,0 +1,54 @@ +--- +title: ImageBind +sidebarTitle: ImageBind +--- + +import { + PyEmbeddingImagebindSetup, + PyEmbeddingImagebindImageSearch, + PyEmbeddingImagebindAudioSearch, + PyEmbeddingImagebindTextSearch, +} from '/snippets/integrations.mdx'; + +We have support for [imagebind](https://github.com/facebookresearch/ImageBind) model embeddings. You can download our version of the packaged model via - `pip install imagebind-packaged==0.1.2`. + +This function is registered as `imagebind` and supports Audio, Video and Text modalities(extending to Thermal,Depth,IMU data): + +| Parameter | Type | Default Value | Description | +|---|---|---|---| +| `name` | `str` | `"imagebind_huge"` | Name of the model. | +| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. | +| `normalize` | `bool` | `False` | set to `True` to normalize your inputs before model ingestion. | + +Below is an example demonstrating how the API works: + + + + {PyEmbeddingImagebindSetup} + + + +Now, we can search using any modality: + +#### image search {#image-search} + + + {PyEmbeddingImagebindImageSearch} + + +#### audio search {#audio-search} + + + + {PyEmbeddingImagebindAudioSearch} + + +#### Text search {#text-search} +You can add any input query and fetch the result as follows: + + + {PyEmbeddingImagebindTextSearch} + + + +If you have any questions about the embeddings API, supported models, or see a relevant model missing, please raise an issue [on GitHub](https://github.com/lancedb/lancedb/issues). diff --git a/docs/web/integrations/embedding/instructor.mdx b/docs/web/integrations/embedding/instructor.mdx new file mode 100644 index 000000000..aea4d443e --- /dev/null +++ b/docs/web/integrations/embedding/instructor.mdx @@ -0,0 +1,39 @@ +--- +title: Instructor +sidebarTitle: Instructor +--- + +import { PyEmbeddingInstructorUsage } from '/snippets/integrations.mdx'; + +[Instructor](https://instructor-embedding.github.io/) is an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g. classification, retrieval, clustering, text evaluation, etc.) and domains (e.g. science, finance, etc.) by simply providing the task instruction, without any finetuning. + +If you want to calculate customized embeddings for specific sentences, you can follow the unified template to write instructions. + + +Represent the `domain` `text_type` for `task_objective`: + +- `domain` is optional, and it specifies the domain of the text, e.g. science, finance, medicine, etc. +- `text_type` is required, and it specifies the encoding unit, e.g. sentence, document, paragraph, etc. +- `task_objective` is optional, and it specifies the objective of embedding, e.g. retrieve a document, classify the sentence, etc. + + +More information about the model can be found at the [source URL](https://github.com/xlang-ai/instructor-embedding). + +| Argument | Type | Default | Description | +|---|---|---|---| +| `name` | `str` | "hkunlp/instructor-base" | The name of the model to use | +| `batch_size` | `int` | `32` | The batch size to use when generating embeddings | +| `device` | `str` | `"cpu"` | The device to use when generating embeddings | +| `show_progress_bar` | `bool` | `True` | Whether to show a progress bar when generating embeddings | +| `normalize_embeddings` | `bool` | `True` | Whether to normalize the embeddings | +| `quantize` | `bool` | `False` | Whether to quantize the model | +| `source_instruction` | `str` | `"represent the document for retrieval"` | The instruction for the source column | +| `query_instruction` | `str` | `"represent the document for retrieving the most similar documents"` | The instruction for the query | + + + + + + {PyEmbeddingInstructorUsage} + + \ No newline at end of file diff --git a/docs/web/integrations/embedding/jina.mdx b/docs/web/integrations/embedding/jina.mdx new file mode 100644 index 000000000..70457180d --- /dev/null +++ b/docs/web/integrations/embedding/jina.mdx @@ -0,0 +1,49 @@ +--- +title: Jina +sidebarTitle: Jina +--- + +import { + PyEmbeddingJinaText, + PyEmbeddingJinaMultimodal, +} from '/snippets/integrations.mdx'; + +## Text Embedding Models {#text-embedding-models} + +Jina embeddings are used to generate embeddings for text and image data. +You also need to set the `JINA_API_KEY` environment variable to use the Jina API. + +You can find a list of supported models under [https://jina.ai/embeddings/](https://jina.ai/embeddings/) + +Supported parameters (to be passed in `create` method) are: + +| Parameter | Type | Default Value | Description | +|---|---|---|---| +| `name` | `str` | `"jina-clip-v1"` | The model ID of the jina model to use | + +Usage Example: + + + + {PyEmbeddingJinaText} + + + +## Multimodal Embedding Models {#multimodal-embedding-models} + +Jina embeddings can also be used to embed both text and image data, only some of the models support image data and you can check the list +under [https://jina.ai/embeddings/](https://jina.ai/embeddings/) + +Supported parameters (to be passed in `create` method) are: + +| Parameter | Type | Default Value | Description | +|---|---|---|---| +| `name` | `str` | `"jina-clip-v1"` | The model ID of the jina model to use | + +Usage Example: + + + + {PyEmbeddingJinaMultimodal} + + diff --git a/docs/web/integrations/embedding/ollama.mdx b/docs/web/integrations/embedding/ollama.mdx new file mode 100644 index 000000000..120cde8f2 --- /dev/null +++ b/docs/web/integrations/embedding/ollama.mdx @@ -0,0 +1,25 @@ +--- +title: Ollama +sidebarTitle: Ollama +--- + +import { PyEmbeddingOllamaUsage } from '/snippets/integrations.mdx'; + +Generate embeddings via the [ollama](https://github.com/ollama/ollama-python) python library. More details: + +- [Ollama docs on embeddings](https://github.com/ollama/ollama/blob/main/docs/api.md#generate-embeddings) +- [Ollama blog on embeddings](https://ollama.com/blog/embedding-models) + +| Parameter | Type | Default Value | Description | +|------------------------|----------------------------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------| +| `name` | `str` | `nomic-embed-text` | The name of the model. | +| `host` | `str` | `http://localhost:11434` | The Ollama host to connect to. | +| `options` | `ollama.Options` or `dict` | `None` | Additional model parameters listed in the documentation for the Modelfile such as `temperature`. | +| `keep_alive` | `float` or `str` | `"5m"` | Controls how long the model will stay loaded into memory following the request. | +| `ollama_client_kwargs` | `dict` | `{}` | kwargs that can be past to the `ollama.Client`. | + + + + {PyEmbeddingOllamaUsage} + + diff --git a/docs/web/integrations/embedding/openai.mdx b/docs/web/integrations/embedding/openai.mdx new file mode 100644 index 000000000..cdd60d7cd --- /dev/null +++ b/docs/web/integrations/embedding/openai.mdx @@ -0,0 +1,20 @@ +--- +title: OpenAI +sidebarTitle: OpenAI +--- + +import { PyEmbeddingOpenaiBasic } from '/snippets/integrations.mdx'; + +LanceDB registers the OpenAI embeddings function in the registry by default, as `openai`. Below are the parameters that you can customize when creating the instances: + +| Parameter | Type | Default Value | Description | +|---|---|---|---| +| `name` | `str` | `"text-embedding-ada-002"` | The name of the model. | +| `dim` | `int` | Model default | For OpenAI's newer text-embedding-3 model, we can specify a dimensionality that is smaller than the 1536 size. This feature supports it | +| `use_azure` | bool | `False` | Set true to use Azure OpenAI SDK | + + + + {PyEmbeddingOpenaiBasic} + + \ No newline at end of file diff --git a/docs/web/integrations/embedding/openclip.mdx b/docs/web/integrations/embedding/openclip.mdx new file mode 100644 index 000000000..118bb26c1 --- /dev/null +++ b/docs/web/integrations/embedding/openclip.mdx @@ -0,0 +1,46 @@ +--- +title: OpenCLIP +sidebarTitle: OpenCLIP +--- + +import { + PyEmbeddingOpenclipSetup, + PyEmbeddingOpenclipTextSearch, + PyEmbeddingOpenclipImageSearch, +} from '/snippets/integrations.mdx'; + +We support CLIP model embeddings using the open source alternative, [open-clip](https://github.com/mlfoundations/open_clip) which supports various customizations. It is registered as `open-clip` and supports the following customizations: + +| Parameter | Type | Default Value | Description | +|---|---|---|---| +| `name` | `str` | `"ViT-B-32"` | The name of the model. | +| `pretrained` | `str` | `"laion2b_s34b_b79k"` | The name of the pretrained model to load. | +| `device` | `str` | `"cpu"` | The device to run the model on. Can be `"cpu"` or `"gpu"`. | +| `batch_size` | `int` | `64` | The number of images to process in a batch. | +| `normalize` | `bool` | `True` | Whether to normalize the input images before feeding them to the model. | + +This embedding function supports ingesting images as both bytes and urls. You can query them using both text and other images. + + +LanceDB supports ingesting images directly from accessible links. + + + + + {PyEmbeddingOpenclipSetup} + + +Now we can search using text from both the default vector column and the custom vector column + + + {PyEmbeddingOpenclipTextSearch} + + + +Because we're using a multimodal embedding function, we can also search using images + + + + {PyEmbeddingOpenclipImageSearch} + + diff --git a/docs/web/integrations/embedding/sentence-transformers.mdx b/docs/web/integrations/embedding/sentence-transformers.mdx new file mode 100644 index 000000000..aa0c9d1ca --- /dev/null +++ b/docs/web/integrations/embedding/sentence-transformers.mdx @@ -0,0 +1,169 @@ +--- +title: Sentence Transformers +sidebarTitle: Sentence Transformers +--- + +import { PyEmbeddingSentenceTransformersBaai } from '/snippets/integrations.mdx'; + +Allows you to set parameters when registering a `sentence-transformers` object. + + +Sentence transformer embeddings are normalized by default. It is recommended to use normalized embeddings for similarity search. + + + +The `trust_remote_code` parameter defaults to `True`, which allows models to execute arbitrary code from their Hugging Face repository during loading. If you are loading untrusted models, set `trust_remote_code=False` to prevent remote code execution. + + +| Parameter | Type | Default Value | Description | +|---|---|---|---| +| `name` | `str` | `all-MiniLM-L6-v2` | The name of the model | +| `device` | `str` | `cpu` | The device to run the model on (can be `cpu` or `gpu`) | +| `normalize` | `bool` | `True` | Whether to normalize the input text before feeding it to the model | +| `trust_remote_code` | `bool` | `True` | Whether to trust and execute remote code from the model's Huggingface repository | + + +
+```markdown + - sentence-transformers/all-MiniLM-L12-v2 + - sentence-transformers/paraphrase-mpnet-base-v2 + - sentence-transformers/gtr-t5-base + - sentence-transformers/LaBSE + - sentence-transformers/all-MiniLM-L6-v2 + - sentence-transformers/bert-base-nli-max-tokens + - sentence-transformers/bert-base-nli-mean-tokens + - sentence-transformers/bert-base-nli-stsb-mean-tokens + - sentence-transformers/bert-base-wikipedia-sections-mean-tokens + - sentence-transformers/bert-large-nli-cls-token + - sentence-transformers/bert-large-nli-max-tokens + - sentence-transformers/bert-large-nli-mean-tokens + - sentence-transformers/bert-large-nli-stsb-mean-tokens + - sentence-transformers/distilbert-base-nli-max-tokens + - sentence-transformers/distilbert-base-nli-mean-tokens + - sentence-transformers/distilbert-base-nli-stsb-mean-tokens + - sentence-transformers/distilroberta-base-msmarco-v1 + - sentence-transformers/distilroberta-base-msmarco-v2 + - sentence-transformers/nli-bert-base-cls-pooling + - sentence-transformers/nli-bert-base-max-pooling + - sentence-transformers/nli-bert-base + - sentence-transformers/nli-bert-large-cls-pooling + - sentence-transformers/nli-bert-large-max-pooling + - sentence-transformers/nli-bert-large + - sentence-transformers/nli-distilbert-base-max-pooling + - sentence-transformers/nli-distilbert-base + - sentence-transformers/nli-roberta-base + - sentence-transformers/nli-roberta-large + - sentence-transformers/roberta-base-nli-mean-tokens + - sentence-transformers/roberta-base-nli-stsb-mean-tokens + - sentence-transformers/roberta-large-nli-mean-tokens + - sentence-transformers/roberta-large-nli-stsb-mean-tokens + - sentence-transformers/stsb-bert-base + - sentence-transformers/stsb-bert-large + - sentence-transformers/stsb-distilbert-base + - sentence-transformers/stsb-roberta-base + - sentence-transformers/stsb-roberta-large + - sentence-transformers/xlm-r-100langs-bert-base-nli-mean-tokens + - sentence-transformers/xlm-r-100langs-bert-base-nli-stsb-mean-tokens + - sentence-transformers/xlm-r-base-en-ko-nli-ststb + - sentence-transformers/xlm-r-bert-base-nli-mean-tokens + - sentence-transformers/xlm-r-bert-base-nli-stsb-mean-tokens + - sentence-transformers/xlm-r-large-en-ko-nli-ststb + - sentence-transformers/bert-base-nli-cls-token + - sentence-transformers/all-distilroberta-v1 + - sentence-transformers/multi-qa-MiniLM-L6-dot-v1 + - sentence-transformers/multi-qa-distilbert-cos-v1 + - sentence-transformers/multi-qa-distilbert-dot-v1 + - sentence-transformers/multi-qa-mpnet-base-cos-v1 + - sentence-transformers/multi-qa-mpnet-base-dot-v1 + - sentence-transformers/nli-distilroberta-base-v2 + - sentence-transformers/all-MiniLM-L6-v1 + - sentence-transformers/all-mpnet-base-v1 + - sentence-transformers/all-mpnet-base-v2 + - sentence-transformers/all-roberta-large-v1 + - sentence-transformers/allenai-specter + - sentence-transformers/average_word_embeddings_glove.6B.300d + - sentence-transformers/average_word_embeddings_glove.840B.300d + - sentence-transformers/average_word_embeddings_komninos + - sentence-transformers/average_word_embeddings_levy_dependency + - sentence-transformers/clip-ViT-B-32-multilingual-v1 + - sentence-transformers/clip-ViT-B-32 + - sentence-transformers/distilbert-base-nli-stsb-quora-ranking + - sentence-transformers/distilbert-multilingual-nli-stsb-quora-ranking + - sentence-transformers/distilroberta-base-paraphrase-v1 + - sentence-transformers/distiluse-base-multilingual-cased-v1 + - sentence-transformers/distiluse-base-multilingual-cased-v2 + - sentence-transformers/distiluse-base-multilingual-cased + - sentence-transformers/facebook-dpr-ctx_encoder-multiset-base + - sentence-transformers/facebook-dpr-ctx_encoder-single-nq-base + - sentence-transformers/facebook-dpr-question_encoder-multiset-base + - sentence-transformers/facebook-dpr-question_encoder-single-nq-base + - sentence-transformers/gtr-t5-large + - sentence-transformers/gtr-t5-xl + - sentence-transformers/gtr-t5-xxl + - sentence-transformers/msmarco-MiniLM-L-12-v3 + - sentence-transformers/msmarco-MiniLM-L-6-v3 + - sentence-transformers/msmarco-MiniLM-L12-cos-v5 + - sentence-transformers/msmarco-MiniLM-L6-cos-v5 + - sentence-transformers/msmarco-bert-base-dot-v5 + - sentence-transformers/msmarco-bert-co-condensor + - sentence-transformers/msmarco-distilbert-base-dot-prod-v3 + - sentence-transformers/msmarco-distilbert-base-tas-b + - sentence-transformers/msmarco-distilbert-base-v2 + - sentence-transformers/msmarco-distilbert-base-v3 + - sentence-transformers/msmarco-distilbert-base-v4 + - sentence-transformers/msmarco-distilbert-cos-v5 + - sentence-transformers/msmarco-distilbert-dot-v5 + - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-lng-aligned + - sentence-transformers/msmarco-distilbert-multilingual-en-de-v2-tmp-trained-scratch + - sentence-transformers/msmarco-distilroberta-base-v2 + - sentence-transformers/msmarco-roberta-base-ance-firstp + - sentence-transformers/msmarco-roberta-base-v2 + - sentence-transformers/msmarco-roberta-base-v3 + - sentence-transformers/multi-qa-MiniLM-L6-cos-v1 + - sentence-transformers/nli-mpnet-base-v2 + - sentence-transformers/nli-roberta-base-v2 + - sentence-transformers/nq-distilbert-base-v1 + - sentence-transformers/paraphrase-MiniLM-L12-v2 + - sentence-transformers/paraphrase-MiniLM-L3-v2 + - sentence-transformers/paraphrase-MiniLM-L6-v2 + - sentence-transformers/paraphrase-TinyBERT-L6-v2 + - sentence-transformers/paraphrase-albert-base-v2 + - sentence-transformers/paraphrase-albert-small-v2 + - sentence-transformers/paraphrase-distilroberta-base-v1 + - sentence-transformers/paraphrase-distilroberta-base-v2 + - sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 + - sentence-transformers/paraphrase-multilingual-mpnet-base-v2 + - sentence-transformers/paraphrase-xlm-r-multilingual-v1 + - sentence-transformers/quora-distilbert-base + - sentence-transformers/quora-distilbert-multilingual + - sentence-transformers/sentence-t5-base + - sentence-transformers/sentence-t5-large + - sentence-transformers/sentence-t5-xxl + - sentence-transformers/sentence-t5-xl + - sentence-transformers/stsb-distilroberta-base-v2 + - sentence-transformers/stsb-mpnet-base-v2 + - sentence-transformers/stsb-roberta-base-v2 + - sentence-transformers/stsb-xlm-r-multilingual + - sentence-transformers/xlm-r-distilroberta-base-paraphrase-v1 + - sentence-transformers/clip-ViT-L-14 + - sentence-transformers/clip-ViT-B-16 + - sentence-transformers/use-cmlm-multilingual + - sentence-transformers/all-MiniLM-L12-v1 +``` +
+ + +You can also load many other model architectures from the library. For example models from sources such as BAAI, Nomic, Salesforce Research, etc. See this HF hub page for all [supported models](https://huggingface.co/models?library=sentence-transformers). + + + +Here is an example that uses the BAAI embedding model from the Hugging Face Hub [supported models](https://huggingface.co/models?library=sentence-transformers). + + + + {PyEmbeddingSentenceTransformersBaai} + + + +Visit sentence-transformers [HuggingFace HUB](https://huggingface.co/sentence-transformers) page for more information on the available models. + diff --git a/docs/web/integrations/embedding/superlinked.mdx b/docs/web/integrations/embedding/superlinked.mdx new file mode 100644 index 000000000..f35796230 --- /dev/null +++ b/docs/web/integrations/embedding/superlinked.mdx @@ -0,0 +1,140 @@ +--- +title: Superlinked +sidebarTitle: Superlinked +--- + +[Superlinked](https://superlinked.com) is a self-hosted inference engine (SIE) for embedding, reranking, and extraction. The `sie-lancedb` package registers SIE as a first-class embedding function in LanceDB's embeddings registry, so embeddings are computed automatically on insert and search. You need a running SIE instance - see the [Superlinked quickstart](https://superlinked.com/docs) for deployment options. + +## Installation {#installation} + + +```bash Python icon=Python +pip install sie-lancedb +``` + +```bash TypeScript icon=js +npm install @superlinked/sie-lancedb @lancedb/lancedb +``` + + +## Registered functions {#registered-functions} + +Importing `sie_lancedb` registers two embedding functions in LanceDB's registry: + +| Name | Purpose | +|---|---| +| `"sie"` | Dense text embeddings | +| `"sie-multivector"` | ColBERT-style late interaction with MaxSim scoring | + +Supported parameters on `.create()`: + +| Parameter | Type | Description | +|---|---|---| +| `model` | `str` | Any of 85+ SIE-supported models (e.g. `BAAI/bge-m3`, `NovaSearch/stella_en_400M_v5`, `jinaai/jina-colbert-v2`) | +| `base_url` | `str` | URL of the SIE endpoint (e.g. `http://localhost:8080`) | + +## Usage {#usage} + +```py Python icon=Python +import lancedb +from lancedb.embeddings import get_registry +from lancedb.pydantic import LanceModel, Vector +import sie_lancedb # registers "sie" and "sie-multivector" + +sie = get_registry().get("sie").create( + model="BAAI/bge-m3", + base_url="http://localhost:8080", +) + +class Documents(LanceModel): + text: str = sie.SourceField() + vector: Vector(sie.ndims()) = sie.VectorField() + +db = lancedb.connect("~/.lancedb") +table = db.create_table("docs", schema=Documents, mode="overwrite") + +table.add([ + {"text": "Machine learning is a subset of AI."}, + {"text": "Neural networks use multiple layers."}, + {"text": "Python is popular for ML development."}, +]) + +results = table.search("What is deep learning?").limit(3).to_list() +``` + +LanceDB handles embedding generation for both inserts and queries automatically, based on the `SourceField` / `VectorField` declarations on the schema. + +## Hybrid search with reranker {#hybrid-search-with-reranker} + +`SIEReranker` plugs into LanceDB's hybrid search pipeline. It uses SIE's cross-encoder `score()` to rerank combined vector + full-text search results. You need a full-text search index on the column first: + +```py Python icon=Python +from sie_lancedb import SIEReranker + +# Create FTS index for hybrid search +table.create_fts_index("text", replace=True) + +results = ( + table.search("What is deep learning?", query_type="hybrid") + .rerank(SIEReranker(model="jinaai/jina-reranker-v2-base-multilingual")) + .limit(5) + .to_list() +) + +for r in results: + print(f"{r['_relevance_score']:.3f} {r['text']}") +``` + +The reranker also works with pure vector or pure FTS search via `.rerank()`. + +## ColBERT and multivector {#colbert-and-multivector} +`SIEMultiVectorEmbeddingFunction` (registered as `"sie-multivector"`) works with LanceDB's native `MultiVector` type and MaxSim scoring for ColBERT and ColPali models: + +```py Python icon=Python +from lancedb.pydantic import MultiVector + +sie_colbert = get_registry().get("sie-multivector").create( + model="jinaai/jina-colbert-v2", + base_url="http://localhost:8080", +) + +class ColBERTDocs(LanceModel): + text: str = sie_colbert.SourceField() + vector: MultiVector(sie_colbert.ndims()) = sie_colbert.VectorField() + +table = db.create_table("colbert_docs", schema=ColBERTDocs, mode="overwrite") +table.add([{"text": "Machine learning is a subset of AI."}]) + +# MaxSim search - query and document multivectors compared token-by-token +results = table.search("What is ML?").limit(5).to_list() +``` + +## Entity extraction {#entity-extraction} + +`SIEExtractor` adds entity extraction to LanceDB's data-enrichment workflows. Extract entities from a text column and merge the results back as a structured Arrow column - enabling filtered search on extracted entities: + +```py Python icon=Python +from sie_lancedb import SIEExtractor + +extractor = SIEExtractor( + base_url="http://localhost:8080", + model="urchade/gliner_multi-v2.1", +) + +extractor.enrich_table( + table, + source_column="text", + target_column="entities", + labels=["person", "technology", "organization"], + id_column="id", +) +``` + +The `entities` column stores structured Arrow data (`list>`), so you can filter on extracted entities in queries. + +## Links {#links} + +- [`sie-lancedb` on PyPI](https://pypi.org/project/sie-lancedb/) +- [`@superlinked/sie-lancedb` on npm](https://www.npmjs.com/package/@superlinked/sie-lancedb) +- [Superlinked on GitHub](https://github.com/superlinked/sie) +- [Superlinked docs](https://superlinked.com/docs) diff --git a/docs/web/integrations/embedding/voyageai.mdx b/docs/web/integrations/embedding/voyageai.mdx new file mode 100644 index 000000000..54d155d1b --- /dev/null +++ b/docs/web/integrations/embedding/voyageai.mdx @@ -0,0 +1,60 @@ +--- +title: VoyageAI +sidebarTitle: VoyageAI +--- + +import { PyEmbeddingVoyageaiUsage, PyEmbeddingVoyageaiMultimodal } from '/snippets/integrations.mdx'; + +Voyage AI provides cutting-edge embedding and rerankers. + + +Using voyageai API requires voyageai package, which can be installed using `pip install voyageai`. Voyage AI embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification. +You also need to set the `VOYAGE_API_KEY` environment variable to use the VoyageAI API. + +Supported models are: + +- voyage-4-large (best retrieval quality, 1024 default dimensions, supports 256/512/1024/2048) +- voyage-4 (balanced general-purpose, 1024 default dimensions, supports 256/512/1024/2048) +- voyage-4-lite (optimized for latency/cost, 1024 default dimensions, supports 256/512/1024/2048) +- voyage-context-3 +- voyage-3.5 +- voyage-3.5-lite +- voyage-3 +- voyage-3-lite +- voyage-finance-2 +- voyage-multilingual-2 +- voyage-law-2 +- voyage-code-2 +- voyage-multimodal-3.5 (multimodal - supports text, images, and video) + + +**Multimodal Model:** `voyage-multimodal-3.5` supports text, images, and video inputs. It outputs 1024-dimensional embeddings by default, configurable via the `output_dimension` parameter (256, 512, 1024, 2048). See the [VoyageAI multimodal embeddings documentation](https://docs.voyageai.com/docs/multimodal-embeddings) for more details. + + +Supported parameters (to be passed in `create` method) are: + +| Parameter | Type | Default Value | Description | +|---|---|--------|---------| +| `name` | `str` | `None` | The model ID of the model to use. Supported models: voyage-4-large, voyage-4, voyage-4-lite, voyage-3, voyage-3-lite, voyage-3.5, voyage-3.5-lite, voyage-context-3, voyage-finance-2, voyage-multilingual-2, voyage-law-2, voyage-code-2, voyage-multimodal-3.5 | +| `input_type` | `str` | `None` | Type of the input text. Default to None. Other options: query, document. | +| `truncation` | `bool` | `True` | Whether to truncate the input texts to fit within the context length. | +| `output_dimension` | `int` | `None` | Output embedding dimension. Only supported by `voyage-multimodal-3.5`. Valid options: 256, 512, 1024 (default), 2048. | + + +Usage Example: + + + + {PyEmbeddingVoyageaiUsage} + + + +### Multimodal Example {#multimodal-example} + +The `voyage-multimodal-3.5` model can embed text alongside images. You can use image URLs, file paths, or PIL Image objects: + + + + {PyEmbeddingVoyageaiMultimodal} + + \ No newline at end of file diff --git a/docs/web/integrations/index.mdx b/docs/web/integrations/index.mdx new file mode 100644 index 000000000..640628684 --- /dev/null +++ b/docs/web/integrations/index.mdx @@ -0,0 +1,17 @@ +--- +title: Integrations +sidebarTitle: Overview +description: Connect LanceDB with popular AI providers, frameworks, and data platforms +--- + +LanceDB seamlessly plugs into the rest of your AI and data engineering stack. Use the sections +below to jump straight into the guides that matter for your workflow. + + +| Group | Description | +|:----------------|:-------------| +| [Embedding models](/integrations/embedding/) | Connect with popular embedding model providers including OpenAI, Cohere, Hugging Face, and more | +| [Reranking models](/integrations/reranking/) | Enhance search results with advanced reranking models and techniques | +| [AI platforms & frameworks](/integrations/ai/) | Integrate with LangChain, LlamaIndex, Kiln, and other AI development frameworks | +| [Data platforms & frameworks](/integrations/data/) | Integrate LanceDB with popular data tools and platforms like DuckDB, Pydantic and dlt | + diff --git a/docs/web/integrations/lerobotdataset.mdx b/docs/web/integrations/lerobotdataset.mdx new file mode 100644 index 000000000..33c28cd49 --- /dev/null +++ b/docs/web/integrations/lerobotdataset.mdx @@ -0,0 +1,94 @@ +--- +title: "LeRobotDataset" +sidebarTitle: "LeRobotDataset" +description: "Use Lance-backed LeRobotDataset loaders and LanceDB to inspect, filter, and train on robotics datasets from the Hugging Face Hub." +--- + +import { + PyFrameworksLerobotFilterFrames, + PyFrameworksLerobotLancedbImageDataset, + PyFrameworksLerobotLancedbVideoDataset, + PyFrameworksLerobotOpenLanceTables, +} from '/snippets/integrations.mdx'; + +[LeRobot](https://huggingface.co/docs/lerobot/index) is Hugging Face's open-source robotics stack for collecting data, training policies, running simulations, and sharing robotics datasets and models on the Hub. + +[LeRobotDataset v3.0](https://huggingface.co/docs/lerobot/lerobot-dataset-v3) standardizes robot learning data across sensorimotor time series, actions, multi-camera video, and task metadata. Its v3 layout stores high-frequency tabular signals in Parquet, visual streams as MP4 shards, and metadata that reconstructs episode-level views from larger files. + +Lance pairs well with LeRobot when you need high-performance random access, lazy multimodal blob reads, and a single table interface for curation, search, and training data preparation. The `lerobot-lancedb` package ships Lance-backed `LeRobotDataset` subclasses, and LanceDB can open Lance-formatted LeRobot datasets on the Hub directly through `hf://` URIs. + +## Install {#install} + +```bash +pip install lancedb lance lerobot-lancedb +``` + +## Use Lance-backed LeRobotDataset loaders {#use-lance-backed-lerobotdataset-loaders} + +`LeRobotLanceDataset` is useful when your Lance-backed dataset stores decoded image observations. It's a drop-in replacement for `LeRobotDataset`, so existing policy training code keeps working with the usual PyTorch dataset and dataloader patterns. + + + {PyFrameworksLerobotLancedbImageDataset} + + +For datasets that store camera observations as MP4 video segments, use `LeRobotLanceVideoDataset` instead. + + + {PyFrameworksLerobotLancedbVideoDataset} + + + +Use the image loader for Lance-backed repos that store image frames. Use the video loader for MP4-backed LeRobot datasets such as `lance-format/lerobot-pusht-lance`. + + +## Open LeRobot Lance tables with LanceDB {#open-lerobot-lance-tables-with-lancedb} + +Lance-formatted LeRobot datasets published by `lance-format` expose each `.lance` file under `data/` as a LanceDB table. The PushT dataset, for example, has `frames`, `episodes`, and `videos` tables. + + + {PyFrameworksLerobotOpenLanceTables} + + +Opening the tables directly is handy for inspecting schemas, counting rows, sampling metadata, or building curation workflows before any data reaches the training loop. + +## Filter a frame window {#filter-a-frame-window} + +Most robotics workflows want a deterministic slice by `episode_index`, `frame_index`, or task metadata long before training begins. LanceDB filters those rows without touching the video blobs. + + + {PyFrameworksLerobotFilterFrames} + + +With the filtered set in hand, you can materialize a smaller local LanceDB database, add derived columns, attach embeddings, or build vector and scalar indexes for faster repeated access. + +## Example Lance-formatted LeRobot datasets {#example-lance-formatted-lerobot-datasets} + + + + A Lance-formatted version of `lerobot/pusht` with frame, episode, and video tables. + + + A multi-camera robotics dataset packaged as Lance tables for frame-level and episode-level access. + + + +## More resources {#more-resources} + + + + Hugging Face's guide to the v3 dataset layout, streaming, transforms, and migration. + + + API documentation for the Lance-backed LeRobotDataset implementations. + + + +## When to use each interface {#when-to-use-each-interface} + +| Interface | Best for | +|:---|:---| +| `LeRobotDataset` | Standard LeRobot training loops and policy code | +| `LeRobotLanceDataset` | Drop-in training on Lance-backed image datasets | +| `LeRobotLanceVideoDataset` | Drop-in training on Lance-backed video datasets | +| LanceDB | Interactive inspection, filtering, curation, search, indexing, and materializing subsets | +| `lance.dataset(...)` | Lower-level schema, fragment, index, and blob access | diff --git a/docs/web/integrations/reranking/answerdotai.mdx b/docs/web/integrations/reranking/answerdotai.mdx new file mode 100644 index 000000000..e3f500d49 --- /dev/null +++ b/docs/web/integrations/reranking/answerdotai.mdx @@ -0,0 +1,52 @@ +--- +title: Answer.AI Rerankers +sidebarTitle: "Answer.AI" +description: Use AnswerDotAI's lightweight reranking library with LanceDB. Features unified API for common reranking models, configurable model selection, and comprehensive scoring options. + +--- + +import { PyRerankingAnswerdotaiUsage } from '/snippets/integrations.mdx'; + +# Answer.AI Rerankers + +This integration uses [AnswersDotAI's rerankers](https://github.com/AnswerDotAI/rerankers) to rerank the search results, providing a lightweight, low-dependency, unified API to use all common reranking and cross-encoder models. + +> **Note:** Supported query types – Hybrid, Vector, and FTS. + + + + + {PyRerankingAnswerdotaiUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `model_type` | `str` | `"colbert"` | The type of model to use. Supported model types can be found here: https://github.com/AnswerDotAI/rerankers. | +| `model_name` | `str` | `"answerdotai/answerai-colbert-small-v1"` | The name of the reranker model to use. | +| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. | + + + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). | + +### Vector Search {#vector-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). | + +### FTS Search {#fts-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). | diff --git a/docs/web/integrations/reranking/cohere.mdx b/docs/web/integrations/reranking/cohere.mdx new file mode 100644 index 000000000..e484f3ee0 --- /dev/null +++ b/docs/web/integrations/reranking/cohere.mdx @@ -0,0 +1,57 @@ +--- +title: Cohere Reranker +sidebarTitle: "Cohere" +description: Integrate Cohere's powerful reranking API with LanceDB for enhanced search results. Supports English and multilingual models with configurable scoring options for vector, FTS, and hybrid search. + +--- + +import { PyRerankingCohereUsage } from '/snippets/integrations.mdx'; + +# Cohere Reranker + +This reranker uses the [Cohere](https://cohere.ai/) API to rerank the search results. You can use this reranker by passing `CohereReranker()` to the `rerank()` method. Note that you'll either need to set the `COHERE_API_KEY` environment variable or pass the `api_key` argument to use this reranker. + + +> **Note:** Supported query types – Hybrid, Vector, and FTS. + +```shell +pip install cohere +``` + + + + {PyRerankingCohereUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `model_name` | `str` | `"rerank-english-v2.0"` | The name of the reranker model to use. Available cohere models are: rerank-english-v2.0, rerank-multilingual-v2.0 | +| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. | +| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. | +| `api_key` | `str` | `None` | The API key for the Cohere API. If not provided, the `COHERE_API_KEY` environment variable is used. | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type | + + + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column | +| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) | + +### Vector Search {#vector-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column | +| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) | + +### FTS Search {#fts-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column | +| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`) | diff --git a/docs/web/integrations/reranking/colbert.mdx b/docs/web/integrations/reranking/colbert.mdx new file mode 100644 index 000000000..463b3d78d --- /dev/null +++ b/docs/web/integrations/reranking/colbert.mdx @@ -0,0 +1,50 @@ +--- +title: ColBERT Reranker +sidebarTitle: "ColBERT" +description: Enhance search results with ColBERT's contextual reranking in LanceDB. Features efficient model deployment, device optimization, and flexible scoring options for vector, FTS, and hybrid search. + +--- + +import { PyRerankingColbertUsage } from '/snippets/integrations.mdx'; + +# ColBERT Reranker + +This reranker uses ColBERT model to rerank the search results. You can use this reranker by passing `ColbertReranker()` to the `rerank()` method. +> **Note:** Supported query types – Hybrid, Vector, and FTS. + + + + + {PyRerankingColbertUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `model_name` | `str` | `"colbert-ir/colbertv2.0"` | The name of the reranker model to use.| +| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. | +| `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. | + + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). | + +### Vector Search {#vector-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). | + +### FTS Search {#fts-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). | diff --git a/docs/web/integrations/reranking/jina.mdx b/docs/web/integrations/reranking/jina.mdx new file mode 100644 index 000000000..4624149d2 --- /dev/null +++ b/docs/web/integrations/reranking/jina.mdx @@ -0,0 +1,54 @@ +--- +title: Jina Reranker +sidebarTitle: "Jina AI" +description: Integrate Jina's multilingual reranking API with LanceDB for improved search results. Features model selection, API key management, and flexible scoring options for all search types. + +--- + +import { PyRerankingJinaUsage } from '/snippets/integrations.mdx'; + +# Jina Reranker + +This reranker uses the [Jina](https://jina.ai/reranker/) API to rerank the search results. You can use this reranker by passing `JinaReranker()` to the `rerank()` method. Note that you'll either need to set the `JINA_API_KEY` environment variable or pass the `api_key` argument to use this reranker. + + +> **Note:** Supported query types – Hybrid, Vector, and FTS. + + + + + {PyRerankingJinaUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `model_name` | `str` | `"jina-reranker-v2-base-multilingual"` | The name of the reranker model to use. You can find the list of available models in https://jina.ai/reranker. | +| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. | +| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. | +| `api_key` | `str` | `None` | The API key for the Jina API. If not provided, the `JINA_API_KEY` environment variable is used. | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. | + + + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). | + +### Vector Search {#vector-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). | + +### FTS Search {#fts-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). | diff --git a/docs/web/integrations/reranking/openai.mdx b/docs/web/integrations/reranking/openai.mdx new file mode 100644 index 000000000..413f95e67 --- /dev/null +++ b/docs/web/integrations/reranking/openai.mdx @@ -0,0 +1,51 @@ +--- +title: OpenAI Reranker (Experimental) +sidebarTitle: "OpenAI" +description: Explore experimental search reranking using OpenAI's GPT models in LanceDB. Features configurable model selection, API key management, and comprehensive scoring options for all search types. + +--- + +import { PyRerankingOpenaiUsage } from '/snippets/integrations.mdx'; + +# OpenAI Reranker (Experimental) + +This reranker uses OpenAI chat model to rerank the search results. You can use this reranker by passing `OpenAI()` to the `rerank()` method. +> **Note:** Supported query types – Hybrid, Vector, and FTS. + +> **Warning:** This reranker is experimental. OpenAI does not have a dedicated reranking model, so it uses a chat model under the hood. + + + + {PyRerankingOpenaiUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `model_name` | `str` | `"gpt-4-turbo-preview"` | The name of the reranker model to use.| +| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type. | +| `api_key` | `str` | `None` | The API key to use. If None, will use the OPENAI_API_KEY environment variable. + + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). | + +### Vector Search {#vector-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). | + +### FTS Search {#fts-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). | diff --git a/docs/web/integrations/reranking/voyageai.mdx b/docs/web/integrations/reranking/voyageai.mdx new file mode 100644 index 000000000..62e9a16da --- /dev/null +++ b/docs/web/integrations/reranking/voyageai.mdx @@ -0,0 +1,56 @@ +--- +title: VoyageAI Reranker +sidebarTitle: "Voyage AI" +description: Integrate VoyageAI's cutting-edge reranking models with LanceDB. Features model selection, API key management, and comprehensive scoring options for all search types. + +--- + +import { PyRerankingVoyageaiUsage } from '/snippets/integrations.mdx'; + +# VoyageAI Reranker + +Voyage AI provides cutting-edge embedding and rerankers. + +This reranker uses the [VoyageAI](https://docs.voyageai.com/docs/) API to rerank the search results. You can use this reranker by passing `VoyageAIReranker()` to the `rerank()` method. Note that you'll either need to set the `VOYAGE_API_KEY` environment variable or pass the `api_key` argument to use this reranker. + + +> **Note:** Supported query types – Hybrid, Vector, and FTS. + + + + + {PyRerankingVoyageaiUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `model_name` | `str` | `None` | The name of the reranker model to use. Available models are: rerank-2, rerank-2-lite | +| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. | +| `top_n` | `str` | `None` | The number of results to return. If None, will return all results. | +| `api_key` | `str` | `None` | The API key for the Voyage AI API. If not provided, the `VOYAGE_API_KEY` environment variable is used. | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score. If "all" is supported, will return relevance score along with the vector and/or fts scores depending on query type | +| `truncation` | `bool` | `None` | Whether to truncate the input to satisfy the "context length limit" on the query and the documents. | + + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column | +| `all` | ❌ Not Supported | Returns have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) | + +### Vector Search {#vector-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column | +| `all` | ✅ Supported | Returns have vector(`_distance`) along with Hybrid Search score(`_relevance_score`) | + +### FTS Search {#fts-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Returns only have the `_relevance_score` column | +| `all` | ✅ Supported | Returns have FTS(`score`) along with Hybrid Search score(`_relevance_score`) | diff --git a/docs/web/integrations/reranking/watsonx.mdx b/docs/web/integrations/reranking/watsonx.mdx new file mode 100644 index 000000000..775af4f15 --- /dev/null +++ b/docs/web/integrations/reranking/watsonx.mdx @@ -0,0 +1,63 @@ +--- +title: Watsonx Reranker +sidebarTitle: "IBM watsonx.ai" +description: Rerank LanceDB search results with the IBM watsonx.ai text rerank API. Supports vector, FTS, and hybrid search with configurable models, projects, and spaces. + +--- + +import { PyRerankingWatsonxUsage } from '/snippets/integrations.mdx'; + +# Watsonx Reranker + +This reranker uses the [IBM watsonx.ai](https://cloud.ibm.com/docs/apis/watsonx-ai#text-rerank) text rerank API to reorder search results. Pass `WatsonxReranker()` to the `rerank()` method on a query. Credentials come from the `WATSONX_API_KEY` and `WATSONX_PROJECT_ID` (or `WATSONX_SPACE_ID`) environment variables, or can be passed explicitly as arguments. + +> **Note:** Supported query types – Hybrid, Vector, and FTS. + +```shell +pip install ibm-watsonx-ai +``` + + + + {PyRerankingWatsonxUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `model_name` | `str` | `"cross-encoder/ms-marco-minilm-l-12-v2"` | The rerank model ID. See [supported rerank models](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx#rerank). | +| `column` | `str` | `"text"` | The name of the table column to use as document input. | +| `top_n` | `int` | `None` | The number of results to return. If `None`, all results are returned. | +| `return_score` | `str` | `"relevance"` | Options are `"relevance"` or `"all"`. Controls which score columns are kept on the result. | +| `api_key` | `str` | `None` | IBM Cloud API key. Falls back to the `WATSONX_API_KEY` environment variable. | +| `project_id` | `str` | `None` | watsonx.ai project ID. Falls back to `WATSONX_PROJECT_ID`. Mutually exclusive with `space_id`. | +| `space_id` | `str` | `None` | watsonx.ai deployment space ID. Falls back to `WATSONX_SPACE_ID`. Mutually exclusive with `project_id`. | +| `url` | `str` | `"https://us-south.ml.cloud.ibm.com"` | watsonx.ai service URL. | +| `truncate_input_tokens` | `int` | `None` | Truncate each document to this many tokens before scoring. | + + +You must supply exactly one of `project_id` or `space_id` (either as an argument or via its environment variable). Setting both, or neither, raises a `ValueError`. + + +## Supported scores for each query type {#supported-scores-for-each-query-type} + +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). | + +### Vector Search {#vector-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). | + +### FTS Search {#fts-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). | diff --git a/docs/web/integrations/stable-worldmodel.mdx b/docs/web/integrations/stable-worldmodel.mdx new file mode 100644 index 000000000..ee35e2159 --- /dev/null +++ b/docs/web/integrations/stable-worldmodel.mdx @@ -0,0 +1,107 @@ +--- +title: "Stable World Model" +sidebarTitle: "Stable World Model" +description: "Use Stable World Model with LanceDB-backed datasets for reproducible world model research, fast data loading, and compact storage." +--- + +import { + PyFrameworksStableWorldmodelCollectLance, + PyFrameworksStableWorldmodelConvert, + PyFrameworksStableWorldmodelEvaluate, + PyFrameworksStableWorldmodelLoadLance, +} from '/snippets/integrations.mdx'; + +[Stable World Model](https://github.com/galilai-group/stable-worldmodel) is a research platform for collecting data, training world models, and evaluating policies with model-predictive control across standardized environments. + +The LanceDB integration is built into Stable World Model's data format registry. Lance is the default backend for collected datasets, so a path ending in `.lance` gives you an append-friendly LanceDB table with episode-contiguous rows and fast indexed reads. + +Random access speed is the bottleneck for world model training, since the loop repeatedly samples temporal windows from high-dimensional observations, actions, and rewards. The faster those windows arrive, the more GPU time goes into training rather than waiting on the data loader. + +## Install {#install} + +```bash +pip install stable-worldmodel +``` + +Datasets and checkpoints are stored under `$STABLEWM_HOME`, which defaults to `~/.stable_worldmodel/`. + +## Collect data into Lance {#collect-data-into-lance} + +Stable World Model uses Lance by default when you collect to a `.lance` path. +Replace `your_expert_policy` with the expert or scripted policy you use to collect demonstrations. + + + {PyFrameworksStableWorldmodelCollectLance} + + +Every writer accepts a `mode` argument such as `append`, `overwrite`, or `error`. The default is append, so re-running collection extends the existing dataset. + +## Load a Lance dataset for training {#load-a-lance-dataset-for-training} + +The dataset loader autodetects the Lance format from the path. + + + {PyFrameworksStableWorldmodelLoadLance} + + +Your model code stays focused on the world model objective while LanceDB handles the storage layout and read path. + +## Evaluate with model-predictive control {#evaluate-with-model-predictive-control} + +After training a world model on the Lance-backed dataset, Stable World Model can evaluate it with planning solvers such as CEM. +Replace `world_model` with the trained model object from your training loop. + + + {PyFrameworksStableWorldmodelEvaluate} + + +## Convert between formats {#convert-between-formats} + +Stable World Model can convert between registered dataset formats. A common workflow is to collect in Lance for fast training reads, then export to the video layout for compact inspection artifacts. + + + {PyFrameworksStableWorldmodelConvert} + + +## Throughput {#throughput} + +The Stable World Model README reports the following PushT benchmark results from `scripts/benchmark/compare_h5_lance.py`: + +| Format | Source | Cache | samples/s | ms/step | +|:---|:---|:---|---:|---:| +| HDF5 | local | no-cache | 1,416.1 | 45.2 | +| HDF5 | local | cached | 1,474.0 | 43.4 | +| LanceDB | local | no-cache | 4,814.8 | 13.3 | +| LanceDB | local | cached | 4,431.3 | 14.4 | +| Video | local | - | 1,330.6 | 48.1 | +| LanceDB | s3 | no-cache | 3,183.7 | 20.1 | +| LanceDB | s3 | cached | 3,253.2 | 19.7 | +| HDF5 | s3 | no-cache | 9.1 | 7,032.5 | +| HDF5 | s3 | cached | 756.5 | 84.6 | + +In that benchmark, local LanceDB reached about **3.4x** the no-cache throughput of local HDF5, while S3-backed LanceDB reached about **350x** the no-cache throughput of S3-backed HDF5. Even with cache enabled, S3-backed LanceDB was about **4.3x** faster than S3-backed HDF5. + +These numbers come from the Stable World Model project's own benchmark setup, so they're best read as a reproducible directional baseline that may shift across environments, models, and storage configurations. + +## Storage {#storage} + +The same README reports these local storage sizes for the benchmark dataset: + +| Format | Local size | +|:---|---:| +| HDF5 | 43.12 GB | +| LanceDB | 13.31 GB | +| Video | 496.29 MB | + +LanceDB used about **69% less local storage than HDF5** in the reported benchmark, while preserving a table interface built for fast training reads and append-heavy collection. + +## More resources {#more-resources} + + + + Installation, quick start, supported formats, benchmarks, environments, solvers, and citation. + + + Full upstream documentation with tutorials, API references, and guides. + + diff --git a/docs/web/lance.mdx b/docs/web/lance.mdx new file mode 100644 index 000000000..d6db5f17c --- /dev/null +++ b/docs/web/lance.mdx @@ -0,0 +1,99 @@ +--- +title: "Lance format" +sidebarTitle: "Lance format" +description: "Open-source lakehouse format for multimodal AI." +icon: "/static/assets/logo/lance-logo-gray.svg" +--- + +[Lance](https://lance.org/) is an open-source, columnar lakehouse format for multimodal AI. +It provides a file format, table format, and lightweight catalog spec, allowing developers +to build a complete open lakehouse on top of object storage. + +Building on top of open foundations and optimizing the format for random access +(without compromising scan performance) enables +high-performance vector search, full-text search, indexing, and feature engineering capabilities. +[LanceDB](/enterprise) builds on these capabilities so teams can work with one multimodal data layer +instead of moving data across separate storage, search, feature, and training systems. + + + Visit the Lance format documentation to learn more about its design, features, and how it enables the multimodal lakehouse. + + +## Capabilities of the Lance format {#capabilities-of-the-lance-format} + +Capability | What it enables +--- | --- +Multimodal storage | Store images, video, audio, text, embeddings, annotations, metadata, features, and more, all in one table. +First-class blob API | Store large binary objects such as images, video, audio, and model artifacts in blob columns with lazy reads and streaming byte access. +Fast random access and scans | Sample, shuffle, and retrieve individual rows efficiently without giving up high-throughput sequential reads. +Flexible data evolution | Add, drop, rename, or alter columns as datasets change, often without rewriting existing data files. +Versioned tables | Reproduce experiments, restore previous states, and tie downstream artifacts to the exact table version they used. +Hybrid search and indexing | Combine vector search, full-text search, and scalar filters on the same dataset with Lance indexes. +Open lakehouse interoperability | Build on object storage and connect Lance tables to open engines such as PyTorch, Ray, Spark, Trino, DuckDB and Polars. + +## Key concepts {#key-concepts} + +The following concepts are core to the Lance format: + + + + **Arrow-native, columnar storage** and **interoperability** with the open lakehouse ecosystem (including other file formats and compute engines). + + + **Zero-copy** data evolution, meaning you can easily add derived columns (like features or embeddings) at a later time, **without full table rewrites**. Only new data is written; expensive existing data (like images/videos) remain untouched. + + + Data is **versioned**, with each insert operation creating a new version of the dataset and an update to the manifest that tracks versions via metadata + + + + +### Data versioning {#data-versioning} + +Data in Lance tables are versioned -- this helps keep LanceDB scalable and consistent. +We do not immediately blow away old versions when creating new ones because other clients might be +in the middle of querying the old version. It's important to retain older versions for as long as they +might be queried. + +Each version contains metadata and just the new/updated data in your transaction. So if you have 100 +versions, they aren't 100 duplicates of the same data. However, they do have 100x the metadata overhead +of a single version, which can result in slower queries. + +### Data compaction {#data-compaction} + +As you insert more data, your dataset will grow and you'll need to perform compaction to maintain query +throughput (i.e., keep latencies down to a minimum). Compaction is the process of merging fragments +together to reduce the amount of metadata that needs to be managed, and to reduce the number of files +that need to be opened while scanning the dataset. + +Running compaction on a Lance dataset will do the following: + +- Remove deleted rows from fragments +- Remove dropped columns from fragments +- Merge small fragments into larger ones + +Compaction focuses on read performance, not immediate disk reclamation. During compaction, Lance writes +new compacted files while older files are still referenced by previous table versions. This means disk +usage can increase temporarily until old versions are cleaned up. + +### Data deletion and recovery {#data-deletion-and-recovery} + +Although Lance allows you to delete rows from a dataset, it does not actually delete the data immediately. +It simply marks the row as deleted in the `DataFile` that represents a fragment. + +For a given version of the dataset, each fragment can have up to one deletion file (if no rows were ever +deleted from that fragment, it will not have a deletion file). This is important to keep in mind because +it means that the data is still there, and can be recovered if needed, as long as that version still +exists based on your backup policy. + + + Lance is a separate open source project. Check out its documentation to learn more. + diff --git a/docs/web/namespaces/index.mdx b/docs/web/namespaces/index.mdx new file mode 100644 index 000000000..e7f4adc3c --- /dev/null +++ b/docs/web/namespaces/index.mdx @@ -0,0 +1,226 @@ +--- +title: "Namespaces and the Catalog Model" +sidebarTitle: "Overview" +description: "Understand LanceDB as a catalog-level abstraction over Lance's table format, and learn how namespaces help organize Lance tables." +icon: "sitemap" +keywords: ["namespace", "catalog", "lance format", "table format", "lancedb"] +--- + +Despite its name, LanceDB is not a "database" in the traditional sense -- it is a **Multimodal Lakehouse** that builds on the table abstraction, +similar to many other lakehouse projects. LanceDB exposes a catalog-level abstraction over the Lance table format, via a *namespace spec*. +If you're coming from traditional databases or lakehouses, you can think of a namespace as the catalog path that says where a table name lives. + +Lance provides the **file** and **table** formats to store and manage your data and indexes. +LanceDB operates at the catalog layer (used to organize, discover, and operate on many Lance tables) +and provides a compute engine on top of the Lance format. + +This is why many SDK methods in LanceDB, like `create_table`, `open_table`, `drop_table`, and +`rename_table`, accept namespace input. The SDK methods expose that input in the idiom of each +language: Python uses `namespace_path`, Rust uses builder methods like `.namespace(...)`, and +TypeScript uses `namespacePath` arguments. + +## Namespace hierarchy {#namespace-hierarchy} + +Namespaces are generalizations of catalog specs that give platform developers a clean way to present Lance tables in the structures users expect. The diagram below shows how the hierarchy can go beyond a single level. +A namespace can contain a collection of tables, and it can also contain namespaces recursively. + +![](/static/assets/images/namespaces/lance-namespace.png) + +Before diving into examples, it helps to keep two terms in mind: the **namespace client** is the abstraction that presents a consistent namespace API, while the **namespace implementation** is the concrete backend that resolves namespaces and table locations (for example, a local directory or an external catalog). +If you want to go deeper, see the Lance format [namespace documentation](https://lance.org/format/namespace/). + +## Namespace paths and names {#namespace-paths-and-names} + +A namespace path is a list of components. For example, `["prod", "search"]` means the `search` +namespace inside the `prod` namespace. The empty path, `[]`, means the root namespace. + +Each component is a name, not a filesystem path segment. Namespace names can't be empty, and each +component can contain only letters, numbers, underscores, hyphens, and periods. That keeps the same +identifier usable across local directory namespaces and REST namespace identifiers. + +## Directory namespaces {#directory-namespaces} + +The simplest namespace model in LanceDB is a single root namespace, often represented by one +directory: + +```bash +./local_lancedb (root) +└── prod + └── search + └── user (table) + └── data (table) +``` + +As a user of LanceDB OSS, you might never notice namespaces at first, because LanceDB exposes the single-level hierarchy shown above, with the data stored in the `data/` directory, where the root namespace is implicit. Connecting to this namespace is as simple as connecting to the catalog root: + + +```python Python icon="python" +import lancedb + +# Connect to the directory namespace root +db = lancedb.connect("./local_lancedb") +``` + +```typescript TypeScript icon="square-js" +import * as lancedb from "@lancedb/lancedb"; + +// Connect to the directory namespace root +const db = await lancedb.connect("./local_lancedb"); +``` + +```rust Rust icon="rust" +use lancedb::connect; + +// Connect to the directory namespace root +let db = connect("./local_lancedb").execute().await?; +``` + + +This creates the default namespace directory (`data/`) under the specified root path. + +You can also explicitly connect to a namespace using `lancedb.connect_namespace(...)` with the directory namespace implementation: + + +```python Python icon="python" +import lancedb + +# Local namespace-backed catalog root (DirectoryNamespace) +# See https://lance.org/format/namespace/dir/catalog-spec/ +db = lancedb.connect_namespace("dir", {"root": "./local_lancedb"}) + +table_name = "user" +data = [{"id": 1, "vector": [0.1, 0.2], "name": "alice"}] + +table = db.create_table(table_name, data=data, mode="create") +print(f"Created table: {table.name}") +# Created table: user +``` + +```typescript TypeScript icon="square-js" +import * as lancedb from "@lancedb/lancedb"; + +// Local namespace-backed catalog root (DirectoryNamespace) +// See https://lance.org/format/namespace/dir/catalog-spec/ +const db = await lancedb.connectNamespace("dir", { root: "./local_lancedb" }); + +const table = await db.createTable( + "user", + [{ id: 1, vector: [0.1, 0.2], name: "alice" }], + { mode: "create" }, +); +console.log(`Created table: ${table.name}`); +// Created table: user +``` + +```rust Rust icon="rust" +use std::collections::HashMap; +use std::sync::Arc; +use arrow_schema::{DataType, Field, Schema}; + +// Local namespace-backed catalog root (DirectoryNamespace) +// See https://lance.org/format/namespace/dir/catalog-spec/ +let mut properties = HashMap::new(); +properties.insert("root".to_string(), "./local_lancedb".to_string()); +let db = lancedb::connect_namespace("dir", properties) + .execute() + .await?; + +let schema = Arc::new(Schema::new(vec![ + Field::new("id", DataType::Int64, false), +])); +let table = db + .create_empty_table("user", schema) + .execute() + .await?; +println!("Created table: {}", table.name()); +// Created table: user +``` + + + +- For simple use cases in LanceDB OSS, you don't need to go too deep into namespaces. +- To integrate LanceDB with external catalogs and to use it as a true **multimodal lakehouse**, it's useful to understand the different namespace implementations and how to use them in your organization's setup. + + +## Remote or external catalog namespaces {#remote-or-external-catalog-namespaces} + +The example above showed local directory-based namespaces. LanceDB also supports namespaces backed by remote object stores and external catalogs, via the REST namespace implementation. + +For remote object stores with central metadata/catalog services (either commercial or open source), +use the REST namespace implementation. It is backed by REST routes +(for example `POST /v1/namespace/{id}/create` and `GET /v1/namespace/{id}/list`) and server-provided table locations. + +For authentication, any property prefixed with `headers` is forwarded as an HTTP header +(for example `headers.Authorization` becomes `Authorization`, and `headers.X-API-Key` becomes `X-API-Key`). +LanceDB Enterprise REST requests use the `x-api-key` header for API-key authentication. Deployments +that route multiple databases through the same endpoint can also use headers such as +`x-lancedb-database` or `x-lancedb-database-prefix` for database context. + + +```python Python icon="python" +import os +import lancedb + +# Remote namespace-backed catalog root (RestNamespace) +# See https://lance.org/format/namespace/rest/catalog-spec/ +db = lancedb.connect_namespace( + "rest", + { + "uri": "https://.internal..com", + "headers.x-api-key": os.environ["API_KEY"], + # or: + # "headers.Authorization": f"Bearer {os.environ['REST_AUTH_TOKEN']}", + }, +) +``` + +```typescript TypeScript icon="square-js" +import * as lancedb from "@lancedb/lancedb"; + +// Remote namespace-backed catalog root (RestNamespace) +// See https://lance.org/format/namespace/rest/catalog-spec/ +const db = await lancedb.connectNamespace("rest", { + uri: "https://.internal..com", + headers: { + "x-api-key": process.env.API_KEY ?? "", + // or: + // Authorization: `Bearer ${process.env.REST_AUTH_TOKEN}`, + }, +}); +``` + +```rust Rust icon="rust" +use std::collections::HashMap; + +// Remote namespace-backed catalog root (RestNamespace) +// See https://lance.org/format/namespace/rest/catalog-spec/ +let mut properties = HashMap::new(); +properties.insert( + "uri".to_string(), + "https://.internal..com".to_string(), +); +properties.insert( + "headers.x-api-key".to_string(), + std::env::var("API_KEY")?, +); +// or: +// properties.insert( +// "headers.Authorization".to_string(), +// format!("Bearer {}", std::env::var("REST_AUTH_TOKEN")?), +// ); +let db = lancedb::connect_namespace("rest", properties) + .execute() + .await?; +``` + +[LanceDB Enterprise](/enterprise) operates a REST namespace server on top of the Lance format, so any REST client that can speak the REST namespace API +contract can be used to interact with it. For authentication examples in LanceDB Enterprise, visit +the [Namespaces in SDKs](/namespaces/usage#namespaces-in-lancedb-enterprise) page. + +## Best practices {#best-practices} + +Below, we list some best practices for working with namespaces: +- For simple use cases and single, stand-alone applications, the directory-based root namespace is sufficient and requires no special configuration. +- For remote storage locations, introduce explicit namespaces when multiple teams, environments, or domains share the same catalog. +- Treat namespace paths as stable identifiers (for example `"prod/search"`, `"staging/recs"`). +- For maintainability reasons, avoid hard-coding object-store table paths in application code -- instead, prefer catalog identifiers + namespaces. diff --git a/docs/web/namespaces/usage.mdx b/docs/web/namespaces/usage.mdx new file mode 100644 index 000000000..5c9096cd0 --- /dev/null +++ b/docs/web/namespaces/usage.mdx @@ -0,0 +1,149 @@ +--- +title: "Using Namespaces" +sidebarTitle: "Using namespaces" +description: "Use LanceDB's namespace-aware table and catalog APIs in Python, TypeScript, and Rust." +icon: "folder-tree" +keywords: ["namespace", "create_table", "open_table", "list_tables", "catalog"] +--- + +import { + PyNamespaceTableOps, + PyNamespaceAdminOps, + TsNamespaceTableOps, + TsNamespaceAdminOps, + RsNamespaceAdminOps, + RsNamespaceTableOps, +} from '/snippets/connection.mdx'; + +As your table organization needs grow over time and your projects become more complex, you can use namespaces to organize your tables in a way that reflects your business domains, teams, or environments. + +As described in the [Namespaces and Catalog Model](/namespaces) section, namespaces are LanceDB's way of generalizing catalog specs, providing developers a clean way to manage hierarchical organization of tables in the catalog. The SDKs treat a namespace as a path and can use it for table resolution when you use LanceDB outside the root namespace. + +## Table operations with namespace paths {#table-operations-with-namespace-paths} + +Let's imagine a scenario where your table management needs have evolved, and you now have the following multi-level structure to organize your tables outside the root namespace. +``` +./local_lancedb (root) +└── prod + └── search + └── user (table) + └── data (table) + └── recommendations + └── user (table) + └── data (data) +``` + +Below, we show how you would express table operations within that namespace. Each item in the namespace +list (`["prod", "search"]`) represents a level in the namespace hierarchy, and the table name is +specified when you create, open, list, or drop it. + +The SDK methods expose the namespace path in the idiom of each language: + +- Python: pass `namespace_path=["prod", "search"]` to table operations. +- Rust: call builder methods such as `.namespace(vec!["prod".to_string(), "search".to_string()])`. +- TypeScript: pass a `namespacePath` array, for example `await db.openTable("user", ["prod", "search"])`. + + + + {PyNamespaceTableOps} + + + + {TsNamespaceTableOps} + + + + {RsNamespaceTableOps} + + + + +Using namespaces is **optional** in LanceDB, and most basic use cases do not require to work with them. +An empty namespace (`[]`), which is the default, means "root namespace", and the data will be stored in +the `data/` directory under the specified root path. + + +## Namespace management APIs {#namespace-management-apis} + +You can open/create/drop tables inside a namespace path (like `["prod", "search"]`). +All three SDKs expose namespace lifecycle operations directly. +In Python, use `lancedb.connect_namespace(...)` when calling namespace lifecycle methods such as +`create_namespace`, `list_namespaces`, `describe_namespace`, and `drop_namespace`. +In TypeScript, use `lancedb.connectNamespace(...)` and call `createNamespace`, `listNamespaces`, +`describeNamespace`, and `dropNamespace` on the returned `Connection`. +In Rust, use `lancedb::connect_namespace(...)` and call `create_namespace`, `list_namespaces`, +and `drop_namespace`. + + + + {PyNamespaceAdminOps} + + + + {TsNamespaceAdminOps} + + + + {RsNamespaceAdminOps} + + + +Namespace creation and deletion have modes that control what happens when the target already exists, +doesn't exist, or contains data: + +- Create mode: `create` fails if the namespace already exists, `exist_ok` keeps the existing namespace, and `overwrite` replaces it. +- Drop mode: `fail` reports an error when the namespace doesn't exist, and `skip` treats a missing namespace as a successful no-op. +- Drop behavior: `restrict` keeps non-empty namespaces from being dropped, and `cascade` drops child namespaces and tables first. + +Namespace path components can't be empty. Each component can contain only letters, numbers, +underscores, hyphens, and periods. + +Listing APIs return the immediate children of the requested namespace path. Use `limit` with the +returned `page_token` to page through large catalogs; pass an empty namespace path (`[]`) when you +want to list from the root namespace. + +## Namespaces in LanceDB Enterprise {#namespaces-in-lancedb-enterprise} + +In LanceDB Enterprise deployments, configure namespace-backed federated databases in a TOML file under your deployment's `config` directory. +LanceDB Enterprise supports both directory-based (`ns_impl = "dir"`) and REST-based (`ns_impl = "rest"`) namespace implementations. +The example below shows how to configure a directory-based namespace implementation in LanceDB Enterprise. +```toml +# Federated database configuration for DirectoryNamespace +# This example uses minio storage +[federated_dbs.federated_dir_test] +ns_impl = "dir" +root = "s3:///" +"storage.region" = "us-east-1" +"storage.endpoint" = "http://localhost:9000" +"storage.access_key_id" = "minioadmin" +"storage.secret_access_key" = "minioadmin" +"storage.allow_http" = "true" +# Far future expiration (year 2100) +"storage.expires_at_millis" = "4102444800000" +``` + +The example above uses MinIO, but the same approach applies to other cloud object storage platforms based on your deployment. + +For REST-based namespace servers, you can specify the namespace implementation as `"rest"` with forwarding prefixed headers +for authentication and context propagation. + +```toml +[federated_dbs.federated_rest_test] +ns_impl = "rest" +uri = "http://.internal.catalog.com" +forward_header_prefixes = ["X-forward"] +``` + +With `forward_header_prefixes = ["X-forward"]`, any incoming header starting with `X-forward` is forwarded to +`http://.internal.catalog.com`. This is useful for auth propagation, for example sending +`X-forward-authorization: Bearer xxxx`. + +For the LanceDB REST API itself, requests use `x-api-key` for API-key authentication. If your endpoint +serves more than one database, LanceDB can also use headers such as `x-lancedb-database` or +`x-lancedb-database-prefix` to route the request to the right database context. + +## Related references {#related-references} + +- [Client SDK API references](/api-reference) +- [REST API Reference](/api-reference/rest) +- [Namespaces and the Catalog Model](/namespaces) diff --git a/docs/web/performance.mdx b/docs/web/performance.mdx new file mode 100644 index 000000000..0d54f7eb0 --- /dev/null +++ b/docs/web/performance.mdx @@ -0,0 +1,241 @@ +--- +title: "Performance Tips and Best Practices" +sidebarTitle: "Performance Tips" +description: "Optimize LanceDB for your workload across ingestion, indexing, querying, and maintenance." +icon: "gauge-high" +keywords: ["performance", "tuning", "best practices", "optimization", "ingestion", "indexing", "vector search", "filtering", "compaction", "oss", "enterprise"] +--- + +LanceDB is performant by default. This page covers performance best practices that matter when you want to ensure you get the right performance for a specific workload. Use the table below to jump to the area relevant to what you're working on. + +| When you're working on... | Read | +|---------------------------|------| +| Loading data into a table | [Ingestion](#ingestion) | +| Running filtered or vector queries at scale | [Indexing](#indexing) | +| Iterating over large result sets (training, export, migration) | [Querying](#querying) | +| Keeping a long-lived dataset healthy | [Maintenance](#maintenance) | +| Inspecting query plans | [Diagnostics](#diagnostics) | + + +When using Python with multiprocessing, use `spawn` rather than `fork`. LanceDB is multi-threaded internally, and `fork` plus a multi-threaded process is unsafe. + + +## Ingestion {#ingestion} + +If ingestion is taking longer than expected on a large dataset, the cause is almost always how `add()` is called: each call commits a new version and a new fragment, so a per-row loop pays that per-call overhead at every row. The best practice is to pick the ingestion mode that matches your data shape — bulk ingestion when the data is already materialized, or iterator ingestion when it's streamed or computed on the fly. + + +**Why `merge_insert()` is significantly slower than `add()`** + +A merge has to scan existing data to find matches on the join key (or look them up via a scalar index, if one exists), and then delete-and-reinsert any updated rows in a single transaction; `add()` simply appends new fragments. + +Use `add()` for pure appends, and reach for `merge_insert()` only when you need upsert or conditional-insert logic. When you do use it, build a scalar index on the join column first — otherwise the match step falls back to a full column scan, which is the dominant cost at scale. + + +### Bulk ingestion: for data you already have {#bulk-ingestion-for-data-you-already-have} + +For materialized inputs (Arrow Tables, DataFrames) and file-backed sources (`pyarrow.dataset(...)`), LanceDB auto-parallelizes the write across workers, estimating the partition count from the data size — more partitions means more concurrent writes and higher throughput, up to the CPU core count. + +```python Python icon="python" +table.add(arrow_table) # in-memory +table.add(df) # pandas +table.add(ds.dataset("data/", format="parquet")) # streams from disk, still parallelized +``` + +Pass `progress=True` to watch it happen: LanceDB shows a live tqdm bar with rows written, throughput in MB/s, and active worker count. + +```python Python icon="python" +table.add(ds.dataset("data/", format="parquet"), progress=True) +``` + +```text +71%|███████▏ | 710000/1000000 [00:12<00:05, 58.8kit/s, 42.3 MB/s | 8/8 workers] +``` + +For larger-than-memory data, prefer scanning a file-backed dataset (`ds.dataset(...)`) over a hand-built `pyarrow.RecordBatchReader`: a `Dataset` can be counted and rescanned, so LanceDB knows the row count upfront (better auto-parallelism) and can retry a failed write from the start — a reader can only be consumed once, so neither is possible. Reach for the iterator path below only when the data genuinely can't be backed by files, e.g. to apply custom data transformations as you ingest. + +For very large initial loads, create the table empty first; passing data directly to `create_table(name, data)` skips the auto-parallel path. + +### Iterator ingestion: for data you transform on the fly {#iterator-ingestion-for-data-you-transform-on-the-fly} + +If each row needs work before it can be written — applying a custom transformation as you ingest, for example — the data doesn't exist as a file you can point `ds.dataset(...)` at. The best practice is to pass an iterator of `pyarrow.RecordBatch` instead; LanceDB consumes one batch at a time as you produce them. + +```python Python icon="python" +def stream(): + for raw in source: + vectors = model.encode(raw["text"]) + yield pa.RecordBatch.from_pydict({**raw, "vector": vectors}) + +table.add(stream()) +``` + +Use decently large chunks of several thousand rows or more, rather than yielding single-row batches. + + +**Set `write_parallelism` yourself for large inputs** + +A reader can't be counted or rescanned the way a `Dataset` can (see above), so LanceDB can't auto-size parallelism for it — set `write_parallelism` explicitly for large inputs: + +```python Python icon="python" +table.add(stream(), write_parallelism=4) +``` + +Each partition becomes its own fragment, so don't over-allocate on a small input — budget one unit of parallelism per ~100K rows or ~1 GB of data as a rule of thumb. + + +### Bulk ingests into a remote table {#bulk-ingests-into-a-remote-table} + +Enterprise + +This section applies only to remote tables (`db://` connections). Embedded (local) writes are unaffected. + +When you write to a remote LanceDB Enterprise table, the client splits each write partition into one or more HTTP `insert` parts and uploads them under a single upload id. Each part is streamed rather than buffered, so peak memory stays bounded. But each part still has to finish within the client read timeout while the server writes it to object storage, so on very large ingests an oversized part can run past that timeout and surface as: + +```text +lancedb.remote.errors.HttpError: operation timed out +``` + +Two environment variables control how parts are cut. Both are picked up automatically by the Python and TypeScript clients, and are the only way to tune this from those SDKs: + +| Variable | Default | What it controls | +|----------|---------|------------------| +| `LANCE_CLIENT_MAX_BYTES_PER_REQUEST` | 8 GiB | Maximum size of a single insert part, in LZ4-compressed Arrow IPC bytes. Set to `0` to disable splitting and send one request per partition. | +| `LANCE_CLIENT_MAX_REQUEST_DURATION` | Half the read timeout | Maximum wall-clock time, in whole seconds, that any one insert part may stay open. Set to `0` to disable the time-based cut. | + +A part is cut when it hits either limit, whichever comes first. Lower the byte budget when large ingests hit the read timeout; lower the duration when uploads are slow or throttled and the byte budget isn't the limiting factor. + +```bash +export LANCE_CLIENT_MAX_BYTES_PER_REQUEST=1073741824 # 1 GiB +export LANCE_CLIENT_MAX_REQUEST_DURATION=120 # 2 minutes +``` + +Splitting into more parts does not change the final table. The server stages every part under the shared upload id and merges them atomically when the write completes. + +## Indexing {#indexing} + +### Vector indexes {#vector-indexes} + +If vector search latency climbs with table size (i.e., queries that ran in milliseconds on a small table take seconds as it grows to millions of rows), the cause is the default brute-force scan over every vector. That works fine below ~100K vectors, but past that you should build a dedicated vector index. Pick the type by your data shape: + +| Index | When to use | +|-------|-------------| +| `IVF_PQ` | General-purpose default; what Enterprise builds automatically. Higher accuracy than RQ at small dimensions (≤ 256). | +| `IVF_RQ` | Maximum compression on high-dim vectors, faster builds than PQ. | +| `IVF_HNSW_SQ` | Best recall/latency for unfiltered search; higher latency variance under selective filters. | +| `IVF_FLAT` | Required for binary vectors with `hamming`. | + +The distance metric is fixed once the index is built. Pick the distance metric based on how the embedding model was trained: `cosine` (unnormalized), `dot` (already-normalized, best performance), `l2` (general-purpose, default), `hamming` (binary). For parameter tuning, see [Vector Indexing](/indexing/vector-index). + +### Scalar indexes {#scalar-indexes} + +If filtered queries slow down as the table grows — even when the filter is selective — the cause is a full column scan: without a scalar index, LanceDB evaluates the `where(...)` predicate on every row, and the same applies to `merge_insert` join keys. The best practice is to build a scalar index on every column you filter or join on, picking the type by the column's shape: + +| Index | Best for | +|-------|----------| +| `BTREE` (default) | Numeric, string, temporal columns with mostly distinct values | +| `BITMAP` | Boolean and low-cardinality columns (< ~1,000 distinct values) | +| `LABEL_LIST` | `List` columns queried with `array_has_any` / `array_has_all` | + +See [Scalar Indexing](/indexing/scalar-index). + +### Full-text search {#full-text-search} + +If your full-text index is much larger than expected, or takes longer than expected to build, the cause is usually phrase-query flags being enabled when they aren't needed: `with_position=True` and `remove_stop_words=False` both significantly inflate index size and build time. The best practice is to keep the defaults for most workloads, and only enable those flags when you actually need to search for phrases. See [FTS Indexing](/indexing/fts-index) configuration options for the full set of options. + +## Compaction and cleanup {#compaction-and-cleanup} + +Two things accumulate on a long-lived table as more and more data gets added to it: + +- **Many small fragments build up as you write**, slowing down queries that have to scan across more files. **Compaction** merges them back into larger fragments. +- **Old versions build up as the table changes**, growing disk usage beyond the live data size (LanceDB retains them for time-travel and rollback). **Cleanup** prunes versions older than a retention window. + +In OSS, you run them yourself via `optimize()`, which bundles both into a single call: + +```python Python icon="python" +from datetime import timedelta +table.optimize() # 7-day default retention +table.optimize(cleanup_older_than=timedelta(days=1)) # reclaim space sooner +``` + +The best practice is to run `optimize()` after large writes or on a schedule. It also bundles incremental index updates — see [Reindexing](/indexing/reindexing) for the breakdown. Updates also move rows out of the vector index, so they remain searchable but unindexed — rebuild the index after large update batches. + +LanceDB Enterprise handles both compaction and cleanup automatically. + +## Querying {#querying} + +Three knobs materially affect query latency, memory use, and recall. Be deliberate about each one on every query: + + + + If queries return more data than you need or take longer than expected, the cause is usually projecting more columns than necessary, or letting the result count run unbounded. The best practice is to always pass both `select()` and `limit()`: + + ```python Python icon="python" + table.search(emb).select(["id", "title"]).limit(20) + ``` + + + If a query with `prefilter=False` returns fewer than `limit` results — sometimes zero — the cause is that post-filter applies the predicate after the top-k is selected, so only candidates that already passed the search are filtered. Pre-filter is the default and guarantees every result satisfies the predicate. Switch to `prefilter=False` only when fewer-than-`limit` results are acceptable. See [Filtering](/search/filtering). + + + If recall is lower than expected, the cause is that the search-time knobs limit how many candidates the index considers. Adjust based on your index type: + + - Quantized indexes (PQ, RQ, SQ): `refine_factor` pulls extra candidates and re-scores them on full vectors. + - HNSW-backed indexes: `ef` at search time. Start at `1.5 × k`, raise toward `10 × k` if recall is short. + - IVF candidate breadth: `nprobes` is auto-tuned; override only when a selective pre-filter leaves too few neighbors. + + + +For hybrid search, the default `RRFReranker()` combines vector and FTS results into a single ranking via reciprocal rank fusion. See [Hybrid Search](/search/hybrid-search). + +### Avoid materializing the whole table {#avoid-materializing-the-whole-table} + +If you need every row in the table (for training, export, or migration), calling `to_pandas()` or `to_arrow()` will run you out of memory on any non-trivial dataset — both materialize the full table at once. The best practice is to iterate via `table.search(...)` or `table.query(...)`, which work the same way in both LanceDB OSS and Enterprise. + +In OSS, you can also stream batches through the underlying Lance dataset directly — useful when you need filter pushdown or fragment-level parallelism via `ds.scanner(...)`: + +```python Python icon="python" +ds = table.to_lance() +for batch in ds.to_batches(columns=["id", "text"], batch_size=10000): + process(batch) +``` + + +**API disparity between OSS and Enterprise.** + +LanceDB OSS exposes the `to_pandas()`, `to_arrow()`, and `table.to_lance()` methods for direct Lance dataset access. Enterprise's `RemoteTable` exposes none of these. Only `table.search(...)` and `table.query(...)`. In general, it's always best to go through `search()` and `query()` to keep your code portable across both OSS and Enterprise. + + +### Diagnostics {#diagnostics} + +To analyze a slow query, inspect what the query engine actually did and the state of the indexes it touched. These two tools surface that information — use them in this order: + +```python Python icon="python" +print(table.search(emb).where("year > 2000").limit(10).analyze_plan()) +print(table.index_stats("vector_idx")) # num_unindexed_rows should be ~0 +``` + +`analyze_plan()` returns the execution plan with per-stage timings, so you can see where the query actually spent its time. `index_stats()` shows how many rows are still unindexed — you want `num_unindexed_rows` to be ~0. In `analyze_plan`, look for: + +| Plan pattern | Fix | +|--------------|-----| +| `LanceScan` with high `bytes_read` / `iops` | Add a missing index, project columns with `select()`, or check that the dataset has been compacted | +| Multiple sequential filters | Reorder filter conditions | + +[Optimize Query Performance](/search/optimize-queries) walks through a fully worked before/after example, including how `KNNVectorDistance` and `output_batches` change once indexes are in place. + +## Where to go next {#where-to-go-next} + + + + Read execution plans, find the bottleneck. + + + Index types, parameters, and tuning in depth. + + + Pre- vs post-filter, scalar indexes, list columns. + + + Benchmark methodology and reference latency numbers. + + diff --git a/docs/web/quickstart.mdx b/docs/web/quickstart.mdx new file mode 100644 index 000000000..a60a347e0 --- /dev/null +++ b/docs/web/quickstart.mdx @@ -0,0 +1,472 @@ +--- +title: Quickstart +sidebarTitle: "Quickstart" +description: "Get started with LanceDB in minutes." +icon: rocket +--- +import { + PyConnect, + PyConnectAsync, + PyConnectEnterpriseQuickstart, + PyConnectObjectStorage, + PyConnectObjectStorageAsync, + RsConnect, + RsConnectEnterpriseQuickstart, + RsConnectObjectStorage, + TsConnect, + TsConnectEnterpriseQuickstart, + TsConnectObjectStorage, +} from '/snippets/connection.mdx'; +import { + PyQuickstartData, + PyQuickstartCreateTable, + PyQuickstartCreateTableAsync, + PyQuickstartAddFeature, + PyQuickstartCurateWithMetadata, + PyQuickstartMultimodalBytes, + PyQuickstartQueryFeature, + PyQuickstartVectorSearch1, + PyQuickstartVectorSearch1Async, + PyQuickstartOutputPandas, + RsQuickstartAddFeature, + RsQuickstartCurateWithMetadata, + RsQuickstartCreateTable, + RsQuickstartData, + RsQuickstartDefineStruct, + RsQuickstartMultimodalBytes, + RsQuickstartQueryFeature, + RsQuickstartVectorSearch1, + TsQuickstartAddFeature, + TsQuickstartCurateWithMetadata, + TsQuickstartCreateTable, + TsQuickstartData, + TsQuickstartMultimodalBytes, + TsQuickstartQueryFeature, + TsQuickstartVectorSearch1, +} from '/snippets/quickstart.mdx'; + +As described in [the landing page](/), LanceDB provides one data layer for +curation, feature engineering, search and retrieval, and model training. Whether you are preparing +training data, building a RAG or agentic retrieval system, reviewing examples, or adding model-generated +features, you'll work with the same underlying table and search primitives. + +Let's get started in just a few steps! + +## 1\. Install LanceDB {#1-install-lancedb} + +Install LanceDB in your client SDK. + + +```bash pip icon="terminal" +pip install lancedb +``` + +```bash uv icon="terminal" +uv add lancedb + +# Or, in an existing virtual environment: +uv pip install lancedb +``` + +```bash TypeScript icon=js +npm install @lancedb/lancedb +``` + +```bash Rust icon=Rust +cargo add lancedb +``` + + +### Python pre-release builds {#python-pre-release-builds} + +To pick up the latest features and bug fixes +before the next stable release, install a pre-release from LanceDB's Fury index. + + +```bash pip icon="terminal" +pip install --pre --extra-index-url https://pypi.fury.io/lancedb/ lancedb +``` + +```bash uv icon="terminal" +uv venv +uv pip install --prerelease allow --index https://pypi.fury.io/lancedb/ lancedb + +# To add to pyproject.toml, use: +uv add --prerelease allow --index https://pypi.fury.io/lancedb/ lancedb +``` + + + +Pre-release builds receive the same level of testing as stable releases, but their availability is not guaranteed +for more than 6 months after release. For real-world workloads, we recommend you use the latest stable release +as far as possible. + + + +The default `lancedb` wheel targets `x86-64-haswell` (AVX2 + FMA + F16C). On older x86_64 CPUs without AVX2 +(Intel Sandy Bridge, Ivy Bridge, Westmere; AMD Bulldozer, Piledriver, Steamroller), `import lancedb` crashes +with `Illegal instruction`. + +Install `lancedb-compat` instead. It exposes the same API (`import lancedb` still works), is built at the +`x86-64-v2` baseline, and uses runtime SIMD dispatch to still leverage AVX2, FMA, or AVX-512 when available: + +```bash pip icon="terminal" +pip install lancedb-compat +``` + +The two packages share the same `lancedb/` namespace and conflict at install time, so pick one. To switch, +uninstall the other first (`pip uninstall lancedb && pip install lancedb-compat`). + + +## 2\. Connect to a LanceDB database {#2-connect-to-a-lancedb-database} + +LanceDB supports several URI patterns to connect to a database. + +- A local filesystem path (when using it as an embedded library) +- A `db://...` URI (when using LanceDB Enterprise) +- An object storage URI: `s3://...`, `gs://...`, or `az://...` (when connecting directly from the client SDK) + +### Connect via local directory path {#connect-via-local-directory-path} + +The simplest way to begin is to use LanceDB as an embedded library. Import LanceDB in your +client SDK of choice and point to a local directory path. + + + + {PyConnect} + + + + {PyConnectAsync} + + + + {TsConnect} + + + + { "use lancedb::connect;\n" } + { "\n" } + {RsConnect} + + + + +### Connect via object storage URIs {#connect-via-object-storage-uris} + +You can also connect directly to object storage from the client SDK: + + + + {PyConnectObjectStorage} + + + + {PyConnectObjectStorageAsync} + + + + {TsConnectObjectStorage} + + + + {RsConnectObjectStorage} + + + +For credentials, endpoints, and provider-specific options, see +[Configuring storage](/storage/configuration). + +### Connect to LanceDB Enterprise {#connect-to-lancedb-enterprise} + +If you're using LanceDB Enterprise, you can connect to the remote database using the +`db://` URI along with the API key, region, and cluster endpoint you received from the +LanceDB team. Pass the cluster endpoint via `host_override` so the client routes +requests to your deployment. + + + + { "import lancedb\n\n" } + {PyConnectEnterpriseQuickstart} + + + + { "import * as lancedb from \"@lancedb/lancedb\";\n\n" } + {TsConnectEnterpriseQuickstart} + + + + { "use lancedb::connect;\n\n" } + {RsConnectEnterpriseQuickstart} + + + + +`host_override` is the full URL of your cluster endpoint, including the scheme +(`https://`) and a port if your deployment listens on a non-default one +(e.g. `https://your-enterprise-endpoint.com:443`). If you don't have the +endpoint, [contact the LanceDB team](mailto:contact@lancedb.com). + + +To learn more about `RemoteTable` semantics and how Enterprise differs operationally from +embedded LanceDB, see the [Enterprise overview](/enterprise). + +## 3\. Create a new table {#3-create-a-new-table} + +Let's create a small table of characters from the kingdom of Camelot. Each row stores source text, +metadata, structured fields, and a vector embedding in the same LanceDB table. + + +The embeddings we use in this example are synthetic and for demonstration purposes only. In a real AI +data workflow, you would generate them from text, images, audio, or video using an embedding model of choice. + + +Each row has source text, metadata, structured fields, and a vector: + +```json +{ + "id": "2", + "name": "Merlin", + "role": "Wizard", + "description": "Advisor and prophet with deep magical knowledge.", + "stats": { "strength": 2, "magic": 5, "leadership": 4, "wisdom": 5 }, + "vector": [0.2, 0.9, 0.4, 0.9] +} +``` + +The full raw records are included below: + + + + + {PyQuickstartData} + + + + {TsQuickstartData} + + + + {RsQuickstartDefineStruct} + {RsQuickstartData} + + + + +You can now create a LanceDB table from those records. The code below creates a LanceDB table +with the appropriate schema and ingests the data. + + + + {PyQuickstartCreateTable} + + + + {PyQuickstartCreateTableAsync} + + + + {TsQuickstartCreateTable} + + + + {RsQuickstartCreateTable} + + + +## 4\. Semantic search {#4-semantic-search} + +Search is a useful capability for all kinds of AI data pipelines. Below, we do a vector similarity +search for samples similar to a "_wise magical advisor_" (transforming the natural language query to +an embedding), and project only the columns needed by the next step. + +Search (which requires random access) is a ubiquitous access pattern that appears in many workloads: +whether you're building a RAG or recommendation system, serving agent memory, or curating a training +dataset. + + + + {PyQuickstartVectorSearch1} + + + + {PyQuickstartVectorSearch1Async} + + + + {TsQuickstartVectorSearch1} + + + + {RsQuickstartVectorSearch1} + + + +The example for Python above shows how to convert results to a Polars DataFrame. +Depending on your language, you can collect query results as a list/array of objects or DataFrames +to be used downstream in your application. + + + Use the `to_pandas()` method to convert query results into a Pandas DataFrame. + + {PyQuickstartOutputPandas} + + + +## 5\. Curation {#5-curation} + +Searching for relevant results can be more useful when combined with metadata filters. +In this tiny example, we filter to examples with high `magic` stats. + + + + {PyQuickstartCurateWithMetadata} + + + + {TsQuickstartCurateWithMetadata} + + + + {RsQuickstartCurateWithMetadata} + + + +When working with large datasets, it's common to use the same pattern to filter on quality labels, +train/eval splits, numeric fields, categorical values, timestamp windows, or generated tags and labels. + +## 6\. Add a derived feature {#6-add-a-derived-feature} + +Feature engineering is the process of cleaning up your data and creating new signals that +help your model learn, make better predictions, or your agent retrieve more useful information. +In the example below, we add a `power_score` column from the structured `stats` fields. +Lance supports data evolution, so you can add new columns without rewriting the entire table. + + + + {PyQuickstartAddFeature} + + + + {TsQuickstartAddFeature} + + + + { "use lancedb::table::NewColumnTransform;\n\n" } + {RsQuickstartAddFeature} + + + +Next, you can query a compact view of the new feature: + + + + {PyQuickstartQueryFeature} + + + + {TsQuickstartQueryFeature} + + + + {RsQuickstartQueryFeature} + + + +| name | role | power_score | +| --- | --- | --- | +| King Arthur | King | 3.5 | +| Merlin | Wizard | 4.0 | +| Sir Lancelot | Knight | 3.0 | + +The same workflow is used for data preparation tasks when adding derived features, cached model signals, review scores, or dataset +quality indicators. + +## 7\. Store multimodal data {#7-store-multimodal-data} + +Multimodal data is a first-class citizen in LanceDB. Binary data (image, audio, video, etc.) is +stored as blobs or inline Arrow binary types in a LanceDB column, and they benefit from the same +table operations and data versioning semantics as other data types. All the data is governed +in the same table, so you can search, filter, and retrieve multimodal records together with structured +fields, metadata, and embeddings. + +In this example, the +[`lancedb/magical_kingdom`](https://huggingface.co/datasets/lancedb/magical_kingdom) dataset stores +character images, descriptions, structured stats, image embeddings, and text embeddings together. + +Say we downloaded the image for Sir Lancelot from that dataset locally. You can read the image bytes +in your client SDK and store them in a LanceDB column. The image bytes can be used for downstream tasks +like retrieval, evaluation, or training. + +
+ Sir Lancelot from the lancedb/magical_kingdom dataset +
+ +These snippets load the local image file and store the bytes in an `image` column: + + + + {PyQuickstartMultimodalBytes} + + + + {TsQuickstartMultimodalBytes} + + + + {RsQuickstartMultimodalBytes} + + + +For more examples, see the [multimodal data](/tables/multimodal) section. + +## Code {#code} + +See the full code for these examples (including helper functions) in the +`quickstart` file for the appropriate client language in the +[files provided in the repo](https://github.com/lancedb/docs/tree/main/tests). + + +## Next steps {#next-steps} +You've learned how to install LanceDB, connect, create one table for AI data, retrieve related +examples, curate with metadata, add a derived feature, and represent multimodal records. These same +primitives apply across the AI data lifecycle, from data preparation and feature engineering to +retrieval, evaluation, and training. + +Continue to the table and search guides to build on this example with schema options, appends, +updates, versioning, indexing, full-text search, hybrid search, and reranking. + + + + Build on this quickstart with table creation, updates, and schema tips. + + + Learn how to build Retrieval-Augmented Generation (RAG) applications using LanceDB. + + + Create vector, full-text, and scalar indexes to speed up queries on larger datasets. + + + Use LanceDB for projected, shuffled, random-access reads in training workflows. + + diff --git a/docs/web/reranking/cross_encoder.mdx b/docs/web/reranking/cross_encoder.mdx new file mode 100644 index 000000000..1bb9660b9 --- /dev/null +++ b/docs/web/reranking/cross_encoder.mdx @@ -0,0 +1,53 @@ +--- +title: Cross Encoder Reranker +sidebarTitle: "Cross Encoder" +description: Implement semantic search reranking in LanceDB using Cross Encoder models. Features configurable model selection, device optimization, and comprehensive scoring options for all search types. + +--- + +import { PyRerankingCrossEncoderUsage } from '/snippets/integrations.mdx'; + +# Cross Encoder Reranker + +This reranker uses Cross Encoder models from sentence-transformers to rerank the search results. You can use this reranker by passing `CrossEncoderReranker()` to the `rerank()` method. +> **Note:** Supported query types – Hybrid, Vector, and FTS. + + + + + {PyRerankingCrossEncoderUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `model_name` | `str` | `"cross-encoder/ms-marco-TinyBERT-L-6"` | The name of the reranker model to use.| +| `column` | `str` | `"text"` | The name of the column to use as input to the cross encoder model. | +| `device` | `str` | `None` | The device to use for the cross encoder model. If None, will use "cuda" if available, otherwise "cpu". | +| `trust_remote_code` | `bool` | `True` | Passed to Sentence Transformers model loading. Set this to `False` when you only want to load models that do not require custom repository code. | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", returns only `_relevance_score`. If "all" is supported, returns relevance score along with the vector and/or FTS scores depending on query type. | + +The reranker loads the model locally through `sentence-transformers`, so install the local model +runtime dependencies you need, such as PyTorch and any device-specific acceleration packages. + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ❌ Not Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). | + +### Vector Search {#vector-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have vector(`_distance`) along with Hybrid Search score(`_relevance_score`). | + +### FTS Search {#fts-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have FTS(`score`) along with Hybrid Search score(`_relevance_score`). | diff --git a/docs/web/reranking/custom-reranker.mdx b/docs/web/reranking/custom-reranker.mdx new file mode 100644 index 000000000..f19108b02 --- /dev/null +++ b/docs/web/reranking/custom-reranker.mdx @@ -0,0 +1,134 @@ +--- +title: Building Custom Rerankers +sidebarTitle: "Custom rerankers" +description: Learn how to create custom rerankers in LanceDB by extending the base Reranker class. +icon: "code" +--- + +You can build your own custom reranker in LanceDB by subclassing the base `Reranker` class. At a +minimum, you need to implement `rerank_hybrid()`, which is the logic that combines vector and +full-text search results. Beyond that, you can optionally implement `rerank_vector()` and +`rerank_fts()` if you want your reranker to also handle pure vector or pure full-text searches. + +Decide up front which surfaces — hybrid, pure vector, or pure full-text — your reranker should +cover, and only override the ones you need. The base class leaves `rerank_vector()` and +`rerank_fts()` unimplemented, so calling `.rerank(...)` on a single-modality search you haven't +overridden raises `NotImplementedError` rather than silently returning unsorted results. That's a +useful guard, but worth knowing about before you wire up a query path you didn't plan for. + +The Python base class exposes hybrid, vector-only, and FTS-only rerank hooks. TypeScript and Rust +currently expose the custom reranker interface for hybrid reranking. In Rust, a custom reranker must +also satisfy the trait bounds `Debug + Send + Sync`. + +## Interface {#interface} + +The `Reranker` base interface comes with a `merge_results()` method that can be used to combine the +results of semantic and full-text search. This is a vanilla merging algorithm that simply concatenates +the results and removes the duplicates without taking the scores into consideration. It only keeps the +first copy of the row encountered. This works well in cases that don't require the scores of semantic +and full-text search to combine the results. If you want to use the scores or want to support +`return_score="all"`, you'll need to implement your own merging algorithm. The base +`return_score` option accepts only `"relevance"` and `"all"`. + +Whichever methods you override, your reranker has one job on the way out: attach a +`_relevance_score` column with the most relevant rows at the top. LanceDB will reject the result +if that column is missing, and downstream `.limit(...)` calls trust the order you return, so +sort descending before handing the table back. + +For vector-only reranking in Python, pass a text query to `.rerank(..., query_string="...")`. +The vector query itself may be numeric, but `rerank_vector(query, vector_results)` still receives +a string query so your reranker can score each candidate against the user's text intent. + +Below, we show the pseudocode of a custom reranker that combines the results of semantic and full-text +search using a linear combination of the scores: + + +```python Python icon="python" +from lancedb.rerankers import Reranker +import pyarrow as pa + +class MyReranker(Reranker): + def __init__(self, param1, param2, ..., return_score="relevance"): + super().__init__(return_score) + self.param1 = param1 + self.param2 = param2 + + def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table): + # Use the built-in merging function + combined_result = self.merge_results(vector_results, fts_results) + + # Do something with the combined results + # ... + + # Return the combined results + return combined_result + + def rerank_vector(self, query: str, vector_results: pa.Table): + # Do something with the vector results + # ... + + # Return the vector results + return vector_results + + def rerank_fts(self, query: str, fts_results: pa.Table): + # Do something with the FTS results + # ... + + # Return the FTS results + return fts_results +``` + + +## Example {#example} + +As an example, let's build custom reranker that enhances the Cohere Reranker by accepting a filter +query, and accepts any other `CohereReranker` params as `kwargs`. + + +```python Python icon="python" +from typing import List, Union +import pandas as pd +from lancedb.rerankers import CohereReranker + +class ModifiedCohereReranker(CohereReranker): + def __init__(self, filters: Union[str, List[str]], **kwargs): + super().__init__(**kwargs) + filters = filters if isinstance(filters, list) else [filters] + self.filters = filters + + def rerank_hybrid(self, query: str, vector_results: pa.Table, fts_results: pa.Table)-> pa.Table: + combined_result = super().rerank_hybrid(query, vector_results, fts_results) + df = combined_result.to_pandas() + for filter in self.filters: + df = df.query("not text.str.contains(@filter)") + + return pa.Table.from_pandas(df) + + def rerank_vector(self, query: str, vector_results: pa.Table)-> pa.Table: + vector_results = super().rerank_vector(query, vector_results) + df = vector_results.to_pandas() + for filter in self.filters: + df = df.query("not text.str.contains(@filter)") + + return pa.Table.from_pandas(df) + + def rerank_fts(self, query: str, fts_results: pa.Table)-> pa.Table: + fts_results = super().rerank_fts(query, fts_results) + df = fts_results.to_pandas() + for filter in self.filters: + df = df.query("not text.str.contains(@filter)") + + return pa.Table.from_pandas(df) +``` + + + +Under the hood, `vector_results` and `fts_results` are PyArrow tables. You can learn more about +PyArrow tables [here](https://arrow.apache.org/docs/python). The advantage of PyArrow tables is their +interoperability -- you can easily convert them to Pandas/Polars DataFrames, `PyDict`, `PyList`, etc. + +The benefits are also bidirectional -- just as you can easily convert PyArrow tables _to_ Pandas +DataFrames using the `to_pandas()` method -- you can perform DataFrame transformations +and just as easily convert the DataFrame back to PyArrow tables using `pa.Table.from_pandas()` method +as shown in the example above. + diff --git a/docs/web/reranking/eval.mdx b/docs/web/reranking/eval.mdx new file mode 100644 index 000000000..ab4c1b71b --- /dev/null +++ b/docs/web/reranking/eval.mdx @@ -0,0 +1,103 @@ +--- +title: "Evaluating Hybrid Search Performance" +sidebarTitle: "Evaluation" +description: Learn about evaluating hybrid search performance in LanceDB. +icon: "chart-bar" +--- + +Hybrid search is an often misused and/or misunderstood term. In this section, we're using +the definition of "hybrid search" to mean using a combination of keyword-based and vector search. +Because the vector search operates in a dense embedding space and keyword-based search operate +in a sparse embedding space, their relevance scores cannot be directly compared. +Combining results from multiple searches thus requires a reranking step. + +Before evaluating hybrid search, build an FTS index on the text column and use a table with embedding +metadata or explicit query vectors for the vector side. Otherwise the evaluation is measuring setup +fallbacks or errors rather than the reranker. + +## Reranking strategies {#reranking-strategies} + +There are two common approaches for reranking search results from multiple sources. + +- **Score-based**: Calculate final relevance scores from the individual search algorithm scores. Examples: Reciprocal Rank Fusion (the default in LanceDB), mean reciprocal rank fusion, and weighted linear combination of semantic and keyword-based search scores. + +- **Relevance-based**: Discards the existing scores and calculates the relevance of each search result-query pair. Example: Cross Encoder models + + +If you call `.rerank()` on a hybrid query without passing a reranker, LanceDB defaults to +`RRFReranker()` — a score-based reranker that uses Reciprocal Rank Fusion. This is the +score-based path most readers encounter first; `LinearCombinationReranker` is an alternative +score-based strategy you opt into explicitly. + + +By default, rerankers return `_relevance_score`. Pass `return_score="all"` when a reranker +supports it, and you also need the original vector or FTS scores for debugging. +Evaluation code can rely on returned rows being ordered by descending `_relevance_score`. Empty +reranked result sets still include the `_relevance_score` column. + +The hybrid `rerank(...)` method also accepts a `normalize` argument that controls how the raw +vector and FTS scores are made comparable before reranking: + +- `normalize="score"` (the default) — normalizes the raw vector and FTS scores directly. +- `normalize="rank"` — converts each result list to ranks first, then normalizes. + +This choice materially affects score-based rerankers (such as `LinearCombinationReranker`), so +when you evaluate score-based strategies, treat `normalize` as a tunable hyperparameter +alongside the reranker itself. + +For score-based evaluation, the main built-in knobs are: + +- `RRFReranker(K=60)`: rank fusion with a positive smoothing constant. +- `MRRReranker(weight_vector=0.5, weight_fts=0.5)`: weighted reciprocal rank fusion where the weights must sum to `1.0`. +- `LinearCombinationReranker(weight=0.7, fill=1.0)`: blends vector and FTS scores directly; `fill` controls how strongly to penalize a result missing from one side. + +Even though there are many more strategies for reranking, there are no "universally best" +ones that work well for all cases, because reranking quality is dataset and application specific. +Evaluating whether a reranking strategy is a good fit is also a challenge. In the next +section, we discuss an example evaluation of different reranking strategies on a sample dataset. + +## Example evaluation {#example-evaluation} + +The table below shows our evaluation results from an experiment comparing multiple rerankers on +~800 hybrid search queries. This is a modified version of an evaluation script by +[LlamaIndex](https://github.com/run-llama/finetune-embedding/blob/main/evaluate.ipynb) that measures +hit-rate \@ top-k. + +### Using OpenAI `text-embedding-ada-002` {#using-openai-text-embedding-ada-002} + +Vector Search baseline: **0.64** + +| Reranker | Top-3 | Top-5 | Top-10 | +| --- | --- | --- | --- | +| Linear Combination | `0.73` | `0.74` | `0.85` | +| Cross Encoder | `0.71` | `0.70` | `0.77` | +| Cohere | `0.81` | `0.81` | `0.85` | +| ColBERT | `0.68` | `0.68` | `0.73` | + + + + +### Using OpenAI `text-embedding-3-small` {#using-openai-text-embedding-3-small} + +Vector Search baseline: **0.59** + +| Reranker | Top-3 | Top-5 | Top-10 | +| --- | --- | --- | --- | +| Linear Combination | `0.68` | `0.70` | `0.84` | +| Cross Encoder | `0.72` | `0.72` | `0.79` | +| Cohere | `0.79` | `0.79` | `0.84` | +| ColBERT | `0.70` | `0.70` | `0.76` | + + + +## Conclusion {#conclusion} + +The results show that the reranking methods can significantly improve the search relevance. However, +the improvement we saw was not consistent across all rerankers. In reality, the choice of reranker +likely depends on the dataset and the application. + +It's also important to note that the reranking methods are not a +replacement for the search methods they supplement. They are complementary and it's likely that you'd +have to tune them together to get the best results. The latency vs. recall tradeoff is also an +important factor to consider when choosing the reranker. Hopefully this evaluation +gives you a starting point for your own experiments with hybrid search in LanceDB! diff --git a/docs/web/reranking/index.mdx b/docs/web/reranking/index.mdx new file mode 100644 index 000000000..eb1244934 --- /dev/null +++ b/docs/web/reranking/index.mdx @@ -0,0 +1,133 @@ +--- +title: "Reranking Search Results" +sidebarTitle: "Overview" +description: "Use a reranker to improve search relevance." +icon: "sort-amount-down" +keywords: ["re-ranking", "reranker", "rerank"] +--- + +import { PyRerankingLinearCombinationUsage, PyRerankingRrfUsage, PyRerankingCohereUsage } from '/snippets/integrations.mdx'; + +Reranking is the process of re-ordering search results to improve relevance, often using a +different model than the one used for the initial search. LanceDB has built-in support for reranking +with models from Cohere, Sentence-Transformers, and more. + +### Quickstart {#quickstart} + +To use a reranker, you run a search and pass the results to the `rerank()` method. The examples below +move from the simplest, model-free rerankers to a model-based one. Each is a complete, runnable script. + +**1. Linear combination (simplest).** `LinearCombinationReranker` normalizes the vector and full-text +scores and blends them with a single `weight` (default `0.7`, favoring the vector score). It runs no +model, and reranks [hybrid search](/search/hybrid-search) results. + + + + {PyRerankingLinearCombinationUsage} + + + +**2. Reciprocal Rank Fusion.** `RRFReranker` fuses results by rank position instead of raw score, so it +sidesteps having to make vector and full-text scores comparable. It loads no model either, and it's the +default reranker for hybrid search. + + + + {PyRerankingRrfUsage} + + + +**3. Cohere (model-based).** For higher relevance, a model-based reranker scores each result against the +query with a trained model. `CohereReranker` works with vector, full-text, or hybrid search, and needs +the `cohere` package plus either `COHERE_API_KEY` in the environment or an `api_key` argument. + + + + {PyRerankingCohereUsage} + + + +Reach for the model-free rerankers (`LinearCombinationReranker`, `RRFReranker`) when cost and latency +matter most; reach for a model-based one like `CohereReranker` or `CrossEncoderReranker` when you need +higher relevance and can afford to score every query and document pair with a model. + +### Supported Rerankers {#supported-rerankers} + +LanceDB supports the following rerankers out of the box. The first three are score-based and run no +model; the rest are model-based. The built-in rerankers are documented in this section; the hosted +providers that need an API key live under [integrations](/integrations/reranking). + +| Reranker | Default model | +| --------------------------- | -------------------------------------- | +| `RRFReranker` | None (reciprocal rank fusion) | +| `LinearCombinationReranker` | None (weighted score blend) | +| `MRRReranker` | None (weighted reciprocal rank) | +| `CohereReranker` | `rerank-english-v3.0` | +| `CrossEncoderReranker` | `cross-encoder/ms-marco-TinyBERT-L-6` | +| `ColbertReranker` | `colbert-ir/colbertv2.0` | +| `AnswerdotaiRerankers` | `answerdotai/answerai-colbert-small-v1`| +| `JinaReranker` | `jina-reranker-v2-base-multilingual` | +| `OpenaiReranker` | `gpt-4-turbo-preview` | +| `VoyageAIReranker` | No default (model name required) | +| `WatsonxReranker` | `cross-encoder/ms-marco-minilm-l-12-v2`| + +The model-based rerankers need their provider package installed, and the hosted ones +(`CohereReranker`, `JinaReranker`, `OpenaiReranker`, `VoyageAIReranker`, `WatsonxReranker`) also need an API key, passed +as an `api_key` argument or set in the provider-specific environment variable. + +Rerankers add `_relevance_score` and return rows ordered by descending relevance. Python rerankers +accept `return_score="relevance"` or `return_score="all"`; use `"all"` when you want to keep the +original vector distance or FTS score columns for debugging. Model-based rerankers read from +`column="text"` by default, so either return that column in the search results or pass a different +column. + +Use `refine_factor` on vector or hybrid queries when you're reranking approximate IVF-PQ results and +want a larger candidate pool before the final ranking step. A value of `3` asks LanceDB to fetch +`limit * 3` candidates, refine them with the full vectors, and keep the requested `limit`. Higher +values can improve recall, but they also increase query latency. + + +**SDK coverage differs across languages** + +The provider-specific rerankers in the table above +(`CohereReranker`, `CrossEncoderReranker`, `ColbertReranker`, and others under `lancedb.rerankers`) +are currently **Python-only**. The TypeScript and Rust SDKs currently expose hybrid reranking through +the generic `Reranker` interface (`rerankHybrid` / `rerank_hybrid`) and the built-in `RRFReranker`. +In TypeScript, create the built-in RRF reranker with `await RRFReranker.create(k)`. To use a +model-based reranker from TypeScript or Rust, you must implement the hybrid reranker interface +yourself. + + + +### Multi-vector reranking {#multi-vector-reranking} +Most rerankers support reranking based on multiple vectors. To rerank based on multiple vectors, you can pass a list of vectors to the `rerank` method. Here's an example of how to rerank based on multiple vector columns using the `CrossEncoderReranker`: + + +```python Python icon="python" +from lancedb.rerankers import CrossEncoderReranker + +reranker = CrossEncoderReranker() + +query = "hello" + +# `deduplicate=True` requires `_rowid` on every input result set, +# so call `.with_row_id(True)` on each search before passing it in. +res1 = table.search(query, vector_column_name="vector").limit(3).with_row_id(True) +res2 = table.search(query, vector_column_name="text_vector").limit(3).with_row_id(True) +res3 = table.search(query, vector_column_name="meta_vector").limit(3).with_row_id(True) + +reranked = reranker.rerank_multivector([res1, res2, res3], deduplicate=True) +``` + + +- Passing `deduplicate=True` to `rerank_multivector(...)` raises a `ValueError` if any of the +input result sets is missing the `_rowid` column. Therefore, it's recommended to add `.with_row_id(True)` to every +`table.search(...)` call before reranking, or omit `deduplicate=True` if you don't need it. +- `RRFReranker.rerank_multivector(...)` always requires `_rowid` on its inputs, regardless of +the `deduplicate` flag. + +## Creating Custom Rerankers {#creating-custom-rerankers} + +LanceDB also allows you to create custom rerankers by extending the base `Reranker` class. The custom reranker +should implement the `rerank` method that takes a list of search results and returns a reranked list of +search results. This is covered in more detail in the [creating custom rerankers](/reranking/custom-reranker/) section. diff --git a/docs/web/reranking/linear_combination.mdx b/docs/web/reranking/linear_combination.mdx new file mode 100644 index 000000000..8917815b6 --- /dev/null +++ b/docs/web/reranking/linear_combination.mdx @@ -0,0 +1,43 @@ +--- +title: Linear Combination Reranker +sidebarTitle: "Linear Combination" +description: Learn about LanceDB's deprecated Linear Combination Reranker for combining semantic and full-text search scores. + +--- + +import { PyRerankingLinearCombinationUsage } from '/snippets/integrations.mdx'; + +# Linear Combination Reranker + +> **Note:** This reranker is deprecated. Use the `RRFReranker` if you need a score-based reranker. + +The Linear Combination Reranker combines the results of semantic and full-text search using a linear combination of the scores. The weights for the linear combination can be specified, and defaults to 0.7, i.e, 70% weight for semantic search and 30% weight for full-text search. + +> **Note:** Supported query type – Hybrid search only. + + + + + {PyRerankingLinearCombinationUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `weight` | `float` | `0.7` | The weight to use for the semantic search score. The weight for the full-text search score is `1 - weight`. | +| `fill` | `float` | `1.0` | Score used when a result is missing from one side of the hybrid query. The default strongly penalizes missing vector or FTS matches. | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", returns only `_relevance_score`. If "all", returns all scores from the vector and FTS search along with the relevance score. | + +`weight` must be between `0` and `1`. If either the vector or FTS side returns no rows, the reranker +returns the non-empty side with `_relevance_score` attached rather than failing. + + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column | +| `all` | ✅ Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_distance`) | diff --git a/docs/web/reranking/mrr.mdx b/docs/web/reranking/mrr.mdx new file mode 100644 index 000000000..b517f8b5a --- /dev/null +++ b/docs/web/reranking/mrr.mdx @@ -0,0 +1,48 @@ +--- +title: MRR Reranker +sidebarTitle: "MRR Algorithm" +description: Combine and rerank search results using Mean Reciprocal Rank (MRR) algorithm in LanceDB. Supports weighted scoring for hybrid and multivector search. + +--- + +import { PyRerankingMrrUsage } from '/snippets/integrations.mdx'; + +# MRR Reranker + +This reranker uses the Mean Reciprocal Rank (MRR) algorithm to combine and rerank search results from vector and full-text search. You can use this reranker by passing `MRRReranker()` to the `rerank()` method. The MRR algorithm calculates the average of reciprocal ranks across different search results, providing a balanced way to merge results from multiple ranking systems. + +> **Note:** Supported query types – Hybrid and Multivector search. + + + + {PyRerankingMrrUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `weight_vector` | `float` | `0.5` | Weight for vector search results (0.0 to 1.0). | +| `weight_fts` | `float` | `0.5` | Weight for FTS search results (0.0 to 1.0). | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score`. If "all", will return all scores from the vector and FTS search along with the relevance score. | + +**Note:** `weight_vector` + `weight_fts` must equal 1.0. + +For multivector reranking, input result sets need `_rowid` so LanceDB can identify the same row +across the ranked lists. Add `.with_row_id(True)` to each vector search before passing the results +to the reranker. + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). | + +### Multivector Search {#multivector-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Results only have the `_relevance_score` column. | +| `all` | ✅ Supported | Results have vector distances from all searches along with `_relevance_score`. | diff --git a/docs/web/reranking/rrf.mdx b/docs/web/reranking/rrf.mdx new file mode 100644 index 000000000..3a58cec98 --- /dev/null +++ b/docs/web/reranking/rrf.mdx @@ -0,0 +1,56 @@ +--- +title: Reciprocal Rank Fusion Reranker +sidebarTitle: "RRF Algorithm" +description: Learn about LanceDB's default Reciprocal Rank Fusion (RRF) reranker for hybrid search. Implements the Cormack et al. algorithm for optimal search result ranking. + +--- + +import { PyRerankingRrfUsage } from '/snippets/integrations.mdx'; + +# Reciprocal Rank Fusion Reranker + +**Reciprocal Rank Fusion (RRF)** is a model-free way to merge several ranked result lists into a +single ordering. Rather than comparing raw similarity scores (which aren't directly comparable +across, say, a vector search and a full-text search), RRF looks only at each document's *rank +position* in each list. It scores every document with the formula `1 / (rank + K)`, sums those +contributions across the lists, and re-sorts by the total. Documents that rank highly in more than +one search rise to the top. Because there's no model to load or call, it's fast and cheap, which is +why it's the default reranker for LanceDB hybrid search. The implementation follows the +[Cormack et al. paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf). + +> **Supported query types:** hybrid search and [multi-vector reranking](/reranking#multi-vector-reranking). +> Because RRF fuses two or more ranked lists, it can't rerank a single vector or full-text result set +> on its own. Calling `rerank_vector` or `rerank_fts` on an `RRFReranker` raises `NotImplementedError`. + + + + + {PyRerankingRrfUsage} + + + +## Accepted Arguments {#accepted-arguments} +| Argument | Type | Default | Description | +| --- | --- | --- | --- | +| `K` | `int` | `60` | A constant used in the RRF formula (default is 60). Experiments indicate that k = 60 was near-optimal, but that the choice is not critical. | +| `return_score` | `str` | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score`. If "all", will return all scores from the vector and FTS search along with the relevance score. | + +`K` must be greater than `0`. In TypeScript, construct the built-in reranker with +`await RRFReranker.create(k)` before passing it to `.rerank(...)`. + +## Multi-vector reranking {#multi-vector-reranking} + +`RRFReranker` can also fuse the results of several vector searches with `rerank_multivector`, applying +the same rank-fusion algorithm across more than two lists. Every input result set must include the +`_rowid` column, so add `.with_row_id(True)` to each `table.search(...)` call before reranking, +otherwise the call raises a `ValueError`. See [multi-vector reranking](/reranking#multi-vector-reranking) +for a full example. + +## Supported Scores for each query type {#supported-scores-for-each-query-type} +You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type: + +### Hybrid Search {#hybrid-search} +|`return_score`| Status | Description | +| --- | --- | --- | +| `relevance` | ✅ Supported | Returned rows only have the `_relevance_score` column. | +| `all` | ✅ Supported | Returned rows have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`). | diff --git a/docs/web/search/filtering.mdx b/docs/web/search/filtering.mdx new file mode 100644 index 000000000..b8ebf37d2 --- /dev/null +++ b/docs/web/search/filtering.mdx @@ -0,0 +1,266 @@ +--- +title: "Metadata Filtering in LanceDB" +sidebarTitle: Filtering +description: Filter search results in LanceDB based on metadata fields. +icon: "filter" +--- + +LanceDB supports filtering features of query results based on metadata fields. +While joint vector and metadata search at scale presents a significant challenge, +LanceDB achieves sub-100ms latency at thousands of QPS, enabling efficient vector search +with filtering capabilities even on datasets containing billions of records. + +**Pre-filtering** means LanceDB applies the metadata `where(...)` condition before running vector search, so the search only considers rows that already match the filter. **Post-filtering** means LanceDB runs vector search first and only then filters the returned candidates. Pre-filtering is enabled by default. In practice, pre-filtering is better when the filter is part of the result contract; post-filtering can be lower-latency for expensive or non-indexable filters, but it can return fewer than `limit` rows, or even zero, if the nearest neighbors do not pass the filter. + +On hybrid queries, the same `where(...)` filter is applied to both the vector and full-text halves of the query. The prefilter or postfilter choice controls whether that happens before each subquery scores candidates or after the subquery top-k is produced. + +## Chaining `where` clauses {#chaining-where-clauses} + +In more recent LanceDB SDK versions (see the callout box below for the exact version numbers), you can call `where(...)` (Python and TypeScript) or `only_if(...)` (Rust) more than once on the same query builder. Each additional filter is combined with the previous one using logical `AND`, so `where("a > 0").where("b < 10")` is equivalent to `where("(a > 0) AND (b < 10)")`. + +For a fixed predicate, writing one `where(...)` clause with `AND` is just as valid and often clearer. Chaining is mainly useful when code composes filters incrementally, such as applying a shared base predicate in a helper and then adding a per-call predicate at the query site. + + +```python Python icon="python" +# Both filters apply: item is in the list AND price is above 15. +result = ( + table.search([100, 102]) + .where("item IN ('foo', 'bar', 'baz')") + .where("price > 15.0") + .limit(3) + .to_arrow() +) +``` + +```typescript TypeScript icon="square-js" +// Both filters apply: item is in the list AND price is above 15. +const result = await table + .search([100, 102]) + .where("item IN ('foo', 'bar', 'baz')") + .where("price > 15.0") + .limit(3) + .toArray(); +``` + + + +In Python SDK versions before `0.34.0` and Rust/TypeScript SDK versions before `0.31.0`, a second `where(...)` or `only_if(...)` call replaced the first filter, so only the last predicate was applied. If your code needs to run on those older versions, write a single predicate with `AND` instead of chaining calls. When upgrading to the latest SDKs, review any existing chained filters and drop earlier calls you no longer want to apply. + + +## Example: Metadata Filtering {#example-metadata-filtering} + +To illustrate filtering capabilities, let's try four data points with combinations of vectors and metadata: + + +```python Python icon="python" +data = [ + {"vector": [3.1, 4.1], "item": "foo", "price": 10.0}, + {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}, + {"vector": [10.2, 100.8], "item": "baz", "price": 30.0}, + {"vector": [1.4, 9.5], "item": "fred", "price": 40.0}, +] +table = db.create_table("metadata_filter_example", data=data, mode="overwrite") +``` + +```typescript TypeScript icon="square-js" +const data = [ + { vector: [3.1, 4.1], item: "foo", price: 10.0 }, + { vector: [5.9, 26.5], item: "bar", price: 20.0 }, + { vector: [10.2, 100.8], item: "baz", price: 30.0 }, + { vector: [1.4, 9.5], item: "fred", price: 40.0 }, +]; + +const tableName = "metadata_filter_example"; +const table = await db.createTable(tableName, data, { + mode: "overwrite", +}); +``` + + +### Filtering Without Vector Search {#filtering-without-vector-search} + +You can always filter your data without search. This is useful when you need to query based on metadata: + + +```python Python icon="python" +filtered_no_search_result = ( + table.search() + .where("(item IN ('foo', 'bar', 'baz')) AND (price > 15.0)") + .limit(3) + .to_arrow() +) +``` + +```typescript TypeScript icon="square-js" +const filteredResult = await table + .query() + .where("(item IN ('foo', 'bar', 'baz')) AND (price > 15.0)") + .limit(3) + .toArray(); +``` + + + +If your table is large, this could potentially return a very large amount of data. Please be sure to use a `limit` clause unless you're sure you want to return the whole result set. + + +### Pre-Filtering with Vector Search {#pre-filtering-with-vector-search} + + +```python Python icon="python" +filtered_result = ( + table.search([100, 102]) + .where("(item IN ('foo', 'bar')) AND (price > 15.0)") + .limit(3) + .to_arrow() +) +``` + +```typescript TypeScript icon="square-js" +const results = await table + .search([100, 102]) + .where("(item IN ('foo', 'bar')) AND (price > 15.0)") + .toArray(); +``` + + +### Post-Filtering with Vector Search {#post-filtering-with-vector-search} + + +```python Python icon="python" +post_filtered_result = ( + table.search([100, 102]) + .where("(item IN ('foo', 'bar')) AND (price > 15.0)", prefilter=False) + .limit(3) + .to_arrow() +) +``` + +```typescript TypeScript icon="square-js" +const postFilteredResult = await (table.search([100, 102]) as VectorQuery) + .where("(item IN ('foo', 'bar')) AND (price > 15.0)") + .postfilter() + .limit(3) + .toArray(); +``` + + + +When querying large tables, omitting a `limit` clause may overwhelm resources and return excessive data. It's always recommended +to be mindful of the potential impact on performance and costs when working with really large tables. + + +## Filtering with SQL {#filtering-with-sql} + +Because it's built on top of DataFusion, LanceDB embraces the utilization of standard SQL expressions as predicates for filtering operations. SQL can be used during vector search, update, and deletion operations. + +LanceDB supports a growing list of SQL expressions: + +| SQL Expression | Description | +|:---------------|:-------------| +| `>, >=, <, <=, =` | Comparison operators | +| `AND`, `OR`, `NOT` | Logical operators | +| `IS NULL`, `IS NOT NULL` | Null checks | +| `IS TRUE`, `IS NOT TRUE`, `IS FALSE`, `IS NOT FALSE` | Boolean checks | +| `IN` | Value matching from a set | +| `LIKE`, `NOT LIKE` | Pattern matching | +| `CAST` | Type conversion | +| `regexp_match(column, pattern)` | Regular expression matching | +| [DataFusion Functions](https://datafusion.apache.org/user-guide/sql/scalar_functions.html) | Additional SQL functions | + +### Simple SQL Filters {#simple-sql-filters} + +For example, the following filter string is acceptable: + + +```python Python icon="python" +tbl.search([100, 102]).where( + "(item IN ('foo', 'baz')) AND (price > 20.0)" +).to_arrow() +``` + +```typescript TypeScript icon="square-js" +await table + .search([100, 102]) + .where("(item IN ('foo', 'baz')) AND (price > 20.0)") + .toArray(); +``` + + +### Advanced SQL Filters {#advanced-sql-filters} + +If your column name contains special characters, upper-case characters, or is a [SQL Keyword](https://docs.rs/sqlparser/latest/sqlparser/keywords/index.html), +you can use backtick (`` ` ``) to escape it. For nested fields, each segment of the +path must be wrapped in backticks. + +```sql +`CUBE` = 10 AND `UpperCaseName` = '3' AND `column name with space` IS NOT NULL +AND `nested with space`.`inner with space` < 2 +``` + + +Field names containing periods (.) are NOT supported. + + +### Dates, Timestamps, Decimals {#dates-timestamps-decimals} + +Literals for dates, timestamps, and decimals can be written by writing the string +value after the type name. For example: + + +```sql SQL icon="SQL" +date_col = date '2021-01-01' +and timestamp_col = timestamp '2021-01-01 00:00:00' +and decimal_col = decimal(8,3) '1.000' +``` + + +For timestamp columns, the precision can be specified as a number in the type +parameter. Microsecond precision (6) is the default. + +| SQL | Time unit | +|:-------------- |:------------ | +| `timestamp(0)` | Seconds | +| `timestamp(3)` | Milliseconds | +| `timestamp(6)` | Microseconds | +| `timestamp(9)` | Nanoseconds | + +## Apache Arrow Mapping {#apache-arrow-mapping} + +LanceDB internally stores data in [Apache Arrow](https://arrow.apache.org/) format. +The mapping from SQL types to Arrow types is: + +| SQL type | Arrow type | +|:--------------------------------------------------------- |:------------------ | +| `boolean` | `Boolean` | +| `tinyint` / `tinyint unsigned` | `Int8` / `UInt8` | +| `smallint` / `smallint unsigned` | `Int16` / `UInt16` | +| `int` or `integer` / `int unsigned` or `integer unsigned` | `Int32` / `UInt32` | +| `bigint` / `bigint unsigned` | `Int64` / `UInt64` | +| `float` | `Float32` | +| `double` | `Float64` | +| `decimal(precision, scale)` | `Decimal128` | +| `date` | `Date32` | +| `timestamp` | `Timestamp` [^1] | +| `string` | `Utf8` | +| `binary` | `Binary` | + + +## Best Practices {#best-practices} + +**Scalar Indexes**: We strongly recommend creating scalar indices on columns used for filtering, whether combined with a search operation or applied independently (e.g., for updates or deletions). + +For best performance with large tables or high query volumes: + +- Build a scalar index on frequently filtered columns +- Use exact column names in filters (e.g., `user_id` instead of `USER_ID`) +- Avoid complex transformations in filter expressions (keep them simple) +- When running concurrent queries, use connection pooling for better throughput + +For a column of type LIST(T), you can use `LABEL_LIST` to create a scalar index. Then you should leverage DataFusion's [array functions](https://datafusion.apache.org/user-guide/sql/scalar_functions.html#array-functions) like `array_has_any` or `array_has_all` for optimized filtering. + +## Limitations {#limitations} + +Both **pre-filtering** and **post-filtering** can yield false positives. For pre-filtering, if the filter is too selective, it might eliminate relevant items that the vector search would have otherwise identified as a good match. In this case, increasing `nprobes` parameter will help reduce such false positives. It is recommended to call `bypass_vector_index()` if you know that the filter is highly selective. + +Similarly, a highly selective post-filter can lead to false positives. Increasing both `nprobes` and `refine_factor` can mitigate this issue. When deciding between pre-filtering and post-filtering, pre-filtering is generally the safer choice if you're uncertain. diff --git a/docs/web/search/fts-examples.mdx b/docs/web/search/fts-examples.mdx new file mode 100644 index 000000000..dff442673 --- /dev/null +++ b/docs/web/search/fts-examples.mdx @@ -0,0 +1,561 @@ +--- +title: Full-Text Search Examples +sidebarTitle: FTS Examples +description: Worked examples of fuzzy search, boosting, boolean queries, and substring search with LanceDB full-text search. +icon: "book-open" +--- + +These worked examples build on the concepts from the [Full-Text Search guide](/search/full-text-search). They walk through creating sample tables, building FTS indices, and running fuzzy, phrase, boosted, boolean, and substring queries. + +## Fuzzy Search and Boosting Example {#fuzzy-search-and-boosting-example} + +### Generate Data {#generate-data} + +First, let's create a table with sample text data for testing fuzzy search: + + +```python Python icon="python" +import lancedb +import numpy as np +import pandas as pd +import random + +# Connect to LanceDB +db = lancedb.connect( + uri="db://your-project-slug", + api_key="your-api-key", + region="us-east-1" +) + +# Generate 100 rows of random " " text +table_name = "fts-fuzzy-boosting-test" +vectors = [np.random.randn(128) for _ in range(100)] +verbs = ("runs", "hits", "jumps", "drives", "barfs") +adv = ("crazily.", "dutifully.", "foolishly.", "merrily.", "occasionally.") +adj = ("adorable", "clueless", "dirty", "odd", "stupid") + +def sentence(nouns): + return " ".join(random.choice(words) for words in (nouns, verbs, adv, adj)) + +text = [sentence(("puppy", "car")) for _ in range(100)] +text2 = [sentence(("rabbit", "girl", "monkey")) for _ in range(100)] +count = [random.randint(1, 10000) for _ in range(100)] +``` + +```typescript TypeScript icon="square-js" +import * as lancedb from "@lancedb/lancedb" + +const db = await lancedb.connect({ + uri: "db://your-project-slug", + apiKey: "your-api-key", + region: "us-east-1" +}); + +// Generate 100 rows of random " " text +const tableName = "fts-fuzzy-boosting-test-ts"; +const n = 100; +const verbs = ["runs", "hits", "jumps", "drives", "barfs"]; +const adverbs = ["crazily", "dutifully", "foolishly", "merrily", "occasionally"]; +const adjectives = ["adorable", "clueless", "dirty", "odd", "stupid"]; + +const pick = (words: string[]) => words[Math.floor(Math.random() * words.length)]; +const sentence = (nouns: string[]) => + [nouns, verbs, adverbs, adjectives].map(pick).join(" "); + +const vectors = Array.from({ length: n }, () => + Array.from({ length: 128 }, () => Math.random() * 2 - 1) +); +const text = Array.from({ length: n }, () => sentence(["puppy", "car"])); +const text2 = Array.from({ length: n }, () => sentence(["rabbit", "girl", "monkey"])); +const count = Array.from({ length: n }, () => Math.floor(Math.random() * 10000) + 1); +``` + + +### Create Table {#create-table} + + +```python Python icon="python" +# Create table with sample data +table = db.create_table( + table_name, + data=pd.DataFrame({ + "vector": vectors, + "id": [i % 2 for i in range(100)], + "text": text, + "text2": text2, + "count": count, + }), + mode="overwrite" +) +``` + +```typescript TypeScript icon="square-js" +// Create table with sample data +const data = makeArrowTable( + vectors.map((vector, i) => ({ + vector, + id: i % 2, + text: text[i], + text2: text2[i], + count: count[i], + })) +); + +const table = await db.createTable(tableName, data, { mode: "overwrite" }); +``` + + +### Construct FTS Index {#construct-fts-index} + +Create a full-text search index on the first text column: + + +```python Python icon="python" +# Create FTS index on first text column +table.create_fts_index("text") +wait_for_index(table, "text_idx") +``` + +```typescript TypeScript icon="square-js" +// Create FTS index on first text column +await table.createIndex("text", { config: Index.fts() }); +await waitForIndex(table, "text_idx"); +``` + + +Then, create an index on the second text column: + + +```python Python icon="python" +# Create FTS index on second text column +table.create_fts_index("text2") +wait_for_index(table, "text2_idx") +``` + +```typescript TypeScript icon="square-js" +// Create FTS index on second text column +await table.createIndex("text2", { config: Index.fts() }); +await waitForIndex(table, "text2_idx"); +``` + + +### Basic and Fuzzy Search {#basic-and-fuzzy-search} + +Now we can perform basic, fuzzy, and prefix match searches: + +#### Basic Exact Search {#basic-exact-search} + + +```python Python icon="python" +from lancedb.query import MatchQuery + +# Basic match (exact search) +basic_match_results = ( + table.search(MatchQuery("crazily", "text")) + .select(["id", "text"]) + .limit(100) + .to_pandas() +) +``` + +```typescript TypeScript icon="square-js" +import { MatchQuery } from "@lancedb/lancedb"; + +// Basic match (exact search) +const basicMatchResults = await table.query() + .fullTextSearch(new MatchQuery("crazily", "text")) + .select(["id", "text"]) + .limit(100) + .toArray(); +``` + + +#### Fuzzy Search with Typos {#fuzzy-search-with-typos} + + +```python Python icon="python" +# Fuzzy match (allows typos) +fuzzy_results = ( + table.search(MatchQuery("craziou", "text", fuzziness=2)) + .select(["id", "text"]) + .limit(100) + .to_pandas() +) +``` + +```typescript TypeScript icon="square-js" +// Fuzzy match (allows typos) +const fuzzyResults = await table.query() + .fullTextSearch(new MatchQuery("craziou", "text", { + fuzziness: 2, + })) + .select(["id", "text"]) + .limit(100) + .toArray(); +``` + + +#### Prefix based Match {#prefix-based-match} + +Prefix-based match allows you to search for documents containing words that start with a specific prefix. + + +```python Python icon="python" +# Fuzzy match (allows typos) +fuzzy_results = ( + table.search(MatchQuery("cra", "text", prefix_length=3)) + .select(["id", "text"]) + .limit(100) + .to_pandas() +) +``` + +```typescript TypeScript icon="square-js" +// Fuzzy match (allows typos) +const fuzzyResults = await table.query() + .fullTextSearch(new MatchQuery("cra", "text", { + prefixLength: 3, + })) + .select(["id", "text"]) + .limit(100) + .toArray(); +``` + + +### Phrase Match {#phrase-match} + +Phrase matching enables you to search for exact sequences of words. Unlike regular text search +which matches individual terms independently, phrase matching requires words to appear in the +specified order with no intervening terms. + + +Phrase queries are supported but only for a single column; providing multiple columns with a quoted phrase raises an error. + + +Phrase matching is particularly useful for: + +- Searching for specific multi-word expressions +- Matching exact titles or quotes +- Finding precise word combinations in a specific order + + +```python Python icon="python" +# Exact phrase match +from lancedb.query import PhraseQuery + +phrase_results = ( + table.search(PhraseQuery("puppy runs", "text")) + .select(["id", "text"]) + .limit(100) + .to_pandas() +) +``` + +```typescript TypeScript icon="square-js" +import { PhraseQuery } from "@lancedb/lancedb"; + +// Exact phrase match +const phraseResults = await table.query() + .fullTextSearch(new PhraseQuery("puppy runs", "text")) + .select(["id", "text"]) + .limit(100) + .toArray(); +``` + + +#### Flexible Phrase Match {#flexible-phrase-match} +To provide more flexible phrase matching, LanceDB supports the `slop` parameter. This allows you to match phrases where the terms appear close to each other, even if they are not directly adjacent or in the exact order, as long as they are within the specified `slop` value. + +For example, the phrase query "puppy merrily" would not return any results by default. However, if you set `slop=1`, it will match phrases like "puppy jumps merrily", "puppy runs merrily", and similar variations where one word appears between "puppy" and "merrily". + + +```python Python icon="python" +# Flexible phrase match with slop=1 for 'puppy merrily' +from lancedb.query import PhraseQuery + +phrase_results = ( + table.search(PhraseQuery("puppy merrily", "text", slop=1)) + .select(["id", "text"]) + .limit(100) + .to_pandas() +) +``` + +```typescript TypeScript icon="square-js" +import { PhraseQuery } from "@lancedb/lancedb"; + +// Flexible phrase match with slop=1 for 'puppy runs' +const phraseResults = await table.query() + .fullTextSearch(new PhraseQuery("puppy runs", "text", { slop: 1 })) + .select(["id", "text"]) + .limit(100) + .toArray(); +``` + + +### Search with Boosting {#search-with-boosting} + +Boosting allows you to control the relative importance of different search terms or fields +in your queries. This feature is particularly useful when you need to: + +* Prioritize matches in certain columns +* Promote specific terms while demoting others +* Fine-tune relevance scoring for better search results + +| Parameter | Type | Default | Description | +| -------------- | ----- | -------- | ------------------------------------------------------------------ | +| positive | Query | required | The primary query terms to match and promote in results | +| negative | Query | required | Terms to demote in the search results | +| negative_boost | float | 0.5 | Multiplier for negative matches (lower values = stronger demotion) | + + +```python Python icon="python" +from lancedb.query import MatchQuery, BoostQuery, MultiMatchQuery + +# Boost data with 'runs' in text more than 'puppy' in text +boosting_results = ( + table.search( + BoostQuery( + MatchQuery("runs", "text"), + MatchQuery("puppy", "text"), + negative_boost=0.2, + ), + ) + .select(["id", "text"]) + .limit(100) + .to_pandas() +) + +# Search across both text and text2 +multi_match_results = ( + table.search(MultiMatchQuery("crazily", ["text", "text2"])) + .select(["id", "text", "text2"]) + .limit(100) + .to_pandas() +) + +# Search with field boosting +multi_match_boosting_results = ( + table.search( + MultiMatchQuery("crazily", ["text", "text2"], boosts=[1.0, 2.0]), + ) + .select(["id", "text", "text2"]) + .limit(100) + .to_pandas() +) +``` + +```typescript TypeScript icon="square-js" +import { MatchQuery, BoostQuery, MultiMatchQuery } from "@lancedb/lancedb"; + +// Boosting Example +const boostingResults = await table.query() + .fullTextSearch(new BoostQuery(new MatchQuery("runs", "text"), new MatchQuery("puppy", "text"), { + negativeBoost: 0.2, + })) + .select(["id", "text"]) + .limit(100) + .toArray(); + +// Search across both text fields +const multiMatchResults = await table.query() + .fullTextSearch(new MultiMatchQuery("crazily", ["text", "text2"])) + .select(["id", "text", "text2"]) + .limit(100) + .toArray(); + +// Search with field boosting +const multiMatchBoostingResults = await table.query() + .fullTextSearch(new MultiMatchQuery("crazily", ["text", "text2"], { + boosts: [1.0, 2.0], + })) + .select(["id", "text", "text2"]) + .limit(100) + .toArray(); +``` + + + +- Use fuzzy search when handling user input that may contain typos or variations +- Apply field boosting to prioritize matches in more important columns +- Combine fuzzy search with boosting for robust and precise search results + +**Recommendations for optimal FTS performance:** + +- Create full-text search indices on text columns that will be frequently searched +- For hybrid search combining text and vectors, see our [hybrid search guide](/search/hybrid-search/) +- For performance benchmarks, check our [benchmark results](/enterprise/benchmarks/) +- For complex queries, use SQL to combine FTS with other filter conditions + + +### Boolean Queries {#boolean-queries} +LanceDB supports boolean logic in full-text search, allowing you to combine multiple queries using `and` and `or` operators. This is useful when you want to match documents that satisfy multiple conditions (intersection) or at least one of several conditions (union). + +#### Combining Two Match Queries {#combining-two-match-queries} + +In Python, you can combine two MatchQuery objects using either the `and` function or the `&` operator (e.g., `MatchQuery("puppy", "text") and MatchQuery("merrily", "text")`); both methods are supported and yield the same result. Similarly, you can use either the `or` function or the `|` operator to perform an or query. + +In TypeScript, boolean queries are constructed using the `BooleanQuery` class with a list of [Occur, subquery] pairs. For example, to perform an AND query: + +```sql SQL icon="code" +BooleanQuery([ +[Occur.Must, new MatchQuery("puppy", "text")], +[Occur.Must, new MatchQuery("merrily", "text")], +]) +``` + +This approach allows you to specify complex boolean logic by combining multiple subqueries with different Occur values (such as `Must`, `Should`, or `MustNot`). + + +**Which queries are allowed?** + +A boolean query must include at least one `SHOULD` or `MUST` clause. Queries that contain only a `MUST_NOT` clause are not allowed. + + + +```python Python icon="python" +from lancedb.query import MatchQuery + +# Example: Find documents containing both "puppy" and "merrily" +and_query = MatchQuery("puppy", "text") & MatchQuery("merrily", "text") +and_results = ( + table.search(and_query) + .select(["id", "text"]) + .limit(100) + .to_pandas() +) + +# Example: Find documents containing either "puppy" or "merrily" +or_query = MatchQuery("puppy", "text") | MatchQuery("merrily", "text") +or_results = ( + table.search(or_query) + .select(["id", "text"]) + .limit(100) + .to_pandas() +) +``` + +```typescript TypeScript icon="square-js" expandable=true +import { MatchQuery, BooleanQuery, Occur } from "@lancedb/lancedb"; + +// Flexible boolean queries with MatchQuery + +// Find documents containing both "puppy" and "merrily" +const mustResults = await table + .search( + new BooleanQuery([ + [Occur.Must, new MatchQuery("puppy", "text")], + [Occur.Must, new MatchQuery("merrily", "text")], + ]), + ) + .select(["id", "text"]) + .limit(100) + .toArray(); + +// Find documents containing either "puppy" or "merrily" +const shouldResults = await table + .search( + new BooleanQuery([ + [Occur.Should, new MatchQuery("puppy", "text")], + [Occur.Should, new MatchQuery("merrily", "text")], + ]), + ) + .select(["id", "text"]) + .limit(100) + .toArray(); +``` + + + +**How to use booleans?** + +- Use `and`/`&`(Python), `Occur.Must`(Typescript) for intersection (documents must match all queries). +- Use `or`/`|`(Python), `Occur.Should`(Typescript) for union (documents must match at least one query). + + +## Substring Search Example {#substring-search-example} + +LanceDB supports searching for substrings in text columns using n-gram tokenization. This is useful for finding partial matches within text content. + +### Setting Up the Table {#setting-up-the-table} + +First, create a table with sample text data and configure n-gram tokenization: + + +```python Python icon="python" +import pyarrow as pa +import lancedb + +db = lancedb.connect(":memory:") + +data = pa.table({"text": ["hello world", "lance database", "lance is cool"]}) +table = db.create_table("test", data=data) +table.create_fts_index("text", base_tokenizer="ngram") +``` + + +### Basic Substring Search {#basic-substring-search} + +With the default n-gram settings (minimum length of 3), you can search for substrings of length 3 or more: + + +```python Python icon="python" +results = table.search("lan", query_type="fts").limit(10).to_list() +assert len(results) == 2 +assert set(r["text"] for r in results) == {"lance database", "lance is cool"} + +results = ( + table.search("nce", query_type="fts").limit(10).to_list() +) # spellchecker:disable-line +assert len(results) == 2 +assert set(r["text"] for r in results) == {"lance database", "lance is cool"} +``` + + +### Handling Short Substrings {#handling-short-substrings} + +By default, the minimum n-gram length is 3, so shorter substrings like "la" won't match: + + +```python Python icon="python" +results = table.search("la", query_type="fts").limit(10).to_list() +assert len(results) == 0 +``` + + +### Customizing N-gram Parameters {#customizing-n-gram-parameters} + +You can customize the n-gram behavior by adjusting the minimum length and using prefix-only matching: + + +```python Python icon="python" +table.create_fts_index( + "text", + base_tokenizer="ngram", + replace=True, + ngram_min_length=2, + prefix_only=True, +) +``` + + +### Testing Custom N-gram Settings {#testing-custom-n-gram-settings} + +With the new settings, you can now search for shorter substrings and use prefix-only matching: + + +```python Python icon="python" +results = table.search("lan", query_type="fts").limit(10).to_list() +assert len(results) == 2 +assert set(r["text"] for r in results) == {"lance database", "lance is cool"} + +results = ( + table.search("nce", query_type="fts").limit(10).to_list() +) # spellchecker:disable-line +assert len(results) == 0 + +results = table.search("la", query_type="fts").limit(10).to_list() +assert len(results) == 2 +assert set(r["text"] for r in results) == {"lance database", "lance is cool"} +``` + diff --git a/docs/web/search/full-text-search.mdx b/docs/web/search/full-text-search.mdx new file mode 100644 index 000000000..50633745a --- /dev/null +++ b/docs/web/search/full-text-search.mdx @@ -0,0 +1,565 @@ +--- +title: Full-Text Search (FTS) +sidebarTitle: Full-Text Search (FTS) +description: Learn how to implement full-text search in LanceDB using BM25 for keyword-based retrieval. +icon: "book" +--- + +import { + PyFtsPrefiltering, + PyFtsPostfiltering, + PyFtsIncrementalIndex, +} from '/snippets/search.mdx' + +LanceDB provides support for Full-Text Search via Lance, allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions. + +## Basic Usage {#basic-usage} + +Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search, the FTS index must be created before you can search via keywords. + +### Table Setup {#table-setup} + +First, open or create the table you want to search: + + +```python Python icon="python" +import lancedb +from lancedb.index import FTS + +uri = "data/sample-lancedb" +db = lancedb.connect(uri) + +table = db.create_table( + "my_table_fts", + data=[ + {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"}, + {"vector": [5.9, 26.5], "text": "There are several kittens playing"}, + ], +) +``` + +```ts TypeScript icon="square-js" +import * as lancedb from "@lancedb/lancedb"; +const uri = "data/sample-lancedb" +const db = await lancedb.connect(uri); + +const data = [ + { vector: [3.1, 4.1], text: "Frodo was a happy puppy" }, + { vector: [5.9, 26.5], text: "There are several kittens playing" }, +]; +const tbl = await db.createTable("my_table", data, { mode: "overwrite" }); +``` + +```rust Rust icon="rust" +let uri = "data/sample-lancedb"; +let db = connect(uri).execute().await?; +let initial_data: Box = create_some_records()?; +let tbl = db + .create_table("my_table", initial_data) + .execute() + .await?; +``` + + +### Construct FTS Index {#construct-fts-index} + +Create a full-text search index on your text column: + + +In Python, this page shows the synchronous `create_fts_index(...)` form. For the +asynchronous equivalent (`await table.create_index("text", config=FTS(...))`), see +[FTS index](/indexing/fts-index). + + + +```python Python icon="python" +table.create_fts_index("text") +``` + +```typescript TypeScript icon="square-js" +await tbl.createIndex("text", { + config: lancedb.Index.fts(), +}); +``` + +```rust Rust icon="rust" +tbl + .create_index(&["text"], Index::FTS(FtsIndexBuilder::default())) + .execute() + .await?; +``` + + +### Full-text Search {#full-text-search} + +Perform full-text search and retrieve results: + + +```python Python icon="python" +results = table.search("puppy") + .limit(10) + .select(["text"]) + .to_list() +# [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}] +``` + +```typescript TypeScript icon="square-js" +const results = await tbl + .search("puppy", "fts") + .select(["text"]) + .limit(10) + .toArray(); +``` + +```rust Rust icon="Rust" +let results = tbl + .query() + .full_text_search(FullTextSearchQuery::new("puppy".to_owned())) + .select(lancedb::query::Select::Columns(vec!["text".to_owned()])) + .limit(10) + .execute() + .await?; +``` + + +The search is conducted on all indexed columns by default, so it's useful when there are multiple indexed columns. + +If you want to specify which columns to search use `fts_columns="text"` + + +LanceDB automatically searches on the existing FTS index if the input to the search is of type `str`. If you provide a vector as input, LanceDB will search the ANN index instead. + + +If a table has more than one FTS index, specify the indexed text column in the query. In Python you can use `fts_columns` or the query builder's `nearest_to_text(..., columns=...)`; in TypeScript, use `query().nearestToText(..., columns)`. The newer Lance-native FTS does not accept legacy Tantivy-only index parameters. + +### Keeping the index up to date {#keeping-the-index-up-to-date} + +Rows you add after building an FTS index aren't part of the index until you optimize the table. Until then, queries fall back to a flat scan over the unindexed fragments to keep results complete, which slows them down as the unindexed tail grows. Call `table.optimize()` to fold new rows into the existing index — it's the same operation used for vector indexes: + + + +{PyFtsIncrementalIndex} + + +```typescript TypeScript icon="square-js" +await tbl.add([{ vector: [3.1, 4.1], text: "Frodo was a happy puppy" }]); +await tbl.optimize(); +``` + +```rust Rust icon="rust" +tbl.add(new_data).execute().await?; +tbl.optimize(OptimizeAction::All).await?; +``` + + +A useful rule of thumb is to call `optimize()` after roughly 100,000 row changes or 20 data-modification operations, whichever comes first. For tables with continuous ingest, schedule it on a cadence that keeps `num_unindexed_rows` (from `table.index_stats(...)`) close to zero. If you want to skip the flat scan over unindexed rows entirely — for example, on a hot read path where stale results are acceptable — call `.fast_search()` on the query so the search returns only indexed results. + +## Advanced Usage {#advanced-usage} + +### Tokenize Table Data {#tokenize-table-data} + +By default, the text is tokenized by splitting on punctuation and whitespaces, and would filter out words that are longer than 40 characters. All words are converted to lowercase. + +Stemming is useful for improving search results by reducing words to their root form, e.g. "running" to "run". LanceDB supports stemming for Arabic, Danish, Dutch, English, Finnish, French, German, Greek, Hungarian, Italian, Norwegian, Portuguese, Romanian, Russian, Spanish, Swedish, Tamil, and Turkish. You should set the `base_tokenizer` parameter rather than `tokenizer_name` because you cannot customize the tokenizer if `tokenizer_name` is specified. + +Tokenization and language filters are separate settings. `base_tokenizer` controls how text is split into searchable tokens. `language` controls stemming and stop-word removal when `stem=True` or `remove_stop_words=True`; choose the tokenizer for CJK or mixed-language segmentation. + +For example, to enable stemming for English: + + +```python Python icon="python" +table.create_fts_index("text", language="English", replace=True) +``` + + +The tokenizer is customizable, you can specify how the tokenizer splits the text, and how it filters out words, etc. + +**Default index parameters:** +- `base_tokenizer`: `"simple"` +- `language`: English +- `with_position`: false +- `max_token_length`: 40 +- `lower_case`: true +- `stem`: true +- `remove_stop_words`: true +- `ascii_folding`: true +- `custom_stop_words`: `None` — pass a `list[str]` to drop additional words beyond the language defaults. Requires `remove_stop_words=True`. + +For multilingual use cases, use `base_tokenizer="icu"` for unicode-aware word segmentation on mixed-language text. ICU stands for [International Components for Unicode](https://icu.unicode.org/). The ICU tokenizer uses bundled ICU4X segmenter data, so it does not require external tokenizer model files. It is a good default when documents mix languages or include scripts where the simple tokenizer would keep an unspaced span as one large token. + +The Python API also supports tokenizer implementations that load language model files. Use `base_tokenizer="jieba/default"` for Jieba tokenization, which segments Chinese text into searchable word tokens when the text is written without spaces between words. Use Lindera-backed tokenizers for dictionary-based East Asian morphological segmentation, such as `base_tokenizer="lindera/ipadic"` for Japanese or `base_tokenizer="lindera/ko-dic"` for Korean when you have installed and compiled that Lindera model. These are language-specific tokenizers; ICU is the broader mixed-language option. + + +```python Python icon="python" +table.create_fts_index( + "text", + base_tokenizer="jieba/default", + stem=False, + remove_stop_words=False, + ascii_folding=False, + replace=True, +) +``` + + +Model-backed tokenizers require tokenizer model files in Lance's language model home. Lance looks under the default platform data directory for `lance/language_models`, or you can set `LANCE_LANGUAGE_MODEL_HOME` to point to a different model root: + +```bash +export LANCE_LANGUAGE_MODEL_HOME=/path/to/lance/language_models +``` + +For example, `jieba/default` is resolved under `/jieba/default/...`, `lindera/ipadic` under `/lindera/ipadic/...`, and `lindera/ko-dic` under `/lindera/ko-dic/...`. + + +Built-in stop-word removal supports Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Norwegian, Portuguese, Russian, Spanish, and Swedish. If you use another stemming language, such as Arabic, Greek, Romanian, Tamil, or Turkish, set `remove_stop_words=False` or pass `custom_stop_words`. + + +For example, for language with accents, you can specify the tokenizer to use `ascii_folding` to remove accents, e.g. 'é' to 'e': + + +```python Python icon="python" +table.create_fts_index( + "text", + language="French", + stem=True, + ascii_folding=True, + replace=True, + ) +``` + + +### Filtering Options {#filtering-options} + +LanceDB full text search supports to filter the search results by a condition, both pre-filtering and post-filtering are supported. + +This can be invoked via the familiar `where` syntax. + +With pre-filtering: + + + + +{PyFtsPrefiltering} + + +```typescript TypeScript icon="square-js" +await tbl +.search("puppy") +.select(["id", "doc"]) +.limit(10) +.where("meta='foo'") +.prefilter(true) +.toArray(); +``` + +```rust Rust icon="Rust" +table + .query() + .full_text_search(FullTextSearchQuery::new("puppy".to_owned())) + .select(lancedb::query::Select::Columns(vec!["doc".to_owned()])) + .limit(10) + .only_if("meta='foo'") + .execute() + .await?; +``` + + +With post-filtering: + + + +{PyFtsPostfiltering} + + +```typescript TypeScript icon="square-js" +await tbl +.search("apple") +.select(["id", "doc"]) +.limit(10) +.where("meta='foo'") +.prefilter(false) +.toArray(); +``` + +```rust Rust icon="Rust" +table + .query() + .full_text_search(FullTextSearchQuery::new(words[0].to_owned())) + .select(lancedb::query::Select::Columns(vec!["doc".to_owned()])) + .postfilter() + .limit(10) + .only_if("meta='foo'") + .execute() + .await?; +``` + + +### Phrase vs. Terms Queries {#phrase-vs-terms-queries} + + +Lance-based FTS doesn't support queries using boolean operators `OR`, `AND` in the search string. + + +For full-text search you can specify either a **phrase** query like `"the old man and the sea"`, +or a **terms** search query like `old man sea`. + +To search for a phrase, the index must be created with `with_position=True` and `remove_stop_words=False`: + + +```python Python icon="python" +table.create_fts_index("text", with_position=True, replace=True) +``` + + +This will allow you to search for phrases, but it will also significantly increase the index size and indexing time. + +### Fuzzy Search {#fuzzy-search} + +Fuzzy search allows you to find matches even when the search terms contain typos or slight variations. +LanceDB uses the classic [Levenshtein distance](https://en.wikipedia.org/wiki/Levenshtein_distance) +to find similar terms within a specified edit distance. + +| Parameter | Type | Default | Description | +| -------------- | ---- | ------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------- | +| fuzziness | int | 0 | Maximum edit distance allowed for each term. If not specified, automatically set based on term length: 0 for length ≤ 2, 1 for length ≤ 5, 2 for length > 5 | +| max_expansions | int | 50 | Maximum number of terms to consider for fuzzy matching. Higher values may improve recall but increase search time | + +For a complete walkthrough that creates a sample table and demonstrates fuzzy search and relevance boosting, see the [fuzzy search example](/search/fts-examples#fuzzy-search-and-boosting-example). + +### Search for Substring {#search-for-substring} + +LanceDB supports searching for substrings in the text column, you can set the `base_tokenizer` parameter to `"ngram"` to enable this feature, and use the parameters `ngram_min_length` and `ngram_max_length` to control the length of the substrings: + +| Parameter | Type | Default | Description | +| ---------------- | ---- | ------- | -------------------------------------------------- | +| ngram_min_length | int | 3 | Minimum length of the n-grams to search for | +| ngram_max_length | int | 3 | Maximum length of the n-grams to search for | +| prefix_only | bool | false | Whether to only search for prefixes of the n-grams | + + +## More Examples {#more-examples} + +For complete worked examples of fuzzy search, prefix matching, phrase matching, boosting, boolean queries, and substring search — including sample data generation and index setup — see [Full-Text Search Examples](/search/fts-examples). + +## Full-Text Search on Array Fields {#full-text-search-on-array-fields} + +LanceDB supports full-text search on string array columns, enabling efficient keyword-based search across multiple values within a single field (e.g., tags, keywords). + +### Setting Up the Connection {#setting-up-the-connection} + +Connect to your LanceDB instance: + + +```python Python icon="python" +import lancedb + +# Connect to LanceDB +db = lancedb.connect( + uri="db://your-project-slug", + api_key="your-api-key", + region="us-east-1" +) +``` + +```typescript TypeScript icon="square-js" expandable=true +import * as lancedb from "@lancedb/lancedb" + +const db = await lancedb.connect({ + uri: "db://your-project-slug", + apiKey: "your-api-key", + region: "us-east-1" +}); +``` + + +### Defining the Schema {#defining-the-schema} + +Create a schema that includes an array field for tags: + + +```python Python icon="python" +table_name = "fts-array-field-test" +schema = pa.schema([ + pa.field("id", pa.string()), + pa.field("tags", pa.list_(pa.string())), + pa.field("description", pa.string()) +]) +``` + +```typescript TypeScript icon="square-js" expandable=true +const tableName = "fts-array-field-test-ts"; + +// Create schema +const schema = new Schema([ + new Field("id", new Utf8(), false), + new Field("tags", new List(new Field("item", new Utf8()))), + new Field("description", new Utf8(), false) +]); +``` + + +### Creating Sample Data {#creating-sample-data} + +Generate sample data with array fields containing tags: + + +```python Python icon="python" expandable=true +# Generate sample data +data = { + "id": [f"doc_{i}" for i in range(10)], + "tags": [ + ["python", "machine learning", "data science"], + ["deep learning", "neural networks", "AI"], + ["database", "indexing", "search"], + ["vector search", "embeddings", "AI"], + ["full text search", "indexing", "database"], + ["python", "web development", "flask"], + ["machine learning", "deep learning", "pytorch"], + ["database", "SQL", "postgresql"], + ["search engine", "elasticsearch", "indexing"], + ["AI", "transformers", "NLP"] + ], + "description": [ + "Python for data science projects", + "Deep learning fundamentals", + "Database indexing techniques", + "Vector search implementations", + "Full-text search guide", + "Web development with Python", + "Machine learning with PyTorch", + "Database management systems", + "Search engine optimization", + "AI and NLP applications" + ] +} +``` + +```typescript TypeScript icon="square-js" expandable=true +// Generate sample data +const data = makeArrowTable( + Array(10).fill(0).map((_, i) => ({ + id: `doc_${i}`, + tags: [ + ["python", "machine learning", "data science"], + ["deep learning", "neural networks", "AI"], + ["database", "indexing", "search"], + ["vector search", "embeddings", "AI"], + ["full text search", "indexing", "database"], + ["python", "web development", "flask"], + ["machine learning", "deep learning", "pytorch"], + ["database", "SQL", "postgresql"], + ["search engine", "elasticsearch", "indexing"], + ["AI", "transformers", "NLP"] + ][i], + description: [ + "Python for data science projects", + "Deep learning fundamentals", + "Database indexing techniques", + "Vector search implementations", + "Full-text search guide", + "Web development with Python", + "Machine learning with PyTorch", + "Database management systems", + "Search engine optimization", + "AI and NLP applications" + ][i] + })), + { schema } +); +``` + + +### Creating the Table and Adding Data {#creating-the-table-and-adding-data} + +Create the table and populate it with the sample data: + + +```python Python icon="python" +# Create table and add data +table = db.create_table(table_name, schema=schema, mode="overwrite") +table_data = pa.Table.from_pydict(data, schema=schema) +table.add(table_data) +``` + +```typescript TypeScript icon="square-js" +// Create table +const table = await db.createTable(tableName, data, { mode: "overwrite" }); +console.log(`Created table: ${tableName}`); +``` + + +### Building the Full-Text Search Index {#building-the-full-text-search-index} + +Create an FTS index on the tags column to enable efficient text search: + + +```python Python icon="python" +# Create FTS index +table.create_fts_index("tags") +wait_for_index(table, "tags_idx") +``` + +```typescript TypeScript icon="square-js" +// Create FTS index +console.log("Creating FTS index on 'tags' column..."); +await table.createIndex("tags", { + config: Index.fts() +}); + +// Wait for index +const ftsIndexName = "tags_idx"; +await waitForIndex(table, ftsIndexName); +``` + + +### Performing Fuzzy Search {#performing-fuzzy-search} + +Search for terms with typos using fuzzy matching: + + +```python Python icon="python" +# Search examples +print("\nSearching for 'learning' in tags with a typo:") +result = ( + table.search(MatchQuery("learnin", column="tags", fuzziness=1)) + .select(['id', 'tags', 'description']) + .to_arrow() +) +``` + +```typescript TypeScript icon="square-js"> +// Search examples +console.log("\nSearching for 'learning' in tags with a typo:"); +const fuzzyResults = await table.query() + .fullTextSearch(new MatchQuery("learnin", "tags", { + fuzziness: 2, + })) + .select(["id", "tags", "description"]) + .toArray(); +console.log(fuzzyResults); +``` + + +### Performing Phrase Search {#performing-phrase-search} + +Search for exact phrases within the array fields: + + +```python Python icon="python" +print("\nSearching for 'machine learning' in tags:") +result = ( + table.search(PhraseQuery("machine learning", column="tags")) + .select(['id', 'tags', 'description']) + .to_arrow() +) +``` + +```typescript TypeScript icon="square-js" +console.log("\nSearching for 'machine learning' in tags:"); +const phraseResults = await table.query() + .fullTextSearch(new PhraseQuery("machine learning", "tags")) + .select(["id", "tags", "description"]) + .toArray(); +console.log(phraseResults); +``` + diff --git a/docs/web/search/hybrid-search.mdx b/docs/web/search/hybrid-search.mdx new file mode 100644 index 000000000..d4109c653 --- /dev/null +++ b/docs/web/search/hybrid-search.mdx @@ -0,0 +1,355 @@ +--- +title: Hybrid Search +sidebarTitle: Hybrid search +description: Learn how to perform hybrid search in LanceDB by combining vector and full-text search techniques with reranking. +icon: "search" +--- + +In certain cases, you may want to retrieve documents that are semantically similar to a given query, +but also prioritize specific keywords. This is an example of **hybrid search**, a query method that combines +multiple search techniques. + +For detailed examples, look at this [Python Notebook](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/saas_examples/python_notebook/Hybrid_search.ipynb) or the [**TypeScript Example**](https://github.com/lancedb/vectordb-recipes/tree/main/examples/saas_examples/ts_example/hybrid-search) + +## Example: Hybrid Search {#example-hybrid-search} + +### 1\. Setup {#1-setup} +Import the necessary libraries and dependencies for working with LanceDB, OpenAI embeddings, and reranking. + + +```python Python icon="python" +import os +import lancedb +import openai +from lancedb.embeddings import get_registry +from lancedb.pydantic import LanceModel, Vector +``` + +```typescript TypeScript icon="square-js" +import * as lancedb from "@lancedb/lancedb"; +import "@lancedb/lancedb/embedding/openai"; +import { Utf8 } from "apache-arrow"; +``` + + +### 2\. Connect to LanceDB {#2-connect-to-lancedb} +Establish a connection to your LanceDB instance, with different options for Enterprise setups or open source. + +OSS + + +```python Python icon="python" +uri = "data/sample-lancedb" +db = lancedb.connect(uri) +``` + +```typescript TypeScript icon="square-js" +import * as lancedb from "@lancedb/lancedb"; +import * as arrow from "apache-arrow"; + +const databaseDir = "data/sample-lancedb"; +const db = await lancedb.connect(databaseDir); +``` + + +Enterprise + +For LanceDB Enterprise, set the `db://` URI, region and the host override to your private cloud endpoint: + + +```python Python icon="python" +host_override = os.environ.get("LANCEDB_HOST_OVERRIDE") + +db = lancedb.connect( + uri=uri, + api_key=api_key, + region=region, + host_override=host_override +) +``` + +```typescript TypeScript icon="square-js" +import * as lancedb from "@lancedb/lancedb"; +import * as arrow from "apache-arrow"; + +const uri = "db://my-lancedb-instance/my-database"; +const apiKey = process.env.LANCEDB_API_KEY; +const region = process.env.LANCEDB_REGION; +const hostOverride = process.env.LANCEDB_HOST_OVERRIDE; + +const db = await lancedb.connect(uri, { + apiKey, + region + hostOverride, +}); +``` + + + + +### 3\. Configure Embedding Model {#3-configure-embedding-model} +Set up the any embedding model that will convert text into vector representations for semantic search. + + +```python Python icon="python" +embeddings = get_registry().get("sentence-transformers").create() +``` + +```typescript TypeScript icon="square-js" +const embedFunc = lancedb.embedding.getRegistry().get("openai")?.create({ + model: "text-embedding-ada-002", +}) as lancedb.embedding.EmbeddingFunction; +``` + + +### 4\. Create Table and Schema {#4-create-table-and-schema} +Define the data structure for your documents, including both the text content and its vector representation. + + +```python Python icon="python" +class Documents(LanceModel): + text: str = embeddings.SourceField() + vector: Vector(embeddings.ndims()) = embeddings.VectorField() + +table_name = "hybrid_search_example" +table = db.create_table(table_name, schema=Documents, mode="overwrite") +``` + +```typescript TypeScript icon="square-js" +const documentSchema = lancedb.embedding.LanceSchema({ + text: embedFunc.sourceField(new Utf8()), + vector: embedFunc.vectorField(), +}); + +const tableName = "hybrid_search_example"; +const table = await db.createEmptyTable(tableName, documentSchema, { + mode: "overwrite", +}); +``` + + +### 5\. Add Data {#5-add-data} +Insert sample documents into your table, which will be used for both semantic and keyword search. + + +```python Python icon="python" +data = [ + {"text": "rebel spaceships striking from a hidden base"}, + {"text": "have won their first victory against the evil Galactic Empire"}, + {"text": "during the battle rebel spies managed to steal secret plans"}, + {"text": "to the Empire's ultimate weapon the Death Star"}, +] +table.add(data=data) +``` + +```typescript TypeScript icon="square-js" +const data = [ + { text: "rebel spaceships striking from a hidden base" }, + { text: "have won their first victory against the evil Galactic Empire" }, + { text: "during the battle rebel spies managed to steal secret plans" }, + { text: "to the Empire's ultimate weapon the Death Star" }, +]; +await table.add(data); +console.log(`Created table: ${tableName} with ${data.length} rows`); +``` + + +### 6\. Build Full Text Index {#6-build-full-text-index} +Create a full-text search index on the text column to enable keyword-based search capabilities. + + +```python Python icon="python" +table.create_fts_index("text") +wait_for_index(table, "text_idx") +``` + +```typescript TypeScript icon="square-js" +console.log("Creating full-text search index..."); +await table.createIndex("text", { + config: lancedb.Index.fts(), +}); +await waitForIndex(table as any, "text_idx"); +``` + + +### 7\. Set Reranker [Optional] {#7-set-reranker-optional} +Initialize the reranker that will combine and rank results from both semantic and keyword search. By default, lancedb uses RRF reranker, but you can choose other rerankers like `Cohere`, `CrossEncoder`, or others lister in integrations section. + + +```python Python icon="python" +reranker = RRFReranker() +``` + +```typescript TypeScript icon="square-js" +const reranker = await lancedb.rerankers.RRFReranker.create(); +``` + + +### 8\. Hybrid Search {#8-hybrid-search} +Perform a hybrid search query that combines semantic similarity with keyword matching, using the specified reranker to merge and rank the results. + + +```python Python icon="python" +results = ( + table.search( + "flower moon", + query_type="hybrid", + vector_column_name="vector", + fts_columns="text", + ) + .rerank(reranker) + .limit(10) + .to_pandas() +) + +print("Hybrid search results:") +print(results) +``` + +```typescript TypeScript icon="square-js" +console.log("Performing hybrid search..."); +const queryVector = await embedFunc.computeQueryEmbeddings("full moon in May"); +const hybridResults = await table + .query() + .fullTextSearch("flower moon") + .nearestTo(queryVector) + .rerank(reranker) + .select(["text"]) + .limit(10) + .toArray(); + +console.log("Hybrid search results:"); +console.log(hybridResults); +``` + + +### 9\. Hybrid Search - Explicit Vector and Text Query pattern {#9-hybrid-search-explicit-vector-and-text-query-pattern} +You can also pass the vector and text query explicitly. This is useful if you're not using the embedding API or if you're using a separate embedder service. + + +```python Python icon="python" +vector_query = [0.1, 0.2, 0.3, 0.4, 0.5] +text_query = "flower moon" +( + table.search(query_type="hybrid") + .vector(vector_query) + .text(text_query) + .limit(5) + .to_pandas() +) +``` + + +## Query controls {#query-controls} + +Hybrid queries inherit the same builder API as vector and FTS queries, so the same knobs for filtering, distance bounds, and row identity apply. These compose with `.rerank(...)` and the explicit `.vector()` / `.text()` form shown above. + + +Always set `.limit(...)` on production hybrid queries. LanceDB's default search limit is 10, but an +explicit cap gives you a clear top-k contract to tune before reranking. + + +### Returning row IDs {#returning-row-ids} + +Pass `with_row_id(True)` (Python) or `withRowId()` (TypeScript) to include the internal `_rowid` column in the results. This is useful for joining hybrid results back to a primary table, or for deduping across multiple queries: + + +```python Python icon="python" +results = ( + table.search("flower moon", query_type="hybrid") + .with_row_id(True) + .limit(10) + .to_pandas() +) +# results now contains a `_rowid` column alongside `_relevance_score` +``` + +```typescript TypeScript icon="square-js" +const results = await table + .query() + .fullTextSearch("flower moon") + .nearestTo(queryVector) + .withRowId() + .limit(10) + .toArray(); +``` + + +### Bounding vector distance {#bounding-vector-distance} + +`distance_range(lower, upper)` (Python) and `distanceRange(lower, upper)` (TypeScript) constrain the vector half of the hybrid query to the half-open interval `[lower, upper)`. This is helpful when you want to cap how far semantic candidates can drift from the query vector before reranking: + + +```python Python icon="python" +results = ( + table.search("flower moon", query_type="hybrid") + .distance_range(lower_bound=0.0, upper_bound=0.4) + .limit(10) + .to_pandas() +) +``` + +```typescript TypeScript icon="square-js" +const results = await table + .query() + .fullTextSearch("flower moon") + .nearestTo(queryVector) + .distanceRange(0.0, 0.4) + .limit(10) + .toArray(); +``` + + +Either bound can be omitted to leave that side unbounded. + +### Prefilter vs. postfilter {#prefilter-vs-postfilter} + +When the query carries a metadata filter via `where(...)`, you can choose whether the filter runs before or after the vector and FTS sub-queries. **Prefiltering** (the default) applies `where` to the candidate set before scoring, which is usually what you want — it shrinks the working set and benefits from any scalar indexes on the filter columns. **Postfiltering** runs the filter on the already-ranked top-k from each sub-query; this can be faster when the filter is non-selective or unindexed, but it may return fewer than `limit` rows because some of the top-k may be filtered out. + + +```python Python icon="python" +# Prefilter (default): filter applied before scoring +table.search("flower moon", query_type="hybrid") \ + .where("category = 'film'", prefilter=True) \ + .limit(10) \ + .to_pandas() + +# Postfilter: filter applied after the sub-queries return top-k +table.search("flower moon", query_type="hybrid") \ + .where("category = 'film'", prefilter=False) \ + .limit(10) \ + .to_pandas() +``` + +```typescript TypeScript icon="square-js" +// Prefilter (default): just call .where(...) +await table.query() + .fullTextSearch("flower moon") + .nearestTo(queryVector) + .where("category = 'film'") + .limit(10) + .toArray(); + +// Postfilter: chain .postfilter() after .where(...) +await table.query() + .fullTextSearch("flower moon") + .nearestTo(queryVector) + .where("category = 'film'") + .postfilter() + .limit(10) + .toArray(); +``` + + +The choice gets baked into both sub-queries, so the vector and FTS halves see the filter applied the same way. Use [`explain_plan`](/search/optimize-queries#analyzing-non-vector-queries) on a hybrid query to see whether the filter pushed into the scan or ran as a separate `FilterExec` step. + +## More on Reranking {#more-on-reranking} + +You can perform hybrid search in LanceDB by combining the results of semantic and full-text search via a reranking algorithm of your choice. LanceDB comes with [**built-in rerankers**](https://docs.lancedb.com/reranking) and you can implement your own **custom reranker** as well. + +By default, LanceDB uses `RRFReranker()`, which uses reciprocal rank fusion score, to combine and rerank the results of semantic and full-text search. You can customize the hyperparameters as needed or write your own custom reranker. Here's how you can use any of the available rerankers: + +| Argument | Type | Default | Description | +|:---------|:-----|:--------|:------------| +| `normalize` | `str` | `"score"` | The method to normalize the scores. Can be `rank` or `score`. If `rank`, the scores are converted to ranks and then normalized. If `score`, the scores are normalized directly. | +| `reranker` | `Reranker` | `RRF()` | The reranker to use. If not specified, the default reranker is used. | diff --git a/docs/web/search/index.mdx b/docs/web/search/index.mdx new file mode 100644 index 000000000..b6f168d75 --- /dev/null +++ b/docs/web/search/index.mdx @@ -0,0 +1,26 @@ +--- +title: Search +sidebarTitle: Overview +description: "Comprehensive guide to all search capabilities in LanceDB including vector search, full-text search, hybrid search, and more." +icon: "list" +--- + +| Feature | Description | +|:---------------|:------------| +| [Vector Search](/search/vector-search/) | Semantic similarity search with multiple distance metrics | +| [Multivector Search](/search/multivector-search/) | Search using multiple vector embeddings per document | +| [Full-Text Search](/search/full-text-search/) | Keyword-based search with BM25 and pre-filtering | +| [Hybrid Search](/search/hybrid-search/) | Combines vector and full-text search with reranking | +| [Filtering](/search/filtering/) | Filter results based on metadata fields | +| [SQL Queries](/search/sql/index) | SQL query capabilities for data exploration and analytics | + +## Before you search {#before-you-search} + +- Vector search can run without an ANN index as an exhaustive scan. That's useful while prototyping, but build a vector index before relying on low-latency searches over larger tables. +- Full-text and hybrid text search require an FTS index on the text column you query. If a table has multiple FTS indexes, specify the target column. FTS also supports phrase, boolean, boosted, multi-match, and fuzzy query forms when you need more than plain terms. +- Phrase FTS queries require an index created with token positions enabled. +- Multivector search currently uses cosine similarity and accepts either one query vector or a matrix of query vectors; every query vector must match the inner dimension of the multivector column. +- Set an explicit `.limit(...)` for production queries. The default top-k is 10 for search builders, + but spelling it out makes latency and result-count assumptions visible. Query builders also + support controls such as prefilter/postfilter, distance ranges, row-id inclusion, offset + pagination, and Arrow/Pandas/list result materialization. diff --git a/docs/web/search/multivector-search.mdx b/docs/web/search/multivector-search.mdx new file mode 100644 index 000000000..a11ca48b3 --- /dev/null +++ b/docs/web/search/multivector-search.mdx @@ -0,0 +1,246 @@ +--- +title: "Multivector Search" +sidebarTitle: Multivector search +description: Learn how to perform multivector search in LanceDB to handle multiple vector embeddings per document, which is ideal for late-interaction models like ColBERT and ColPaLi. +icon: "braille" +--- + +LanceDB's multivector support enables you to store and search multiple vector embeddings for a single item. + +This capability is particularly valuable when working with late-interaction models like ColBERT and ColPaLi, which generate multiple embeddings per document. + +In this tutorial, you'll create a table with multiple vector embeddings per document and learn how to perform multivector search. For more end-to-end examples, see the [VectorDB recipes repository](https://github.com/lancedb/vectordb-recipes/tree/main/examples). + +## Multivector Support {#multivector-support} + +Each item in your dataset can have a column containing multiple vectors, which LanceDB can efficiently index and search. When performing a search, you can query with either a single vector embedding or multiple vector embeddings. + + +Currently, only the `cosine` metric is supported for multivector search. The vector value type can be `float16`, `float32`, or `float64`. + + +Each query vector must match the inner vector dimension in the multivector column. This applies to both single-vector queries and multi-vector query matrices. + +## Computing Similarity {#computing-similarity} + +MaxSim (Maximum Similarity) is a key concept in late-interaction models that: + +- Computes the maximum similarity between each query embedding and all document embeddings +- Sums these maximum similarities to get the final relevance score +- Effectively captures fine-grained semantic matches between query and document tokens + +The MaxSim calculation can be expressed as: + +$$ +\text{MaxSim}(Q, D) = \sum_{i=1}^{|Q|} \max_{j=1}^{|D|} \text{sim}(q_i, d_j) +$$ + +Where $sim$ is the similarity function (e.g., cosine similarity). + +$$ +Q = \{q_1, q_2, ..., q_{|Q|}\} +$$ + +$Q$ represents the query embeddings, and $D = \{d_1, d_2, ..., d_{|D|}\}$ represents the document embeddings. + +## Using Multivector Search {#using-multivector-search} + +### 1\. Setup {#1-setup} + +Connect to LanceDB and import the required libraries. + + +```python Python icon="python" +import lancedb +import numpy as np +import pyarrow as pa + +db = lancedb.connect( + uri="db://your-project-slug", + api_key="your-api-key", + region="your-region" +) +``` + + +### 2\. Define Schema {#2-define-schema} + +Define a schema that specifies a multivector field. A multivector field is a nested list structure in which each document contains multiple vectors. In this case, we'll create a schema with: + +1. An ID field as an integer (int64) +2. A vector field that is a list of lists of float32 values + - The outer list represents multiple vectors per document + - Each inner list is a 256-dimensional vector + - Using float32 for memory efficiency while maintaining precision + + +```python Python icon="python" +db = lancedb.connect("data/multivector_demo") +schema = pa.schema( + [ + pa.field("id", pa.int64()), + # float16, float32, and float64 are supported + pa.field("vector", pa.list_(pa.list_(pa.float32(), 256))), + ] +) +``` + + +### 3\. Generate Multivectors {#3-generate-multivectors} + +Generate sample data where each document contains multiple vector embeddings, which can represent different aspects or views of the same document. + +In this example, we create **1024 documents** where each document has **2 random vectors** of **dimension 256**, simulating a real-world scenario where you might have multiple embeddings per item. + + +```python Python icon="python" +data = [ + { + "id": i, + "vector": np.random.random(size=(2, 256)).tolist(), # Each document has 2 vectors + } + for i in range(1024) +] +``` + + +### 4\. Create a Table {#4-create-a-table} + +Create a table with the defined schema and sample data, which will store multiple vectors per document for similarity search. + + +```python Python icon="python" +tbl = db.create_table("multivector_example", data=data, schema=schema) +``` + + +### 5\. Build an Index {#5-build-an-index} + +Only cosine similarity is supported for multivector search operations. +For faster search, build the standard `IVF_PQ` index over your vectors: + + +```python Python icon="python" +tbl.create_index(metric="cosine", vector_column_name="vector") +``` + + + +**Indexing matters more for multivector tables than for single-vector ones.** + +A brute-force scan over a multivector column has to compare every query vector to every document vector in every row, so the cost grows with both the row count and the number of vectors per row. +In LanceDB OSS, the query will just run, so a large unindexed multivector table can stall a process for a long time before returning results. + +On LanceDB Enterprise, the brute-force KNN safety check applies a stricter row threshold to multivector columns — roughly 10× lower than for single-vector columns. So an unindexed multivector table will start being rejected with a "vector search would use brute-force KNN" error well before a comparable single-vector table would. Build the index before you start hitting it from production traffic, even if the dataset is small enough that you'd skip indexing for a single-vector workload. + + +### 6\. Query a Single Vector {#6-query-a-single-vector} + +When searching with a single query vector, it will be compared against all vectors in each document, and the similarity scores will be aggregated to find the most relevant documents. + + +```python Python icon="python" +query = np.random.random(256) +results_single = tbl.search(query).limit(5).to_pandas() +``` + + +### 7\. Query Multiple Vectors {#7-query-multiple-vectors} + +With multiple query vectors, LanceDB calculates similarity using late interaction, a late-interaction technique that computes relevance by finding the best-matching pairs between query and document vectors. This approach provides more nuanced matching while maintaining fast retrieval speeds. + + +```python Python icon="python" +query_multi = np.random.random(size=(2, 256)) +results_multi = tbl.search(query_multi).limit(5).to_pandas() +``` + + + +Visit the [Hugging Face embedding integration](/integrations/embedding/huggingface/) page for information on embedding models. + +## Simple Example: ColBERT Embeddings {#simple-example-colbert-embeddings} + +[ColBERT](https://arxiv.org/abs/2004.12832) is the most well-known late-interaction retrieval model that +represents each document and query as multiple token embeddings and scores matches by taking the best +token-to-token similarities (MaxSim) across them. + +Install the dependencies before running this example: + +```bash +pip install pylate lancedb pandas +``` + + +```python Python icon="python" +import numpy as np +import pyarrow as pa +import lancedb +from pylate import models + +# 1) Load a late-interaction model via PyLate +# PyLate docs show ColBERT() + encode(..., is_query=...) :contentReference[oaicite:2]{index=2} +model = models.ColBERT(model_name_or_path="lightonai/GTE-ModernColBERT-v1") + +# You can discover dim from one embedding (avoid guessing) +dim = model.encode(["hello"], is_query=True)[0].shape[1] + +# 2) Create a LanceDB table with a multivector column +db = lancedb.connect("./pylate_lancedb") +schema = pa.schema([ + pa.field("doc_id", pa.string()), + pa.field("text", pa.string()), + # multivector: list> :contentReference[oaicite:3]{index=3} + pa.field("mv", pa.list_(pa.list_(pa.float32(), dim))), +]) + +docs = [ + {"doc_id": "1", "text": "The train to Tokyo leaves at 5pm."}, + {"doc_id": "2", "text": "That Pho restaurant in Hanoi is highly rated."}, + {"doc_id": "3", "text": "This is a noodle bar in Osaka, Japan."}, +] + +# 3) Encode documents with PyLate (token vectors per doc) +doc_texts = [d["text"] for d in docs] +doc_embs = model.encode(doc_texts, is_query=False) # list/array of (T, dim) per doc :contentReference[oaicite:4]{index=4} + +rows = [] +for d, emb in zip(docs, doc_embs): + emb = np.asarray(emb, dtype=np.float32) + rows.append({**d, "mv": emb.tolist()}) + +tbl = db.create_table("docs", data=rows, schema=schema, mode="overwrite") + +# 4) Build an index + query using a query matrix. +# Multivector brute-force scales with rows × vectors-per-row, so build the index +# at much smaller dataset sizes than you would for single-vector search — and +# always before exposing the table to remote traffic. +tbl.create_index(vector_column_name="mv", metric="cosine") + +query = "Tell me about ramen in Japan" +q_emb = np.asarray(model.encode([query], is_query=True)[0], dtype=np.float32) # (Tq, dim) :contentReference[oaicite:5]{index=5} + +out = tbl.search(q_emb).limit(5).to_pandas() # multivector search accepts a matrix :contentReference[oaicite:6]{index=6} +print(out[["doc_id", "text"]]) +``` + + +Late-interaction model implementations evolve rapidly, so it's a good idea to check the latest popular models +when trying multivector search. + +## Advanced Example: XTR Embeddings {#advanced-example-xtr-embeddings} + +[ConteXtualized Token Retriever (XTR)](https://arxiv.org/abs/2304.01982) is a late-interaction retrieval model that represents text as token-level vectors instead of a single embedding. +This lets search score token-to-token matches (MaxSim), which can improve fine-grained relevance. + +The notebook linked below shows how to integrate XTR, which prioritizes critical document +tokens during the initial retrieval stage and removes the gathering stage to improve performance significantly. +By focusing on the most semantically salient tokens early in the process, XTR reduces computational complexity +while improving recall and ensuring rapid identification of candidate documents. + + + diff --git a/docs/web/search/optimize-queries.mdx b/docs/web/search/optimize-queries.mdx new file mode 100644 index 000000000..244e87633 --- /dev/null +++ b/docs/web/search/optimize-queries.mdx @@ -0,0 +1,483 @@ +--- +title: "Optimize Query Performance" +sidebarTitle: Query optimization +description: "Analyze and optimize query performance in LanceDB." +icon: "gauge" +--- + +LanceDB provides two powerful tools for query analysis and optimization: `explain_plan` and `analyze_plan`. Let's take a better look at how they work: + +| Method | Purpose | Description | +| :------------- | :----------------- | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| `explain_plan` | Query Analysis | Print the resolved query plan to understand how the query will be executed. Helpful for identifying slow queries or unexpected query results. | +| `analyze_plan` | Performance Tuning | Execute the query and return a physical execution plan annotated with runtime metrics including execution time, number of rows processed, and I/O stats. Essential for performance tuning and debugging. | + +## Query Analysis Tools {#query-analysis-tools} + +### Inspect a query plan {#inspect-a-query-plan} + +Reveals the logical query plan before execution, helping you identify potential issues with query structure and index usage. This tool is useful for: + +- Verifying query optimization strategies +- Validating index selection +- Understanding query execution order +- Detecting missing indices + +### Analyze a query plan {#analyze-a-query-plan} +Executes the query and provides detailed runtime metrics, including: + +- Operation duration (`_elapsed_compute_`) +- Data processing statistics (`_output_rows_`, `_bytes_read_`) +- Index effectiveness (`_index_comparisons_`, `_indices_loaded_`) +- Resource utilization (`_iops_`, `_requests_`) + +Together, these tools offer a comprehensive view of query performance, from planning to execution. Use `explain_plan` to verify your query structure and `analyze_plan` to measure and optimize actual performance. + +Metadata filters are prefiltered by default, which usually shows the filter pushed into the +`LanceScan` or index scan. If you set `prefilter=False`, expect a separate `FilterExec` after +search instead; that can be useful for some expensive filters, but it changes both latency and +the number of rows available after filtering. + +## Reading the Execution Plan {#reading-the-execution-plan} + +To demonstrate query performance analysis, we'll use a table containing 1.2M rows sampled from the [Wikipedia dataset](https://huggingface.co/datasets/wikimedia/wikipedia). Initially, the table has no indices, allowing us to observe the impact of optimization. + +Let's examine a vector search query that: + +- Filters rows where `identifier` is between 0 and 1,000,000 +- Returns the top 100 matches +- Projects specific columns: `chunk_index`, `title`, and `identifier` + + +```python Python icon="python" +# explain_plan +query_explain_plan = ( + table.search(query_embed) + .where("identifier > 0 AND identifier < 1000000") + .select(["chunk_index", "title", "identifier"]) + .limit(100) + .explain_plan(True) +) +``` + +```typescript TypeScript icon="square-js" +// explain_plan +const explainPlan = await table + .search(queryEmbed) + .where("identifier > 0 AND identifier < 1000000") + .select(["chunk_index", "title", "identifier"]) + .limit(100) + .explainPlan(true); +``` + + +### Execution Plan Components {#execution-plan-components} + +The execution plan reveals the sequence of operations performed to execute your query. Let's examine each component: + +``` +ProjectionExec: expr=[chunk_index@4 as chunk_index, title@5 as title, identifier@1 as identifier, _distance@3 as _distance] + RemoteTake: columns="vector, identifier, _rowid, _distance, chunk_index, title" + CoalesceBatchesExec: target_batch_size=1024 + GlobalLimitExec: skip=0, fetch=100 + FilterExec: _distance@3 IS NOT NULL + SortExec: TopK(fetch=100), expr=[_distance@3 ASC NULLS LAST], preserve_partitioning=[false] + KNNVectorDistance: metric=l2 + FilterExec: identifier@1 > 0 AND identifier@1 < 1000000 + LanceScan: uri=***, projection=[vector, identifier], row_id=true, row_addr=false, ordered=false +``` + +#### 1\. Base Layer (LanceScan) {#1-base-layer-lancescan} + +- Initial data scan loading only specified columns to minimize I/O +- Unordered scan enabling parallel processing + +``` +LanceScan: +- projection=[vector, identifier] +- row_id=true, row_addr=false, ordered=false +``` + +#### 2\. First Filter {#2-first-filter} + +- Apply requested filter on `identifier` column +- Reduces the number of vectors that need KNN computation + +``` +FilterExec: identifier@1 > 0 AND identifier@1 < 1000000 +``` + +#### 3\. Vector Search {#3-vector-search} + +- Computes L2 (Euclidean) distances between query vector and all vectors that passed the filter + +``` +KNNVectorDistance: metric=l2 +``` + +#### 4\. Results Processing {#4-results-processing} + +- Filters out null distance results +- Sorts by distance and takes top 100 results +- Processes in batches of 1024 for optimal memory usage + +``` +SortExec: TopK(fetch=100) +- expr=[_distance@3 ASC NULLS LAST] +- preserve_partitioning=[false] +FilterExec: _distance@3 IS NOT NULL +GlobalLimitExec: skip=0, fetch=100 +CoalesceBatchesExec: target_batch_size=1024 +``` + +#### 5\. Data Retrieval {#5-data-retrieval} + +- `RemoteTake` is a key component of Lance's I/O cache +- Handles efficient data retrieval from remote storage locations +- Fetches specific rows and columns needed for the final output +- Optimizes network bandwidth by only retrieving required data + +``` +RemoteTake: columns="vector, identifier, _rowid, _distance, chunk_index, title" +``` + +#### 6\. Final Output {#6-final-output} + +- Returns only requested columns and maintains column ordering + +```python +ProjectionExec: expr=[chunk_index@4 as chunk_index, title@5 as title, identifier@1 as identifier, _distance@3 as _distance] +``` + +This plan demonstrates a basic search without index optimizations: it performs a full scan and filter before vector search. + +## Performance Analysis {#performance-analysis} + +Let's use `analyze_plan` to run the query and analyze the query performance, which will help us identify potential bottlenecks: + + +```python Python icon="python" +# analyze_plan +query_analyze_plan = ( + table.search(query_embed) + .where("identifier > 0 AND identifier < 1000000") + .select(["chunk_index", "title", "identifier"]) + .limit(100) + .analyze_plan() +) +``` + +```typescript TypeScript icon="square-js" +// analyze_plan +const analyzePlan = await table + .search(queryEmbed) + .where("identifier > 0 AND identifier < 1000000") + .select(["chunk_index", "title", "identifier"]) + .limit(100) + .analyzePlan(); +``` + + +### Performance Metrics Analysis {#performance-metrics-analysis} + +``` +ProjectionExec: expr=[chunk_index@4 as chunk_index, title@5 as title, identifier@1 as identifier, _distance@3 as _distance], metrics=[output_rows=100, elapsed_compute=1.424µs] + RemoteTake: columns="vector, identifier, _rowid, _distance, chunk_index, title", metrics=[output_rows=100, elapsed_compute=175.53097ms, output_batches=1, remote_takes=100] + CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=100, elapsed_compute=2.748µs] + GlobalLimitExec: skip=0, fetch=100, metrics=[output_rows=100, elapsed_compute=1.819µs] + FilterExec: _distance@3 IS NOT NULL, metrics=[output_rows=100, elapsed_compute=10.275µs] + SortExec: TopK(fetch=100), expr=[_distance@3 ASC NULLS LAST], preserve_partitioning=[false], metrics=[output_rows=100, elapsed_compute=39.259451ms, row_replacements=546] + KNNVectorDistance: metric=l2, metrics=[output_rows=1099508, elapsed_compute=56.783526ms, output_batches=1076] + FilterExec: identifier@1 > 0 AND identifier@1 < 1000000, metrics=[output_rows=1099508, elapsed_compute=17.136819ms] + LanceScan: uri=***, projection=[vector, identifier], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1200000, elapsed_compute=21.348178ms, bytes_read=1852931072, iops=78, requests=78] +``` + +#### 1\. Data Loading (LanceScan) {#1-data-loading-lancescan} + +- Scanned 1,200,000 rows from the LanceDB table +- Read 1.86GB of data in 78 I/O operations +- Only loaded necessary columns (`vector` and `identifier`) +- Unordered scan for parallel processing + +#### 2\. Filtering and Search {#2-filtering-and-search} + +- Applied prefilter condition (`identifier > 0 AND identifier < 1000000`) +- Reduced dataset from 1.2M to 1,099,508 rows +- KNN search used L2 (Euclidean) distance metric +- Vector comparisons processed in 1076 batches + +#### 3\. Results Processing {#3-results-processing} + +- KNN results sorted by distance (TopK with fetch=100) +- Null distances filtered out +- Batches coalesced to target size of 1024 rows +- Additional columns fetched for final results +- Remote take operation for 100 results +- Final projection of required columns + +### Distributed metrics on remote tables {#distributed-metrics-on-remote-tables} + +Enterprise + +When you call `analyze_plan` against a LanceDB Enterprise table, the query runs across a pool of workers. By default the returned plan aggregates each operator's metrics into a single value, matching the local single-node output. Pass a `distributed_metrics` mode when you need to see how work was split across workers: + +| Mode | What it shows | When to use it | +| :------------ | :---------------------------------------------------------------------------------- | :------------------------------------------------------------------------------------------------------ | +| `aggregate` | One summary value per operator (default). Same shape as local `analyze_plan`. | Everyday performance checks and comparisons against a local plan. | +| `per_worker` | Metrics reported separately for each worker that participated in the query. | Diagnosing stragglers or uneven work distribution across the cluster. | +| `full` | Both the aggregate summary and the per-worker breakdown. | Deep investigations where you need the totals *and* the per-worker detail in a single output. | + +The parameter only affects remote plans — local queries always return the aggregate output regardless of what you pass. + + +```python Python icon="python" +# Show a per-worker breakdown for a remote query +plan = ( + table.search(query_embed) + .where("identifier > 0 AND identifier < 1000000") + .limit(100) + .analyze_plan(distributed_metrics="per_worker") +) +print(plan) +``` + +```typescript TypeScript icon="square-js" +// Show a per-worker breakdown for a remote query +const plan = await table + .search(queryEmbed) + .where("identifier > 0 AND identifier < 1000000") + .limit(100) + .analyzePlan("per_worker"); +``` + + + +Use `per_worker` or `full` sparingly — the plan output grows with the size of your worker pool. Stick with the default `aggregate` mode for routine tuning. + + +### Key Observations {#key-observations} + +- Vector search is the primary bottleneck (1,099,508 vector comparisons) +- Significant I/O overhead (1.86GB data read) +- Full table scan due to lack of indices +- Substantial optimization potential through proper index implementation + +## Optimized Query Execution {#optimized-query-execution} + +After creating vector and scalar indices, the execution plan shows: + +``` +ProjectionExec: expr=[chunk_index@3 as chunk_index, title@4 as title, identifier@2 as identifier, _distance@0 as _distance] + RemoteTake: columns="_distance, _rowid, identifier, chunk_index, title" + CoalesceBatchesExec: target_batch_size=1024 + GlobalLimitExec: skip=0, fetch=100 + SortExec: TopK(fetch=100), expr=[_distance@0 ASC NULLS LAST], preserve_partitioning=[false] + ANNSubIndex: name=vector_idx, k=100, deltas=1 + ANNIvfPartition: uuid=83916fd5-fc45-4977-bad9-1f0737539bb9, nprobes=20, deltas=1 + ScalarIndexQuery: query=AND(identifier > 0,identifier < 1000000) +``` + +### Optimized Plan Analysis {#optimized-plan-analysis} + +#### 1\. Scalar Index Query {#1-scalar-index-query} + +``` +ScalarIndexQuery: query=AND(identifier > 0,identifier < 1000000) +metrics=[ + output_rows=2 + index_comparisons=2,301,824 + indices_loaded=2 + output_batches=1 + parts_loaded=562 + elapsed_compute=86.979354ms +] +``` + +- Range filter using scalar index +- Only 2 index files and 562 scalar index parts loaded +- 2.3M index comparisons for matches + +#### 2\. Vector Search {#2-vector-search} + +``` +ANNSubIndex: name=vector_idx, k=100, deltas=1 +metrics=[ + output_rows=2,000 + index_comparisons=25,893 + indices_loaded=0 + output_batches=20 + parts_loaded=20 + elapsed_compute=111.849043ms +] +``` + +- IVF index with 20 probes +- Only 20 index parts loaded +- 25,893 vector comparisons +- 2,000 matching vectors + +#### 3\. Results Processing {#3-results-processing-2} + +``` +SortExec: TopK(fetch=100), expr=[_distance@0 ASC NULLS LAST], preserve_partitioning=[false] +GlobalLimitExec: skip=0, fetch=100 +CoalesceBatchesExec: target_batch_size=1024 +``` + +- Sorts by distance +- Limits to top 100 results +- Batches into groups of 1024 + +#### 4\. Data Fetching {#4-data-fetching} + +``` +RemoteTake: columns="_distance, _rowid, identifier, chunk_index, title" +metrics=[output_rows=100, elapsed_compute=113.491859ms, output_batches=1, remote_takes=100] +``` + +- Single output batch +- One remote take per row + +#### 5\. Final Projection {#5-final-projection} + +``` +ProjectionExec: expr=[chunk_index@3 as chunk_index, title@4 as title, identifier@2 as identifier, _distance@0 as _distance] +``` + +- Returns specified columns: chunk_index, title, identifier, and distance + +### Performance Improvements {#performance-improvements} + +#### 1\. Initial Data Access {#1-initial-data-access} + +``` +ScalarIndexQuery metrics: +- indices_loaded=2 +- parts_loaded=562 +- output_batches=1 +``` + +- Before: Full table scan of 1.2M rows, 1.86GB data +- After: Only 2 indices and 562 scalar index parts loaded +- Benefit: Eliminated table scans for prefilter + +#### 2\. Vector Search Efficiency {#2-vector-search-efficiency} + +``` +ANNSubIndex: +- index_comparisons=25,893 +- indices_loaded=0 +- parts_loaded=20 +- output_batches=20 +``` + +- Before: L2 calculations on 1,099,508 vectors +- After: + - 99.8% reduction in vector comparisons + - Decreased output batches from 1,076 to 20 + +#### 3\. Data Retrieval Optimization {#3-data-retrieval-optimization} + +``` +RemoteTake: +- remote_takes=100 +- output_batches=1 +``` + +- RemoteTake operation remains consistent + +## Performance Optimization Guide {#performance-optimization-guide} + +### 1\. Index Implementation {#1-index-implementation} + +#### When to Create Indices {#when-to-create-indices} + +- Columns used in WHERE clauses +- Vector columns for similarity searches +- Join columns used in `merge_insert` + +#### Index Type Selection {#index-type-selection} + +| Data Type | Recommended Index | Use Case | +| ----------- | ------------------ | ---------------------------------------- | +| Vector | IVF_PQ/IVF_HNSW_SQ/IVF_HNSW_FLAT | Approximate nearest neighbor search | +| Scalar | B-Tree | Range queries and sorting | +| Categorical | Bitmap | Multi-value filters and set operations | +| `List` | Label_list | Multi-label classification and filtering | + + +Use `table.index_stats()` to monitor index coverage. +A well-optimized table should have `num_unindexed_rows ~ 0`. + + +### 2\. Query Plan Optimization {#2-query-plan-optimization} + +#### Common Patterns and Fixes {#common-patterns-and-fixes} + +| Plan Pattern | Optimization | +| ------------------------------------------ | -------------------------------------------- | +| LanceScan with high _bytes_read_ or _iops_ | Add missing index | +| | Use `select()` to limit returned columns | +| | Check whether the dataset has been compacted | +| Multiple sequential filters | Reorder filter conditions | + +!!! note "Regular Performance Analysis" + Regularly analyze your query plans to identify and address performance bottlenecks. + The `analyze_plan` output provides detailed metrics to guide optimization efforts. + +### 3\. Getting Started with Optimization {#3-getting-started-with-optimization} + +For vector search performance: + +- Create ANN index on your vector column(s) as described in the [index guide](/indexing/vector-index/) +- If you often filter by metadata, create [scalar indices](/indexing/scalar-index/) on those columns + +## Analyzing non-vector queries {#analyzing-non-vector-queries} + +`explain_plan` and `analyze_plan` aren't vector-specific — they're available on every query builder, including FTS and hybrid. The most common reason to look at the plan for a non-vector query is to confirm whether your `where` clause pushed into the scan (good) or ran as a separate `FilterExec` step on top of the search results (often slower, and a hint that the filter column needs a scalar index). + +### FTS queries {#fts-queries} + + +```python Python icon="python" +plan = ( + table.search("puppy", query_type="fts") + .where("category = 'animals'", prefilter=True) + .limit(10) + .explain_plan(True) +) +print(plan) +``` + +```typescript TypeScript icon="square-js" +const plan = await table + .query() + .fullTextSearch("puppy") + .where("category = 'animals'") + .limit(10) + .explainPlan(true); +``` + + +In an indexed FTS plan you should see a `MatchQuery` (or other FTS execution node) reading from the inverted index, with the metadata filter pushed down. If the plan shows a `LanceScan` followed by `FilterExec` over the entire text column, the FTS index either isn't covering the column or the filter isn't using a scalar index — both worth investigating. + +### Hybrid queries {#hybrid-queries} + +For hybrid queries, `explain_plan` returns the reranker label followed by the vector and FTS sub-plans, indented for readability: + + +```python Python icon="python" +plan = ( + table.search("flower moon", query_type="hybrid") + .where("category = 'film'", prefilter=True) + .limit(10) + .explain_plan(True) +) +print(plan) +# RRFReranker(...) +# +# +``` + + +`analyze_plan` does the same, but executes both sub-queries and labels them as `Vector Search Plan:` and `FTS Search Plan:` in the output. This is the easiest way to see whether the filter pushed into both halves uniformly, and which half is dominating latency. diff --git a/docs/web/search/vector-search.mdx b/docs/web/search/vector-search.mdx new file mode 100644 index 000000000..185761fbb --- /dev/null +++ b/docs/web/search/vector-search.mdx @@ -0,0 +1,453 @@ +--- +title: "Vector Search" +sidebarTitle: Vector search +description: "Learn how to run vector search queries in LanceDB. Includes best practices, tips and examples." +icon: "arrow-up-right-dots" +--- +import { + PyConfigureDistanceMetric as ConfigureDistanceMetric, + PySelectVectorColumn as SelectVectorColumn, + PyIndexNestedColumn as IndexNestedColumn, + PyExactVsApproximateDistances as ExactVsApproximateDistances, + PyVectorSearchPrefilter as VectorSearchPrefilter, + PyVectorSearchPostfilter as VectorSearchPostfilter, + PyMultivectorSearch as MultivectorSearch, + PySearchDistanceRange as SearchDistanceRange, + PySearchBinaryVectors as SearchBinaryVectors, + PyBatchSearch as BatchSearch, + PyFastSearch as FastSearch, + PyBruteForceSearch as BruteForceSearch, + PyBypassVectorIndex as BypassVectorIndex, + TsSearch2, + TsSelectVectorColumn, + TsIndexNestedColumn, + TsExactVsApproximate, + TsVectorSearchPrefilter, + TsVectorSearchPostfilter, + TsDistanceRange, + TsBinarySearch, + TsBatchSearch, + TsFastSearch, + TsBruteForceSearch, + TsBypassVectorIndex, + RsConfigureDistanceMetric, + RsExactVsApproximate, + RsVectorSearchPrefilter, + RsVectorSearchPostfilter, + RsSearchDistanceRange, + RsFastSearch, + RsBruteForceSearch, + RsBypassVectorIndex, + RsBatchSearch, + RsBinarySearch, +} from '/snippets/search.mdx'; + +Vector search is a technique used to search for similar items based on their vector representations, called embeddings. It is also known as similarity search, nearest neighbor search, or approximate nearest neighbor search. + +![](/static/assets/images/search/vector-db-basics.png) + +Raw data (e.g. text, images, audio, etc.) is converted into embeddings via an embedding model, which are then stored in a multimodal lakehouse like LanceDB. To perform similarity search at scale, an index is created on the stored embeddings, which can then used to perform fast lookups. + +## Supported distance metrics {#supported-distance-metrics} + +Distance metrics determine how LanceDB compares vectors to find similar matches. Euclidean or `l2` is the default, and used for general-purpose similarity, `cosine` for unnormalized embeddings, `dot` for normalized embeddings (best performance), or `hamming` for binary vectors. + + +Ensure you always use the same distance metric that your embedding model was trained with. Most modern embedding models use cosine similarity, so `cosine` is often the best choice. However, if your vectors are normalized, you should use `dot` for best performance. + + +The right metric improves both search accuracy and query performance. Currently, LanceDB supports the following metrics: + +| Distance metric | Mathematical form | Notes | +|---|---|---| +| `l2` | $\|x-y\|_2=\sqrt{\sum_i (x_i-y_i)^2}$ | Measures the straight-line distance between two points in vector space. Calculated as the square root of the sum of squared differences between corresponding vector components. | +| `cosine` | $1-\frac{x\cdot y}{\|x\|_2\|y\|_2}$ | Measures directional difference between vectors. Computed as 1 minus cosine similarity (the dot product normalized by both vector magnitudes), so vector length does not affect the score. Use for unnormalized vectors. | +| `dot` | $x\cdot y=\sum_i x_i y_i$ | Calculates the sum of products of corresponding vector components. Provides raw similarity scores without normalization, sensitive to vector magnitudes. Use for normalized vectors for best performance. | +| `hamming` | $\sum_i \mathbf{1}[x_i\neq y_i]$ | Counts the number of positions where corresponding bits differ between binary vectors. Only applicable to binary vectors stored as packed uint8 arrays. | + +For indexed search, supported distance metrics vary by index type: + +| Index type | Supported distance metrics | +|---|---| +| `IVF_FLAT` | `["l2", "cosine", "dot", "hamming"]` | +| `IVF_PQ` | `["l2", "cosine", "dot"]` | +| `IVF_SQ` | `["l2", "cosine", "dot"]` | +| `IVF_RQ` | `["l2", "cosine", "dot"]` | +| `IVF_HNSW_FLAT` | `["l2", "cosine", "dot"]` | +| `IVF_HNSW_PQ` | `["l2", "cosine", "dot"]` | +| `IVF_HNSW_SQ` | `["l2", "cosine", "dot"]` | + +### Configure Distance Metric {#configure-distance-metric} + +By default, `l2` will be used as metric type. You can specify the metric type as +`cosine` or `dot` if required (`hamming` is supported for `IVF_FLAT` index only). + +**Note:** You can configure the distance metric during search only if there's no vector index. If a vector index exists, the distance metric will always be the one you specified when creating the index. + + + +{ConfigureDistanceMetric} + + + +{TsSearch2} + + + +{RsConfigureDistanceMetric} + + + +Here you can see the same search but using `cosine` similarity instead of `l2` distance. The result focuses on vector direction rather than absolute distance, which works better for normalized embeddings. + +Set `.limit(...)` on vector searches you run in applications. You can page through results with `.offset(...)` and include LanceDB's internal row id with `.with_row_id()` / `.withRowId()` when you need a stable handle for follow-up operations. + +## Selecting the vector column {#selecting-the-vector-column} + +If your table has exactly one vector column, you can omit the column name and LanceDB will pick it for you. This works for both top-level columns (such as `vector`) and vector fields nested inside a struct (such as `image.embedding`). + +When LanceDB can't infer a single column, it raises a `ValueError` (Python) or rejects the query (Node/Rust). Two cases trigger this: + +- No vector column: the schema has no `fixed_size_list` or `list` of floats. +- Multiple candidates: more than one column matches the query's dimension. The error lists every candidate path so you can pick one explicitly. + +To disambiguate, pass the field path with dot notation. Wrap any segment that contains characters outside `[A-Za-z0-9_]` in backticks (for example, `` `image-meta`.`embedding.v1` ``). + + + +{SelectVectorColumn} + + + +{TsSelectVectorColumn} + + + +The same field-path syntax works when creating an index on a nested vector column: + + + +{IndexNestedColumn} + + + +{TsIndexNestedColumn} + + + +When several columns share a name across structs (for example, `image.embedding` and `text.embedding`), LanceDB still picks the one whose dimension matches your query vector. If two candidates have the same dimension, you must pass the column name explicitly. + +## Vector Search With ANN Index {#vector-search-with-ann-index} + +Instead of performing an exhaustive search on the entire database for each and every query, approximate nearest neighbour (ANN) algorithms use an index to narrow down the search space, which significantly reduces query latency. + +The trade-off is that the results are not guaranteed to be the true nearest neighbors of the query, but are usually "good enough" for most use cases. + +Use ANN search for large-scale applications where speed matters more than perfect recall. LanceDB uses approximate nearest neighbor algorithms to deliver fast results without examining every vector in your dataset. + + +When a vector index is used, `_distance` is not always the true distance between full vectors. On quantized ANN indexes, LanceDB may compute `_distance` from the compressed representation for speed. Use `refine_factor` when you want reranking on full vectors. + + +### Exact vs Approximate Distances {#exact-vs-approximate-distances} + +When doing vector search, the meaning of "distance" depends on whether you are using an index and whether `refine_factor` is specified as part of your query. +`nprobes` controls how many partitions are searched to find candidates, `approx_mode` controls the query-time speed/recall trade-off for RQ-quantized indexes, and `refine_factor` controls how many candidates are rescored on full vectors for better distance fidelity and reranking quality. + +The table below summarizes the behavior of `_distance` in search results based on your query configuration: + +| Query mode | Neighbor quality | `_distance` in results | +| :--- | :--- | :--- | +| No index or `.bypass_vector_index()` | Exact kNN (100% recall) | True distance on full vectors | +| Indexed ANN, no `refine_factor` | Approximate neighbors | Distance on the index representation: exact for flat indexes, approximate for quantized indexes | +| Indexed ANN + `refine_factor(1)` | Approximate neighbors (same candidate set) | Distances recomputed on full vectors for reranked candidates | +| Indexed ANN + `refine_factor(>1)` | Better recall than no refine (usually) | Distances recomputed on full vectors for reranked candidates | + + + +{ExactVsApproximateDistances} + + + +{TsExactVsApproximate} + + + +{RsExactVsApproximate} + + + +For deeper tuning guidance on indexing and performance estimation, see the [vector indexes](/indexing/vector-index/#search-configuration) page, +For tuning `nprobes`, see below. + +### Tuning approximate mode {#tuning-approximate-mode} + +Use `approx_mode` when you want to adjust the speed/recall trade-off for approximate vector search at query time. This setting currently applies only to RQ-quantized indexes, such as `IVF_RQ`; other index types ignore it. + +The supported values are: + +| Value | Behavior | +| :--- | :--- | +| `fast` | Prefer lower query latency, which can reduce recall. | +| `normal` | Use the default balance between query latency and recall. This only has an effect for RQ indexes built with `num_bits > 1`. | +| `accurate` | Prefer higher recall, which can increase query latency. | + +You can change `approx_mode` per query without rebuilding the index. For RQ indexes built with `num_bits=1`, `normal` uses the same one-bit scoring path as `fast`. If you also set `refine_factor`, LanceDB first uses `approx_mode` while finding candidates, then reranks the selected candidates on full vectors. + +### Tuning `nprobes` {#tuning-nprobes} + +- `nprobes` controls how many partitions are searched at query time. +- `nprobes` improves candidate recall, but does not by itself make `_distance` exact. +- By default, LanceDB automatically tunes `nprobes` to achieve the best performance without noticeably sacrificing accuracy. +- In most cases, leave `nprobes` unset and use the auto-tuned value. +- Only tune `nprobes` manually when recall is below your target, or when you need even higher performance for your workload. +- If recall is too low, increase `nprobes` gradually, but after a certain threshold, increasing `nprobes` yields only marginal accuracy gains. +- If you need higher performance and have recall headroom, decrease `nprobes` gradually. + +For filtered ANN searches, you can also set `minimum_nprobes` and `maximum_nprobes`. LanceDB starts +with the minimum and can scan more partitions up to the maximum if the filter leaves too few +candidates. Calling `nprobes(n)` fixes both values to `n`, which disables that adaptive behavior. + +### Vector Search with Prefiltering {#vector-search-with-prefiltering} + +This is the default vector search setting. You can use prefiltering to boost query performance by reducing the search space before vector calculations begin. The system first applies your filter criteria to the dataset, then conducts vector search operations only on the remaining relevant subset. + + + +{VectorSearchPrefilter} + + + +{TsVectorSearchPrefilter} + + + +{RsVectorSearchPrefilter} + + + +This filters out rows where label ≤ 2 before doing vector search, then picks specific columns from the top 5 matches. + +The `.where("label > 2")` applies a filter before vector search, `.select(["text", "keywords", "label"])` chooses specific columns to return, and `.limit(5)` restricts results to the top `5` most similar vectors. + +As a result, you'll see a result with just the data you want from the most similar vectors. + +### Vector Search with Postfiltering {#vector-search-with-postfiltering} + +Use postfiltering to prioritize vector similarity by searching the full dataset first, then applying metadata filters to the top results. This approach ensures you get the most similar vectors before filtering, which can be crucial when similarity is more important than metadata constraints. + + + +{VectorSearchPostfilter} + + + +{TsVectorSearchPostfilter} + + + +{RsVectorSearchPostfilter} + + + +Here you can see how to do vector search first to get the most similar vectors, then filter by label > 1 on those results. + +The `prefilter=False` parameter tells LanceDB to apply the filter after vector search instead of before, `.where("label > 1")` filters the top results by metadata, and `.select()` chooses which columns to include. + +In the end, you receive a query result with the best matches that also meet your metadata requirements. + + +[Post-filtering](/search/filtering/#post-filtering-with-vector-search) in LanceDB applies +the filter condition after obtaining the nearest neighbors based on vector similarity. + + +## Multivector Search {#multivector-search} + +Use multivector search when your documents contain multiple embeddings and you need sophisticated matching between query and document vector pairs. The late interaction approach finds the most relevant combinations across all available embeddings and provides nuanced similarity scoring. + +Only `cosine` similarity is supported as the distance metric for multivector search operations. +Every query vector must match the inner dimension of the multivector column; LanceDB rejects mismatched query dimensions rather than guessing how to reshape them. + + + +{MultivectorSearch} + + + +Here you can see how to take 2 query vectors and find the best matching pairs between them and document vectors using late interaction. The `np.random.random(size=(2, 256))` creates a 2×256 array with two random query vectors, `.limit(5)` returns the top 5 best document-query combinations, and `.to_pandas()` provides results in a DataFrame format. + +**Read more:** [Multivector search](/search/multivector-search/) + +## Advanced Search Scenarios {#advanced-search-scenarios} + +### Search With Distance Range {#search-with-distance-range} + +Use `distance_range` search when you need vectors within particular similarity bounds rather than just the closest neighbors. The system filters results to only include vectors that fall within your specified distance thresholds from the query. + + + +{SearchDistanceRange} + + + +{TsDistanceRange} + + + +{RsSearchDistanceRange} + + + +This shows three ways to search within distance ranges: bounded, upper bound only, and lower bound only. + +The `distance_range()` method filters results by similarity thresholds - the first example finds vectors with distance between `0.1` and `0.5`, the second finds vectors closer than `0.5`, and the third finds vectors farther than `0.1`. + + Each approach returns Arrow tables with vectors that fall within your specified distance thresholds. + +### Search With Binary Vectors {#search-with-binary-vectors} + +Use binary vector search for scenarios involving binary embeddings, such as those produced by hashing algorithms. The system stores these efficiently as packed uint8 arrays and uses Hamming distance calculations to determine vector similarity. + + +The number of dimensions of the binary vector must be a multiple of 8. A vector of dimensionality 128 will be stored as a `uint8` array of size 16. + + + + +{SearchBinaryVectors} + + + +{TsBinarySearch} + + + +{RsBinarySearch} + + + +Here you can see how to set up a table for binary vectors, pack them efficiently into bytes, and search using Hamming distance. + +The schema defines a 32-byte vector field (256 bits ÷ 8), `np.random.randint(0, 2, size=256)` creates binary vectors, `np.packbits()` compresses them to bytes, and `.distance_type("hamming")` specifies `hamming` distance for similarity calculation. + +The search produces an Arrow table with binary vectors ranked by how many bits differ from the query. + +## Scaling Vector Search {#scaling-vector-search} + +### Batch Search {#batch-search} + +Use batch search to handle multiple query vectors simultaneously. This gives you significant efficiency gains over individual queries. LanceDB processes all vectors in parallel and organizes results with a `query_index` field that maps each result set back to its originating query. + + + +{BatchSearch} + + + +{TsBatchSearch} + + + +{RsBatchSearch} + + + +This takes 5 query embeddings and finds the top 5 matches for each one in a single batch operation. + +The `load_dataset()` loads embeddings from a Hugging Face dataset, `query_embeds` contains `5` query vectors, and `.search(query_embeds)` processes all queries simultaneously. + +The final query result contains all results, including a `query_index` to tell you which query each result came from. + + +When processing batch queries, the results include a `query_index` field +to explicitly associate each result set with its corresponding query in +the input batch. + + +### Search With Asynchronous Indexing {#search-with-asynchronous-indexing} + +To optimize for speed over completeness, enable the `fast_search` flag in your query to skip searching unindexed data. + +While vector indexing occurs asynchronously, newly added vectors are immediately +searchable through a fallback brute-force search mechanism. This ensures zero +latency between data insertion and searchability, though it may temporarily +increase query response times. + + + +{FastSearch} + + + +{TsFastSearch} + + + +{RsFastSearch} + + + +Here you can see how to turn on fast search mode to skip unindexed vectors and only look through indexed data for speed. + +The `fast_search=True` parameter tells LanceDB to only search indexed vectors, skipping any recently added data that hasn't been indexed yet. + +You'll obtain a query result with the top `5` matches from indexed vectors, but might miss data that was just added. + +## Brute Force Search {#brute-force-search} + +### Search With No Index {#search-with-no-index} + +The simplest way to perform vector search is to perform a brute force search, without an index, where the distance between the query vector and all the vectors in the database are computed, with the top-k closest vectors returned. + +This is equivalent to a k-nearest neighbours (kNN) search in vector space. + +Choose brute force search when you need guaranteed 100% recall, typically with smaller datasets where query speed isn't the primary concern. The system scans every vector in the table and calculates precise distances to find the exact nearest neighbors. + + + +{BruteForceSearch} + + + +{TsBruteForceSearch} + + + +{RsBruteForceSearch} + + + +This carries out a brute force search through every vector in the table to find the 3 closest matches to a random 1536-dimensional query. You'll get back a list of the most similar vectors with exact distances. + +![](/static/assets/images/search/knn_search.png) + +As you can imagine, the brute force approach is not scalable for datasets larger than a few hundred thousand vectors, as the latency of the search grows linearly with the size of the dataset. This is where approximate nearest neighbour (ANN) algorithms come in. + +### Bypass the Vector Index {#bypass-the-vector-index} + +Use `bypass_vector_index` to get exact, ground-truth results by performing exhaustive searches across all vectors. Instead of relying on approximate methods, the system directly compares your query against every vector in the table, ensuring 100% recall at the cost of increased query time. + + + +{BypassVectorIndex} + + + +{TsBypassVectorIndex} + + + +{RsBypassVectorIndex} + + + +This skips the approximate index and checks every single vector for exact, ground-truth results. + +The `.bypass_vector_index()` method forces LanceDB to perform an exhaustive search through all vectors instead of using the approximate nearest neighbor index, ensuring exact results but at the cost of slower performance. + +The output is a query result with the top 5 exact matches, guaranteeing 100% recall but taking longer to run. + +This approach is particularly useful when: +- Evaluating ANN index quality +- Calculating recall metrics to tune index parameters +- Ensuring exact results for critical applications diff --git a/docs/web/snippets/ann_indexes.mdx b/docs/web/snippets/ann_indexes.mdx new file mode 100644 index 000000000..ef4021e16 --- /dev/null +++ b/docs/web/snippets/ann_indexes.mdx @@ -0,0 +1,12 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const TsImport = "import * as lancedb from \"@lancedb/lancedb\";\nimport type { VectorQuery } from \"@lancedb/lancedb\";\n"; + +export const TsIngest = "const db = await lancedb.connect(databaseDir);\n\nconst data = Array.from({ length: 5_000 }, (_, i) => ({\n vector: Array(128).fill(i),\n id: `${i}`,\n content: \"\",\n longId: `${i}`,\n}));\n\nconst table = await db.createTable(\"my_vectors\", data, {\n mode: \"overwrite\",\n});\nawait table.createIndex(\"vector\", {\n config: lancedb.Index.ivfPq({\n numPartitions: 10,\n numSubVectors: 16,\n }),\n});\n"; + +export const TsSearch1 = "const search = table.search(Array(128).fill(1.2)).limit(2) as VectorQuery;\nconst results1 = await search.nprobes(20).refineFactor(10).toArray();\n"; + +export const TsSearch2 = "const results2 = await table\n .search(Array(128).fill(1.2))\n .where(\"id != '1141'\")\n .limit(2)\n .toArray();\n"; + +export const TsSearch3 = "const results3 = await table\n .search(Array(128).fill(1.2))\n .select([\"id\"])\n .limit(2)\n .toArray();\n"; + diff --git a/docs/web/snippets/basic_usage.mdx b/docs/web/snippets/basic_usage.mdx new file mode 100644 index 000000000..d377efa9c --- /dev/null +++ b/docs/web/snippets/basic_usage.mdx @@ -0,0 +1,100 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyBasicAddColumns = "table.add_columns(\n {\n \"power\": \"cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)\"\n }\n)\n"; + +export const PyBasicAddData = "magical_characters = [\n {\n \"id\": 9,\n \"name\": \"Morgan le Fay\",\n \"role\": \"Sorceress\",\n \"description\": \"A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.\",\n \"vector\": [0.10, 0.84, 0.25, 0.70],\n \"stats\": { \"strength\": 2, \"courage\": 3, \"magic\": 5, \"wisdom\": 4 }\n },\n {\n \"id\": 10,\n \"name\": \"The Lady of the Lake\",\n \"role\": \"Mystical Guardian\",\n \"description\": \"A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.\",\n \"vector\": [0.00, 0.90, 0.58, 0.88],\n \"stats\": { \"strength\": 2, \"courage\": 3, \"magic\": 5, \"wisdom\": 5 }\n }\n]\ntable.add(magical_characters)\n"; + +export const PyBasicAsyncApi = "import lancedb\n\nasync_db = await lancedb.connect_async(uri)\nasync_table = await async_db.create_table(\n \"camelot_async\",\n data=data,\n mode=\"overwrite\",\n)\n\nquery_vector = [0.03, 0.85, 0.61, 0.90]\nasync_results = await (\n await async_table.search(query_vector)\n).limit(5).select([\"name\", \"role\", \"description\"]).to_polars()\nprint(async_results)\n"; + +export const PyBasicCreateEmptyTable = "schema = pa.schema(\n [\n pa.field(\"id\", pa.uint16()),\n pa.field(\"name\", pa.string()),\n pa.field(\"role\", pa.string()),\n pa.field(\"description\", pa.string()),\n pa.field(\"vector\", pa.list_(pa.float32(), 4)),\n pa.field(\n \"stats\",\n pa.struct(\n [\n pa.field(\"strength\", pa.int8()),\n pa.field(\"courage\", pa.int8()),\n pa.field(\"magic\", pa.int8()),\n pa.field(\"wisdom\", pa.int8()),\n ]\n ),\n ),\n ]\n)\ndb.create_table(\"camelot_pa\", schema=schema, mode=\"overwrite\")\n"; + +export const PyBasicCreateTable = "table = db.create_table(\"camelot\", data=data, mode=\"overwrite\")\n"; + +export const PyBasicCreateTablePandas = "pandas_df = pd.DataFrame(data)\ntable_pd = db.create_table(\"camelot_pd\", data=pandas_df, mode=\"overwrite\")\n"; + +export const PyBasicCreateTablePolars = "polars_df = pl.DataFrame(data)\ntable_pl = db.create_table(\"camelot_pl\", data=polars_df, mode=\"overwrite\")\n"; + +export const PyBasicDeleteRows = "table.delete('role = \"Traitor Knight\"')\n"; + +export const PyBasicDropColumns = "table.drop_columns([\"power\"])\n"; + +export const PyBasicDropTable = "db.drop_table(\"camelot\")\n"; + +export const PyBasicImports = "import json\n\nimport lancedb\nimport pandas as pd\nimport polars as pl\nimport pyarrow as pa\n"; + +export const PyBasicOpenTable = "table = db.open_table(\"camelot\")\n"; + +export const PyBasicSortPolars = "# Sort Polars DataFrame by power in descending order\nprint(r4.sort(\"power\", descending=True).limit(5))\n"; + +export const PyBasicVectorSearch = "query_vector = [0.03, 0.85, 0.61, 0.90]\nresult = table.search(query_vector).limit(5).to_polars()\nprint(result)\n"; + +export const PyBasicVectorSearchQ1 = "# Who are the characters similar to \"wizard\"?\nquery_vector_1 = [0.03, 0.85, 0.61, 0.90]\nr1 = (\n table.search(query_vector_1)\n .limit(5)\n .select([\"name\", \"role\", \"description\"])\n .to_polars()\n)\nprint(r1)\n"; + +export const PyBasicVectorSearchQ2 = "# Who are the characters with high magic stats?\nquery_vector_2 = [0.03, 0.85, 0.61, 0.90]\nr2 = (\n table.search(query_vector_2)\n .where(\"stats.magic > 3\")\n .select([\"name\", \"role\", \"description\"])\n .limit(5)\n .to_polars()\n)\nprint(r2)\n"; + +export const PyBasicVectorSearchQ3 = "# Who are the strongest characters?\nr3 = (\n table.search()\n .where(\"stats.strength > 3\")\n .select([\"name\", \"role\", \"description\"])\n .limit(5)\n .to_polars()\n)\nprint(r3)\n"; + +export const PyBasicVectorSearchQ4 = "# Who are the strongest characters?\nr4 = (\n table.search()\n .select([\"name\", \"role\", \"description\", \"power\"])\n .to_polars()\n)\nprint(r4)\n"; + +export const PyDataLoad = "with open(data_path, \"r\") as f:\n data = json.load(f)\n"; + +export const TsBasicAddColumns = "await table.addColumns([\n {\n name: \"power\",\n valueSql:\n \"cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)\",\n },\n]);\n"; + +export const TsBasicAddData = "const magicalCharacters = [\n {\n id: 9,\n name: \"Morgan le Fay\",\n role: \"Sorceress\",\n description:\n \"A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.\",\n vector: [0.1, 0.84, 0.25, 0.7],\n stats: { strength: 2, courage: 3, magic: 5, wisdom: 4 },\n },\n {\n id: 10,\n name: \"The Lady of the Lake\",\n role: \"Mystical Guardian\",\n description:\n \"A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.\",\n vector: [0.0, 0.9, 0.58, 0.88],\n stats: { strength: 2, courage: 3, magic: 5, wisdom: 5 },\n },\n];\nawait table.add(magicalCharacters);\n"; + +export const TsBasicCreateEmptyTable = "const schema = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Int16()),\n new arrow.Field(\"name\", new arrow.Utf8()),\n new arrow.Field(\"role\", new arrow.Utf8()),\n new arrow.Field(\"description\", new arrow.Utf8()),\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 4,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n new arrow.Field(\n \"stats\",\n new arrow.Struct([\n new arrow.Field(\"strength\", new arrow.Int8()),\n new arrow.Field(\"courage\", new arrow.Int8()),\n new arrow.Field(\"magic\", new arrow.Int8()),\n new arrow.Field(\"wisdom\", new arrow.Int8()),\n ]),\n ),\n]);\nawait db.createEmptyTable(\"camelot_empty\", schema, { mode: \"overwrite\" });\n"; + +export const TsBasicCreateTable = "let table = await db.createTable(\"camelot\", data, {\n mode: \"overwrite\",\n});\n"; + +export const TsBasicDeleteRows = "await table.delete('role = \"Traitor Knight\"');\n"; + +export const TsBasicDropColumns = "await table.dropColumns([\"power\"]);\n"; + +export const TsBasicDropTable = "await db.dropTable(\"camelot\");\n"; + +export const TsBasicImports = "import * as lancedb from \"@lancedb/lancedb\";\nimport * as arrow from \"apache-arrow\";\n"; + +export const TsBasicOpenTable = "table = await db.openTable(\"camelot\");\n"; + +export const TsBasicVectorSearch = "const queryVector = [0.03, 0.85, 0.61, 0.9];\nconst result = await table.search(queryVector).limit(5).toArray();\nconsole.log(result);\n"; + +export const TsBasicVectorSearchQ1 = "// Who are the characters similar to \"wizard\"?\nconst queryVector1 = [0.03, 0.85, 0.61, 0.9];\nconst r1 = await table\n .search(queryVector1)\n .limit(5)\n .select([\"name\", \"role\", \"description\"])\n .toArray();\nconsole.log(r1);\n"; + +export const TsBasicVectorSearchQ2 = "// Who are the characters similar to \"wizard\" with high magic stats?\nconst queryVector2 = [0.03, 0.85, 0.61, 0.9];\nconst r2 = await table\n .search(queryVector2)\n .where(\"stats.magic > 3\")\n .select([\"name\", \"role\", \"description\"])\n .limit(5)\n .toArray();\nconsole.log(r2);\n"; + +export const TsBasicVectorSearchQ3 = "// Who are the strongest characters?\nconst r3 = await table\n .query()\n .where(\"stats.strength > 3\")\n .select([\"name\", \"role\", \"description\"])\n .limit(5)\n .toArray();\nconsole.log(r3);\n"; + +export const TsBasicVectorSearchQ4 = "// Who are the strongest characters?\nconst r4 = await table\n .query()\n .select([\"name\", \"role\", \"description\", \"power\"])\n .toArray();\nconsole.log(r4);\n"; + +export const TsDataLoad = "const data = JSON.parse(fs.readFileSync(dataPath, \"utf-8\"));\n"; + +export const RsBasicAddColumns = "table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"power\".to_string(),\n \"cast(((stats.strength + stats.courage + stats.magic + stats.wisdom) / 4.0) as float)\"\n .to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\n"; + +export const RsBasicAddData = "let magical_characters = vec![\n Character {\n id: 9,\n name: \"Morgan le Fay\".to_string(),\n role: \"Sorceress\".to_string(),\n description: \"A powerful enchantress, Arthur's half-sister, and a complex figure who oscillates between aiding and opposing Camelot.\".to_string(),\n vector: [0.10, 0.84, 0.25, 0.70],\n stats: Stats {\n strength: 2,\n courage: 3,\n magic: 5,\n wisdom: 4,\n },\n },\n Character {\n id: 10,\n name: \"The Lady of the Lake\".to_string(),\n role: \"Mystical Guardian\".to_string(),\n description: \"A mysterious supernatural figure associated with Avalon, known for giving Arthur the sword Excalibur.\".to_string(),\n vector: [0.00, 0.90, 0.58, 0.88],\n stats: Stats {\n strength: 2,\n courage: 3,\n magic: 5,\n wisdom: 5,\n },\n },\n];\ntable\n .add(characters_to_reader(camelot_schema(), &magical_characters))\n .execute()\n .await\n .unwrap();\n"; + +export const RsBasicCreateEmptyTable = "let schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int16, false),\n Field::new(\"name\", DataType::Utf8, false),\n Field::new(\"role\", DataType::Utf8, false),\n Field::new(\"description\", DataType::Utf8, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n false,\n ),\n Field::new(\n \"stats\",\n DataType::Struct(arrow_schema::Fields::from(vec![\n Arc::new(Field::new(\"strength\", DataType::Int8, false)),\n Arc::new(Field::new(\"courage\", DataType::Int8, false)),\n Arc::new(Field::new(\"magic\", DataType::Int8, false)),\n Arc::new(Field::new(\"wisdom\", DataType::Int8, false)),\n ])),\n false,\n ),\n]));\ndb.create_empty_table(\"camelot_empty\", schema)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsBasicCreateTable = "let mut table = db\n .create_table(\"camelot\", characters_to_reader(schema.clone(), &data))\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsBasicDeleteRows = "table.delete(\"role = 'Traitor Knight'\").await.unwrap();\n"; + +export const RsBasicDropColumns = "table.drop_columns(&[\"power\"]).await.unwrap();\n"; + +export const RsBasicDropTable = "db.drop_table(\"camelot\", &[]).await.unwrap();\n"; + +export const RsBasicImports = "use arrow_array::types::Float32Type;\nuse arrow_array::{\n FixedSizeListArray, Int8Array, Int16Array, RecordBatch, RecordBatchIterator, StringArray,\n StructArray,\n};\nuse arrow_schema::{DataType, Field, FieldRef, Schema};\nuse futures_util::TryStreamExt;\nuse lancedb::database::CreateTableMode;\nuse lancedb::query::{ExecutableQuery, QueryBase, Select};\nuse lancedb::{connect, table::NewColumnTransform};\n"; + +export const RsBasicOpenTable = "table = db.open_table(\"camelot\").execute().await.unwrap();\n"; + +export const RsBasicVectorSearch = "let query_vector = [0.03, 0.85, 0.61, 0.90];\nlet result = table\n .query()\n .nearest_to(&query_vector)\n .unwrap()\n .limit(5)\n .execute()\n .await\n .unwrap()\n .try_collect::>()\n .await\n .unwrap();\nprintln!(\"{result:?}\");\n"; + +export const RsBasicVectorSearchQ1 = "// Who are the characters similar to \"wizard\"?\nlet query_vector_1 = [0.03, 0.85, 0.61, 0.90];\nlet r1 = table\n .query()\n .nearest_to(&query_vector_1)\n .unwrap()\n .limit(5)\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n ]))\n .execute()\n .await\n .unwrap()\n .try_collect::>()\n .await\n .unwrap();\nprintln!(\"{r1:?}\");\n"; + +export const RsBasicVectorSearchQ2 = "// Who are the characters similar to \"wizard\" with high magic stats?\nlet query_vector_2 = [0.03, 0.85, 0.61, 0.90];\nlet r2 = table\n .query()\n .nearest_to(&query_vector_2)\n .unwrap()\n .only_if(\"stats.magic > 3\")\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n ]))\n .limit(5)\n .execute()\n .await\n .unwrap()\n .try_collect::>()\n .await\n .unwrap();\nprintln!(\"{r2:?}\");\n"; + +export const RsBasicVectorSearchQ3 = "// Who are the strongest characters?\nlet r3 = table\n .query()\n .only_if(\"stats.strength > 3\")\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n ]))\n .limit(5)\n .execute()\n .await\n .unwrap()\n .try_collect::>()\n .await\n .unwrap();\nprintln!(\"{r3:?}\");\n"; + +export const RsBasicVectorSearchQ4 = "// Who are the strongest characters?\nlet r4 = table\n .query()\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n \"power\".to_string(),\n ]))\n .execute()\n .await\n .unwrap()\n .try_collect::>()\n .await\n .unwrap();\nprintln!(\"{r4:?}\");\n"; + +export const RsDataLoad = "let data: Vec =\n serde_json::from_str(&fs::read_to_string(camelot_json_path()).unwrap()).unwrap();\n"; + diff --git a/docs/web/snippets/build_with_ai_agents.mdx b/docs/web/snippets/build_with_ai_agents.mdx new file mode 100644 index 000000000..1a82d6fb6 --- /dev/null +++ b/docs/web/snippets/build_with_ai_agents.mdx @@ -0,0 +1,7 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyCamelotBatches = "import json\nfrom collections.abc import Iterator\nfrom pathlib import Path\n\n\ndef validated_batches(\n input_path: Path, batch_size: int\n) -> Iterator[list[dict]]:\n raw_records = json.loads(input_path.read_text())\n asset_root = input_path.parent.parent\n batch: list[dict] = []\n\n for raw in raw_records:\n payload = dict(raw)\n image_path = asset_root / payload.pop(\"img\")\n payload[\"image_filename\"] = image_path.name\n payload[\"image\"] = image_path.read_bytes()\n\n character = Character.model_validate(payload)\n batch.append(character.model_dump(mode=\"python\"))\n\n if len(batch) == batch_size:\n yield batch\n batch = []\n\n if batch:\n yield batch\n"; + +export const PyCamelotOssIngestion = "import lancedb\n\n\ndef ingest_oss(\n input_path: Path,\n uri: str = \"data/camelot.lancedb\",\n table_name: str = \"camelot_multimodal\",\n batch_size: int = 64,\n):\n db = lancedb.connect(uri)\n if table_name in db.list_tables():\n raise ValueError(\n f\"Table {table_name!r} already exists. Choose a fresh table name.\"\n )\n\n table = db.create_table(table_name, schema=Character)\n for batch in validated_batches(input_path, batch_size):\n table.add(batch)\n\n table.optimize()\n return table\n\n\nif __name__ == \"__main__\":\n ingest_oss(Path(\"data/camelot.json\"))\n"; + +export const PyCamelotSchema = "from lancedb.pydantic import LanceModel\nfrom pydantic import ConfigDict\n\n\nclass Stats(LanceModel):\n model_config = ConfigDict(strict=True, extra=\"forbid\")\n\n strength: int\n courage: int\n magic: int\n wisdom: int\n\n\nclass Character(LanceModel):\n model_config = ConfigDict(strict=True, extra=\"forbid\")\n\n id: int\n name: str\n role: str\n description: str\n stats: Stats\n image_filename: str\n image: bytes\n"; diff --git a/docs/web/snippets/connection.mdx b/docs/web/snippets/connection.mdx new file mode 100644 index 000000000..c7d8a187d --- /dev/null +++ b/docs/web/snippets/connection.mdx @@ -0,0 +1,36 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyConnect = "import lancedb\n\nuri = \"ex_lancedb\"\ndb = lancedb.connect(uri)\n"; + +export const PyConnectAsync = "import lancedb\n\nuri = \"ex_lancedb\"\nasync_db = await lancedb.connect_async(uri)\n"; + +export const PyConnectEnterpriseQuickstart = "uri = \"db://your-database-uri\"\napi_key = \"your-api-key\"\nregion = \"us-east-1\"\nhost_override = \"https://your-enterprise-endpoint.com\"\n\ndb = lancedb.connect(\n uri=uri,\n api_key=api_key,\n region=region,\n host_override=host_override,\n)\n"; + +export const PyConnectObjectStorage = "import lancedb\n\nuri = \"s3://your-bucket/path\"\n# You can also use \"gs://your-bucket/path\" or \"az://your-container/path\".\ndb = lancedb.connect(uri)\n"; + +export const PyConnectObjectStorageAsync = "import lancedb\n\nuri = \"s3://your-bucket/path\"\n# You can also use \"gs://your-bucket/path\" or \"az://your-container/path\".\nasync_db = await lancedb.connect_async(uri)\n"; + +export const PyNamespaceAdminOps = "import lancedb\n\ndb = lancedb.connect_namespace(\"dir\", {\"root\": \"./local_lancedb\"})\nnamespace = [\"prod\", \"search\"]\n\ndb.create_namespace([\"prod\"])\ndb.create_namespace([\"prod\", \"search\"])\n\nchild_namespaces = db.list_namespaces(namespace_path=[\"prod\"]).namespaces\nprint(f\"Child namespaces under {namespace}: {child_namespaces}\")\n# Child namespaces under ['prod', 'search']: ['search']\n\nmetadata = db.describe_namespace([\"prod\", \"search\"])\nprint(f\"Metadata for namespace {namespace}: {metadata}\")\n# Metadata for namespace ['prod', 'search']: properties=None\n\ndb.drop_namespace([\"prod\", \"search\"], mode=\"skip\")\ndb.drop_namespace([\"prod\"], mode=\"skip\")\n"; + +export const PyNamespaceTableOps = "import lancedb\n\ndb = lancedb.connect_namespace(\"dir\", {\"root\": \"./local_lancedb\"})\n\n# Create namespace tree: prod/search\ndb.create_namespace([\"prod\"], mode=\"exist_ok\")\ndb.create_namespace([\"prod\", \"search\"], mode=\"exist_ok\")\ndb.create_namespace([\"prod\", \"recommendations\"], mode=\"exist_ok\")\n\ndb.create_table(\n \"user\",\n data=[{\"id\": 1, \"vector\": [0.1, 0.2], \"name\": \"alice\"}],\n namespace_path=[\"prod\", \"search\"],\n mode=\"create\", # use \"overwrite\" only if you want to replace existing table\n)\n\ndb.create_table(\n \"user\",\n data=[{\"id\": 2, \"vector\": [0.3, 0.4], \"name\": \"bob\"}],\n namespace_path=[\"prod\", \"recommendations\"],\n mode=\"create\", # use \"overwrite\" only if you want to replace existing table\n)\n\n# Verify\nprint(db.list_namespaces()) # ['prod']\nprint(db.list_namespaces(namespace_path=[\"prod\"])) # ['recommendations', 'search']\nprint(db.list_tables(namespace_path=[\"prod\", \"search\"])) # ['user']\nprint(db.list_tables(namespace_path=[\"prod\", \"recommendations\"])) # ['user']\n"; + +export const TsConnect = "import * as lancedb from \"@lancedb/lancedb\";\n\nasync function connectExample(uri: string) {\n const db = await lancedb.connect(uri);\n return db;\n}\n"; + +export const TsConnectEnterpriseQuickstart = "const uri = \"db://your-database-uri\";\nconst apiKey = \"your-api-key\";\nconst region = \"us-east-1\";\nconst hostOverride = \"https://your-enterprise-endpoint.com\";\n\nconst db = await lancedb.connect(uri, {\n apiKey,\n region,\n hostOverride,\n});\n"; + +export const TsConnectObjectStorage = "async function connectObjectStorageExample() {\n const uri = \"s3://your-bucket/path\";\n // You can also use \"gs://your-bucket/path\" or \"az://your-container/path\".\n const db = await lancedb.connect(uri);\n return db;\n}\n"; + +export const TsNamespaceAdminOps = "const db = await lancedb.connectNamespace(\"dir\", { root: \"./local_lancedb\" });\nconst namespace = [\"prod\", \"search\"];\n\nawait db.createNamespace([\"prod\"]);\nawait db.createNamespace([\"prod\", \"search\"]);\n\nconst childNamespaces = (await db.listNamespaces([\"prod\"])).namespaces;\nconsole.log(`Child namespaces under ${JSON.stringify(namespace)}:`, childNamespaces);\n// Child namespaces under [\"prod\",\"search\"]: [ 'search' ]\n\nconst metadata = await db.describeNamespace([\"prod\", \"search\"]);\nconsole.log(`Metadata for namespace ${JSON.stringify(namespace)}:`, metadata);\n\nawait db.dropNamespace([\"prod\", \"search\"], { mode: \"skip\" });\nawait db.dropNamespace([\"prod\"], { mode: \"skip\" });\n"; + +export const TsNamespaceTableOps = "const db = await lancedb.connectNamespace(\"dir\", { root: \"./local_lancedb\" });\n\n// Create namespace tree: prod/search and prod/recommendations\nawait db.createNamespace([\"prod\"], { mode: \"exist_ok\" });\nawait db.createNamespace([\"prod\", \"search\"], { mode: \"exist_ok\" });\nawait db.createNamespace([\"prod\", \"recommendations\"], { mode: \"exist_ok\" });\n\nawait db.createTable(\n \"user\",\n [{ id: 1, vector: [0.1, 0.2], name: \"alice\" }],\n [\"prod\", \"search\"],\n { mode: \"create\" }, // use \"overwrite\" only if you want to replace existing table\n);\n\nawait db.createTable(\n \"user\",\n [{ id: 2, vector: [0.3, 0.4], name: \"bob\" }],\n [\"prod\", \"recommendations\"],\n { mode: \"create\" },\n);\n\n// Verify\nconsole.log((await db.listNamespaces()).namespaces); // [\"prod\"]\nconsole.log((await db.listNamespaces([\"prod\"])).namespaces); // [\"recommendations\", \"search\"]\nconsole.log(await db.tableNames([\"prod\", \"search\"])); // [\"user\"]\nconsole.log(await db.tableNames([\"prod\", \"recommendations\"])); // [\"user\"]\n"; + +export const RsConnect = "async fn connect_example(uri: &str) {\n let db = connect(uri).execute().await.unwrap();\n let _ = db;\n}\n"; + +export const RsConnectEnterpriseQuickstart = "let uri = \"db://your-database-uri\";\nlet api_key = \"your-api-key\";\nlet region = \"us-east-1\";\nlet host_override = \"https://your-enterprise-endpoint.com\";\n"; + +export const RsConnectObjectStorage = "let uri = \"s3://your-bucket/path\";\n// You can also use \"gs://your-bucket/path\" or \"az://your-container/path\".\n"; + +export const RsNamespaceAdminOps = "let mut properties = std::collections::HashMap::new();\nproperties.insert(\"root\".to_string(), \"./local_lancedb\".to_string());\nlet db = lancedb::connect_namespace(\"dir\", properties).execute().await?;\nlet namespace = vec![\"prod\".to_string(), \"search\".to_string()];\n\ndb.create_namespace(lance_namespace::models::CreateNamespaceRequest {\n id: Some(vec![\"prod\".to_string()]),\n ..Default::default()\n})\n.await?;\ndb.create_namespace(lance_namespace::models::CreateNamespaceRequest {\n id: Some(namespace.clone()),\n ..Default::default()\n})\n.await?;\n\nlet child_namespaces = db\n .list_namespaces(lance_namespace::models::ListNamespacesRequest {\n id: Some(vec![\"prod\".to_string()]),\n ..Default::default()\n })\n .await?;\nprintln!(\n \"Child namespaces under {:?}: {:?}\",\n namespace, child_namespaces\n);\n// Child namespaces under [\"prod\", \"search\"]: [\"search\"]\n\ndb.drop_namespace(lance_namespace::models::DropNamespaceRequest {\n id: Some(namespace.clone()),\n ..Default::default()\n})\n.await?;\ndb.drop_namespace(lance_namespace::models::DropNamespaceRequest {\n id: Some(vec![\"prod\".to_string()]),\n ..Default::default()\n})\n.await?;\n"; + +export const RsNamespaceTableOps = "let conn = connect(uri).execute().await?;\nlet search_namespace = vec![\"prod\".to_string(), \"search\".to_string()];\nlet recommendations_namespace = vec![\"prod\".to_string(), \"recommendations\".to_string()];\n\nlet schema = std::sync::Arc::new(arrow_schema::Schema::new(vec![\n arrow_schema::Field::new(\"id\", arrow_schema::DataType::Int64, false),\n]));\n\nconn.create_empty_table(\"user\", schema.clone())\n .namespace(search_namespace.clone())\n .execute()\n .await?;\n\nconn.create_empty_table(\"user\", schema)\n .namespace(recommendations_namespace.clone())\n .execute()\n .await?;\n\nlet search_table_names = conn\n .table_names()\n .namespace(search_namespace)\n .execute()\n .await?;\nlet recommendation_table_names = conn\n .table_names()\n .namespace(recommendations_namespace)\n .execute()\n .await?;\n\nprintln!(\"{search_table_names:?}\"); // [\"user\"]\nprintln!(\"{recommendation_table_names:?}\"); // [\"user\"]\n"; + diff --git a/docs/web/snippets/custom_embedding_function.mdx b/docs/web/snippets/custom_embedding_function.mdx new file mode 100644 index 000000000..bdbb591d8 --- /dev/null +++ b/docs/web/snippets/custom_embedding_function.mdx @@ -0,0 +1,8 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const TsCallCustomFunction = "const registry = getRegistry();\n\nconst sentenceTransformer = await registry\n .get(\"sentence-transformers\")!\n .create();\n\nconst schema = LanceSchema({\n vector: sentenceTransformer.vectorField(),\n text: sentenceTransformer.sourceField(),\n});\n\nconst db = await lancedb.connect(databaseDir);\nconst table = await db.createEmptyTable(\"table\", schema, {\n mode: \"overwrite\",\n});\n\nawait table.add([{ text: \"hello\" }, { text: \"world\" }]);\n\nconst results = await table.search(\"greeting\").limit(1).toArray();\n"; + +export const TsEmbeddingImpl = "@register(\"sentence-transformers\")\nclass SentenceTransformersEmbeddings extends TextEmbeddingFunction {\n name = \"Xenova/all-miniLM-L6-v2\";\n #ndims!: number;\n extractor!: FeatureExtractionPipeline;\n\n async init() {\n this.extractor = await pipeline(\"feature-extraction\", this.name, {\n dtype: \"fp32\",\n });\n this.#ndims = await this.generateEmbeddings([\"hello\"]).then(\n (e) => e[0].length,\n );\n }\n\n ndims() {\n return this.#ndims;\n }\n\n toJSON() {\n return {\n name: this.name,\n };\n }\n async generateEmbeddings(texts: string[]) {\n const output = await this.extractor(texts, {\n pooling: \"mean\",\n normalize: true,\n });\n return output.tolist();\n }\n}\n"; + +export const TsImports = "import * as lancedb from \"@lancedb/lancedb\";\nimport {\n LanceSchema,\n TextEmbeddingFunction,\n getRegistry,\n register,\n} from \"@lancedb/lancedb/embedding\";\n"; + diff --git a/docs/web/snippets/embedding.mdx b/docs/web/snippets/embedding.mdx new file mode 100644 index 000000000..8db4472dc --- /dev/null +++ b/docs/web/snippets/embedding.mdx @@ -0,0 +1,46 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyAsyncOpenaiEmbeddings = "db = await lancedb.connect_async(uri)\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n text: str = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n\ntable = await db.create_table(\"words\", schema=Words, mode=\"overwrite\")\nawait table.add([{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}])\n\nquery = \"greetings\"\nactual = await (await table.search(query)).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n"; + +export const PyCreateEmbeddingFunction = "func = get_registry().get(\"openai\").create(\n name=\"text-embedding-3-small\",\n max_retries=7,\n)\n"; + +export const PyEmbeddingFunction = "from functools import cached_property\n\nfrom lancedb.embeddings import TextEmbeddingFunction, register\n\nclass MyEmbeddingModel:\n def __init__(self, model_name: str):\n self.model_name = model_name\n\n def encode(self, texts: list[str]) -> list[list[float]]:\n return [[1.0, 2.0, 3.0] for _ in texts]\n\n@register(\"my-embedder\")\nclass MyTextEmbedder(TextEmbeddingFunction):\n model_name: str = \"my-model\"\n\n def generate_embeddings(self, texts: list[str]) -> list[list[float]]:\n # Your embedding logic here\n return self._model.encode(texts)\n\n def ndims(self) -> int:\n # Return the dimensionality of the embeddings\n return len(self.generate_embeddings([\"test\"])[0])\n\n @cached_property\n def _model(self) -> MyEmbeddingModel:\n # Initialize your model once\n return MyEmbeddingModel(self.model_name)\n"; + +export const PyImports = "from lancedb.pydantic import LanceModel, Vector\nfrom lancedb.embeddings import get_registry\n"; + +export const PyManualQueryEmbeddings = "db = lancedb.connect(\"/tmp/db\")\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n text: str = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add([{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}])\n\nquery_vector = func.generate_embeddings([\"greetings\"])[0]\n# --8<-- [start:manual_query_search]\n# query_vector is assumed to already be generated by your embedding function\nactual = table.search(query_vector).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n# --8<-- [end:manual_query_search]\n"; + +export const PyManualQuerySearch = "# query_vector is assumed to already be generated by your embedding function\nactual = table.search(query_vector).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n"; + +export const PyOpenaiEmbeddings = "db = lancedb.connect(\"/tmp/db\")\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n text: str = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add([{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}])\n\nquery = \"greetings\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n"; + +export const PyRegisterDevice = "import torch\n\nregistry = get_registry()\nif torch.cuda.is_available():\n registry.set_var(\"device\", \"cuda\")\n\nfunc = registry.get(\"huggingface\").create(device=\"$var:device:cpu\")\n"; + +export const PyRegisterSecret = "registry = get_registry()\nregistry.set_var(\"api_key\", \"sk-...\")\n\nfunc = registry.get(\"openai\").create(api_key=\"$var:api_key\")\n"; + +export const TsCreateEmbeddingFunction = "const func = getRegistry().get(\"openai\")!.create({\n model: \"text-embedding-3-small\",\n});\n"; + +export const TsEmbeddingFunction = "const db = await lancedb.connect(databaseDir);\n\n@register(\"my_embedding\")\nclass MyEmbeddingFunction extends EmbeddingFunction {\n constructor(optionsRaw = {}) {\n super();\n const options = this.resolveVariables(optionsRaw);\n // Initialize using options\n }\n ndims() {\n return 3;\n }\n protected getSensitiveKeys(): string[] {\n return [];\n }\n embeddingDataType(): Float {\n return new Float32();\n }\n async computeQueryEmbeddings(_data: string) {\n // This is a placeholder for a real embedding function\n return [1, 2, 3];\n }\n async computeSourceEmbeddings(data: string[]) {\n // This is a placeholder for a real embedding function\n return Array.from({ length: data.length }).fill([\n 1, 2, 3,\n ]) as number[][];\n }\n}\n\nconst func = new MyEmbeddingFunction();\n\nconst data = [{ text: \"pepperoni\" }, { text: \"pineapple\" }];\n\n// Option 1: manually specify the embedding function\nconst table = await db.createTable(\"vectors\", data, {\n embeddingFunction: {\n function: func,\n sourceColumn: \"text\",\n vectorColumn: \"vector\",\n },\n mode: \"overwrite\",\n});\n\n// Option 2: provide the embedding function through a schema\n\nconst schema = LanceSchema({\n text: func.sourceField(new Utf8()),\n vector: func.vectorField(),\n});\n\nconst table2 = await db.createTable(\"vectors2\", data, {\n schema,\n mode: \"overwrite\",\n});\n"; + +export const TsImports = "import * as lancedb from \"@lancedb/lancedb\";\nimport \"@lancedb/lancedb/embedding/openai\";\nimport { LanceSchema, getRegistry, register } from \"@lancedb/lancedb/embedding\";\nimport { EmbeddingFunction } from \"@lancedb/lancedb/embedding\";\nimport { type Float, Float32, Utf8 } from \"apache-arrow\";\n"; + +export const TsManualQueryEmbeddings = "const db = await lancedb.connect(databaseDir);\nconst func = getRegistry()\n .get(\"openai\")\n ?.create({ model: \"text-embedding-ada-002\" }) as EmbeddingFunction;\n\nconst wordsSchema = LanceSchema({\n text: func.sourceField(new Utf8()),\n vector: func.vectorField(),\n});\nconst tbl = await db.createEmptyTable(\"words\", wordsSchema, {\n mode: \"overwrite\",\n});\nawait tbl.add([{ text: \"hello world\" }, { text: \"goodbye world\" }]);\n\nconst queryVector = await func.computeQueryEmbeddings(\"greetings\");\n// --8<-- [start:manual_query_search]\n// queryVector is assumed to already be generated by your embedding function\nconst actual = (await tbl.search(queryVector).limit(1).toArray())[0];\n// --8<-- [end:manual_query_search]\n"; + +export const TsManualQuerySearch = "// queryVector is assumed to already be generated by your embedding function\nconst actual = (await tbl.search(queryVector).limit(1).toArray())[0];\n"; + +export const TsOpenaiEmbeddings = "const db = await lancedb.connect(databaseDir);\nconst func = getRegistry()\n .get(\"openai\")\n ?.create({ model: \"text-embedding-ada-002\" }) as EmbeddingFunction;\n\nconst wordsSchema = LanceSchema({\n text: func.sourceField(new Utf8()),\n vector: func.vectorField(),\n});\nconst tbl = await db.createEmptyTable(\"words\", wordsSchema, {\n mode: \"overwrite\",\n});\nawait tbl.add([{ text: \"hello world\" }, { text: \"goodbye world\" }]);\n\nconst query = \"greetings\";\nconst actual = (await tbl.search(query).limit(1).toArray())[0];\n"; + +export const TsRegisterModelFallback = "const registry = getRegistry();\nregistry.setVar(\"openai_model\", \"text-embedding-3-large\");\n\nconst func = registry.get(\"openai\")!.create({\n model: \"$var:openai_model:text-embedding-3-small\",\n});\n"; + +export const TsRegisterSecret = "const registry = getRegistry();\nregistry.setVar(\"api_key\", \"sk-...\");\n\nconst func = registry.get(\"openai\")!.create({\n apiKey: \"$var:api_key\",\n});\n"; + +export const RsCreateEmbeddingFunction = "use std::sync::Arc;\n\nuse lancedb::embeddings::openai::OpenAIEmbeddingFunction;\n\nlet api_key = std::env::var(\"OPENAI_API_KEY\").expect(\"OPENAI_API_KEY is not set\");\nlet embedding = Arc::new(\n OpenAIEmbeddingFunction::new_with_model(api_key, \"text-embedding-3-small\")\n .expect(\"failed to create OpenAI embedding function\"),\n);\n"; + +export const RsEmbeddingFunction = "use std::{borrow::Cow, sync::Arc};\n\nuse arrow_array::{Array, FixedSizeListArray, Float32Array};\nuse arrow_schema::{DataType, Field, Schema};\nuse lancedb::{\n connect,\n embeddings::{EmbeddingDefinition, EmbeddingFunction},\n Result,\n};\n\n#[derive(Debug, Clone)]\nstruct MyTextEmbedder {\n dim: usize,\n}\n\nimpl EmbeddingFunction for MyTextEmbedder {\n fn name(&self) -> &str {\n \"my-embedder\"\n }\n\n fn source_type(&self) -> Result> {\n Ok(Cow::Owned(DataType::Utf8))\n }\n\n fn dest_type(&self) -> Result> {\n Ok(Cow::Owned(DataType::new_fixed_size_list(\n DataType::Float32,\n self.dim as i32,\n true,\n )))\n }\n\n fn compute_source_embeddings(&self, source: Arc) -> Result> {\n let values = Arc::new(Float32Array::from(vec![1.0f32; source.len() * self.dim]));\n let field = Arc::new(Field::new(\"item\", DataType::Float32, true));\n Ok(Arc::new(FixedSizeListArray::new(\n field,\n self.dim as i32,\n values,\n None,\n )))\n }\n\n fn compute_query_embeddings(&self, _input: Arc) -> Result> {\n unimplemented!()\n }\n}\n\n#[tokio::main]\nasync fn main() -> Result<()> {\n let db = connect(\"./mydb\").execute().await?;\n db.embedding_registry()\n .register(\"my-embedder\", Arc::new(MyTextEmbedder { dim: 3 }))?;\n\n let schema = Arc::new(Schema::new(vec![Field::new(\"text\", DataType::Utf8, false)]));\n db.create_empty_table(\"mytable\", schema)\n .add_embedding(EmbeddingDefinition::new(\n \"text\",\n \"my-embedder\",\n Some(\"vector\"),\n ))?\n .execute()\n .await?;\n\n Ok(())\n}\n"; + +export const RsManualQueryEmbeddings = "use std::{iter::once, sync::Arc};\n\nuse arrow_array::{record_batch, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::StreamExt;\nuse lancedb::{\n connect,\n embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},\n query::{ExecutableQuery, QueryBase},\n Result,\n};\n\n#[tokio::main]\nasync fn main() -> Result<()> {\n let db = connect(\"./mydb\").execute().await?;\n let api_key = std::env::var(\"OPENAI_API_KEY\").expect(\"OPENAI_API_KEY is not set\");\n let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(\n api_key,\n \"text-embedding-3-large\",\n )?);\n db.embedding_registry().register(\"openai\", embedding.clone())?;\n\n let schema = Arc::new(Schema::new(vec![Field::new(\"text\", DataType::Utf8, false)]));\n let table = db\n .create_empty_table(\"mytable\", schema)\n .add_embedding(EmbeddingDefinition::new(\"text\", \"openai\", Some(\"vector\")))?\n .execute()\n .await?;\n\n table\n .add(record_batch!((\"text\", Utf8, [\"This is a test.\", \"Another example.\"]))?)\n .execute()\n .await?;\n\n // Manually generate embeddings for the query (Enterprise path)\n let query = Arc::new(StringArray::from_iter_values(once(\"test example\")));\n let query_vector = embedding.compute_query_embeddings(query)?;\n // --8<-- [start:manual_query_search]\n // query_vector is assumed to already be generated by your embedding function\n let mut results = table.vector_search(query_vector)?.limit(5).execute().await?;\n\n while let Some(batch) = results.next().await {\n println!(\"{:?}\", batch?);\n }\n // --8<-- [end:manual_query_search]\n\n Ok(())\n}\n"; + +export const RsManualQuerySearch = "// query_vector is assumed to already be generated by your embedding function\nlet mut results = table.vector_search(query_vector)?.limit(5).execute().await?;\n\nwhile let Some(batch) = results.next().await {\n println!(\"{:?}\", batch?);\n}\n"; + +export const RsOpenaiEmbeddings = "use std::{iter::once, sync::Arc};\n\nuse arrow_array::{record_batch, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::StreamExt;\nuse lancedb::{\n connect,\n embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},\n query::{ExecutableQuery, QueryBase},\n Result,\n};\n\n#[tokio::main]\nasync fn main() -> Result<()> {\n let db = connect(\"./mydb\").execute().await?;\n let api_key = std::env::var(\"OPENAI_API_KEY\").expect(\"OPENAI_API_KEY is not set\");\n let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(\n api_key,\n \"text-embedding-3-large\",\n )?);\n\n db.embedding_registry().register(\"openai\", embedding.clone())?;\n\n let schema = Arc::new(Schema::new(vec![Field::new(\"text\", DataType::Utf8, false)]));\n let table = db\n .create_empty_table(\"mytable\", schema)\n .add_embedding(EmbeddingDefinition::new(\"text\", \"openai\", Some(\"vector\")))?\n .execute()\n .await?;\n\n table\n .add(record_batch!((\"text\", Utf8, [\"This is a test.\", \"Another example.\"]))?)\n .execute()\n .await?;\n\n let query = Arc::new(StringArray::from_iter_values(once(\"test example\")));\n let query_vector = embedding.compute_query_embeddings(query)?;\n let mut results = table.vector_search(query_vector)?.limit(5).execute().await?;\n\n while let Some(batch) = results.next().await {\n println!(\"{:?}\", batch?);\n }\n\n Ok(())\n}\n"; + diff --git a/docs/web/snippets/filtering.mdx b/docs/web/snippets/filtering.mdx new file mode 100644 index 000000000..f4fc0cae8 --- /dev/null +++ b/docs/web/snippets/filtering.mdx @@ -0,0 +1,8 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const TsSearch = "const _result = await tbl\n .search(Array(1536).fill(0.5))\n .limit(1)\n .where(\"id = 10\")\n .toArray();\n"; + +export const TsSqlSearch = "await tbl.query().where(\"id = 10\").limit(10).toArray();\n"; + +export const TsVecSearch = "const result = await (\n tbl.search(Array(1536).fill(0)) as lancedb.VectorQuery\n)\n .where(\"(item IN ('item 0', 'item 2')) AND (id > 10)\")\n .postfilter()\n .toArray();\n"; + diff --git a/docs/web/snippets/full_text_search.mdx b/docs/web/snippets/full_text_search.mdx new file mode 100644 index 000000000..7d71409ee --- /dev/null +++ b/docs/web/snippets/full_text_search.mdx @@ -0,0 +1,4 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const TsFullTextSearch = "const result = await tbl\n .query()\n .nearestToText(\"apple\")\n .select([\"id\", \"doc\"])\n .limit(10)\n .toArray();\nexpect(result.length).toBe(10);\n"; + diff --git a/docs/web/snippets/geneva_dependency_verification.mdx b/docs/web/snippets/geneva_dependency_verification.mdx new file mode 100644 index 000000000..5afae693a --- /dev/null +++ b/docs/web/snippets/geneva_dependency_verification.mdx @@ -0,0 +1,12 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyCondaClusterInline = "from geneva.cluster.builder import KubeRayClusterBuilder\n\ncluster = (\n KubeRayClusterBuilder.create(\"my-cluster\")\n .ray_init_kwargs({\n \"runtime_env\": {\n \"conda\": {\n \"channels\": [\"conda-forge\"],\n \"dependencies\": [\n \"python=3.10\",\n \"ffmpeg<8\",\n \"torchvision=0.22.1\",\n ],\n },\n \"config\": {\"eager_install\": True},\n }\n })\n .build()\n)\n"; + +export const PyCondaClusterPath = "from geneva.cluster.builder import KubeRayClusterBuilder\n\ncluster = (\n KubeRayClusterBuilder.create(\"my-cluster\")\n .ray_init_kwargs({\n \"runtime_env\": {\"conda\": \"environment.yml\"}\n })\n .build()\n)\n"; + +export const PyEnvVarsViaCluster = "from geneva.cluster.builder import KubeRayClusterBuilder\nimport os\n\ncluster = (\n KubeRayClusterBuilder.create(\"my-cluster\")\n .ray_init_kwargs({\n \"runtime_env\": {\n \"env_vars\": {\n \"AWS_ACCESS_KEY_ID\": os.environ[\"AWS_ACCESS_KEY_ID\"],\n \"AWS_SECRET_ACCESS_KEY\": os.environ[\"AWS_SECRET_ACCESS_KEY\"],\n }\n }\n })\n .build()\n)\n"; + +export const PyPipManifest = "import geneva\nfrom geneva.manifest.builder import PipManifestBuilder\n\nmanifest = (\n PipManifestBuilder.create(\"my-manifest\")\n .pip([\n \"numpy==1.26.4\",\n \"torch==2.0.1\",\n \"attrs==23.2.0\",\n ])\n .build()\n)\n\nconn = geneva.connect(\"s3://my-bucket/my-db\")\nconn.define_manifest(\"my-manifest\", manifest)\nwith conn.context(cluster=\"my-cluster\", manifest=\"my-manifest\"):\n conn.open_table(\"my-table\").backfill(\"my-column\")\n"; + +export const PyQuickFixManifest = "from geneva.manifest.builder import PipManifestBuilder\n\nmanifest = PipManifestBuilder.create(\"fix\").pip([\"numpy==1.26.4\"]).build()\n"; + diff --git a/docs/web/snippets/geneva_profiling_memory.mdx b/docs/web/snippets/geneva_profiling_memory.mdx new file mode 100644 index 000000000..f6addc420 --- /dev/null +++ b/docs/web/snippets/geneva_profiling_memory.mdx @@ -0,0 +1,22 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyBoundedCache = "from functools import lru_cache\n\nclass GoodEmbedding:\n def __init__(self):\n self._embed = None\n\n def setup(self):\n model = load_model()\n self._embed = lru_cache(maxsize=1024)(model.embed)\n\n def __call__(self, text: str) -> list[float]:\n if self._embed is None:\n self.setup()\n return self._embed(text)\n"; + +export const PyConfidenceCheck = "def __call__(self, x):\n scratch = bytearray(8 * 1024 * 1024) # 8 MiB\n self._scratches.append(scratch) # <-- deliberate leak\n return ...\n"; + +export const PyLeakyAggregator = "class BadAggregator:\n def __init__(self):\n self.history = []\n\n def __call__(self, batch: pa.RecordBatch) -> pa.Array:\n self.history.append(batch) # holds every batch ever processed\n ...\n"; + +export const PyLeakyCache = "class BadEmbedding:\n def __init__(self):\n self.cache: dict[str, list[float]] = {}\n\n def __call__(self, text: str) -> list[float]:\n if text not in self.cache:\n self.cache[text] = self.model.embed(text)\n return self.cache[text]\n"; + +export const PyLeakyClosure = "class BadDeferred:\n def __init__(self):\n self.work_queue = []\n\n def __call__(self, x: pa.Array) -> pa.Array:\n # Lambda captures `x` by reference — the whole Array stays alive\n self.work_queue.append(lambda: expensive(x))\n ...\n"; + +export const PyLogMemory = "import resource, pyarrow as pa\n\ndef log_memory(seq: int) -> None:\n rss_bytes = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n # ru_maxrss is bytes on macOS, KiB on Linux:\n import sys\n if sys.platform != \"darwin\":\n rss_bytes *= 1024\n arrow_live = pa.total_allocated_bytes()\n print(\n f\"seq={seq} \"\n f\"rss_mb={rss_bytes // 1024**2} \"\n f\"arrow_live_mb={arrow_live // 1024**2} \"\n f\"gap_mb={(rss_bytes - arrow_live) // 1024**2}\",\n flush=True,\n )\n"; + +export const PyMemrayTrackerUdf = "import os, pathlib, uuid\nfrom typing import Any\nimport memray\nimport geneva\nimport pyarrow as pa\n\n_MEMRAY_OUT_DIR_ENV = \"MY_UDF_MEMRAY_OUT_DIR\"\n\n\n@geneva.udf(data_type=pa.list_(pa.float32(), 512))\nclass MyEmbedding:\n def __init__(self):\n self.model = None\n self._tracker: Any = None # memray.Tracker, when profiling is on\n\n def setup(self):\n # Open a memray tracker per worker process, if requested. Each\n # worker writes its own .bin file so traces don't collide.\n out_dir = os.environ.get(_MEMRAY_OUT_DIR_ENV)\n if out_dir:\n pathlib.Path(out_dir).mkdir(parents=True, exist_ok=True)\n bin_path = pathlib.Path(out_dir) / (\n f\"memray-{os.getpid()}-{uuid.uuid4().hex}.bin\"\n )\n self._tracker = memray.Tracker(\n str(bin_path), native_traces=False, follow_fork=False\n )\n self._tracker.__enter__()\n self.model = load_model()\n\n def __call__(self, text: str) -> list[float]:\n if self.model is None:\n self.setup()\n return self.model.embed(text)\n"; + +export const PyRayClusterProfile = "from geneva.runners.ray._mgr import ray_cluster\n\nwith ray_cluster(\n local=True,\n extra_env={\"MY_UDF_MEMRAY_OUT_DIR\": \"/tmp/my-udf-profile\"},\n):\n table.backfill(\"embedding\", concurrency=1)\n"; + +export const PyStatefulUdfClass = "@geneva.udf(data_type=pa.list_(pa.float32(), 512))\nclass MyEmbedding:\n def __init__(self):\n self.model = None\n\n def setup(self):\n self.model = load_model() # allocated once per actor\n\n def __call__(self, text: str) -> list[float]:\n if self.model is None:\n self.setup()\n return self.model.embed(text)\n"; + +export const PyTorchInferenceMode = "def __call__(self, text: str) -> list[float]:\n with torch.inference_mode(): # <-- prevents autograd graph retention\n return self.model.encode(text)\n"; + diff --git a/docs/web/snippets/geneva_scalar_udtfs.mdx b/docs/web/snippets/geneva_scalar_udtfs.mdx new file mode 100644 index 000000000..a51feb545 --- /dev/null +++ b/docs/web/snippets/geneva_scalar_udtfs.mdx @@ -0,0 +1,20 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyAddColumnsScalarUdtf = "@udf(data_type=pa.list_(pa.float32(), 512))\ndef clip_embedding(clip_bytes: bytes) -> list[float]:\n return embed_model.encode(clip_bytes)\n\n# Add an embedding column to the clips table\nclips.add_columns({\"embedding\": clip_embedding})\n\n# Backfill computes embeddings for all existing clips\nclips.backfill(\"embedding\")\n"; + +export const PyChainingUdtfViews = "# videos → clips (1:N)\nclips = db.create_udtf_view(\n \"clips\", source=videos.search(None), udtf=extract_clips\n)\n\n# clips → frames (1:N)\nframes = db.create_udtf_view(\n \"frames\", source=clips.search(None), udtf=extract_frames\n)\n"; + +export const PyCreateScalarUdtfView = "import geneva\n\ndb = geneva.connect(\"/data/mydb\")\nvideos = db.open_table(\"videos\")\n\n# Create the 1:N materialized view\nclips = db.create_udtf_view(\n \"clips\",\n source=videos.search(None).select([\"video_path\", \"metadata\"]),\n udtf=extract_clips,\n)\n\n# Populate — runs the UDTF on every source row\nclips.refresh()\n"; + +export const PyDocumentChunkingFull = "from geneva import connect, chunker, udf\nfrom typing import Iterator, NamedTuple\nimport pyarrow as pa\n\nclass Chunk(NamedTuple):\n chunk_index: int\n chunk_text: str\n\n@chunker\ndef chunk_document(text: str) -> Iterator[Chunk]:\n \"\"\"Split a document into overlapping chunks.\"\"\"\n words = text.split()\n chunk_size = 500\n overlap = 50\n for i, start in enumerate(range(0, len(words), chunk_size - overlap)):\n chunk_words = words[start:start + chunk_size]\n yield Chunk(chunk_index=i, chunk_text=\" \".join(chunk_words))\n\ndb = connect(\"/data/mydb\")\ndocs = db.open_table(\"documents\")\n\n# Create chunked view — inherits doc_id, title, etc. from source\nchunks = db.create_udtf_view(\n \"doc_chunks\",\n source=docs.search(None).select([\"doc_id\", \"title\", \"text\"]),\n udtf=chunk_document,\n)\nchunks.refresh()\n\n# Add embeddings to chunks for semantic search\n@udf(data_type=pa.list_(pa.float32(), 1536))\ndef embed_text(chunk_text: str) -> list[float]:\n return embedding_model.encode(chunk_text)\n\nchunks.add_columns({\"embedding\": embed_text})\nchunks.backfill(\"embedding\") # Backfills embeddings on all existing chunks\n\n# Query — parent columns available alongside chunk columns\nchunks.search(None).select([\"doc_id\", \"title\", \"chunk_text\", \"embedding\"]).to_pandas()\n"; + +export const PyDocumentChunkingUdtf = "from geneva import chunker\nfrom typing import Iterator, NamedTuple\n\nclass Chunk(NamedTuple):\n chunk_index: int\n chunk_text: str\n\n@chunker\ndef chunk_document(text: str) -> Iterator[Chunk]:\n \"\"\"Split a document into overlapping chunks.\"\"\"\n words = text.split()\n chunk_size = 500\n overlap = 50\n for i, start in enumerate(range(0, len(words), chunk_size - overlap)):\n chunk_words = words[start:start + chunk_size]\n yield Chunk(chunk_index=i, chunk_text=\" \".join(chunk_words))\n"; + +export const PyIncrementalRefresh = "# Add new videos to the source table\nvideos.add(new_video_data)\n\n# Incremental refresh — only processes the new videos\nclips.refresh()\n"; + +export const PyScalarUdtfBatch = "@chunker(batch=True, output_schema=clip_schema)\ndef extract_clips(batch: pa.RecordBatch) -> pa.RecordBatch:\n \"\"\"Process rows in batches. Same 1:N semantic per row.\"\"\"\n ...\n"; + +export const PyScalarUdtfIterator = "from geneva import chunker\nfrom typing import Iterator, NamedTuple\n\nclass Clip(NamedTuple):\n clip_start: float\n clip_end: float\n clip_bytes: bytes\n\n@chunker\ndef extract_clips(video_path: str, duration: float) -> Iterator[Clip]:\n \"\"\"Yields multiple clips per video.\"\"\"\n clip_length = 10.0\n for start in range(0, int(duration), int(clip_length)):\n end = min(start + clip_length, duration)\n clip_data = extract_video_segment(video_path, start, end)\n yield Clip(clip_start=start, clip_end=end, clip_bytes=clip_data)\n"; + +export const PyScalarUdtfList = "@chunker\ndef extract_clips(video_path: str, duration: float) -> list[Clip]:\n clips = []\n for start in range(0, int(duration), 10):\n end = min(start + 10, duration)\n clips.append(Clip(clip_start=start, clip_end=end, clip_bytes=b\"...\"))\n return clips\n"; + diff --git a/docs/web/snippets/geneva_udfs_index.mdx b/docs/web/snippets/geneva_udfs_index.mdx new file mode 100644 index 000000000..923102a3a --- /dev/null +++ b/docs/web/snippets/geneva_udfs_index.mdx @@ -0,0 +1,8 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyRegistrationScalarUdtf = "db = geneva.connect(\"/data/mydb\")\ndb.create_udtf_view(\"my_view\", source=my_source, udtf=my_chunker)\n"; + +export const PyRegistrationUdf = "mock_table.add_columns({\"col\": my_udf})\n"; + +export const PyRegistrationUdtf = "db = geneva.connect(\"/data/mydb\")\ndb.create_udtf_view(\"my_view\", source=my_source, udtf=my_udtf)\n"; + diff --git a/docs/web/snippets/indexing.mdx b/docs/web/snippets/indexing.mdx new file mode 100644 index 000000000..278a097ec --- /dev/null +++ b/docs/web/snippets/indexing.mdx @@ -0,0 +1,72 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyFtsIndexAsync = "import asyncio\n\nimport lancedb\nimport polars as pl\nfrom lancedb.index import FTS\n\ndata = pl.DataFrame(\n {\n \"id\": [1, 2],\n \"text\": [\n \"His first language is spanish\",\n \"Her first language is english\",\n ],\n }\n)\n\nasync def main(data: pl.DataFrame):\n uri = \"ex_lancedb\"\n db = await lancedb.connect_async(uri)\n tbl = await db.create_table(\"my_text\", data=data, mode=\"overwrite\")\n\n await tbl.create_index(\"text\", config=FTS(language=\"English\"))\n\n response = await tbl.search(\"spanish\", query_type=\"fts\")\n result = await response.limit(1).to_polars()\n print(result)\n return result\n\nif __name__ == \"__main__\":\n asyncio.run(main(data))\n"; + +export const PyFtsIndexCreate = "table_name = \"fts-index-create\"\ntable = db.open_table(table_name)\ntable.create_fts_index(\"text\")\n"; + +export const PyFtsIndexNested = "from lancedb.query import MatchQuery, PhraseQuery\n\ntable = db.open_table(\"fts-index-nested\")\n\n# Index a text leaf inside a struct column using a dotted path.\ntable.create_fts_index(\"payload.text\", with_position=True)\n\n# The same dotted path works in MatchQuery and PhraseQuery.\nmatches = (\n table.search(MatchQuery(\"puppy\", \"payload.text\")).limit(5).to_list()\n)\nphrases = (\n table.search(PhraseQuery(\"puppy runs\", \"payload.text\"))\n .limit(5)\n .to_list()\n)\n"; + +export const PyFtsIndexWait = "table_name = \"fts-index-wait\"\n\ntable = db.open_table(table_name)\ntable.create_fts_index(\"text\")\n\nindex_name = \"text_idx\"\ntable.wait_for_index([index_name])\n"; + +export const PyGpuIndexCuda = "table.create_index(\n num_partitions=256,\n num_sub_vectors=96,\n accelerator=\"cuda\",\n)\n"; + +export const PyGpuIndexMps = "table.create_index(\n num_partitions=256,\n num_sub_vectors=96,\n accelerator=\"mps\",\n)\n"; + +export const PyReindexingIncremental = "table = db.open_table(\"reindexing_incremental\")\ntable.add([{\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"}])\ntable.optimize()\n"; + +export const PyScalarIndexBuild = "tbl = db.open_table(\"scalar_index_build\")\ntbl.create_scalar_index(\"book_id\")\ntbl.create_scalar_index(\"publisher\", index_type=\"BITMAP\")\n"; + +export const PyScalarIndexFilter = "table = db.open_table(\"books\")\nresult = table.search().where(\"book_id = 2\").limit(10).to_pandas()\n"; + +export const PyScalarIndexNestedFields = "import pyarrow as pa\nfrom lancedb.index import BTree\n\nmetadata_type = pa.struct(\n [\n pa.field(\"user_id\", pa.int32()),\n pa.field(\"user.id\", pa.int32()),\n ]\n)\ndata = pa.Table.from_arrays(\n [\n pa.array([1, 2, 3], type=pa.int32()),\n pa.array(\n [\n {\"user_id\": 10, \"user.id\": 100},\n {\"user_id\": 20, \"user.id\": 200},\n {\"user_id\": 30, \"user.id\": 300},\n ],\n type=metadata_type,\n ),\n ],\n names=[\"user_id\", \"metadata\"],\n)\ntable = await db.create_table(\"nested_scalar_index\", data)\n\n# Index a nested struct field.\nawait table.create_index(\n \"metadata.user_id\", config=BTree(), name=\"nested_user_id_idx\"\n)\n\n# Escape literal dots inside a segment with backticks.\nawait table.create_index(\n \"metadata.`user.id`\", config=BTree(), name=\"escaped_user_id_idx\"\n)\n\n# `columns` is returned as the canonical path you passed in.\nfor index in await table.list_indices():\n print(index.name, index.columns)\n# nested_user_id_idx ['metadata.user_id']\n# escaped_user_id_idx ['metadata.`user.id`']\n"; + +export const PyScalarIndexOptimize = "table.add([{\"vector\": [7, 8], \"book_id\": 4}])\ntable.optimize()\n"; + +export const PyScalarIndexPrefilter = "table = db.open_table(\"book_with_embeddings\")\ntable.search([1.2] * 2).where(\"book_id != 3\").limit(10).to_pandas()\n"; + +export const PyScalarIndexUuidData = "def generate_random_names():\n base_names = [\"Alice\", \"Bob\", \"Carla\", \"David\", \"Eve\", \"Frank\", \"Grace\"]\n letter = random.choice(string.ascii_uppercase)\n return f\"{random.choice(base_names)} {letter}.\"\n\ndef generate_uuids(num_items):\n return [uuid.uuid4().bytes for _ in range(num_items)]\n\n# Generate some UUIDs and random names\nn = 7\nuuids = generate_uuids(n)\nnames = [generate_random_names() for _ in range(n)]\n"; + +export const PyScalarIndexUuidTable = "table_name = \"index-on-uuid\"\n\nuuid_array = pa.array(uuids, pa.uuid())\nname_array = pa.array(names, pa.string())\nschema = pa.schema(\n [\n pa.field(\"id\", pa.uuid()),\n pa.field(\"name\", pa.string()),\n ]\n)\ndata_table = pa.Table.from_arrays([uuid_array, name_array], schema=schema)\ntable = db.create_table(table_name, data=data_table, mode=\"overwrite\")\n"; + +export const PyScalarIndexUuidType = "import pyarrow as pa\n"; + +export const PyScalarIndexUuidUpsert = "new_users = [\n {\"id\": uuid.uuid4().bytes, \"name\": \"Hannah D.\"},\n {\"id\": uuid.uuid4().bytes, \"name\": \"Ian B.\"},\n]\n# Insert or update using the UUID index\ntable.merge_insert(\n \"id\"\n).when_matched_update_all().when_not_matched_insert_all().execute(new_users)\n"; + +export const PyScalarIndexUuidWait = "index_name = \"id_idx\"\ntable.create_scalar_index(\"id\")\ntable.wait_for_index([index_name])\n"; + +export const PyScalarIndexWait = "index_name = \"label_idx\"\ntable.wait_for_index([index_name])\n"; + +export const PyVectorIndexAsyncConfig = "import lancedb\nimport numpy as np\nfrom lancedb.index import IvfPq\n\nasync def main():\n data = [\n {\"id\": i, \"vector\": np.random.random(8).astype(np.float32).tolist()}\n for i in range(512)\n ]\n\n db = await lancedb.connect_async(\"ex_lancedb\")\n table = await db.create_table(\n \"vector_index_async\", data=data, mode=\"overwrite\"\n )\n\n await table.create_index(\n \"vector\",\n config=IvfPq(\n distance_type=\"cosine\",\n num_partitions=16,\n num_sub_vectors=4,\n ),\n )\n return await table.list_indices()\n"; + +export const PyVectorIndexBinaryAddData = "table.add(data)\n"; + +export const PyVectorIndexBinaryBuildIndex = "table.create_index(\n metric=\"hamming\",\n vector_column_name=\"vector\",\n index_type=\"IVF_FLAT\",\n)\n"; + +export const PyVectorIndexBinarySchema = "table = tmp_db.create_table(table_name, schema=schema, mode=\"overwrite\")\n"; + +export const PyVectorIndexBinarySearch = "query = np.random.randint(0, 2, size=ndim)\nquery = np.packbits(query)\ndf = table.search(query).metric(\"hamming\").limit(10).to_pandas()\ndf.vector = df.vector.apply(np.unpackbits)\n"; + +export const PyVectorIndexBuildHnsw = "table.create_index(index_type=\"IVF_HNSW_SQ\")\n"; + +export const PyVectorIndexBuildIvf = "table_name = \"vector-index-build-ivf\"\ntable = db.open_table(table_name)\ntable.create_index(\n metric=\"cosine\",\n vector_column_name=\"keywords_embeddings\",\n)\n"; + +export const PyVectorIndexBypassRecall = "query = np.random.random(128)\nk = 10\n\n# Ground truth: flat (exhaustive) scan, ignoring the ANN index.\ntruth = set(table.search(query).bypass_vector_index().limit(k).to_pandas()[\"id\"])\n\n# ANN results with the current nprobes setting.\nann = set(table.search(query).nprobes(20).limit(k).to_pandas()[\"id\"])\n\nrecall_at_k = len(truth & ann) / k\n"; + +export const PyVectorIndexCheckStatus = "index_name = \"keywords_embeddings_idx\"\ntable.wait_for_index([index_name])\nprint(table.index_stats(index_name))\n"; + +export const PyVectorIndexConfigureIvf = "table.create_index(metric=\"l2\", num_partitions=16, num_sub_vectors=4)\n"; + +export const PyVectorIndexCustomName = "# Override the default `{column}_idx` convention by passing `name=...`.\ntable.create_index(\n metric=\"cosine\",\n vector_column_name=\"keywords_embeddings\",\n name=\"my_custom_index\",\n)\ntable.wait_for_index([\"my_custom_index\"])\nprint(table.index_stats(\"my_custom_index\"))\n"; + +export const PyVectorIndexDistanceRange = "# Only return results whose distance falls within [0.0, 0.5).\n# Useful for near-duplicate detection or thresholded similarity search.\n(\n table.search(np.random.random(128))\n .distance_range(lower_bound=0.0, upper_bound=0.5)\n .limit(10)\n .to_pandas()\n)\n"; + +export const PyVectorIndexNestedField = "# The vector column `embedding` is nested inside the `image` struct.\n# Pass its full dotted path as `vector_column_name`; the same path is used\n# at query time and is what `list_indices()` reports under `columns`.\ntable.create_index(\n vector_column_name=\"image.embedding\",\n num_partitions=1,\n num_sub_vectors=1,\n name=\"image_embedding_idx\",\n)\n\nresults = (\n table.search([0.0, 1.0], vector_column_name=\"image.embedding\")\n .limit(1)\n .to_list()\n)\n"; + +export const PyVectorIndexNprobes = "# Always scan 10 partitions; scan up to 50 only if the initial pass\n# returns fewer than `limit` results (common with narrow filters).\n(\n table.search(np.random.random(128))\n .minimum_nprobes(10)\n .maximum_nprobes(50)\n .where(\"id > 100\")\n .limit(5)\n .to_pandas()\n)\n"; + +export const PyVectorIndexQueryHnsw = "tbl = table\ntbl.search(np.random.random((16))).limit(2).to_pandas()\n"; + +export const PyVectorIndexQueryIvf = "tbl = table\ntbl.search(np.random.random((1536))).limit(2).nprobes(20).refine_factor(\n 10\n).to_pandas()\n"; + +export const PyVectorIndexSetup = "table_name = \"vector-index-tbl\"\ntable = db.open_table(table_name)\n"; + diff --git a/docs/web/snippets/integrations.mdx b/docs/web/snippets/integrations.mdx new file mode 100644 index 000000000..de11f10ad --- /dev/null +++ b/docs/web/snippets/integrations.mdx @@ -0,0 +1,182 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyEmbeddingAwsUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\nmodel = get_registry().get(\"bedrock-text\").create()\n\nclass TextModel(LanceModel):\n text: str = model.SourceField()\n vector: Vector(model.ndims()) = model.VectorField()\n\ndf = pd.DataFrame({\"text\": [\"hello world\", \"goodbye world\"]})\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"bedrock-demo\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\n\ntbl.add(df)\nrs = tbl.search(\"hello\").limit(1).to_pandas()\nprint(rs.head())\n"; + +export const PyEmbeddingCohereUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\ncohere = (\n EmbeddingFunctionRegistry.get_instance()\n .get(\"cohere\")\n .create(name=\"embed-multilingual-v2.0\")\n)\n\nclass TextModel(LanceModel):\n text: str = cohere.SourceField()\n vector: Vector(cohere.ndims()) = cohere.VectorField()\n\ndata = [{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"cohere-demo\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\ntbl.add(data)\n"; + +export const PyEmbeddingColpaliSetup = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nimport requests\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, MultiVector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"colpali-demo\"))\nfunc = get_registry().get(\"colpali\").create()\n\nclass Images(LanceModel):\n label: str\n image_uri: str = func.SourceField()\n image_bytes: bytes = func.SourceField()\n vector: MultiVector(func.ndims()) = func.VectorField()\n vec_from_bytes: MultiVector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"images\", schema=Images)\nlabels = [\"cat\", \"dog\", \"horse\"]\nuris = [\n \"http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg\",\n \"http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg\",\n \"http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg\",\n]\nimage_bytes = [requests.get(uri).content for uri in uris]\ntable.add(\n pd.DataFrame({\"label\": labels, \"image_uri\": uris, \"image_bytes\": image_bytes})\n)\n"; + +export const PyEmbeddingColpaliTextSearch = "actual = (\n table.search(\"a furry pet\", vector_column_name=\"vector\")\n .limit(1)\n .to_pydantic(Images)[0]\n)\nprint(actual.label)\n"; + +export const PyEmbeddingGeminiUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\nmodel = get_registry().get(\"gemini-text\").create()\n\nclass TextModel(LanceModel):\n text: str = model.SourceField()\n vector: Vector(model.ndims()) = model.VectorField()\n\ndf = pd.DataFrame({\"text\": [\"hello world\", \"goodbye world\"]})\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"gemini-demo\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\n\ntbl.add(df)\nrs = tbl.search(\"hello\").limit(1).to_pandas()\nprint(rs.head())\n"; + +export const PyEmbeddingHuggingfaceUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"huggingface-demo\"))\nmodel = get_registry().get(\"huggingface\").create(name=\"facebook/bart-base\")\n\nclass Words(LanceModel):\n text: str = model.SourceField()\n vector: Vector(model.ndims()) = model.VectorField()\n\ndf = pd.DataFrame({\"text\": [\"hi hello sayonara\", \"goodbye world\"]})\ntable = db.create_table(\"greets\", schema=Words)\ntable.add(df)\nquery = \"old greeting\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n"; + +export const PyEmbeddingIbmUsage = "import os\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\nwatsonx_embed = (\n EmbeddingFunctionRegistry.get_instance()\n .get(\"watsonx\")\n .create(\n name=\"ibm/slate-125m-english-rtrvr\",\n api_key=os.environ.get(\"WATSONX_API_KEY\"),\n project_id=os.environ.get(\"WATSONX_PROJECT_ID\"),\n )\n)\n\nclass TextModel(LanceModel):\n text: str = watsonx_embed.SourceField()\n vector: Vector(watsonx_embed.ndims()) = watsonx_embed.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"watsonx-demo\"))\ntbl = db.create_table(\"watsonx_test\", schema=TextModel, mode=\"overwrite\")\ntbl.add(data)\n\nrs = tbl.search(\"hello\").limit(1).to_pandas()\nprint(rs.head())\n"; + +export const PyEmbeddingImagebindAudioSearch = "query_audio = \"./assets/car_audio2.wav\"\nactual = table.search(query_audio).limit(1).to_pydantic(ImageBindModel)[0]\nprint(actual.text == \"car\")\n"; + +export const PyEmbeddingImagebindImageSearch = "query_image = \"./assets/dog_image2.jpg\"\nactual = table.search(query_image).limit(1).to_pydantic(ImageBindModel)[0]\nprint(actual.text == \"dog\")\n"; + +export const PyEmbeddingImagebindSetup = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(\"/tmp/imagebind-db\")\nfunc = get_registry().get(\"imagebind\").create()\n\nclass ImageBindModel(LanceModel):\n text: str\n image_uri: str = func.SourceField()\n audio_path: str\n vector: Vector(func.ndims()) = func.VectorField()\n\ntext_list = [\"A dog.\", \"A car\", \"A bird\"]\nimage_paths = [\n \"./assets/dog_image.jpg\",\n \"./assets/car_image.jpg\",\n \"./assets/bird_image.jpg\",\n]\naudio_paths = [\n \"./assets/dog_audio.wav\",\n \"./assets/car_audio.wav\",\n \"./assets/bird_audio.wav\",\n]\n\ninputs = [\n {\"text\": a, \"audio_path\": b, \"image_uri\": c}\n for a, b, c in zip(text_list, audio_paths, image_paths)\n]\n\ntable = db.create_table(\"img_bind\", schema=ImageBindModel)\ntable.add(inputs)\n"; + +export const PyEmbeddingImagebindTextSearch = "query = \"an animal which flies and tweets\"\nactual = table.search(query).limit(1).to_pydantic(ImageBindModel)[0]\nprint(actual.text == \"bird\")\n"; + +export const PyEmbeddingInstructorUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ninstructor = (\n get_registry()\n .get(\"instructor\")\n .create(\n source_instruction=\"represent the document for retrieval\",\n query_instruction=\"represent the document for retrieving the most similar documents\",\n )\n)\n\nclass Schema(LanceModel):\n vector: Vector(instructor.ndims()) = instructor.VectorField()\n text: str = instructor.SourceField()\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"instructor-demo\"))\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\n\ntexts = [\n {\n \"text\": \"Capitalism has been dominant in the Western world since the end of feudalism.\"\n },\n {\n \"text\": \"The disparate impact theory is especially controversial under the Fair Housing Act.\"\n },\n {\n \"text\": \"Disparate impact in United States labor law refers to practices in employment.\"\n },\n]\n\ntbl.add(texts)\n"; + +export const PyEmbeddingJinaMultimodal = "import os\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nimport requests\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\nos.environ[\"JINA_API_KEY\"] = os.environ.get(\"JINA_API_KEY\", \"jina_*\")\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"jina-images\"))\nfunc = get_registry().get(\"jina\").create()\n\nclass Images(LanceModel):\n label: str\n image_uri: str = func.SourceField()\n image_bytes: bytes = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n vec_from_bytes: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"images\", schema=Images)\nlabels = [\"cat\", \"cat\", \"dog\", \"dog\", \"horse\", \"horse\"]\nuris = [\n \"http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg\",\n \"http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg\",\n \"http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg\",\n \"http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg\",\n \"http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg\",\n \"http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg\",\n]\nimage_bytes = [requests.get(uri).content for uri in uris]\ntable.add(\n pd.DataFrame({\"label\": labels, \"image_uri\": uris, \"image_bytes\": image_bytes})\n)\n"; + +export const PyEmbeddingJinaText = "import os\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\nos.environ[\"JINA_API_KEY\"] = os.environ[\"JINA_API_KEY\"]\n\njina_embed = (\n EmbeddingFunctionRegistry.get_instance()\n .get(\"jina\")\n .create(name=\"jina-embeddings-v2-base-en\")\n)\n\nclass TextModel(LanceModel):\n text: str = jina_embed.SourceField()\n vector: Vector(jina_embed.ndims()) = jina_embed.VectorField()\n\ndata = [{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"jina-text\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\n\ntbl.add(data)\n"; + +export const PyEmbeddingOllamaUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"ollama-demo\"))\nfunc = get_registry().get(\"ollama\").create(name=\"nomic-embed-text\")\n\nclass Words(LanceModel):\n text: str = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add(\n [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n ]\n)\n\nquery = \"greetings\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n"; + +export const PyEmbeddingOpenaiBasic = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb_path = Path(tempfile.mkdtemp()) / \"openai-embeddings\"\ndb = lancedb.connect(str(db_path))\nfunc = get_registry().get(\"openai\").create(name=\"text-embedding-ada-002\")\n\nclass Words(LanceModel):\n text: str = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"words\", schema=Words, mode=\"overwrite\")\ntable.add(\n [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n ]\n)\n\nquery = \"greetings\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n"; + +export const PyEmbeddingOpenclipImageSearch = "import io\n\nfrom PIL import Image\n\nquery_image_uri = \"http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg\"\nimage_bytes = requests.get(query_image_uri).content\nquery_image = Image.open(io.BytesIO(image_bytes))\nactual = table.search(query_image).limit(1).to_pydantic(Images)[0]\nprint(actual.label == \"dog\")\n\nother = (\n table.search(query_image, vector_column_name=\"vec_from_bytes\")\n .limit(1)\n .to_pydantic(Images)[0]\n)\nprint(other.label)\n"; + +export const PyEmbeddingOpenclipSetup = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\nimport requests\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"openclip-demo\"))\nfunc = get_registry().get(\"open-clip\").create()\n\nclass Images(LanceModel):\n label: str\n image_uri: str = func.SourceField()\n image_bytes: bytes = func.SourceField()\n vector: Vector(func.ndims()) = func.VectorField()\n vec_from_bytes: Vector(func.ndims()) = func.VectorField()\n\ntable = db.create_table(\"images\", schema=Images)\nlabels = [\"cat\", \"cat\", \"dog\", \"dog\", \"horse\", \"horse\"]\nuris = [\n \"http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg\",\n \"http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg\",\n \"http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg\",\n \"http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg\",\n \"http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg\",\n \"http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg\",\n]\nimage_bytes = [requests.get(uri).content for uri in uris]\ntable.add(\n pd.DataFrame({\"label\": labels, \"image_uri\": uris, \"image_bytes\": image_bytes})\n)\n"; + +export const PyEmbeddingOpenclipTextSearch = "actual = table.search(\"man's best friend\").limit(1).to_pydantic(Images)[0]\nprint(actual.label)\n\nfrombytes = (\n table.search(\"man's best friend\", vector_column_name=\"vec_from_bytes\")\n .limit(1)\n .to_pydantic(Images)[0]\n)\nprint(frombytes.label)\n"; + +export const PyEmbeddingSentenceTransformersBaai = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"sentence-transformers\"))\nmodel = (\n get_registry()\n .get(\"sentence-transformers\")\n .create(name=\"BAAI/bge-small-en-v1.5\", device=\"cpu\")\n)\n\nclass Words(LanceModel):\n text: str = model.SourceField()\n vector: Vector(model.ndims()) = model.VectorField()\n\ntable = db.create_table(\"words\", schema=Words)\ntable.add(\n [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n ]\n)\n\nquery = \"greetings\"\nactual = table.search(query).limit(1).to_pydantic(Words)[0]\nprint(actual.text)\n"; + +export const PyEmbeddingVoyageaiMultimodal = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\n# Create multimodal embedding function with custom dimension\nvoyageai = (\n EmbeddingFunctionRegistry.get_instance()\n .get(\"voyageai\")\n .create(name=\"voyage-multimodal-3.5\", output_dimension=512)\n)\n\nclass ImageModel(LanceModel):\n image_uri: str = voyageai.SourceField()\n vector: Vector(voyageai.ndims()) = voyageai.VectorField()\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"voyageai-multimodal\"))\ntbl = db.create_table(\"images\", schema=ImageModel, mode=\"overwrite\")\n\n# Add images using URLs\ntbl.add(\n [\n {\"image_uri\": \"https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/300px-PNG_transparency_demonstration_1.png\"},\n ]\n)\n\n# Search with text query\nresults = tbl.search(\"dice\").limit(1).to_list()\nprint(results)\n"; + +export const PyEmbeddingVoyageaiUsage = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.embeddings import EmbeddingFunctionRegistry\nfrom lancedb.pydantic import LanceModel, Vector\n\nvoyageai = (\n EmbeddingFunctionRegistry.get_instance().get(\"voyageai\").create(name=\"voyage-3\")\n)\n\nclass TextModel(LanceModel):\n text: str = voyageai.SourceField()\n vector: Vector(voyageai.ndims()) = voyageai.VectorField()\n\ndata = [{\"text\": \"hello world\"}, {\"text\": \"goodbye world\"}]\n\ndb = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"voyageai-demo\"))\ntbl = db.create_table(\"test\", schema=TextModel, mode=\"overwrite\")\n\ntbl.add(data)\n"; + +export const PyFrameworksAgnoAgent = "agent = Agent(\n model=OpenAIResponses(id=\"gpt-5-mini\"),\n knowledge=knowledge,\n search_knowledge=True,\n instructions=\"Search the transcript and answer only from retrieved context.\",\n markdown=True,\n)\n"; + +export const PyFrameworksAgnoCliChat = "agent.print_response(\n \"Summarize the loaded video transcript in 5 concise bullet points.\",\n stream=True,\n)\nwhile True:\n question = input(\"You: \").strip()\n if question.lower() in {\"exit\", \"quit\", \"bye\"}:\n break\n agent.print_response(question, stream=True)\n"; + +export const PyFrameworksAgnoIngestYoutube = "youtube_url = \"https://www.youtube.com/watch?v=wl6mFyXoxos\"\nvideo_id = extract_video_id(youtube_url)\nytt = YouTubeTranscriptApi()\ntranscript_segments = ytt.fetch(video_id, languages=[\"en\", \"en-US\"]).to_raw_data()\ntranscript_text = \" \".join(segment[\"text\"] for segment in transcript_segments)\n\nknowledge.insert(\n name=f\"YouTube Transcript ({video_id})\",\n text_content=transcript_text,\n metadata={\"source\": \"youtube\", \"video_id\": video_id, \"video_url\": youtube_url},\n)\n"; + +export const PyFrameworksAgnoSetup = "import os\nimport re\n\nfrom agno.agent import Agent\nfrom agno.knowledge.embedder.openai import OpenAIEmbedder\nfrom agno.knowledge.knowledge import Knowledge\nfrom agno.models.openai import OpenAIResponses\nfrom agno.vectordb.lancedb import LanceDb, SearchType\nfrom youtube_transcript_api import YouTubeTranscriptApi\n\nif \"OPENAI_API_KEY\" not in os.environ:\n os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n\ndef extract_video_id(youtube_url: str) -> str:\n match = re.search(r\"(?<=v=)[\\w-]+\", youtube_url) or re.search(\n r\"(?<=be/)[\\w-]+\", youtube_url\n )\n if not match:\n raise ValueError(\"Could not parse YouTube video ID from URL\")\n return match.group(0)\n\nknowledge = Knowledge(\n vector_db=LanceDb(\n uri=\"./tmp/lancedb\",\n table_name=\"youtube_transcripts\",\n search_type=SearchType.hybrid,\n embedder=OpenAIEmbedder(id=\"text-embedding-3-small\"),\n ),\n)\n"; + +export const PyFrameworksLangchainAddImages = "image_uris = [\"./assets/image-1.png\", \"./assets/image-2.png\"]\nvector_store.add_images(uris=image_uris)\n# here image_uris are local fs paths to the images.\n"; + +export const PyFrameworksLangchainAddTexts = "vector_store.add_texts(texts=[\"test_123\"], metadatas=[{\"source\": \"wiki\"}])\n\n# Additionaly, to explore the table you can load it into a df or save it in a csv file:\n\ntbl = vector_store.get_table()\nprint(\"tbl:\", tbl)\npd_df = tbl.to_pandas()\npd_df.to_csv(\"docsearch.csv\", index=False)\n\n# you can also create a new vector store object using an older connection object:\nvector_store = LanceDB(connection=tbl, embedding=embeddings)\n"; + +export const PyFrameworksLangchainCreateIndex = "# for creating vector index\nvector_store.create_index(vector_col=\"vector\", metric=\"cosine\")\n\n# for creating scalar index(for non-vector columns)\nvector_store.create_index(col_name=\"text\")\n"; + +export const PyFrameworksLangchainMaxMarginalRelevance = "result = docsearch.max_marginal_relevance_search(query=\"text\")\nresult_texts = [doc.page_content for doc in result]\nprint(result_texts)\n\n# search by vector :\nresult = docsearch.max_marginal_relevance_search_by_vector(\n embeddings.embed_query(\"text\")\n)\nresult_texts = [doc.page_content for doc in result]\nprint(result_texts)\n"; + +export const PyFrameworksLangchainQuickStart = "import os\n\nfrom langchain.document_loaders import TextLoader\nfrom langchain.vectorstores import LanceDB\nfrom langchain_openai import OpenAIEmbeddings\nfrom langchain_text_splitters import CharacterTextSplitter\n\nos.environ[\"OPENAI_API_KEY\"] = \"sk-...\"\n\nloader = TextLoader(\n \"../../modules/state_of_the_union.txt\"\n) # Replace with your data path\ndocuments = loader.load()\n\ndocuments = CharacterTextSplitter().split_documents(documents)\nembeddings = OpenAIEmbeddings()\n\ndocsearch = LanceDB.from_documents(documents, embeddings)\nquery = \"What did the president say about Ketanji Brown Jackson\"\ndocs = docsearch.similarity_search(query)\nprint(docs[0].page_content)\n"; + +export const PyFrameworksLangchainSimilaritySearch = "docs = docsearch.similarity_search(query)\nprint(docs[0].page_content)\n"; + +export const PyFrameworksLangchainSimilaritySearchByVector = "docs = docsearch.similarity_search_by_vector(query)\nprint(docs[0].page_content)\n"; + +export const PyFrameworksLangchainSimilaritySearchByVectorWithScores = "query_embedding = embeddings.embed_query(\"text\")\ndocs = docsearch.similarity_search_by_vector_with_relevance_scores(query_embedding)\nprint(\"relevance score - \", docs[0][1])\nprint(\"text- \", docs[0][0].page_content[:1000])\n"; + +export const PyFrameworksLangchainSimilaritySearchWithScores = "docs = docsearch.similarity_search_with_relevance_scores(query)\nprint(\"relevance score - \", docs[0][1])\nprint(\"text- \", docs[0][0].page_content[:1000])\n"; + +export const PyFrameworksLangchainVectorStoreConfig = "db_url = \"db://lang_test\" # url of db you created\napi_key = \"xxxxx\" # your API key\nregion = \"us-east-1-dev\" # your selected region\n\nvector_store = LanceDB(\n uri=db_url,\n api_key=api_key, # (dont include for local API)\n region=region, # (dont include for local API)\n embedding=embeddings,\n table_name=\"langchain_test\", # Optional\n)\n"; + +export const PyFrameworksLerobotFilterFrames = "frame_rows = (\n frames.search()\n .where(\"episode_index = 0 AND frame_index < 10\", prefilter=True)\n .select([\"episode_index\", \"frame_index\", \"timestamp\", \"action\"])\n .limit(10)\n .to_list()\n)\n\nfor row in frame_rows:\n print(row[\"episode_index\"], row[\"frame_index\"], row[\"timestamp\"])\n"; + +export const PyFrameworksLerobotLancedbImageDataset = "from lerobot_lancedb import LeRobotLanceDataset\n\ndataset = LeRobotLanceDataset(\n repo_id=\"your-org/your-lerobot-lance-images\",\n delta_timestamps={\n \"observation.images.front\": [-0.2, -0.1, 0.0],\n },\n return_uint8=True,\n)\n\nsample = dataset[0]\nprint(sample[\"observation.state\"].shape)\nprint(sample[\"action\"].shape)\n"; + +export const PyFrameworksLerobotLancedbVideoDataset = "from lerobot_lancedb import LeRobotLanceVideoDataset\n\nvideo_dataset = LeRobotLanceVideoDataset(\n repo_id=\"lance-format/lerobot-pusht-lance\",\n delta_timestamps={\n \"observation.images.image\": [-0.2, -0.1, 0.0],\n },\n return_uint8=True,\n)\n\nvideo_sample = video_dataset[0]\nprint(video_sample[\"observation.images.image\"].shape)\n"; + +export const PyFrameworksLerobotOpenLanceTables = "import lancedb\n\ndb = lancedb.connect(\"hf://datasets/lance-format/lerobot-pusht-lance/data\")\nframes = db.open_table(\"frames\")\nepisodes = db.open_table(\"episodes\")\nvideos = db.open_table(\"videos\")\n\nprint(len(frames), len(episodes), len(videos))\nprint(frames.schema)\n"; + +export const PyFrameworksLlamaindexAddReranker = "from lancedb.rerankers import ColbertReranker\n\nreranker = ColbertReranker()\nvector_store._add_reranker(reranker)\n"; + +export const PyFrameworksLlamaindexFiltering = "from llama_index.core.vector_stores import (\n FilterCondition,\n FilterOperator,\n MetadataFilter,\n MetadataFilters,\n)\n\nquery_filters = MetadataFilters(\n filters=[\n MetadataFilter(\n key=\"creation_date\", operator=FilterOperator.EQ, value=\"2024-05-23\"\n ),\n MetadataFilter(key=\"file_size\", value=75040, operator=FilterOperator.GT),\n ],\n condition=FilterCondition.AND,\n)\n"; + +export const PyFrameworksLlamaindexHybridSearch = "from lancedb.rerankers import ColbertReranker\n\nreranker = ColbertReranker()\nvector_store._add_reranker(reranker)\n\nquery_engine = index.as_query_engine(\n filters=query_filters,\n vector_store_kwargs={\n \"query_type\": \"hybrid\",\n },\n)\n\nresponse = query_engine.query(\"How much did Viaweb charge per month?\")\n"; + +export const PyFrameworksLlamaindexQuickStart = "import logging\nimport sys\nimport textwrap\n\nimport openai\n\n# Uncomment to see debug logs\n# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\nfrom llama_index.core import (\n Document,\n SimpleDirectoryReader,\n StorageContext,\n VectorStoreIndex,\n)\nfrom llama_index.vector_stores.lancedb import LanceDBVectorStore\n\nopenai.api_key = \"sk-...\"\n\ndocuments = SimpleDirectoryReader(\"./data/your-data-dir/\").load_data()\nprint(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].hash)\n\n## For LanceDB Enterprise :\n# vector_store = LanceDBVectorStore(\n# uri=\"db://db_name\", # your remote DB URI\n# api_key=\"sk_..\", # lancedb enterprise api key\n# region=\"your-region\" # the region you configured\n# host_override=\"https://your-host.com\" # if you have a custom host, otherwise omit this\n# )\n\nvector_store = LanceDBVectorStore(\n uri=\"./lancedb\", mode=\"overwrite\", query_type=\"vector\"\n)\nstorage_context = StorageContext.from_defaults(vector_store=vector_store)\n\nindex = VectorStoreIndex.from_documents(documents, storage_context=storage_context)\nlance_filter = \"metadata.file_name = 'paul_graham_essay.txt' \"\nretriever = index.as_retriever(vector_store_kwargs={\"where\": lance_filter})\nresponse = retriever.retrieve(\"What did the author do growing up?\")\n"; + +export const PyFrameworksPydanticBaseExample = "table = db.create_table(\"docs\", schema=LanceDocs, mode=\"overwrite\")\ntable.add(\n [\n {\"text\": \"hello world\", \"vector\": [1.0, 0.0]},\n {\"text\": \"goodbye world\", \"vector\": [0.0, 1.0]},\n ]\n)\nresults = table.search(\"hello world\").limit(1).to_pydantic(LanceDocs)\nprint(results[0].text)\n"; + +export const PyFrameworksPydanticBaseModel = "class LanceDocs(LanceModel):\n text: str\n vector: Vector(2)\n"; + +export const PyFrameworksPydanticImports = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nfrom lancedb.pydantic import LanceModel, Vector\n"; + +export const PyFrameworksPydanticSetUrl = "db = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"pydantic-docs\"))\n"; + +export const PyFrameworksPydanticTypeConversion = "from typing import List, Optional\n\nimport pyarrow as pa\nimport pydantic\nfrom lancedb.pydantic import Vector, pydantic_to_schema\n\nclass FooModel(pydantic.BaseModel):\n id: int\n s: str\n vec: Vector(1536) # fixed_size_list[1536]\n li: List[int]\n\nschema = pydantic_to_schema(FooModel)\nassert schema == pa.schema(\n [\n pa.field(\"id\", pa.int64(), False),\n pa.field(\"s\", pa.utf8(), False),\n pa.field(\"vec\", pa.list_(pa.float32(), 1536)),\n pa.field(\"li\", pa.list_(pa.int64()), False),\n ]\n)\n"; + +export const PyFrameworksPydanticVectorField = "import pyarrow as pa\nimport pydantic\nfrom lancedb.pydantic import Vector, pydantic_to_schema\n\nclass MyModel(pydantic.BaseModel):\n id: int\n url: str\n embeddings: Vector(768)\n\nschema = pydantic_to_schema(MyModel)\nassert schema == pa.schema(\n [\n pa.field(\"id\", pa.int64(), False),\n pa.field(\"url\", pa.utf8(), False),\n pa.field(\"embeddings\", pa.list_(pa.float32(), 768)),\n ]\n)\n"; + +export const PyFrameworksStableWorldmodelCollectLance = "import stable_worldmodel as swm\n\nworld = swm.World(\"swm/PushT-v1\", num_envs=8)\nworld.set_policy(your_expert_policy)\nworld.collect(\"data/pusht_demo.lance\", episodes=100, seed=0)\n"; + +export const PyFrameworksStableWorldmodelConvert = "swm.data.convert(\n \"data/pusht_demo.lance\",\n \"data/pusht_video\",\n dest_format=\"video\",\n fps=30,\n)\n"; + +export const PyFrameworksStableWorldmodelEvaluate = "from stable_worldmodel.policy import PlanConfig, WorldModelPolicy\nfrom stable_worldmodel.solver import CEMSolver\n\nsolver = CEMSolver(model=world_model, num_samples=300)\npolicy = WorldModelPolicy(solver=solver, config=PlanConfig(horizon=10))\n\nworld.set_policy(policy)\nresults = world.evaluate(episodes=50)\nprint(f\"Success Rate: {results['success_rate']:.1f}%\")\n"; + +export const PyFrameworksStableWorldmodelLoadLance = "dataset = swm.data.load_dataset(\"data/pusht_demo.lance\", num_steps=16)\n\nbatch = dataset[0]\nprint(batch.keys())\n"; + +export const PyPlatformsDltAdapterImport = "from dlt.destinations.adapters import lancedb_adapter\n"; + +export const PyPlatformsDltAdapterUsage = "load_info = pipeline.run(\n lancedb_adapter(\n movies_source,\n embed=\"Title\",\n )\n)\n"; + +export const PyPlatformsDltPipeline = "# Import necessary modules\nimport dlt\nfrom rest_api import rest_api_source\n\n# Configure the REST API source\nmovies_source = rest_api_source(\n {\n \"client\": {\n \"base_url\": \"https://www.omdbapi.com/\",\n \"auth\": { # authentication strategy for the OMDb API\n \"type\": \"api_key\",\n \"name\": \"apikey\",\n \"api_key\": dlt.secrets[\n \"sources.rest_api.api_token\"\n ], # read API credentials directly from secrets.toml\n \"location\": \"query\",\n },\n \"paginator\": { # pagination strategy for the OMDb API\n \"type\": \"page_number\",\n \"base_page\": 1,\n \"total_path\": \"totalResults\",\n \"maximum_page\": 5,\n },\n },\n \"resources\": [ # list of API endpoints to request\n {\n \"name\": \"movie_search\",\n \"endpoint\": {\n \"path\": \"/\",\n \"params\": {\n \"s\": \"godzilla\",\n \"type\": \"movie\",\n },\n },\n }\n ],\n }\n)\n\nif __name__ == \"__main__\":\n # Create a pipeline object\n pipeline = dlt.pipeline(\n pipeline_name=\"movies_pipeline\",\n destination=\"lancedb\", # this tells dlt to load the data into LanceDB\n dataset_name=\"movies_data_pipeline\",\n )\n\n # Run the pipeline\n load_info = pipeline.run(movies_source)\n\n # pretty print the information on data that was loaded\n print(load_info)\n"; + +export const PyPlatformsDuckdbCreateTable = "import lancedb\n\ndb = lancedb.connect(\"data/sample-lancedb\")\ndata = [\n {\"vector\": [3.1, 4.1], \"item\": \"foo\", \"price\": 10.0},\n {\"vector\": [5.9, 26.5], \"item\": \"bar\", \"price\": 20.0},\n]\ntable = db.create_table(\"pd_table\", data=data)\n"; + +export const PyPlatformsDuckdbMeanPrice = "duckdb.query(\"SELECT mean(price) FROM arrow_table\")\n"; + +export const PyPlatformsDuckdbQueryTable = "import duckdb\n\narrow_table = table.to_lance()\n\nduckdb.query(\"SELECT * FROM arrow_table\")\n"; + +export const PyPlatformsPandasAsyncExample = "async def run_pandas_async_example() -> None:\n async_db = await lancedb.connect_async(\n str(Path(tempfile.mkdtemp()) / \"pandas-async\")\n )\n async_df = pd.DataFrame(\n [\n {\"id\": \"10\", \"text\": \"sage\", \"vector\": [0.6, 0.4, 0.8]},\n {\"id\": \"11\", \"text\": \"bard\", \"vector\": [0.2, 0.7, 0.3]},\n ]\n )\n async_table = await async_db.create_table(\n \"creatures_async\", data=async_df, mode=\"overwrite\"\n )\n async_results = await (\n async_table.search([0.6, 0.4, 0.8])\n .select([\"text\", \"_distance\"])\n .limit(1)\n .to_pandas()\n )\n print(async_results)\n\nasyncio.run(run_pandas_async_example())\n"; + +export const PyPlatformsPandasCreateTable = "pandas_df = pd.DataFrame(\n [\n {\"id\": \"1\", \"text\": \"dragon\", \"vector\": [0.9, 0.1, 0.3]},\n {\"id\": \"2\", \"text\": \"griffin\", \"vector\": [0.4, 0.5, 0.2]},\n {\"id\": \"3\", \"text\": \"phoenix\", \"vector\": [0.7, 0.3, 0.6]},\n ]\n)\npandas_db = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"pandas-demo\"))\npandas_table = pandas_db.create_table(\"creatures\", data=pandas_df, mode=\"overwrite\")\n"; + +export const PyPlatformsPandasImports = "import asyncio\nimport tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport pandas as pd\n"; + +export const PyPlatformsPandasVectorSearch = "pandas_results = (\n pandas_table.search([0.9, 0.1, 0.3])\n .select([\"text\", \"_distance\"])\n .limit(1)\n .to_pandas()\n)\nprint(pandas_results)\n"; + +export const PyPlatformsPolarsCreateTable = "birds = pl.DataFrame(\n {\n \"text\": [\"phoenix\", \"sparrow\"],\n \"vector\": [\n [0.1, 0.2, 0.3],\n [0.8, 0.6, 0.5],\n ],\n }\n)\npolars_db = lancedb.connect(str(Path(tempfile.mkdtemp()) / \"polars-demo\"))\npolars_table = polars_db.create_table(\n \"birds\", data=birds.to_arrow(), mode=\"overwrite\"\n)\n"; + +export const PyPlatformsPolarsImports = "import tempfile\nfrom pathlib import Path\n\nimport lancedb\nimport polars as pl\nfrom lancedb.pydantic import LanceModel, Vector\n"; + +export const PyPlatformsPolarsLazyframe = "lazy_frame = polars_table.to_polars().lazy()\nprint(lazy_frame.select([\"text\"]).collect())\n"; + +export const PyPlatformsPolarsPydantic = "class BirdModel(LanceModel):\n text: str\n vector: Vector(3)\n\nschema_table = polars_db.create_table(\n \"birds_schema\", schema=BirdModel, mode=\"overwrite\"\n)\nschema_table.add(birds.to_dicts())\n"; + +export const PyPlatformsPolarsVectorSearch = "polars_results = (\n polars_table.search([0.1, 0.2, 0.3])\n .select([\"text\", \"_distance\"])\n .limit(1)\n .to_polars()\n)\nprint(polars_results)\n"; + +export const PyPlatformsVoxel51BackendFlag = "import fiftyone.brain as fob\n\n# Re-run similarity creation using the LanceDB backend explicitly\nfob.compute_similarity(\n dataset,\n model=\"clip-vit-base32-torch\",\n brain_key=\"lancedb_index\",\n backend=\"lancedb\",\n)\n"; + +export const PyPlatformsVoxel51BackendParams = "lancedb_index = fob.compute_similarity(\n dataset,\n model=\"clip-vit-base32-torch\",\n backend=\"lancedb\",\n brain_key=\"lancedb_index\",\n table_name=\"your-table\",\n metric=\"euclidean\",\n uri=\"/tmp/lancedb\",\n)\n"; + +export const PyPlatformsVoxel51BrainConfig = "import fiftyone.brain as fob\n\n# Print your current brain config\nprint(fob.brain_config)\n"; + +export const PyPlatformsVoxel51Cleanup = "# Step 5 (optional): Cleanup\n\n# Delete the LanceDB table\nlancedb_index.cleanup()\n\n# Delete run record from FiftyOne\ndataset.delete_brain_run(\"lancedb_index\")\n"; + +export const PyPlatformsVoxel51ComputeSimilarity = "# Steps 2 and 3: Compute embeddings and create a similarity index\nlancedb_index = fob.compute_similarity(\n dataset,\n model=\"clip-vit-base32-torch\",\n brain_key=\"lancedb_index\",\n backend=\"lancedb\",\n)\n"; + +export const PyPlatformsVoxel51LoadDataset = "import fiftyone as fo\nimport fiftyone.brain as fob\nimport fiftyone.zoo as foz\n\n# Step 1: Load your data into FiftyOne\ndataset = foz.load_zoo_dataset(\"quickstart\")\n"; + +export const PyPlatformsVoxel51SortBySimilarity = "# Step 4: Query your data\nquery = dataset.first().id # query by sample ID\nview = dataset.sort_by_similarity(\n query,\n brain_key=\"lancedb_index\",\n k=10, # limit to 10 most similar samples\n)\n"; + +export const PyRerankingAnswerdotaiUsage = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import AnswerdotaiRerankers\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = AnswerdotaiRerankers()\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const PyRerankingCohereUsage = "import os\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import CohereReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = CohereReranker(api_key=os.environ[\"COHERE_API_KEY\"])\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const PyRerankingColbertUsage = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import ColbertReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = ColbertReranker()\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const PyRerankingCrossEncoderUsage = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import CrossEncoderReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = CrossEncoderReranker()\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const PyRerankingJinaUsage = "import os\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import JinaReranker\n\nembedder = get_registry().get(\"jina\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = JinaReranker(api_key=os.environ[\"JINA_API_KEY\"])\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const PyRerankingLinearCombinationUsage = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import LinearCombinationReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = LinearCombinationReranker()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const PyRerankingMrrUsage = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import MRRReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = MRRReranker(weight_vector=0.7, weight_fts=0.3)\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n\n# Run multivector search across multiple vector columns\nrs1 = tbl.search(\"hello\").limit(10).with_row_id(True).to_arrow()\nrs2 = tbl.search(\"greeting\").limit(10).with_row_id(True).to_arrow()\ncombined = MRRReranker().rerank_multivector([rs1, rs2])\n"; + +export const PyRerankingOpenaiUsage = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import OpenaiReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = OpenaiReranker()\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const PyRerankingRrfUsage = "import lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import RRFReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = RRFReranker()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const PyRerankingVoyageaiUsage = "import os\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import VoyageAIReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\nreranker = VoyageAIReranker(model_name=\"rerank-2\")\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const PyRerankingWatsonxUsage = "import os\n\nimport lancedb\nfrom lancedb.embeddings import get_registry\nfrom lancedb.pydantic import LanceModel, Vector\nfrom lancedb.rerankers import WatsonxReranker\n\nembedder = get_registry().get(\"sentence-transformers\").create()\ndb = lancedb.connect(\"~/.lancedb\")\n\nclass Schema(LanceModel):\n text: str = embedder.SourceField()\n vector: Vector(embedder.ndims()) = embedder.VectorField()\n\ndata = [\n {\"text\": \"hello world\"},\n {\"text\": \"goodbye world\"},\n]\ntbl = db.create_table(\"test\", schema=Schema, mode=\"overwrite\")\ntbl.add(data)\n\n# Credentials pulled from WATSONX_API_KEY and WATSONX_PROJECT_ID (or WATSONX_SPACE_ID)\nreranker = WatsonxReranker(\n api_key=os.environ[\"WATSONX_API_KEY\"],\n project_id=os.environ[\"WATSONX_PROJECT_ID\"],\n)\n\n# Run vector search with a reranker\nresult = tbl.search(\"hello\").rerank(reranker=reranker).to_list()\n\n# Run FTS search with a reranker\nresult = tbl.search(\"hello\", query_type=\"fts\").rerank(reranker=reranker).to_list()\n\n# Run hybrid search with a reranker\ntbl.create_fts_index(\"text\", replace=True)\nresult = (\n tbl.search(\"hello\", query_type=\"hybrid\").rerank(reranker=reranker).to_list()\n)\n"; + +export const TsFrameworksGenkitCustomIndexer = "export const menuPdfIndexer = lancedbIndexerRef({\n // Using all defaults, for dbUri, tableName, and embedder, etc\n});\n\nconst chunkingConfig = {\n minLength: 1000,\n maxLength: 2000,\n splitter: \"sentence\",\n overlap: 100,\n delimiters: \"\",\n} as any;\n\nasync function extractTextFromPdf(filePath: string) {\n const pdfFile = path.resolve(filePath);\n const dataBuffer = await readFile(pdfFile);\n const data = await pdf(dataBuffer);\n return data.text;\n}\n\nexport const indexMenu = ai.defineFlow(\n {\n name: \"indexMenu\",\n inputSchema: z.string().describe(\"PDF file path\"),\n outputSchema: z.void(),\n },\n async (filePath: string) => {\n filePath = path.resolve(filePath);\n\n // Read the pdf.\n const pdfTxt = await ai.run(\"extract-text\", () => extractTextFromPdf(filePath));\n\n // Divide the pdf text into segments.\n const chunks = await ai.run(\"chunk-it\", async () => chunk(pdfTxt, chunkingConfig));\n\n // Convert chunks of text into documents to store in the index.\n const documents = chunks.map((text) => {\n return Document.fromText(text, { filePath });\n });\n\n // Add documents to the index.\n await ai.index({\n indexer: menuPdfIndexer,\n documents,\n options: {\n writeMode: WriteMode.Overwrite,\n } as any,\n });\n },\n);\n"; + +export const TsFrameworksGenkitCustomRetriever = "export const menuRetriever = lancedbRetrieverRef({\n tableName: \"table\", // Use the same table name as the indexer.\n displayName: \"Menu\", // Use a custom display name.\n});\n\nexport const menuQAFlow = ai.defineFlow(\n { name: \"Menu\", inputSchema: z.string(), outputSchema: z.string() },\n async (input: string) => {\n // retrieve relevant documents\n const docs = await ai.retrieve({\n retriever: menuRetriever,\n query: input,\n options: {\n k: 3,\n },\n });\n\n const extractedContent = docs.map((doc) => {\n if (doc.content && Array.isArray(doc.content) && doc.content.length > 0) {\n if (doc.content[0].media && doc.content[0].media.url) {\n return doc.content[0].media.url;\n }\n }\n return \"No content found\";\n });\n\n console.log(\"Extracted content:\", extractedContent);\n\n const { text } = await ai.generate({\n model: gemini(\"gemini-2.0-flash\"),\n prompt: `\nYou are acting as a helpful AI assistant that can answer \nquestions about the food available on the menu at Genkit Grub Pub.\n\nUse only the context provided to answer the question.\nIf you don't know, do not make up an answer.\nDo not add or change items on the menu.\n\nContext:\n${extractedContent.join(\"\\n\\n\")}\n\nQuestion: ${input}`,\n docs,\n });\n\n return text;\n },\n);\n"; + +export const TsFrameworksGenkitUsage = "import { lancedbIndexerRef, lancedb, lancedbRetrieverRef, WriteMode } from \"genkitx-lancedb\";\nimport { textEmbedding004, vertexAI } from \"@genkit-ai/vertexai\";\nimport { gemini } from \"@genkit-ai/vertexai\";\nimport { z, genkit } from \"genkit\";\nimport { Document } from \"genkit/retriever\";\nimport { chunk } from \"llm-chunk\";\nimport { readFile } from \"fs/promises\";\nimport path from \"path\";\nimport pdf from \"pdf-parse/lib/pdf-parse\";\n\nconst ai = genkit({\n plugins: [\n // vertexAI provides the textEmbedding004 embedder\n vertexAI(),\n\n // the local vector store requires an embedder to translate from text to vector\n lancedb([\n {\n dbUri: \".db\", // optional lancedb uri, default to .db\n tableName: \"table\", // optional table name, default to table\n embedder: textEmbedding004,\n },\n ]),\n ],\n});\n"; + diff --git a/docs/web/snippets/ivf_pq.mdx b/docs/web/snippets/ivf_pq.mdx new file mode 100644 index 000000000..fd1c6360b --- /dev/null +++ b/docs/web/snippets/ivf_pq.mdx @@ -0,0 +1,6 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const RsCreateIndex = "// For this example, `table` is a lancedb::Table with a column named\n// \"vector\" that is a vector column with dimension 128.\n\n// By default, if the column \"vector\" appears to be a vector column,\n// then an IVF_PQ index with reasonable defaults is created.\ntable\n .create_index(&[\"vector\"], Index::Auto)\n .execute()\n .await?;\n// For advanced cases, it is also possible to specifically request an\n// IVF_PQ index and provide custom parameters.\ntable\n .create_index(\n &[\"vector\"],\n Index::IvfPq(\n // Here we specify advanced indexing parameters. In this case\n // we are creating an index that my have better recall than the\n // default but is also larger and slower.\n IvfPqIndexBuilder::default()\n // This overrides the default distance type of l2\n .distance_type(DistanceType::Cosine)\n // With 1000 rows this have been ~31 by default\n .num_partitions(50)\n // With dimension 128 this would have been 8 by default\n .num_sub_vectors(16),\n ),\n )\n .execute()\n .await?;\n"; + +export const RsSearch1 = "let query_vector = [1.0; 128];\n// By default the index will find the 10 closest results using default\n// search parameters that give a reasonable tradeoff between accuracy\n// and search latency\nlet mut results = table\n .vector_search(&query_vector)?\n // Note: you should always set the distance_type to match the value used\n // to train the index\n .distance_type(DistanceType::Cosine)\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n// We can also provide custom search parameters. Here we perform a\n// slower but more accurate search\nlet mut results = table\n .vector_search(&query_vector)?\n .distance_type(DistanceType::Cosine)\n // Override the default of 10 to get more rows\n .limit(15)\n // Override the default of 20 to search more partitions\n .nprobes(30)\n // Override the default of None to apply a refine step\n .refine_factor(1)\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\nOk(())\n"; + diff --git a/docs/web/snippets/merge_insert.mdx b/docs/web/snippets/merge_insert.mdx new file mode 100644 index 000000000..bdd55a631 --- /dev/null +++ b/docs/web/snippets/merge_insert.mdx @@ -0,0 +1,8 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const TsInsertIfNotExists = "const table2 = await db.createTable(\"domains\", [\n { domain: \"google.com\", name: \"Google\" },\n { domain: \"github.com\", name: \"GitHub\" },\n]);\n\nconst newDomains = [\n { domain: \"google.com\", name: \"Google\" },\n { domain: \"facebook.com\", name: \"Facebook\" },\n];\nawait table2\n .mergeInsert(\"domain\")\n .whenNotMatchedInsertAll()\n .execute(newDomains);\nawait table2.countRows(); // 3\n"; + +export const TsReplaceRange = "const table3 = await db.createTable(\"chunks\", [\n { doc_id: 0, chunk_id: 0, text: \"Hello\" },\n { doc_id: 0, chunk_id: 1, text: \"World\" },\n { doc_id: 1, chunk_id: 0, text: \"Foo\" },\n { doc_id: 1, chunk_id: 1, text: \"Bar\" },\n]);\n\nconst newChunks = [{ doc_id: 1, chunk_id: 0, text: \"Baz\" }];\n\nawait table3\n .mergeInsert([\"doc_id\", \"chunk_id\"])\n .whenMatchedUpdateAll()\n .whenNotMatchedInsertAll()\n .whenNotMatchedBySourceDelete({ where: \"doc_id = 1\" })\n .execute(newChunks);\n\nawait table3.countRows(\"doc_id = 1\"); // 1\n"; + +export const TsUpsertBasic = "const table = await db.createTable(\"users\", [\n { id: 0, name: \"Alice\" },\n { id: 1, name: \"Bob\" },\n]);\n\nconst newUsers = [\n { id: 1, name: \"Bobby\" },\n { id: 2, name: \"Charlie\" },\n];\nawait table\n .mergeInsert(\"id\")\n .whenMatchedUpdateAll()\n .whenNotMatchedInsertAll()\n .execute(newUsers);\n\nawait table.countRows(); // 3\n"; + diff --git a/docs/web/snippets/multimodal.mdx b/docs/web/snippets/multimodal.mdx new file mode 100644 index 000000000..22c3617a1 --- /dev/null +++ b/docs/web/snippets/multimodal.mdx @@ -0,0 +1,54 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyBlobApiIngest = "import lancedb\n\ndb = lancedb.connect(db_path_factory(\"blob_db\"))\n \n# Create sample data\ndata = [\n {\"id\": 1, \"video\": b\"fake_video_bytes_1\"},\n {\"id\": 2, \"video\": b\"fake_video_bytes_2\"}\n]\n \n# Create the table\ntbl = db.create_table(\"videos\", data=data, schema=schema)\n"; + +export const PyBlobApiSchema = "import pyarrow as pa\n\n# Define schema with Blob API metadata for lazy loading\nschema = pa.schema([\n pa.field(\"id\", pa.int64()),\n pa.field(\n \"video\", \n pa.large_binary(), \n metadata={\"lance-encoding:blob\": \"true\"} # Enable Blob API\n ),\n])\n"; + +export const PyBlobApiToPandas = "# Default: blob columns come back lazily\ndf_lazy = tbl.to_pandas()\n\n# Materialize blob bytes eagerly\ndf_bytes = tbl.to_pandas(blob_mode=\"bytes\")\n\n# Return descriptors instead of payloads\ndf_desc = tbl.to_pandas(blob_mode=\"descriptions\")\n\n# Forward extra kwargs to PyArrow's to_pandas\ndf_typed = tbl.to_pandas(split_blocks=True, self_destruct=True)\n"; + +export const PyCreateDummyData = "# Create some dummy images\ndef create_dummy_image(color):\n img = Image.new('RGB', (100, 100), color=color)\n buf = io.BytesIO()\n img.save(buf, format='PNG')\n return buf.getvalue()\n\n# Create dataset with metadata, vectors, and image blobs\ndata = [\n {\n \"id\": 1,\n \"filename\": \"red_square.png\",\n \"vector\": np.random.rand(128).astype(np.float32),\n \"image_blob\": create_dummy_image('red'),\n \"label\": \"red\"\n },\n {\n \"id\": 2,\n \"filename\": \"blue_square.png\",\n \"vector\": np.random.rand(128).astype(np.float32),\n \"image_blob\": create_dummy_image('blue'),\n \"label\": \"blue\"\n }\n]\n"; + +export const PyDefineSchema = "# Define schema explictly to ensure image_blob is treated as binary\nschema = pa.schema([\n pa.field(\"id\", pa.int32()),\n pa.field(\"filename\", pa.string()),\n pa.field(\"vector\", pa.list_(pa.float32(), 128)),\n pa.field(\"image_blob\", pa.binary()), # Important: Use pa.binary() for blobs\n pa.field(\"label\", pa.string())\n])\n"; + +export const PyIngestData = "tbl = db.create_table(\"images\", data=data, schema=schema, mode=\"overwrite\")\n"; + +export const PyMultimodalImports = "import lancedb\nimport pyarrow as pa\nimport pandas as pd\nimport numpy as np\nimport io\nfrom PIL import Image\n"; + +export const PyProcessResults = "# Convert back to PIL Image\nfor _, row in results.iterrows():\n image_bytes = row['image_blob']\n image = Image.open(io.BytesIO(image_bytes))\n print(f\"Retrieved image: {row['filename']}, Size: {image.size}\")\n # You can now use 'image' with other libraries or display it\n"; + +export const PyQueryToPandasKwargs = "# Plain scan query: blob_mode is supported end to end\ndf_lazy = (\n tbl.search()\n .where(\"id = 1\")\n .select([\"id\", \"video\"])\n .to_pandas(blob_mode=\"lazy\")\n)\n\n# Same call shape works on async query builders\ndf_bytes = await (\n tbl_async.query()\n .where(\"id = 1\")\n .select([\"id\", \"video\"])\n .to_pandas(blob_mode=\"bytes\")\n)\n\n# Vector / FTS / hybrid queries can't materialize blob columns,\n# so omit them from the projection\ndf_vec = (\n tbl.search(query_vector)\n .limit(10)\n .select([\"id\", \"vector\"])\n .to_pandas(split_blocks=True, self_destruct=True)\n)\n"; + +export const PySearchData = "# Search for similar images\nquery_vector = np.random.rand(128).astype(np.float32)\nresults = tbl.search(query_vector).limit(1).to_pandas()\n"; + +export const TsBlobApiIngest = "const blobData = lancedb.makeArrowTable(\n [\n { id: 1, video: Buffer.from(\"fake_video_bytes_1\") },\n { id: 2, video: Buffer.from(\"fake_video_bytes_2\") },\n ],\n { schema: blobSchema },\n);\nconst blobTable = await db.createTable(\"videos\", blobData, {\n mode: \"overwrite\",\n});\n"; + +export const TsBlobApiSchema = "const blobSchema = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Int64()),\n new arrow.Field(\n \"video\",\n new arrow.LargeBinary(),\n true,\n new Map([[\"lance-encoding:blob\", \"true\"]]),\n ),\n]);\n"; + +export const TsCreateDummyData = "const createDummyImage = (color: string): Uint8Array => {\n const pngHeader = Uint8Array.from([137, 80, 78, 71, 13, 10, 26, 10]);\n return Buffer.concat([Buffer.from(pngHeader), Buffer.from(color, \"utf8\")]);\n};\n\nconst data = [\n {\n id: 1,\n filename: \"red_square.png\",\n vector: Array.from({ length: 128 }, (_, i) => (i % 16) / 16),\n image_blob: createDummyImage(\"red\"),\n label: \"red\",\n },\n {\n id: 2,\n filename: \"blue_square.png\",\n vector: Array.from({ length: 128 }, (_, i) => ((i + 8) % 16) / 16),\n image_blob: createDummyImage(\"blue\"),\n label: \"blue\",\n },\n];\n"; + +export const TsDefineSchema = "const schema = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Int32()),\n new arrow.Field(\"filename\", new arrow.Utf8()),\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 128,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n new arrow.Field(\"image_blob\", new arrow.Binary()),\n new arrow.Field(\"label\", new arrow.Utf8()),\n]);\n"; + +export const TsIngestData = "const multimodalData = lancedb.makeArrowTable(data, { schema });\nconst tbl = await db.createTable(\"images\", multimodalData, {\n mode: \"overwrite\",\n});\n"; + +export const TsMultimodalImports = "import * as arrow from \"apache-arrow\";\nimport { Buffer } from \"node:buffer\";\nimport * as lancedb from \"@lancedb/lancedb\";\n"; + +export const TsProcessResults = "for (const row of results) {\n const imageBytes = row.image_blob as Uint8Array;\n console.log(\n `Retrieved image: ${row.filename}, Byte length: ${imageBytes.length}`,\n );\n}\n"; + +export const TsSearchData = "const queryVector = Array.from({ length: 128 }, (_, i) => (i % 16) / 16);\nconst results = await tbl.search(queryVector).limit(1).toArray();\n"; + +export const RsBlobApiIngest = "let blob_rows = vec![\n (1_i64, b\"fake_video_bytes_1\".to_vec()),\n (2_i64, b\"fake_video_bytes_2\".to_vec()),\n];\n\nlet blob_schema = Arc::new(blob_schema);\nlet blob_batch = RecordBatch::try_new(\n blob_schema.clone(),\n vec![\n Arc::new(Int64Array::from_iter_values(blob_rows.iter().map(|row| row.0))),\n Arc::new(LargeBinaryArray::from_iter_values(\n blob_rows.iter().map(|row| row.1.as_slice()),\n )),\n ],\n)\n.unwrap();\nlet blob_reader: Box =\n Box::new(RecordBatchIterator::new(vec![Ok(blob_batch)].into_iter(), blob_schema));\nlet blob_table = db\n .create_table(\"videos\", blob_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsBlobApiSchema = "let blob_metadata = HashMap::from([(\n \"lance-encoding:blob\".to_string(),\n \"true\".to_string(),\n)]);\nlet blob_schema = Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"video\", DataType::LargeBinary, true).with_metadata(blob_metadata),\n]);\n"; + +export const RsCreateDummyData = "let create_dummy_image = |color: u8| -> Vec {\n let mut png_like = vec![137, 80, 78, 71, 13, 10, 26, 10];\n png_like.push(color);\n png_like\n};\n\nlet data = vec![\n (\n 1_i32,\n \"red_square.png\",\n vec![0.1_f32; 128],\n create_dummy_image(1),\n \"red\",\n ),\n (\n 2_i32,\n \"blue_square.png\",\n vec![0.2_f32; 128],\n create_dummy_image(2),\n \"blue\",\n ),\n];\n"; + +export const RsDefineSchema = "let schema = Schema::new(vec![\n Field::new(\"id\", DataType::Int32, false),\n Field::new(\"filename\", DataType::Utf8, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n false,\n ),\n Field::new(\"image_blob\", DataType::Binary, false),\n Field::new(\"label\", DataType::Utf8, false),\n]);\n"; + +export const RsIngestData = "let schema = Arc::new(schema);\nlet image_batch = RecordBatch::try_new(\n schema.clone(),\n vec![\n Arc::new(Int32Array::from_iter_values(data.iter().map(|row| row.0))),\n Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.1))),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n data.iter()\n .map(|row| Some(row.2.iter().copied().map(Some).collect::>())),\n 128,\n ),\n ),\n Arc::new(BinaryArray::from_iter_values(\n data.iter().map(|row| row.3.as_slice()),\n )),\n Arc::new(StringArray::from_iter_values(data.iter().map(|row| row.4))),\n ],\n)\n.unwrap();\nlet image_reader: Box =\n Box::new(RecordBatchIterator::new(vec![Ok(image_batch)].into_iter(), schema.clone()));\nlet table = db\n .create_table(\"images\", image_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsMultimodalImports = "use std::collections::HashMap;\nuse std::sync::Arc;\n\nuse arrow_array::types::Float32Type;\nuse arrow_array::{\n BinaryArray, FixedSizeListArray, Int32Array, Int64Array, LargeBinaryArray, RecordBatch,\n RecordBatchIterator, StringArray,\n};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures_util::TryStreamExt;\nuse lancedb::connect;\nuse lancedb::database::CreateTableMode;\nuse lancedb::query::{ExecutableQuery, QueryBase};\n"; + +export const RsProcessResults = "for batch in &results {\n let filenames = batch\n .column_by_name(\"filename\")\n .unwrap()\n .as_any()\n .downcast_ref::()\n .unwrap();\n let images = batch\n .column_by_name(\"image_blob\")\n .unwrap()\n .as_any()\n .downcast_ref::()\n .unwrap();\n\n for row in 0..batch.num_rows() {\n let image_bytes = images.value(row);\n println!(\n \"Retrieved image: {}, Byte length: {}\",\n filenames.value(row),\n image_bytes.len()\n );\n }\n}\n"; + +export const RsSearchData = "let query_vector = vec![0.1_f32; 128];\nlet results = table\n .query()\n .nearest_to(query_vector)\n .unwrap()\n .limit(1)\n .execute()\n .await\n .unwrap()\n .try_collect::>()\n .await\n .unwrap();\n"; + diff --git a/docs/web/snippets/openai.mdx b/docs/web/snippets/openai.mdx new file mode 100644 index 000000000..f279e4797 --- /dev/null +++ b/docs/web/snippets/openai.mdx @@ -0,0 +1,6 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const RsImports = "\nuse std::{iter::once, sync::Arc};\n\nuse arrow_array::{Float64Array, Int32Array, RecordBatch, RecordBatchIterator, StringArray};\nuse arrow_schema::{DataType, Field, Schema};\nuse futures::StreamExt;\nuse lancedb::{\n arrow::IntoArrow,\n connect,\n embeddings::{openai::OpenAIEmbeddingFunction, EmbeddingDefinition, EmbeddingFunction},\n query::{ExecutableQuery, QueryBase},\n Result,\n};\n"; + +export const RsOpenaiEmbeddings = "#[tokio::main]\nasync fn main() -> Result<()> {\n let tempdir = tempfile::tempdir().unwrap();\n let tempdir = tempdir.path().to_str().unwrap();\n let api_key = std::env::var(\"OPENAI_API_KEY\").expect(\"OPENAI_API_KEY is not set\");\n let embedding = Arc::new(OpenAIEmbeddingFunction::new_with_model(\n api_key,\n \"text-embedding-3-large\",\n )?);\n\n let db = connect(tempdir).execute().await?;\n db.embedding_registry()\n .register(\"openai\", embedding.clone())?;\n\n let table = db\n .create_table(\"vectors\", make_data())\n .add_embedding(EmbeddingDefinition::new(\n \"text\",\n \"openai\",\n Some(\"embeddings\"),\n ))?\n .execute()\n .await?;\n\n let query = Arc::new(StringArray::from_iter_values(once(\"something warm\")));\n let query_vector = embedding.compute_query_embeddings(query)?;\n let mut results = table\n .vector_search(query_vector)?\n .limit(1)\n .execute()\n .await?;\n\n let rb = results.next().await.unwrap()?;\n let out = rb\n .column_by_name(\"text\")\n .unwrap()\n .as_any()\n .downcast_ref::()\n .unwrap();\n let text = out.iter().next().unwrap().unwrap();\n println!(\"Closest match: {}\", text);\n Ok(())\n}\n"; + diff --git a/docs/web/snippets/quickstart.mdx b/docs/web/snippets/quickstart.mdx new file mode 100644 index 000000000..810a0c0b5 --- /dev/null +++ b/docs/web/snippets/quickstart.mdx @@ -0,0 +1,76 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyQuickstartAddData = "more_data = [\n {\n \"id\": \"4\",\n \"name\": \"Morgana\",\n \"role\": \"Sorceress\",\n \"description\": \"Powerful sorceress of Avalon.\",\n \"stats\": {\"strength\": 2, \"magic\": 5, \"leadership\": 4, \"wisdom\": 4},\n \"vector\": [0.3, 0.9, 0.6, 0.8],\n \"power_score\": 3.75,\n },\n]\n\n# Add data to table\ntable.add(more_data)\n"; + +export const PyQuickstartAddFeature = "table.add_columns(\n {\n \"power_score\": \"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)\"\n }\n)\n"; + +export const PyQuickstartCreateTable = "table = db.create_table(\"characters\", data=data, mode=\"overwrite\")\n"; + +export const PyQuickstartCreateTableAsync = "async_table = await async_db.create_table(\n \"characters\",\n data=data,\n mode=\"overwrite\",\n)\n"; + +export const PyQuickstartCreateTableNoOverwrite = "table = db.create_table(\"characters\", data=data)\n"; + +export const PyQuickstartCurateWithMetadata = "curated = (\n table.search(query_vector)\n .where(\"stats.magic >= 4\")\n .select([\"name\", \"role\", \"description\", \"_distance\"])\n .limit(2)\n .to_polars()\n)\nprint(curated)\n"; + +export const PyQuickstartData = "data = [\n {\n \"id\": \"1\",\n \"name\": \"King Arthur\",\n \"role\": \"King\",\n \"description\": \"Leader of Camelot and wielder of Excalibur.\",\n \"stats\": {\"strength\": 4, \"magic\": 1, \"leadership\": 5, \"wisdom\": 4},\n \"vector\": [0.7, 0.1, 0.9, 0.7],\n },\n {\n \"id\": \"2\",\n \"name\": \"Merlin\",\n \"role\": \"Wizard\",\n \"description\": \"Advisor and prophet with deep magical knowledge.\",\n \"stats\": {\"strength\": 2, \"magic\": 5, \"leadership\": 4, \"wisdom\": 5},\n \"vector\": [0.2, 0.9, 0.4, 0.9],\n },\n {\n \"id\": \"3\",\n \"name\": \"Sir Lancelot\",\n \"role\": \"Knight\",\n \"description\": \"Legendary knight known for courage and combat skill.\",\n \"stats\": {\"strength\": 5, \"magic\": 1, \"leadership\": 3, \"wisdom\": 3},\n \"vector\": [0.9, 0.1, 0.5, 0.4],\n },\n]\n"; + +export const PyQuickstartDataAsync = "data = [\n {\n \"id\": \"1\",\n \"name\": \"King Arthur\",\n \"role\": \"King\",\n \"description\": \"Leader of Camelot and wielder of Excalibur.\",\n \"stats\": {\"strength\": 4, \"magic\": 1, \"leadership\": 5, \"wisdom\": 4},\n \"vector\": [0.7, 0.1, 0.9, 0.7],\n },\n {\n \"id\": \"2\",\n \"name\": \"Merlin\",\n \"role\": \"Wizard\",\n \"description\": \"Advisor and prophet with deep magical knowledge.\",\n \"stats\": {\"strength\": 2, \"magic\": 5, \"leadership\": 4, \"wisdom\": 5},\n \"vector\": [0.2, 0.9, 0.4, 0.9],\n },\n {\n \"id\": \"3\",\n \"name\": \"Sir Lancelot\",\n \"role\": \"Knight\",\n \"description\": \"Legendary knight known for courage and combat skill.\",\n \"stats\": {\"strength\": 5, \"magic\": 1, \"leadership\": 3, \"wisdom\": 3},\n \"vector\": [0.9, 0.1, 0.5, 0.4],\n },\n]\n"; + +export const PyQuickstartMultimodalBytes = "from pathlib import Path\n\nimage_path = Path(\"docs/static/assets/images/quickstart/sir-lancelot.jpg\")\nimage_bytes = image_path.read_bytes()\n\nmultimodal_table = db.create_table(\n \"character_images\",\n data=[\n {\n \"id\": \"lancelot\",\n \"description\": \"Portrait of Sir Lancelot\",\n \"image\": image_bytes,\n \"vector\": [0.9, 0.1, 0.5, 0.4],\n }\n ],\n mode=\"overwrite\",\n)\n"; + +export const PyQuickstartOpenTable = "table = db.open_table(\"characters\")\n"; + +export const PyQuickstartOutputPandas = "# Ensure you run `pip install pandas` beforehand\nresult = table.search(query_vector).limit(2).to_pandas()\nprint(result)\n"; + +export const PyQuickstartQueryFeature = "features = table.search().select([\"name\", \"role\", \"power_score\"]).to_polars()\nprint(features)\n"; + +export const PyQuickstartVectorSearch1 = "# Search for examples similar to a \"wise magical advisor\"\nquery_vector = [0.2, 0.8, 0.4, 0.9]\n\n# Ensure you run `pip install polars` beforehand\nresult = (\n table.search(query_vector)\n .select([\"name\", \"role\", \"description\", \"_distance\"])\n .limit(2)\n .to_polars()\n)\nprint(result)\n"; + +export const PyQuickstartVectorSearch1Async = "# Search for examples similar to a \"wise magical advisor\"\nquery_vector = [0.2, 0.8, 0.4, 0.9]\n\n# Ensure you run `pip install polars` beforehand\nasync_result = await (\n await async_table.search(query_vector)\n).select([\"name\", \"role\", \"description\", \"_distance\"]).limit(2).to_polars()\nprint(async_result)\n"; + +export const PyQuickstartVectorSearch2 = "# Search for examples similar to a \"powerful sorceress\"\nquery_vector = [0.3, 0.9, 0.6, 0.8]\n\nresults = table.search(query_vector).limit(2).to_polars()\nprint(results)\n"; + +export const TsQuickstartAddData = "const moreData = [\n {\n id: \"4\",\n name: \"Morgana\",\n role: \"Sorceress\",\n description: \"Powerful sorceress of Avalon.\",\n stats: { strength: 2, magic: 5, leadership: 4, wisdom: 4 },\n vector: [0.3, 0.9, 0.6, 0.8],\n power_score: 3.75,\n },\n];\n\n// Add data to table\nawait table.add(moreData);\n"; + +export const TsQuickstartAddFeature = "await table.addColumns([\n {\n name: \"power_score\",\n valueSql:\n \"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)\",\n },\n]);\n"; + +export const TsQuickstartCreateTable = "let table = await db.createTable(\"characters\", data, { mode: \"overwrite\" });\n"; + +export const TsQuickstartCreateTableNoOverwrite = "table = await db.createTable(\"characters\", data);\n"; + +export const TsQuickstartCurateWithMetadata = "const curated = await table\n .search(queryVector)\n .where(\"stats.magic >= 4\")\n .select([\"name\", \"role\", \"description\", \"_distance\"])\n .limit(2)\n .toArray();\nconsole.table(curated);\n"; + +export const TsQuickstartData = "const data = [\n {\n id: \"1\",\n name: \"King Arthur\",\n role: \"King\",\n description: \"Leader of Camelot and wielder of Excalibur.\",\n stats: { strength: 4, magic: 1, leadership: 5, wisdom: 4 },\n vector: [0.7, 0.1, 0.9, 0.7],\n },\n {\n id: \"2\",\n name: \"Merlin\",\n role: \"Wizard\",\n description: \"Advisor and prophet with deep magical knowledge.\",\n stats: { strength: 2, magic: 5, leadership: 4, wisdom: 5 },\n vector: [0.2, 0.9, 0.4, 0.9],\n },\n {\n id: \"3\",\n name: \"Sir Lancelot\",\n role: \"Knight\",\n description: \"Legendary knight known for courage and combat skill.\",\n stats: { strength: 5, magic: 1, leadership: 3, wisdom: 3 },\n vector: [0.9, 0.1, 0.5, 0.4],\n },\n];\n"; + +export const TsQuickstartMultimodalBytes = "const arrow = await import(\"apache-arrow\");\nconst path = await import(\"node:path\");\nconst { readFile } = await import(\"node:fs/promises\");\n\nconst imagePath = path.resolve(\n \"../../docs/static/assets/images/quickstart/sir-lancelot.jpg\",\n);\nconst imageBytes = await readFile(imagePath);\nconst imageSchema = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Utf8()),\n new arrow.Field(\"description\", new arrow.Utf8()),\n new arrow.Field(\"image\", new arrow.Binary()),\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 4,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n]);\nconst imageData = lancedb.makeArrowTable(\n [\n {\n id: \"lancelot\",\n description: \"Portrait of Sir Lancelot\",\n image: imageBytes,\n vector: [0.9, 0.1, 0.5, 0.4],\n },\n ],\n { schema: imageSchema },\n);\nconst multimodalTable = await db.createTable(\n \"character_images\",\n imageData,\n { mode: \"overwrite\" },\n);\n"; + +export const TsQuickstartOpenTable = "table = await db.openTable(\"characters\");\n"; + +export const TsQuickstartOutputArray = "result = await table.search(queryVector).limit(2).toArray();\nconsole.table(result);\n"; + +export const TsQuickstartQueryFeature = "const features = await table\n .query()\n .select([\"name\", \"role\", \"power_score\"])\n .toArray();\nconsole.table(features);\n"; + +export const TsQuickstartVectorSearch1 = "// Search for examples similar to a \"wise magical advisor\"\nlet queryVector = [0.2, 0.8, 0.4, 0.9];\n\nlet result = await table\n .search(queryVector)\n .select([\"name\", \"role\", \"description\", \"_distance\"])\n .limit(2)\n .toArray();\nconsole.table(result);\n"; + +export const TsQuickstartVectorSearch2 = "// Search for examples similar to a \"powerful sorceress\"\nqueryVector = [0.3, 0.9, 0.6, 0.8];\n\nconst results = await table.search(queryVector).limit(2).toArray();\nconsole.table(results);\n"; + +export const RsQuickstartAddFeature = "table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"power_score\".to_string(),\n \"cast(((stats.strength + stats.magic + stats.leadership + stats.wisdom) / 4.0) as float)\"\n .to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\n"; + +export const RsQuickstartCreateTable = "let schema = characters_schema();\nlet table = db\n .create_table(\"characters\", characters_to_reader(schema.clone(), &data))\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsQuickstartCreateTableNoOverwrite = "let table = db\n .create_table(\"characters\", characters_to_reader(schema.clone(), &data))\n .execute()\n .await\n .unwrap();\n"; + +export const RsQuickstartCurateWithMetadata = "let curated: DataFrame = table\n .query()\n .nearest_to(&query_vector)\n .unwrap()\n .only_if(\"stats.magic >= 4\")\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n \"_distance\".to_string(),\n ]))\n .limit(2)\n .execute()\n .await\n .unwrap()\n .into_polars()\n .await\n .unwrap();\nprintln!(\"{curated:?}\");\n"; + +export const RsQuickstartData = "let data = vec![\n Character {\n id: \"1\".to_string(),\n name: \"King Arthur\".to_string(),\n role: \"King\".to_string(),\n description: \"Leader of Camelot and wielder of Excalibur.\".to_string(),\n stats: Stats {\n strength: 4,\n magic: 1,\n leadership: 5,\n wisdom: 4,\n },\n vector: [0.7, 0.1, 0.9, 0.7],\n },\n Character {\n id: \"2\".to_string(),\n name: \"Merlin\".to_string(),\n role: \"Wizard\".to_string(),\n description: \"Advisor and prophet with deep magical knowledge.\".to_string(),\n stats: Stats {\n strength: 2,\n magic: 5,\n leadership: 4,\n wisdom: 5,\n },\n vector: [0.2, 0.9, 0.4, 0.9],\n },\n Character {\n id: \"3\".to_string(),\n name: \"Sir Lancelot\".to_string(),\n role: \"Knight\".to_string(),\n description: \"Legendary knight known for courage and combat skill.\".to_string(),\n stats: Stats {\n strength: 5,\n magic: 1,\n leadership: 3,\n wisdom: 3,\n },\n vector: [0.9, 0.1, 0.5, 0.4],\n },\n];\n"; + +export const RsQuickstartDefineStruct = "// Define structs representing the data schema\n#[derive(Debug, Clone, Serialize, Deserialize)]\nstruct Stats {\n strength: i8,\n magic: i8,\n leadership: i8,\n wisdom: i8,\n}\n\n#[derive(Debug, Clone, Serialize, Deserialize)]\nstruct Character {\n id: String,\n name: String,\n role: String,\n description: String,\n stats: Stats,\n vector: [f32; 4],\n}\n\nfn characters_schema() -> Arc {\n Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::LargeUtf8, false),\n Field::new(\"name\", DataType::LargeUtf8, false),\n Field::new(\"role\", DataType::LargeUtf8, false),\n Field::new(\"description\", DataType::LargeUtf8, false),\n Field::new(\n \"stats\",\n DataType::Struct(arrow_schema::Fields::from(vec![\n Arc::new(Field::new(\"strength\", DataType::Int8, false)),\n Arc::new(Field::new(\"magic\", DataType::Int8, false)),\n Arc::new(Field::new(\"leadership\", DataType::Int8, false)),\n Arc::new(Field::new(\"wisdom\", DataType::Int8, false)),\n ])),\n false,\n ),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n false,\n ),\n ]))\n}\n"; + +export const RsQuickstartMultimodalBytes = "use std::sync::Arc;\n\nuse arrow_array::{\n BinaryArray, FixedSizeListArray, LargeStringArray, RecordBatch, RecordBatchIterator,\n};\nuse arrow_schema::{DataType, Field, Schema};\n\nlet image_path = std::path::Path::new(env!(\"CARGO_MANIFEST_DIR\"))\n .join(\"../../docs/static/assets/images/quickstart/sir-lancelot.jpg\");\nlet image_bytes = std::fs::read(image_path).unwrap();\n\nlet image_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::LargeUtf8, false),\n Field::new(\"description\", DataType::LargeUtf8, false),\n Field::new(\"image\", DataType::Binary, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n false,\n ),\n]));\nlet image_vectors = [[0.9_f32, 0.1, 0.5, 0.4]];\nlet image_batch = RecordBatch::try_new(\n image_schema.clone(),\n vec![\n Arc::new(LargeStringArray::from_iter_values([\"lancelot\"])),\n Arc::new(LargeStringArray::from_iter_values([\n \"Portrait of Sir Lancelot\",\n ])),\n Arc::new(BinaryArray::from_iter_values([image_bytes.as_slice()])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n image_vectors\n .iter()\n .map(|vector| Some(vector.iter().copied().map(Some).collect::>())),\n 4,\n ),\n ),\n ],\n)\n.unwrap();\nlet image_reader: Box = Box::new(\n RecordBatchIterator::new(vec![Ok(image_batch)].into_iter(), image_schema),\n);\nlet multimodal_table = db\n .create_table(\"character_images\", image_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsQuickstartOutputArray = "let result: DataFrame = table\n .query()\n .nearest_to(&query_vector)\n .unwrap()\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n \"_distance\".to_string(),\n ]))\n .limit(2)\n .execute()\n .await\n .unwrap()\n .into_polars()\n .await\n .unwrap();\nprintln!(\"{result:?}\");\n"; + +export const RsQuickstartQueryFeature = "let features: DataFrame = table\n .query()\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"power_score\".to_string(),\n ]))\n .execute()\n .await\n .unwrap()\n .into_polars()\n .await\n .unwrap();\nprintln!(\"{features:?}\");\n"; + +export const RsQuickstartVectorSearch1 = "// Search for examples similar to a \"wise magical advisor\"\nlet query_vector = [0.2, 0.8, 0.4, 0.9];\n\nlet result: DataFrame = table\n .query()\n .nearest_to(&query_vector)\n .unwrap()\n .select(Select::Columns(vec![\n \"name\".to_string(),\n \"role\".to_string(),\n \"description\".to_string(),\n \"_distance\".to_string(),\n ]))\n .limit(2)\n .execute()\n .await\n .unwrap()\n .into_polars()\n .await\n .unwrap();\nprintln!(\"{result:?}\");\n"; + diff --git a/docs/web/snippets/search.mdx b/docs/web/snippets/search.mdx new file mode 100644 index 000000000..4d9f0eccb --- /dev/null +++ b/docs/web/snippets/search.mdx @@ -0,0 +1,172 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyBasicFts = "uri = \"data/sample-lancedb\"\ndb = lancedb.connect(uri)\n\ntable = db.create_table(\n \"my_table_fts\",\n data=[\n {\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"},\n {\"vector\": [5.9, 26.5], \"text\": \"There are several kittens playing\"},\n ],\n mode=\"overwrite\",\n)\n\ntable.create_fts_index(\"text\")\ntable.search(\"puppy\").limit(10).select([\"text\"]).to_list()\n# [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]\n# ...\n"; + +export const PyBasicFtsAsync = "uri = \"data/sample-lancedb\"\nasync_db = await lancedb.connect_async(uri)\n\nasync_tbl = await async_db.create_table(\n \"my_table_fts_async\",\n data=[\n {\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"},\n {\"vector\": [5.9, 26.5], \"text\": \"There are several kittens playing\"},\n ],\n mode=\"overwrite\",\n)\n\n# async API uses our native FTS algorithm\nawait async_tbl.create_index(\"text\", config=FTS())\nawait (await async_tbl.search(\"puppy\")).select([\"text\"]).limit(10).to_list()\n# [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]\n# ...\n"; + +export const PyBasicHybridSearch = "data = [\n {\"text\": \"rebel spaceships striking from a hidden base\"},\n {\"text\": \"have won their first victory against the evil Galactic Empire\"},\n {\"text\": \"during the battle rebel spies managed to steal secret plans\"},\n {\"text\": \"to the Empire's ultimate weapon the Death Star\"},\n]\nuri = \"data/sample-lancedb\"\ndb = lancedb.connect(uri)\ntable = db.create_table(\"documents\", schema=Documents)\n# ingest docs with auto-vectorization\ntable.add(data)\n# Create a fts index before the hybrid search\ntable.create_fts_index(\"text\")\n# hybrid search with default re-ranker\ntable.search(\"flower moon\", query_type=\"hybrid\").to_pandas()\n"; + +export const PyBasicHybridSearchAsync = "uri = \"data/sample-lancedb\"\nasync_db = await lancedb.connect_async(uri)\ndata = [\n {\"text\": \"rebel spaceships striking from a hidden base\"},\n {\"text\": \"have won their first victory against the evil Galactic Empire\"},\n {\"text\": \"during the battle rebel spies managed to steal secret plans\"},\n {\"text\": \"to the Empire's ultimate weapon the Death Star\"},\n]\nasync_tbl = await async_db.create_table(\"documents_async\", schema=Documents)\n# ingest docs with auto-vectorization\nawait async_tbl.add(data)\n# Create a fts index before the hybrid search\nawait async_tbl.create_index(\"text\", config=FTS())\ntext_query = \"flower moon\"\n# hybrid search with default re-ranker\nawait (await async_tbl.search(\"flower moon\", query_type=\"hybrid\")).to_pandas()\n"; + +export const PyBatchSearch = "# Load a batch of query embeddings\nquery_dataset = load_dataset(\n \"sunhaozhepy/ag_news_sbert_keywords_embeddings\", split=\"test[5000:5005]\"\n)\nquery_embeds = query_dataset[\"keywords_embeddings\"]\nbatch_results = table.search(query_embeds).limit(5).to_pandas()\nprint(batch_results)\n"; + +export const PyBruteForceSearch = "tbl.search(np.random.random((1536))).limit(3).to_list()\n"; + +export const PyBypassVectorIndex = "table.search(embedding).bypass_vector_index().limit(5).to_pandas()\n"; + +export const PyClassDefinition = "class Metadata(BaseModel):\n source: str\n timestamp: datetime\n\n\nclass Document(BaseModel):\n content: str\n meta: Metadata\n\n\nclass LanceSchema(LanceModel):\n id: str\n vector: Vector(1536)\n payload: Document\n"; + +export const PyClassDocuments = "class Documents(LanceModel):\n vector: Vector(embeddings.ndims()) = embeddings.VectorField()\n text: str = embeddings.SourceField()\n"; + +export const PyConfigureDistanceMetric = "tbl.search(np.random.random((1536))).distance_type(\"cosine\").limit(10).to_list()\n"; + +export const PyCreateTableAsyncWithNestedSchema = "# Let's add 100 sample rows to our dataset\ndata = [\n LanceSchema(\n id=f\"id{i}\",\n vector=np.random.randn(1536),\n payload=Document(\n content=f\"document{i}\",\n meta=Metadata(source=f\"source{i % 10}\", timestamp=datetime.now()),\n ),\n )\n for i in range(100)\n]\n\nasync_tbl = await async_db.create_table(\n \"documents_async\", data=data, mode=\"overwrite\"\n)\n"; + +export const PyCreateTableWithNestedSchema = "# Let's add 100 sample rows to our dataset\ndata = [\n LanceSchema(\n id=f\"id{i}\",\n vector=np.random.randn(1536),\n payload=Document(\n content=f\"document{i}\",\n meta=Metadata(source=f\"source{i % 10}\", timestamp=datetime.now()),\n ),\n )\n for i in range(100)\n]\n\n# Synchronous client\ntbl = db.create_table(\"documents\", data=data, mode=\"overwrite\")\n"; + +export const PyExactVsApproximateDistances = "# Indexed ANN search without refinement (fast, approximate `_distance`)\nfast_results = (\n table.search(embedding)\n .limit(10)\n .to_pandas()\n)\n\n# Recompute distances on full vectors for reranked candidates\nexact_distance_results = (\n table.search(embedding)\n .limit(10)\n .refine_factor(1)\n .to_pandas()\n)\n\n# Rerank a larger candidate set for better recall (higher latency)\nhigher_recall_results = (\n table.search(embedding)\n .limit(10)\n .refine_factor(20)\n .to_pandas()\n)\n"; + +export const PyExhaustiveSearch = "uri = \"data/sample-lancedb\"\ndb = lancedb.connect(uri)\ndata = [\n {\"vector\": row, \"item\": f\"item {i}\"}\n for i, row in enumerate(np.random.random((10_000, 1536)).astype(\"float32\"))\n]\ntbl = db.create_table(\"vector_search\", data=data, mode=\"overwrite\")\ntbl.search(np.random.random((1536))).limit(10).to_list()\n"; + +export const PyExhaustiveSearchAsync = "uri = \"data/sample-lancedb\"\nasync_db = await lancedb.connect_async(uri)\ndata = [\n {\"vector\": row, \"item\": f\"item {i}\"}\n for i, row in enumerate(np.random.random((10_000, 1536)).astype(\"float32\"))\n]\nasync_tbl = await async_db.create_table(\n \"vector_search_async\", data=data, mode=\"overwrite\"\n)\n(await (await async_tbl.search(np.random.random((1536)))).limit(10).to_list())\n"; + +export const PyExhaustiveSearchAsyncCosine = "(\n await (await async_tbl.search(np.random.random((1536))))\n .distance_type(\"cosine\")\n .limit(10)\n .to_list()\n)\n"; + +export const PyExhaustiveSearchCosine = "tbl.search(np.random.random((1536))).distance_type(\"cosine\").limit(10).to_list()\n"; + +export const PyFastSearch = "table.search(embedding, fast_search=True).limit(5).to_pandas()\n"; + +export const PyFtsConfigFolding = "table.create_fts_index(\n \"text\",\n language=\"French\",\n stem=True,\n ascii_folding=True,\n replace=True,\n)\n"; + +export const PyFtsConfigFoldingAsync = "await async_tbl.create_index(\n \"text\", config=FTS(language=\"French\", stem=True, ascii_folding=True)\n)\n"; + +export const PyFtsConfigStem = "table.create_fts_index(\"text\", tokenizer_name=\"en_stem\", replace=True)\n"; + +export const PyFtsConfigStemAsync = "await async_tbl.create_index(\n \"text\", config=FTS(language=\"English\", stem=True, remove_stop_words=True)\n)\n"; + +export const PyFtsIncrementalIndex = "table.add([{\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"}])\ntable.optimize()\n"; + +export const PyFtsIncrementalIndexAsync = "await async_tbl.add([{\"vector\": [3.1, 4.1], \"text\": \"Frodo was a happy puppy\"}])\nawait async_tbl.optimize()\n"; + +export const PyFtsPostfiltering = "table.search(\"puppy\").limit(10).where(\"text='foo'\", prefilter=False).to_list()\n"; + +export const PyFtsPostfilteringAsync = "await (\n (await async_tbl.search(\"puppy\"))\n .limit(10)\n .where(\"text='foo'\")\n .postfilter()\n .to_list()\n)\n"; + +export const PyFtsPrefiltering = "table.search(\"puppy\").limit(10).where(\"text='foo'\", prefilter=True).to_list()\n"; + +export const PyFtsPrefilteringAsync = "await (await async_tbl.search(\"puppy\")).limit(10).where(\"text='foo'\").to_list()\n"; + +export const PyFtsWithPosition = "table.create_fts_index(\"text\", with_position=True, replace=True)\n"; + +export const PyFtsWithPositionAsync = "await async_tbl.create_index(\"text\", config=FTS(with_position=True))\n"; + +export const PyHybridSearchPassVectorText = "vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]\ntext_query = \"flower moon\"\n(\n table.search(query_type=\"hybrid\")\n .vector(vector_query)\n .text(text_query)\n .limit(5)\n .to_pandas()\n)\n"; + +export const PyHybridSearchPassVectorTextAsync = "vector_query = [0.1, 0.2, 0.3, 0.4, 0.5]\ntext_query = \"flower moon\"\nawait (\n async_tbl.query()\n .nearest_to(vector_query)\n .nearest_to_text(text_query)\n .limit(5)\n .to_pandas()\n)\n"; + +export const PyImportDatetime = "from datetime import datetime\n"; + +export const PyImportEmbeddings = "from lancedb.embeddings import get_registry\n"; + +export const PyImportLancedb = "import lancedb\n"; + +export const PyImportLancedbFts = "from lancedb.index import FTS\n"; + +export const PyImportLancedbPydantic = "from lancedb.pydantic import Vector, LanceModel\n"; + +export const PyImportNumpy = "from lancedb.query import BoostQuery, MatchQuery\nimport numpy as np\nimport pyarrow as pa\n"; + +export const PyImportOpenai = "import openai\n"; + +export const PyImportOs = "import os\n"; + +export const PyImportPydanticBaseModel = "from pydantic import BaseModel\n"; + +export const PyIndexNestedColumn = "table.create_index(vector_column_name=\"image.embedding\")\n"; + +export const PyMultivectorSearch = "query_multi = np.random.random(size=(2, 256))\nresults_multi = tbl.search(query_multi).limit(5).to_pandas()\n"; + +export const PyOpenaiEmbeddings = "# Ingest embedding function in LanceDB table\n# Configuring the environment variable OPENAI_API_KEY\nif \"OPENAI_API_KEY\" not in os.environ:\n # OR set the key here as a variable\n openai.api_key = \"sk-...\"\nembeddings = get_registry().get(\"openai\").create()\n"; + +export const PySearchBinaryVectors = "import numpy as np\nimport pyarrow as pa\n\nschema = pa.schema(\n [\n pa.field(\"id\", pa.int64()),\n # for dim=256, lance stores every 8 bits in a byte\n # so the vector field should be a list of 256 / 8 = 32 bytes\n pa.field(\"vector\", pa.list_(pa.uint8(), 32)),\n ]\n)\ntbl = db.create_table(\"my_binary_vectors\", schema=schema)\n\ndata = []\nfor i in range(1024):\n vector = np.random.randint(0, 2, size=256)\n # pack the binary vector into bytes to save space\n packed_vector = np.packbits(vector)\n data.append(\n {\n \"id\": i,\n \"vector\": packed_vector,\n }\n )\ntbl.add(data)\n\nquery = np.random.randint(0, 2, size=256)\npacked_query = np.packbits(query)\ntbl.search(packed_query).distance_type(\"hamming\").to_arrow()\n"; + +export const PySearchDistanceRange = "query = np.random.random(256)\n\n# Search for the vectors within the range of [0.1, 0.5)\ntbl.search(query).distance_range(0.1, 0.5).to_arrow()\n\n# Search for the vectors with the distance less than 0.5\ntbl.search(query).distance_range(upper_bound=0.5).to_arrow()\n\n# Search for the vectors with the distance greater or equal to 0.1\ntbl.search(query).distance_range(lower_bound=0.1).to_arrow()\n"; + +export const PySearchResultAsList = "tbl.search(np.random.randn(1536)).to_list()\n"; + +export const PySearchResultAsPandas = "tbl.search(np.random.randn(1536)).to_pandas()\n"; + +export const PySearchResultAsPandasFlatten1 = "tbl.search(np.random.randn(1536)).to_pandas(flatten=1)\n"; + +export const PySearchResultAsPandasFlattenTrue = "tbl.search(np.random.randn(1536)).to_pandas(flatten=True)\n"; + +export const PySearchResultAsPyarrow = "tbl.search(np.random.randn(1536)).to_arrow()\n"; + +export const PySearchResultAsPydantic = "tbl.search(np.random.randn(1536)).to_pydantic(LanceSchema)\n"; + +export const PySearchResultAsyncAsList = "await (await async_tbl.search(np.random.randn(1536))).to_list()\n"; + +export const PySearchResultAsyncAsPandas = "await (await async_tbl.search(np.random.randn(1536))).to_pandas()\n"; + +export const PySearchResultAsyncAsPyarrow = "await (await async_tbl.search(np.random.randn(1536))).to_arrow()\n"; + +export const PySelectVectorColumn = "import pyarrow as pa\n\nschema = pa.schema([\n pa.field(\"id\", pa.int32()),\n pa.field(\n \"image\",\n pa.struct([pa.field(\"embedding\", pa.list_(pa.float32(), 2))]),\n ),\n])\ntable = db.create_table(\n \"nested\",\n data=[{\"id\": 0, \"image\": {\"embedding\": [0.0, 1.0]}}],\n schema=schema,\n)\n\n# Inferred: the only vector leaf is `image.embedding`.\ntable.search([0.0, 1.0]).limit(1).to_list()\n\n# Explicit: required when more than one vector column matches.\ntable.search([0.0, 1.0], vector_column_name=\"image.embedding\").limit(1).to_list()\n"; + +export const PyVectorSearchPostfilter = "results_post_filtered = (\n table.search(query_embed)\n .where(\"label > 1\", prefilter=False)\n .select([\"text\", \"keywords\", \"label\"])\n .limit(5)\n .to_pandas()\n)\n\nprint(\"Vector search results with post-filter:\")\nprint(results_post_filtered)\n"; + +export const PyVectorSearchPrefilter = "from datasets import load_dataset\n\n# Load query vector from dataset\nquery_dataset = load_dataset(\"sunhaozhepy/ag_news_sbert_keywords_embeddings\", split=\"test[5000:5001]\")\nprint(f\"Query keywords: {query_dataset[0]['keywords']}\")\nquery_embed = query_dataset[\"keywords_embeddings\"][0]\n\n# Open table and perform search\ntable_name = \"lancedb-enterprise-quickstart\"\ntable = db.open_table(table_name)\n\n# Vector search with filters (pre-filtering is the default)\nsearch_results = (\n table.search(query_embed)\n .where(\"label > 2\")\n .select([\"text\", \"keywords\", \"label\"])\n .limit(5)\n .to_pandas()\n)\n\nprint(\"Search results (with pre-filtering):\")\nprint(search_results)\n"; + +export const TsBatchSearch = "// Batch query\nconsole.log(\"Performing batch vector search...\");\nconst batchSize = 5;\nconst queryVectors = Array.from({ length: batchSize }, () =>\n Array.from({ length: dimensions }, () => Math.random() * 2 - 1),\n);\nlet batchQuery = table.search(queryVectors[0]) as lancedb.VectorQuery;\nfor (let i = 1; i < batchSize; i++) {\n batchQuery = batchQuery.addQueryVector(queryVectors[i]);\n}\nconst batchResults = await batchQuery\n .select([\"text\", \"keywords\", \"label\"])\n .limit(5)\n .toArray();\nconsole.log(\"Batch vector search results:\");\nconsole.log(batchResults);\n"; + +export const TsBinarySearch = "const tbl = await db.createTable(\"binary_vectors\", data, {\n mode: \"overwrite\",\n});\nawait tbl.createIndex(\"vector\", {\n config: lancedb.Index.ivfFlat({\n numPartitions: 10,\n distanceType: \"hamming\",\n }),\n});\n\nconst query = Array(32)\n .fill(1)\n .map(() => Math.floor(Math.random() * 255));\nconst results = await tbl.query().nearestTo(query).limit(10).toArray();\n"; + +export const TsBruteForceSearch = "const tbl = await db.openTable(\"my_vectors\");\n\nconst results1 = await tbl.search(Array(128).fill(1.2)).limit(3).toArray();\n"; + +export const TsBypassVectorIndex = "await table\n .query()\n .nearestTo(embedding)\n .bypassVectorIndex()\n .limit(5)\n .toArray();\n"; + +export const TsDistanceRange = "const results3 = await (\n tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery\n)\n .distanceType(\"cosine\")\n .distanceRange(0.1, 0.2)\n .limit(10)\n .toArray();\n"; + +export const TsExactVsApproximate = "// Indexed ANN search without refinement (fast, approximate `_distance`)\nconst fastResults = await (table.search(embedding) as lancedb.VectorQuery)\n .limit(10)\n .toArray();\n\n// Recompute distances on full vectors for reranked candidates\nconst exactDistanceResults = await (\n table.search(embedding) as lancedb.VectorQuery\n)\n .limit(10)\n .refineFactor(1)\n .toArray();\n\n// Rerank a larger candidate set for better recall (higher latency)\nconst higherRecallResults = await (\n table.search(embedding) as lancedb.VectorQuery\n)\n .limit(10)\n .refineFactor(20)\n .toArray();\n"; + +export const TsFastSearch = "await table\n .query()\n .nearestTo(embedding)\n .fastSearch()\n .limit(5)\n .toArray();\n"; + +export const TsImport = "import * as lancedb from \"@lancedb/lancedb\";\n"; + +export const TsImportBinUtil = "import { Field, FixedSizeList, Int32, Schema, Uint8 } from \"apache-arrow\";\n"; + +export const TsIndexNestedColumn = "await table.createIndex(\"image.embedding\");\n"; + +export const TsIngestBinaryData = "const schema = new Schema([\n new Field(\"id\", new Int32(), true),\n new Field(\"vec\", new FixedSizeList(32, new Field(\"item\", new Uint8()))),\n]);\nconst data = lancedb.makeArrowTable(\n Array(1_000)\n .fill(0)\n .map((_, i) => ({\n // the 256 bits would be store in 32 bytes,\n // if your data is already in this format, you can skip the packBits step\n id: i,\n vec: lancedb.packBits(Array(256).fill(i % 2)),\n })),\n { schema: schema },\n);\n\nconst tbl = await db.createTable(\"binary_table\", data);\nawait tbl.createIndex(\"vec\", {\n config: lancedb.Index.ivfFlat({\n numPartitions: 10,\n distanceType: \"hamming\",\n }),\n});\n"; + +export const TsSearch1 = "const db = await lancedb.connect(databaseDir);\nconst tbl = await db.openTable(\"my_vectors\");\n\nconst results1 = await tbl.search(Array(128).fill(1.2)).limit(10).toArray();\n"; + +export const TsSearch2 = "const results2 = await (\n tbl.search(Array(128).fill(1.2)) as lancedb.VectorQuery\n)\n .distanceType(\"cosine\")\n .limit(10)\n .toArray();\n"; + +export const TsSearchBinaryData = "const query = Array(32)\n .fill(1)\n .map(() => Math.floor(Math.random() * 255));\nconst results = await tbl.query().nearestTo(query).limit(10).toArrow();\n"; + +export const TsSelectVectorColumn = "const table = await db.openTable(\"nested\");\n\n// Inferred: LanceDB finds the single nested vector leaf automatically.\nawait table.query().nearestTo([0.0, 1.0]).limit(1).toArray();\n\n// Explicit: required when more than one vector column matches.\nawait table\n .query()\n .nearestTo([0.0, 1.0])\n .column(\"image.embedding\")\n .limit(1)\n .toArray();\n"; + +export const TsVectorSearchPostfilter = "const vectorResultsWithPostFilter = await (\n table.search(queryEmbed) as lancedb.VectorQuery\n)\n .where(\"label > 2\")\n .postfilter()\n .select([\"text\", \"keywords\", \"label\"])\n .limit(5)\n .toArray();\n\nconsole.log(\"Vector search results with post-filter:\");\nconsole.log(vectorResultsWithPostFilter);\n"; + +export const TsVectorSearchPrefilter = "// Generate a sample 768-dimension embedding vector (typical for BERT-based models)\n// In real applications, you would get this from an embedding model\nconst dimensions = 768;\nconst queryEmbed = Array.from(\n { length: dimensions },\n () => Math.random() * 2 - 1,\n);\n\n// Open table and perform search\nconst tableName = \"lancedb-enterprise-quickstart\";\nconst table = await db.openTable(tableName);\n\n// Vector search with filters (pre-filtering is the default)\nconst vectorResults = await table\n .search(queryEmbed)\n .where(\"label > 2\")\n .select([\"text\", \"keywords\", \"label\"])\n .limit(5)\n .toArray();\n\nconsole.log(\"Search results (with pre-filtering):\");\nconsole.log(vectorResults);\n"; + +export const RsBatchSearch = "// Search multiple query vectors in one call. Each result row carries a\n// `query_index` mapping it back to the query it matched.\nlet mut results = table\n .vector_search(&query_1)?\n .add_query_vector(&query_2)?\n .limit(5)\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + +export const RsBinarySearch = "// Binary vectors use `hamming` distance over the packed uint8 bytes.\nlet query: Arc = Arc::new(UInt8Array::from(vec![1u8; NUM_BYTES as usize]));\nlet mut results = tbl\n .vector_search(query)?\n .distance_type(DistanceType::Hamming)\n .limit(10)\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + +export const RsBruteForceSearch = "// A plain vector search returns the top-k closest rows.\nlet mut results = table.vector_search(&query_vector)?.limit(3).execute().await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + +export const RsBypassVectorIndex = "// Force an exhaustive (flat) scan for exact, ground-truth results.\nlet mut results = table\n .vector_search(&query_vector)?\n .bypass_vector_index()\n .limit(5)\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + +export const RsConfigureDistanceMetric = "// Use the same distance metric the index was trained with.\nlet mut results = table\n .vector_search(&query_vector)?\n .distance_type(DistanceType::Cosine)\n .limit(10)\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + +export const RsExactVsApproximate = "// Approximate ANN search (fast, distances may come from the index representation)\nlet mut fast_results = table.vector_search(&query_vector)?.limit(10).execute().await?;\nwhile let Some(batch) = fast_results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n\n// Rerank a larger candidate set on full vectors for better recall\nlet mut refined_results = table\n .vector_search(&query_vector)?\n .limit(10)\n .refine_factor(20)\n .execute()\n .await?;\nwhile let Some(batch) = refined_results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + +export const RsFastSearch = "// Skip unindexed data for lower latency.\nlet mut results = table\n .vector_search(&query_vector)?\n .fast_search()\n .limit(5)\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + +export const RsSearchDistanceRange = "// Only return rows whose distance falls within [0.1, 0.5).\nlet mut results = table\n .vector_search(&query_vector)?\n .distance_range(Some(0.1), Some(0.5))\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + +export const RsVectorSearchPostfilter = "// Apply the filter after vector search by calling postfilter().\nlet mut results = table\n .vector_search(&query_vector)?\n .only_if(\"id > 100\")\n .postfilter()\n .select(Select::columns(&[\"id\"]))\n .limit(5)\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + +export const RsVectorSearchPrefilter = "// Prefiltering is the default: the filter is applied before vector search.\nlet mut results = table\n .vector_search(&query_vector)?\n .only_if(\"id > 100\")\n .select(Select::columns(&[\"id\"]))\n .limit(5)\n .execute()\n .await?;\nwhile let Some(batch) = results.try_next().await? {\n println!(\"{:?}\", batch);\n}\n"; + diff --git a/docs/web/snippets/sentence-transformers.mdx b/docs/web/snippets/sentence-transformers.mdx new file mode 100644 index 000000000..bf4aec126 --- /dev/null +++ b/docs/web/snippets/sentence-transformers.mdx @@ -0,0 +1,14 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const TsQuickstartConnect = "const db = await lancedb.connect(\"data/sample-lancedb\");\n"; + +export const TsQuickstartCreateTable = "const table = await db.createEmptyTable(\"words\", wordsSchema, {\n mode: \"overwrite\",\n});\nawait table.add([{ text: \"hello world\" }, { text: \"goodbye world\" }]);\n"; + +export const TsQuickstartImports = "import * as lancedb from \"@lancedb/lancedb\";\nimport \"@lancedb/lancedb/embedding/transformers\";\nimport { Utf8 } from \"apache-arrow\";\n"; + +export const TsQuickstartInitModel = "const model = (await lancedb.embedding\n .getRegistry()\n .get(\"huggingface\")\n ?.create()) as lancedb.embedding.EmbeddingFunction;\n"; + +export const TsQuickstartQuery = "const query = \"greetings\";\nconst actual = (await table.search(query).limit(1).toArray())[0];\nconsole.log(actual.text);\n"; + +export const TsQuickstartSchema = "const wordsSchema = lancedb.embedding.LanceSchema({\n text: model.sourceField(new Utf8()),\n vector: model.vectorField(),\n});\n"; + diff --git a/docs/web/snippets/simple.mdx b/docs/web/snippets/simple.mdx new file mode 100644 index 000000000..92710bfc4 --- /dev/null +++ b/docs/web/snippets/simple.mdx @@ -0,0 +1,24 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const RsAdd = "let new_data = create_some_records()?;\ntbl.add(new_data).execute().await.unwrap();\n"; + +export const RsConnect = "#[tokio::main]\nasync fn main() -> Result<()> {\n if std::path::Path::new(\"data\").exists() {\n std::fs::remove_dir_all(\"data\").unwrap();\n }\n // --8<-- [start:connect_uri]\n let uri = \"data/sample-lancedb\";\n let db = connect(uri).execute().await?;\n // --8<-- [end:connect_uri]\n\n // --8<-- [start:list_names]\n println!(\"{:?}\", db.table_names().execute().await?);\n // --8<-- [end:list_names]\n let tbl = create_table(&db).await?;\n create_index(&tbl).await?;\n let batches = search(&tbl).await?;\n println!(\"{:?}\", batches);\n\n create_empty_table(&db).await.unwrap();\n\n // --8<-- [start:delete]\n tbl.delete(\"id > 24\").await.unwrap();\n // --8<-- [end:delete]\n\n // --8<-- [start:drop_table]\n db.drop_table(\"my_table\").await.unwrap();\n // --8<-- [end:drop_table]\n Ok(())\n}\n"; + +export const RsConnectUri = "let uri = \"data/sample-lancedb\";\nlet db = connect(uri).execute().await?;\n"; + +export const RsCreateEmptyTable = "let schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int32, false),\n Field::new(\"item\", DataType::Utf8, true),\n]));\ndb.create_empty_table(\"empty_table\", schema).execute().await\n"; + +export const RsCreateIndex = "table.create_index(&[\"vector\"], Index::Auto).execute().await\n"; + +export const RsCreateTable = "let initial_data = create_some_records()?;\nlet tbl = db\n .create_table(\"my_table\", initial_data)\n .execute()\n .await\n .unwrap();\n"; + +export const RsDelete = "tbl.delete(\"id > 24\").await.unwrap();\n"; + +export const RsDropTable = "db.drop_table(\"my_table\").await.unwrap();\n"; + +export const RsListNames = "println!(\"{:?}\", db.table_names().execute().await?);\n"; + +export const RsOpenExistingTbl = "let table = db.open_table(\"my_table\").execute().await.unwrap();\n"; + +export const RsSearch = "table\n .query()\n .limit(2)\n .nearest_to(&[1.0; 128])?\n .execute()\n .await?\n .try_collect::>()\n .await\n"; + diff --git a/docs/web/snippets/storage.mdx b/docs/web/snippets/storage.mdx new file mode 100644 index 000000000..942fd8aec --- /dev/null +++ b/docs/web/snippets/storage.mdx @@ -0,0 +1,64 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyStorageAzureAccount = "db = lancedb.connect(\n \"az://my-container/my-database\",\n storage_options={\n \"account_name\": \"some-account\",\n \"account_key\": \"some-key\",\n },\n)\n"; + +export const PyStorageAzureSas = "db = lancedb.connect(\n \"az://my-container/my-database\",\n storage_options={\n \"azure_storage_account_name\": \"some-account\",\n \"azure_storage_sas_token\": \"\",\n },\n)\n"; + +export const PyStorageConnectAzure = "db = lancedb.connect(\"az://bucket/path\")\n"; + +export const PyStorageConnectGcs = "db = lancedb.connect(\"gs://bucket/path\")\n"; + +export const PyStorageConnectS3 = "db = lancedb.connect(\"s3://bucket/path\")\n"; + +export const PyStorageConnectTimeout = "db = lancedb.connect(\n \"s3://bucket/path\",\n storage_options={\"timeout\": \"60s\"},\n)\n"; + +export const PyStorageGcsServiceAccount = "db = lancedb.connect(\n \"gs://my-bucket/my-database\",\n storage_options={\n \"service_account\": \"path/to/service-account.json\",\n },\n)\n"; + +export const PyStorageS3Ddb = "db = lancedb.connect(\n \"s3+ddb://bucket/path?ddbTableName=my-dynamodb-table\",\n)\n"; + +export const PyStorageS3DdbLocal = "db = lancedb.connect(\n \"s3+ddb://bucket/path?ddbTableName=my-dynamodb-table\",\n storage_options={\n \"endpoint\": \"http://localhost:4566\",\n \"dynamodb_endpoint\": \"http://localhost:4566\",\n \"allow_http\": \"true\",\n },\n)\n"; + +export const PyStorageS3Express = "db = lancedb.connect(\n \"s3://my-bucket--use1-az4--x-s3/path\",\n storage_options={\n \"region\": \"us-east-1\",\n \"s3_express\": \"true\",\n },\n)\n"; + +export const PyStorageS3Minio = "db = lancedb.connect(\n \"s3://bucket/path\",\n storage_options={\n \"region\": \"us-east-1\",\n \"endpoint\": \"http://minio:9000\",\n },\n)\n"; + +export const PyStorageS3SseKms = "db = lancedb.connect(\n \"s3://bucket/path\",\n storage_options={\n \"aws_server_side_encryption\": \"aws:kms\",\n \"aws_sse_kms_key_id\": \"\",\n },\n)\n"; + +export const PyStorageTableTimeout = "table = db.create_table(\n \"table\",\n [{\"a\": 1, \"b\": 2}],\n storage_options={\"timeout\": \"60s\"},\n)\n"; + +export const PyStorageTigrisConnect = "db = lancedb.connect(\n \"s3://your-bucket/path\",\n storage_options={\n \"endpoint\": \"https://t3.storage.dev\",\n \"region\": \"auto\",\n },\n)\n"; + +export const PyStorageCosConnect = "db = lancedb.connect(\n \"cos://my-bucket/my-database\",\n storage_options={\n \"secret_id\": \"\",\n \"secret_key\": \"\",\n \"region\": \"ap-guangzhou\",\n },\n)\n"; + +export const PyStorageGoosefsConnect = "db = lancedb.connect(\"goosefs://my-namespace/my-database\")\n"; + +export const TsStorageAzureAccount = "async function storageAzureAccount() {\n const db = await lancedb.connect(\n \"az://my-container/my-database\",\n {\n storageOptions: {\n accountName: \"some-account\",\n accountKey: \"some-key\",\n },\n },\n );\n return db;\n}\n"; + +export const TsStorageAzureSas = "async function storageAzureSas() {\n const db = await lancedb.connect(\n \"az://my-container/my-database\",\n {\n storageOptions: {\n azureStorageAccountName: \"some-account\",\n azureStorageSasToken: \"\",\n },\n },\n );\n return db;\n}\n"; + +export const TsStorageConnectAzure = "async function storageConnectAzure() {\n const db = await lancedb.connect(\"az://bucket/path\");\n return db;\n}\n"; + +export const TsStorageConnectGcs = "async function storageConnectGcs() {\n const db = await lancedb.connect(\"gs://bucket/path\");\n return db;\n}\n"; + +export const TsStorageConnectS3 = "async function storageConnectS3() {\n const db = await lancedb.connect(\"s3://bucket/path\");\n return db;\n}\n"; + +export const TsStorageConnectTimeout = "async function storageConnectTimeout() {\n const db = await lancedb.connect(\"s3://bucket/path\", {\n storageOptions: { timeout: \"60s\" },\n });\n return db;\n}\n"; + +export const TsStorageGcsServiceAccount = "async function storageGcsServiceAccount() {\n const db = await lancedb.connect(\n \"gs://my-bucket/my-database\",\n {\n storageOptions: {\n serviceAccount: \"path/to/service-account.json\",\n },\n },\n );\n return db;\n}\n"; + +export const TsStorageS3Ddb = "async function storageS3Ddb() {\n const db = await lancedb.connect(\n \"s3+ddb://bucket/path?ddbTableName=my-dynamodb-table\",\n );\n return db;\n}\n"; + +export const TsStorageS3DdbLocal = "async function storageS3DdbLocal() {\n const db = await lancedb.connect(\n \"s3+ddb://bucket/path?ddbTableName=my-dynamodb-table\",\n {\n storageOptions: {\n endpoint: \"http://localhost:4566\",\n dynamodbEndpoint: \"http://localhost:4566\",\n allowHttp: \"true\",\n },\n },\n );\n return db;\n}\n"; + +export const TsStorageS3Express = "async function storageS3Express() {\n const db = await lancedb.connect(\n \"s3://my-bucket--use1-az4--x-s3/path\",\n {\n storageOptions: {\n region: \"us-east-1\",\n s3Express: \"true\",\n },\n },\n );\n return db;\n}\n"; + +export const TsStorageS3Minio = "async function storageS3Minio() {\n const db = await lancedb.connect(\"s3://bucket/path\", {\n storageOptions: {\n region: \"us-east-1\",\n endpoint: \"http://minio:9000\",\n },\n });\n return db;\n}\n"; + +export const TsStorageS3SseKms = "async function storageS3SseKms() {\n const db = await lancedb.connect(\"s3://bucket/path\", {\n storageOptions: {\n awsServerSideEncryption: \"aws:kms\",\n awsSseKmsKeyId: \"\",\n },\n });\n return db;\n}\n"; + +export const TsStorageTableTimeout = "async function storageTableTimeout() {\n const db = await lancedb.connect(\"s3://bucket/path\");\n const table = await db.createTable(\n \"table\",\n [{ a: 1, b: 2 }],\n { storageOptions: { timeout: \"60s\" } },\n );\n return table;\n}\n"; + +export const TsStorageTigrisConnect = "async function storageTigrisConnect() {\n const db = await lancedb.connect(\n \"s3://your-bucket/path\",\n {\n storageOptions: {\n endpoint: \"https://t3.storage.dev\",\n region: \"auto\",\n },\n },\n );\n return db;\n}\n"; + +export const TsStorageGoosefsConnect = "async function storageGoosefsConnect() {\n const db = await lancedb.connect(\"goosefs://my-namespace/my-database\");\n return db;\n}\n"; + diff --git a/docs/web/snippets/tables.mdx b/docs/web/snippets/tables.mdx new file mode 100644 index 000000000..0d8f213db --- /dev/null +++ b/docs/web/snippets/tables.mdx @@ -0,0 +1,370 @@ +{/* Auto-generated by scripts/mdx_snippets_gen.py. Do not edit manually. */} + +export const PyAddColumnsCalculated = "# Add a discounted price column (10% discount)\ntable.add_columns({\"discounted_price\": \"cast((price * 0.9) as float)\"})\n"; + +export const PyAddColumnsDefaultValues = "# Add a stock status column with default value\ntable.add_columns({\"in_stock\": \"cast(true as boolean)\"})\n"; + +export const PyAddColumnsNullable = "# Add a nullable timestamp column\ntable.add_columns({\"last_ordered\": \"cast(NULL as timestamp)\"})\n"; + +export const PyAddDataNestedModel = "from lancedb.pydantic import LanceModel, Vector\nfrom pydantic import BaseModel\n\nclass Document(BaseModel):\n content: str\n source: str\n\nclass NestedSchema(LanceModel):\n id: str\n vector: Vector(128)\n document: Document\n\n# Create table with nested schema\ntable_name = \"nested_model_example\"\ntable = db.create_table(table_name, schema=NestedSchema, mode=\"overwrite\")\n"; + +export const PyAddDataPydanticModel = "from lancedb.pydantic import LanceModel, Vector\n\n# Define a Pydantic model\nclass Content(LanceModel):\n movie_id: int\n vector: Vector(128)\n genres: str\n title: str\n imdb_id: int\n\n @property\n def imdb_url(self) -> str:\n return f\"https://www.imdb.com/title/tt{self.imdb_id}\"\n\n# Create table with Pydantic model schema\ntable_name = \"pydantic_example\"\ntable = db.create_table(table_name, schema=Content, mode=\"overwrite\")\n"; + +export const PyAddDataToTable = "import pyarrow as pa\n\n# create an empty table with schema\ndata = [\n {\"vector\": [3.1, 4.1], \"item\": \"foo\", \"price\": 10.0},\n {\"vector\": [5.9, 26.5], \"item\": \"bar\", \"price\": 20.0},\n {\"vector\": [10.2, 100.8], \"item\": \"baz\", \"price\": 30.0},\n {\"vector\": [1.4, 9.5], \"item\": \"fred\", \"price\": 40.0},\n]\n\nschema = pa.schema(\n [\n pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n pa.field(\"item\", pa.utf8()),\n pa.field(\"price\", pa.float32()),\n ]\n)\n\ntable_name = \"basic_ingestion_example\"\ntable = db.create_table(table_name, schema=schema, mode=\"overwrite\")\n# Add data\ntable.add(data)\n"; + +export const PyAddFeatureColumnsSql = "table.add_columns(\n {\n \"price_per_id\": \"cast(price / id as float)\",\n \"price_log\": \"ln(price)\",\n \"price_score\": \"cast(price / (price + 100.0) as float)\",\n }\n)\n"; + +export const PyAddFromDataset = "import pyarrow.dataset as ds\n\ndataset = ds.dataset(data_path, format=\"parquet\")\ndb = tmp_db\ntable = db.create_table(\"my_table\", schema=dataset.schema, mode=\"overwrite\")\ntable.add(dataset)\n"; + +export const PyAlterColumnsDataType = "# Change price from int32 to int64 for larger numbers\ntable.alter_columns({\"path\": \"price\", \"data_type\": pa.int64()})\n"; + +export const PyAlterColumnsMultiple = "# Rename, change type, and make nullable in one operation\ntable.alter_columns(\n {\n \"path\": \"sale_price\",\n \"rename\": \"final_price\",\n \"data_type\": pa.float64(),\n \"nullable\": True,\n }\n)\n"; + +export const PyAlterColumnsNullable = "# Make the name column nullable\ntable.alter_columns({\"path\": \"name\", \"nullable\": True})\n"; + +export const PyAlterColumnsRename = "# Rename discount_price to sale_price\ntable.alter_columns({\"path\": \"discount_price\", \"rename\": \"sale_price\"})\n"; + +export const PyAlterColumnsWithExpression = "# For custom transforms, create a new column from a SQL expression.\nexpression_table = tmp_db.create_table(\n \"schema_evolution_expression_example\",\n [{\"id\": 1, \"price_text\": \"$100\"}],\n mode=\"overwrite\",\n)\n\nexpression_table.add_columns(\n {\"price_numeric\": \"cast(replace(price_text, '$', '') as int)\"}\n)\nexpression_table.drop_columns([\"price_text\"])\nexpression_table.alter_columns({\"path\": \"price_numeric\", \"rename\": \"price\"})\n"; + +export const PyAlterVectorColumn = "vector_dim = 768 # Your embedding dimension\ntable_name = \"vector_alter_example\"\ndb = tmp_db\ndata = [\n {\n \"id\": 1,\n \"embedding\": np.random.random(vector_dim).tolist(),\n },\n]\ntable = db.create_table(table_name, data, mode=\"overwrite\")\n\ntable.alter_columns(\n dict(path=\"embedding\", data_type=pa.list_(pa.float32(), vector_dim))\n)\n"; + +export const PyBatchDataInsertion = "import pyarrow as pa\n\ndef make_batches():\n for i in range(5): # Create 5 batches\n yield pa.RecordBatch.from_arrays(\n [\n pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)),\n pa.array([f\"item{i * 2 + 1}\", f\"item{i * 2 + 2}\"]),\n pa.array([float((i * 2 + 1) * 10), float((i * 2 + 2) * 10)]),\n ],\n [\"vector\", \"item\", \"price\"],\n )\n\nschema = pa.schema(\n [\n pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n pa.field(\"item\", pa.utf8()),\n pa.field(\"price\", pa.float32()),\n ]\n)\n# Create table with batches\ntable_name = \"batch_ingestion_example\"\ntable = db.create_table(table_name, make_batches(), schema=schema, mode=\"overwrite\")\n"; + +export const PyBranchCreate = "# Fork an isolated, writable branch from main's latest version.\n# `create` returns a table handle scoped to the new branch.\nbranch = table.branches.create(\"exp\")\n"; + +export const PyBranchDelete = "# Delete the branch and its branch-local history. Data on main is safe.\ntable.branches.delete(\"exp\")\n"; + +export const PyBranchIndex = "# Build and validate indexes on a branch before using the configuration on\n# main.\ndev = products.branches.create(\"index-dev\")\n\n# A vector (ANN) index and a full-text search index, both branch-scoped.\ndev.create_index(\n \"vector\",\n config=IvfPq(distance_type=\"cosine\", num_partitions=1, num_sub_vectors=2),\n)\ndev.create_index(\"text\", config=FTS())\n\n# Both indexes live only on the branch; main still has none.\nprint([ix.name for ix in dev.list_indices()]) # branch: two indexes\nprint([ix.name for ix in products.list_indices()]) # main: [] (untouched)\n"; + +export const PyBranchReopen = "# Reopen an existing branch by name from the table handle...\nchecked_out = table.branches.checkout(\"exp\")\n# ...or open it directly from the database connection.\nbranch_handle = db.open_table(\"quotes_branches_example\", branch=\"exp\")\nprint(checked_out.count_rows(), branch_handle.count_rows()) # both 4\n"; + +export const PyBranchUpsertToMain = "# This is a row-level upsert, not a merge of branch histories.\n# `merge_insert` updates matching rows and inserts new rows using a stable\n# unique key. Filter the branch read if you only want to apply some results.\nrows_to_apply = candidate.to_arrow()\n(\n table.merge_insert(\"id\")\n .when_matched_update_all() # update rows that already exist on main\n .when_not_matched_insert_all() # insert rows that are new on the branch\n .execute(rows_to_apply)\n)\n"; + +export const PyBranchWrite = "# Writes land on the branch handle only; main is left untouched.\nbranch.add([{\"id\": 4, \"author\": \"Lancelot\", \"quote\": \"For the realm!\"}])\nprint(branch.count_rows()) # 4 rows on the branch\nprint(table.count_rows()) # 3 rows; main is unaffected\n\n# List every branch, each mapped to its metadata (including its fork point).\nprint(table.branches.list())\n"; + +export const PyConsistencyCheckoutLatest = "uri = str(tmp_db.uri)\nwriter_db = lancedb.connect(uri)\nreader_db = lancedb.connect(uri)\nwriter_table = writer_db.create_table(\n \"consistency_checkout_latest_table\", [{\"id\": 1}], mode=\"overwrite\"\n)\nreader_table = reader_db.open_table(\"consistency_checkout_latest_table\")\n\nwriter_table.add([{\"id\": 2}])\nrows_before_refresh = reader_table.count_rows()\nprint(f\"Rows before checkout_latest: {rows_before_refresh}\")\n\nreader_table.checkout_latest()\nrows_after_refresh = reader_table.count_rows()\nprint(f\"Rows after checkout_latest: {rows_after_refresh}\")\n"; + +export const PyConsistencyEventual = "from datetime import timedelta\n\nuri = str(tmp_db.uri)\nwriter_db = lancedb.connect(uri)\nreader_db = lancedb.connect(uri, read_consistency_interval=timedelta(seconds=3600))\nwriter_table = writer_db.create_table(\n \"consistency_eventual_table\", [{\"id\": 1}], mode=\"overwrite\"\n)\nreader_table = reader_db.open_table(\"consistency_eventual_table\")\nwriter_table.add([{\"id\": 2}])\nrows_after_write = reader_table.count_rows()\nprint(f\"Rows visible before eventual refresh interval: {rows_after_write}\")\n"; + +export const PyConsistencyStrong = "from datetime import timedelta\n\nuri = str(tmp_db.uri)\nwriter_db = lancedb.connect(uri)\nreader_db = lancedb.connect(uri, read_consistency_interval=timedelta(0))\nwriter_table = writer_db.create_table(\n \"consistency_strong_table\", [{\"id\": 1}], mode=\"overwrite\"\n)\nreader_table = reader_db.open_table(\"consistency_strong_table\")\nwriter_table.add([{\"id\": 2}])\nrows_after_write = reader_table.count_rows()\nprint(f\"Rows visible with strong consistency: {rows_after_write}\")\n"; + +export const PyCreateEmptyTable = "import pyarrow as pa\n\nschema = pa.schema(\n [\n pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n pa.field(\"item\", pa.string()),\n pa.field(\"price\", pa.float32()),\n ]\n)\ndb = tmp_db\ntbl = db.create_table(\"test_empty_table\", schema=schema, mode=\"overwrite\")\n"; + +export const PyCreateEmptyTablePydantic = "from lancedb.pydantic import LanceModel, Vector\n\nclass Item(LanceModel):\n vector: Vector(2)\n item: str\n price: float\n\ndb = tmp_db\ntbl = db.create_table(\n \"test_empty_table_new\", schema=Item.to_arrow_schema(), mode=\"overwrite\"\n)\n"; + +export const PyCreateTableConflictHandling = "# Idempotent open: reuse the existing table if it exists.\n# The provided data is ignored; the schema is validated against the\n# existing table and a mismatch raises an error.\ntbl = db.create_table(\"conflict_table\", data, exist_ok=True)\n\n# Overwrite: drop the existing table and create a new one with the\n# provided data. This permanently discards the old table's data.\ntbl = db.create_table(\"conflict_table\", data, mode=\"overwrite\")\n"; + +export const PyCreateTableCustomSchema = "import pyarrow as pa\n\ncustom_schema = pa.schema(\n [\n pa.field(\"vector\", pa.list_(pa.float32(), 4)),\n pa.field(\"lat\", pa.float32()),\n pa.field(\"long\", pa.float32()),\n ]\n)\n\ndata = [\n {\"vector\": [1.1, 1.2, 1.3, 1.4], \"lat\": 45.5, \"long\": -122.7},\n {\"vector\": [0.2, 1.8, 0.4, 3.6], \"lat\": 40.1, \"long\": -74.1},\n]\ndb = tmp_db\ntbl = db.create_table(\n \"my_table_custom_schema\", data, schema=custom_schema, mode=\"overwrite\"\n)\n"; + +export const PyCreateTableFromArrow = "import numpy as np\nimport pyarrow as pa\n\ndim = 16\ntotal = 2\nschema = pa.schema(\n [pa.field(\"vector\", pa.list_(pa.float16(), dim)), pa.field(\"text\", pa.string())]\n)\ndata = pa.Table.from_arrays(\n [\n pa.array(\n [np.random.randn(dim).astype(np.float16) for _ in range(total)],\n pa.list_(pa.float16(), dim),\n ),\n pa.array([\"foo\", \"bar\"]),\n ],\n [\"vector\", \"text\"],\n)\ndb = tmp_db\ntbl = db.create_table(\"f16_tbl\", data, schema=schema, mode=\"overwrite\")\n"; + +export const PyCreateTableFromDicts = "data = [\n {\"vector\": [1.1, 1.2], \"lat\": 45.5, \"long\": -122.7},\n {\"vector\": [0.2, 1.8], \"lat\": 40.1, \"long\": -74.1},\n]\ndb = tmp_db\ndb.create_table(\"test_table\", data, mode=\"overwrite\")\ntbl = db[\"test_table\"]\ntbl.head()\n"; + +export const PyCreateTableFromIterator = "import pyarrow as pa\n\nschema = pa.schema(\n [\n pa.field(\"vector\", pa.list_(pa.float32(), 4)),\n pa.field(\"item\", pa.utf8()),\n pa.field(\"price\", pa.float32()),\n ]\n)\n\ndef make_batches():\n for i in range(5):\n yield pa.RecordBatch.from_arrays(\n [\n pa.array(\n [[3.1, 4.1, 5.1, 6.1], [5.9, 26.5, 4.7, 32.8]],\n pa.list_(pa.float32(), 4),\n ),\n pa.array([\"foo\", \"bar\"]),\n pa.array([10.0, 20.0]),\n ],\n [\"vector\", \"item\", \"price\"],\n )\n\ndb = tmp_db\ndb.create_table(\"batched_table\", make_batches(), schema=schema, mode=\"overwrite\")\n"; + +export const PyCreateTableFromPandas = "import pandas as pd\n\ndata = pd.DataFrame(\n {\n \"vector\": [[1.1, 1.2, 1.3, 1.4], [0.2, 1.8, 0.4, 3.6]],\n \"lat\": [45.5, 40.1],\n \"long\": [-122.7, -74.1],\n }\n)\ndb = tmp_db\ndb.create_table(\"my_table_pandas\", data, mode=\"overwrite\")\ndb[\"my_table_pandas\"].head()\n"; + +export const PyCreateTableFromPolars = "import polars as pl\n\ndata = pl.DataFrame(\n {\n \"vector\": [[3.1, 4.1], [5.9, 26.5]],\n \"item\": [\"foo\", \"bar\"],\n \"price\": [10.0, 20.0],\n }\n)\ndb = tmp_db\ntbl = db.create_table(\"my_table_pl\", data, mode=\"overwrite\")\n"; + +export const PyCreateTableFromPydantic = "from lancedb.pydantic import LanceModel, Vector\n\nclass Content(LanceModel):\n movie_id: int\n vector: Vector(128)\n genres: str\n title: str\n imdb_id: int\n\n @property\n def imdb_url(self) -> str:\n return f\"https://www.imdb.com/title/tt{self.imdb_id}\"\n\ndb = tmp_db\ntbl = db.create_table(\"movielens_small\", schema=Content, mode=\"overwrite\")\n"; + +export const PyCreateTableNestedSchema = "from lancedb.pydantic import LanceModel, Vector\n\n# --8<-- [start:tables_document_model]\nfrom pydantic import BaseModel\n\nclass Document(BaseModel):\n content: str\n source: str\n\n# --8<-- [end:tables_document_model]\n\nclass NestedSchema(LanceModel):\n id: str\n vector: Vector(1536)\n document: Document\n\ndb = tmp_db\ntbl = db.create_table(\"nested_table\", schema=NestedSchema, mode=\"overwrite\")\n"; + +export const PyDeleteOperation = "# delete data\npredicate = \"id = 3\"\ntable.delete(predicate)\n"; + +export const PyDropColumnsMultiple = "# Remove the second temporary column\ntable.drop_columns([\"temp_col2\"])\n"; + +export const PyDropColumnsSingle = "# Remove the first temporary column\ntable.drop_columns([\"temp_col1\"])\n"; + +export const PyDropTable = "db = tmp_db\n# Create a table first\ndata = [{\"vector\": [1.1, 1.2], \"lat\": 45.5}]\ndb.create_table(\"my_table\", data, mode=\"overwrite\")\n\n# Drop the table\ndb.drop_table(\"my_table\")\n"; + +export const PyInsertIfNotExists = "import pyarrow as pa\n\ntable = db.create_table(\n \"users_example\",\n data=pa.table(\n {\n \"id\": [1, 2],\n \"name\": [\"Alice\", \"Bob\"],\n \"login_count\": [10, 20],\n }\n ),\n mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n {\n \"id\": [2, 3],\n \"name\": [\"Bobby\", \"Charlie\"],\n \"login_count\": [21, 5],\n }\n)\n\n(table.merge_insert(\"id\").when_not_matched_insert_all().execute(incoming_users))\n"; + +export const PyMergeDeleteMissingBySource = "import pyarrow as pa\n\ntable = db.create_table(\n \"users_example\",\n data=pa.table(\n {\n \"id\": [1, 2, 3],\n \"name\": [\"Alice\", \"Bob\", \"Charlie\"],\n \"login_count\": [10, 20, 5],\n }\n ),\n mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n {\n \"id\": [2, 3],\n \"name\": [\"Bobby\", \"Charlie\"],\n \"login_count\": [21, 5],\n }\n)\n\n(\n table.merge_insert(\"id\")\n .when_matched_update_all()\n .when_not_matched_insert_all()\n .when_not_matched_by_source_delete()\n .execute(incoming_users)\n)\n"; + +export const PyMergeMatchedUpdateOnly = "import pyarrow as pa\n\ntable = db.create_table(\n \"users_example\",\n data=pa.table(\n {\n \"id\": [1, 2],\n \"name\": [\"Alice\", \"Bob\"],\n \"login_count\": [10, 20],\n }\n ),\n mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n {\n \"id\": [2, 3],\n \"name\": [\"Bobby\", \"Charlie\"],\n \"login_count\": [21, 5],\n }\n)\n\n(table.merge_insert(\"id\").when_matched_update_all().execute(incoming_users))\n"; + +export const PyMergePartialColumns = "import pyarrow as pa\n\ntable = db.create_table(\n \"users_example\",\n data=pa.table(\n {\n \"id\": [1, 2],\n \"name\": [\"Alice\", \"Bob\"],\n \"login_count\": [10, 20],\n }\n ),\n mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n {\n \"id\": [2, 3],\n \"name\": [\"Bobby\", \"Charlie\"],\n }\n)\n\n(\n table.merge_insert(\"id\")\n .when_matched_update_all()\n .when_not_matched_insert_all()\n .execute(incoming_users)\n)\n"; + +export const PyMergeUpdateInsert = "import pyarrow as pa\n\ntable = db.create_table(\n \"users_example\",\n data=pa.table(\n {\n \"id\": [1, 2],\n \"name\": [\"Alice\", \"Bob\"],\n \"login_count\": [10, 20],\n }\n ),\n mode=\"overwrite\",\n)\n\nincoming_users = pa.table(\n {\n \"id\": [2, 3],\n \"name\": [\"Bobby\", \"Charlie\"],\n \"login_count\": [21, 5],\n }\n)\n\n(\n table.merge_insert(\"id\")\n .when_matched_update_all()\n .when_not_matched_insert_all()\n .execute(incoming_users)\n)\n"; + +export const PyOpenExistingTable = "db = tmp_db\n# Create a table first\ndata = [{\"vector\": [1.1, 1.2], \"lat\": 45.5, \"long\": -122.7}]\ndb.create_table(\"test_table\", data, mode=\"overwrite\")\n\n# List table names\nprint(db.list_tables().tables)\n\n# Open existing table\ntbl = db.open_table(\"test_table\")\n"; + +export const PySchemaAddSetup = "table_name = \"schema_evolution_add_example\"\nif data is None:\n data = [\n {\n \"id\": 1,\n \"name\": \"Laptop\",\n \"price\": 1200.00,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 2,\n \"name\": \"Smartphone\",\n \"price\": 800.00,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 3,\n \"name\": \"Headphones\",\n \"price\": 150.00,\n \"vector\": np.random.random(128).tolist(),\n },\n ]\ntable = tmp_db.create_table(table_name, data, mode=\"overwrite\")\n"; + +export const PySchemaAlterSetup = "table_name = \"schema_evolution_alter_example\"\nif data is None:\n data = [\n {\n \"id\": 1,\n \"name\": \"Laptop\",\n \"price\": 1200,\n \"discount_price\": 1080.0,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 2,\n \"name\": \"Smartphone\",\n \"price\": 800,\n \"discount_price\": 720.0,\n \"vector\": np.random.random(128).tolist(),\n },\n ]\nschema = pa.schema(\n {\n \"id\": pa.int64(),\n \"name\": pa.string(),\n \"price\": pa.int32(),\n \"discount_price\": pa.float64(),\n \"vector\": pa.list_(pa.float32(), 128),\n }\n)\ntable = tmp_db.create_table(table_name, data, schema=schema, mode=\"overwrite\")\n"; + +export const PySchemaDropSetup = "if data is None:\n data = [\n {\n \"id\": 1,\n \"name\": \"Laptop\",\n \"price\": 1200.00,\n \"temp_col1\": \"X\",\n \"temp_col2\": 100,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 2,\n \"name\": \"Smartphone\",\n \"price\": 800.00,\n \"temp_col1\": \"Y\",\n \"temp_col2\": 200,\n \"vector\": np.random.random(128).tolist(),\n },\n {\n \"id\": 3,\n \"name\": \"Headphones\",\n \"price\": 150.00,\n \"temp_col1\": \"Z\",\n \"temp_col2\": 300,\n \"vector\": np.random.random(128).tolist(),\n },\n ]\ntable = tmp_db.create_table(\"schema_evolution_drop_example\", data, mode=\"overwrite\")\n"; + +export const PySchemaFieldMetadataMerge = "# Set two metadata keys on the `category` field.\nres = table.update_field_metadata(\n {\"path\": \"category\", \"metadata\": {\"unit\": \"label\", \"pii\": \"false\"}}\n)\nprint(res.version)\n\n# Merge: add a new key, delete one with None, keep the rest.\ntable.update_field_metadata(\n {\"path\": \"category\", \"metadata\": {\"source\": \"import\", \"pii\": None}}\n)\n\n# Arrow stores field metadata as bytes.\nassert table.schema.field(\"category\").metadata == {\n b\"unit\": b\"label\",\n b\"source\": b\"import\",\n}\n"; + +export const PySchemaFieldMetadataReplace = "table.update_field_metadata(\n {\n \"path\": \"category\",\n \"metadata\": {\"owner\": \"search-team\"},\n \"replace\": True,\n }\n)\n"; + +export const PyTablesBasicConnect = "import lancedb\n\nuri = \"data/sample-lancedb\"\ndb = lancedb.connect(uri)\n"; + +export const PyTablesDocumentModel = "from pydantic import BaseModel\n\nclass Document(BaseModel):\n content: str\n source: str\n"; + +export const PyTablesImports = "import lancedb\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pytest\nfrom numpy.random import randint, random\n"; + +export const PyTablesTzValidator = "from datetime import datetime\nfrom zoneinfo import ZoneInfo\n\nfrom lancedb.pydantic import LanceModel\nfrom pydantic import Field, ValidationError, ValidationInfo, field_validator\n\ntzname = \"America/New_York\"\ntz = ZoneInfo(tzname)\n\nclass TestModel(LanceModel):\n dt_with_tz: datetime = Field(json_schema_extra={\"tz\": tzname})\n\n @field_validator(\"dt_with_tz\")\n @classmethod\n def tz_must_match(cls, dt: datetime) -> datetime:\n assert dt.tzinfo == tz\n return dt\n\nok = TestModel(dt_with_tz=datetime.now(tz))\n\ntry:\n TestModel(dt_with_tz=datetime.now(ZoneInfo(\"Asia/Shanghai\")))\n assert 0 == 1, \"this should raise ValidationError\"\nexcept ValidationError:\n print(\"A ValidationError was raised.\")\n pass\n"; + +export const PyUpdateConnectEnterprise = "import lancedb\n\ndb = lancedb.connect(\n uri=\"db://your-project-slug\",\n api_key=\"your-api-key\",\n region=\"us-east-1\",\n)\n"; + +export const PyUpdateConnectLocal = "import lancedb\n\ndb = lancedb.connect(\"./data\")\n"; + +export const PyUpdateExampleTableSetup = "import pyarrow as pa\n\ntable = db.create_table(\n \"users_example\",\n data=pa.table(\n {\n \"id\": [1, 2],\n \"name\": [\"Alice\", \"Bob\"],\n \"login_count\": [10, 20],\n }\n ),\n mode=\"overwrite\",\n)\n"; + +export const PyUpdateOperation = "import pyarrow as pa\n\ntable = db.create_table(\n \"users_example\",\n data=pa.table(\n {\n \"id\": [1, 2],\n \"name\": [\"Alice\", \"Bob\"],\n \"login_count\": [10, 20],\n }\n ),\n mode=\"overwrite\",\n)\ntable.update(where=\"id = 2\", values={\"name\": \"Bobby\"})\n"; + +export const PyUpdateOptimizeCleanup = "from datetime import timedelta\n\ntable.optimize(cleanup_older_than=timedelta(days=1))\n"; + +export const PyUpdateUsingSql = "import pyarrow as pa\n\ntable = db.create_table(\n \"users_example\",\n data=pa.table(\n {\n \"id\": [1, 2],\n \"name\": [\"Alice\", \"Bob\"],\n \"login_count\": [10, 20],\n }\n ),\n mode=\"overwrite\",\n)\ntable.update(where=\"id = 2\", values_sql={\"login_count\": \"login_count + 1\"})\n"; + +export const PyVersioningAddData = "more_data = [\n {\n \"id\": 4,\n \"author\": \"Richard Daniel Sanchez\",\n \"quote\": \"That's the way the news goes!\",\n },\n {\"id\": 5, \"author\": \"Morty\", \"quote\": \"Aww geez, Rick!\"},\n]\ntable.add(more_data)\n"; + +export const PyVersioningBasicSetup = "import pyarrow as pa\n\ndb = tmp_db\n\ntable_name = \"quotes_versioning_example\"\ndata = [\n {\"id\": 1, \"author\": \"Richard\", \"quote\": \"Wubba Lubba Dub Dub!\"},\n {\"id\": 2, \"author\": \"Morty\", \"quote\": \"Rick, what's going on?\"},\n {\n \"id\": 3,\n \"author\": \"Richard\",\n \"quote\": \"I turned myself into a pickle, Morty!\",\n },\n]\n\n# Define schema\nschema = pa.schema(\n [\n pa.field(\"id\", pa.int64()),\n pa.field(\"author\", pa.string()),\n pa.field(\"quote\", pa.string()),\n ]\n)\n\ntable = db.create_table(table_name, data, schema=schema, mode=\"overwrite\")\n"; + +export const PyVersioningCheckInitialVersion = "versions = table.list_versions()\ncurrent_version = table.version\nprint(f\"Number of versions after creation: {len(versions)}\")\nprint(f\"Current version: {current_version}\")\n"; + +export const PyVersioningCheckVersionsAfterMod = "versions = table.list_versions()\nversion_count_after_mod = len(versions)\nversion_after_mod = table.version\nprint(f\"Number of versions after modifications: {version_count_after_mod}\")\nprint(f\"Current version: {version_after_mod}\")\n"; + +export const PyVersioningCheckoutLatest = "table.checkout_latest()\n"; + +export const PyVersioningDeleteData = "table.delete(\"author = 'Morty'\")\nrows_after_deletion = table.count_rows()\nprint(f\"Number of rows after deletion: {rows_after_deletion}\")\n"; + +export const PyVersioningListAllVersions = "versions = table.list_versions()\nfor v in versions:\n print(f\"Version {v['version']}, created at {v['timestamp']}\")\n"; + +export const PyVersioningRollback = "table.restore(version_after_mod)\nversions = table.list_versions()\nversion_count_after_rollback = len(versions)\nprint(f\"Total number of versions after rollback: {version_count_after_rollback}\")\n"; + +export const PyVersioningTags = "# Create a tag pointing at a specific version\ntable.tags.create(\"baseline\", 1)\ntable.tags.create(\"with-edits\", table.version)\n\n# List all tags on this table\nprint(table.tags.list())\n\n# Look up the version a tag points at\nprint(table.tags.get_version(\"baseline\"))\n\n# Move an existing tag to a different version\ntable.tags.update(\"baseline\", 2)\n\n# Check out a version by tag name\ntable.checkout(\"baseline\")\nprint(table.version)\n\n# Delete a tag (does not delete the underlying version)\ntable.tags.delete(\"with-edits\")\n\n# Return to the latest version\ntable.checkout_latest()\n"; + +export const PyVersioningUpdateData = "table.update(where=\"author='Richard'\", values={\"author\": \"Richard Daniel Sanchez\"})\nrows_after_update = table.count_rows(\"author = 'Richard Daniel Sanchez'\")\nprint(f\"Rows updated to Richard Daniel Sanchez: {rows_after_update}\")\n"; + +export const TsAddColumnsCalculated = "// Add a discounted price column (10% discount)\nawait schemaAddTable.addColumns([\n {\n name: \"discounted_price\",\n valueSql: \"cast((price * 0.9) as float)\",\n },\n]);\n"; + +export const TsAddColumnsDefaultValues = "// Add a stock status column with default value\nawait schemaAddTable.addColumns([\n {\n name: \"in_stock\",\n valueSql: \"cast(true as boolean)\",\n },\n]);\n"; + +export const TsAddColumnsNullable = "// Add a nullable timestamp column\nawait schemaAddTable.addColumns([\n {\n name: \"last_ordered\",\n valueSql: \"cast(NULL as timestamp)\",\n },\n]);\n"; + +export const TsAddFeatureColumnsSql = "await schemaAddTable.addColumns([\n {\n name: \"price_per_id\",\n valueSql: \"cast(price / id as float)\",\n },\n {\n name: \"price_log\",\n valueSql: \"ln(price)\",\n },\n {\n name: \"price_score\",\n valueSql: \"cast(price / (price + 100.0) as float)\",\n },\n]);\n"; + +export const TsAlterColumnsDataType = "// Change price from int32 to int64 for larger numbers\nawait schemaAlterTable.alterColumns([\n { path: \"price\", dataType: new arrow.Int64() },\n]);\n"; + +export const TsAlterColumnsMultiple = "// Rename, change type, and make nullable in one operation\nawait schemaAlterTable.alterColumns([\n {\n path: \"sale_price\",\n rename: \"final_price\",\n dataType: new arrow.Float64(),\n nullable: true,\n },\n]);\n"; + +export const TsAlterColumnsNullable = "// Make the name column nullable\nawait schemaAlterTable.alterColumns([{ path: \"name\", nullable: true }]);\n"; + +export const TsAlterColumnsRename = "// Rename discount_price to sale_price\nawait schemaAlterTable.alterColumns([\n { path: \"discount_price\", rename: \"sale_price\" },\n]);\n"; + +export const TsAlterColumnsWithExpression = "// For custom transforms, create a new column from a SQL expression.\nconst expressionTable = await db.createTable(\n \"schema_evolution_expression_example\",\n [{ id: 1, price_text: \"$100\" }],\n { mode: \"overwrite\" },\n);\n\nawait expressionTable.addColumns([\n {\n name: \"price_numeric\",\n valueSql: \"cast(replace(price_text, '$', '') as int)\",\n },\n]);\nawait expressionTable.dropColumns([\"price_text\"]);\nawait expressionTable.alterColumns([\n { path: \"price_numeric\", rename: \"price\" },\n]);\n"; + +export const TsAlterVectorColumn = "const oldDim = 384;\nconst newDim = 1024;\nconst vectorSchema = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Int64()),\n new arrow.Field(\n \"embedding\",\n new arrow.FixedSizeList(\n oldDim,\n new arrow.Field(\"item\", new arrow.Float16(), true),\n ),\n true,\n ),\n]);\nconst vectorData = lancedb.makeArrowTable(\n [{ id: 1, embedding: Array.from({ length: oldDim }, () => Math.random()) }],\n { schema: vectorSchema },\n);\nconst vectorTable = await db.createTable(\"vector_alter_example\", vectorData, {\n mode: \"overwrite\",\n});\n\n// Changing FixedSizeList dimensions (384 -> 1024) is not supported via alterColumns.\n// Use addColumns + dropColumns + alterColumns(rename) to replace the column.\nawait vectorTable.addColumns([\n {\n name: \"embedding_v2\",\n valueSql: `arrow_cast(NULL, 'FixedSizeList(${newDim}, Float16)')`,\n },\n]);\nawait vectorTable.dropColumns([\"embedding\"]);\nawait vectorTable.alterColumns([{ path: \"embedding_v2\", rename: \"embedding\" }]);\n"; + +export const TsBranchCreate = "// Fork an isolated, writable branch from main's latest version.\n// `create` returns a table handle scoped to the new branch.\nconst branch = await branches.create(\"exp\");\n"; + +export const TsBranchDelete = "// Delete the branch and its branch-local history. Data on main is safe.\nawait branches.delete(\"exp\");\n"; + +export const TsBranchIndex = "// Build and validate indexes on a branch before using the configuration on\n// main.\nconst dev = await productBranches.create(\"index-dev\");\n\n// A vector (ANN) index and a full-text search index, both branch-scoped.\nawait dev.createIndex(\"vector\", {\n config: lancedb.Index.ivfPq({\n distanceType: \"cosine\",\n numPartitions: 1,\n numSubVectors: 2,\n }),\n});\nawait dev.createIndex(\"text\", { config: lancedb.Index.fts() });\n\n// Both indexes live only on the branch; main still has none.\nconsole.log((await dev.listIndices()).map((ix) => ix.name)); // branch: two indexes\nconsole.log((await products.listIndices()).map((ix) => ix.name)); // main: [] (untouched)\n"; + +export const TsBranchReopen = "// Reopen an existing branch by name from the table handle...\nconst checkedOut = await branches.checkout(\"exp\");\n// ...or open it directly from the database connection.\nconst branchHandle = await db.openTable(\n \"quotes_branches_example\",\n undefined,\n { branch: \"exp\" },\n);\nconsole.log(await checkedOut.countRows(), await branchHandle.countRows()); // both 4\n"; + +export const TsBranchUpsertToMain = "// This is a row-level upsert, not a merge of branch histories.\n// `mergeInsert` updates matching rows and inserts new rows using a stable\n// unique key. Filter the branch read if you only want to apply some results.\nconst rowsToApply = await candidate.toArrow();\nawait table\n .mergeInsert(\"id\")\n .whenMatchedUpdateAll() // update rows that already exist on main\n .whenNotMatchedInsertAll() // insert rows that are new on the branch\n .execute(rowsToApply);\n"; + +export const TsBranchWrite = "// Writes land on the branch handle only; main is left untouched.\nawait branch.add([{ id: 4, author: \"Lancelot\", quote: \"For the realm!\" }]);\nconsole.log(await branch.countRows()); // 4 rows on the branch\nconsole.log(await table.countRows()); // 3 rows; main is unaffected\n\n// List every branch, each mapped to its metadata (including its fork point).\nconsole.log(await branches.list());\n"; + +export const TsConsistencyCheckoutLatest = "const checkoutWriterDb = await lancedb.connect(databaseDir);\nconst checkoutReaderDb = await lancedb.connect(databaseDir);\nconst checkoutWriterTable = await checkoutWriterDb.createTable(\n \"consistency_checkout_latest_table\",\n [{ id: 1 }],\n { mode: \"overwrite\" },\n);\nconst checkoutReaderTable = await checkoutReaderDb.openTable(\n \"consistency_checkout_latest_table\",\n);\nawait checkoutWriterTable.add([{ id: 2 }]);\nconst rowsBeforeRefresh = await checkoutReaderTable.countRows();\nconsole.log(`Rows before checkoutLatest: ${rowsBeforeRefresh}`);\nawait checkoutReaderTable.checkoutLatest();\nconst rowsAfterRefresh = await checkoutReaderTable.countRows();\nconsole.log(`Rows after checkoutLatest: ${rowsAfterRefresh}`);\n"; + +export const TsConsistencyEventual = "const eventualWriterDb = await lancedb.connect(databaseDir);\nconst eventualReaderDb = await lancedb.connect(databaseDir, {\n readConsistencyInterval: 3600,\n});\nconst eventualWriterTable = await eventualWriterDb.createTable(\n \"consistency_eventual_table\",\n [{ id: 1 }],\n { mode: \"overwrite\" },\n);\nconst eventualReaderTable = await eventualReaderDb.openTable(\n \"consistency_eventual_table\",\n);\nawait eventualWriterTable.add([{ id: 2 }]);\nconst eventualRowsAfterWrite = await eventualReaderTable.countRows();\nconsole.log(\n `Rows visible before eventual refresh interval: ${eventualRowsAfterWrite}`,\n);\n"; + +export const TsConsistencyStrong = "const strongWriterDb = await lancedb.connect(databaseDir);\nconst strongReaderDb = await lancedb.connect(databaseDir, {\n readConsistencyInterval: 0,\n});\nconst strongWriterTable = await strongWriterDb.createTable(\n \"consistency_strong_table\",\n [{ id: 1 }],\n { mode: \"overwrite\" },\n);\nconst strongReaderTable = await strongReaderDb.openTable(\n \"consistency_strong_table\",\n);\nawait strongWriterTable.add([{ id: 2 }]);\nconst strongRowsAfterWrite = await strongReaderTable.countRows();\nconsole.log(`Rows visible with strong consistency: ${strongRowsAfterWrite}`);\n"; + +export const TsCreateEmptyTable = "const emptySchema = new arrow.Schema([\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 2,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n new arrow.Field(\"item\", new arrow.Utf8()),\n new arrow.Field(\"price\", new arrow.Float32()),\n]);\nconst emptyTable = await db.createEmptyTable(\n \"test_empty_table\",\n emptySchema,\n {\n mode: \"overwrite\",\n },\n);\n"; + +export const TsCreateTableConflictHandling = "// Idempotent open: reuse the existing table if it exists.\n// The provided data is ignored; the schema is validated against the\n// existing table and a mismatch raises an error.\nlet conflictTable = await db.createTable(\"conflict_table\", data, {\n existOk: true,\n});\n\n// Overwrite: drop the existing table and create a new one with the\n// provided data. This permanently discards the old table's data.\nconflictTable = await db.createTable(\"conflict_table\", data, {\n mode: \"overwrite\",\n});\n"; + +export const TsCreateTableCustomSchema = "const customSchema = new arrow.Schema([\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 4,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n new arrow.Field(\"lat\", new arrow.Float32()),\n new arrow.Field(\"long\", new arrow.Float32()),\n]);\n\nconst customSchemaData = lancedb.makeArrowTable(\n [\n { vector: [1.1, 1.2, 1.3, 1.4], lat: 45.5, long: -122.7 },\n { vector: [0.2, 1.8, 0.4, 3.6], lat: 40.1, long: -74.1 },\n ],\n { schema: customSchema },\n);\nconst customSchemaTable = await db.createTable(\n \"my_table_custom_schema\",\n customSchemaData,\n { mode: \"overwrite\" },\n);\n"; + +export const TsCreateTableFromArrow = "const arrowSchema = new arrow.Schema([\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 16,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n new arrow.Field(\"text\", new arrow.Utf8()),\n]);\nconst arrowData = lancedb.makeArrowTable(\n [\n { vector: Array(16).fill(0.1), text: \"foo\" },\n { vector: Array(16).fill(0.2), text: \"bar\" },\n ],\n { schema: arrowSchema },\n);\nconst arrowTable = await db.createTable(\"f32_tbl\", arrowData, {\n mode: \"overwrite\",\n});\n"; + +export const TsCreateTableFromDicts = "type Location = {\n vector: number[];\n lat: number;\n long: number;\n};\n\nconst data: Location[] = [\n { vector: [1.1, 1.2], lat: 45.5, long: -122.7 },\n { vector: [0.2, 1.8], lat: 40.1, long: -74.1 },\n];\nconst table = await db.createTable(\"test_table\", data, {\n mode: \"overwrite\",\n});\n"; + +export const TsCreateTableFromIterator = "const batchSchema = new arrow.Schema([\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 4,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n new arrow.Field(\"item\", new arrow.Utf8()),\n new arrow.Field(\"price\", new arrow.Float32()),\n]);\n\nconst tableForBatches = await db.createEmptyTable(\n \"batched_table\",\n batchSchema,\n {\n mode: \"overwrite\",\n },\n);\n\nconst rows = Array.from({ length: 10 }, (_, i) => ({\n vector: [i + 0.1, i + 0.2, i + 0.3, i + 0.4],\n item: `item-${i + 1}`,\n price: (i + 1) * 10,\n}));\n\nconst chunkSize = 2;\nfor (let i = 0; i < rows.length; i += chunkSize) {\n const batch = lancedb.makeArrowTable(rows.slice(i, i + chunkSize), {\n schema: batchSchema,\n });\n await tableForBatches.add(batch);\n}\n"; + +export const TsDeleteOperation = "// delete data\nconst predicate = \"id = 3\";\nawait table.delete(predicate);\n"; + +export const TsDropColumnsMultiple = "// Remove the second temporary column\nawait schemaDropTable.dropColumns([\"temp_col2\"]);\n"; + +export const TsDropColumnsSingle = "// Remove the first temporary column\nawait schemaDropTable.dropColumns([\"temp_col1\"]);\n"; + +export const TsDropTable = "await db.createTable(\"my_table\", [{ vector: [1.1, 1.2], lat: 45.5 }], {\n mode: \"overwrite\",\n});\n\nawait db.dropTable(\"my_table\");\n"; + +export const TsInsertIfNotExists = "const table = await db.createTable(\n \"users_example\",\n [\n { id: 1, name: \"Alice\", login_count: 10 },\n { id: 2, name: \"Bob\", login_count: 20 },\n ],\n { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n { id: 2, name: \"Bobby\", login_count: 21 },\n { id: 3, name: \"Charlie\", login_count: 5 },\n];\n\nawait table\n .mergeInsert(\"id\")\n .whenNotMatchedInsertAll()\n .execute(incomingUsers);\n"; + +export const TsMergeDeleteMissingBySource = "const table = await db.createTable(\n \"users_example\",\n [\n { id: 1, name: \"Alice\", login_count: 10 },\n { id: 2, name: \"Bob\", login_count: 20 },\n { id: 3, name: \"Charlie\", login_count: 5 },\n ],\n { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n { id: 2, name: \"Bobby\", login_count: 21 },\n { id: 3, name: \"Charlie\", login_count: 5 },\n];\n\nawait table\n .mergeInsert(\"id\")\n .whenMatchedUpdateAll()\n .whenNotMatchedInsertAll()\n .whenNotMatchedBySourceDelete()\n .execute(incomingUsers);\n"; + +export const TsMergeMatchedUpdateOnly = "const table = await db.createTable(\n \"users_example\",\n [\n { id: 1, name: \"Alice\", login_count: 10 },\n { id: 2, name: \"Bob\", login_count: 20 },\n ],\n { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n { id: 2, name: \"Bobby\", login_count: 21 },\n { id: 3, name: \"Charlie\", login_count: 5 },\n];\n\nawait table\n .mergeInsert(\"id\")\n .whenMatchedUpdateAll()\n .execute(incomingUsers);\n"; + +export const TsMergePartialColumns = "const table = await db.createTable(\n \"users_example\",\n [\n { id: 1, name: \"Alice\", login_count: 10 },\n { id: 2, name: \"Bob\", login_count: 20 },\n ],\n { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n { id: 2, name: \"Bobby\" },\n { id: 3, name: \"Charlie\" },\n];\n\nawait table\n .mergeInsert(\"id\")\n .whenMatchedUpdateAll()\n .whenNotMatchedInsertAll()\n .execute(incomingUsers);\n"; + +export const TsMergeUpdateInsert = "const table = await db.createTable(\n \"users_example\",\n [\n { id: 1, name: \"Alice\", login_count: 10 },\n { id: 2, name: \"Bob\", login_count: 20 },\n ],\n { mode: \"overwrite\" },\n);\n\nconst incomingUsers = [\n { id: 2, name: \"Bobby\", login_count: 21 },\n { id: 3, name: \"Charlie\", login_count: 5 },\n];\n\nawait table\n .mergeInsert(\"id\")\n .whenMatchedUpdateAll()\n .whenNotMatchedInsertAll()\n .execute(incomingUsers);\n"; + +export const TsOpenExistingTable = "const openTableData = [{ vector: [1.1, 1.2], lat: 45.5, long: -122.7 }];\nawait db.createTable(\"test_table_open\", openTableData, {\n mode: \"overwrite\",\n});\n\nconsole.log(await db.tableNames());\n\nconst openedTable = await db.openTable(\"test_table_open\");\n"; + +export const TsSchemaAddSetup = "const schemaAddData = [\n {\n id: 1,\n name: \"Laptop\",\n price: 1200.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 2,\n name: \"Smartphone\",\n price: 800.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 3,\n name: \"Headphones\",\n price: 150.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n];\nconst schemaAddTable = await db.createTable(\n \"schema_evolution_add_example\",\n schemaAddData,\n { mode: \"overwrite\" },\n);\n"; + +export const TsSchemaAlterSetup = "const schemaAlter = new arrow.Schema([\n new arrow.Field(\"id\", new arrow.Int64()),\n new arrow.Field(\"name\", new arrow.Utf8()),\n new arrow.Field(\"price\", new arrow.Int32()),\n new arrow.Field(\"discount_price\", new arrow.Float64()),\n new arrow.Field(\n \"vector\",\n new arrow.FixedSizeList(\n 128,\n new arrow.Field(\"item\", new arrow.Float32(), true),\n ),\n ),\n]);\nconst schemaAlterData = lancedb.makeArrowTable(\n [\n {\n id: 1,\n name: \"Laptop\",\n price: 1200,\n discount_price: 1080.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 2,\n name: \"Smartphone\",\n price: 800,\n discount_price: 720.0,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n ],\n { schema: schemaAlter },\n);\nconst schemaAlterTable = await db.createTable(\n \"schema_evolution_alter_example\",\n schemaAlterData,\n { mode: \"overwrite\" },\n);\n"; + +export const TsSchemaDropSetup = "const schemaDropData = [\n {\n id: 1,\n name: \"Laptop\",\n price: 1200.0,\n temp_col1: \"X\",\n temp_col2: 100,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 2,\n name: \"Smartphone\",\n price: 800.0,\n temp_col1: \"Y\",\n temp_col2: 200,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n {\n id: 3,\n name: \"Headphones\",\n price: 150.0,\n temp_col1: \"Z\",\n temp_col2: 300,\n vector: Array.from({ length: 128 }, () => Math.random()),\n },\n];\nconst schemaDropTable = await db.createTable(\n \"schema_evolution_drop_example\",\n schemaDropData,\n { mode: \"overwrite\" },\n);\n"; + +export const TsSchemaFieldMetadataMerge = "// Set two metadata keys on the `category` field.\nconst res = await fieldMetadataTable.updateFieldMetadata([\n { path: \"category\", metadata: { unit: \"label\", pii: \"false\" } },\n]);\nconsole.log(res.version);\n\n// Merge: add a new key, delete one via null, keep the rest.\nawait fieldMetadataTable.updateFieldMetadata([\n { path: \"category\", metadata: { source: \"import\", pii: null } },\n]);\n"; + +export const TsSchemaFieldMetadataReplace = "await fieldMetadataTable.updateFieldMetadata([\n {\n path: \"category\",\n metadata: { owner: \"search-team\" },\n replace: true,\n },\n]);\n"; + +export const TsUpdateConnectEnterprise = "const db = await lancedb.connect(\"db://your-project-slug\", {\n apiKey: \"your-api-key\",\n region: \"us-east-1\",\n});\n"; + +export const TsUpdateConnectLocal = "const db = await lancedb.connect(\"./data\");\n"; + +export const TsUpdateExampleTableSetup = "const table = await db.createTable(\n \"users_example\",\n [\n { id: 1, name: \"Alice\", login_count: 10 },\n { id: 2, name: \"Bob\", login_count: 20 },\n ],\n { mode: \"overwrite\" },\n);\n"; + +export const TsUpdateOperation = "const table = await db.createTable(\n \"users_example\",\n [\n { id: 1, name: \"Alice\", login_count: 10 },\n { id: 2, name: \"Bob\", login_count: 20 },\n ],\n { mode: \"overwrite\" },\n);\nawait table.update({ where: \"id = 2\", values: { name: \"Bobby\" } });\n"; + +export const TsUpdateOptimizeCleanup = "const olderThan = new Date();\nolderThan.setDate(olderThan.getDate() - 1);\nawait table.optimize({ cleanupOlderThan: olderThan });\n"; + +export const TsUpdateUsingSql = "const table = await db.createTable(\n \"users_example\",\n [\n { id: 1, name: \"Alice\", login_count: 10 },\n { id: 2, name: \"Bob\", login_count: 20 },\n ],\n { mode: \"overwrite\" },\n);\nawait table.update({\n where: \"id = 2\",\n valuesSql: { login_count: \"login_count + 1\" },\n});\n"; + +export const TsVersioningAddData = "const moreData = [\n {\n id: 4,\n author: \"Richard Daniel Sanchez\",\n quote: \"That's the way the news goes!\",\n },\n { id: 5, author: \"Morty\", quote: \"Aww geez, Rick!\" },\n];\nawait table.add(moreData);\n"; + +export const TsVersioningBasicSetup = "const tableName = \"quotes_versioning_example\";\nconst data = [\n { id: 1, author: \"Richard\", quote: \"Wubba Lubba Dub Dub!\" },\n { id: 2, author: \"Morty\", quote: \"Rick, what's going on?\" },\n {\n id: 3,\n author: \"Richard\",\n quote: \"I turned myself into a pickle, Morty!\",\n },\n];\nconst table = await db.createTable(tableName, data, { mode: \"overwrite\" });\n"; + +export const TsVersioningCheckInitialVersion = "const versions = await table.listVersions();\nconst currentVersion = await table.version();\nconsole.log(`Number of versions after creation: ${versions.length}`);\nconsole.log(`Current version: ${currentVersion}`);\n"; + +export const TsVersioningCheckVersionsAfterMod = "const versionsAfterMod = await table.listVersions();\nconst versionCountAfterMod = versionsAfterMod.length;\nconst versionAfterMod = await table.version();\nconsole.log(\n `Number of versions after modifications: ${versionCountAfterMod}`,\n);\nconsole.log(`Current version: ${versionAfterMod}`);\n"; + +export const TsVersioningCheckoutLatest = "await table.checkoutLatest();\n"; + +export const TsVersioningDeleteData = "await table.delete(\"author = 'Morty'\");\nconst rowsAfterDeletion = await table.countRows();\nconsole.log(`Number of rows after deletion: ${rowsAfterDeletion}`);\n"; + +export const TsVersioningListAllVersions = "const allVersions = await table.listVersions();\nfor (const v of allVersions) {\n console.log(`Version ${v.version}, created at ${v.timestamp}`);\n}\n"; + +export const TsVersioningRollback = "await table.checkout(versionAfterMod);\nawait table.restore();\nconst versionsAfterRollback = await table.listVersions();\nconst versionCountAfterRollback = versionsAfterRollback.length;\nconsole.log(\n `Total number of versions after rollback: ${versionCountAfterRollback}`,\n);\n"; + +export const TsVersioningTags = "const tags = await tagsTable.tags();\n\n// Create a tag pointing at a specific version\nawait tags.create(\"baseline\", 1);\nawait tags.create(\"with-edits\", await tagsTable.version());\n\n// List all tags on this table\nconsole.log(await tags.list());\n\n// Look up the version a tag points at\nconsole.log(await tags.getVersion(\"baseline\"));\n\n// Move an existing tag to a different version\nawait tags.update(\"baseline\", 2);\n\n// Check out a version by tag name\nawait tagsTable.checkout(\"baseline\");\nconsole.log(await tagsTable.version());\n\n// Delete a tag (does not delete the underlying version)\nawait tags.delete(\"with-edits\");\n\n// Return to the latest version\nawait tagsTable.checkoutLatest();\n"; + +export const TsVersioningUpdateData = "await table.update({\n where: \"author = 'Richard'\",\n values: { author: \"Richard Daniel Sanchez\" },\n});\nconst rowsAfterUpdate = await table.countRows(\n \"author = 'Richard Daniel Sanchez'\",\n);\nconsole.log(`Rows updated to Richard Daniel Sanchez: ${rowsAfterUpdate}`);\n"; + +export const RsAddColumnsCalculated = "// Add a discounted price column (10% discount)\nschema_add_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"discounted_price\".to_string(),\n \"cast((price * 0.9) as float)\".to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\n"; + +export const RsAddColumnsDefaultValues = "// Add a stock status column with default value\nschema_add_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"in_stock\".to_string(),\n \"cast(true as boolean)\".to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\n"; + +export const RsAddColumnsNullable = "// Add a nullable timestamp column\nschema_add_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"last_ordered\".to_string(),\n \"cast(NULL as timestamp)\".to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\n"; + +export const RsAddFeatureColumnsSql = "schema_add_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![\n (\n \"price_per_id\".to_string(),\n \"cast(price / id as float)\".to_string(),\n ),\n (\"price_log\".to_string(), \"ln(price)\".to_string()),\n (\n \"price_score\".to_string(),\n \"cast(price / (price + 100.0) as float)\".to_string(),\n ),\n ]),\n None,\n )\n .await\n .unwrap();\n"; + +export const RsAlterColumnsDataType = "// Change price from int32 to int64 for larger numbers\nschema_alter_table\n .alter_columns(&[ColumnAlteration::new(\"price\".to_string()).cast_to(DataType::Int64)])\n .await\n .unwrap();\n"; + +export const RsAlterColumnsMultiple = "// Rename, change type, and make nullable in one operation\nschema_alter_table\n .alter_columns(&[ColumnAlteration::new(\"sale_price\".to_string())\n .rename(\"final_price\".to_string())\n .cast_to(DataType::Float64)\n .set_nullable(true)])\n .await\n .unwrap();\n"; + +export const RsAlterColumnsNullable = "// Make the name column nullable\nschema_alter_table\n .alter_columns(&[ColumnAlteration::new(\"name\".to_string()).set_nullable(true)])\n .await\n .unwrap();\n"; + +export const RsAlterColumnsRename = "// Rename discount_price to sale_price\nschema_alter_table\n .alter_columns(&[ColumnAlteration::new(\"discount_price\".to_string())\n .rename(\"sale_price\".to_string())])\n .await\n .unwrap();\n"; + +export const RsAlterColumnsWithExpression = "// For custom transforms, create a new column from a SQL expression.\nlet expression_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"price_text\", DataType::Utf8, false),\n]));\nlet expression_batch = RecordBatch::try_new(\n expression_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1])),\n Arc::new(StringArray::from(vec![\"$100\"])),\n ],\n)\n.unwrap();\nlet expression_reader: Box = Box::new(RecordBatchIterator::new(\n vec![Ok(expression_batch)].into_iter(),\n expression_schema.clone(),\n));\nlet expression_table = db\n .create_table(\"schema_evolution_expression_example\", expression_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\nexpression_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"price_numeric\".to_string(),\n \"cast(replace(price_text, '$', '') as int)\".to_string(),\n )]),\n None,\n )\n .await\n .unwrap();\nexpression_table.drop_columns(&[\"price_text\"]).await.unwrap();\nexpression_table\n .alter_columns(&[ColumnAlteration::new(\"price_numeric\".to_string())\n .rename(\"price\".to_string())])\n .await\n .unwrap();\n"; + +export const RsAlterVectorColumn = "let old_dim = 384;\nlet new_dim = 1024;\nlet vector_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\n \"embedding\",\n DataType::FixedSizeList(\n Arc::new(Field::new(\"item\", DataType::Float32, true)),\n old_dim,\n ),\n true,\n ),\n]));\nlet vector_batch = RecordBatch::try_new(\n vector_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![Some(vec![Some(0.1_f32); old_dim as usize])],\n old_dim,\n ),\n ),\n ],\n)\n.unwrap();\nlet vector_reader: Box =\n Box::new(RecordBatchIterator::new(vec![Ok(vector_batch)].into_iter(), vector_schema.clone()));\nlet vector_table = db\n .create_table(\"vector_alter_example\", vector_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\n// Changing FixedSizeList dimensions (384 -> 1024) is not supported via alter_columns.\n// Use add_columns + drop_columns + alter_columns(rename) to replace the column.\nvector_table\n .add_columns(\n NewColumnTransform::SqlExpressions(vec![(\n \"embedding_v2\".to_string(),\n format!(\"arrow_cast(NULL, 'FixedSizeList({}, Float32)')\", new_dim),\n )]),\n None,\n )\n .await\n .unwrap();\nvector_table.drop_columns(&[\"embedding\"]).await.unwrap();\nvector_table\n .alter_columns(&[ColumnAlteration::new(\"embedding_v2\".to_string())\n .rename(\"embedding\".to_string())])\n .await\n .unwrap();\n"; + +export const RsBranchCreate = "// Fork an isolated, writable branch from main's latest version.\n// `create_branch` returns a table handle scoped to the new branch.\nlet branch = branches_table\n .create_branch(\"exp\", Ref::Version(None, None))\n .await\n .unwrap();\n"; + +export const RsBranchDelete = "// Delete the branch and its branch-local history. Data on main is safe.\nbranches_table.delete_branch(\"exp\").await.unwrap();\n"; + +export const RsBranchIndex = "use lancedb::index::scalar::FtsIndexBuilder;\nuse lancedb::index::Index;\n\n// Build and validate indexes on a branch before using the configuration on\n// main.\nlet dev = products\n .create_branch(\"index-dev\", Ref::Version(None, None))\n .await\n .unwrap();\n\n// A vector (ANN) index and a full-text search index, both branch-scoped.\ndev.create_index(&[\"vector\"], Index::Auto)\n .execute()\n .await\n .unwrap();\ndev.create_index(&[\"text\"], Index::FTS(FtsIndexBuilder::default()))\n .execute()\n .await\n .unwrap();\n\n// Both indexes live only on the branch; main still has none.\nprintln!(\"Branch indexes: {}\", dev.list_indices().await.unwrap().len()); // 2\nprintln!(\"Main indexes: {}\", products.list_indices().await.unwrap().len()); // 0\n"; + +export const RsBranchReopen = "// Reopen an existing branch by name from the table handle...\nlet checked_out = branches_table.checkout_branch(\"exp\", None).await.unwrap();\n// ...or open it directly via the connection's builder.\nlet opened = db\n .open_table(\"quotes_branches_example\")\n .branch(\"exp\")\n .execute()\n .await\n .unwrap();\nprintln!(\n \"Reopened rows: {}, {}\",\n checked_out.count_rows(None).await.unwrap(),\n opened.count_rows(None).await.unwrap()\n); // both 4\n"; + +export const RsBranchUpsertToMain = "// This is a row-level upsert, not a merge of branch histories.\n// `merge_insert` updates matching rows and inserts new rows using a stable\n// unique key. Filter the branch read if you only want to apply some results.\nlet schema = candidate.schema().await.unwrap();\nlet batches = candidate\n .query()\n .execute()\n .await\n .unwrap()\n .try_collect::>()\n .await\n .unwrap();\nlet rows_to_apply = RecordBatchIterator::new(batches.into_iter().map(Ok), schema);\n\nlet mut merge = branches_table.merge_insert(&[\"id\"]);\nmerge\n .when_matched_update_all(None) // update rows that already exist on main\n .when_not_matched_insert_all(); // insert rows that are new on the branch\nmerge.execute(Box::new(rows_to_apply)).await.unwrap();\n"; + +export const RsBranchWrite = "// Writes land on the branch handle only; main is left untouched.\nbranch\n .add(make_quotes_reader(vec![(4, \"Lancelot\", \"For the realm!\")]))\n .execute()\n .await\n .unwrap();\nprintln!(\"Branch rows: {}\", branch.count_rows(None).await.unwrap()); // 4\nprintln!(\"Main rows: {}\", branches_table.count_rows(None).await.unwrap()); // 3\n\n// List every branch, each mapped to its metadata (including its fork point).\nprintln!(\"Branches: {:?}\", branches_table.list_branches().await.unwrap());\n"; + +export const RsConsistencyCheckoutLatest = "let checkout_writer_db = connect(&db_uri).execute().await.unwrap();\nlet checkout_reader_db = connect(&db_uri).execute().await.unwrap();\nlet checkout_writer_table = checkout_writer_db\n .create_table(\n \"consistency_checkout_latest_table\",\n make_users_reader(vec![1], vec![\"Alice\"], None),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\nlet checkout_reader_table = checkout_reader_db\n .open_table(\"consistency_checkout_latest_table\")\n .execute()\n .await\n .unwrap();\ncheckout_writer_table\n .add(make_users_reader(vec![2], vec![\"Bob\"], None))\n .execute()\n .await\n .unwrap();\nlet rows_before_refresh = checkout_reader_table.count_rows(None).await.unwrap();\nprintln!(\"Rows before checkout_latest: {}\", rows_before_refresh);\ncheckout_reader_table.checkout_latest().await.unwrap();\nlet rows_after_refresh = checkout_reader_table.count_rows(None).await.unwrap();\nprintln!(\"Rows after checkout_latest: {}\", rows_after_refresh);\n"; + +export const RsConsistencyEventual = "let eventual_writer_db = connect(&db_uri).execute().await.unwrap();\nlet eventual_reader_db = connect(&db_uri)\n .read_consistency_interval(StdDuration::from_secs(3600))\n .execute()\n .await\n .unwrap();\nlet eventual_writer_table = eventual_writer_db\n .create_table(\n \"consistency_eventual_table\",\n make_users_reader(vec![1], vec![\"Alice\"], None),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\nlet eventual_reader_table = eventual_reader_db\n .open_table(\"consistency_eventual_table\")\n .execute()\n .await\n .unwrap();\neventual_writer_table\n .add(make_users_reader(vec![2], vec![\"Bob\"], None))\n .execute()\n .await\n .unwrap();\nlet eventual_rows_after_write = eventual_reader_table.count_rows(None).await.unwrap();\nprintln!(\n \"Rows visible before eventual refresh interval: {}\",\n eventual_rows_after_write\n);\n"; + +export const RsConsistencyStrong = "let strong_writer_db = connect(&db_uri).execute().await.unwrap();\nlet strong_reader_db = connect(&db_uri)\n .read_consistency_interval(StdDuration::from_secs(0))\n .execute()\n .await\n .unwrap();\nlet strong_writer_table = strong_writer_db\n .create_table(\n \"consistency_strong_table\",\n make_users_reader(vec![1], vec![\"Alice\"], None),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\nlet strong_reader_table = strong_reader_db\n .open_table(\"consistency_strong_table\")\n .execute()\n .await\n .unwrap();\nstrong_writer_table\n .add(make_users_reader(vec![2], vec![\"Bob\"], None))\n .execute()\n .await\n .unwrap();\nlet strong_rows_after_write = strong_reader_table.count_rows(None).await.unwrap();\nprintln!(\n \"Rows visible with strong consistency: {}\",\n strong_rows_after_write\n);\n"; + +export const RsCreateEmptyTable = "let empty_schema = Arc::new(Schema::new(vec![\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n false,\n ),\n Field::new(\"item\", DataType::Utf8, false),\n Field::new(\"price\", DataType::Float32, false),\n]));\nlet empty_table = db\n .create_empty_table(\"test_empty_table\", empty_schema)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsCreateTableConflictHandling = "// Idempotent open: reuse the existing table if it exists.\n// The provided data is ignored; the schema is validated against the\n// existing table and a mismatch raises an error.\nlet _conflict_table = db\n .create_table(\"conflict_table\", exist_ok_reader)\n .mode(CreateTableMode::exist_ok(|req| req))\n .execute()\n .await\n .unwrap();\n\n// Overwrite: drop the existing table and create a new one with the\n// provided data. This permanently discards the old table's data.\nlet conflict_table = db\n .create_table(\"conflict_table\", overwrite_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsCreateTableCustomSchema = "let custom_schema = Arc::new(Schema::new(vec![\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n false,\n ),\n Field::new(\"lat\", DataType::Float32, false),\n Field::new(\"long\", DataType::Float32, false),\n]));\n\nlet custom_batch = RecordBatch::try_new(\n custom_schema.clone(),\n vec![\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![\n Some(vec![Some(1.1), Some(1.2), Some(1.3), Some(1.4)]),\n Some(vec![Some(0.2), Some(1.8), Some(0.4), Some(3.6)]),\n ],\n 4,\n ),\n ),\n Arc::new(Float32Array::from(vec![45.5, 40.1])),\n Arc::new(Float32Array::from(vec![-122.7, -74.1])),\n ],\n)\n.unwrap();\nlet custom_reader: Box =\n Box::new(RecordBatchIterator::new(vec![Ok(custom_batch)].into_iter(), custom_schema.clone()));\nlet custom_table = db\n .create_table(\"my_table_custom_schema\", custom_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsCreateTableFromArrow = "let arrow_schema = Arc::new(Schema::new(vec![\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 16),\n false,\n ),\n Field::new(\"text\", DataType::Utf8, false),\n]));\n\nlet arrow_batch = RecordBatch::try_new(\n arrow_schema.clone(),\n vec![\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![Some(vec![Some(0.1); 16]), Some(vec![Some(0.2); 16])],\n 16,\n ),\n ),\n Arc::new(StringArray::from(vec![\"foo\", \"bar\"])),\n ],\n)\n.unwrap();\nlet arrow_reader: Box =\n Box::new(RecordBatchIterator::new(vec![Ok(arrow_batch)].into_iter(), arrow_schema.clone()));\nlet arrow_table = db\n .create_table(\"arrow_table_example\", arrow_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsCreateTableFromDicts = "struct Location {\n vector: [f32; 2],\n lat: f32,\n long: f32,\n}\n\nlet data = vec![\n Location {\n vector: [1.1, 1.2],\n lat: 45.5,\n long: -122.7,\n },\n Location {\n vector: [0.2, 1.8],\n lat: 40.1,\n long: -74.1,\n },\n];\n\nlet schema = Arc::new(Schema::new(vec![\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n false,\n ),\n Field::new(\"lat\", DataType::Float32, false),\n Field::new(\"long\", DataType::Float32, false),\n]));\n\nlet batch = RecordBatch::try_new(\n schema.clone(),\n vec![\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n data.iter()\n .map(|row| Some(row.vector.iter().copied().map(Some).collect::>())),\n 2,\n ),\n ),\n Arc::new(Float32Array::from_iter_values(\n data.iter().map(|row| row.lat),\n )),\n Arc::new(Float32Array::from_iter_values(\n data.iter().map(|row| row.long),\n )),\n ],\n)\n.unwrap();\nlet reader: Box =\n Box::new(RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema.clone()));\nlet table = db\n .create_table(\"test_table\", reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsCreateTableFromIterator = "let batch_schema = Arc::new(Schema::new(vec![\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 4),\n false,\n ),\n Field::new(\"item\", DataType::Utf8, false),\n Field::new(\"price\", DataType::Float32, false),\n]));\n\nlet batches = (0..5)\n .map(|i| {\n RecordBatch::try_new(\n batch_schema.clone(),\n vec![\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![\n Some(vec![Some(3.1 + i as f32), Some(4.1), Some(5.1), Some(6.1)]),\n Some(vec![\n Some(5.9),\n Some(26.5 + i as f32),\n Some(4.7),\n Some(32.8),\n ]),\n ],\n 4,\n ),\n ),\n Arc::new(StringArray::from(vec![\n format!(\"item{}\", i * 2 + 1),\n format!(\"item{}\", i * 2 + 2),\n ])),\n Arc::new(Float32Array::from(vec![\n ((i * 2 + 1) * 10) as f32,\n ((i * 2 + 2) * 10) as f32,\n ])),\n ],\n )\n .unwrap()\n })\n .collect::>();\n\nlet batch_reader: Box =\n Box::new(RecordBatchIterator::new(batches.into_iter().map(Ok), batch_schema.clone()));\nlet batch_table = db\n .create_table(\"batched_table\", batch_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsDeleteOperation = "// delete data\nlet predicate = \"id = 3\";\ntable.delete(predicate).await.unwrap();\n"; + +export const RsDropColumnsMultiple = "// Remove the second temporary column\nschema_drop_table.drop_columns(&[\"temp_col2\"]).await.unwrap();\n"; + +export const RsDropColumnsSingle = "// Remove the first temporary column\nschema_drop_table.drop_columns(&[\"temp_col1\"]).await.unwrap();\n"; + +export const RsDropTable = "let drop_schema = Arc::new(Schema::new(vec![\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n false,\n ),\n Field::new(\"lat\", DataType::Float32, false),\n]));\nlet drop_batch = RecordBatch::try_new(\n drop_schema.clone(),\n vec![\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![Some(vec![Some(1.1), Some(1.2)])],\n 2,\n ),\n ),\n Arc::new(Float32Array::from(vec![45.5])),\n ],\n)\n.unwrap();\nlet drop_reader: Box =\n Box::new(RecordBatchIterator::new(vec![Ok(drop_batch)].into_iter(), drop_schema.clone()));\ndb.create_table(\"my_table\", drop_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\ndb.drop_table(\"my_table\", &[]).await.unwrap();\n"; + +export const RsInsertIfNotExists = "let table = db\n .create_table(\n \"users_example\",\n make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert.when_not_matched_insert_all();\nmerge_insert\n .execute(make_users_reader(\n vec![2, 3],\n vec![\"Bobby\", \"Charlie\"],\n Some(vec![21, 5]),\n ))\n .await\n .unwrap();\n"; + +export const RsMergeDeleteMissingBySource = "let table = db\n .create_table(\n \"users_example\",\n make_users_reader(\n vec![1, 2, 3],\n vec![\"Alice\", \"Bob\", \"Charlie\"],\n Some(vec![10, 20, 5]),\n ),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert\n .when_matched_update_all(None)\n .when_not_matched_insert_all()\n .when_not_matched_by_source_delete(None);\nmerge_insert\n .execute(make_users_reader(\n vec![2, 3],\n vec![\"Bobby\", \"Charlie\"],\n Some(vec![21, 5]),\n ))\n .await\n .unwrap();\n"; + +export const RsMergeMatchedUpdateOnly = "let table = db\n .create_table(\n \"users_example\",\n make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert.when_matched_update_all(None);\nmerge_insert\n .execute(make_users_reader(\n vec![2, 3],\n vec![\"Bobby\", \"Charlie\"],\n Some(vec![21, 5]),\n ))\n .await\n .unwrap();\n"; + +export const RsMergePartialColumns = "let table = db\n .create_table(\n \"users_example\",\n make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert\n .when_matched_update_all(None)\n .when_not_matched_insert_all();\nmerge_insert\n .execute(make_users_reader(vec![2, 3], vec![\"Bobby\", \"Charlie\"], None))\n .await\n .unwrap();\n"; + +export const RsMergeUpdateInsert = "let table = db\n .create_table(\n \"users_example\",\n make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\nlet mut merge_insert = table.merge_insert(&[\"id\"]);\nmerge_insert\n .when_matched_update_all(None)\n .when_not_matched_insert_all();\nmerge_insert\n .execute(make_users_reader(\n vec![2, 3],\n vec![\"Bobby\", \"Charlie\"],\n Some(vec![21, 5]),\n ))\n .await\n .unwrap();\n"; + +export const RsOpenExistingTable = "let open_schema = Arc::new(Schema::new(vec![\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 2),\n false,\n ),\n Field::new(\"lat\", DataType::Float32, false),\n Field::new(\"long\", DataType::Float32, false),\n]));\nlet open_batch = RecordBatch::try_new(\n open_schema.clone(),\n vec![\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![Some(vec![Some(1.1), Some(1.2)])],\n 2,\n ),\n ),\n Arc::new(Float32Array::from(vec![45.5])),\n Arc::new(Float32Array::from(vec![-122.7])),\n ],\n)\n.unwrap();\nlet open_reader: Box =\n Box::new(RecordBatchIterator::new(vec![Ok(open_batch)].into_iter(), open_schema.clone()));\ndb.create_table(\"test_table\", open_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n\nprintln!(\"{:?}\", db.table_names().execute().await.unwrap());\n\nlet opened_table = db.open_table(\"test_table\").execute().await.unwrap();\n"; + +export const RsSchemaAddSetup = "let schema_add_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"name\", DataType::Utf8, false),\n Field::new(\"price\", DataType::Float64, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n false,\n ),\n]));\nlet schema_add_batch = RecordBatch::try_new(\n schema_add_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1, 2, 3])),\n Arc::new(StringArray::from(vec![\"Laptop\", \"Smartphone\", \"Headphones\"])),\n Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![\n Some(vec![Some(0.1_f32); 128]),\n Some(vec![Some(0.2_f32); 128]),\n Some(vec![Some(0.3_f32); 128]),\n ],\n 128,\n ),\n ),\n ],\n)\n.unwrap();\nlet schema_add_reader: Box = Box::new(RecordBatchIterator::new(\n vec![Ok(schema_add_batch)].into_iter(),\n schema_add_schema.clone(),\n));\nlet schema_add_table = db\n .create_table(\"schema_evolution_add_example\", schema_add_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsSchemaAlterSetup = "let schema_alter_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"name\", DataType::Utf8, false),\n Field::new(\"price\", DataType::Int32, false),\n Field::new(\"discount_price\", DataType::Float64, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n false,\n ),\n]));\nlet schema_alter_batch = RecordBatch::try_new(\n schema_alter_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1, 2])),\n Arc::new(StringArray::from(vec![\"Laptop\", \"Smartphone\"])),\n Arc::new(Int32Array::from(vec![1200, 800])),\n Arc::new(Float64Array::from(vec![1080.0, 720.0])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![Some(vec![Some(0.1_f32); 128]), Some(vec![Some(0.2_f32); 128])],\n 128,\n ),\n ),\n ],\n)\n.unwrap();\nlet schema_alter_reader: Box = Box::new(RecordBatchIterator::new(\n vec![Ok(schema_alter_batch)].into_iter(),\n schema_alter_schema.clone(),\n));\nlet schema_alter_table = db\n .create_table(\"schema_evolution_alter_example\", schema_alter_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsSchemaDropSetup = "let schema_drop_schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"name\", DataType::Utf8, false),\n Field::new(\"price\", DataType::Float64, false),\n Field::new(\"temp_col1\", DataType::Utf8, false),\n Field::new(\"temp_col2\", DataType::Int32, false),\n Field::new(\n \"vector\",\n DataType::FixedSizeList(Arc::new(Field::new(\"item\", DataType::Float32, true)), 128),\n false,\n ),\n]));\nlet schema_drop_batch = RecordBatch::try_new(\n schema_drop_schema.clone(),\n vec![\n Arc::new(Int64Array::from(vec![1, 2, 3])),\n Arc::new(StringArray::from(vec![\"Laptop\", \"Smartphone\", \"Headphones\"])),\n Arc::new(Float64Array::from(vec![1200.0, 800.0, 150.0])),\n Arc::new(StringArray::from(vec![\"X\", \"Y\", \"Z\"])),\n Arc::new(Int32Array::from(vec![100, 200, 300])),\n Arc::new(\n FixedSizeListArray::from_iter_primitive::(\n vec![\n Some(vec![Some(0.1_f32); 128]),\n Some(vec![Some(0.2_f32); 128]),\n Some(vec![Some(0.3_f32); 128]),\n ],\n 128,\n ),\n ),\n ],\n)\n.unwrap();\nlet schema_drop_reader: Box = Box::new(RecordBatchIterator::new(\n vec![Ok(schema_drop_batch)].into_iter(),\n schema_drop_schema.clone(),\n));\nlet schema_drop_table = db\n .create_table(\"schema_evolution_drop_example\", schema_drop_reader)\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsSchemaFieldMetadataMerge = "// Set two metadata keys on the `category` field.\nlet res = field_metadata_table\n .update_field_metadata(&[FieldMetadataUpdate::new(\"category\")\n .set(\"unit\", \"label\")\n .set(\"pii\", \"false\")])\n .await\n .unwrap();\nprintln!(\"version: {}\", res.version);\n\n// Merge: add a new key, delete one with `.remove`, keep the rest.\nfield_metadata_table\n .update_field_metadata(&[FieldMetadataUpdate::new(\"category\")\n .set(\"source\", \"import\")\n .remove(\"pii\")])\n .await\n .unwrap();\n"; + +export const RsSchemaFieldMetadataReplace = "field_metadata_table\n .update_field_metadata(&[FieldMetadataUpdate::new(\"category\")\n .set(\"owner\", \"search-team\")\n .replace()])\n .await\n .unwrap();\n"; + +export const RsUpdateConnectEnterprise = "let uri = \"db://your-project-slug\";\nlet api_key = \"your-api-key\";\nlet region = \"us-east-1\";\n"; + +export const RsUpdateConnectLocal = "let db = connect(\"./data\").execute().await.unwrap();\n"; + +export const RsUpdateExampleTableSetup = "let table = db\n .create_table(\n \"users_example\",\n make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsUpdateMakeUsersReader = "fn make_users_reader(\n ids: Vec,\n names: Vec<&str>,\n login_counts: Option>,\n) -> Box {\n let mut fields = vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"name\", DataType::Utf8, false),\n ];\n let mut columns: Vec> =\n vec![Arc::new(Int64Array::from(ids)), Arc::new(StringArray::from(names))];\n\n if let Some(login_counts) = login_counts {\n fields.push(Field::new(\"login_count\", DataType::Int64, true));\n columns.push(Arc::new(Int64Array::from(login_counts)));\n }\n\n let schema = Arc::new(Schema::new(fields));\n let batch = RecordBatch::try_new(schema.clone(), columns).unwrap();\n let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);\n Box::new(reader)\n}\n"; + +export const RsUpdateOperation = "let table = db\n .create_table(\n \"users_example\",\n make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\ntable\n .update()\n .only_if(\"id = 2\")\n .column(\"name\", \"'Bobby'\")\n .execute()\n .await\n .unwrap();\n"; + +export const RsUpdateOptimizeCleanup = "table\n .optimize(OptimizeAction::Prune {\n older_than: Some(Duration::days(1)),\n delete_unverified: None,\n error_if_tagged_old_versions: None,\n })\n .await\n .unwrap();\n"; + +export const RsUpdateUsingSql = "let table = db\n .create_table(\n \"users_example\",\n make_users_reader(vec![1, 2], vec![\"Alice\", \"Bob\"], Some(vec![10, 20])),\n )\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\ntable\n .update()\n .only_if(\"id = 2\")\n .column(\"login_count\", \"login_count + 1\")\n .execute()\n .await\n .unwrap();\n"; + +export const RsVersioningAddData = "let more_data = vec![\n (4, \"Richard Daniel Sanchez\", \"That's the way the news goes!\"),\n (5, \"Morty\", \"Aww geez, Rick!\"),\n];\ntable\n .add(make_quotes_reader(more_data))\n .execute()\n .await\n .unwrap();\n"; + +export const RsVersioningBasicSetup = "let table_name = \"quotes_versioning_example\";\nlet data = vec![\n (1, \"Richard\", \"Wubba Lubba Dub Dub!\"),\n (2, \"Morty\", \"Rick, what's going on?\"),\n (3, \"Richard\", \"I turned myself into a pickle, Morty!\"),\n];\n\nlet table = db\n .create_table(table_name, make_quotes_reader(data))\n .mode(CreateTableMode::Overwrite)\n .execute()\n .await\n .unwrap();\n"; + +export const RsVersioningCheckInitialVersion = "let versions = table.list_versions().await.unwrap();\nlet current_version = table.version().await.unwrap();\nprintln!(\"Number of versions after creation: {}\", versions.len());\nprintln!(\"Current version: {}\", current_version);\n"; + +export const RsVersioningCheckVersionsAfterMod = "let versions_after_mod = table.list_versions().await.unwrap();\nlet version_count_after_mod = versions_after_mod.len();\nlet version_after_mod = table.version().await.unwrap();\nprintln!(\n \"Number of versions after modifications: {}\",\n version_count_after_mod\n);\nprintln!(\"Current version: {}\", version_after_mod);\n"; + +export const RsVersioningCheckoutLatest = "table.checkout_latest().await.unwrap();\n"; + +export const RsVersioningDeleteData = "table.delete(\"author = 'Morty'\").await.unwrap();\nlet rows_after_deletion = table.count_rows(None).await.unwrap();\nprintln!(\"Number of rows after deletion: {}\", rows_after_deletion);\n"; + +export const RsVersioningListAllVersions = "let all_versions = table.list_versions().await.unwrap();\nfor v in &all_versions {\n println!(\"Version {}, created at {}\", v.version, v.timestamp);\n}\n"; + +export const RsVersioningMakeQuotesReader = "fn make_quotes_reader(rows: Vec<(i64, &str, &str)>) -> Box {\n let ids: Vec = rows.iter().map(|(id, _, _)| *id).collect();\n let authors: Vec<&str> = rows.iter().map(|(_, author, _)| *author).collect();\n let quotes: Vec<&str> = rows.iter().map(|(_, _, quote)| *quote).collect();\n\n let schema = Arc::new(Schema::new(vec![\n Field::new(\"id\", DataType::Int64, false),\n Field::new(\"author\", DataType::Utf8, false),\n Field::new(\"quote\", DataType::Utf8, false),\n ]));\n\n let batch = RecordBatch::try_new(\n schema.clone(),\n vec![\n Arc::new(Int64Array::from(ids)),\n Arc::new(StringArray::from(authors)),\n Arc::new(StringArray::from(quotes)),\n ],\n )\n .unwrap();\n let reader = RecordBatchIterator::new(vec![Ok(batch)].into_iter(), schema);\n Box::new(reader)\n}\n"; + +export const RsVersioningRollback = "table.checkout(version_after_mod).await.unwrap();\ntable.restore().await.unwrap();\nlet versions_after_rollback = table.list_versions().await.unwrap();\nlet version_count_after_rollback = versions_after_rollback.len();\nprintln!(\n \"Total number of versions after rollback: {}\",\n version_count_after_rollback\n);\n"; + +export const RsVersioningTags = "let mut tags = tags_table.tags().await.unwrap();\n\n// Create a tag pointing at a specific version\ntags.create(\"baseline\", 1).await.unwrap();\nlet current_version = tags_table.version().await.unwrap();\ntags.create(\"with-edits\", current_version).await.unwrap();\n\n// List all tags on this table\nlet all_tags = tags.list().await.unwrap();\nprintln!(\"Tags: {:?}\", all_tags);\n\n// Look up the version a tag points at\nlet baseline_version = tags.get_version(\"baseline\").await.unwrap();\nprintln!(\"baseline -> v{}\", baseline_version);\n\n// Move an existing tag to a different version\ntags.update(\"baseline\", 2).await.unwrap();\n\n// Check out a version by tag name (separate method in Rust)\ntags_table.checkout_tag(\"baseline\").await.unwrap();\nprintln!(\"Current version: {}\", tags_table.version().await.unwrap());\n\n// Delete a tag (does not delete the underlying version)\ntags.delete(\"with-edits\").await.unwrap();\n\n// Return to the latest version\ntags_table.checkout_latest().await.unwrap();\n"; + +export const RsVersioningUpdateData = "table\n .update()\n .only_if(\"author = 'Richard'\")\n .column(\"author\", \"'Richard Daniel Sanchez'\")\n .execute()\n .await\n .unwrap();\nlet rows_after_update = table\n .count_rows(Some(\"author = 'Richard Daniel Sanchez'\".to_string()))\n .await\n .unwrap();\nprintln!(\n \"Rows updated to Richard Daniel Sanchez: {}\",\n rows_after_update\n);\n"; + diff --git a/docs/web/static/assets/images/demos/sda_hero.jpg b/docs/web/static/assets/images/demos/sda_hero.jpg new file mode 100644 index 0000000000000000000000000000000000000000..afe4322ed9e6225e1ca5f9c96b856099d754b36c GIT binary patch literal 343978 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