docs(nodejs): correct LangChain result handling

docs/src/js/README.md

@@ -34,6 +34,36 @@ const results = await table.vectorSearch([0.1, 0.3]).limit(20).toArray();
 console.log(results);
 ```
 
+### Use an Existing Table with LangChain
+
+When wrapping an existing table with `@langchain/community`, open the table
+with LanceDB and pass the resulting table handle to LangChain. The LangChain
+`uri` and `tableName` options are used when creating a table; they do not open
+an existing table for search.
+
+```javascript
+import { LanceDB as LangChainLanceDB } from "@langchain/community/vectorstores/lancedb";
+import * as lancedb from "@lancedb/lancedb";
+
+const db = await lancedb.connect("data/sample-lancedb");
+const table = await db.openTable("my_table");
+const vectorStore = new LangChainLanceDB(embeddings, {
+  table,
+  textKey: "item",
+});
+
+const documents = await vectorStore.similaritySearch("foo", 5);
+```
+
+Affected versions of `@langchain/community` do not map LanceDB's `_distance`
+column to the score returned by `similaritySearchVectorWithScore`. Query the
+table directly when you need the numeric vector distance:
+
+```javascript
+const results = await table.vectorSearch(queryVector).limit(5).toArray();
+console.log(results[0]._distance);
+```
+
 The [quickstart](https://docs.lancedb.com/quickstart/) contains more complete examples.
 
 ## Development

nodejs/README.md

@@ -30,6 +30,36 @@ const results = await table.vectorSearch([0.1, 0.3]).limit(20).toArray();
 console.log(results);
 ```
 
+### Use an Existing Table with LangChain
+
+When wrapping an existing table with `@langchain/community`, open the table
+with LanceDB and pass the resulting table handle to LangChain. The LangChain
+`uri` and `tableName` options are used when creating a table; they do not open
+an existing table for search.
+
+```javascript
+import { LanceDB as LangChainLanceDB } from "@langchain/community/vectorstores/lancedb";
+import * as lancedb from "@lancedb/lancedb";
+
+const db = await lancedb.connect("data/sample-lancedb");
+const table = await db.openTable("my_table");
+const vectorStore = new LangChainLanceDB(embeddings, {
+  table,
+  textKey: "item",
+});
+
+const documents = await vectorStore.similaritySearch("foo", 5);
+```
+
+Affected versions of `@langchain/community` do not map LanceDB's `_distance`
+column to the score returned by `similaritySearchVectorWithScore`. Query the
+table directly when you need the numeric vector distance:
+
+```javascript
+const results = await table.vectorSearch(queryVector).limit(5).toArray();
+console.log(results[0]._distance);
+```
+
 The [quickstart](https://docs.lancedb.com/quickstart/) contains more complete examples.
 
 ## Development

python/python/lancedb/db.py

@@ -707,9 +707,6 @@ class LanceDBConnection(DBConnection):
         self._namespace_client_properties = namespace_client_properties
         if _inner is not None:
             self._conn = _inner
-            # Native-derived wrappers resolve this in their async reconstruction
-            # path so construction never synchronously re-enters LOOP.
-            self._read_consistency_interval = read_consistency_interval
             self._cached_namespace_client = None
             return
 
@@ -759,14 +756,11 @@ class LanceDBConnection(DBConnection):
         # storage_options.  Also, this class really shouldn't be holding any state
         # beyond _conn.
         self._conn = AsyncConnection(LOOP.run(do_connect()))
-        # Keep property access synchronous so debugger introspection cannot wait on
-        # the background loop while that thread is suspended at a breakpoint.
-        self._read_consistency_interval = read_consistency_interval
         self._cached_namespace_client: Optional[LanceNamespace] = None
 
     @property
     def read_consistency_interval(self) -> Optional[timedelta]:
-        return self._read_consistency_interval
+        return LOOP.run(self._conn.get_read_consistency_interval())
 
     @property
     def session(self) -> Optional[Session]:
@@ -777,16 +771,8 @@ class LanceDBConnection(DBConnection):
         return self._conn.uri
 
     @classmethod
-    def from_inner(
-        cls,
-        inner: LanceDbConnection,
-        read_consistency_interval: Optional[timedelta],
-    ):
-        return cls(
-            None,
-            read_consistency_interval=read_consistency_interval,
-            _inner=inner,
-        )
+    def from_inner(cls, inner: LanceDbConnection):
+        return cls(None, _inner=inner)
 
     def __repr__(self) -> str:
         return f"{self.__class__.__name__}(uri={self._conn.uri!r})"

python/python/lancedb/embeddings/instructor.py

@@ -3,7 +3,6 @@
 
 
 from typing import List
-from urllib.parse import unquote, urlparse
 
 import numpy as np
 
@@ -126,20 +125,9 @@ class InstructorEmbeddingFunction(TextEmbeddingFunction):
 
     @weak_lru(maxsize=1)
     def get_model(self):
-        huggingface_hub = attempt_import_or_raise("huggingface_hub", "huggingface-hub")
-        missing = object()
-        original_cached_download = getattr(huggingface_hub, "cached_download", missing)
-        if original_cached_download is missing:
-            huggingface_hub.cached_download = _cached_download(huggingface_hub)
-
-        try:
-            instructor_embedding = attempt_import_or_raise(
-                "InstructorEmbedding", "InstructorEmbedding"
-            )
-        finally:
-            if original_cached_download is missing:
-                del huggingface_hub.cached_download
-
+        instructor_embedding = attempt_import_or_raise(
+            "InstructorEmbedding", "InstructorEmbedding"
+        )
         torch = attempt_import_or_raise("torch", "torch")
 
         model = instructor_embedding.INSTRUCTOR(self.name)
@@ -152,44 +140,3 @@ class InstructorEmbeddingFunction(TextEmbeddingFunction):
                 model, {torch.nn.Linear}, dtype=torch.qint8
             )
         return model
-
-
-def _cached_download(huggingface_hub):
-    """Provide the legacy download API used by sentence-transformers 2.2.x."""
-
-    def cached_download(
-        *,
-        url,
-        cache_dir=None,
-        force_filename=None,
-        library_name=None,
-        library_version=None,
-        user_agent=None,
-        use_auth_token=None,
-        **_,
-    ):
-        path = urlparse(url).path.lstrip("/")
-        try:
-            repo_id, resolved_path = path.split("/resolve/", maxsplit=1)
-            revision, filename = resolved_path.split("/", maxsplit=1)
-        except ValueError as err:
-            raise ValueError(f"Unsupported Hugging Face Hub URL: {url}") from err
-
-        repo_id = unquote(repo_id)
-        revision = unquote(revision)
-        filename = unquote(filename)
-        # sentence-transformers derives force_filename from this Hub path with
-        # os.path.join. Using the URL path beneath local_dir produces the same
-        # local destination without sending Windows separators to the Hub.
-        return huggingface_hub.hf_hub_download(
-            repo_id=repo_id,
-            filename=filename,
-            revision=revision,
-            local_dir=cache_dir,
-            library_name=library_name,
-            library_version=library_version,
-            user_agent=user_agent,
-            token=use_auth_token,
-        )
-
-    return cached_download

python/python/lancedb/permutation.py

@@ -226,7 +226,7 @@ class PermutationBuilder:
 
         async def do_execute():
             inner_tbl = await self._async.execute()
-            return await LanceTable.from_inner(inner_tbl)
+            return LanceTable.from_inner(inner_tbl)
 
         return LOOP.run(do_execute())
 

python/python/lancedb/table.py

@@ -2182,15 +2182,11 @@ class LanceTable(Table):
         return self.name
 
     @classmethod
-    async def from_inner(cls, tbl: LanceDBTable):
-        from .db import AsyncConnection, LanceDBConnection
+    def from_inner(cls, tbl: LanceDBTable):
+        from .db import LanceDBConnection
 
         async_tbl = AsyncTable(tbl)
-        inner_conn = tbl.database()
-        read_consistency_interval = await AsyncConnection(
-            inner_conn
-        ).get_read_consistency_interval()
-        conn = LanceDBConnection.from_inner(inner_conn, read_consistency_interval)
+        conn = LanceDBConnection.from_inner(tbl.database())
         return cls(
             conn,
             async_tbl.name,

python/python/tests/test_db.py

@@ -77,23 +77,6 @@ def test_sync_repr_does_not_use_background_loop(tmp_path, monkeypatch):
     assert repr(table) == f"LanceTable(name='test', _conn={db!r})"
 
 
-def test_read_consistency_interval_does_not_use_background_loop(tmp_path, monkeypatch):
-    from lancedb.background_loop import LOOP
-    from lancedb.db import LanceDBConnection
-
-    consistency_interval = timedelta(seconds=5)
-    db = lancedb.connect(tmp_path, read_consistency_interval=consistency_interval)
-    db_from_inner = LanceDBConnection.from_inner(db._inner, consistency_interval)
-
-    def fail_run(*args, **kwargs):
-        raise AssertionError("properties should not use the Python background loop")
-
-    monkeypatch.setattr(LOOP, "run", fail_run)
-
-    assert db.read_consistency_interval == consistency_interval
-    assert db_from_inner.read_consistency_interval == consistency_interval
-
-
 def test_ingest_pd(tmp_path):
     db = lancedb.connect(tmp_path)
 

python/python/tests/test_embeddings.py

@@ -1,11 +1,8 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-import ntpath
 import os
 import pickle
-import sys
-from types import ModuleType
 from typing import List, Optional, Union
 from unittest.mock import MagicMock, patch
 
@@ -525,59 +522,6 @@ def test_embedding_function_safe_model_dump(embedding_type):
             )
 
 
-def test_instructor_embedding_supports_huggingface_hub_without_cached_download(
-    tmp_path, monkeypatch
-):
-    from lancedb.embeddings.instructor import InstructorEmbeddingFunction
-
-    hub_download = MagicMock(return_value="/cache/1_Pooling/config.json")
-    huggingface_hub = ModuleType("huggingface_hub")
-    huggingface_hub.hf_hub_download = hub_download
-    torch = ModuleType("torch")
-    monkeypatch.setitem(sys.modules, "huggingface_hub", huggingface_hub)
-    monkeypatch.setitem(sys.modules, "torch", torch)
-    monkeypatch.delitem(sys.modules, "InstructorEmbedding", raising=False)
-    monkeypatch.syspath_prepend(str(tmp_path))
-
-    (tmp_path / "InstructorEmbedding.py").write_text(
-        "from huggingface_hub import cached_download\n\n"
-        "class INSTRUCTOR:\n"
-        "    def __init__(self, name):\n"
-        "        self.name = name\n"
-    )
-
-    embedding = InstructorEmbeddingFunction.create(show_progress_bar=False)
-    instructor_model = embedding.get_model()
-
-    assert instructor_model.name == "hkunlp/instructor-base"
-    assert not hasattr(huggingface_hub, "cached_download")
-
-    instructor_embedding = sys.modules["InstructorEmbedding"]
-    path = instructor_embedding.cached_download(
-        url=(
-            "https://huggingface.co/hkunlp/instructor-base/resolve/abc123/"
-            "1_Pooling/config.json"
-        ),
-        cache_dir="/cache",
-        force_filename=ntpath.join("1_Pooling", "config.json"),
-        library_name="sentence-transformers",
-        library_version="2.2.2",
-        use_auth_token="token",
-    )
-
-    assert path == "/cache/1_Pooling/config.json"
-    hub_download.assert_called_once_with(
-        repo_id="hkunlp/instructor-base",
-        filename="1_Pooling/config.json",
-        revision="abc123",
-        local_dir="/cache",
-        library_name="sentence-transformers",
-        library_version="2.2.2",
-        user_agent=None,
-        token="token",
-    )
-
-
 @patch("time.sleep")
 def test_retry(mock_sleep):
     test_function = MagicMock(side_effect=[Exception] * 9 + ["result"])

python/python/tests/test_permutation.py

@@ -6,7 +6,6 @@ import math
 import pytest
 
 from lancedb import DBConnection, Table, connect
-from lancedb.background_loop import LOOP
 from lancedb.permutation import Permutation, Permutations, permutation_builder
 
 
@@ -32,25 +31,6 @@ def test_split_random_ratios(mem_db):
     assert 65 <= split_1_count <= 75  # ~70% ± tolerance
 
 
-def test_execute_does_not_reenter_background_loop(tmp_path, monkeypatch):
-    import threading
-
-    db = connect(tmp_path)
-    tbl = db.create_table("test_table", pa.table({"x": range(10)}))
-    original_run = LOOP.run
-
-    def fail_on_reentry(future):
-        assert threading.current_thread() is not LOOP.thread
-        return original_run(future)
-
-    monkeypatch.setattr(LOOP, "run", fail_on_reentry)
-
-    permutation_tbl = permutation_builder(tbl).execute()
-
-    assert permutation_tbl.count_rows() == 10
-    assert permutation_tbl._conn.read_consistency_interval is None
-
-
 def test_split_random_counts(mem_db):
     """Test random splitting with absolute counts."""
     tbl = mem_db.create_table(

python/python/tests/test_table.py

@@ -6,7 +6,6 @@ import os
 import sys
 import threading
 import warnings
-from concurrent.futures import ThreadPoolExecutor
 from datetime import date, datetime, timedelta
 from time import sleep
 from typing import List
@@ -2125,27 +2124,6 @@ def test_delete(mem_db: DBConnection):
     assert table.to_arrow()["id"].to_pylist() == [1]
 
 
-def test_concurrent_deletes_are_thread_safe(mem_db: DBConnection):
-    num_workers = 8
-    table = mem_db.create_table(
-        "my_table", data=[{"id": row_id} for row_id in range(num_workers)]
-    )
-    barrier = threading.Barrier(num_workers)
-
-    def delete(row_id: int):
-        barrier.wait()
-        return table.delete(f"id = {row_id}")
-
-    with ThreadPoolExecutor(max_workers=num_workers) as pool:
-        results = list(pool.map(delete, range(num_workers)))
-
-    assert all(result.num_deleted_rows == 1 for result in results)
-    assert sorted(result.version for result in results) == list(
-        range(2, num_workers + 2)
-    )
-    assert table.count_rows() == 0
-
-
 def test_delete_expr(mem_db: DBConnection):
     table = mem_db.create_table(
         "my_table",

python/src/table.rs

@@ -745,9 +745,6 @@ impl Table {
 
     #[allow(private_interfaces)]
     pub fn delete(self_: PyRef<'_, Self>, condition: PredicateArg) -> PyResult<Bound<'_, PyAny>> {
-        // Do not hold the Python borrow across the await.  The cloned Rust table
-        // handle is thread-safe and allows deletes on the same Python table to
-        // run concurrently without PyO3 reporting "Already borrowed".
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {
             let result = match &condition {