Bump version: 0.34.0-beta.7 → 0.34.0

Bump version: 0.34.0-beta.6 → 0.34.0-beta.7
feat: update lance dependency to v8.0.0
2026-08-27 08:28:28 +00:00 · 2026-07-02 15:59:10 +00:00 · 2026-07-02 15:59:09 +00:00 · 2026-07-02 23:00:26 +08:00
111 changed files with 605 additions and 10852 deletions
@@ -1,14 +1,6 @@
 ---
 name: lancedb-branch-ops
-description: >-
+description: Branch management for LanceDB tables via the REST API. Use this skill whenever someone wants to create, delete, list, or switch branches on a LanceDB table — or needs to make sure a write (metadata update, index build, etc.) lands on a specific branch instead of main. Invoke it even without the word "branch" if context makes clear they want an experimental copy of a table, want to isolate changes, or want to confirm a mutation didn't touch main. Covers: branches/list, branches/create, branches/delete, and passing "branch" in describe/update_field_metadata/create_index to target a non-main version.
  Manage LanceDB table branches through the REST API: list, create, and delete
  branches; target schema reads, field-metadata updates, and index creation to a
  named branch; and verify that branch changes remain isolated from main. Use
  when a task involves branch lifecycle, an experimental or isolated table
  version, directing an operation to a non-main branch, or confirming that a
  mutation did not affect main. This skill also explains that LanceDB has no
  checkout operation; each request selects its target branch in the request
  body.
 ---
 ## Goal
@@ -1,79 +0,0 @@
 ---
 name: lancedb
 description: Use when writing, reviewing, debugging, or documenting LanceDB pipelines in Python or TypeScript, especially code that should work across local LanceDB OSS tables and remote LanceDB Enterprise/Cloud tables. Helps avoid non-portable full-table materialization, choose idiomatic query/search patterns, and apply LanceDB performance defaults for ingestion, indexing, filtering, and diagnostics.
 ---
 # Building LanceDB Pipelines
 Use this skill to produce LanceDB pipelines that are portable between local and remote tables (for LanceDB Enterprise/Cloud) and idiomatic for the selected SDK.
 ## LanceDB Table Modes
 LanceDB has two common execution modes:
 - **Local table**: embedded, open source, in-process LanceDB. The client opens data from a local path or object storage URI and executes queries in the application process.
 - **Remote table**: LanceDB Enterprise/Cloud table opened through a `db://...` URI. The data may be very large, commonly backed by object storage, and queried through a remote service.
 Do NOT assume local-only table helpers exist on remote tables. If the user asks for LanceDB Enterprise, Cloud, `db://...`, production remote access, or a remote table, focus on the remote table path: use `search()` / `query()`, keep reads bounded with `select()` and `limit()`, and avoid table-level full materialization APIs.
 ## Workflow
 1. Identify the SDK: Python, TypeScript, or both.
 2. Identify the table mode: local/embedded OSS, remote Enterprise/Cloud, or portable across both. If the user says "LanceDB Enterprise", choose the remote table path.
 3. Read the matching language branch before writing or changing code:
   - Python patterns: `references/python/patterns.md`
   - Python API quick reference: `references/python/api_reference.md`
   - Python performance guidance: `references/python/performance.md`
   - TypeScript patterns: `references/typescript/patterns.md`
   - TypeScript API quick reference: `references/typescript/api_reference.md`
   - TypeScript performance guidance: `references/typescript/performance.md`
 4. Start with `patterns.md` for the selected SDK. Read `api_reference.md` when choosing method names or return collectors. Read `performance.md` when the task involves ingestion, indexing, filtering, query tuning, diagnostics, or large datasets.
 5. For Python schemas, favor Pydantic models and validate records before writing. Use PyArrow schemas when Arrow-native, streaming, or highly dynamic data makes them materially better suited.
 6. Prefer `search()` or `query()` builders with explicit `select()` and `limit()` for reads.
 7. Avoid table-level full materialization in remote or portable code. This is the main local-vs-remote read pitfall.
 8. After a successful embedded OSS ingestion, call `table.optimize()`. Do not call it for Enterprise/Cloud; remote maintenance is automatic.
 9. For remote Enterprise/Cloud writes, never drop-then-reuse or `mode="overwrite"` the same table name — see "Enterprise: never drop-then-reuse the same table name" below. This is the main local-vs-remote write pitfall.
 10. If reviewing an existing file or repo, run `scripts/check_materialization.py` on the relevant paths and inspect each finding before editing.
 11. Cross-check unfamiliar or non-trivial API claims against the source tree instead of relying on memory.
 ## Core Portability Rule
 Do not write code that assumes a local table API will exist on a remote table. Remote tables can be very large, so whole-table materialization helpers are intentionally unavailable or unsafe.
 This does **not** mean result conversion is forbidden. Bounded query/search result collection is normal:
 - Python: `table.search(...).select([...]).limit(10).to_pandas()`
 - TypeScript: `await table.search(...).select([...]).limit(10).toArray()`
 The unsafe pattern is table-level or unbounded collection, plus local-only dataset escape hatches in remote code:
 - Python: `table.to_pandas()`, `table.to_arrow()`, `table.to_polars()`; `table.to_lance()` is local/OSS-only dataset access, not materialization
 - TypeScript: `await table.toArrow()`, `await table.query().toArray()` without `limit()`
 ## Enterprise: never drop-then-reuse the same table name
 LanceDB Enterprise/Cloud splits a **control plane** (DDL: create/drop/rename) from a **data plane** (query nodes that serve reads). Query nodes cache the resolved dataset for a table name for up to `table_cache_ttl` — **default 300 seconds (5 minutes)**. After you drop or overwrite a table, the control plane updates immediately but the data plane keeps serving the *old* dataset until that cache entry expires. During the window the two planes disagree.
 The failure this causes: you `drop_table("t")` then immediately `create_table("t", ...)` (or `create_table("t", ..., mode="overwrite")`). The DDL returns success, but every query against `t` returns **`500 Internal Server Error`** (the query node resolves the stale/deleted dataset), and a fresh `describe` may still show the *old* schema/version. It looks like your write silently failed; it didn't — the name is cached.
 **`mode="overwrite"` has the same problem** — it is a drop+create of the same name under the hood.
 Rules for portable Enterprise ingestion:
 1. **Never reuse a table name you just dropped/overwrote within the cache TTL.** Do not use `mode="overwrite"` to replace an existing Enterprise table in place.
 2. To (re)load data, **write to a fresh table name** (e.g. `<table>_v2`, or a run-stamped suffix). A brand-new name has no cached data-plane entry, so writes and reads work immediately.
 3. Before creating, `list_tables()` and **fail loudly if the name already exists** rather than overwriting — prompt for a new name.
 4. To land on a specific final name that is currently occupied by an old table: drop the old table, **wait out the TTL (~5 min), then `rename_table(fresh_name, final_name)`**. Renaming onto a name whose old dataset is still cached hits the same race, so the wait is mandatory. `rename_table` is a supported control-plane op.
 5. When you hand a table name back to a human, tell them which step still needs the propagation wait (usually: "the old `t` was dropped; run the rename in ~5 minutes").
 This is Enterprise/Cloud-specific. Local/OSS tables have no separate data plane, so `mode="overwrite"` and immediate same-name reuse are fine there.
 ## Script
 Run the scanner when reviewing or modifying an existing codebase:
 ```bash
 python skills/lancedb/scripts/check_materialization.py path/to/file_or_dir
 ```
 The script reports likely unsafe full-table materialization in Python and TypeScript. Treat results as review prompts, not automatic proof of a bug.
@@ -1,105 +0,0 @@
 # Python API Reference
 Quick method reference for Python LanceDB code. Cross-check source for non-trivial claims.
 ## Connect
 ```python
 import lancedb
 db = lancedb.connect("./camelot-db")         # local/OSS
 db = lancedb.connect("db://my-db", api_key=api_key, region=region)  # remote
 ```
 **Place the local database directory next to the script/entrypoint that opens it** (i.e. resolve the path relative to the script, `Path(__file__).parent / "camelot-db"`), not buried under a shared `data/` folder. The Lance dataset is the database, not a data file — keeping it beside its code makes ownership obvious and paths stable regardless of the working directory the script is launched from.
 **Do not name the directory `lancedb`** (e.g. `./lancedb`, `./data/lancedb`). It collides with the imported `lancedb` package name, which is confusing to read and easy to shadow in scripts. Give it a name derived from the repo or dataset with a clear prefix/suffix — for example `./<dataset>-db`, `./<repo>_lancedb`, or `./vectordb`.
 Async:
 ```python
 db = await lancedb.connect_async("./camelot-db")
 ```
 ## Table Reads
 | Task | Preferred API |
 | --- | --- |
 | Vector search | `table.search(query_vector).limit(k)` |
 | Full scan with filters/projection (sync) | `table.search().where(...).select(...).limit(...)` |
 | Full scan with filters/projection (async) | `table.query().where(...).select(...).limit(...)` |
 | Filter | `.where("col > 10")` |
 | Projection | `.select(["id", "text"])` |
 | Bound result count | `.limit(20)` |
 | Collect bounded result as Python objects (default, no extra deps) | `.to_list()` on query/search result |
 | Collect bounded result as Arrow (default, `pyarrow` always available) | `.to_arrow()` on query/search result |
 | Collect bounded result as pandas (only if project uses pandas) | `.to_pandas()` on query/search result |
 | Collect bounded result as Polars (only if project uses polars) | `.to_polars()` on query/search result |
 ## Sync vs Async Scan API
 The plain-scan entry point differs between the sync and async clients. **Verified against `lancedb` 0.34.0** — re-check if the pinned version changes:
 - **Sync** (`lancedb.connect(...)`): the table has **no `.query()` method**. Use `.search()` with no argument for a plain scan; it returns a query builder that supports `.where()`, `.select()`, `.limit()`, and the `.to_list()` / `.to_arrow()` / `.to_pandas()` / `.to_polars()` collectors.
  ```python
  rows = table.search().where("status = 'ready'").select(["id", "text"]).limit(20).to_list()
  ```
 - **Async** (`lancedb.connect_async(...)`): the table has **both** `.query()` and `.search()`. Use `.query()` for a plain scan.
  ```python
  rows = await async_table.query().where("status = 'ready'").select(["id", "text"]).limit(20).to_list()
  ```
 Do not call `table.query()` on a sync table — it raises `AttributeError`.
 ## Local vs Remote Table Methods
 | API | Local table | Remote table | Agent guidance |
 | --- | --- | --- | --- |
 | `table.search(...)` | Yes | Yes | Preferred read path (sync + async) |
 | `table.query()` | Async only | Async only | Sync scan path is `table.search()`; `.query()` is the async scan builder |
 | `table.to_pandas()` | Yes | No / unsafe for portability | Avoid in portable code |
 | `table.to_arrow()` | Yes | No / unsafe for portability | Avoid in portable code |
 | `table.to_polars()` | Yes | No / unsafe for portability | Avoid in portable code |
 | `table.to_lance()` | Yes | No | Local/OSS escape hatch only |
 ## Indexes
 Use `create_index(...)` for vector indexes and modern index configs. Use scalar indexes for filtered or merge keys.
 Common calls:
 ```python
 table.create_index("vector")
 table.create_scalar_index("status")
 table.create_fts_index("text")
 ```
 Check source docs before specifying advanced index config names or parameters.
 ## Filtering And Recall Knobs
 ```python
 table.search(query_vector).where("status = 'ready'")  # pre-filter by default
 table.search(query_vector).where("status = 'ready'", prefilter=False)
 table.search(query_vector).limit(10).refine_factor(20)
 table.search(query_vector).limit(10).nprobes(50)
 ```
 Use post-filtering only when fewer than `limit` results are acceptable.
 ## Diagnostics
 ```python
 print(table.search(query_vector).where("year > 2000").limit(10).analyze_plan())
 print(table.index_stats("vector_idx"))
 ```
 Use these before changing indexes or search tuning.
 ## Maintenance
 ```python
 table.optimize()
 ```
 Call this after every successful local/OSS ingestion. It handles compaction, cleanup of old versions according to retention, and index optimization. Do not add this for LanceDB Enterprise/Cloud remote tables; Enterprise handles compaction and cleanup automatically from cluster configuration.
@@ -1,173 +0,0 @@
 # Python Patterns
 Use these patterns when writing Python code with `lancedb`.
 ## Before Writing Code
 Choose the output type from what the project actually depends on. **Do not assume `pandas` or `polars` is installed** — they are heavy dependencies that many LanceDB projects do not use. `pyarrow`, by contrast, ships as a LanceDB dependency and is always available, so it is a safe default to lean on.
 Default output (after applying `select()` and `limit()`):
 - **Python objects**: `.to_list()` — a list of dicts, no extra dependencies. Prefer this for scripts, examples, and agent-generated code unless there is a reason to do otherwise.
 - **PyArrow**: `.to_arrow()` — a `pyarrow.Table`, when the surrounding code is Arrow-native or you need columnar/zero-copy handoff.
 Only reach for a DataFrame when the project *already* declares that dependency:
 - Pandas projects (pandas in `pyproject.toml`/requirements): `.to_pandas()`.
 - Polars projects (polars declared): `.to_polars()`.
 If unsure, check the dependency manifest or the imports in surrounding files. When in doubt, use `.to_list()` or `.to_arrow()`.
 ## Schema Design and Validation
 Favor `LanceModel` and Pydantic validation for Python schemas. They keep field
 types readable, validate source records before a write, and map directly to a
 LanceDB schema. Use `Vector(dimension)` for fixed-size vectors:
 ```python
 from lancedb.pydantic import LanceModel, Vector
 class Document(LanceModel):
    id: int
    text: str
    vector: Vector(384, nullable=False)
 rows = [Document.model_validate(row) for row in source_rows]
 table = db.create_table("documents", schema=Document)
 table.add(rows)
 ```
 Use PyArrow schemas instead when the pipeline is already Arrow-native, needs
 record-batch streaming, or has runtime schema requirements that would make a
 Pydantic model harder to understand. Declare Pydantic as a direct project
 dependency when application code imports it, even if LanceDB also depends on it.
 ## Recommended Patterns
 ### Bounded search or query
 Use this for application reads, examples, notebooks, and agent-generated scripts:
 ```python
 results = (
    table.search(query_vector)
    .where("status = 'ready'")
    .select(["id", "text"])
    .limit(20)
    .to_list()  # or .to_arrow(); .to_pandas()/.to_polars() only if the project uses them
 )
 ```
 Why: `search()` works across local and remote tables and on both the sync and async clients. `select()` avoids fetching unused columns. `limit()` prevents accidental full-table reads. `.to_list()` and `.to_arrow()` avoid assuming pandas/polars is installed (see "Before Writing Code").
 For a **plain scan** (no query vector), the entry point differs by client:
 ```python
 # Sync client: no .query() method — use .search() with no argument.
 rows = table.search().where("status = 'ready'").select(["id", "text"]).limit(20).to_list()
 # Async client: use .query().
 rows = await async_table.query().where("status = 'ready'").select(["id", "text"]).limit(20).to_list()
 ```
 `table.query()` on a sync table raises `AttributeError` (verified on `lancedb` 0.34.0). See the "Sync vs Async Scan API" section in `api_reference.md`.
 ### Bounded query result conversion
 It is fine to collect bounded query/search results:
 ```python
 arrow_table = table.search().select(["id"]).limit(100).to_arrow()  # sync plain scan
 rows = table.search(query_vector).limit(10).to_list()
 df = table.search(query_vector).limit(10).to_pandas()  # only if pandas is a project dep
 ```
 ### Local-only Lance dataset API
 `table.to_lance()` does not itself materialize the full dataset. It returns the underlying `lance.LanceDataset`, making the table accessible through the PyLance dataset API. Use it when the task is explicitly local/OSS and needs Lance dataset methods not exposed by LanceDB:
 ```python
 # Local/OSS only: RemoteTable does not expose table.to_lance().
 ds = table.to_lance()
 for batch in ds.to_batches(columns=["id", "text"], batch_size=10_000):
    process(batch)
 ```
 ### Async Python
 Keep the same shape and bound the result before collecting:
 ```python
 results = await (
    async_table.query()
    .where("status = 'ready'")
    .select(["id", "text"])
    .limit(20)
    .to_list()  # or .to_arrow()
 )
 ```
 ## Anti-Patterns
 **Avoid the following anti-patterns in your code.**
 ### Table-level full materialization
 Avoid whole-table collectors in portable or large-table code:
 ```python
 df = table.to_pandas()
 arrow_table = table.to_arrow()
 polars_df = table.to_polars()
 ```
 Why: local tables expose these whole-table collectors, but remote tables intentionally do not — a remote production table can be far larger than a local development table, so it is easy to accidentally pull the entire table into memory.
 `table.to_lance()` is different: it is not a full materialization call, but it is still local/OSS-only and should not appear in code meant to run against remote Enterprise tables.
 ### Unbounded result collection
 Avoid query/search collection without a meaningful limit:
 ```python
 rows = table.search().to_list()               # unbounded plain scan
 rows = table.search(query_vector).to_list()   # unbounded vector search
 ```
 Prefer `select(...).limit(...)` before collecting; for large reads, stream in batches instead.
 ### Per-row writes
 Avoid loops that write one row per call:
 ```python
 for row in rows:
    table.add([row])  # one commit + fragment per row
 ```
 Each `add()` creates a new version and fragment. Pass the whole batch in a single call, or chunk very large inputs:
 ```python
 table.add(rows)  # single commit
 # for very large inputs, add batches of several thousand rows
 ```
 After the final successful write to an embedded OSS table, call
 `table.optimize()`. Skip this for Enterprise/Cloud tables because their
 maintenance is automatic.
 ### Drop-then-reuse the same table name (Enterprise/Cloud)
 Avoid dropping or overwriting a remote table and then reusing that name right away:
 ```python
 db.drop_table("my_table")
 table = db.create_table("my_table", data=rows)          # reads 500 for ~5 min
 table = db.create_table("my_table", data=rows, mode="overwrite")  # same problem
 ```
 Why: Enterprise/Cloud splits DDL (control plane) from query serving (data plane). The data plane caches the dataset behind a table name for up to `table_cache_ttl` (default 300s / 5 min), so after a drop/overwrite the DDL succeeds but queries against the reused name return `500 Internal Server Error` until the cache expires — and a fresh `describe` may still show the old schema. Instead, write to a **fresh name**, use `list_tables()` and fail if it already exists, then `rename_table(fresh, final)` onto the final name only after the old table's drop has propagated (~5 min). See the "Enterprise: never drop-then-reuse the same table name" section in `SKILL.md`. Local/OSS tables have no separate data plane — overwrite freely there.
 ### Guessing performance fixes
 Avoid changing `nprobes`, `refine_factor`, or index types before checking the query plan and index stats. Diagnose first, then tune one knob at a time.
@@ -1,131 +0,0 @@
 # Python Performance Guidance
 Use this when writing Python code that ingests data, queries large tables, builds indexes, or investigates latency.
 ## Ingestion
 ### Recommended: validate schemas and records with Pydantic
 Favor `LanceModel` for readable Python schema definitions and validate source
 records before writing. Use PyArrow directly for Arrow-native or streaming
 pipelines where it is the clearer representation.
 ```python
 from lancedb.pydantic import LanceModel, Vector
 class Document(LanceModel):
    id: int
    text: str
    vector: Vector(384, nullable=False)
 rows = [Document.model_validate(row) for row in source_rows]
 table = db.create_table("documents", schema=Document)
 table.add(rows)
 ```
 ### Recommended: bulk ingestion for materialized data
 ```python
 table.add(arrow_table)
 table.add(df)
 table.add(pa.dataset("data/", format="parquet"))
 ```
 For very large initial loads, create the table empty first, then call `add(...)`. Passing data directly to `create_table(name, data)` can skip the auto-parallel write path.
 ### Recommended: iterator ingestion for generated or streamed data
 ```python
 def batches():
    for raw in source:
        vectors = model.encode(raw["text"])
        yield pa.RecordBatch.from_pydict({**raw, "vector": vectors})
 table.add(batches())
 ```
 Use chunks of several thousand rows or more when practical. Tiny batches and per-row writes create many small fragments.
 ### Anti-pattern: per-row `add()`
 ```python
 for row in rows:
    table.add([row])
 ```
 Each call creates a version and fragment. This slows ingestion and later queries.
 ## Indexing
 - Build a vector index once brute-force vector search becomes too slow. As a rule of thumb, local brute force is fine below roughly 100K vectors; beyond that, build an index.
 - Use `IVF_PQ` as the general-purpose default. Enterprise builds this automatically.
 - Use scalar indexes for filtered columns and merge/upsert keys.
 - Use `BTREE` for mostly distinct numeric/string/temporal columns, `BITMAP` for booleans and low-cardinality columns, and `LABEL_LIST` for list membership queries.
 - Keep full-text defaults unless phrase queries require position data.
 ## Querying
 Always be explicit:
 ```python
 table.search(query_vector).select(["id", "title"]).limit(20)
 ```
 - `select()` reduces bytes read and transferred.
 - `limit()` prevents accidental full-table materialization.
 - Pre-filtering is the default and guarantees returned rows satisfy the predicate.
 - Use post-filtering only when fewer than `limit` results are acceptable.
 ## Recall Tuning
 Tune one knob at a time:
 - Quantized indexes: raise `refine_factor` to rescore more candidates on full vectors.
 - HNSW-backed indexes: raise `ef`; start around `1.5 * k`, increase 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.
 ## Maintenance
 After every successful embedded OSS/local ingestion, call `table.optimize()`.
 Do not add this to LanceDB Enterprise/Cloud remote table code; remote compaction
 and cleanup are handled automatically based on the Enterprise cluster
 configuration.
 Why local maintenance is needed:
 - Frequent writes can create many small fragments. Queries then need to scan across more files, which can increase latency.
 - Updates, deletes, and appends create new table versions. Old versions are retained for time travel and rollback, which can grow disk usage.
 - Indexes may have newly added rows that are not yet fully optimized into the index structure.
 For local/OSS tables, run `optimize()` after the final successful ingestion
 write. Also run it after later batches of update/delete operations or on a
 regular maintenance schedule:
 ```python
 table.optimize()
 ```
 If the user wants more aggressive local disk cleanup, pass a shorter cleanup retention window:
 ```python
 from datetime import timedelta
 table.optimize(cleanup_older_than=timedelta(days=1))
 ```
 Do not use very short cleanup windows when the application depends on time travel, rollback, or old versions.
 ## Diagnostics
 Before changing code or indexes, inspect:
 ```python
 print(table.search(query_vector).where("year > 2000").limit(10).analyze_plan())
 print(table.index_stats("vector_idx"))
 ```
 Look for high scan bytes, missing indexes, fragmented data, and unindexed rows.
 ## Python Multiprocessing
 When using multiprocessing, use `spawn` rather than `fork`. LanceDB is multi-threaded internally, and `fork` plus a multi-threaded process is unsafe.
@@ -1,78 +0,0 @@
 # TypeScript API Reference
 Quick method reference for TypeScript LanceDB code. Cross-check source for non-trivial claims.
 ## Connect
 ```typescript
 import * as lancedb from "@lancedb/lancedb";
 const db = await lancedb.connect("./camelot-db");
 ```
 **Place the local database directory next to the script/entrypoint that opens it** (resolve the path relative to the module, e.g. via `import.meta.dirname` / `__dirname`), not buried under a shared `data/` folder. The Lance dataset is the database, not a data file — keeping it beside its code makes ownership obvious and paths stable regardless of the working directory the script is launched from.
 **Do not name the directory `lancedb`** (e.g. `./lancedb`, `./data/lancedb`). It collides with the imported `lancedb` package/namespace, which is confusing to read. Give it a name derived from the repo or dataset with a clear prefix/suffix — for example `./<dataset>-db`, `./<repo>_lancedb`, or `./vectordb`.
 Remote connections use `db://...` plus Enterprise/Cloud credentials and deployment settings. Check current source/docs for exact connection options.
 ## Table Reads
 | Task | Preferred API |
 | --- | --- |
 | Vector search | `table.search(queryVector).limit(k)` |
 | Full scan with filters/projection | `table.query().where(...).select(...).limit(...)` |
 | Filter | `.where("col > 10")` |
 | Projection | `.select(["id", "text"])` |
 | Bound result count | `.limit(20)` |
 | Collect bounded result as objects | `.toArray()` on query/search result |
 | Collect bounded result as Arrow | `.toArrow()` on query/search result |
 | Stream result batches | `for await (const batch of table.query()...)` |
 ## Local vs Remote Safety
 | API | Agent guidance |
 | --- | --- |
 | `table.search(...)` | Preferred read path |
 | `table.query()` | Preferred scan/filter path |
 | `await table.toArrow()` | Avoid in portable or large-table code |
 | `await table.query().toArray()` with no `limit()` | Avoid; unbounded collection |
 | `await table.query().toArrow()` with no `limit()` | Avoid; unbounded collection |
 ## Indexes
 ```typescript
 await table.createIndex("vector");
 await table.createIndex("status");
 ```
 Use vector indexes for large vector search workloads and scalar indexes for filtered columns or merge/upsert keys. Check source/docs before specifying advanced index options.
 ## Filtering And Recall Knobs
 ```typescript
 await table.search(queryVector).where("status = 'ready'").limit(10).toArray();
 await table.search(queryVector).limit(10).refineFactor(20).toArray();
 await table.search(queryVector).limit(10).nprobes(50).toArray();
 await table.search(queryVector).limit(10).ef(100).toArray();
 await table.search(queryVector).where("status = 'ready'").postfilter().limit(10).toArray();
 ```
 Use `postfilter()` only when fewer than `limit` results are acceptable.
 ## Diagnostics
 ```typescript
 console.log(await table.search(queryVector).where("year > 2000").limit(10).analyzePlan());
 console.log(await table.indexStats("vector_idx"));
 ```
 Use these before changing indexes or search tuning.
 ## Maintenance
 ```typescript
 await table.optimize();
 ```
 Call this after every successful local/OSS ingestion. It handles compaction, cleanup of old versions according to retention, and index optimization. Do not add this for LanceDB Enterprise/Cloud remote tables; Enterprise handles compaction and cleanup automatically from cluster configuration.
@@ -1,100 +0,0 @@
 # TypeScript Patterns
 Use these patterns when writing TypeScript code with `@lancedb/lancedb`.
 ## Recommended Patterns
 ### Bounded query
 Use this for application reads, scripts, and examples:
 ```typescript
 const rows = await table
  .query()
  .where("status = 'ready'")
  .select(["id", "text"])
  .limit(20)
  .toArray();
 ```
 ### Bounded vector search
 ```typescript
 const rows = await table
  .search(queryVector)
  .select(["id", "text"])
  .limit(20)
  .toArray();
 ```
 ### Batch streaming for larger reads
 When the task needs many rows, avoid collecting everything at once:
 ```typescript
 for await (const batch of table
  .query()
  .where("status = 'ready'")
  .select(["id", "text"])
  .limit(10_000)) {
  process(batch);
 }
 ```
 ## Anti-Patterns
 **Avoid the following anti-patterns in your code.**
 ### Table-level full materialization
 Avoid whole-table collectors in portable or large-table code:
 ```typescript
 const tableArrow = await table.toArrow();
 ```
 Why: local tables expose these whole-table collectors, but remote tables intentionally do not — a remote production table can be far larger than a local development table, so it is easy to accidentally pull the entire table into memory.
 ### Unbounded result collection
 Avoid query/search collection without a meaningful limit:
 ```typescript
 const rows = await table.query().toArray();              // unbounded plain scan
 const rows = await table.search(queryVector).toArray();  // unbounded vector search
 ```
 Prefer `select(...).limit(...)` before collecting; for large reads, stream in batches instead.
 ### Per-row writes
 Avoid loops that write one row per call:
 ```typescript
 for (const row of rows) {
  await table.add([row]); // one commit + fragment per row
 }
 ```
 Each `add()` creates a new version and fragment. Pass the whole batch in a single call, or chunk very large inputs:
 ```typescript
 await table.add(rows); // single commit
 // for very large inputs, add in chunks of several thousand rows
 ```
 ### Drop-then-reuse the same table name (Enterprise/Cloud)
 Avoid dropping or overwriting a remote table and then reusing that name right away:
 ```typescript
 await db.dropTable("my_table");
 const table = await db.createTable("my_table", rows);                    // reads 500 for ~5 min
 const table = await db.createTable("my_table", rows, { mode: "overwrite" }); // same problem
 ```
 Why: Enterprise/Cloud splits DDL (control plane) from query serving (data plane). The data plane caches the dataset behind a table name for up to `table_cache_ttl` (default 300s / 5 min), so after a drop/overwrite the DDL succeeds but queries against the reused name return `500 Internal Server Error` until the cache expires — and a fresh `describe` may still show the old schema. Instead, write to a **fresh name**, use `tableNames()` and fail if it already exists, then `renameTable(fresh, final)` onto the final name only after the old table's drop has propagated (~5 min). See the "Enterprise: never drop-then-reuse the same table name" section in `SKILL.md`. Local/OSS tables have no separate data plane — overwrite freely there.
 ### Guessing performance fixes
 Avoid changing `nprobes`, `refineFactor`, `ef`, or index settings before checking `analyzePlan()` and `indexStats(...)`. Diagnose first, then tune one knob at a time.
@@ -1,78 +0,0 @@
 # TypeScript Performance Guidance
 Use this when writing TypeScript code that ingests data, queries large tables, builds indexes, or investigates latency.
 ## Ingestion
 - Prefer bulk or batched writes.
 - Avoid per-row write loops; they create many small commits/fragments.
 - For generated data, accumulate reasonable batches before adding.
 - For file-backed data, prefer APIs that stream from Arrow/Parquet-style inputs when available.
 ## Indexing
 - Build a vector index once brute-force vector search becomes too slow. As a rule of thumb, local brute force is fine below roughly 100K vectors; beyond that, build an index.
 - Use the general-purpose vector index defaults unless the task has explicit recall/latency requirements.
 - Build scalar indexes for filtered columns and merge/upsert keys.
 - Use full-text index phrase options only when phrase queries require them.
 ## Querying
 Always be explicit:
 ```typescript
 await table.search(queryVector).select(["id", "title"]).limit(20).toArray();
 ```
 - `select()` reduces bytes read and transferred.
 - `limit()` prevents accidental full-table collection.
 - Pre-filtering is the default behavior. Use `postfilter()` only when fewer than `limit` results are acceptable.
 ## Recall Tuning
 Tune one knob at a time:
 - Quantized indexes: raise `refineFactor(...)` to rescore more candidates on full vectors.
 - HNSW-backed indexes: raise `ef(...)`; start around `1.5 * k`, increase toward `10 * k` if recall is short.
 - IVF candidate breadth: `nprobes(...)` is usually auto-tuned; override only when a selective pre-filter leaves too few neighbors.
 ## Maintenance
 After every successful embedded OSS/local ingestion, call `table.optimize()`.
 Do not add this to LanceDB Enterprise/Cloud remote table code; remote compaction
 and cleanup are handled automatically based on the Enterprise cluster
 configuration.
 Why local maintenance is needed:
 - Frequent writes can create many small fragments. Queries then need to scan across more files, which can increase latency.
 - Updates, deletes, and appends create new table versions. Old versions are retained for time travel and rollback, which can grow disk usage.
 - Indexes may have newly added rows that are not yet fully optimized into the index structure.
 For local/OSS tables, run `optimize()` after the final successful ingestion
 write. Also run it after later batches of update/delete operations or on a
 regular maintenance schedule:
 ```typescript
 await table.optimize();
 ```
 If the user wants more aggressive local disk cleanup, pass a shorter cleanup retention window:
 ```typescript
 const olderThan = new Date(Date.now() - 24 * 60 * 60 * 1000);
 await table.optimize({ cleanupOlderThan: olderThan });
 ```
 Do not use very short cleanup windows when the application depends on time travel, rollback, or old versions.
 ## Diagnostics
 Before changing code or indexes, inspect:
 ```typescript
 console.log(await table.search(queryVector).where("year > 2000").limit(10).analyzePlan());
 console.log(await table.indexStats("vector_idx"));
 ```
 Look for high scan cost, missing indexes, fragmented data, and unindexed rows.
@@ -1,135 +0,0 @@
 #!/usr/bin/env python3
 """Scan Python and TypeScript for likely unsafe LanceDB materialization."""
 from __future__ import annotations
 import argparse
 import re
 import sys
 from dataclasses import dataclass
 from pathlib import Path
 PY_FULL_TABLE = re.compile(r"\b\w+\.(to_pandas|to_arrow|to_polars)\s*\(")
 TS_TABLE_TO_ARROW = re.compile(r"\b\w+\.toArrow\s*\(")
 TS_QUERY_COLLECTOR = re.compile(r"\.query\s*\(\s*\)[\s\S]*?\.to(Array|Arrow)\s*\(")
@dataclass
 class Finding:
    path: Path
    line: int
    message: str
    text: str
 def iter_files(paths: list[Path]) -> list[Path]:
    files: list[Path] = []
    for path in paths:
        if path.is_dir():
            files.extend(
                p
                for p in path.rglob("*")
                if p.suffix in {".py", ".ts", ".tsx"} and "node_modules" not in p.parts
            )
        elif path.suffix in {".py", ".ts", ".tsx"}:
            files.append(path)
    return sorted(set(files))
 def line_number(text: str, offset: int) -> int:
    return text.count("\n", 0, offset) + 1
 def scan_python(path: Path, text: str) -> list[Finding]:
    findings: list[Finding] = []
    for match in PY_FULL_TABLE.finditer(text):
        line_start = text.rfind("\n", 0, match.start()) + 1
        line_end = text.find("\n", match.start())
        if line_end == -1:
            line_end = len(text)
        line = text[line_start:line_end].strip()
        if ".search(" in line or ".query(" in line:
            continue
        findings.append(
            Finding(
                path,
                line_number(text, match.start()),
                f"Review Python `{match.group(1)}()` call; table-level materialization is not portable to remote tables.",
                line,
            )
        )
    return findings
 def statement_around(text: str, start: int, end: int) -> str:
    before = max(text.rfind(";", 0, start), text.rfind("\n\n", 0, start))
    after_candidates = [pos for pos in (text.find(";", end), text.find("\n\n", end)) if pos != -1]
    after = min(after_candidates) if after_candidates else len(text)
    return text[before + 1 : after].strip()
 def scan_typescript(path: Path, text: str) -> list[Finding]:
    findings: list[Finding] = []
    for match in TS_TABLE_TO_ARROW.finditer(text):
        stmt = statement_around(text, match.start(), match.end())
        if ".query(" in stmt or ".search(" in stmt:
            continue
        findings.append(
            Finding(
                path,
                line_number(text, match.start()),
                "Review TypeScript `table.toArrow()`-style call; table-level materialization is not portable for large/remote tables.",
                stmt.splitlines()[0].strip(),
            )
        )
    for match in TS_QUERY_COLLECTOR.finditer(text):
        stmt = statement_around(text, match.start(), match.end())
        if ".limit(" in stmt:
            continue
        findings.append(
            Finding(
                path,
                line_number(text, match.start()),
                "Review unbounded TypeScript query collection; add `limit()` or stream batches.",
                stmt.splitlines()[0].strip(),
            )
        )
    return findings
 def scan_file(path: Path) -> list[Finding]:
    text = path.read_text(encoding="utf-8", errors="replace")
    if path.suffix == ".py":
        return scan_python(path, text)
    if path.suffix in {".ts", ".tsx"}:
        return scan_typescript(path, text)
    return []
 def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument("paths", nargs="+", type=Path)
    parser.add_argument(
        "--no-fail", action="store_true", help="Always exit 0 after reporting findings."
    )
    args = parser.parse_args()
    findings: list[Finding] = []
    for path in iter_files(args.paths):
        findings.extend(scan_file(path))
    for finding in findings:
        print(f"{finding.path}:{finding.line}: {finding.message}")
        print(f"  {finding.text}")
    if findings:
        print(
            f"\n{len(findings)} finding(s). Review manually; bounded query result conversion may be OK."
        )
    return 0 if args.no_fail or not findings else 1
 if __name__ == "__main__":
    sys.exit(main())
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.32.0-beta.1"
+current_version = "0.31.0-beta.6"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
@@ -34,16 +34,15 @@ runs:
        maturin-version: "1.12.4"
        command: build
        working-directory: python
-        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc"
+        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/'"
        target: x86_64-unknown-linux-gnu
        manylinux: ${{ inputs.manylinux }}
        args: ${{ inputs.args }}
        before-script-linux: |
          set -e
-          curl -fsSL https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-x86_64.zip -o /tmp/protoc.zip
+          curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-$(uname -m).zip > /tmp/protoc.zip \
-          unzip /tmp/protoc.zip -d /usr/local
+            && unzip /tmp/protoc.zip -d /usr/local \
-          rm /tmp/protoc.zip
+            && rm /tmp/protoc.zip
          /usr/local/bin/protoc --version
    - name: Build Arm Manylinux Wheel
      if: ${{ inputs.arm-build == 'true' }}
      uses: PyO3/maturin-action@v1
@@ -51,14 +50,13 @@ runs:
        maturin-version: "1.12.4"
        command: build
        working-directory: python
-        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc"
+        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/'"
        target: aarch64-unknown-linux-gnu
        manylinux: ${{ inputs.manylinux }}
        args: ${{ inputs.args }}
        before-script-linux: |
          set -e
-          yum install -y clang
+          yum install -y clang \
-          curl -fsSL https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-aarch_64.zip -o /tmp/protoc.zip
+            && curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-aarch_64.zip > /tmp/protoc.zip \
-          unzip /tmp/protoc.zip -d /usr/local
+            && unzip /tmp/protoc.zip -d /usr/local \
-          rm /tmp/protoc.zip
+            && rm /tmp/protoc.zip
          /usr/local/bin/protoc --version
@@ -103,7 +103,7 @@ jobs:
            features: fp16kernels
            pre_build: brew install protobuf
          - target: x86_64-pc-windows-msvc
-            host: windows-2025-8x-x64
+            host: windows-latest
            features: ","
            pre_build: |-
              choco install --no-progress protoc ninja nasm
@@ -111,21 +111,12 @@ jobs:
              # There is an issue where choco doesn't add nasm to the path
              export PATH="$PATH:/c/Program Files/NASM"
              nasm -v
              # Fat LTO of the cdylib is single-threaded and the peak-memory
              # step of the build, and had started hitting rustc-LLVM OOM on the
              # Windows runners. ThinLTO parallelizes it across the runner's
              # cores and keeps peak memory well under the limit.
              export CARGO_PROFILE_RELEASE_LTO=thin
              export CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16
          - target: aarch64-pc-windows-msvc
-            host: windows-2025-8x-x64
+            host: windows-latest
            features: ","
            pre_build: |-
                choco install --no-progress protoc
                rustup target add aarch64-pc-windows-msvc
                # See ThinLTO note on the x86_64-pc-windows-msvc target above.
                export CARGO_PROFILE_RELEASE_LTO=thin
                export CARGO_PROFILE_RELEASE_CODEGEN_UNITS=16
          - target: x86_64-unknown-linux-gnu
            host: ubuntu-latest
            features: fp16kernels
@@ -125,26 +125,10 @@ jobs:
      - uses: rui314/setup-mold@v1
      - name: Make Swap
        run: |
-          swapfile=/swapfile
+          sudo fallocate -l 16G /swapfile
-          min_swap_bytes=$((15 * 1024 * 1024 * 1024))
+          sudo chmod 600 /swapfile
-          active_swap_bytes="$(sudo swapon --show=NAME,SIZE --bytes --noheadings | awk '$1 == "/swapfile" { print $2 }')"
+          sudo mkswap /swapfile
-          if [ -n "$active_swap_bytes" ]; then
+          sudo swapon /swapfile
            if [ "$active_swap_bytes" -ge "$min_swap_bytes" ]; then
              echo "/swapfile is already active with enough space; skipping swap creation"
              exit 0
            fi
            echo "/swapfile is already active but smaller than 16G; using /mnt/lancedb-swapfile"
            swapfile=/mnt/lancedb-swapfile
          fi
          if sudo swapon --show=NAME --noheadings | grep -Fxq "$swapfile"; then
            echo "$swapfile is already active; skipping swap creation"
            exit 0
          fi
          sudo rm -f "$swapfile"
          sudo fallocate -l 16G "$swapfile"
          sudo chmod 600 "$swapfile"
          sudo mkswap "$swapfile"
          sudo swapon "$swapfile"
      - name: Build
        run: cargo build --profile ci --all-features --tests --locked --examples
      - name: Run feature tests
@@ -13,20 +13,20 @@ categories = ["database-implementations"]
 rust-version = "1.91.0"
 [workspace.dependencies]
-lance = { "version" = "=9.0.0-beta.23", default-features = false, "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=8.0.0", default-features = false }
-lance-core = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = "=8.0.0"
-lance-datagen = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = "=8.0.0"
-lance-file = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = "=8.0.0"
-lance-io = { "version" = "=9.0.0-beta.23", default-features = false, "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=8.0.0", default-features = false }
-lance-index = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = "=8.0.0"
-lance-linalg = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = "=8.0.0"
-lance-namespace = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = "=8.0.0"
-lance-namespace-impls = { "version" = "=9.0.0-beta.23", default-features = false, "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=8.0.0", default-features = false }
-lance-table = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = "=8.0.0"
-lance-testing = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = "=8.0.0"
-lance-datafusion = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = "=8.0.0"
-lance-encoding = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = "=8.0.0"
-lance-arrow = { "version" = "=9.0.0-beta.23", "tag" = "v9.0.0-beta.23", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = "=8.0.0"
 ahash = "0.8"
 # Note that this one does not include pyarrow
 arrow = { version = "58.0.0", optional = false }
@@ -54,8 +54,6 @@ half = { "version" = "2.7.1", default-features = false, features = [
 ] }
 futures = "0"
 log = "0.4"
 metrics = "0.24"
 metrics-util = "0.19"
 moka = { version = "0.12", features = ["future"] }
 object_store = "0.13.2"
 pin-project = "1.0.7"
@@ -51,6 +51,18 @@ ignore = [
    # https://rustsec.org/advisories/RUSTSEC-2024-0436
    { id = "RUSTSEC-2024-0436", reason = "transitive via datafusion; awaiting ecosystem migration" },
    # encoding: unmaintained. Reached through lindera-dictionary, which is
    # required by the native Lindera tokenizer path. Lindera has not migrated
    # off this crate yet.
    # https://rustsec.org/advisories/RUSTSEC-2021-0153
    { id = "RUSTSEC-2021-0153", reason = "transitive via lindera-dictionary for native Lindera tokenizer" },
    # fast-float: unsound and unmaintained. Reached only through polars-arrow
    # from the optional Polars integration; replacement requires a Polars
    # dependency upgrade.
    # https://rustsec.org/advisories/RUSTSEC-2024-0379
    { id = "RUSTSEC-2024-0379", reason = "transitive via polars-arrow; waiting on Polars migration" },
    # tantivy: segfault on malformed input due to missing bounds check.
    # Pulled in via lance for full-text search. We only feed tantivy
    # documents we construct ourselves, not attacker-controlled bytes.
@@ -68,6 +80,18 @@ ignore = [
    # https://rustsec.org/advisories/RUSTSEC-2025-0119
    { id = "RUSTSEC-2025-0119", reason = "transitive via hf-hub/indicatif; cosmetic formatting crate" },
    # bincode: unmaintained. Reached through lindera and lindera-dictionary,
    # which are required by the native Lindera tokenizer path. Lindera has not
    # migrated to another serialization format yet.
    # https://rustsec.org/advisories/RUSTSEC-2025-0141
    { id = "RUSTSEC-2025-0141", reason = "transitive via lindera/lindera-dictionary for native Lindera tokenizer" },
    # lru: soundness issue in IterMut. Reached only through aws-sdk-s3 in
    # LanceDB's dev-dependency graph; LanceDB does not use that iterator
    # directly. Clearing this requires the AWS SDK chain to update lru.
    # https://rustsec.org/advisories/RUSTSEC-2026-0002
    { id = "RUSTSEC-2026-0002", reason = "transitive via aws-sdk-s3 dev-dependency; waiting on AWS SDK lru upgrade" },
    # rustls-webpki 0.101.7 (old major line): name-constraint checks for
    # URI / wildcard names. Pulled in only via the legacy rustls 0.21 chain
    # from aws-smithy-http-client. The 0.103 line we actively use is patched.
@@ -84,23 +108,17 @@ ignore = [
    # https://rustsec.org/advisories/RUSTSEC-2026-0104
    { id = "RUSTSEC-2026-0104", reason = "only affects rustls-webpki 0.101 from legacy aws-smithy/rustls 0.21 chain" },
    # rand 0.8.5: soundness issue only when ThreadRng reseeds inside a custom
    # logger. Reached through several transitive chains. LanceDB does not use
    # rand from a custom logger; upgrade once all pinned chains accept 0.8.6+.
    # https://rustsec.org/advisories/RUSTSEC-2026-0097
    { id = "RUSTSEC-2026-0097", reason = "transitive rand 0.8.5; LanceDB does not call ThreadRng from custom logging" },
    # pyo3 advisories in the Python bindings; tracked pending a patched pyo3 release.
    # https://rustsec.org/advisories/RUSTSEC-2026-0176
    # https://rustsec.org/advisories/RUSTSEC-2026-0177
    { id = "RUSTSEC-2026-0176", reason = "pyo3 in Python bindings; awaiting patched pyo3 release" },
    { id = "RUSTSEC-2026-0177", reason = "pyo3 in Python bindings; awaiting patched pyo3 release" },
    # quick-xml < 0.41.0: quadratic runtime on duplicate attribute names (DoS).
    # quick-xml < 0.41.0: unbounded namespace-declaration allocation in NsReader (DoS).
    # Pulled in transitively by inferno (dev-only flame-graph dep), lance-namespace-impls
    # (git dep from lance), and opendal/reqsign (cloud storage XML parsing). The XML
    # parsed by opendal/reqsign comes from trusted cloud-storage endpoints (S3, GCS,
    # Azure), not attacker-controlled input. Clearing requires upstream crates to migrate
    # to quick-xml >= 0.41.0.
    # https://rustsec.org/advisories/RUSTSEC-2026-0194
    # https://rustsec.org/advisories/RUSTSEC-2026-0195
    { id = "RUSTSEC-2026-0194", reason = "transitive via inferno/lance/opendal; XML from trusted cloud endpoints, not attacker-controlled" },
    { id = "RUSTSEC-2026-0195", reason = "transitive via inferno/lance/opendal; XML from trusted cloud endpoints, not attacker-controlled" },
 ]
 # ---------------------------------------------------------------------------
@@ -14,7 +14,7 @@ Add the following dependency to your `pom.xml`:
 <dependency>
    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-core</artifactId>
-    <version>0.32.0-beta.1</version>
+    <version>0.31.0-beta.6</version>
 </dependency>
 ```
@@ -398,26 +398,6 @@ Drop an index from the table.
 ***
 ### getLsmWriteSpec()
 ```ts
 abstract getLsmWriteSpec(): Promise<undefined | LsmWriteSpec>
 ```
 Read the [LsmWriteSpec](../interfaces/LsmWriteSpec.md) currently installed on this table.
 Resolves to `undefined` when the MemWAL LSM write path is not enabled (no
 spec has been set, or it was removed with [Table#unsetLsmWriteSpec](Table.md#unsetlsmwritespec)).
 The returned spec — including its `maintainedIndexes` and
 `writerConfigDefaults` — mirrors what was passed to
 [Table#setLsmWriteSpec](Table.md#setlsmwritespec).
 #### Returns
 `Promise`&lt;`undefined` \| [`LsmWriteSpec`](../interfaces/LsmWriteSpec.md)&gt;
 ***
 ### indexStats()
 ```ts
@@ -934,32 +914,6 @@ Return the table as an arrow table
 ***
 ### tokenize()
 ```ts
 abstract tokenize(query, options): Promise<FtsToken[]>
 ```
 Tokenize a full-text search query using the tokenizer configured on an FTS index.
 Specify exactly one of `column` or `indexName`.
 Model-backed tokenizers such as `jieba/*` and `lindera/*` are rebuilt in
 the client process from index metadata. For remote tables, this means the
 same tokenizer model files must also exist locally.
 #### Parameters
 * **query**: `string`
 * **options**: [`TokenizeTableOptions`](../type-aliases/TokenizeTableOptions.md)
 #### Returns
 `Promise`&lt;[`FtsToken`](../interfaces/FtsToken.md)[]&gt;
 ***
 ### unsetLsmWriteSpec()
 ```ts
@@ -1,42 +0,0 @@
 [**@lancedb/lancedb**](../README.md) • **Docs**
 ***
 [@lancedb/lancedb](../globals.md) / instrumentLanceDbMetrics
 # Function: instrumentLanceDbMetrics()
 ```ts
 function instrumentLanceDbMetrics(meterProvider?): boolean
 ```
 Register LanceDB metrics as OpenTelemetry observable instruments.
 Installs a process-global metrics recorder and creates one observable
 instrument per LanceDB metric (currently object store request counts, bytes,
 latency, errors, and throttles) on the given (or global) `MeterProvider`. The
 configured `MetricReader` then collects them on its own schedule.
 Counters and gauges map directly to observable counters/gauges. Because
 OpenTelemetry has no asynchronous histogram instrument, each histogram is
 exported Prometheus-style as cumulative `le` bucket counts (`<name>_bucket`,
 with an `le` attribute) plus `<name>_count` and `<name>_sum`.
 Requires `@opentelemetry/api` (a dependency) and, to actually export, an
 OpenTelemetry SDK such as `@opentelemetry/sdk-metrics`.
 ## Parameters
 * **meterProvider?**: `MeterProvider`
    The provider to register instruments on. Defaults to the
    global provider from `@opentelemetry/api`.
 ## Returns
 `boolean`
 `true` if the recorder is installed and instruments are registered.
  `false` if a different `metrics` recorder is already installed in this
  process (only one global recorder is permitted), in which case a warning is
  emitted and no instruments are created. Calling this more than once is safe;
  instruments are created only on the first successful call.
@@ -1,26 +0,0 @@
 [**@lancedb/lancedb**](../README.md) • **Docs**
 ***
 [@lancedb/lancedb](../globals.md) / tokenize
 # Function: tokenize()
 ```ts
 function tokenize(query, options?): Promise<FtsToken[]>
 ```
 Tokenize a full-text search query using an explicit tokenizer.
 This does not require a table or FTS index. The tokenizer options match
 [Index.fts](../classes/Index.md#fts).
 ## Parameters
 * **query**: `string`
 * **options?**: `Partial`&lt;[`TokenizeOptions`](../interfaces/TokenizeOptions.md)&gt;
 ## Returns
 `Promise`&lt;[`FtsToken`](../interfaces/FtsToken.md)[]&gt;
@@ -72,7 +72,6 @@
 - [FragmentStatistics](interfaces/FragmentStatistics.md)
 - [FragmentSummaryStats](interfaces/FragmentSummaryStats.md)
 - [FtsOptions](interfaces/FtsOptions.md)
 - [FtsToken](interfaces/FtsToken.md)
 - [FullTextQuery](interfaces/FullTextQuery.md)
 - [FullTextSearchOptions](interfaces/FullTextSearchOptions.md)
 - [HnswPqOptions](interfaces/HnswPqOptions.md)
@@ -108,7 +107,6 @@
 - [TimeoutConfig](interfaces/TimeoutConfig.md)
 - [TlsConfig](interfaces/TlsConfig.md)
 - [TokenResponse](interfaces/TokenResponse.md)
 - [TokenizeOptions](interfaces/TokenizeOptions.md)
 - [UpdateFieldMetadataResult](interfaces/UpdateFieldMetadataResult.md)
 - [UpdateOptions](interfaces/UpdateOptions.md)
 - [UpdateResult](interfaces/UpdateResult.md)
@@ -118,7 +116,6 @@
 ## Type Aliases
 - [BaseTokenizer](type-aliases/BaseTokenizer.md)
 - [Data](type-aliases/Data.md)
 - [DataLike](type-aliases/DataLike.md)
 - [FieldLike](type-aliases/FieldLike.md)
@@ -128,15 +125,12 @@
 - [RecordBatchLike](type-aliases/RecordBatchLike.md)
 - [SchemaLike](type-aliases/SchemaLike.md)
 - [TableLike](type-aliases/TableLike.md)
 - [TokenizeTableOptions](type-aliases/TokenizeTableOptions.md)
 ## Functions
 - [RecordBatchIterator](functions/RecordBatchIterator.md)
 - [connect](functions/connect.md)
 - [connectNamespace](functions/connectNamespace.md)
 - [instrumentLanceDbMetrics](functions/instrumentLanceDbMetrics.md)
 - [makeArrowTable](functions/makeArrowTable.md)
 - [packBits](functions/packBits.md)
 - [permutationBuilder](functions/permutationBuilder.md)
 - [tokenize](functions/tokenize.md)
@@ -23,7 +23,7 @@ whether to remove punctuation
 ### baseTokenizer?
 ```ts
-optional baseTokenizer: BaseTokenizer;
+optional baseTokenizer: "raw" | "simple" | "whitespace" | "ngram";
 ```
 The tokenizer to use when building the index.
@@ -37,10 +37,6 @@ The following tokenizers are available:
 "raw" - Raw tokenizer. This tokenizer does not split the text into tokens and indexes the entire text as a single token.
 "icu" - ICU dictionary-based word segmentation.
 "icu/split" - ICU segmentation with simple-style delimiter splitting.
 ***
 ### language?
@@ -1,29 +0,0 @@
 [**@lancedb/lancedb**](../README.md) • **Docs**
 ***
 [@lancedb/lancedb](../globals.md) / FtsToken
 # Interface: FtsToken
 Token produced by the tokenizer configured on a full-text search index.
 ## Properties
 ### position
 ```ts
 position: number;
 ```
 Token position used by full-text query matching.
 ***
 ### text
 ```ts
 text: string;
 ```
 Token text after tokenizer filters have been applied.
@@ -8,14 +8,6 @@
 ## Properties
 ### clumpSize?
 ```ts
 optional clumpSize: number;
 ```
 ***
 ### counts?
 ```ts
@@ -1,109 +0,0 @@
 [**@lancedb/lancedb**](../README.md) • **Docs**
 ***
 [@lancedb/lancedb](../globals.md) / TokenizeOptions
 # Interface: TokenizeOptions
 Options for tokenizing a full-text search query without a table index.
 ## Properties
 ### asciiFolding?
 ```ts
 optional asciiFolding: boolean;
 ```
 Whether to fold ASCII characters.
 ***
 ### baseTokenizer?
 ```ts
 optional baseTokenizer: BaseTokenizer;
 ```
 The tokenizer to use. The default is "simple".
 ***
 ### language?
 ```ts
 optional language: string;
 ```
 Language for stemming and stop words.
 ***
 ### lowercase?
 ```ts
 optional lowercase: boolean;
 ```
 Whether to lowercase tokens.
 ***
 ### maxTokenLength?
 ```ts
 optional maxTokenLength: number;
 ```
 Maximum token length; tokens longer than this are ignored.
 ***
 ### ngramMaxLength?
 ```ts
 optional ngramMaxLength: number;
 ```
 N-gram maximum length.
 ***
 ### ngramMinLength?
 ```ts
 optional ngramMinLength: number;
 ```
 N-gram minimum length.
 ***
 ### prefixOnly?
 ```ts
 optional prefixOnly: boolean;
 ```
 Whether to only emit token prefixes for the n-gram tokenizer.
 ***
 ### removeStopWords?
 ```ts
 optional removeStopWords: boolean;
 ```
 Whether to remove stop words.
 ***
 ### stem?
 ```ts
 optional stem: boolean;
 ```
 Whether to stem tokens.
@@ -1,19 +0,0 @@
 [**@lancedb/lancedb**](../README.md) • **Docs**
 ***
 [@lancedb/lancedb](../globals.md) / BaseTokenizer
 # Type Alias: BaseTokenizer
 ```ts
 type BaseTokenizer:
  | "simple"
  | "whitespace"
  | "raw"
  | "ngram"
  | "icu"
  | "icu/split"
  | `jieba/${string}`
  | `lindera/${string}`;
 ```
@@ -1,11 +0,0 @@
 [**@lancedb/lancedb**](../README.md) • **Docs**
 ***
 [@lancedb/lancedb](../globals.md) / TokenizeTableOptions
 # Type Alias: TokenizeTableOptions
 ```ts
 type TokenizeTableOptions: object | object;
 ```
@@ -8,7 +8,7 @@
    <parent>
      <groupId>com.lancedb</groupId>
      <artifactId>lancedb-parent</artifactId>
-      <version>0.32.0-beta.1</version>
+      <version>0.31.0-beta.6</version>
      <relativePath>../pom.xml</relativePath>
    </parent>
@@ -6,7 +6,7 @@
    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-parent</artifactId>
-    <version>0.32.0-beta.1</version>
+    <version>0.31.0-beta.6</version>
    <packaging>pom</packaging>
    <name>${project.artifactId}</name>
    <description>LanceDB Java SDK Parent POM</description>
@@ -28,7 +28,7 @@
    <properties>
        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
        <arrow.version>15.0.0</arrow.version>
-        <lance-core.version>9.0.0-beta.23</lance-core.version>
+        <lance-core.version>8.0.0</lance-core.version>
        <spotless.skip>false</spotless.skip>
        <spotless.version>2.30.0</spotless.version>
        <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>
@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.32.0-beta.1"
+version = "0.31.0-beta.6"
 publish = false
 license.workspace = true
 description.workspace = true
@@ -44,6 +44,6 @@ aws-lc-rs = "=1.16.3"
 napi-build = "2.3.1"
 [features]
-default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/goosefs", "lancedb/metrics-otel"]
+default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
 fp16kernels = ["lancedb/fp16kernels"]
 remote = ["lancedb/remote"]
@@ -1,114 +0,0 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import {
  MeterProvider,
  type MetricData,
  MetricReader,
 } from "@opentelemetry/sdk-metrics";
 import * as tmp from "tmp";
 import { connect, instrumentLanceDbMetrics } from "../lancedb";
 // snapshotLancedbMetrics is internal plumbing (not part of the public API), so
 // it is imported from the native module rather than the package entry point.
 import { snapshotLancedbMetrics } from "../lancedb/native";
 // The metrics recorder is process-global and installed once, so the whole
 // bridge is exercised in a single test to avoid cross-test global-state coupling.
 // A minimal pull-based reader whose `collect()` we drive directly, invoking the
 // observable-instrument callbacks. `@opentelemetry/sdk-metrics` ships no
 // in-memory reader, so we subclass the abstract base.
 class TestMetricReader extends MetricReader {
  protected async onForceFlush(): Promise<void> {
    // no-op: collection is driven directly via collect()
  }
  protected async onShutdown(): Promise<void> {
    // no-op: nothing to release
  }
 }
 async function metricsByName(
  reader: TestMetricReader,
 ): Promise<Map<string, MetricData>> {
  const collected = await reader.collect();
  const result = new Map<string, MetricData>();
  for (const scope of collected.resourceMetrics.scopeMetrics) {
    for (const metric of scope.metrics) {
      result.set(metric.descriptor.name, metric);
    }
  }
  return result;
 }
 describe("OpenTelemetry metrics bridge", () => {
  let tmpDir: tmp.DirResult;
  beforeEach(() => {
    tmpDir = tmp.dirSync({ unsafeCleanup: true });
  });
  afterEach(() => tmpDir.removeCallback());
  it("snapshot is safe to call regardless of install state", () => {
    expect(Array.isArray(snapshotLancedbMetrics())).toBe(true);
  });
  it("exports object store metrics via observable instruments", async () => {
    const reader = new TestMetricReader();
    const provider = new MeterProvider({ readers: [reader] });
    expect(instrumentLanceDbMetrics(provider)).toBe(true);
    // Generate object store activity on the local filesystem (scheme "file").
    const db = await connect(tmpDir.name);
    const data = Array.from({ length: 256 }, (_, i) => ({ id: i }));
    const table = await db.createTable("t", data);
    expect(await table.countRows()).toBe(256);
    const metrics = await metricsByName(reader);
    const requests = metrics.get("lance_object_store_requests_total");
    expect(requests).toBeDefined();
    // biome-ignore lint/suspicious/noExplicitAny: SDK point shape
    const requestPoints = (requests!.dataPoints as any[]) ?? [];
    expect(requestPoints.length).toBeGreaterThan(0);
    for (const p of requestPoints) {
      // Labelled by `operation` and `base` (the store scheme by default).
      expect(p.attributes).toHaveProperty("base");
      expect(p.attributes).toHaveProperty("operation");
    }
    const totalRequests = requestPoints.reduce((acc, p) => acc + p.value, 0);
    expect(totalRequests).toBeGreaterThan(0);
    // Histograms are decomposed into bucket / count / sum observable counters.
    const bucket = metrics.get(
      "lance_object_store_request_duration_seconds_bucket",
    );
    expect(bucket).toBeDefined();
    // biome-ignore lint/suspicious/noExplicitAny: SDK point shape
    const bucketPoints = (bucket!.dataPoints as any[]) ?? [];
    expect(bucketPoints.length).toBeGreaterThan(0);
    expect(bucketPoints.every((p) => "le" in p.attributes)).toBe(true);
    // The implicit +Inf bucket must be present.
    expect(bucketPoints.some((p) => p.attributes.le === "+Inf")).toBe(true);
    const count = metrics.get(
      "lance_object_store_request_duration_seconds_count",
    );
    expect(count).toBeDefined();
    // biome-ignore lint/suspicious/noExplicitAny: SDK point shape
    const countPoints = (count!.dataPoints as any[]) ?? [];
    expect(countPoints.reduce((acc, p) => acc + p.value, 0)).toBeGreaterThan(0);
    const sum = metrics.get("lance_object_store_request_duration_seconds_sum");
    expect(sum).toBeDefined();
    // biome-ignore lint/suspicious/noExplicitAny: SDK point shape
    const sumPoints = (sum!.dataPoints as any[]) ?? [];
    expect(sumPoints.reduce((acc, p) => acc + p.value, 0)).toBeGreaterThan(0);
    // Unit handling: only `_sum` keeps the histogram's unit (seconds); `_bucket`
    // and `_count` observe cumulative counts and are unitless.
    expect(sum!.descriptor.unit).toBe("s");
    expect(bucket!.descriptor.unit).toBe("");
    expect(count!.descriptor.unit).toBe("");
    await provider.shutdown();
  });
 });
@@ -16,7 +16,6 @@ import {
  PhraseQuery,
  Table,
  connect,
  tokenize,
 } from "../lancedb";
 import {
  Table as ArrowTable,
@@ -2308,75 +2307,6 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
      expect(results2[0].text).toBe(data[1].text);
    });
    test("tokenizes FTS queries by column or index name", async () => {
      const db = await connect(tmpDir.name);
      const data = [
        {
          text: "Running in cafés",
          japanese: "Hello, こんにちは世界!",
          vector: [0.1, 0.2, 0.3],
        },
      ];
      const table = await db.createTable("test", data);
      await table.createIndex("text", {
        config: Index.fts({ baseTokenizer: "simple" }),
      });
      await table.createIndex("japanese", {
        config: Index.fts({
          baseTokenizer: "icu",
          stem: false,
          removeStopWords: false,
        }),
        name: "japanese_icu_idx",
      });
      await expect(table.tokenize("hello", {} as never)).rejects.toThrow(
        "Specify exactly one",
      );
      await expect(
        table.tokenize("hello", {
          column: "text",
          indexName: "text_idx",
        } as never),
      ).rejects.toThrow("Specify exactly one");
      const simpleTokens = await table.tokenize("Running in cafés", {
        column: "text",
      });
      expect(simpleTokens).toEqual([
        { text: "run", position: 0 },
        { text: "cafe", position: 2 },
      ]);
      const icuTokens = await table.tokenize("Hello, こんにちは世界!", {
        indexName: "japanese_icu_idx",
      });
      expect(icuTokens).toEqual([
        { text: "hello", position: 0 },
        { text: "こんにちは", position: 1 },
        { text: "世界", position: 2 },
      ]);
      const directSimpleTokens = await tokenize("Running in cafés", {
        baseTokenizer: "simple",
      });
      expect(directSimpleTokens).toEqual([
        { text: "run", position: 0 },
        { text: "cafe", position: 2 },
      ]);
      const directIcuTokens = await tokenize("Hello, こんにちは世界!", {
        baseTokenizer: "icu",
        stem: false,
        removeStopWords: false,
      });
      expect(directIcuTokens).toEqual([
        { text: "hello", position: 0 },
        { text: "こんにちは", position: 1 },
        { text: "世界", position: 2 },
      ]);
    });
    test("full text search fast search", async () => {
      const db = await connect(tmpDir.name);
      const data = [{ text: "hello world", vector: [0.1, 0.2, 0.3], id: 1 }];
@@ -3062,56 +2992,6 @@ describe("setLsmWriteSpec / unsetLsmWriteSpec", () => {
      }),
    ).rejects.toThrow();
  });
  it("reads back the installed spec via getLsmWriteSpec", async () => {
    const conn = await connect(tmpDir.name);
    const table = await makeTable(conn);
    await table.setUnenforcedPrimaryKey("id");
    // Nothing installed yet.
    expect(await table.getLsmWriteSpec()).toBeUndefined();
    // A real scalar index is needed to name it as a maintained index.
    await table.add([{ id: 1 }, { id: 2 }, { id: 3 }]);
    await table.createIndex("id");
    const indexName = (await table.listIndices())[0].name;
    // Bucket spec round-trips, including maintained indexes and writer config
    // defaults. Lance writer-config keys are canonically snake_case.
    // biome-ignore lint/style/useNamingConvention: Lance writer-config keys are snake_case
    const writerConfigDefaults = { durable_write: "false" };
    await table.setLsmWriteSpec({
      specType: "bucket",
      column: "id",
      numBuckets: 4,
      maintainedIndexes: [indexName],
      writerConfigDefaults,
    });
    const spec = await table.getLsmWriteSpec();
    expect(spec).toBeDefined();
    expect(spec?.specType).toBe("bucket");
    expect(spec?.column).toBe("id");
    expect(spec?.numBuckets).toBe(4);
    expect(spec?.maintainedIndexes).toEqual([indexName]);
    expect(spec?.writerConfigDefaults).toEqual(writerConfigDefaults);
    // After unset, undefined again.
    await table.unsetLsmWriteSpec();
    expect(await table.getLsmWriteSpec()).toBeUndefined();
    // Identity round-trips (column recovered from the schema).
    await table.setLsmWriteSpec({ specType: "identity", column: "id" });
    const identity = await table.getLsmWriteSpec();
    expect(identity?.specType).toBe("identity");
    expect(identity?.column).toBe("id");
    await table.unsetLsmWriteSpec();
    // Unsharded round-trips (no routing column).
    await table.setLsmWriteSpec({ specType: "unsharded" });
    const unsharded = await table.getLsmWriteSpec();
    expect(unsharded?.specType).toBe("unsharded");
    expect(unsharded?.column).toBeFalsy();
  });
 });
 describe("LSM merge insert", () => {
@@ -13,21 +13,13 @@ import {
  Connection as LanceDbConnection,
  JsHeaderProvider as NativeJsHeaderProvider,
  Session,
  tokenize as nativeTokenize,
 } from "./native.js";
 import { HeaderProvider } from "./header";
 import type { BaseTokenizer } from "./indices";
 import type { FtsToken } from "./table";
 // Re-export native header provider for use with connectWithHeaderProvider
 export { JsHeaderProvider as NativeJsHeaderProvider } from "./native.js";
 // OpenTelemetry metrics bridge. Only the high-level entry point is public; the
 // underlying recorder/catalog/snapshot functions remain internal plumbing that
 // `otel.ts` consumes from the native module.
 export { instrumentLanceDbMetrics } from "./otel";
 export {
  AddColumnsSql,
  ConnectionOptions,
@@ -117,7 +109,6 @@ export {
  HnswPqOptions,
  HnswSqOptions,
  FtsOptions,
  BaseTokenizer,
 } from "./indices";
 export {
@@ -128,8 +119,6 @@ export {
  OptimizeOptions,
  Version,
  WriteProgress,
  FtsToken,
  TokenizeTableOptions,
  LsmWriteSpec,
  ColumnAlteration,
  FieldMetadataUpdate,
@@ -161,68 +150,6 @@ export {
 } from "./arrow";
 export { IntoSql, packBits } from "./util";
 /**
 * Options for tokenizing a full-text search query without a table index.
 */
 export interface TokenizeOptions {
  /**
   * The tokenizer to use. The default is "simple".
   */
  baseTokenizer?: BaseTokenizer;
  /** Language for stemming and stop words. */
  language?: string;
  /** Maximum token length; tokens longer than this are ignored. */
  maxTokenLength?: number;
  /** Whether to lowercase tokens. */
  lowercase?: boolean;
  /** Whether to stem tokens. */
  stem?: boolean;
  /** Whether to remove stop words. */
  removeStopWords?: boolean;
  /** Whether to fold ASCII characters. */
  asciiFolding?: boolean;
  /** N-gram minimum length. */
  ngramMinLength?: number;
  /** N-gram maximum length. */
  ngramMaxLength?: number;
  /** Whether to only emit token prefixes for the n-gram tokenizer. */
  prefixOnly?: boolean;
 }
 /**
 * Tokenize a full-text search query using an explicit tokenizer.
 *
 * This does not require a table or FTS index. The tokenizer options match
 * {@link Index.fts}.
 */
 export async function tokenize(
  query: string,
  options?: Partial<TokenizeOptions>,
 ): Promise<FtsToken[]> {
  return await nativeTokenize(
    query,
    options?.baseTokenizer,
    options?.language,
    options?.maxTokenLength,
    options?.lowercase,
    options?.stem,
    options?.removeStopWords,
    options?.asciiFolding,
    options?.ngramMinLength,
    options?.ngramMaxLength,
    options?.prefixOnly,
  );
 }
 /**
 * Connect to a LanceDB instance at the given URI.
 *
@@ -486,16 +486,6 @@ export interface IvfFlatOptions {
  sampleRate?: number;
 }
 export type BaseTokenizer =
  | "simple"
  | "whitespace"
  | "raw"
  | "ngram"
  | "icu"
  | "icu/split"
  | `jieba/${string}`
  | `lindera/${string}`;
 /**
 * Options to create a full text search index
 */
@@ -519,12 +509,8 @@ export interface FtsOptions {
   * "whitespace" - Whitespace tokenizer. This tokenizer splits the text into tokens using whitespace as a delimiter.
   *
   * "raw" - Raw tokenizer. This tokenizer does not split the text into tokens and indexes the entire text as a single token.
   *
   * "icu" - ICU dictionary-based word segmentation.
   *
   * "icu/split" - ICU segmentation with simple-style delimiter splitting.
   */
-  baseTokenizer?: BaseTokenizer;
+  baseTokenizer?: "simple" | "whitespace" | "raw" | "ngram";
  /**
   * language for stemming and stop words
@@ -1,137 +0,0 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import {
  type Attributes,
  type MeterProvider,
  type ObservableResult,
  metrics,
 } from "@opentelemetry/api";
 import {
  lancedbMetricsCatalog,
  registerLancedbMetricsRecorder,
  snapshotLancedbMetrics,
 } from "./native";
 let instrumented = false;
 /**
 * Register LanceDB metrics as OpenTelemetry observable instruments.
 *
 * Installs a process-global metrics recorder and creates one observable
 * instrument per LanceDB metric (currently object store request counts, bytes,
 * latency, errors, and throttles) on the given (or global) `MeterProvider`. The
 * configured `MetricReader` then collects them on its own schedule.
 *
 * Counters and gauges map directly to observable counters/gauges. Because
 * OpenTelemetry has no asynchronous histogram instrument, each histogram is
 * exported Prometheus-style as cumulative `le` bucket counts (`<name>_bucket`,
 * with an `le` attribute) plus `<name>_count` and `<name>_sum`.
 *
 * Requires `@opentelemetry/api` (a dependency) and, to actually export, an
 * OpenTelemetry SDK such as `@opentelemetry/sdk-metrics`.
 *
 * @param meterProvider The provider to register instruments on. Defaults to the
 *   global provider from `@opentelemetry/api`.
 * @returns `true` if the recorder is installed and instruments are registered.
 *   `false` if a different `metrics` recorder is already installed in this
 *   process (only one global recorder is permitted), in which case a warning is
 *   emitted and no instruments are created. Calling this more than once is safe;
 *   instruments are created only on the first successful call.
 */
 export function instrumentLanceDbMetrics(
  meterProvider?: MeterProvider,
 ): boolean {
  if (!registerLancedbMetricsRecorder()) {
    console.warn(
      "Could not install the LanceDB metrics recorder: another `metrics` " +
        "recorder is already installed in this process. LanceDB metrics will " +
        "not be exported via OpenTelemetry.",
    );
    return false;
  }
  if (instrumented) {
    return true;
  }
  const provider = meterProvider ?? metrics.getMeterProvider();
  const meter = provider.getMeter("lancedb");
  const scalarCallback = (metricName: string) => (result: ObservableResult) => {
    for (const point of snapshotLancedbMetrics()) {
      if (point.name === metricName && point.value != null) {
        result.observe(point.value, point.attributes);
      }
    }
  };
  const bucketCallback = (metricName: string) => (result: ObservableResult) => {
    for (const point of snapshotLancedbMetrics()) {
      if (point.name !== metricName || point.buckets == null) {
        continue;
      }
      for (const bucket of point.buckets) {
        const attributes: Attributes = {
          ...point.attributes,
          le: bucket.le,
        };
        result.observe(bucket.cumulativeCount, attributes);
      }
    }
  };
  const fieldCallback =
    (metricName: string, field: "count" | "sum") =>
    (result: ObservableResult) => {
      for (const point of snapshotLancedbMetrics()) {
        if (point.name !== metricName) {
          continue;
        }
        const value = point[field];
        if (value != null) {
          result.observe(value, point.attributes);
        }
      }
    };
  for (const desc of lancedbMetricsCatalog()) {
    const unit = desc.unit ?? "";
    if (desc.kind === "counter") {
      const counter = meter.createObservableCounter(desc.name, {
        unit,
        description: desc.description,
      });
      counter.addCallback(scalarCallback(desc.name));
    } else if (desc.kind === "gauge") {
      const gauge = meter.createObservableGauge(desc.name, {
        unit,
        description: desc.description,
      });
      gauge.addCallback(scalarCallback(desc.name));
    } else if (desc.kind === "histogram") {
      // `_bucket` and `_count` observe cumulative sample counts, not the
      // histogram's measured quantity, so they are unitless; only `_sum`
      // carries the histogram's unit.
      const bucket = meter.createObservableCounter(`${desc.name}_bucket`, {
        description: `${desc.description} (cumulative buckets)`,
      });
      bucket.addCallback(bucketCallback(desc.name));
      const count = meter.createObservableCounter(`${desc.name}_count`, {
        description: `${desc.description} (count)`,
      });
      count.addCallback(fieldCallback(desc.name, "count"));
      const sum = meter.createObservableCounter(`${desc.name}_sum`, {
        unit,
        description: `${desc.description} (sum)`,
      });
      sum.addCallback(fieldCallback(desc.name, "sum"));
    }
  }
  instrumented = true;
  return true;
 }
@@ -158,26 +158,6 @@ export interface Version {
  metadata: Record<string, string>;
 }
 /** Token produced by the tokenizer configured on a full-text search index. */
 export interface FtsToken {
  /** Token text after tokenizer filters have been applied. */
  text: string;
  /** Token position used by full-text query matching. */
  position: number;
 }
 export type TokenizeTableOptions =
  | {
      /** FTS-indexed column whose tokenizer should be used. */
      column: string;
      indexName?: never;
    }
  | {
      /** Name of the FTS index whose tokenizer should be used. */
      indexName: string;
      column?: never;
    };
 /**
 * Specification selecting Lance's MemWAL LSM-style write path for
 * `mergeInsert`.
@@ -605,17 +585,6 @@ export abstract class Table {
   * @returns {Promise<void>}
   */
  abstract unsetLsmWriteSpec(): Promise<void>;
  /**
   * Read the {@link LsmWriteSpec} currently installed on this table.
   *
   * Resolves to `undefined` when the MemWAL LSM write path is not enabled (no
   * spec has been set, or it was removed with {@link Table#unsetLsmWriteSpec}).
   * The returned spec — including its `maintainedIndexes` and
   * `writerConfigDefaults` — mirrors what was passed to
   * {@link Table#setLsmWriteSpec}.
   * @returns {Promise<LsmWriteSpec | undefined>}
   */
  abstract getLsmWriteSpec(): Promise<LsmWriteSpec | undefined>;
  /**
   * Drain and close any cached MemWAL shard writers held for this table.
   *
@@ -736,19 +705,6 @@ export abstract class Table {
  abstract optimize(options?: Partial<OptimizeOptions>): Promise<OptimizeStats>;
  /** List all indices that have been created with {@link Table.createIndex} */
  abstract listIndices(): Promise<IndexConfig[]>;
  /**
   * Tokenize a full-text search query using the tokenizer configured on an FTS index.
   *
   * Specify exactly one of `column` or `indexName`.
   *
   * Model-backed tokenizers such as `jieba/*` and `lindera/*` are rebuilt in
   * the client process from index metadata. For remote tables, this means the
   * same tokenizer model files must also exist locally.
   */
  abstract tokenize(
    query: string,
    options: TokenizeTableOptions,
  ): Promise<FtsToken[]>;
  /** Return the table as an arrow table */
  abstract toArrow(): Promise<ArrowTable>;
@@ -1135,15 +1091,6 @@ export class LocalTable extends Table {
    return await this.inner.unsetLsmWriteSpec();
  }
  async getLsmWriteSpec(): Promise<LsmWriteSpec | undefined> {
    // The native binding types `specType` as a plain `string`; narrow it back
    // to the public union. The Rust `From` impl only ever emits one of the
    // three valid values, so the cast is safe.
    return ((await this.inner.getLsmWriteSpec()) ?? undefined) as
      | LsmWriteSpec
      | undefined;
  }
  async closeLsmWriters(): Promise<void> {
    return await this.inner.closeLsmWriters();
  }
@@ -1206,17 +1153,6 @@ export class LocalTable extends Table {
    return await this.inner.listIndices();
  }
  async tokenize(
    query: string,
    options: TokenizeTableOptions,
  ): Promise<FtsToken[]> {
    return await this.inner.tokenize(
      query,
      options?.column,
      options?.indexName,
    );
  }
  async toArrow(): Promise<ArrowTable> {
    return await this.query().toArrow();
  }
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.32.0-beta.1",
+	"version": "0.31.0-beta.6",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.32.0-beta.1",
+	"version": "0.31.0-beta.6",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.32.0-beta.1",
+	"version": "0.31.0-beta.6",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.32.0-beta.1",
+	"version": "0.31.0-beta.6",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.32.0-beta.1",
+	"version": "0.31.0-beta.6",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",
@@ -1,6 +1,6 @@
 {
  "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.32.0-beta.1",
+  "version": "0.31.0-beta.6",
  "os": [
    "win32"
  ],
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.32.0-beta.1",
+	"version": "0.31.0-beta.6",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",
@@ -1,12 +1,12 @@
 {
  "name": "@lancedb/lancedb",
-  "version": "0.32.0-beta.1",
+  "version": "0.31.0-beta.6",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "@lancedb/lancedb",
-      "version": "0.32.0-beta.1",
+      "version": "0.31.0-beta.6",
      "cpu": [
        "x64",
        "arm64"
@@ -18,7 +18,6 @@
        "win32"
      ],
      "dependencies": {
        "@opentelemetry/api": "^1.9.0",
        "reflect-metadata": "^0.2.2"
      },
      "devDependencies": {
@@ -28,7 +27,6 @@
        "@biomejs/biome": "^1.7.3",
        "@jest/globals": "^29.7.0",
        "@napi-rs/cli": "3.7.0",
        "@opentelemetry/sdk-metrics": "^1.30.0",
        "@types/axios": "^0.14.0",
        "@types/jest": "^29.1.2",
        "@types/node": "22.7.4",
@@ -4150,75 +4148,6 @@
        "@octokit/openapi-types": "^27.0.0"
      }
    },
    "node_modules/@opentelemetry/api": {
      "version": "1.9.1",
      "resolved": "https://registry.npmjs.org/@opentelemetry/api/-/api-1.9.1.tgz",
      "integrity": "sha512-gLyJlPHPZYdAk1JENA9LeHejZe1Ti77/pTeFm/nMXmQH/HFZlcS/O2XJB+L8fkbrNSqhdtlvjBVjxwUYanNH5Q==",
      "license": "Apache-2.0",
      "engines": {
        "node": ">=8.0.0"
      }
    },
    "node_modules/@opentelemetry/core": {
      "version": "1.30.1",
      "resolved": "https://registry.npmjs.org/@opentelemetry/core/-/core-1.30.1.tgz",
      "integrity": "sha512-OOCM2C/QIURhJMuKaekP3TRBxBKxG/TWWA0TL2J6nXUtDnuCtccy49LUJF8xPFXMX+0LMcxFpCo8M9cGY1W6rQ==",
      "dev": true,
      "license": "Apache-2.0",
      "dependencies": {
        "@opentelemetry/semantic-conventions": "1.28.0"
      },
      "engines": {
        "node": ">=14"
      },
      "peerDependencies": {
        "@opentelemetry/api": ">=1.0.0 <1.10.0"
      }
    },
    "node_modules/@opentelemetry/resources": {
      "version": "1.30.1",
      "resolved": "https://registry.npmjs.org/@opentelemetry/resources/-/resources-1.30.1.tgz",
      "integrity": "sha512-5UxZqiAgLYGFjS4s9qm5mBVo433u+dSPUFWVWXmLAD4wB65oMCoXaJP1KJa9DIYYMeHu3z4BZcStG3LC593cWA==",
      "dev": true,
      "license": "Apache-2.0",
      "dependencies": {
        "@opentelemetry/core": "1.30.1",
        "@opentelemetry/semantic-conventions": "1.28.0"
      },
      "engines": {
        "node": ">=14"
      },
      "peerDependencies": {
        "@opentelemetry/api": ">=1.0.0 <1.10.0"
      }
    },
    "node_modules/@opentelemetry/sdk-metrics": {
      "version": "1.30.1",
      "resolved": "https://registry.npmjs.org/@opentelemetry/sdk-metrics/-/sdk-metrics-1.30.1.tgz",
      "integrity": "sha512-q9zcZ0Okl8jRgmy7eNW3Ku1XSgg3sDLa5evHZpCwjspw7E8Is4K/haRPDJrBcX3YSn/Y7gUvFnByNYEKQNbNog==",
      "dev": true,
      "license": "Apache-2.0",
      "dependencies": {
        "@opentelemetry/core": "1.30.1",
        "@opentelemetry/resources": "1.30.1"
      },
      "engines": {
        "node": ">=14"
      },
      "peerDependencies": {
        "@opentelemetry/api": ">=1.3.0 <1.10.0"
      }
    },
    "node_modules/@opentelemetry/semantic-conventions": {
      "version": "1.28.0",
      "resolved": "https://registry.npmjs.org/@opentelemetry/semantic-conventions/-/semantic-conventions-1.28.0.tgz",
      "integrity": "sha512-lp4qAiMTD4sNWW4DbKLBkfiMZ4jbAboJIGOQr5DvciMRI494OapieI9qiODpOt0XBr1LjIDy1xAGAnVs5supTA==",
      "dev": true,
      "license": "Apache-2.0",
      "engines": {
        "node": ">=14"
      }
    },
    "node_modules/@protobufjs/aspromise": {
      "version": "1.1.2",
      "resolved": "https://registry.npmjs.org/@protobufjs/aspromise/-/aspromise-1.1.2.tgz",
@@ -11,7 +11,7 @@
    "ann"
  ],
  "private": false,
-  "version": "0.32.0-beta.1",
+  "version": "0.31.0-beta.6",
  "main": "dist/index.js",
  "exports": {
    ".": "./dist/index.js",
@@ -44,7 +44,6 @@
    "@biomejs/biome": "^1.7.3",
    "@jest/globals": "^29.7.0",
    "@napi-rs/cli": "3.7.0",
    "@opentelemetry/sdk-metrics": "^1.30.0",
    "@types/axios": "^0.14.0",
    "@types/jest": "^29.1.2",
    "@types/node": "22.7.4",
@@ -93,7 +92,6 @@
    "version": "napi version"
  },
  "dependencies": {
    "@opentelemetry/api": "^1.9.0",
    "reflect-metadata": "^0.2.2"
  },
  "optionalDependencies": {
@@ -8,9 +8,6 @@ importers:
  .:
    dependencies:
      '@opentelemetry/api':
        specifier: ^1.9.0
        version: 1.9.1
      apache-arrow:
        specifier: '>=15.0.0 <=18.1.0'
        version: 18.1.0
@@ -36,9 +33,6 @@ importers:
      '@napi-rs/cli':
        specifier: 3.7.0
        version: 3.7.0(@emnapi/core@1.10.0)(@emnapi/runtime@1.10.0)(@types/node@22.7.4)
      '@opentelemetry/sdk-metrics':
        specifier: ^1.30.0
        version: 1.30.1(@opentelemetry/api@1.9.1)
      '@types/axios':
        specifier: ^0.14.0
        version: 0.14.4
@@ -1313,32 +1307,6 @@ packages:
  '@octokit/types@16.0.0':
    resolution: {integrity: sha512-sKq+9r1Mm4efXW1FCk7hFSeJo4QKreL/tTbR0rz/qx/r1Oa2VV83LTA/H/MuCOX7uCIJmQVRKBcbmWoySjAnSg==}
  '@opentelemetry/api@1.9.1':
    resolution: {integrity: sha512-gLyJlPHPZYdAk1JENA9LeHejZe1Ti77/pTeFm/nMXmQH/HFZlcS/O2XJB+L8fkbrNSqhdtlvjBVjxwUYanNH5Q==}
    engines: {node: '>=8.0.0'}
  '@opentelemetry/core@1.30.1':
    resolution: {integrity: sha512-OOCM2C/QIURhJMuKaekP3TRBxBKxG/TWWA0TL2J6nXUtDnuCtccy49LUJF8xPFXMX+0LMcxFpCo8M9cGY1W6rQ==}
    engines: {node: '>=14'}
    peerDependencies:
      '@opentelemetry/api': '>=1.0.0 <1.10.0'
  '@opentelemetry/resources@1.30.1':
    resolution: {integrity: sha512-5UxZqiAgLYGFjS4s9qm5mBVo433u+dSPUFWVWXmLAD4wB65oMCoXaJP1KJa9DIYYMeHu3z4BZcStG3LC593cWA==}
    engines: {node: '>=14'}
    peerDependencies:
      '@opentelemetry/api': '>=1.0.0 <1.10.0'
  '@opentelemetry/sdk-metrics@1.30.1':
    resolution: {integrity: sha512-q9zcZ0Okl8jRgmy7eNW3Ku1XSgg3sDLa5evHZpCwjspw7E8Is4K/haRPDJrBcX3YSn/Y7gUvFnByNYEKQNbNog==}
    engines: {node: '>=14'}
    peerDependencies:
      '@opentelemetry/api': '>=1.3.0 <1.10.0'
  '@opentelemetry/semantic-conventions@1.28.0':
    resolution: {integrity: sha512-lp4qAiMTD4sNWW4DbKLBkfiMZ4jbAboJIGOQr5DvciMRI494OapieI9qiODpOt0XBr1LjIDy1xAGAnVs5supTA==}
    engines: {node: '>=14'}
  '@protobufjs/aspromise@1.1.2':
    resolution: {integrity: sha512-j+gKExEuLmKwvz3OgROXtrJ2UG2x8Ch2YZUxahh+s1F2HZ+wAceUNLkvy6zKCPVRkU++ZWQrdxsUeQXmcg4uoQ==}
@@ -4957,27 +4925,6 @@ snapshots:
    dependencies:
      '@octokit/openapi-types': 27.0.0
  '@opentelemetry/api@1.9.1': {}
  '@opentelemetry/core@1.30.1(@opentelemetry/api@1.9.1)':
    dependencies:
      '@opentelemetry/api': 1.9.1
      '@opentelemetry/semantic-conventions': 1.28.0
  '@opentelemetry/resources@1.30.1(@opentelemetry/api@1.9.1)':
    dependencies:
      '@opentelemetry/api': 1.9.1
      '@opentelemetry/core': 1.30.1(@opentelemetry/api@1.9.1)
      '@opentelemetry/semantic-conventions': 1.28.0
  '@opentelemetry/sdk-metrics@1.30.1(@opentelemetry/api@1.9.1)':
    dependencies:
      '@opentelemetry/api': 1.9.1
      '@opentelemetry/core': 1.30.1(@opentelemetry/api@1.9.1)
      '@opentelemetry/resources': 1.30.1(@opentelemetry/api@1.9.1)
  '@opentelemetry/semantic-conventions@1.28.0': {}
  '@protobufjs/aspromise@1.1.2':
    optional: true
@@ -9,11 +9,8 @@ use lancedb::index::vector::{
    IvfFlatIndexBuilder, IvfHnswPqIndexBuilder, IvfHnswSqIndexBuilder, IvfPqIndexBuilder,
    IvfRqIndexBuilder,
 };
 use lancedb::tokenize as lancedb_tokenize;
 use napi_derive::napi;
 use crate::error::NapiErrorExt;
 use crate::table::FtsToken;
 use crate::util::parse_distance_type;
 #[napi]
@@ -33,65 +30,6 @@ impl Index {
    }
 }
 #[napi(catch_unwind)]
 #[allow(dead_code, clippy::too_many_arguments)]
 pub fn tokenize(
    query: String,
    base_tokenizer: Option<String>,
    language: Option<String>,
    max_token_length: Option<u32>,
    lower_case: Option<bool>,
    stem: Option<bool>,
    remove_stop_words: Option<bool>,
    ascii_folding: Option<bool>,
    ngram_min_length: Option<u32>,
    ngram_max_length: Option<u32>,
    prefix_only: Option<bool>,
 ) -> napi::Result<Vec<FtsToken>> {
    let mut opts = FtsIndexBuilder::default();
    if let Some(base_tokenizer) = base_tokenizer {
        opts = opts.base_tokenizer(base_tokenizer);
    }
    if let Some(language) = language {
        opts = opts.language(&language).map_err(|_| {
            napi::Error::from_reason(format!(
                "LanceDB does not support the requested language: '{}'",
                language
            ))
        })?;
    }
    if let Some(max_token_length) = max_token_length {
        opts = opts.max_token_length(Some(max_token_length as usize));
    }
    if let Some(lower_case) = lower_case {
        opts = opts.lower_case(lower_case);
    }
    if let Some(stem) = stem {
        opts = opts.stem(stem);
    }
    if let Some(remove_stop_words) = remove_stop_words {
        opts = opts.remove_stop_words(remove_stop_words);
    }
    if let Some(ascii_folding) = ascii_folding {
        opts = opts.ascii_folding(ascii_folding);
    }
    if let Some(ngram_min_length) = ngram_min_length {
        opts = opts.ngram_min_length(ngram_min_length);
    }
    if let Some(ngram_max_length) = ngram_max_length {
        opts = opts.ngram_max_length(ngram_max_length);
    }
    if let Some(prefix_only) = prefix_only {
        opts = opts.ngram_prefix_only(prefix_only);
    }
    Ok(lancedb_tokenize(&query, &opts)
        .default_error()?
        .into_iter()
        .map(FtsToken::from)
        .collect())
 }
 #[napi]
 impl Index {
    #[napi(factory)]
@@ -12,7 +12,6 @@ mod header;
 mod index;
 mod iterator;
 pub mod merge;
 pub mod otel;
 pub mod permutation;
 mod query;
 pub mod remote;
@@ -1,119 +0,0 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 //! Node.js bindings over [`lancedb::metrics_otel`].
 //!
 //! The aggregation, catalog, and histogram bucketing all live in the LanceDB
 //! core crate; this module only converts the core snapshot types into napi
 //! objects and exposes the three entry points to JavaScript, where
 //! `lancedb/otel.ts` bridges them into the user's OpenTelemetry `MeterProvider`.
 use std::collections::HashMap;
 use lancedb::metrics_otel::{MetricPoint as CoreMetricPoint, MetricValue};
 use napi_derive::napi;
 /// One cumulative histogram bucket: all samples with value `<= le`.
 #[napi(object)]
 pub struct MetricBucket {
    /// The inclusive upper bound of the bucket, or `"+Inf"` for the final bucket.
    pub le: String,
    /// Cumulative number of samples less than or equal to `le`.
    pub cumulative_count: f64,
 }
 /// One aggregated metric data point. For counters and gauges only `value` is
 /// set; for histograms `buckets` (cumulative `le` counts), `count`, and `sum`
 /// are set.
 #[napi(object)]
 pub struct MetricPoint {
    pub name: String,
    pub kind: String,
    pub attributes: HashMap<String, String>,
    pub value: Option<f64>,
    pub buckets: Option<Vec<MetricBucket>>,
    pub count: Option<f64>,
    pub sum: Option<f64>,
 }
 impl From<CoreMetricPoint> for MetricPoint {
    fn from(point: CoreMetricPoint) -> Self {
        let kind = point.kind.as_str().to_string();
        let (value, buckets, count, sum) = match point.value {
            MetricValue::Scalar(v) => (Some(v), None, None, None),
            MetricValue::Histogram {
                buckets,
                count,
                sum,
            } => (
                None,
                Some(
                    buckets
                        .into_iter()
                        // Counts stay well within the f64-exact integer range
                        // (2^53), so this cast is lossless in practice and keeps
                        // the values plain JS numbers for OpenTelemetry.
                        .map(|(le, cumulative_count)| MetricBucket {
                            le,
                            cumulative_count: cumulative_count as f64,
                        })
                        .collect(),
                ),
                Some(count as f64),
                Some(sum),
            ),
        };
        Self {
            name: point.name,
            kind,
            attributes: point.attributes,
            value,
            buckets,
            count,
            sum,
        }
    }
 }
 /// A described metric, used by the JavaScript layer to create instruments up front.
 #[napi(object)]
 pub struct MetricDescription {
    pub name: String,
    pub kind: String,
    pub unit: Option<String>,
    pub description: String,
 }
 /// Install the LanceDB metrics recorder as the process-global `metrics` recorder.
 ///
 /// Returns `true` if the recorder is installed (now or previously). Returns
 /// `false` if a *different* recorder is already installed — `metrics` allows
 /// only one global recorder per process, so LanceDB cannot coexist with another.
 #[napi]
 pub fn register_lancedb_metrics_recorder() -> bool {
    lancedb::metrics_otel::register_metrics_recorder()
 }
 /// The catalog of described LanceDB metrics. Empty until the recorder is installed.
 #[napi]
 pub fn lancedb_metrics_catalog() -> Vec<MetricDescription> {
    lancedb::metrics_otel::metrics_catalog()
        .into_iter()
        .map(|desc| MetricDescription {
            name: desc.name,
            kind: desc.kind.as_str().to_string(),
            unit: desc.unit,
            description: desc.description,
        })
        .collect()
 }
 /// A point-in-time snapshot of every recorded metric. Empty until the recorder
 /// is installed.
 #[napi]
 pub fn snapshot_lancedb_metrics() -> Vec<MetricPoint> {
    lancedb::metrics_otel::snapshot_metrics()
        .into_iter()
        .map(MetricPoint::from)
        .collect()
 }
@@ -16,7 +16,6 @@ pub struct SplitRandomOptions {
    pub counts: Option<Vec<i64>>,
    pub fixed: Option<i64>,
    pub seed: Option<i64>,
    pub clump_size: Option<i64>,
    pub split_names: Option<Vec<String>>,
 }
@@ -126,15 +125,10 @@ impl PermutationBuilder {
        };
        let seed = options.seed.map(|s| s as u64);
        let clump_size = options.clump_size.map(|c| c as u64);
        self.modify(|builder| {
            builder.with_split_strategy(
-                SplitStrategy::Random {
+                SplitStrategy::Random { seed, sizes },
                    seed,
                    sizes,
                    clump_size,
                },
                options.split_names.clone(),
            )
        })
@@ -8,8 +8,8 @@ use chrono::{DateTime, Utc};
 use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};
 use lancedb::table::{
    AddDataMode, ColumnAlteration as LanceColumnAlteration, Duration,
-    FieldMetadataUpdate as LanceFieldMetadataUpdate, FtsToken as LanceDbFtsToken,
+    FieldMetadataUpdate as LanceFieldMetadataUpdate, NewColumnTransform, OptimizeAction,
-    NewColumnTransform, OptimizeAction, OptimizeOptions, Ref, Table as LanceDbTable,
+    OptimizeOptions, Ref, Table as LanceDbTable,
 };
 use napi::bindgen_prelude::*;
 use napi::threadsafe_function::{ThreadsafeFunction, ThreadsafeFunctionCallMode};
@@ -411,16 +411,6 @@ impl Table {
            .default_error()
    }
    #[napi(catch_unwind)]
    pub async fn get_lsm_write_spec(&self) -> napi::Result<Option<LsmWriteSpec>> {
        let spec = self
            .inner_ref()?
            .get_lsm_write_spec()
            .await
            .default_error()?;
        Ok(spec.map(LsmWriteSpec::from))
    }
    #[napi(catch_unwind)]
    pub async fn close_lsm_writers(&self) -> napi::Result<()> {
        self.inner_ref()?.close_lsm_writers().await.default_error()
@@ -574,27 +564,6 @@ impl Table {
            .collect::<Vec<_>>())
    }
    #[napi(catch_unwind)]
    pub async fn tokenize(
        &self,
        query: String,
        column: Option<String>,
        index_name: Option<String>,
    ) -> napi::Result<Vec<FtsToken>> {
        let table = self.inner_ref()?;
        let tokens = match (column.as_deref(), index_name.as_deref()) {
            (Some(_), Some(_)) | (None, None) => {
                return Err(napi::Error::from_reason(
                    "Specify exactly one of 'column' or 'indexName'",
                ));
            }
            (Some(column), None) => table.tokenize_with_column(&query, column).await,
            (None, Some(index_name)) => table.tokenize(&query, index_name).await,
        }
        .default_error()?;
        Ok(tokens.into_iter().map(FtsToken::from).collect())
    }
    #[napi(catch_unwind)]
    pub async fn index_stats(&self, index_name: String) -> napi::Result<Option<IndexStatistics>> {
        let tbl = self.inner_ref()?;
@@ -702,24 +671,6 @@ impl From<lancedb::index::IndexConfig> for IndexConfig {
    }
 }
 #[napi(object)]
 /// A token produced by the tokenizer configured on a full-text search index.
 pub struct FtsToken {
    /// The token text after the index tokenizer has applied its filters.
    pub text: String,
    /// The token position used by full-text query matching.
    pub position: u32,
 }
 impl From<LanceDbFtsToken> for FtsToken {
    fn from(token: LanceDbFtsToken) -> Self {
        Self {
            text: token.text,
            position: token.position,
        }
    }
 }
 /// Specification selecting Lance's MemWAL LSM-style write path for
 /// `mergeInsert`.
 ///
@@ -777,47 +728,6 @@ impl TryFrom<LsmWriteSpec> for lancedb::table::LsmWriteSpec {
    }
 }
 impl From<lancedb::table::LsmWriteSpec> for LsmWriteSpec {
    fn from(spec: lancedb::table::LsmWriteSpec) -> Self {
        use lancedb::table::LsmWriteSpec as Native;
        match spec {
            Native::Bucket {
                column,
                num_buckets,
                maintained_indexes,
                writer_config_defaults,
            } => Self {
                spec_type: "bucket".to_string(),
                column: Some(column),
                num_buckets: Some(num_buckets),
                maintained_indexes: Some(maintained_indexes),
                writer_config_defaults: Some(writer_config_defaults),
            },
            Native::Identity {
                column,
                maintained_indexes,
                writer_config_defaults,
            } => Self {
                spec_type: "identity".to_string(),
                column: Some(column),
                num_buckets: None,
                maintained_indexes: Some(maintained_indexes),
                writer_config_defaults: Some(writer_config_defaults),
            },
            Native::Unsharded {
                maintained_indexes,
                writer_config_defaults,
            } => Self {
                spec_type: "unsharded".to_string(),
                column: None,
                num_buckets: None,
                maintained_indexes: Some(maintained_indexes),
                writer_config_defaults: Some(writer_config_defaults),
            },
        }
    }
 }
 /// Statistics about a compaction operation.
 #[napi(object)]
 #[derive(Clone, Debug)]
@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.35.0-beta.2"
+current_version = "0.34.0"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.35.0-beta.2"
+version = "0.34.0"
 publish = false
 edition.workspace = true
 description = "Python bindings for LanceDB"
@@ -47,6 +47,6 @@ pyo3-build-config = { version = "0.28", features = [
 ] }
 [features]
-default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/cos", "lancedb/goosefs", "lancedb/metrics-otel"]
+default = ["remote",  "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface"]
 fp16kernels = ["lancedb/fp16kernels"]
 remote = ["lancedb/remote"]
@@ -1,135 +0,0 @@
 #!/usr/bin/env python3
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 """Benchmark for StreamingDataset throughput.
 Sweeps read_batch_size from 1 to 16384 to show how amortising the per-request
 overhead scales.  Each row at each chunk size is timed via the real
 StreamingDataset so the numbers reflect production code.
 Run with:
    cd python
    uv run --extra tests benchmarks/bench_streaming_dataloader.py
 Optional env vars:
    BENCH_NUM_ROWS   — total rows in the table (default 49152 = 24 × 2048)
    BENCH_NUM_SPLITS — number of splits (default 24)
    BENCH_STEPS      — round-robin cycles to time per chunk size (default 100)
    BENCH_ROW_BYTES  — approximate bytes per row padded with a binary column
                       (default 4096, mimics a small embedding/image patch)
 """
 import os
 import time
 import tempfile
 import pyarrow as pa
 import lancedb
 from lancedb.streaming import StreamingDataset
 # ---------------------------------------------------------------------------
 # Configuration
 # ---------------------------------------------------------------------------
 NUM_SPLITS = int(os.environ.get("BENCH_NUM_SPLITS", 24))
 # Default: 2048 rows per split so every chunk size up to 16Ki has ≥1 full
 # chunk (except 16Ki itself which gets a single full-split fetch — still valid).
 NUM_ROWS = int(os.environ.get("BENCH_NUM_ROWS", NUM_SPLITS * 2048))
 STEPS = int(os.environ.get("BENCH_STEPS", 100))
 ROW_BYTES = int(os.environ.get("BENCH_ROW_BYTES", 4096))
 assert NUM_ROWS % NUM_SPLITS == 0, "NUM_ROWS must be divisible by NUM_SPLITS"
 CHUNK_SIZES = [1, 4, 16, 64, 256, 1024, 4096, 16384]
 # ---------------------------------------------------------------------------
 # Table helpers
 # ---------------------------------------------------------------------------
 def make_table(db_path: str) -> lancedb.table.Table:
    db = lancedb.connect(db_path)
    payload = b"x" * ROW_BYTES
    data = pa.table(
        {
            "id": pa.array(range(NUM_ROWS), type=pa.int32()),
            "payload": pa.array([payload] * NUM_ROWS, type=pa.large_binary()),
        }
    )
    return db.create_table("bench", data, mode="overwrite")
 # ---------------------------------------------------------------------------
 # Timing
 # ---------------------------------------------------------------------------
 def bench_chunk(table, chunk_size: int, steps: int) -> tuple[int, float]:
    """Return (rows_drained, elapsed_seconds) for one timed run."""
    total_rows = steps * NUM_SPLITS
    ds = StreamingDataset(
        table, num_splits=NUM_SPLITS, shuffle_seed=42, read_batch_size=chunk_size
    )
    count = 0
    t0 = time.perf_counter()
    for _ in ds:
        count += 1
        if count >= total_rows:
            break
    return count, time.perf_counter() - t0
 # ---------------------------------------------------------------------------
 # Main
 # ---------------------------------------------------------------------------
 def main() -> None:
    rows_per_split = NUM_ROWS // NUM_SPLITS
    print("Benchmark config:")
    print(
        f"  NUM_ROWS={NUM_ROWS}  NUM_SPLITS={NUM_SPLITS}  "
        f"rows/split={rows_per_split}  STEPS={STEPS}  ROW_BYTES={ROW_BYTES}"
    )
    print(f"  ~{NUM_ROWS * ROW_BYTES / 1024 / 1024:.1f} MB total table size")
    print()
    with tempfile.TemporaryDirectory() as tmp:
        print("Creating table...", flush=True)
        table = make_table(tmp)
        cols = (
            f"{'chunk':>6}  {'rows':>6}  {'elapsed':>8}  {'rows/s':>10}  {'ms/step':>9}"
        )
        print(f"\n{cols}")
        print("-" * 52)
        for chunk in CHUNK_SIZES:
            # Warm-up pass (one step's worth of rows)
            warmup_ds = StreamingDataset(
                table, num_splits=NUM_SPLITS, shuffle_seed=42, read_batch_size=chunk
            )
            warmup_count = 0
            for _ in warmup_ds:
                warmup_count += 1
                if warmup_count >= NUM_SPLITS:
                    break
            drained, elapsed = bench_chunk(table, chunk, STEPS)
            rows_per_sec = drained / elapsed if elapsed > 0 else float("inf")
            ms_per_step = elapsed / STEPS * 1000
            print(
                f"{chunk:>6}  {drained:>6}  {elapsed:>7.3f}s  "
                f"{rows_per_sec:>10.0f}  {ms_per_step:>8.1f}ms"
            )
    print()
    print("Done.")
 if __name__ == "__main__":
    main()
@@ -47,10 +47,6 @@ repository = "https://github.com/lancedb/lancedb"
 pylance = [
    "pylance>=5.0.0b5",
 ]
 # A library only needs the OpenTelemetry API; the application supplies and
 # configures the SDK (the actual exporter/reader). See
 # https://opentelemetry.io/docs/languages/python/instrumentation/
 otel = ["opentelemetry-api"]
 tests = [
    "aiohttp>=3.9.0",
    "boto3>=1.28.57",
@@ -65,7 +61,6 @@ tests = [
    "pylance>=5.0.0b5",
    "requests>=2.31.0",
    "datafusion>=52,<53",
    "opentelemetry-sdk>=1.30.0",
 ]
 dev = [
    "ruff>=0.3.0",
@@ -6,22 +6,19 @@ import importlib.metadata
 import os
 from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
-from typing import Dict, Optional, Union, Any, List, Iterable
+from typing import Dict, Optional, Union, Any, List
 __version__ = importlib.metadata.version("lancedb")
 from ._lancedb import connect as lancedb_connect
 from ._lancedb import FtsToken
 from ._lancedb import tokenize as _tokenize
 from .common import URI, sanitize_uri
 from urllib.parse import urlparse
 from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .remote import ClientConfig
 from .remote.db import RemoteDBConnection
 from .expr import Expr, col, lit, func
-from .schema import blob, vector, BlobType
+from .schema import vector
 from .table import AsyncTable, Table
 from .types import BaseTokenizerType
 from ._lancedb import Session
 from .namespace import (
    connect_namespace,
@@ -152,14 +149,8 @@ def connect(
    For object storage, use a URI prefix:
-    >>> db = lancedb.connect(  # doctest: +SKIP
+    >>> db = lancedb.connect("s3://my-bucket/lancedb",
-    ...     "s3://my-bucket/lancedb",
+    ...                      storage_options={"aws_access_key_id": "***"})
    ...     storage_options={
    ...         "aws_access_key_id": "***",
    ...         "aws_secret_access_key": "***",
    ...         "aws_region": "us-east-1",
    ...     },
    ... )
    For tests and temporary data, use an in-memory database:
@@ -249,40 +240,6 @@ def connect(
    )
 def tokenize(
    query: str,
    *,
    base_tokenizer: BaseTokenizerType = "simple",
    language: str = "English",
    max_token_length: Optional[int] = 40,
    lower_case: bool = True,
    stem: bool = True,
    remove_stop_words: bool = True,
    ascii_folding: bool = True,
    ngram_min_length: int = 3,
    ngram_max_length: int = 3,
    prefix_only: bool = False,
 ) -> Iterable[FtsToken]:
    """Tokenize a full-text search query using an explicit tokenizer.
    This does not require a table or FTS index. The tokenizer options match
    :class:`lancedb.index.FTS`.
    """
    return _tokenize(
        query,
        base_tokenizer=base_tokenizer,
        language=language,
        max_token_length=max_token_length,
        lower_case=lower_case,
        stem=stem,
        remove_stop_words=remove_stop_words,
        ascii_folding=ascii_folding,
        ngram_min_length=ngram_min_length,
        ngram_max_length=ngram_max_length,
        prefix_only=prefix_only,
    )
 WORKER_PROPERTY_PREFIX = "_lancedb_worker_"
@@ -493,21 +450,17 @@ async def connect_async(
 __all__ = [
    "connect",
    "connect_async",
    "tokenize",
    "connect_namespace",
    "connect_namespace_async",
    "AsyncConnection",
    "AsyncLanceNamespaceDBConnection",
    "AsyncTable",
    "FtsToken",
    "col",
    "Expr",
    "func",
    "lit",
    "URI",
    "sanitize_uri",
    "blob",
    "BlobType",
    "vector",
    "DBConnection",
    "LanceDBConnection",
@@ -1,420 +0,0 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 """Blob fetch API and v2 projection helpers."""
 from __future__ import annotations
 import io
 from collections.abc import Awaitable, Callable, Iterable
 from typing import TYPE_CHECKING, Optional, Union
 import pyarrow as pa
 from .expr import Expr
 from .schema import blob_v2_column_paths
 from .types import BlobMode, QueryProjection, QueryProjectionSpec
 from .util import get_uri_scheme
 if TYPE_CHECKING:
    from _typeshed import WriteableBuffer
    from .remote.table import RemoteTable
    from .table import AsyncTable, Table
 BLOB_MODE_TO_HANDLING = {
    "lazy": "blobs_descriptions",
    "bytes": "all_binary",
    "descriptions": "blobs_descriptions",
 }
 ROW_ID_FIELD_NAME = "_lance_row_id"
 FetchBlobsSync = Callable[[str, pa.Table], pa.Array | pa.ChunkedArray]
 FetchBlobsAsync = Callable[[str, pa.Table], Awaitable[pa.Array | pa.ChunkedArray]]
 class BlobFile(io.RawIOBase):
    """Seekable lazy handle from :meth:`~lancedb.table.Table.fetch_blob_files`.
    Bytes load on ``read`` or ``read_range``, not when the handle is opened.
    Use :meth:`aread` from async code.
    """
    def __init__(self, inner) -> None:
        self._inner = inner
    async def aread(self) -> bytes:
        return await self._inner.read()
    def close(self) -> None:
        self._inner.close()
    @property
    def closed(self) -> bool:
        return self._inner.is_closed()
    def readable(self) -> bool:
        return True
    def seekable(self) -> bool:
        return True
    def seek(self, offset: int, whence: int = io.SEEK_SET) -> int:
        if whence == io.SEEK_SET:
            self._inner.seek(offset)
        elif whence == io.SEEK_CUR:
            self._inner.seek(self._inner.tell() + offset)
        elif whence == io.SEEK_END:
            self._inner.seek(self._inner.size() + offset)
        else:
            raise ValueError(f"invalid whence: {whence}")
        return self._inner.tell()
    def tell(self) -> int:
        return self._inner.tell()
    def size(self) -> int:
        return self._inner.size()
    def readall(self) -> bytes:
        return self._inner.read_bytes()
    def read(self, size: int = -1) -> bytes:
        if size == -1:
            return self._inner.read_bytes()
        return super().read(size)
    def read_range(self, offset: int, length: int) -> bytes:
        return self._inner.read_range(offset, length)
    def readinto(self, b: WriteableBuffer) -> int:
        view = memoryview(b).cast("B")
        chunk = self._inner.read_up_to(len(view))
        view[: len(chunk)] = chunk
        return len(chunk)
    def __repr__(self) -> str:
        return f"<BlobFile size={self.size()}>"
 def validate_blob_mode(blob_mode: BlobMode) -> None:
    if blob_mode not in BLOB_MODE_TO_HANDLING:
        modes = ", ".join(repr(mode) for mode in BLOB_MODE_TO_HANDLING)
        raise ValueError(f"blob_mode must be one of {modes}, got {blob_mode!r}")
 def supports_blob_auto_row_id(table: Table | AsyncTable | RemoteTable) -> bool:
    """Blob auto row-id applies to native tables, not LanceDB Cloud."""
    from .remote.table import RemoteTable
    if isinstance(table, RemoteTable):
        return False
    inner = getattr(table, "_inner", None)
    if inner is not None:
        uri = inner.database().uri
        if isinstance(uri, str) and get_uri_scheme(uri) == "db":
            return False
    return True
 def projection_includes_blob_column(
    projection: QueryProjection,
    blob_columns: Iterable[str],
 ) -> bool:
    columns = set(blob_columns)
    if not columns:
        return False
    if projection is None:
        return True
    for output, source in _iter_projection_pairs(projection):
        if output in columns or source in columns:
            return True
    return False
 def blob_v2_projection_sources(
    schema: pa.Schema,
    projection: QueryProjection,
 ) -> dict[str, str]:
    blob_columns = blob_v2_column_paths(schema)
    if not blob_columns:
        return {}
    columns = set(blob_columns)
    if projection is None:
        return {column: column for column in blob_columns}
    return {
        output: source
        for output, source in _iter_projection_pairs(projection)
        if source in columns
    }
 def v2_projection_needs_row_id(
    schema: pa.Schema,
    projection: QueryProjection,
    *,
    with_row_id: bool,
 ) -> bool:
    if with_row_id:
        return False
    return projection_includes_blob_column(projection, blob_v2_column_paths(schema))
 def blob_auto_row_id_for_scan(
    table: Table | AsyncTable | RemoteTable,
    schema: pa.Schema,
    projection: QueryProjection,
    *,
    with_row_id: bool | None,
 ) -> bool:
    if with_row_id is not None:
        return False
    if not supports_blob_auto_row_id(table):
        return False
    return v2_projection_needs_row_id(schema, projection, with_row_id=False)
 def finalize_blob_query_table(
    tbl: pa.Table,
    *,
    user_requested_row_id: bool,
    blob_auto_row_id: bool,
    blob_paths: Iterable[str] = (),
 ) -> pa.Table:
    if user_requested_row_id or not blob_auto_row_id:
        return tbl
    return stash_auto_row_ids(tbl, blob_paths)
 async def replace_v2_blob_columns_with_bytes(
    tbl: pa.Table,
    blob_sources: dict[str, str],
    fetch_blobs: FetchBlobsAsync,
 ) -> pa.Table:
    for output_name, source_name in blob_sources.items():
        if output_name not in tbl.column_names:
            continue
        blobs = await fetch_blobs(source_name, tbl)
        tbl = _set_blob_column(tbl, output_name, blobs)
    return tbl
 def replace_v2_blob_columns_with_bytes_sync(
    tbl: pa.Table,
    blob_sources: dict[str, str],
    fetch_blobs: FetchBlobsSync,
 ) -> pa.Table:
    for output_name, source_name in blob_sources.items():
        if output_name not in tbl.column_names:
            continue
        blobs = fetch_blobs(source_name, tbl)
        tbl = _set_blob_column(tbl, output_name, blobs)
    return tbl
 def stash_auto_row_ids(tbl: pa.Table, blob_paths: Iterable[str]) -> pa.Table:
    if "_rowid" not in tbl.column_names:
        raise ValueError("query result has no '_rowid' column to hide")
    present_paths = [p for p in blob_paths if p.split(".")[0] in tbl.column_names]
    if not present_paths:
        raise ValueError("query result has no blob v2 column to carry a row id")
    row_ids = tbl["_rowid"]
    if isinstance(row_ids, pa.ChunkedArray):
        row_ids = row_ids.combine_chunks()
    row_ids = row_ids.cast(pa.uint64())
    for path in present_paths:
        tbl = _embed_row_id_in_column(tbl, path, row_ids)
    return tbl.drop_columns(["_rowid"])
 def read_row_ids_from_hits(hits: pa.Table, blob_column: str) -> list[int]:
    if "_rowid" in hits.column_names:
        return hits["_rowid"].to_pylist()
    try:
        leaf = _leaf_struct_column(hits, blob_column)
        if ROW_ID_FIELD_NAME in leaf.type.names:
            return leaf.field(ROW_ID_FIELD_NAME).to_pylist()
    except KeyError:
        pass
    # blob_column is the source name; aliased projections use the output name in hits.
    row_ids = _find_row_id_in_any_column(hits)
    if row_ids is not None:
        return row_ids
    raise ValueError(
        f"query result has no '_rowid' column and no '{ROW_ID_FIELD_NAME}' "
        f"field on blob column '{blob_column}'. Pass fresh blob query "
        "results, call .with_row_id(True), or pass a list of row ids."
    )
 def _find_row_id_in_any_column(tbl: pa.Table) -> Optional[list[int]]:
    for name in tbl.column_names:
        column = tbl.column(name)
        if isinstance(column, pa.ChunkedArray):
            column = column.combine_chunks()
        row_ids = _find_row_id_in_struct(column)
        if row_ids is not None:
            return row_ids
    return None
 def _find_row_id_in_struct(array: pa.Array) -> Optional[list[int]]:
    if not pa.types.is_struct(array.type):
        return None
    if ROW_ID_FIELD_NAME in array.type.names:
        return array.field(ROW_ID_FIELD_NAME).to_pylist()
    for i in range(array.type.num_fields):
        row_ids = _find_row_id_in_struct(array.field(i))
        if row_ids is not None:
            return row_ids
    return None
 def _iter_projection_pairs(
    projection: QueryProjectionSpec,
 ) -> Iterable[tuple[str, str]]:
    if isinstance(projection, dict):
        for name, expr in projection.items():
            if isinstance(expr, str):
                yield name, expr
            elif isinstance(expr, Expr):
                yield name, expr.to_sql()
        return
    for column in projection:
        if isinstance(column, str):
            yield column, column
        elif isinstance(column, tuple) and len(column) == 2:
            name, expr = column
            if isinstance(expr, str):
                yield name, expr
            elif isinstance(expr, Expr):
                yield name, expr.to_sql()
 def _set_blob_column(tbl: pa.Table, output_name: str, blobs: pa.Array) -> pa.Table:
    index = tbl.schema.get_field_index(output_name)
    return tbl.set_column(index, pa.field(output_name, blobs.type), [blobs])
 def _embed_row_id_in_column(tbl: pa.Table, path: str, row_ids: pa.Array) -> pa.Table:
    def add_row_id(children: list, child_fields: list) -> None:
        children.append(row_ids)
        child_fields.append(pa.field(ROW_ID_FIELD_NAME, pa.uint64(), nullable=False))
    return _transform_struct_column(tbl, path, add_row_id)
 def strip_auto_row_ids(tbl: pa.Table, blob_paths: Iterable[str]) -> pa.Table:
    """Remove any `_lance_row_id` field embedded in blob descriptor structs.
    For read-only descriptor views (`blob_mode="descriptions"`) that never
    fetch bytes, so have no use for the row id.
    """
    def drop_row_id(children: list, child_fields: list) -> None:
        for i, field in enumerate(child_fields):
            if field.name == ROW_ID_FIELD_NAME:
                del children[i], child_fields[i]
                return
    for path in blob_paths:
        if path.split(".")[0] not in tbl.column_names:
            continue
        tbl = _transform_struct_column(tbl, path, drop_row_id)
    return tbl
 def _transform_struct_column(
    tbl: pa.Table, path: str, leaf_transform: Callable[[list, list], None]
 ) -> pa.Table:
    top_name, *rest = path.split(".")
    top_index = tbl.schema.get_field_index(top_name)
    top_field = tbl.schema.field(top_index)
    top_array = tbl.column(top_name)
    if isinstance(top_array, pa.ChunkedArray):
        top_array = top_array.combine_chunks()
    new_array, new_field = _rebuild_struct(top_array, top_field, rest, leaf_transform)
    return tbl.set_column(top_index, new_field, new_array)
 def _rebuild_struct(
    struct_array: pa.StructArray,
    struct_field: pa.Field,
    remaining_path: list[str],
    leaf_transform: Callable[[list, list], None],
 ) -> tuple[pa.StructArray, pa.Field]:
    null_mask = struct_array.is_null()
    if not remaining_path:
        children = [struct_array.field(i) for i in range(struct_array.type.num_fields)]
        child_fields = list(struct_array.type)
        leaf_transform(children, child_fields)
        new_array = pa.StructArray.from_arrays(
            children, fields=child_fields, mask=null_mask
        )
    else:
        child_name = remaining_path[0]
        child_index = struct_array.type.get_field_index(child_name)
        child_array = struct_array.field(child_index)
        child_field = struct_array.type.field(child_index)
        new_child_array, new_child_field = _rebuild_struct(
            child_array, child_field, remaining_path[1:], leaf_transform
        )
        children = []
        child_fields = []
        for i in range(struct_array.type.num_fields):
            field = struct_array.type.field(i)
            if field.name == child_name:
                children.append(new_child_array)
                child_fields.append(new_child_field)
            else:
                children.append(struct_array.field(i))
                child_fields.append(field)
        new_array = pa.StructArray.from_arrays(
            children, fields=child_fields, mask=null_mask
        )
    new_field = pa.field(
        struct_field.name,
        new_array.type,
        nullable=struct_field.nullable,
        metadata=struct_field.metadata,
    )
    return new_array, new_field
 def _leaf_struct_column(tbl: pa.Table, path: str) -> pa.StructArray:
    parts = path.split(".")
    column = tbl.column(parts[0])
    if isinstance(column, pa.ChunkedArray):
        column = column.combine_chunks()
    for part in parts[1:]:
        column = column.field(part)
    return column
 def _normalize_blob_row_ids(
    row_ids: Union[list[int], pa.Table], blob_column: str
 ) -> list[int]:
    if isinstance(row_ids, pa.Table):
        return read_row_ids_from_hits(row_ids, blob_column)
    if isinstance(row_ids, (pa.Array, pa.ChunkedArray)):
        raise ValueError(
            "pass a query table with _rowid, not a column array "
            "(use fetch_blobs('image', hits), not fetch_blobs('image', hits['image']))"
        )
    return list(row_ids)
 def _wrap_blob_files(handles: Iterable[object]) -> list[Optional[BlobFile]]:
    return [BlobFile(handle) if handle is not None else None for handle in handles]
@@ -1,5 +1,4 @@
-from datetime import date, datetime, timedelta
+from datetime import datetime, timedelta
 from decimal import Decimal
 from typing import Dict, List, Optional, Tuple, Any, TypedDict, Union, Literal
 import pyarrow as pa
@@ -25,45 +24,11 @@ from lance_namespace import (
    ListTablesResponse,
 )
 from .remote import ClientConfig
 from .types import BaseTokenizerType
 IvfHnswPq: type[HnswPq] = HnswPq
 IvfHnswSq: type[HnswSq] = HnswSq
 IvfHnswFlat: type[HnswFlat] = HnswFlat
 class MetricPoint:
    name: str
    kind: str
    attributes: Dict[str, str]
    value: Optional[float]
    buckets: Optional[List[Tuple[str, int]]]
    count: Optional[int]
    sum: Optional[float]
 class MetricDescription:
    name: str
    kind: str
    unit: Optional[str]
    description: str
 def register_lancedb_metrics_recorder() -> bool: ...
 def lancedb_metrics_catalog() -> List[MetricDescription]: ...
 def snapshot_lancedb_metrics() -> List[MetricPoint]: ...
 def tokenize(
    query: str,
    *,
    base_tokenizer: BaseTokenizerType = "simple",
    language: str = "English",
    max_token_length: Optional[int] = 40,
    lower_case: bool = True,
    stem: bool = True,
    remove_stop_words: bool = True,
    ascii_folding: bool = True,
    ngram_min_length: int = 3,
    ngram_max_length: int = 3,
    prefix_only: bool = False,
 ) -> List["FtsToken"]: ...
 class PyExpr:
    """A type-safe DataFusion expression node (Rust-side handle)."""
@@ -88,9 +53,7 @@ class PyExpr:
    def to_sql(self) -> str: ...
 def expr_col(name: str) -> PyExpr: ...
-def expr_lit(
+def expr_lit(value: Union[bool, int, float, str, bytes]) -> PyExpr: ...
    value: Union[bool, int, float, str, bytes, date, datetime, Decimal],
 ) -> PyExpr: ...
 def expr_func(name: str, args: List[PyExpr]) -> PyExpr: ...
 class Session:
@@ -196,17 +159,6 @@ class Connection(object):
        self,
    ) -> Dict[str, Any]: ...
 class BlobFile:
    async def read(self) -> bytes: ...
    def read_bytes(self) -> bytes: ...
    def close(self) -> None: ...
    def is_closed(self) -> bool: ...
    def seek(self, position: int) -> None: ...
    def tell(self) -> int: ...
    def size(self) -> int: ...
    def read_range(self, offset: int, length: int) -> bytes: ...
    def read_up_to(self, length: int) -> bytes: ...
 class Table:
    def name(self) -> str: ...
    def __repr__(self) -> str: ...
@@ -253,13 +205,6 @@ class Table:
    async def prewarm_index(self, index_name: str) -> None: ...
    async def prewarm_data(self, columns: Optional[List[str]] = None) -> None: ...
    async def list_indices(self) -> list[IndexConfig]: ...
    async def tokenize(
        self,
        query: str,
        *,
        column: Optional[str] = None,
        index_name: Optional[str] = None,
    ) -> list[FtsToken]: ...
    async def delete(self, filter: Union[str, PyExpr]) -> DeleteResult: ...
    async def add_columns(self, columns: list[tuple[str, str]]) -> AddColumnsResult: ...
    async def add_columns_with_schema(self, schema: pa.Schema) -> AddColumnsResult: ...
@@ -281,7 +226,6 @@ class Table:
    async def set_unenforced_primary_key(self, columns: List[str]) -> None: ...
    async def set_lsm_write_spec(self, spec: LsmWriteSpec) -> None: ...
    async def unset_lsm_write_spec(self) -> None: ...
    async def get_lsm_write_spec(self) -> Optional[LsmWriteSpec]: ...
    async def close_lsm_writers(self) -> None: ...
    @property
    def tags(self) -> Tags: ...
@@ -291,13 +235,6 @@ class Table:
    def query(self) -> Query: ...
    def take_offsets(self, offsets: list[int]) -> TakeQuery: ...
    def take_row_ids(self, row_ids: list[int]) -> TakeQuery: ...
    async def blob_columns(self) -> list[str]: ...
    async def fetch_blobs(
        self, column: str, row_ids: list[int]
    ) -> pa.LargeBinaryArray: ...
    async def fetch_blob_files(
        self, column: str, row_ids: list[int]
    ) -> list[Optional[BlobFile]]: ...
    def vector_search(self) -> VectorQuery: ...
 class Tags:
@@ -533,10 +470,6 @@ class MergeResult:
    num_attempts: int
    num_rows: int
 class FtsToken:
    text: str
    position: int
 class LsmWriteSpec:
    """Specification selecting Lance's MemWAL LSM-style write path for
    `merge_insert`."""
@@ -4,7 +4,7 @@
 import os
 from functools import cached_property
-from typing import List, Optional, Union
+from typing import List, Union
 import numpy as np
@@ -15,8 +15,6 @@ from .base import TextEmbeddingFunction
 from .registry import register
 from .utils import TEXT, api_key_not_found_help
 EMBEDDING_BATCH_SIZE = 100
@register("gemini-text")
 class GeminiText(TextEmbeddingFunction):
@@ -83,7 +81,6 @@ class GeminiText(TextEmbeddingFunction):
    """
    name: str = "gemini-embedding-001"
    dim: Optional[int] = None
    query_task_type: str = "retrieval_query"
    source_task_type: str = "retrieval_document"
@@ -96,8 +93,6 @@ class GeminiText(TextEmbeddingFunction):
        model_config["ignored_types"] = (cached_property,)
    def ndims(self):
        if self.dim:
            return self.dim
        # TODO: fix hardcoding
        return 768
@@ -138,22 +133,22 @@ class GeminiText(TextEmbeddingFunction):
                contents.append({"parts": [{"text": text}]})
        # Build config
-        config_kwargs = {"output_dimensionality": self.ndims()}
+        config_kwargs = {}
        if task_type:
            config_kwargs["task_type"] = task_type.upper()  # API expects uppercase
-        config = types.EmbedContentConfig(**config_kwargs) if config_kwargs else None
+        # Call embed_content for each content
        # Call embed_content in groups of at most EMBEDDING_BATCH_SIZE docs at a time
        embeddings = []
-        for i in range(0, len(contents), EMBEDDING_BATCH_SIZE):
+        for content in contents:
-            chunk = contents[i : i + EMBEDDING_BATCH_SIZE]
+            config = (
                types.EmbedContentConfig(**config_kwargs) if config_kwargs else None
            )
            response = self.client.models.embed_content(
                model=self.name,
-                contents=chunk,
+                contents=content,
                config=config,
            )
-            embeddings.extend([np.array(e.values) for e in response.embeddings])
+            embeddings.append(response.embeddings[0].values)
        return embeddings
@@ -165,13 +160,5 @@ class GeminiText(TextEmbeddingFunction):
            api_key_not_found_help("google")
        from google import genai as genai_module
        from lancedb import __version__
-        return genai_module.Client(
+        return genai_module.Client(api_key=os.environ.get("GOOGLE_API_KEY"))
            api_key=os.environ.get("GOOGLE_API_KEY"),
            http_options={
                "headers": {
                    "x-goog-api-client": f"lancedb/{__version__}",
                }
            },
        )
@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 from functools import cached_property
-from typing import TYPE_CHECKING, Any, List, Optional, Sequence, Union
+from typing import TYPE_CHECKING, List, Optional, Sequence, Union
 import numpy as np
@@ -56,16 +56,6 @@ class OllamaEmbeddings(TextEmbeddingFunction):
        embeddings = self._compute_embedding(texts)
        return list(embeddings)
    def __getstate__(self) -> dict[str, Any]:
        state = super().__getstate__()
        state["__dict__"] = {
            k: v for k, v in state["__dict__"].items() if k != "_ollama_client"
        }
        return state
    def __setstate__(self, state: dict[str, Any]) -> None:
        super().__setstate__(state)
    @cached_property
    def _ollama_client(self) -> "ollama.Client":
        ollama = attempt_import_or_raise("ollama")
@@ -19,8 +19,6 @@ operators::
 from __future__ import annotations
 from datetime import date, datetime
 from decimal import Decimal
 from typing import Iterable, Union
 import pyarrow as pa
@@ -65,7 +63,7 @@ def _coerce(value: "ExprLike") -> "Expr":
 # Type alias used in annotations.
-ExprLike = Union["Expr", bool, int, float, str, bytes, date, datetime, Decimal]
+ExprLike = Union["Expr", bool, int, float, str, bytes]
 class Expr:
@@ -120,18 +118,10 @@ class Expr:
        """Logical AND (``expr_a & expr_b``)."""
        return Expr(self._inner.and_(_coerce(other)._inner))
    def __rand__(self, other: ExprLike) -> "Expr":
        """Right-hand logical AND (``True & expr``)."""
        return Expr(_coerce(other)._inner.and_(self._inner))
    def __or__(self, other: "Expr") -> "Expr":
        """Logical OR (``expr_a | expr_b``)."""
        return Expr(self._inner.or_(_coerce(other)._inner))
    def __ror__(self, other: ExprLike) -> "Expr":
        """Right-hand logical OR (``False | expr``)."""
        return Expr(_coerce(other)._inner.or_(self._inner))
    def __invert__(self) -> "Expr":
        """Logical NOT (``~expr``)."""
        return Expr(self._inner.not_())
@@ -276,14 +266,13 @@ def col(name: str) -> Expr:
    return Expr(expr_col(name))
-def lit(value: Union[bool, int, float, str, bytes, date, datetime, Decimal]) -> Expr:
+def lit(value: Union[bool, int, float, str, bytes]) -> Expr:
    """Create a literal (constant) value expression.
    Parameters
    ----------
    value:
-        A Python ``bool``, ``int``, ``float``, ``str``, ``bytes``, ``date``,
+        A Python ``bool``, ``int``, ``float``, ``str``, or ``bytes``.
        ``datetime``, or ``Decimal``.
    Examples
    --------
@@ -291,9 +280,6 @@ def lit(value: Union[bool, int, float, str, bytes, date, datetime, Decimal]) ->
    >>> col("price") * lit(1.1)
    Expr((price * 1.1))
    """
    if not isinstance(value, (bool, int, float, str, bytes, date, datetime, Decimal)):
        raise TypeError(f"Unsupported literal type: {type(value).__name__}")
    return Expr(expr_lit(value))
@@ -127,8 +127,6 @@ class FTS:
        - "whitespace": Split text by whitespace, but not punctuation.
        - "raw": No tokenization. The entire text is treated as a single token.
        - "ngram": N-gram tokenizer for substring-style matching.
        - "icu": ICU dictionary-based word segmentation.
        - "icu/split": ICU segmentation with simple-style delimiter splitting.
        - "jieba/*": Jieba tokenizer loaded from Lance's language model home.
        - "lindera/*": Lindera tokenizer loaded from Lance's language model home.
    language : str, default "English"
@@ -1,170 +0,0 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 """Bridge LanceDB's internal metrics into OpenTelemetry.
 LanceDB (through Lance core) publishes metrics (currently object store request
 counts, bytes, latency, errors, and throttles) through the Rust ``metrics``
 facade. This module installs a process-global recorder that aggregates them and
 registers OpenTelemetry observable instruments that report the aggregated values
 into the user's ``MeterProvider``.
 The bridge is generic: every metric LanceDB describes is surfaced automatically,
 with no per-metric Python code. Histograms have no asynchronous OpenTelemetry
 instrument, so each is exported Prometheus-style as cumulative ``le`` buckets
 plus ``_count`` and ``_sum`` observable counters.
 """
 from __future__ import annotations
 import warnings
 from typing import TYPE_CHECKING, Optional
 from ._lancedb import (
    lancedb_metrics_catalog,
    register_lancedb_metrics_recorder,
    snapshot_lancedb_metrics,
 )
 if TYPE_CHECKING:
    from opentelemetry.metrics import MeterProvider
 _INSTRUMENTED = False
 def instrument_lancedb_metrics(
    meter_provider: Optional["MeterProvider"] = None,
 ) -> bool:
    """Register LanceDB metrics as OpenTelemetry observable instruments.
    Installs a process-global metrics recorder and creates one observable
    instrument per LanceDB metric on the given (or global) ``MeterProvider``. The
    user's configured ``MetricReader`` then collects them on its own schedule.
    Counters and gauges map directly to observable counters/gauges. Each
    histogram is exported as cumulative ``le`` bucket counts (``<name>_bucket``,
    with an ``le`` attribute) plus ``<name>_count`` and ``<name>_sum``.
    Parameters
    ----------
    meter_provider : opentelemetry.metrics.MeterProvider, optional
        The provider to register instruments on. Defaults to the global provider
        from ``opentelemetry.metrics.get_meter_provider()``.
    Returns
    -------
    bool
        ``True`` if the recorder is installed and instruments are registered.
        ``False`` if a different ``metrics`` recorder is already installed in
        this process (``metrics`` permits only one global recorder), in which
        case a warning is emitted and no instruments are created.
    Notes
    -----
    Requires the OpenTelemetry API (``pip install lancedb[otel]``) and, to
    actually export, an OpenTelemetry SDK (``pip install opentelemetry-sdk``)
    configured by the application. Calling this more than once is safe;
    instruments are created only on the first successful call.
    """
    global _INSTRUMENTED
    try:
        from opentelemetry.metrics import Observation, get_meter_provider
    except ImportError as exc:
        raise ImportError(
            "instrument_lancedb_metrics requires the OpenTelemetry API/SDK. "
            "Install it with `pip install lancedb[otel]` or "
            "`pip install opentelemetry-sdk`."
        ) from exc
    if not register_lancedb_metrics_recorder():
        warnings.warn(
            "Could not install the LanceDB metrics recorder: another `metrics` "
            "recorder is already installed in this process. LanceDB metrics will "
            "not be exported via OpenTelemetry.",
            stacklevel=2,
        )
        return False
    if _INSTRUMENTED:
        return True
    provider = meter_provider or get_meter_provider()
    meter = provider.get_meter("lancedb")
    def scalar_callback(metric_name: str):
        def callback(_options):
            return [
                Observation(point.value, point.attributes)
                for point in snapshot_lancedb_metrics()
                if point.name == metric_name and point.value is not None
            ]
        return callback
    def bucket_callback(metric_name: str):
        def callback(_options):
            observations = []
            for point in snapshot_lancedb_metrics():
                if point.name != metric_name or point.buckets is None:
                    continue
                for le, cumulative in point.buckets:
                    attributes = dict(point.attributes)
                    attributes["le"] = le
                    observations.append(Observation(cumulative, attributes))
            return observations
        return callback
    def field_callback(metric_name: str, field: str):
        def callback(_options):
            observations = []
            for point in snapshot_lancedb_metrics():
                if point.name != metric_name:
                    continue
                value = getattr(point, field)
                if value is not None:
                    observations.append(Observation(value, point.attributes))
            return observations
        return callback
    for desc in lancedb_metrics_catalog():
        unit = desc.unit or ""
        if desc.kind == "counter":
            meter.create_observable_counter(
                desc.name,
                callbacks=[scalar_callback(desc.name)],
                unit=unit,
                description=desc.description,
            )
        elif desc.kind == "gauge":
            meter.create_observable_gauge(
                desc.name,
                callbacks=[scalar_callback(desc.name)],
                unit=unit,
                description=desc.description,
            )
        elif desc.kind == "histogram":
            # `_bucket` and `_count` observe cumulative sample counts, not the
            # histogram's measured quantity, so they are unitless; only `_sum`
            # carries the histogram's unit.
            meter.create_observable_counter(
                f"{desc.name}_bucket",
                callbacks=[bucket_callback(desc.name)],
                description=f"{desc.description} (cumulative buckets)",
            )
            meter.create_observable_counter(
                f"{desc.name}_count",
                callbacks=[field_callback(desc.name, "count")],
                description=f"{desc.description} (count)",
            )
            meter.create_observable_counter(
                f"{desc.name}_sum",
                callbacks=[field_callback(desc.name, "sum")],
                unit=unit,
                description=f"{desc.description} (sum)",
            )
    _INSTRUMENTED = True
    return True
@@ -11,7 +11,7 @@ import pyarrow as pa
 from ._lancedb import async_permutation_builder, PermutationReader
 from .table import LanceTable, Table
 from .background_loop import LOOP
-from .util import batch_to_tensor, batch_to_tensor_dict, batch_to_tensor_rows
+from .util import batch_to_tensor, batch_to_tensor_rows
 from typing import Any, Callable, Iterator, Literal, Optional, TYPE_CHECKING, Union
 if TYPE_CHECKING:
@@ -65,7 +65,6 @@ class PermutationBuilder:
        counts: Optional[list[int]] = None,
        fixed: Optional[int] = None,
        seed: Optional[int] = None,
        clump_size: Optional[int] = None,
        split_names: Optional[list[str]] = None,
    ) -> "PermutationBuilder":
        """
@@ -88,9 +87,6 @@ class PermutationBuilder:
        Rows will be randomly assigned to splits.  The optional seed can be provided to
        make the assignment deterministic.
        If clump_size is provided, rows are shuffled as contiguous groups of that size,
        preserving I/O locality while still randomising the split assignment.
        The optional split_names can be provided to name the splits.  If not provided,
        the splits can only be referenced by their index.
        """
@@ -99,7 +95,6 @@ class PermutationBuilder:
            counts=counts,
            fixed=fixed,
            seed=seed,
            clump_size=clump_size,
            split_names=split_names,
        )
        return self
@@ -946,7 +941,6 @@ class Permutation:
            "pandas",
            "arrow",
            "torch",
            "torch_row",
            "torch_col",
            "polars",
        ],
@@ -962,19 +956,15 @@ class Permutation:
        - "python_col" - the batch will be a dict of lists (one entry per column)
        - "pandas" - the batch will be a pandas DataFrame
        - "arrow" - the batch will be a pyarrow RecordBatch
-        - "torch" - the batch will be a list of per-row dicts mapping column
+        - "torch" - the batch will be a list of tensors, one per row
          name to a 0-D torch tensor. Works with the default
          ``torch.utils.data.DataLoader`` collate, which stacks the per-row
          dicts back into a dict of batched tensors.
        - "torch_row" - the batch will be a list of tensors, one per row
        - "torch_col" - the batch will be a 2D torch tensor (first dim indexes columns)
        - "polars" - the batch will be a polars DataFrame
        Conversion may or may not involve a data copy.  Lance uses Arrow internally
        and so it is able to zero-copy to the arrow and polars formats.
-        Conversion to torch and torch_col will be zero-copy but will only support a
+        Conversion to torch_col will be zero-copy but will only support a subset of data
-        subset of data types (numeric types).
+        types (numeric types).
        Conversion to numpy and/or pandas will typically be zero-copy for numeric
        types.  Conversion of strings, lists, and structs will require creating python
@@ -995,8 +985,6 @@ class Permutation:
        elif format == "arrow":
            return self.with_transform(Transforms.arrow2arrow)
        elif format == "torch":
            return self.with_transform(batch_to_tensor_dict)
        elif format == "torch_row":
            return self.with_transform(batch_to_tensor_rows)
        elif format == "torch_col":
            return self.with_transform(batch_to_tensor)
@@ -15,12 +15,10 @@ from typing import (
    List,
    Literal,
    Optional,
    Protocol,
    Tuple,
    Type,
    TypeVar,
    Union,
    runtime_checkable,
 )
 import deprecation
@@ -41,21 +39,15 @@ from .expr import Expr
 from .rerankers.base import Reranker
 from .rerankers.rrf import RRFReranker
 from .rerankers.util import check_reranker_result
 from .schema import is_blob_like_field, schema_has_blob_field
 from .util import flatten_columns
-from ._blob import (
+
-    BLOB_MODE_TO_HANDLING,
+BlobMode = Literal["lazy", "bytes", "descriptions"]
-    FetchBlobsAsync,
+
-    FetchBlobsSync,
+_BLOB_MODE_TO_HANDLING = {
-    blob_auto_row_id_for_scan,
+    "lazy": "blobs_descriptions",
-    blob_v2_projection_sources,
+    "bytes": "all_binary",
-    finalize_blob_query_table,
+    "descriptions": "blobs_descriptions",
-    replace_v2_blob_columns_with_bytes,
+}
    replace_v2_blob_columns_with_bytes_sync,
    supports_blob_auto_row_id,
    validate_blob_mode,
 )
 from .types import BlobMode, QueryProjection
 if TYPE_CHECKING:
    import sys
@@ -81,22 +73,25 @@ if TYPE_CHECKING:
 T = TypeVar("T", bound="LanceModel")
-@runtime_checkable
+def _validate_blob_mode(blob_mode: BlobMode) -> None:
-class _LanceScanner(Protocol):
+    if blob_mode not in _BLOB_MODE_TO_HANDLING:
-    projected_schema: pa.Schema | None
+        modes = ", ".join(repr(mode) for mode in _BLOB_MODE_TO_HANDLING)
-    schema: pa.Schema | None
+        raise ValueError(f"blob_mode must be one of {modes}, got {blob_mode!r}")
    def to_pandas(self, blob_mode: BlobMode | None = ..., **kwargs) -> pd.DataFrame: ...
-    def to_pyarrow(self): ...
+def _field_is_blob(field: pa.Field) -> bool:
    metadata = field.metadata or {}
    return metadata.get(b"lance-encoding:blob") == b"true" or (
        metadata.get("lance-encoding:blob") == "true"
    )
    def to_table(self) -> pa.Table: ...
-    def to_reader(self): ...
+def _schema_has_blob_field(schema: pa.Schema) -> bool:
    return any(_field_is_blob(field) for field in schema)
 def _blob_mode_requires_native_pandas(blob_mode: BlobMode, schema: pa.Schema) -> bool:
-    return blob_mode in BLOB_MODE_TO_HANDLING and schema_has_blob_field(schema)
+    return blob_mode in _BLOB_MODE_TO_HANDLING and _schema_has_blob_field(schema)
 def _unsupported_blob_pandas_error(reason: str) -> RuntimeError:
@@ -145,7 +140,13 @@ def _combine_where(
    return f"({existing_sql}) AND ({new_sql})"
-def _projection_to_scanner_kwargs(columns: QueryProjection) -> Dict[str, Any]:
+def _projection_to_scanner_kwargs(
    columns: Optional[
        Union[
            List[str], List[Tuple[str, Union[str, Expr]]], Dict[str, Union[str, Expr]]
        ]
    ],
 ) -> Dict[str, Any]:
    if columns is None:
        return {}
    if isinstance(columns, list):
@@ -170,11 +171,7 @@ def _projection_to_scanner_kwargs(columns: QueryProjection) -> Dict[str, Any]:
 def _scanner_kwargs_for_query(
-    query: Query,
+    query: Query, blob_mode: BlobMode, dataset: Optional[Any] = None
    blob_mode: BlobMode,
    dataset: Optional[Any] = None,
    *,
    with_row_id: Optional[bool] = None,
 ) -> Dict[str, Any]:
    fragments = _scanner_fragments_for_query(query, dataset)
    kwargs = {
@@ -182,10 +179,10 @@ def _scanner_kwargs_for_query(
        "filter": _filter_to_sql(query.filter),
        "limit": query.limit,
        "offset": query.offset,
-        "with_row_id": with_row_id if with_row_id is not None else query.with_row_id,
+        "with_row_id": query.with_row_id,
        "with_row_address": query.with_row_address,
        "fast_search": query.fast_search,
-        "blob_handling": BLOB_MODE_TO_HANDLING[blob_mode],
+        "blob_handling": _BLOB_MODE_TO_HANDLING[blob_mode],
        "fragments": fragments,
    }
    return {key: value for key, value in kwargs.items() if value is not None}
@@ -218,11 +215,11 @@ def _scanner_fragments_for_query(query: Query, dataset: Optional[Any]) -> Option
 def _ensure_lazy_blob_frame(
    df: "pd.DataFrame", schema: pa.Schema, blob_mode: BlobMode
 ) -> "pd.DataFrame":
-    if blob_mode != "lazy" or not schema_has_blob_field(schema) or len(df) == 0:
+    if blob_mode != "lazy" or not _schema_has_blob_field(schema) or len(df) == 0:
        return df
    for field in schema:
-        if not is_blob_like_field(field) or field.name not in df.columns:
+        if not _field_is_blob(field) or field.name not in df.columns:
            continue
        value = df[field.name].iloc[0]
        if value is not None and not hasattr(value, "readall"):
@@ -232,7 +229,7 @@ def _ensure_lazy_blob_frame(
    return df
-def _scanner_to_table(scanner: _LanceScanner) -> pa.Table:
+def _scanner_to_table(scanner: Any) -> pa.Table:
    if hasattr(scanner, "to_pyarrow"):
        reader = scanner.to_pyarrow()
        return reader.read_all()
@@ -242,9 +239,7 @@ def _scanner_to_table(scanner: _LanceScanner) -> pa.Table:
    return reader.read_all()
-def _scanner_to_pandas(
+def _scanner_to_pandas(scanner: Any, blob_mode: BlobMode, **kwargs) -> "pd.DataFrame":
    scanner: _LanceScanner, blob_mode: BlobMode, **kwargs
 ) -> pd.DataFrame:
    schema = getattr(scanner, "projected_schema", None)
    if schema is None:
        schema = getattr(scanner, "schema", None)
@@ -265,71 +260,13 @@ def _scanner_to_pandas(
        return df
    tbl = _scanner_to_table(scanner)
-    if blob_mode == "lazy" and schema_has_blob_field(tbl.schema):
+    if blob_mode == "lazy" and _schema_has_blob_field(tbl.schema):
        raise _unsupported_blob_pandas_error(
            "the Lance scanner does not expose to_pandas"
        )
    return tbl.to_pandas(**kwargs)
 def _finish_plain_scan_pandas(
    scanner: _LanceScanner,
    *,
    blob_mode: BlobMode,
    blob_sources: dict[str, str],
    fetch_blobs: FetchBlobsSync,
    strip_auto_row_id: bool,
    flatten: Optional[Union[int, bool]],
    **kwargs,
 ) -> pd.DataFrame:
    if blob_sources:
        tbl = _scanner_to_table(scanner)
        tbl = replace_v2_blob_columns_with_bytes_sync(tbl, blob_sources, fetch_blobs)
        if strip_auto_row_id and "_rowid" in tbl.column_names:
            tbl = tbl.drop_columns(["_rowid"])
        if flatten is not None:
            tbl = flatten_columns(tbl, flatten)
        return tbl.to_pandas(**kwargs)
    if flatten is not None:
        tbl = flatten_columns(_scanner_to_table(scanner), flatten)
        if strip_auto_row_id and "_rowid" in tbl.column_names:
            tbl = tbl.drop_columns(["_rowid"])
        return tbl.to_pandas(**kwargs)
    df = _scanner_to_pandas(scanner, blob_mode, **kwargs)
    if strip_auto_row_id and "_rowid" in df.columns:
        return df.drop(columns=["_rowid"])
    return df
 async def _finish_plain_scan_pandas_async(
    scanner: _LanceScanner,
    *,
    blob_mode: BlobMode,
    blob_sources: dict[str, str],
    fetch_blobs: FetchBlobsAsync,
    strip_auto_row_id: bool,
    flatten: Optional[Union[int, bool]],
    **kwargs,
 ) -> pd.DataFrame:
    if blob_sources:
        tbl = _scanner_to_table(scanner)
        tbl = await replace_v2_blob_columns_with_bytes(tbl, blob_sources, fetch_blobs)
        if strip_auto_row_id and "_rowid" in tbl.column_names:
            tbl = tbl.drop_columns(["_rowid"])
        if flatten is not None:
            tbl = flatten_columns(tbl, flatten)
        return tbl.to_pandas(**kwargs)
    if flatten is not None:
        tbl = flatten_columns(_scanner_to_table(scanner), flatten)
        if strip_auto_row_id and "_rowid" in tbl.column_names:
            tbl = tbl.drop_columns(["_rowid"])
        return tbl.to_pandas(**kwargs)
    df = _scanner_to_pandas(scanner, blob_mode, **kwargs)
    if strip_auto_row_id and "_rowid" in df.columns:
        return df.drop(columns=["_rowid"])
    return df
 # Pydantic validation function for vector queries
 def ensure_vector_query(
    val: Any,
@@ -737,7 +674,7 @@ class Query(pydantic.BaseModel):
    distance_type: Optional[str] = None
    # which columns to return in the results (dict values may be str or Expr)
-    columns: QueryProjection = None
+    columns: Optional[Union[List[str], Dict[str, Union[str, Expr]]]] = None
    # minimum number of IVF partitions to search
    #
@@ -1021,7 +958,7 @@ class LanceQueryBuilder(ABC):
            Forwarded to pyarrow.Table.to_pandas after query execution and
            optional flattening.
        """
-        validate_blob_mode(blob_mode)
+        _validate_blob_mode(blob_mode)
        output_schema = getattr(self, "output_schema", None)
        if output_schema is not None:
            schema = output_schema()
@@ -1080,11 +1017,6 @@ class LanceQueryBuilder(ABC):
        Execute the query and return the results as a pyarrow
        [RecordBatchReader](https://arrow.apache.org/docs/python/generated/pyarrow.RecordBatchReader.html)
        For v2 blob projections, ``to_batches`` keeps the auto ``_rowid``
        column visible so batch consumers can call ``fetch_blobs``. Use
        ``to_arrow``, ``to_list``, or ``to_pandas`` if you want LanceDB to hide
        auto row ids in the final collected result.
        Parameters
        ----------
        batch_size: int
@@ -1263,42 +1195,6 @@ class LanceQueryBuilder(ABC):
        self._with_row_id = with_row_id
        return self
    def _user_requested_row_id(self) -> bool:
        return self._with_row_id is True
    def _blob_auto_row_id_enabled(self) -> bool:
        if not supports_blob_auto_row_id(self._table):
            return False
        return blob_auto_row_id_for_scan(
            self._table,
            self._table.schema,
            self._columns,
            with_row_id=self._with_row_id,
        )
    def _scan_needs_row_id(self) -> bool:
        return self._user_requested_row_id() or self._blob_auto_row_id_enabled()
    def _query_for_scan(self) -> Query:
        query = self.to_query_object()
        if self._scan_needs_row_id():
            query.with_row_id = True
        return query
    def _finalize_blob_query_table(self, tbl: pa.Table) -> pa.Table:
        blob_auto_row_id = self._blob_auto_row_id_enabled()
        blob_paths = (
            blob_v2_projection_sources(self._table.schema, self._columns).keys()
            if blob_auto_row_id
            else ()
        )
        return finalize_blob_query_table(
            tbl,
            user_requested_row_id=self._user_requested_row_id(),
            blob_auto_row_id=blob_auto_row_id,
            blob_paths=blob_paths,
        )
    def with_row_address(self, with_row_address: bool = True) -> Self:
        """Set whether to return row addresses.
@@ -1475,29 +1371,13 @@ class LanceQueryBuilder(ABC):
            return None
        dataset = self._table.to_lance()
        blob_auto_row_id = self._blob_auto_row_id_enabled()
        blob_sources = (
            blob_v2_projection_sources(self._table.schema, query.columns)
            if blob_mode == "bytes"
            else {}
        )
        scanner = dataset.scanner(
-            **_scanner_kwargs_for_query(
+            **_scanner_kwargs_for_query(query, blob_mode, dataset)
                query,
                "descriptions" if blob_sources else blob_mode,
                dataset,
                with_row_id=query.with_row_id or blob_auto_row_id or bool(blob_sources),
            )
        )
        return _finish_plain_scan_pandas(
            scanner,
            blob_mode=blob_mode,
            blob_sources=blob_sources,
            fetch_blobs=self._table.fetch_blobs,
            strip_auto_row_id=blob_auto_row_id,
            flatten=flatten,
            **kwargs,
        )
        if flatten is not None:
            tbl = flatten_columns(_scanner_to_table(scanner), flatten)
            return tbl.to_pandas(**kwargs)
        return _scanner_to_pandas(scanner, blob_mode, **kwargs)
    @abstractmethod
    def to_query_object(self) -> Query:
@@ -1745,9 +1625,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
            The maximum time to wait for the query to complete.
            If None, wait indefinitely.
        """
-        return self._finalize_blob_query_table(
+        return self.to_batches(timeout=timeout).read_all()
            self.to_batches(timeout=timeout).read_all()
        )
    def to_query_object(self) -> Query:
        """
@@ -1807,7 +1685,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        vector = self._query if isinstance(self._query, list) else self._query.tolist()
        if isinstance(vector[0], np.ndarray):
            vector = [v.tolist() for v in vector]
-        query = self._query_for_scan()
+        query = self.to_query_object()
        result_set = self._table._execute_query(
            query, batch_size=batch_size, timeout=timeout
        )
@@ -1951,7 +1829,8 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
        Parameters
        ----------
        phrase_query: bool, default True
-            If True, then an unquoted string query will be wrapped in quotes.
+            If True, then the query will be wrapped in quotes and
            double quotes replaced by single quotes.
        Returns
        -------
@@ -1961,21 +1840,6 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
        self._phrase_query = phrase_query
        return self
    def _query_with_phrase_semantics(self) -> str | FullTextQuery:
        query = self._query
        if not self._phrase_query:
            return query
        if isinstance(query, str):
            if not query.startswith('"') or not query.endswith('"'):
                return f'"{query}"'
            return query
        if isinstance(query, PhraseQuery):
            return query
        raise TypeError(
            "phrase_query() requires a string or PhraseQuery, "
            f"got {type(query).__name__}"
        )
    def fast_search(self) -> LanceFtsQueryBuilder:
        """
        Skip a flat search of unindexed data. This will improve
@@ -2000,7 +1864,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
            fragments=self._fragments,
            fragment_ids=self._fragment_ids,
            full_text_query=FullTextSearchQuery(
-                query=self._query_with_phrase_semantics(), columns=self._fts_columns
+                query=self._query, columns=self._fts_columns
            ),
            offset=self._offset,
            fast_search=self._fast_search,
@@ -2018,13 +1882,22 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
        self._table._ensure_no_legacy_fts_index()
-        query = self._query_for_scan()
+        query = self._query
        if self._phrase_query:
            if isinstance(query, str):
                if not query.startswith('"') or not query.endswith('"'):
                    self._query = f'"{query}"'
            elif isinstance(query, FullTextQuery) and not isinstance(
                query, PhraseQuery
            ):
                raise TypeError("Please use PhraseQuery for phrase queries.")
        query = self.to_query_object()
        results = self._table._execute_query(query, timeout=timeout)
        results = results.read_all()
        if self._reranker is not None:
            results = self._reranker.rerank_fts(self._query, results)
            check_reranker_result(results)
-        return self._finalize_blob_query_table(results)
+        return results
    def to_batches(
        self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
@@ -2052,9 +1925,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
 class LanceEmptyQueryBuilder(LanceQueryBuilder):
    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
-        return self._finalize_blob_query_table(
+        return self.to_batches(timeout=timeout).read_all()
            self.to_batches(timeout=timeout).read_all()
        )
    def to_query_object(self) -> Query:
        return Query(
@@ -2076,7 +1947,7 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
    def to_batches(
        self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
    ) -> pa.RecordBatchReader:
-        query = self._query_for_scan()
+        query = self.to_query_object()
        return self._table._execute_query(query, batch_size=batch_size, timeout=timeout)
    def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
@@ -2148,13 +2019,14 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
        return vector_query, text_query
-    def phrase_query(self, phrase_query: bool = True) -> LanceHybridQueryBuilder:
+    def phrase_query(self, phrase_query: bool = None) -> LanceHybridQueryBuilder:
        """Set whether to use phrase query.
        Parameters
        ----------
        phrase_query: bool, default True
-            If True, then an unquoted string query will be wrapped in quotes.
+            If True, then the query will be wrapped in quotes and
            double quotes replaced by single quotes.
        Returns
        -------
@@ -2179,25 +2051,15 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
            fts_results = fts_future.result()
            vector_results = vector_future.result()
-        results = self._combine_hybrid_results(
+        return self._combine_hybrid_results(
            fts_results=fts_results,
            vector_results=vector_results,
            norm=self._norm,
            fts_query=self._fts_query._query,
            reranker=self._reranker,
            limit=self._limit,
-            with_row_ids=True,
+            with_row_ids=self._with_row_id,
        )
        return self._finish_hybrid_results(results)
    def _finish_hybrid_results(self, results: pa.Table) -> pa.Table:
        if self._user_requested_row_id():
            return results
        if self._blob_auto_row_id_enabled():
            return self._finalize_blob_query_table(results)
        if "_rowid" in results.column_names:
            return results.drop(["_rowid"])
        return results
    @staticmethod
    def _combine_hybrid_results(
@@ -2638,7 +2500,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
            self._vector_query.ef(self._ef)
        if self._bypass_vector_index:
            self._vector_query.bypass_vector_index()
-        if self._lower_bound is not None or self._upper_bound is not None:
+        if self._lower_bound or self._upper_bound:
            self._vector_query.distance_range(
                lower_bound=self._lower_bound, upper_bound=self._upper_bound
            )
@@ -2668,9 +2530,6 @@ class AsyncQueryBase(object):
        self._with_row_address = None
        self._fragments = None
        self._fragment_ids = None
        self._with_row_id = None
        self._blob_auto_row_id = False
        self._blob_paths: tuple[str, ...] = ()
    def to_query_object(self) -> Query:
        """
@@ -2680,46 +2539,11 @@ class AsyncQueryBase(object):
        python and more easily serializable.
        """
        query = Query.from_inner(self._inner.to_query_request())
        query.with_row_id = self._user_requested_row_id()
        query.with_row_address = self._with_row_address
        query.fragments = self._fragments
        query.fragment_ids = self._fragment_ids
        return query
    def _user_requested_row_id(self) -> bool:
        return self._with_row_id is True
    def _blob_auto_row_id_enabled(self) -> bool:
        return self._blob_auto_row_id
    def _finalize_blob_query_table(self, tbl: pa.Table) -> pa.Table:
        return finalize_blob_query_table(
            tbl,
            user_requested_row_id=self._user_requested_row_id(),
            blob_auto_row_id=self._blob_auto_row_id_enabled(),
            blob_paths=self._blob_paths,
        )
    async def _maybe_add_blob_row_id(self) -> None:
        if self._table is None or not supports_blob_auto_row_id(self._table):
            self._blob_auto_row_id = False
            self._blob_paths = ()
            return
        req = self._inner.to_query_request()
        schema = await self._table.schema()
        self._blob_auto_row_id = blob_auto_row_id_for_scan(
            self._table,
            schema,
            req.select,
            with_row_id=self._with_row_id,
        )
        if not self._blob_auto_row_id:
            self._blob_paths = ()
            return
        self._blob_paths = tuple(blob_v2_projection_sources(schema, req.select).keys())
        self._inner.with_row_id()
    def select(self, columns: Union[List[str], dict[str, str]]) -> Self:
        """
        Return only the specified columns.
@@ -2772,7 +2596,6 @@ class AsyncQueryBase(object):
        """
        Include the _rowid column in the results.
        """
        self._with_row_id = True
        self._inner.with_row_id()
        return self
@@ -2819,7 +2642,6 @@ class AsyncQueryBase(object):
            If not specified, no timeout is applied. If the query does not
            complete within the specified time, an error will be raised.
        """
        await self._maybe_add_blob_row_id()
        return AsyncRecordBatchReader(
            await self._inner.execute(
                max_batch_length=max_batch_length, timeout=timeout
@@ -2850,8 +2672,8 @@ class AsyncQueryBase(object):
            complete within the specified time, an error will be raised.
        """
        batch_iter = await self.to_batches(timeout=timeout)
-        return self._finalize_blob_query_table(
+        return pa.Table.from_batches(
-            pa.Table.from_batches(await batch_iter.read_all(), schema=batch_iter.schema)
+            await batch_iter.read_all(), schema=batch_iter.schema
        )
    async def to_list(self, timeout: Optional[timedelta] = None) -> List[dict]:
@@ -2918,7 +2740,7 @@ class AsyncQueryBase(object):
            Forwarded to pyarrow.Table.to_pandas after query execution and
            optional flattening.
        """
-        validate_blob_mode(blob_mode)
+        _validate_blob_mode(blob_mode)
        if hasattr(self._inner, "output_schema"):
            schema = await self.output_schema()
            if _blob_mode_requires_native_pandas(blob_mode, schema):
@@ -2959,36 +2781,14 @@ class AsyncQueryBase(object):
        if not _query_is_plain_scan(query):
            return None
        schema = await self._table.schema()
        blob_auto_row_id = blob_auto_row_id_for_scan(
            self._table,
            schema,
            query.columns,
            with_row_id=self._with_row_id,
        )
        blob_sources = (
            blob_v2_projection_sources(schema, query.columns)
            if blob_mode == "bytes"
            else {}
        )
        dataset = await self._table._to_lance()
        scanner = dataset.scanner(
-            **_scanner_kwargs_for_query(
+            **_scanner_kwargs_for_query(query, blob_mode, dataset)
                query,
                "descriptions" if blob_sources else blob_mode,
                dataset,
                with_row_id=query.with_row_id or blob_auto_row_id or bool(blob_sources),
            )
        )
        return await _finish_plain_scan_pandas_async(
            scanner,
            blob_mode=blob_mode,
            blob_sources=blob_sources,
            fetch_blobs=self._table.fetch_blobs,
            strip_auto_row_id=blob_auto_row_id,
            flatten=flatten,
            **kwargs,
        )
        if flatten is not None:
            tbl = flatten_columns(_scanner_to_table(scanner), flatten)
            return tbl.to_pandas(**kwargs)
        return _scanner_to_pandas(scanner, blob_mode, **kwargs)
    async def to_polars(
        self,
@@ -3773,24 +3573,9 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
        fts_query = AsyncFTSQuery(self._inner.to_fts_query(), self._table)
        vec_query = AsyncVectorQuery(self._inner.to_vector_query(), self._table)
-        req = fts_query._inner.to_query_request()
+        # save the row ID choice that was made on the query builder and force it
-        blob_auto_row_id = False
+        # to actually fetch the row ids because we need this for reranking
-        blob_paths: tuple[str, ...] = ()
+        with_row_ids = self._inner.get_with_row_id()
        if self._table is not None and supports_blob_auto_row_id(self._table):
            schema = await self._table.schema()
            blob_auto_row_id = blob_auto_row_id_for_scan(
                self._table,
                schema,
                req.select,
                with_row_id=self._with_row_id,
            )
            if blob_auto_row_id:
                blob_paths = tuple(
                    blob_v2_projection_sources(schema, req.select).keys()
                )
        self._blob_auto_row_id = blob_auto_row_id
        self._blob_paths = blob_paths
        fts_query.with_row_id()
        vec_query.with_row_id()
@@ -3806,14 +3591,8 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
            fts_query=fts_query.get_query(),
            reranker=self._reranker,
            limit=self._inner.get_limit(),
-            with_row_ids=True,
+            with_row_ids=with_row_ids,
        )
        if (
            not self._user_requested_row_id()
            and not blob_auto_row_id
            and "_rowid" in result.column_names
        ):
            result = result.drop(["_rowid"])
        return AsyncRecordBatchReader(result, max_batch_length=max_batch_length)
@@ -28,7 +28,6 @@ from lancedb._lancedb import (
    UpdateFieldMetadataResult,
    DeleteResult,
    DropColumnsResult,
    FtsToken,
    IndexConfig,
    LsmWriteSpec,
    MergeResult,
@@ -245,23 +244,6 @@ class RemoteTable(Table):
        """List all the indices on the table"""
        return LOOP.run(self._table.list_indices())
    def tokenize(
        self,
        query: str,
        *,
        column: Optional[str] = None,
        index_name: Optional[str] = None,
    ) -> Iterable[FtsToken]:
        """Tokenize a query using the tokenizer configured on an FTS index.
        Model-backed tokenizers such as ``jieba/*`` and ``lindera/*`` are
        rebuilt in the client process from index metadata, so the same tokenizer
        model files must exist locally.
        """
        return LOOP.run(
            self._table.tokenize(query, column=column, index_name=index_name)
        )
    def index_stats(self, index_uuid: str) -> Optional[IndexStatistics]:
        """List all the stats of a specified index"""
        return LOOP.run(self._table.index_stats(index_uuid))
@@ -930,10 +912,6 @@ class RemoteTable(Table):
        """Not supported on LanceDB Cloud."""
        return LOOP.run(self._table.unset_lsm_write_spec())
    def get_lsm_write_spec(self) -> Optional["LsmWriteSpec"]:
        """Read the installed LsmWriteSpec, or ``None``."""
        return LOOP.run(self._table.get_lsm_write_spec())
    def close_lsm_writers(self) -> None:
        """No-op on LanceDB Cloud (no local shard writers)."""
        return LOOP.run(self._table.close_lsm_writers())
@@ -1012,19 +990,6 @@ class RemoteTable(Table):
            "migrate_v2_manifest_paths() is not supported on the LanceDB Cloud"
        )
    def blob_columns(self) -> list[str]:
        raise NotImplementedError(
            "blob_columns() is not yet supported on the LanceDB Cloud"
        )
    def fetch_blobs(self, column: str, row_ids) -> pa.LargeBinaryArray:
        raise NotImplementedError("fetch_blobs() is not supported on LanceDB Cloud")
    def fetch_blob_files(self, column: str, row_ids):
        raise NotImplementedError(
            "fetch_blob_files() is not supported on LanceDB Cloud"
        )
    def head(self, n=5) -> pa.Table:
        """
        Return the first `n` rows of the table.
@@ -12,7 +12,6 @@ from .rrf import RRFReranker
 from .mrr import MRRReranker
 from .answerdotai import AnswerdotaiRerankers
 from .voyageai import VoyageAIReranker
 from .watsonx import WatsonxReranker
 __all__ = [
    "Reranker",
@@ -26,5 +25,4 @@ __all__ = [
    "AnswerdotaiRerankers",
    "VoyageAIReranker",
    "MRRReranker",
    "WatsonxReranker",
 ]
@@ -1,180 +0,0 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import os
 from functools import cached_property
 from typing import Dict, Optional
 import pyarrow as pa
 from ..util import attempt_import_or_raise
 from .base import Reranker
 DEFAULT_WATSONX_URL = "https://us-south.ml.cloud.ibm.com"
 class WatsonxReranker(Reranker):
    """
    Reranks the results using the IBM watsonx.ai Rerank API.
    Uses the ``ibm_watsonx_ai`` SDK (``Rerank.generate``) under the hood.
    API Docs:
        https://cloud.ibm.com/docs/apis/watsonx-ai#text-rerank
    Supported rerank models:
        https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx#rerank
    Parameters
    ----------
    model_name : str, default "cross-encoder/ms-marco-minilm-l-12-v2"
        The ID of the rerank model to use.
    column : str, default "text"
        The name of the column to use as input to the reranker.
    top_n : int, optional
        Return only the top-n results. If ``None``, all results are returned.
    return_score : str, default "relevance"
        Options are ``"relevance"`` or ``"all"``.
    api_key : str, optional
        IBM Cloud API key.  Falls back to the ``WATSONX_API_KEY`` environment
        variable when not provided.
    project_id : str, optional
        watsonx.ai project ID.  Falls back to the ``WATSONX_PROJECT_ID``
        environment variable when not provided.  Mutually exclusive with
        ``space_id`` — exactly one must be supplied.
    space_id : str, optional
        watsonx.ai deployment space ID.  Falls back to the ``WATSONX_SPACE_ID``
        environment variable when not provided.  Mutually exclusive with
        ``project_id`` — exactly one must be supplied.
    url : str, optional
        watsonx.ai service URL.  Defaults to
        ``"https://us-south.ml.cloud.ibm.com"``.
    truncate_input_tokens : int, optional
        Truncate each input to this many tokens before scoring.  Passed
        directly to the ``parameters`` dict of ``Rerank.generate``.
    """
    def __init__(
        self,
        model_name: str = "cross-encoder/ms-marco-minilm-l-12-v2",
        column: str = "text",
        top_n: Optional[int] = None,
        return_score: str = "relevance",
        api_key: Optional[str] = None,
        project_id: Optional[str] = None,
        space_id: Optional[str] = None,
        url: Optional[str] = None,
        truncate_input_tokens: Optional[int] = None,
    ):
        super().__init__(return_score)
        self.model_name = model_name
        self.column = column
        self.top_n = top_n
        self.api_key = api_key
        self.project_id = project_id
        self.space_id = space_id
        self.url = url
        self.truncate_input_tokens = truncate_input_tokens
    def __str__(self) -> str:
        return f"WatsonxReranker(model_name={self.model_name})"
    @cached_property
    def _client(self):
        ibm_watsonx_ai = attempt_import_or_raise("ibm_watsonx_ai")
        ibm_watsonx_ai_foundation_models = attempt_import_or_raise(
            "ibm_watsonx_ai.foundation_models"
        )
        # --- credentials ---
        api_key = self.api_key or os.environ.get("WATSONX_API_KEY")
        if not api_key:
            raise ValueError(
                "WATSONX_API_KEY not set. Either set it in your environment or "
                "pass it as `api_key` argument to WatsonxReranker."
            )
        credentials = ibm_watsonx_ai.Credentials(
            api_key=api_key,
            url=self.url or DEFAULT_WATSONX_URL,
        )
        # --- project_id / space_id (exactly one required) ---
        project_id = self.project_id or os.environ.get("WATSONX_PROJECT_ID")
        space_id = self.space_id or os.environ.get("WATSONX_SPACE_ID")
        if project_id and space_id:
            raise ValueError("Provide either `project_id` or `space_id`, not both.")
        if not project_id and not space_id:
            raise ValueError(
                "Either WATSONX_PROJECT_ID or WATSONX_SPACE_ID must be set. "
                "Pass one as an argument to WatsonxReranker or set the corresponding "
                "environment variable."
            )
        kwargs: Dict = dict(model_id=self.model_name, credentials=credentials)
        if project_id:
            kwargs["project_id"] = project_id
        else:
            kwargs["space_id"] = space_id
        return ibm_watsonx_ai_foundation_models.Rerank(**kwargs)
    def _build_params(self) -> Dict:
        """Build the ``parameters`` dict forwarded to ``Rerank.generate``."""
        return_options: Dict = {"inputs": True}
        if self.top_n is not None:
            return_options["top_n"] = self.top_n
        params: Dict = {"return_options": return_options}
        if self.truncate_input_tokens is not None:
            params["truncate_input_tokens"] = self.truncate_input_tokens
        return params
    def _rerank(self, result_set: pa.Table, query: str) -> pa.Table:
        result_set = self._handle_empty_results(result_set)
        if len(result_set) == 0:
            return result_set
        docs = result_set[self.column].to_pylist()
        response = self._client.generate(
            query=query,
            inputs=docs,
            params=self._build_params(),
        )
        results = response["results"]
        indices, scores = zip(
            *[(result["index"], result["score"]) for result in results]
        )
        result_set = result_set.take(list(indices))
        result_set = result_set.append_column(
            "_relevance_score", pa.array(scores, type=pa.float32())
        )
        return result_set
    def rerank_hybrid(
        self,
        query: str,
        vector_results: pa.Table,
        fts_results: pa.Table,
    ) -> pa.Table:
        if self.score == "all":
            combined_results = self._merge_and_keep_scores(vector_results, fts_results)
        else:
            combined_results = self.merge_results(vector_results, fts_results)
        combined_results = self._rerank(combined_results, query)
        if self.score == "relevance":
            combined_results = self._keep_relevance_score(combined_results)
        return combined_results
    def rerank_vector(self, query: str, vector_results: pa.Table) -> pa.Table:
        vector_results = self._rerank(vector_results, query)
        if self.score == "relevance":
            vector_results = vector_results.drop_columns(["_distance"])
        return vector_results
    def rerank_fts(self, query: str, fts_results: pa.Table) -> pa.Table:
        fts_results = self._rerank(fts_results, query)
        if self.score == "relevance":
            fts_results = fts_results.drop_columns(["_score"])
        return fts_results
@@ -2,134 +2,10 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
-"""Schema helpers for Lance blob columns."""
+"""Schema related utilities."""
 import pyarrow as pa
 _BLOB_EXTENSION_NAME = "lance.blob.v2"
 _BLOB_V1_KEY = "lance-encoding:blob"
 _ARROW_EXT_NAME_KEY = "ARROW:extension:name"
 class BlobType(pa.ExtensionType):
    """PyArrow extension type for a Lance blob v2 column.
    Queries return descriptors; call :meth:`~lancedb.table.Table.fetch_blob_files`
    for lazy reads or :meth:`~lancedb.table.Table.fetch_blobs` for eager bytes.
    """
    def __init__(self) -> None:
        storage_type = pa.struct(
            [
                pa.field("data", pa.large_binary(), nullable=True),
                pa.field("uri", pa.utf8(), nullable=True),
                pa.field("position", pa.uint64(), nullable=True),
                pa.field("size", pa.uint64(), nullable=True),
            ]
        )
        super().__init__(storage_type, _BLOB_EXTENSION_NAME)
    def __arrow_ext_serialize__(self) -> bytes:
        return b""
    @classmethod
    def __arrow_ext_deserialize__(
        cls, storage_type: pa.DataType, serialized: bytes
    ) -> "BlobType":
        return cls()
    def __reduce__(self):
        # Ensure pickle round-trips on older pyarrow (apache/arrow#35599).
        return type(self).__arrow_ext_deserialize__, (
            self.storage_type,
            self.__arrow_ext_serialize__(),
        )
 try:
    pa.register_extension_type(BlobType())  # type: ignore[arg-type]
 except pa.ArrowKeyError:
    pass
 def _metadata_value(metadata: dict, key: str):
    return metadata.get(key.encode()) or metadata.get(key)
 def _metadata_marks_blob_v2(metadata: dict) -> bool:
    if not metadata:
        return False
    extension_name = _metadata_value(metadata, _ARROW_EXT_NAME_KEY)
    return extension_name in (_BLOB_EXTENSION_NAME, _BLOB_EXTENSION_NAME.encode())
 def _metadata_marks_legacy_blob(metadata: dict) -> bool:
    if not metadata:
        return False
    return _metadata_value(metadata, _BLOB_V1_KEY) in ("true", b"true")
 def is_blob_v2_field(field: pa.Field) -> bool:
    """Return True if `field` declares a blob v2 extension column."""
    field_type = field.type
    if (
        isinstance(field_type, pa.ExtensionType)
        and field_type.extension_name == _BLOB_EXTENSION_NAME
    ):
        return True
    return _metadata_marks_blob_v2(field.metadata or {})
 def is_blob_like_field(field: pa.Field) -> bool:
    """Blob detection for ``to_pandas(blob_mode=...)`` and scanner paths only.
    Matches v2 extension fields on table schema, legacy ``lance-encoding:blob``
    storage columns, and v2 query descriptor fields (the engine tags those with
    the same metadata). Not used for fetch or auto ``_rowid``.
    """
    return is_blob_v2_field(field) or _metadata_marks_legacy_blob(field.metadata or {})
 def _collect_blob_paths(schema: pa.Schema, is_blob) -> list[str]:
    paths: list[str] = []
    def walk(fields, prefix: str) -> None:
        for field in fields:
            path = f"{prefix}.{field.name}" if prefix else field.name
            if is_blob(field):
                paths.append(path)
            elif pa.types.is_struct(field.type):
                walk(field.type, path)
            elif (
                pa.types.is_list(field.type)
                or pa.types.is_large_list(field.type)
                or pa.types.is_fixed_size_list(field.type)
            ):
                walk([field.type.value_field], path)
    walk(schema, "")
    return paths
 def blob_column_paths(schema: pa.Schema) -> list[str]:
    """Dotted paths of blob-like columns (v2 extension or legacy metadata)."""
    return _collect_blob_paths(schema, is_blob_like_field)
 def blob_v2_column_paths(schema: pa.Schema) -> list[str]:
    return _collect_blob_paths(schema, is_blob_v2_field)
 def schema_has_blob_field(schema: pa.Schema) -> bool:
    return bool(blob_column_paths(schema))
 def blob(name: str, nullable: bool = True) -> pa.Field:
    """Create a Lance blob v2 column field."""
    return pa.field(name, BlobType(), nullable=nullable)
 def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataType:
    """A help function to create a vector type.
@@ -1,607 +0,0 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 """Elastic streaming dataloader for PyTorch.
 Provides StreamingDataset, a PyTorch IterableDataset that guarantees:
 - **Elastic determinism**: for a fixed (num_splits, shuffle_seed, epoch) the set
  of samples that forms each global training step is identical regardless of
  world_size or num_workers.
 - **Resumability**: state_dict / load_state_dict capture per-split consumption
  counts so training can resume from an exact mid-epoch position even when the
  distributed topology changes between runs.
 """
 import ctypes
 import logging
 import os
 import random
 import threading
 import time
 from collections import deque
 from concurrent.futures import ThreadPoolExecutor
 from multiprocessing import RawArray
 from typing import Any, Callable, Iterator, Optional
 from torch.utils.data import IterableDataset, get_worker_info
 from .permutation import (
    Permutation,
    Transforms,
    permutation_builder,
    _table_from_pickle_state,
    _table_to_pickle_state,
 )
 logger = logging.getLogger(__name__)
 # Multiplier used to combine shuffle_seed and epoch into a single permutation
 # seed.  Chosen to be a large prime so different (seed, epoch) pairs produce
 # distinct seeds for any practically encountered epoch count.
 _EPOCH_PRIME = 100003
 DEFAULT_READ_BATCH_SIZE = 64
 DEFAULT_PREFETCH_BATCHES = 4
 class StreamingDataset(IterableDataset):
    """An elastic, resumable PyTorch IterableDataset backed by a LanceDB table.
    The table is partitioned into ``num_splits`` fixed splits using a
    deterministic random shuffle controlled by ``shuffle_seed`` and ``epoch``.
    Each rank is assigned a contiguous block of splits, and within a rank each
    DataLoader worker is assigned a contiguous sub-block.  Samples are yielded
    by round-robining over the assigned splits, one sample per split per cycle.
    Internally ``__iter__`` runs a two-stage pipeline:
    - **Stage 1 (I/O)**: one thread pool with ``num_splits * prefetch_batches``
      workers fetches raw ``RecordBatch`` objects from LanceDB in parallel
      across all splits and places them in a per-split raw-batch queue.
    - **Stage 2 (transform)**: a second thread pool with ``os.cpu_count()``
      workers picks up raw batches, applies the transform, and places the
      results in a per-split cooked-row queue.
    The main thread round-robins over the cooked queues, yielding one row per
    split per cycle.
    Parameters
    ----------
    table:
        LanceDB table to stream from.
    num_splits:
        Number of fixed splits to partition the table into.  Must be divisible
        by ``world_size``.  When used with DataLoader workers it must also be
        divisible by ``world_size * num_workers``.  Defaults to ``world_size``.
        If the row count (after any ``filter``) is not evenly divisible by
        ``num_splits``, the surplus rows — at most ``num_splits - 1`` per epoch
        — are silently dropped to keep all splits the same length.
    shuffle:
        Whether to randomly assign rows to splits.  When ``True`` (the
        default) rows are shuffled using ``shuffle_seed`` and ``epoch``.
        When ``False`` rows are divided into splits sequentially in storage
        order, which can be useful for deterministic debugging or evaluation.
    shuffle_seed:
        Base seed for the random permutation.  Combined with ``epoch`` so
        each epoch produces a different ordering.  Pass ``None`` to generate
        a random seed at construction time.
    epoch:
        Current training epoch.  Combined with ``shuffle_seed`` so that each
        epoch produces a different sample ordering.
    rank:
        This process's rank in the distributed training group.
    world_size:
        Total number of processes in the distributed training group.
    read_batch_size:
        Number of rows fetched from each split in a single ``take_offsets``
        call.  Larger values amortise per-request overhead (critical on object
        storage) at the cost of higher memory usage per split buffer.  Defaults
        to ``DEFAULT_READ_BATCH_SIZE`` (64).
    prefetch_batches:
        Number of I/O batches to keep in flight per split.  Higher values
        overlap storage latency with transform and training compute at the cost
        of more memory and threads.  Defaults to ``DEFAULT_PREFETCH_BATCHES``
        (4).
    columns:
        Optional list of column names to read.  When set, only those columns
        are fetched from storage; all others are omitted.  ``None`` (the
        default) reads every column.
    shuffle_clump_size:
        When set, rows are shuffled in contiguous groups of this size rather
        than individually.  Larger clumps improve I/O locality (important on
        object storage) at the cost of reduced randomness.  ``None`` (the
        default) shuffles rows individually.
    filter:
        Optional SQL filter expression (e.g. ``"label = 'dog'"``).  Only rows
        that satisfy the predicate are included in the permutation.  The filter
        is applied during permutation construction so split sizes reflect the
        filtered row count.
    transform:
        Optional callable applied to each ``pyarrow.RecordBatch`` before rows
        are yielded.  Receives one batch at a time and must return an iterable
        whose length equals the number of rows in the batch.  When ``None``
        (the default) rows are returned as plain Python dicts.
    worker_info_override:
        If set, used in place of ``torch.utils.data.get_worker_info()`` to
        determine the DataLoader worker assignment.  Intended for unit tests
        that need to simulate multiple workers without spawning real processes.
        If both this and the real worker info are non-None a warning is logged
        and the override takes precedence.
    """
    def __init__(
        self,
        table,
        *,
        num_splits: Optional[int] = None,
        shuffle: bool = True,
        shuffle_seed: Optional[int] = 0,
        epoch: int = 0,
        rank: int = 0,
        world_size: int = 1,
        read_batch_size: int = DEFAULT_READ_BATCH_SIZE,
        prefetch_batches: int = DEFAULT_PREFETCH_BATCHES,
        columns: Optional[list[str]] = None,
        shuffle_clump_size: Optional[int] = None,
        filter: Optional[str] = None,
        transform: Optional[Callable] = None,
        connection_factory: Optional[Callable[[str], Any]] = None,
        worker_info_override=None,
    ):
        super().__init__()
        if num_splits is None:
            num_splits = world_size
        if shuffle_seed is None:
            shuffle_seed = random.randrange(2**32)
        if num_splits % world_size != 0:
            raise ValueError(
                f"num_splits ({num_splits}) must be divisible by "
                f"world_size ({world_size})"
            )
        self._table = table
        self._num_splits = num_splits
        self._shuffle = shuffle
        self._shuffle_seed = shuffle_seed
        self._epoch = epoch
        self._rank = rank
        self._world_size = world_size
        self._read_batch_size = read_batch_size
        self._prefetch_batches = prefetch_batches
        self._columns = columns
        self._shuffle_clump_size = shuffle_clump_size
        self._filter = filter
        self._transform = transform
        self._connection_factory = connection_factory
        self._worker_info_override = worker_info_override
        # Live references to pipeline state, set only while __iter__ is running
        # in the same process.  Used by the observability properties when the
        # DataLoader runs with num_workers=0.
        self._raw_batches_ref: Optional[list[deque]] = None
        self._cooked_ref: Optional[list[deque]] = None
        self._fetch_head_ref: Optional[list[int]] = None
        self._split_sizes_ref: Optional[list[int]] = None
        self._local_consumed_ref: Optional[list[int]] = None
        # Shared-memory counters written by __iter__ (which may run in a
        # DataLoader worker process) and read by the observability properties
        # in the main process.  RawArray is picklable via the forkserver
        # reduction protocol so it survives the dataset pickle round-trip.
        # Layout: [unscanned_rows, raw_rows, cooked_rows, consumed_rows,
        #          bytes_loaded, fetch_time_us, transform_time_us]
        self._worker_stats: RawArray = RawArray(ctypes.c_int64, 7)
        # Cumulative bytes of Arrow buffer data fetched across all iterations.
        self._bytes_loaded: int = 0
        # Cumulative seconds spent in LanceDB I/O and in transform functions.
        self._fetch_time: float = 0.0
        self._transform_time: float = 0.0
        # Number of samples each split has already been consumed.  At global
        # step boundaries all splits have consumed this many samples, so a
        # single scalar captures the topology-independent checkpoint state.
        self._resume_offset: int = 0
        # Build the permutation table once, deterministically.
        builder = permutation_builder(table)
        if filter is not None:
            builder = builder.filter(filter)
        if shuffle:
            perm_seed = shuffle_seed + epoch * _EPOCH_PRIME
            self._perm_table = builder.split_random(
                fixed=num_splits, seed=perm_seed, clump_size=shuffle_clump_size
            ).execute()
        else:
            self._perm_table = builder.split_sequential(fixed=num_splits).execute()
        # Contiguous block of global split indices assigned to this rank.
        splits_per_rank = num_splits // world_size
        rank_start = rank * splits_per_rank
        self._rank_splits: list[int] = list(
            range(rank_start, rank_start + splits_per_rank)
        )
    def _resolve_my_splits(self) -> list[int]:
        """Return the split indices this instance should read in __iter__."""
        torch_worker_info = get_worker_info()
        if self._worker_info_override is not None:
            if torch_worker_info is not None:
                logger.warning(
                    "worker_info_override is set but get_worker_info() also returned a "
                    "non-None value; ignoring the real torch worker info and using the "
                    "override instead.  This may lead to duplicated or incorrect data "
                    "from the dataset."
                )
            worker_info = self._worker_info_override
        else:
            worker_info = torch_worker_info
        if worker_info is None:
            return self._rank_splits
        num_workers: int = worker_info.num_workers
        worker_id: int = worker_info.id
        n_rank_splits = len(self._rank_splits)
        if n_rank_splits % num_workers != 0:
            raise ValueError(
                f"Number of rank splits ({n_rank_splits}) must be divisible by "
                f"num_workers ({num_workers})"
            )
        splits_per_worker = n_rank_splits // num_workers
        start = worker_id * splits_per_worker
        return self._rank_splits[start : start + splits_per_worker]
    def __iter__(self) -> Iterator[dict[str, Any]]:
        if self._raw_batches_ref is not None:
            raise RuntimeError(
                "StreamingDataset does not support concurrent iteration. "
                "Only one active iterator per dataset instance is allowed."
            )
        my_splits = self._resolve_my_splits()
        if not my_splits:
            return
        # Set identity transform on each Permutation so __getitems__ returns
        # the raw RecordBatch.  Stage 2 applies the real transform.
        permutations: list[Permutation] = []
        for split_idx in my_splits:
            perm = Permutation.from_tables(
                self._table, self._perm_table, split=split_idx
            )
            if self._columns is not None:
                perm = perm.select_columns(self._columns)
            perm = perm.with_transform(lambda batch: batch)
            if self._resume_offset > 0:
                perm = perm.with_skip(self._resume_offset)
            permutations.append(perm)
        n = len(permutations)
        split_sizes = [perm.num_rows for perm in permutations]
        initial_offset = self._resume_offset
        local_consumed = [0] * n
        batch_size = self._read_batch_size
        max_prefetch = self._prefetch_batches
        cpu_workers = os.cpu_count() or 1
        final_transform = (
            self._transform if self._transform is not None else Transforms.arrow2python
        )
        # Per-split pipeline state.
        fetch_head = [0] * n
        io_pending = [deque() for _ in range(n)]  # Future[RecordBatch]
        raw_batches = [deque() for _ in range(n)]  # RecordBatch — fetched, awaiting tx
        tx_pending = [deque() for _ in range(n)]  # Future[list[Any]]
        cooked = [deque() for _ in range(n)]  # rows ready to yield
        # Limit simultaneous transforms to cpu_workers across all splits.
        tx_semaphore = threading.Semaphore(cpu_workers)
        # ── Stage 1 helpers ───────────────────────────────────────────────────
        def _io_call(perm, indices):
            t0 = time.perf_counter()
            batch = perm.__getitems__(indices)
            self._bytes_loaded += batch.nbytes
            self._fetch_time += time.perf_counter() - t0
            return batch
        def _submit_io(i: int) -> None:
            remaining = split_sizes[i] - fetch_head[i]
            if remaining <= 0:
                return
            fetch = min(batch_size, remaining)
            start = fetch_head[i]
            fetch_head[i] += fetch
            perm_i = permutations[i]
            indices = list(range(start, start + fetch))
            io_pending[i].append(io_pool.submit(_io_call, perm_i, indices))
        def _fill_io(i: int) -> None:
            while len(io_pending[i]) < max_prefetch and fetch_head[i] < split_sizes[i]:
                _submit_io(i)
        def _drain_io(i: int) -> None:
            """Move completed I/O futures into raw_batches non-blockingly."""
            while io_pending[i] and io_pending[i][0].done():
                raw_batches[i].append(io_pending[i].popleft().result())
        # ── Stage 2 helpers ───────────────────────────────────────────────────
        def _tx_call_guarded(batch):
            try:
                t0 = time.perf_counter()
                result = final_transform(batch)
                self._transform_time += time.perf_counter() - t0
                return result
            finally:
                tx_semaphore.release()
        def _try_submit_tx(i: int) -> None:
            """Submit transforms for raw_batches[i] up to available capacity."""
            while raw_batches[i] and tx_semaphore.acquire(blocking=False):
                batch = raw_batches[i].popleft()
                tx_pending[i].append(tx_pool.submit(_tx_call_guarded, batch))
        def _drain_tx(i: int) -> None:
            """Move completed transform futures into cooked non-blockingly."""
            while tx_pending[i] and tx_pending[i][0].done():
                cooked[i].extend(tx_pending[i].popleft().result())
        # ── Combined advance ──────────────────────────────────────────────────
        def _advance(i: int) -> None:
            """Non-blocking pipeline pump for split i."""
            _drain_io(i)
            _drain_tx(i)
            _try_submit_tx(i)
            _fill_io(i)
        def _ensure_cooked(i: int) -> None:
            """Ensure cooked[i] has at least one row, blocking if necessary."""
            _advance(i)
            while not cooked[i]:
                if tx_pending[i]:
                    # Wait for the oldest in-flight transform.
                    cooked[i].extend(tx_pending[i].popleft().result())
                    _advance(i)
                elif raw_batches[i]:
                    # Acquire a transform slot (may block briefly if all
                    # cpu_workers are busy with other splits).
                    tx_semaphore.acquire()
                    batch = raw_batches[i].popleft()
                    tx_pending[i].append(tx_pool.submit(_tx_call_guarded, batch))
                elif io_pending[i]:
                    # Block on the oldest in-flight I/O fetch.
                    raw_batches[i].append(io_pending[i].popleft().result())
                    _advance(i)
                else:
                    break  # split exhausted
        # ── Main loop ─────────────────────────────────────────────────────────
        with ThreadPoolExecutor(max_workers=n * max_prefetch) as io_pool:
            with ThreadPoolExecutor(max_workers=cpu_workers) as tx_pool:
                self._raw_batches_ref = raw_batches
                self._cooked_ref = cooked
                self._fetch_head_ref = fetch_head
                self._split_sizes_ref = split_sizes
                self._local_consumed_ref = local_consumed
                try:
                    for i in range(n):
                        _fill_io(i)
                    while True:
                        # Stop when any split is exhausted (all exhaust
                        # simultaneously: equal split sizes + round-robin).
                        if any(local_consumed[i] >= split_sizes[i] for i in range(n)):
                            break
                        for i in range(n):
                            _ensure_cooked(i)
                            row = cooked[i].popleft()
                            local_consumed[i] += 1
                            _advance(i)
                            # After the last split in each cycle: update the
                            # global offset and refresh the shared-memory stats
                            # so the main process can observe pipeline depth
                            # even when __iter__ runs in a worker process.
                            if i == n - 1:
                                self._resume_offset = initial_offset + local_consumed[i]
                                ws = self._worker_stats
                                ws[0] = sum(
                                    split_sizes[j] - fetch_head[j] for j in range(n)
                                )
                                ws[1] = sum(
                                    batch.num_rows for q in raw_batches for batch in q
                                )
                                ws[2] = sum(len(q) for q in cooked)
                                ws[3] = sum(local_consumed)
                                ws[4] = self._bytes_loaded
                                ws[5] = int(self._fetch_time * 1_000_000)
                                ws[6] = int(self._transform_time * 1_000_000)
                            yield row
                finally:
                    self._raw_batches_ref = None
                    self._cooked_ref = None
                    self._fetch_head_ref = None
                    self._split_sizes_ref = None
                    self._local_consumed_ref = None
    @property
    def bytes_loaded(self) -> int:
        """Cumulative bytes of raw Arrow buffer data fetched from storage.
        Measured on the ``RecordBatch`` before any transform is applied, so
        the value reflects actual I/O rather than the size of transformed
        output.  Accumulates across multiple iterations of the same dataset
        instance and is never reset automatically.
        """
        if self._raw_batches_ref is not None:
            return self._bytes_loaded
        return int(self._worker_stats[4])
    @property
    def fetch_time(self) -> float:
        """Cumulative seconds spent waiting for data from LanceDB.
        Measured per batch in the Stage 1 I/O threads as the total elapsed
        time of the ``take_offsets`` call.  Accumulates across all splits and
        all iterations.
        """
        if self._raw_batches_ref is not None:
            return self._fetch_time
        return self._worker_stats[5] / 1_000_000
    @property
    def transform_time(self) -> float:
        """Cumulative seconds spent applying the transform.
        Measured per batch in the Stage 2 transform threads as the elapsed
        time inside the transform callable (or the default ``arrow2python``
        conversion when no transform is set).  Accumulates across all splits
        and all iterations.
        """
        if self._raw_batches_ref is not None:
            return self._transform_time
        return self._worker_stats[6] / 1_000_000
    @property
    def raw_queue_depth(self) -> int:
        """Number of raw rows waiting for a transform thread across all splits.
        A persistently non-zero value means Stage 2 (transform) is the
        bottleneck: I/O is completing faster than transforms can consume
        batches.  Returns 0 when not iterating.
        """
        if self._raw_batches_ref is not None:
            return sum(batch.num_rows for q in self._raw_batches_ref for batch in q)
        return int(self._worker_stats[1])
    @property
    def prefetch_queue_depth(self) -> int:
        """Number of rows transformed and ready to yield across all splits.
        Counts rows whose transform has completed and are sitting in memory
        waiting for the main thread — rows that can be handed off with no
        I/O or CPU wait.  Returns 0 when not iterating.
        """
        if self._cooked_ref is not None:
            return sum(len(q) for q in self._cooked_ref)
        return int(self._worker_stats[2])
    @property
    def unscanned_rows(self) -> int:
        """Number of rows not yet submitted to the I/O stage across all splits.
        Decreases as the I/O stage submits fetch requests.  When this reaches
        zero all data has been requested from storage (though it may not have
        arrived yet).  Returns 0 when not iterating.
        """
        if self._fetch_head_ref is not None:
            return sum(
                size - head
                for size, head in zip(self._split_sizes_ref, self._fetch_head_ref)
            )
        return int(self._worker_stats[0])
    @property
    def consumed_rows(self) -> int:
        """Number of rows already yielded to the caller across all splits.
        Monotonically increases throughout iteration.  Returns 0 when not
        iterating.
        """
        if self._local_consumed_ref is not None:
            return sum(self._local_consumed_ref)
        return int(self._worker_stats[3])
    def __getstate__(self):
        """Support pickling for multi-worker DataLoader (forkserver / spawn).
        The live LanceDB table object contains non-picklable connection state
        (sockets, Rust-backed PyO3 objects).  If a ``connection_factory`` was
        supplied only the table name is serialised; the factory is called in
        the worker to reopen the connection without embedding any credentials.
        Without a factory the table's own picklable reopen state is captured
        via ``_table_to_pickle_state`` (mirrors the ``Permutation`` approach).
        """
        state = self.__dict__.copy()
        # _table: replace with reconnect info (credentials must not be embedded).
        state["_table_name"] = self._table.name
        if self._connection_factory is not None:
            state["_table"] = None
        else:
            state["_table"] = _table_to_pickle_state(self._table)
        # _perm_table: always in-memory; serialise as Arrow data (mirrors
        # how Permutation.__getstate__ handles its permutation_table).
        state["_perm_table"] = (
            self._perm_table.name,
            self._perm_table.to_arrow(),
        )
        for key in (
            "_raw_batches_ref",
            "_cooked_ref",
            "_fetch_head_ref",
            "_split_sizes_ref",
            "_local_consumed_ref",
        ):
            state[key] = None
        return state
    def __setstate__(self, state):
        """Reconnect to LanceDB after unpickling in a worker process."""
        from . import connect as _connect
        table_name = state.pop("_table_name")
        table_state = state.pop("_table")
        perm_name, perm_data = state.pop("_perm_table")
        self.__dict__.update(state)
        if self._connection_factory is not None:
            self._table = self._connection_factory(table_name)
        else:
            self._table = _table_from_pickle_state(table_state)
        self._perm_table = _connect("memory://").create_table(perm_name, perm_data)
    def state_dict(self) -> dict:
        """Snapshot the dataset's consumption state.
        The returned dict is topology-independent: at global step boundaries
        every split has been consumed the same number of times (by the
        round-robin design), so the per-split count is a single uniform value
        that is identical across all ranks and DataLoader workers.
        """
        return {
            "shuffle_seed": self._shuffle_seed,
            "num_splits": self._num_splits,
            "epoch": self._epoch,
            "samples_consumed_per_split": [self._resume_offset] * self._num_splits,
        }
    def load_state_dict(self, state: dict) -> None:
        """Resume from a previously snapshotted state.
        Raises ``ValueError`` if ``num_splits`` or ``shuffle_seed`` differ
        from the checkpoint, since a different split structure or shuffle order
        makes mid-epoch resumption meaningless.
        """
        if state["num_splits"] != self._num_splits:
            raise ValueError(
                f"num_splits mismatch: checkpoint has {state['num_splits']}, "
                f"current dataset has {self._num_splits}"
            )
        if state["shuffle_seed"] != self._shuffle_seed:
            raise ValueError(
                f"shuffle_seed mismatch: checkpoint has {state['shuffle_seed']}, "
                f"current dataset has {self._shuffle_seed}"
            )
        consumed = state["samples_consumed_per_split"]
        # All entries are equal at step boundaries; use the first.
        if isinstance(consumed, list):
            self._resume_offset = consumed[0] if consumed else 0
        else:
            self._resume_offset = int(consumed)
@@ -29,14 +29,6 @@ from urllib.parse import urlparse
 from lancedb.scannable import _register_optional_converters, to_scannable
 from . import __version__
 from ._blob import (
    BlobFile,
    _normalize_blob_row_ids,
    _wrap_blob_files,
    strip_auto_row_ids,
    validate_blob_mode,
 )
 from .types import BlobMode
 from lancedb.arrow import peek_reader
 from lancedb.background_loop import LOOP, embedding_executor
 from .dependencies import (
@@ -96,7 +88,10 @@ from .util import (
    value_to_sql,
 )
 from .index import lang_mapping
-from .schema import blob_v2_column_paths, schema_has_blob_field
+
 BlobMode = Literal["lazy", "bytes", "descriptions"]
 _VALID_BLOB_MODES = ("lazy", "bytes", "descriptions")
 def _should_push_down_query_table(
@@ -105,6 +100,23 @@ def _should_push_down_query_table(
    return namespace_client is not None and "QueryTable" in pushdown_operations
 def _validate_blob_mode(blob_mode: BlobMode) -> None:
    if blob_mode not in _VALID_BLOB_MODES:
        modes = ", ".join(repr(mode) for mode in _VALID_BLOB_MODES)
        raise ValueError(f"blob_mode must be one of {modes}, got {blob_mode!r}")
 def _field_is_blob(field: pa.Field) -> bool:
    metadata = field.metadata or {}
    return metadata.get(b"lance-encoding:blob") == b"true" or (
        metadata.get("lance-encoding:blob") == "true"
    )
 def _schema_has_blob_field(schema: pa.Schema) -> bool:
    return any(_field_is_blob(field) for field in schema)
 _MODEL_BACKED_TOKENIZER_PREFIXES = ("jieba", "lindera")
 _MODEL_BACKED_TOKENIZER_ERRORS = (
    "unknown base tokenizer",
@@ -173,7 +185,6 @@ if TYPE_CHECKING:
        UpdateFieldMetadataResult,
        DeleteResult,
        DropColumnsResult,
        FtsToken,
        LsmWriteSpec,
        MergeResult,
        UpdateResult,
@@ -640,16 +651,6 @@ def _append_vector_columns(
                    col_data = func.compute_source_embeddings_with_retry(
                        batch[conf.source_column]
                    )
                    # Replace vectors with wrong length (including empty lists
                    # returned for inputs like empty strings) with None so that
                    # _handle_bad_vectors can process them according to the
                    # on_bad_vectors policy instead of crashing when PyArrow
                    # tries to cast them into a fixed-size list array.
                    expected_ndims = conf.function.ndims()
                    col_data = [
                        v if v is not None and len(v) == expected_ndims else None
                        for v in col_data
                    ]
                    if no_vector_column:
                        batch = batch.append_column(
                            schema.field(vector_column),
@@ -1148,8 +1149,6 @@ class Table(ABC):
            - "whitespace": Split text by whitespace, but not punctuation.
            - "raw": No tokenization. The entire text is treated as a single token.
            - "ngram": N-Gram tokenizer.
            - "icu": ICU dictionary-based word segmentation.
            - "icu/split": ICU segmentation with simple-style delimiter splitting.
            - "jieba/*": Jieba tokenizer loaded from Lance's language model home.
            - "lindera/*": Lindera tokenizer loaded from Lance's language model home.
        language : str, default "English"
@@ -1514,31 +1513,6 @@ class Table(ABC):
            A query object that can be executed to get the rows.
        """
    @abstractmethod
    def blob_columns(self) -> list[str]:
        """Names of the blob v2 columns declared on this table."""
    @abstractmethod
    def fetch_blobs(
        self, column: str, row_ids: Union[list[int], pa.Table]
    ) -> pa.LargeBinaryArray:
        """Materialize full blob bytes for ``column`` at the given rows.
        Convenience for small payloads. For large values use
        :meth:`fetch_blob_files`.
        """
    @abstractmethod
    def fetch_blob_files(
        self, column: str, row_ids: Union[list[int], pa.Table]
    ) -> "list[Optional[BlobFile]]":
        """Open lazy, seekable :class:`~lancedb._blob.BlobFile` handles.
        Prefer this over :meth:`fetch_blobs` for large payloads. ``row_ids`` is
        a ``list[int]`` or query ``pyarrow.Table`` with ``_rowid`` (or stashed
        row-id metadata). Null rows are ``None``. Local tables only.
        """
    @abstractmethod
    def _execute_query(
        self,
@@ -1802,24 +1776,6 @@ class Table(ABC):
        [Table.create_index][lancedb.table.Table.create_index]
        """
    @abstractmethod
    def tokenize(
        self,
        query: str,
        *,
        column: Optional[str] = None,
        index_name: Optional[str] = None,
    ) -> Iterable[FtsToken]:
        """
        Tokenize a query using the tokenizer configured on an FTS index.
        Specify exactly one of ``column`` or ``index_name``.
        Model-backed tokenizers such as ``jieba/*`` and ``lindera/*`` are
        rebuilt in the client process from index metadata. For remote tables,
        this means the same tokenizer model files must also exist locally.
        """
    @abstractmethod
    def index_stats(self, index_name: str) -> Optional[IndexStatistics]:
        """
@@ -2238,19 +2194,6 @@ class LanceTable(Table):
    def take_row_ids(self, row_ids: list[int]) -> LanceTakeQueryBuilder:
        return LanceTakeQueryBuilder(self._table.take_row_ids(row_ids))
    def blob_columns(self) -> list[str]:
        return LOOP.run(self._table.blob_columns())
    def fetch_blobs(
        self, column: str, row_ids: Union[list[int], pa.Table]
    ) -> pa.LargeBinaryArray:
        return LOOP.run(self._table.fetch_blobs(column, row_ids))
    def fetch_blob_files(
        self, column: str, row_ids: Union[list[int], pa.Table]
    ) -> "list[Optional[BlobFile]]":
        return LOOP.run(self._table.fetch_blob_files(column, row_ids))
    @property
    def tags(self) -> Tags:
        """Tag management for the table.
@@ -2446,14 +2389,9 @@ class LanceTable(Table):
        -------
        pd.DataFrame
        """
-        validate_blob_mode(blob_mode)
+        _validate_blob_mode(blob_mode)
-        if blob_mode == "descriptions" or not schema_has_blob_field(self.schema):
+        if blob_mode == "descriptions" or not _schema_has_blob_field(self.schema):
-            arrow_tbl = self.to_arrow()
+            return self.to_arrow().to_pandas(**kwargs)
            if blob_mode == "descriptions":
                arrow_tbl = strip_auto_row_ids(
                    arrow_tbl, blob_v2_column_paths(self.schema)
                )
            return arrow_tbl.to_pandas(**kwargs)
        if (
            blob_mode == "lazy"
@@ -2462,9 +2400,6 @@ class LanceTable(Table):
        ):
            return self.to_arrow().to_pandas(**kwargs)
        if blob_mode == "bytes" and blob_v2_column_paths(self.schema):
            return self.search().to_pandas(blob_mode=blob_mode, **kwargs)
        return self.to_lance().to_pandas(blob_mode=blob_mode, **kwargs)
    def to_arrow(self) -> pa.Table:
@@ -3765,26 +3700,6 @@ class LanceTable(Table):
        """
        return LOOP.run(self._table.list_indices())
    def tokenize(
        self,
        query: str,
        *,
        column: Optional[str] = None,
        index_name: Optional[str] = None,
    ) -> Iterable[FtsToken]:
        """
        Tokenize a query using the tokenizer configured on an FTS index.
        Specify exactly one of ``column`` or ``index_name``.
        Model-backed tokenizers such as ``jieba/*`` and ``lindera/*`` are
        rebuilt in the client process from index metadata. For remote tables,
        this means the same tokenizer model files must also exist locally.
        """
        return LOOP.run(
            self._table.tokenize(query, column=column, index_name=index_name)
        )
    def index_stats(self, index_name: str) -> Optional[IndexStatistics]:
        """
        Retrieve statistics about an index
@@ -3834,11 +3749,6 @@ class LanceTable(Table):
        [`AsyncTable.unset_lsm_write_spec`][lancedb.AsyncTable.unset_lsm_write_spec]."""
        return LOOP.run(self._table.unset_lsm_write_spec())
    def get_lsm_write_spec(self) -> Optional["LsmWriteSpec"]:
        """Read the installed LsmWriteSpec, or ``None``. See
        [`AsyncTable.get_lsm_write_spec`][lancedb.AsyncTable.get_lsm_write_spec]."""
        return LOOP.run(self._table.get_lsm_write_spec())
    def close_lsm_writers(self) -> None:
        """Close cached MemWAL shard writers. See
        [`AsyncTable.close_lsm_writers`][lancedb.AsyncTable.close_lsm_writers]."""
@@ -4110,16 +4020,7 @@ def _handle_bad_vector_column(
        dim = _infer_vector_dim(vec_arr)
        if dim is None:
            return data
-
+    has_wrong_dim = pc.not_equal(pc.list_value_length(vec_arr), dim)
    is_null = pc.is_null(vec_arr)
    # pc.list_value_length returns null for null list entries, so
    # pc.not_equal(null, dim) also returns null. Use or_kleene so that
    # True OR null = True (Kleene three-valued logic), ensuring null vectors
    # are counted as wrong-dim.
    has_wrong_dim = pc.or_kleene(
        is_null,
        pc.not_equal(pc.list_value_length(vec_arr), dim),
    )
    has_bad_vectors = pc.any(has_nan).as_py() or pc.any(has_wrong_dim).as_py()
@@ -4154,58 +4055,17 @@ def _handle_bad_vector_column(
                raise ValueError(
                    "`fill_value` must not be None if `on_bad_vectors` is 'fill'"
                )
-            vec_arr = _fill_bad_vector_values(vec_arr, dim, fill_value)
+            vec_arr = pc.if_else(
                is_bad,
                pa.scalar([fill_value] * dim, type=vec_arr.type),
                vec_arr,
            )
        else:
            raise ValueError(f"Invalid value for on_bad_vectors: {on_bad_vectors}")
    return data.set_column(position, vector_column_name, vec_arr)
 def _fill_bad_vector_values(
    arr: Union[pa.Array, pa.ChunkedArray],
    dim: int,
    fill_value: float,
 ) -> pa.Array:
    if not isinstance(arr, pa.ChunkedArray):
        arr = pa.chunked_array([arr])
    arr = arr.combine_chunks()
    # A fixed-size slice truncates long vectors and pads short vectors with nulls.
    # Slice an array marking the original child nulls in parallel so padding nulls
    # can be distinguished from null values that were already present.
    sliced = pc.list_slice(arr, 0, dim, return_fixed_size_list=True)
    child_nulls = pc.is_null(arr.values)
    parent_nulls = pc.is_null(arr)
    if pa.types.is_list(arr.type):
        original_child_nulls = pa.ListArray.from_arrays(
            arr.offsets, child_nulls, mask=parent_nulls
        )
    elif pa.types.is_large_list(arr.type):
        original_child_nulls = pa.LargeListArray.from_arrays(
            arr.offsets, child_nulls, mask=parent_nulls
        )
    else:
        original_child_nulls = pa.FixedSizeListArray.from_arrays(
            child_nulls, arr.type.list_size, mask=parent_nulls
        )
    sliced_child_nulls = pc.list_slice(
        original_child_nulls, 0, dim, return_fixed_size_list=True
    )
    needs_fill = pc.is_null(sliced_child_nulls.values)
    values = sliced.values
    if pa.types.is_floating(values.type):
        values_for_nan_check = (
            values.cast(pa.float32()) if pa.types.is_float16(values.type) else values
        )
        needs_fill = pc.or_kleene(needs_fill, pc.is_nan(values_for_nan_check))
    fill_scalar = pa.scalar(fill_value).cast(values.type)
    filled_values = pc.if_else(needs_fill, fill_scalar, values)
    filled = pa.FixedSizeListArray.from_arrays(filled_values, dim)
    return filled.cast(arr.type)
 def has_nan_values(arr: Union[pa.ListArray, pa.ChunkedArray]) -> pa.BooleanArray:
    if isinstance(arr, pa.ChunkedArray):
        values = pa.chunked_array([chunk.flatten() for chunk in arr.chunks])
@@ -4538,17 +4398,6 @@ class AsyncTable:
        """
        await self._inner.unset_lsm_write_spec()
    async def get_lsm_write_spec(self) -> Optional["LsmWriteSpec"]:
        """Read the LsmWriteSpec currently installed on this table.
        Returns ``None`` when the MemWAL LSM write path is not enabled (no
        spec has been set, or it was removed with `unset_lsm_write_spec`).
        The returned spec — including its ``maintained_indexes`` and
        ``writer_config_defaults`` — mirrors what was passed to
        `set_lsm_write_spec`.
        """
        return await self._inner.get_lsm_write_spec()
    async def close_lsm_writers(self) -> None:
        """Drain and close any cached MemWAL shard writers for this table.
@@ -4655,18 +4504,14 @@ class AsyncTable:
        -------
        pd.DataFrame
        """
-        validate_blob_mode(blob_mode)
+        _validate_blob_mode(blob_mode)
-        schema = await self.schema()
+        if blob_mode == "descriptions" or not _schema_has_blob_field(
-        if blob_mode == "descriptions" or not schema_has_blob_field(schema):
+            await self.schema()
-            arrow_tbl = await self.to_arrow()
+        ):
-            if blob_mode == "descriptions":
+            return (await self.to_arrow()).to_pandas(**kwargs)
                arrow_tbl = strip_auto_row_ids(arrow_tbl, blob_v2_column_paths(schema))
            return arrow_tbl.to_pandas(**kwargs)
        if blob_mode == "lazy" and get_uri_scheme(await self.uri()) == "memory":
            return (await self.to_arrow()).to_pandas(**kwargs)
        if blob_mode == "bytes" and blob_v2_column_paths(schema):
            return await self.query().to_pandas(blob_mode=blob_mode, **kwargs)
        return (await self._to_lance()).to_pandas(blob_mode=blob_mode, **kwargs)
    async def to_arrow(self) -> pa.Table:
@@ -5767,24 +5612,6 @@ class AsyncTable:
        """
        return AsyncTakeQuery(self._inner.take_row_ids(row_ids), self)
    async def blob_columns(self) -> list[str]:
        return await self._inner.blob_columns()
    async def fetch_blobs(
        self, column: str, row_ids: Union[list[int], pa.Table]
    ) -> pa.LargeBinaryArray:
        return await self._inner.fetch_blobs(
            column, _normalize_blob_row_ids(row_ids, column)
        )
    async def fetch_blob_files(
        self, column: str, row_ids: Union[list[int], pa.Table]
    ) -> "list[Optional[BlobFile]]":
        handles = await self._inner.fetch_blob_files(
            column, _normalize_blob_row_ids(row_ids, column)
        )
        return _wrap_blob_files(handles)
    @property
    def tags(self) -> AsyncTags:
        """Tag management for the dataset.
@@ -5886,24 +5713,6 @@ class AsyncTable:
        """
        return await self._inner.list_indices()
    async def tokenize(
        self,
        query: str,
        *,
        column: Optional[str] = None,
        index_name: Optional[str] = None,
    ) -> Iterable[FtsToken]:
        """
        Tokenize a query using the tokenizer configured on an FTS index.
        Specify exactly one of ``column`` or ``index_name``.
        Model-backed tokenizers such as ``jieba/*`` and ``lindera/*`` are
        rebuilt in the client process from index metadata. For remote tables,
        this means the same tokenizer model files must also exist locally.
        """
        return await self._inner.tokenize(query, column=column, index_name=index_name)
    async def index_stats(self, index_name: str) -> Optional[IndexStatistics]:
        """
        Retrieve statistics about an index
@@ -1,24 +1,11 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
-from __future__ import annotations
+from typing import Literal
 from typing import Dict, List, Literal, Optional, Tuple, Union
 from .expr import Expr
 # Query type literals
 QueryType = Literal["vector", "fts", "hybrid", "auto"]
 BlobMode = Literal["lazy", "bytes", "descriptions"]
 QueryProjectionSpec = Union[
    List[str],
    List[Tuple[str, Union[str, Expr]]],
    Dict[str, Union[str, Expr]],
 ]
 QueryProjection = Optional[QueryProjectionSpec]
 # Distance type literals
 DistanceType = Literal["l2", "cosine", "dot"]
 DistanceTypeWithHamming = Literal["l2", "cosine", "dot", "hamming"]
@@ -55,7 +42,5 @@ IndexType = Literal[
 ]
 # Tokenizer literals
-BuiltinTokenizerType = Literal[
+BuiltinTokenizerType = Literal["simple", "raw", "whitespace", "ngram"]
    "simple", "raw", "whitespace", "ngram", "icu", "icu/split"
 ]
 BaseTokenizerType = BuiltinTokenizerType | str
@@ -177,10 +177,7 @@ def flatten_columns(tbl: pa.Table, flatten: Optional[Union[int, bool]] = None):
                continue
            else:
                break
-    # `bool` is a subclass of `int`, so guard against it explicitly: `flatten=False`
+    elif isinstance(flatten, int):
    # (and `None`) must mean "do not flatten" rather than falling into the integer
    # branch and raising on the `flatten <= 0` check.
    elif isinstance(flatten, int) and not isinstance(flatten, bool):
        if flatten <= 0:
            raise ValueError(
                "Please specify a positive integer for flatten or the boolean "
@@ -518,38 +515,3 @@ def batch_to_tensor_rows(batch: pa.RecordBatch):
    stacked = torch.tensor(numpy.column_stack(columns))
    rows = list(stacked.unbind(dim=0))
    return rows
 def batch_to_tensor_dict(batch: pa.RecordBatch):
    """
    Convert a PyArrow RecordBatch to a list of per-row dicts of PyTorch Tensors.
    Each column is first converted to a 1-D tensor (zero-copy via DLPack), then
    sliced per row. The result is a list whose length is ``batch.num_rows`` and
    whose items are dicts keyed by column name. This shape composes directly
    with PyTorch's default ``DataLoader`` collate, which stacks the per-row
    dicts back into a dict of batched tensors.
    Fails if torch is not installed.
    Fails if a column's data type is not supported by PyTorch.
    Parameters
    ----------
    batch : pa.RecordBatch
        The record batch to convert.
    Returns
    -------
    list[dict[str, torch.Tensor]]
        One per-row dict per row in the batch. Each dict maps column name to a
        0-D tensor view into the column.
    """
    torch = attempt_import_or_raise("torch", "torch")
    tensors = {
        name: torch.from_dlpack(col)
        for name, col in zip(batch.schema.names, batch.columns)
    }
    return [
        {name: tensor[i] for name, tensor in tensors.items()}
        for i in range(batch.num_rows)
    ]
@@ -1,562 +0,0 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import io
 import pyarrow as pa
 import pyarrow.compute as pc
 import pytest
 import lancedb
 from lancedb._blob import read_row_ids_from_hits, stash_auto_row_ids
 from lancedb.index import FTS
 from lancedb.schema import blob_column_paths, blob_v2_column_paths
 def _blob_table(name, rows):
    db = lancedb.connect("memory:///")
    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
    table = db.create_table(name, schema=schema)
    table.add(rows)
    return table
 def _blob_array(name, values):
    blob_type = lancedb.blob(name).type
    storage_type = blob_type.storage_type
    storage = pa.StructArray.from_arrays(
        [
            pa.array(values, type=pa.large_binary()),
            pa.array([None] * len(values), type=pa.string()),
            pa.array([None] * len(values), type=pa.uint64()),
            pa.array([None] * len(values), type=pa.uint64()),
        ],
        fields=list(storage_type),
    )
    return pa.ExtensionArray.from_storage(blob_type, storage)
 def _row_ids_by_id(table):
    hits = table.search().with_row_id(True).limit(1000).to_arrow()
    assert "_rowid" in hits.column_names
    return dict(zip(hits["id"].to_pylist(), hits["_rowid"].to_pylist()))
 def test_blob_factory_declares_v2_field():
    field = lancedb.blob("image")
    assert isinstance(field.type, pa.ExtensionType)
    assert field.type.extension_name == "lance.blob.v2"
 def test_blob_v2_column_paths_include_list_children():
    schema = pa.schema(
        [
            pa.field("id", pa.int64()),
            pa.field("info", pa.struct([lancedb.blob("blob")])),
            pa.field("images", pa.list_(lancedb.blob("image"))),
            pa.field("large_images", pa.large_list(lancedb.blob("large_image"))),
            pa.field(
                "fixed_images",
                pa.list_(lancedb.blob("fixed_image"), list_size=2),
            ),
        ]
    )
    assert blob_v2_column_paths(schema) == [
        "info.blob",
        "images.image",
        "large_images.large_image",
        "fixed_images.fixed_image",
    ]
 def _legacy_v1_table(name):
    db = lancedb.connect("memory:///")
    schema = pa.schema(
        [
            pa.field("id", pa.int64()),
            pa.field(
                "legacy", pa.large_binary(), metadata={"lance-encoding:blob": "true"}
            ),
        ]
    )
    table = db.create_table(name, schema=schema)
    table.add([{"id": 1, "legacy": b"old"}])
    return table
 def test_blob_v2_column_paths_exclude_legacy_metadata():
    schema = pa.schema(
        [
            pa.field("id", pa.int64()),
            lancedb.blob("image"),
            pa.field(
                "legacy", pa.large_binary(), metadata={"lance-encoding:blob": "true"}
            ),
        ]
    )
    assert blob_v2_column_paths(schema) == ["image"]
    assert blob_column_paths(schema) == ["image", "legacy"]
 def test_blob_v2_paths_match_blob_columns():
    table = _blob_table("paths_match", [{"id": 1, "image": b"x"}])
    assert blob_v2_column_paths(table.schema) == table.blob_columns()
    db = lancedb.connect("memory:///")
    info = pa.StructArray.from_arrays(
        [
            pa.array(["first"], type=pa.string()),
            _blob_array("blob", [b"nested"]),
        ],
        names=["name", "blob"],
    )
    data = pa.Table.from_arrays(
        [pa.array([1], type=pa.int64()), info],
        names=["id", "info"],
    )
    nested = db.create_table("nested_paths", data=data)
    assert blob_v2_column_paths(nested.schema) == nested.blob_columns()
 def test_auto_row_id_stash_round_trip():
    table = _blob_table(
        "stash_round_trip",
        [{"id": 1, "image": b"alpha"}, {"id": 2, "image": b"beta"}],
    )
    hits = table.search().with_row_id(True).limit(10).to_arrow()
    row_ids = hits["_rowid"].to_pylist()
    stashed = stash_auto_row_ids(hits, ["image"])
    assert "_rowid" not in stashed.column_names
    assert stashed.schema.field("image").metadata == hits.schema.field("image").metadata
    assert read_row_ids_from_hits(stashed, "image") == row_ids
 def test_blob_query_omits_auto_row_id():
    table = _blob_table("rowid", [{"id": 1, "image": b"x"}])
    hits = table.search().limit(10).to_arrow()
    assert "_rowid" not in hits.column_names
 def test_blob_query_explicit_row_id_opt_in():
    table = _blob_table("explicit_rowid", [{"id": 1, "image": b"x"}])
    hits = table.search().with_row_id(True).limit(10).to_arrow()
    assert "_rowid" in hits.column_names
 def test_table_to_pandas_descriptions_mode_omits_row_id():
    table = _blob_table("descriptions_no_leak", [{"id": 1, "image": b"x"}])
    df = table.to_pandas(blob_mode="descriptions")
    descriptor = df["image"].iloc[0]
    assert "_lance_row_id" not in descriptor
    assert set(descriptor.keys()) == {"kind", "position", "size", "blob_id", "blob_uri"}
@pytest.mark.asyncio
 async def test_async_table_to_pandas_descriptions_mode_omits_row_id():
    db = await lancedb.connect_async("memory:///")
    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
    table = await db.create_table("descriptions_no_leak_async", schema=schema)
    await table.add([{"id": 1, "image": b"x"}])
    df = await table.to_pandas(blob_mode="descriptions")
    descriptor = df["image"].iloc[0]
    assert "_lance_row_id" not in descriptor
    assert set(descriptor.keys()) == {"kind", "position", "size", "blob_id", "blob_uri"}
 def test_fetch_blobs_round_trip():
    table = _blob_table(
        "round_trip",
        [{"id": 1, "image": b"alpha"}, {"id": 2, "image": b"beta"}],
    )
    by_id = _row_ids_by_id(table)
    blobs = table.fetch_blobs("image", [by_id[1], by_id[2]])
    assert [blobs[0].as_py(), blobs[1].as_py()] == [b"alpha", b"beta"]
 def test_fetch_blobs_accepts_query_result():
    table = _blob_table("from_result", [{"id": 1, "image": b"gamma"}])
    hits = table.search().limit(10).to_arrow()
    assert "_rowid" not in hits.column_names
    blobs = table.fetch_blobs("image", hits)
    assert {blobs[i].as_py() for i in range(len(blobs))} == {b"gamma"}
 def test_fetch_blobs_null_alignment():
    table = _blob_table(
        "nulls",
        [{"id": 1, "image": b"present"}, {"id": 2, "image": None}],
    )
    by_id = _row_ids_by_id(table)
    request = [by_id[1], by_id[2], by_id[1]]
    blobs = table.fetch_blobs("image", request)
    assert len(blobs) == len(request)
    assert blobs[0].as_py() == b"present"
    assert blobs[1].as_py() is None
    assert blobs[2].as_py() == b"present"
 def test_fetch_blobs_nested_path():
    db = lancedb.connect("memory:///")
    info = pa.StructArray.from_arrays(
        [
            pa.array(["first", "second"], type=pa.string()),
            _blob_array("blob", [b"nested-alpha", b"nested-beta"]),
        ],
        names=["name", "blob"],
    )
    data = pa.Table.from_arrays(
        [pa.array([1, 2], type=pa.int64()), info],
        names=["id", "info"],
    )
    table = db.create_table("nested", data=data)
    by_id = _row_ids_by_id(table)
    blobs = table.fetch_blobs("info.blob", [by_id[1], by_id[2]])
    assert [blobs[0].as_py(), blobs[1].as_py()] == [b"nested-alpha", b"nested-beta"]
 def test_fetch_blob_files_lazy_read():
    payload = b"lazy-read" * 100
    table = _blob_table("lazy", [{"id": 1, "image": payload}])
    by_id = _row_ids_by_id(table)
    handles = table.fetch_blob_files("image", [by_id[1]])
    assert len(handles) == 1
    assert handles[0].read() == payload
 def test_fetch_blob_files_null_alignment():
    table = _blob_table(
        "lazy_nulls",
        [{"id": 1, "image": b"here"}, {"id": 2, "image": None}],
    )
    by_id = _row_ids_by_id(table)
    handles = table.fetch_blob_files("image", [by_id[2], by_id[1]])
    assert len(handles) == 2
    assert handles[0] is None
    assert handles[1].read() == b"here"
 def test_fetch_blobs_rejects_non_blob_column():
    table = _blob_table("reject", [{"id": 1, "image": b"x"}])
    with pytest.raises(ValueError, match="not a blob column"):
        table.fetch_blobs("id", [0])
 def test_legacy_v1_query_omits_auto_row_id():
    table = _legacy_v1_table("legacy_v1")
    hits = table.search().select(["legacy"]).limit(10).to_arrow()
    assert "_rowid" not in hits.column_names
 def test_fetch_blobs_rejects_legacy_v1_column():
    table = _legacy_v1_table("legacy_fetch")
    with pytest.raises(ValueError, match="legacy blob column.*blob v2"):
        table.fetch_blobs("legacy", [0])
@pytest.mark.asyncio
 async def test_async_fetch_blob_files_lazy_read():
    db = await lancedb.connect_async("memory:///")
    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
    table = await db.create_table("async_lazy", schema=schema)
    payload = b"async-lazy" * 100
    await table.add([{"id": 1, "image": payload}])
    hits = (
        await table.query().select({"image_alias": "image"}).limit(10).to_arrow()
    ).combine_chunks()
    assert "_rowid" not in hits.column_names
    handles = await table.fetch_blob_files("image", hits)
    assert len(handles) == 1
    assert await handles[0].aread() == payload
 def test_fetch_blobs_from_query_result_without_row_id_raises():
    table = _blob_table("no_rowid", [{"id": 1, "image": b"x"}])
    hits = table.search().select(["id"]).to_arrow()
    assert "_rowid" not in hits.column_names
    with pytest.raises(ValueError, match="_rowid"):
        table.fetch_blobs("image", hits)
 _HYBRID_BLOB_SCHEMA = pa.schema(
    [
        pa.field("id", pa.int64()),
        pa.field("text", pa.utf8()),
        pa.field("vector", pa.list_(pa.float32(), list_size=2)),
        lancedb.blob("image"),
    ]
 )
 _HYBRID_BLOB_ROWS = [
    {"id": 1, "text": "hello alpha", "vector": [1.0, 0.0], "image": b"alpha"},
    {"id": 2, "text": "hello beta", "vector": [0.9, 0.1], "image": b"beta"},
    {"id": 3, "text": "other", "vector": [0.0, 1.0], "image": b"other"},
 ]
 def _hybrid_blob_table(db):
    table = db.create_table("hybrid_blob_fetch", schema=_HYBRID_BLOB_SCHEMA)
    table.add(_HYBRID_BLOB_ROWS)
    table.create_index("text", config=FTS(with_position=False))
    return table
 async def _hybrid_blob_table_async(db):
    table = await db.create_table("hybrid_blob_fetch_async", schema=_HYBRID_BLOB_SCHEMA)
    await table.add(_HYBRID_BLOB_ROWS)
    await table.create_index("text", config=FTS(with_position=False))
    return table
 def test_blob_v2_hybrid_fetch_blobs():
    table = _hybrid_blob_table(lancedb.connect("memory:///"))
    hits = (
        table.search(query_type="hybrid")
        .vector([1.0, 0.0])
        .text("hello")
        .select(["id", "image"])
        .limit(2)
        .to_arrow()
    )
    assert "_rowid" not in hits.column_names
    assert "_lance_row_id" in hits.schema.field("image").type.names
    blobs = table.fetch_blobs("image", hits)
    assert {blobs[i].as_py() for i in range(len(blobs))} == {b"alpha", b"beta"}
@pytest.mark.asyncio
 async def test_blob_v2_hybrid_fetch_blobs_async():
    db = await lancedb.connect_async("memory:///hybrid_blob_fetch_async")
    table = await _hybrid_blob_table_async(db)
    hits = await (
        table.query()
        .nearest_to([1.0, 0.0])
        .nearest_to_text("hello")
        .select(["id", "image"])
        .limit(2)
        .to_arrow()
    )
    assert "_rowid" not in hits.column_names
    assert "_lance_row_id" in hits.schema.field("image").type.names
    blobs = await table.fetch_blobs("image", hits)
    assert {blobs[i].as_py() for i in range(len(blobs))} == {b"alpha", b"beta"}
 def test_blob_file_seek_read_and_read_range():
    payload = _identifiable_payload(1024)
    table = _blob_table("seek_read", [{"id": 1, "image": payload}])
    by_id = _row_ids_by_id(table)
    handle = table.fetch_blob_files("image", [by_id[1]])[0]
    assert handle.seek(100) == 100
    assert handle.read(16) == payload[100:116]
    handle.seek(100)
    assert handle.read_range(500, 8) == payload[500:508]
    assert handle.tell() == 100
    with pytest.raises(ValueError, match="whence"):
        handle.seek(0, 99)
 def test_fetch_blob_files_from_query_partial_read():
    payload = _identifiable_payload(65536)
    table = _blob_table("query_partial", [{"id": 1, "image": payload}])
    hits = table.search().select(["id", "image"]).limit(1).to_arrow()
    assert "_rowid" not in hits.column_names
    handle = table.fetch_blob_files("image", hits)[0]
    assert handle.size() == 65536
    assert handle.read_range(0, 128) == payload[:128]
    assert handle.tell() == 0
    assert handle.seek(40000) == 40000
    assert handle.read(16) == payload[40000:40016]
 def test_blob_file_buffered_reader():
    payload = _identifiable_payload(4096)
    table = _blob_table("buffered_reader", [{"id": 1, "image": payload}])
    hits = table.search().select(["id", "image"]).limit(1).to_arrow()
    handle = table.fetch_blob_files("image", hits)[0]
    reader = io.BufferedReader(handle)
    assert reader.read(8) == payload[:8]
    assert reader.read(8) == payload[8:16]
    assert reader.read() == payload[16:]
 def test_fetch_blob_files_cross_fragment_nulls_and_dups():
    db = lancedb.connect("memory:///")
    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("image")])
    table = db.create_table("cross_fragment", schema=schema)
    table.add([{"id": 1, "image": b"alpha"}])
    table.add([{"id": 2, "image": None}, {"id": 3, "image": b"beta"}])
    by_id = _row_ids_by_id(table)
    request = [by_id[3], by_id[2], by_id[1], by_id[3]]
    handles = table.fetch_blob_files("image", request)
    assert len(handles) == 4
    assert handles[1] is None
    assert handles[0].read() == b"beta"
    assert handles[2].read() == b"alpha"
    assert handles[3].seek(1) == 1
    assert handles[3].read() == b"eta"
 def test_blob_file_pyav_decode_seek(tmp_path):
    av = pytest.importorskip("av")
    import fractions
    clip = tmp_path / "clip.mp4"
    with av.open(str(clip), mode="w") as container:
        stream = container.add_stream("mpeg4", rate=5)
        stream.width, stream.height, stream.pix_fmt = 32, 32, "yuv420p"
        stream.time_base = fractions.Fraction(1, 5)
        for pts in range(5):
            frame = av.VideoFrame(32, 32, "yuv420p")
            frame.pts = pts
            container.mux(stream.encode(frame))
        container.mux(stream.encode(None))
    table = _blob_table("pyav", [{"id": 1, "image": clip.read_bytes()}])
    hits = table.search().select(["image"]).limit(1).to_arrow()
    handle = table.fetch_blob_files("image", hits)[0]
    with av.open(handle) as container:
        stream = container.streams.video[0]
        container.seek(0)
        assert next(container.decode(stream)) is not None
 def test_blob_v2_hybrid_fetch_blob_files_seek():
    table = _hybrid_blob_table(lancedb.connect("memory:///"))
    hits = (
        table.search(query_type="hybrid")
        .vector([1.0, 0.0])
        .text("hello")
        .select(["id", "image"])
        .limit(2)
        .to_arrow()
    )
    assert "_rowid" not in hits.column_names
    handles = table.fetch_blob_files("image", hits)
    assert len(handles) == 2
    assert {handle.read_range(0, 2) for handle in handles} == {b"al", b"be"}
    first = handles[0]
    assert first.seek(1) == 1
    assert first.read(2) in {b"lp", b"et"}
 def test_blob_file_header_sniff_from_search():
    payload = b"%PDF-1.7\n" + bytes(4096)
    table = _blob_table("header_sniff", [{"id": 1, "image": payload}])
    hits = table.search().select(["id", "image"]).limit(1).to_arrow()
    handle = table.fetch_blob_files("image", hits)[0]
    assert handle.read_range(0, 4) == b"%PDF"
    assert handle.tell() == 0
 def test_blob_file_multiple_handles_independent_cursors():
    table = _blob_table(
        "multi_handle",
        [{"id": 1, "image": b"first-payload"}, {"id": 2, "image": b"second-payload"}],
    )
    by_id = _row_ids_by_id(table)
    first, second = table.fetch_blob_files("image", [by_id[1], by_id[2]])
    assert first.seek(6) == 6
    assert second.tell() == 0
    assert first.read(7) == b"payload"
    assert second.read(6) == b"second"
 def test_fetch_blob_files_nested_path_seek():
    db = lancedb.connect("memory:///")
    info = pa.StructArray.from_arrays(
        [
            pa.array(["first", "second"], type=pa.string()),
            _blob_array("blob", [b"nested-alpha", b"nested-beta"]),
        ],
        names=["name", "blob"],
    )
    data = pa.Table.from_arrays(
        [pa.array([1, 2], type=pa.int64()), info],
        names=["id", "info"],
    )
    table = db.create_table("nested_seek", data=data)
    by_id = _row_ids_by_id(table)
    handle = table.fetch_blob_files("info.blob", [by_id[2]])[0]
    assert handle.seek(7) == 7
    assert handle.read() == b"beta"
 def test_fetch_blobs_survives_sort_after_query():
    table = _blob_table(
        "sort_survives",
        [{"id": i, "image": f"payload-{i}".encode()} for i in range(5)],
    )
    hits = table.search().select(["id", "image"]).to_arrow()
    sort_idx = pc.sort_indices(hits["id"], sort_keys=[("id", "descending")])
    sorted_hits = hits.take(sort_idx)
    blobs = table.fetch_blobs("image", sorted_hits)
    expected = [f"payload-{i}".encode() for i in sorted_hits["id"].to_pylist()]
    assert [blobs[i].as_py() for i in range(len(blobs))] == expected
 def test_fetch_blobs_survives_filter_and_sort_after_query():
    table = _blob_table(
        "filter_sort_survives",
        [{"id": i, "image": f"payload-{i}".encode()} for i in range(5)],
    )
    hits = table.search().select(["id", "image"]).to_arrow()
    filtered = hits.filter(pc.field("id") >= 2)
    sort_idx = pc.sort_indices(filtered["id"], sort_keys=[("id", "descending")])
    filtered_sorted = filtered.take(sort_idx)
    blobs = table.fetch_blobs("image", filtered_sorted)
    expected = [f"payload-{i}".encode() for i in filtered_sorted["id"].to_pylist()]
    assert [blobs[i].as_py() for i in range(len(blobs))] == expected
 def test_fetch_blob_files_survives_sort_after_query():
    table = _blob_table(
        "lazy_sort_survives",
        [{"id": i, "image": f"payload-{i}".encode()} for i in range(5)],
    )
    hits = table.search().select(["id", "image"]).to_arrow()
    sort_idx = pc.sort_indices(hits["id"], sort_keys=[("id", "descending")])
    sorted_hits = hits.take(sort_idx)
    handles = table.fetch_blob_files("image", sorted_hits)
    expected = [f"payload-{i}".encode() for i in sorted_hits["id"].to_pylist()]
    assert [handle.read() for handle in handles] == expected
 def test_fetch_blobs_nested_path_survives_sort_after_query():
    db = lancedb.connect("memory:///")
    values = [f"payload-{i}".encode() for i in range(4)]
    info = pa.StructArray.from_arrays(
        [pa.array(["row"] * 4, type=pa.string()), _blob_array("blob", values)],
        names=["name", "blob"],
    )
    data = pa.Table.from_arrays(
        [pa.array(range(4), type=pa.int64()), info],
        names=["id", "info"],
    )
    table = db.create_table("nested_sort_survives", data=data)
    hits = table.search().to_arrow()
    sort_idx = pc.sort_indices(hits["id"], sort_keys=[("id", "descending")])
    sorted_hits = hits.take(sort_idx)
    blobs = table.fetch_blobs("info.blob", sorted_hits)
    expected = [f"payload-{i}".encode() for i in sorted_hits["id"].to_pylist()]
    assert [blobs[i].as_py() for i in range(len(blobs))] == expected
 def _identifiable_payload(size: int) -> bytes:
    block = 256
    return b"".join(bytes([i % 256]) * block for i in range(size // block))
@@ -2,7 +2,6 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import os
 import pickle
 from typing import List, Optional, Union
 from unittest.mock import MagicMock, patch
@@ -243,49 +242,6 @@ def test_embedding_with_bad_results(tmp_path):
    assert tbl["vector"].null_count == 1
 def test_embedding_with_empty_output_vectors(tmp_path):
    """Regression test for issue #1672.
    When an embedding function returns an empty list (e.g. for empty-string
    inputs), _append_vector_columns used to crash because PyArrow cannot cast
    [] into a fixed-size list element.  The fix replaces wrong-length vectors
    with None before building the Arrow array so that _handle_bad_vectors can
    process them normally.
    """
    @register("empty-vec-embedding")
    class EmptyVecEmbeddingFunction(TextEmbeddingFunction):
        def ndims(self):
            return 128
        def generate_embeddings(self, texts: Union[List[str], np.ndarray]) -> list:
            # Simulate a model that returns an empty list for blank inputs
            return [
                [] if text.strip() == "" else np.random.randn(self.ndims()).tolist()
                for text in texts
            ]
    db = lancedb.connect(tmp_path)
    registry = EmbeddingFunctionRegistry.get_instance()
    model = registry.get("empty-vec-embedding").create()
    class Schema(LanceModel):
        text: str = model.SourceField()
        vector: Vector(model.ndims()) = model.VectorField()
    table = db.create_table("test_empty_vec", schema=Schema, mode="overwrite")
    # Should not crash; the row with the empty string should be dropped
    table.add(
        [{"text": "hello world"}, {"text": ""}, {"text": "foo"}],
        on_bad_vectors="drop",
    )
    assert len(table) == 2
    texts = table.to_arrow()["text"].to_pylist()
    assert "" not in texts
 def test_with_existing_vectors(tmp_path):
    @register("mock-embedding")
    class MockEmbeddingFunction(TextEmbeddingFunction):
@@ -592,22 +548,6 @@ def test_openai_no_retry_on_401(mock_sleep):
    assert mock_sleep.call_count == 0
 def test_ollama_embeddings_pickle():
    """OllamaEmbeddings must pickle even after the cached client is created."""
    registry = get_registry()
    model = registry.get("ollama").create(name="nomic-embed-text")
    # Simulate accessing the cached client, which stores it on the instance.
    model.__dict__["_ollama_client"] = MagicMock()
    pickled = pickle.dumps(model)
    restored = pickle.loads(pickled)
    assert restored.name == "nomic-embed-text"
    assert restored.host == "http://localhost:11434"
    assert "_ollama_client" not in restored.__dict__
 def test_url_retrieve_downloads_image():
    """
    Embedding functions like open-clip, siglip, and jinaai use url_retrieve()
@@ -786,97 +786,6 @@ def test_language(mem_db: DBConnection):
    assert len(results) == 0
 def test_tokenize_uses_simple_index_tokenizer(mem_db: DBConnection):
    data = pa.table({"text": ["Running in cafés"], "other": ["Running in cafés"]})
    table = mem_db.create_table("test_tokenize", data=data)
    table.create_index("text", config=FTS(base_tokenizer="simple"))
    tokens = table.tokenize("Running in cafés", column="text")
    assert [(token.text, token.position) for token in tokens] == [
        ("run", 0),
        ("cafe", 2),
    ]
 def test_tokenize_uses_icu_index_tokenizer_by_name(mem_db: DBConnection):
    data = pa.table({"text": ["Hello, こんにちは世界!"]})
    table = mem_db.create_table("test_tokenize_icu", data=data)
    table.create_index(
        "text",
        config=FTS(
            base_tokenizer="icu",
            stem=False,
            remove_stop_words=False,
        ),
        name="text_icu_idx",
    )
    tokens = table.tokenize("Hello, こんにちは世界!", index_name="text_icu_idx")
    assert [(token.text, token.position) for token in tokens] == [
        ("hello", 0),
        ("こんにちは", 1),
        ("世界", 2),
    ]
 def test_tokenize_requires_one_selector(mem_db: DBConnection):
    data = pa.table({"text": ["hello world"]})
    table = mem_db.create_table("test_tokenize_selector", data=data)
    table.create_index("text", config=FTS(), name="text_idx")
    with pytest.raises(ValueError, match="Specify exactly one"):
        table.tokenize("hello")
    with pytest.raises(ValueError, match="Specify exactly one"):
        table.tokenize("hello", column="text", index_name="text_idx")
 def test_tokenize_requires_fts_index(mem_db: DBConnection):
    data = pa.table({"text": ["hello world"]})
    table = mem_db.create_table("test_tokenize_no_index", data=data)
    with pytest.raises(ValueError, match="does not have a full text search index"):
        table.tokenize("hello", column="text")
@pytest.mark.asyncio
 async def test_tokenize_async(async_table):
    await async_table.create_index("text", config=FTS())
    tokens = await async_table.tokenize("Running in cafés", column="text")
    assert [(token.text, token.position) for token in tokens] == [
        ("run", 0),
        ("cafe", 2),
    ]
 def test_tokenize_uses_explicit_simple_tokenizer():
    tokens = ldb.tokenize("Running in cafés", base_tokenizer="simple")
    assert [(token.text, token.position) for token in tokens] == [
        ("run", 0),
        ("cafe", 2),
    ]
 def test_tokenize_uses_explicit_icu_tokenizer():
    tokens = ldb.tokenize(
        "Hello, こんにちは世界!",
        base_tokenizer="icu",
        stem=False,
        remove_stop_words=False,
    )
    assert [(token.text, token.position) for token in tokens] == [
        ("hello", 0),
        ("こんにちは", 1),
        ("世界", 2),
    ]
 def test_fts_on_list(mem_db: DBConnection):
    data = pa.table(
        {
@@ -1175,84 +1084,6 @@ def test_fts_query_to_json():
    assert json_str == expected
 def test_fts_phrase_query_is_preserved_in_query_object():
    query = LanceFtsQueryBuilder(mock.Mock(), "puppy runs").phrase_query()
    query_object = query.to_query_object()
    assert query_object.full_text_query.query == '"puppy runs"'
 def test_fts_phrase_query_execution_preserves_user_text():
    table = mock.Mock()
    table.schema = pa.schema([])
    table._execute_query.return_value = pa.table({"text": ["result"]}).to_reader()
    class CapturingReranker:
        score = "relevance"
        def __init__(self):
            self.queries = []
        def rerank_fts(self, query, results):
            self.queries.append(query)
            return results.append_column("_relevance_score", [[1.0]])
    reranker = CapturingReranker()
    query = (
        LanceFtsQueryBuilder(table, "puppy runs")
        .phrase_query()
        .with_row_id(False)
        .rerank(reranker)
    )
    query.to_arrow()
    backend_query = table._execute_query.call_args.args[0]
    assert (
        backend_query.full_text_query.query,
        reranker.queries,
        query._query,
    ) == ('"puppy runs"', ["puppy runs"], "puppy runs")
 def test_fts_phrase_query_false_preserves_string():
    query = LanceFtsQueryBuilder(mock.Mock(), "puppy runs").phrase_query(False)
    query_object = query.to_query_object()
    assert query_object.full_text_query.query == "puppy runs"
 def test_fts_phrase_query_preserves_fully_quoted_string():
    query = LanceFtsQueryBuilder(mock.Mock(), '"puppy runs"').phrase_query()
    query_object = query.to_query_object()
    assert query_object.full_text_query.query == '"puppy runs"'
 def test_fts_phrase_query_preserves_structured_phrase_query():
    phrase_query = PhraseQuery("puppy runs", "text")
    query = LanceFtsQueryBuilder(mock.Mock(), phrase_query).phrase_query()
    query_object = query.to_query_object()
    assert query_object.full_text_query.query == phrase_query
 def test_fts_phrase_query_rejects_other_structured_queries():
    query = LanceFtsQueryBuilder(
        mock.Mock(), MatchQuery("puppy", "text")
    ).phrase_query()
    with pytest.raises(
        TypeError,
        match=r"phrase_query\(\) requires a string or PhraseQuery, got MatchQuery",
    ):
        query.to_query_object()
 def test_fts_fast_search(table):
    table.create_fts_index("text")
@@ -1,183 +0,0 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 """Unit tests for GeminiText embedding function."""
 import sys
 from unittest.mock import MagicMock, patch
 # Mock google.genai modules before they are imported by gemini_text.py
 mock_google = MagicMock()
 mock_genai = MagicMock()
 mock_types = MagicMock()
 mock_google.genai = mock_genai
 mock_genai.types = mock_types
 sys.modules["google"] = mock_google
 sys.modules["google.genai"] = mock_genai
 sys.modules["google.genai.types"] = mock_types
 import pytest  # noqa: E402
 import numpy as np  # noqa: E402
 from lancedb.embeddings import get_registry  # noqa: E402
 from lancedb import __version__  # noqa: E402
 class TestGeminiText:
    """Tests for GeminiText model registration, configuration, and execution."""
    @pytest.fixture(autouse=True)
    def setup_mocks(self):
        """Set up standard mocks for google-genai Client and Config."""
        # Reset mocks
        mock_genai.reset_mock()
        mock_types.reset_mock()
        self.mock_client = MagicMock()
        mock_genai.Client.return_value = self.mock_client
        # Mock response for embed_content
        self.mock_embedding_1 = MagicMock()
        self.mock_embedding_1.values = [0.1] * 768
        self.mock_embedding_2 = MagicMock()
        self.mock_embedding_2.values = [0.2] * 768
        self.mock_response = MagicMock()
        self.mock_response.embeddings = [self.mock_embedding_1, self.mock_embedding_2]
        self.mock_client.models.embed_content.return_value = self.mock_response
    def test_gemini_registered(self):
        """Test that gemini-text is registered in the embedding function registry."""
        registry = get_registry()
        assert registry.get("gemini-text") is not None
    def test_client_init_headers(self):
        """Test that Client is initialized with the partner-attribution header."""
        with patch.dict("os.environ", {"GOOGLE_API_KEY": "test-key"}):
            with patch("lancedb.embeddings.gemini_text.attempt_import_or_raise"):
                registry = get_registry()
                func = registry.get("gemini-text").create()
                # Access the client property to trigger initialization
                _ = func.client
                mock_genai.Client.assert_called_once_with(
                    api_key="test-key",
                    http_options={
                        "headers": {
                            "x-goog-api-client": f"lancedb/{__version__}",
                        }
                    },
                )
    def test_generate_embeddings_batched(self):
        """Test that multiple texts are sent in a single batched API request."""
        with patch.dict("os.environ", {"GOOGLE_API_KEY": "test-key"}):
            with patch("lancedb.embeddings.gemini_text.attempt_import_or_raise"):
                registry = get_registry()
                func = registry.get("gemini-text").create()
                texts = ["hello", "world"]
                embeddings = func.generate_embeddings(texts)
                # Check embed_content was called exactly once
                self.mock_client.models.embed_content.assert_called_once()
                # Verify call arguments
                call_kwargs = self.mock_client.models.embed_content.call_args.kwargs
                assert call_kwargs["model"] == "gemini-embedding-001"
                assert len(call_kwargs["contents"]) == 2
                assert call_kwargs["contents"][0] == {"parts": [{"text": "hello"}]}
                assert call_kwargs["contents"][1] == {"parts": [{"text": "world"}]}
                # Verify returns are correct numpy arrays
                assert len(embeddings) == 2
                assert isinstance(embeddings[0], np.ndarray)
                assert embeddings[0].shape == (768,)
                assert np.allclose(embeddings[0], 0.1)
                assert np.allclose(embeddings[1], 0.2)
    def test_generate_embeddings_retrieval_document(self):
        """Test that retrieval_document task type prepends the document title part."""
        with patch.dict("os.environ", {"GOOGLE_API_KEY": "test-key"}):
            with patch("lancedb.embeddings.gemini_text.attempt_import_or_raise"):
                registry = get_registry()
                func = registry.get("gemini-text").create(
                    source_task_type="retrieval_document"
                )
                texts = ["doc text"]
                # We need mock to return only 1 embedding since we only pass 1 text
                mock_embedding = MagicMock()
                mock_embedding.values = [0.3] * 768
                self.mock_response.embeddings = [mock_embedding]
                embeddings = func.generate_embeddings(
                    texts, task_type="retrieval_document"
                )
                # Check call arguments for retrieval_document
                call_kwargs = self.mock_client.models.embed_content.call_args.kwargs
                assert call_kwargs["contents"][0] == {
                    "parts": [{"text": "Embedding of a document"}, {"text": "doc text"}]
                }
                mock_types.EmbedContentConfig.assert_called_once_with(
                    output_dimensionality=768, task_type="RETRIEVAL_DOCUMENT"
                )
                assert len(embeddings) == 1
                assert np.allclose(embeddings[0], 0.3)
    def test_custom_dimension(self):
        """Test that custom dimension (dim) can be configured and passed to config."""
        with patch.dict("os.environ", {"GOOGLE_API_KEY": "test-key"}):
            with patch("lancedb.embeddings.gemini_text.attempt_import_or_raise"):
                registry = get_registry()
                func = registry.get("gemini-text").create(dim=3072)
                assert func.ndims() == 3072
                texts = ["hello"]
                mock_embedding = MagicMock()
                mock_embedding.values = [0.5] * 3072
                self.mock_response.embeddings = [mock_embedding]
                _ = func.generate_embeddings(texts)
                mock_types.EmbedContentConfig.assert_called_once_with(
                    output_dimensionality=3072
                )
    def test_generate_embeddings_chunked(self):
        """Test that generate_embeddings chunks texts into groups of 100."""
        with patch.dict("os.environ", {"GOOGLE_API_KEY": "test-key"}):
            with patch("lancedb.embeddings.gemini_text.attempt_import_or_raise"):
                registry = get_registry()
                func = registry.get("gemini-text").create()
                # Passing 250 texts should make 3 calls (100, 100, 50)
                texts = [f"text_{i}" for i in range(250)]
                # Mock client response to return correct number of embeddings per chunk
                def mock_embed_side_effect(model, contents, config=None):
                    mock_resp = MagicMock()
                    mock_embeddings = []
                    for _ in contents:
                        emb = MagicMock()
                        # Each embedding is length 768
                        emb.values = [0.1] * 768
                        mock_embeddings.append(emb)
                    mock_resp.embeddings = mock_embeddings
                    return mock_resp
                self.mock_client.models.embed_content.side_effect = (
                    mock_embed_side_effect
                )
                embeddings = func.generate_embeddings(texts)
                # embed_content should be called 3 times
                assert self.mock_client.models.embed_content.call_count == 3
                assert len(embeddings) == 250
@@ -1,8 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 from unittest import mock
 import lancedb
 from lancedb.query import LanceHybridQueryBuilder
@@ -141,20 +139,6 @@ def test_hybrid_query_distance_range(sync_table: Table):
            assert 0.2 <= dist.as_py() <= 0.5
 def test_hybrid_query_applies_zero_upper_distance_bound(sync_table: Table):
    result = (
        sync_table.search(query_type="hybrid")
        .vector([0.0, 0.4])
        .text("elephant")
        .distance_range(upper_bound=0.0)
        .rerank(RRFReranker(return_score="all"))
        .limit(4)
        .to_arrow()
    )
    assert len(result) == 0
@pytest.mark.asyncio
 async def test_hybrid_query_distance_range_async(table: AsyncTable):
    reranker = RRFReranker(return_score="all")
@@ -193,23 +177,6 @@ async def test_analyze_plan(table: AsyncTable):
    assert "metrics=" in res
 def test_hybrid_phrase_query_is_preserved_in_analyze_plan():
    table = mock.Mock()
    analyzed_queries = []
    table._analyze_plan.side_effect = lambda query: analyzed_queries.append(query) or ""
    (
        LanceHybridQueryBuilder(table)
        .vector([0.1, 0.2])
        .text("puppy runs")
        .phrase_query()
        .analyze_plan()
    )
    assert len(analyzed_queries) == 2
    assert analyzed_queries[1].full_text_query.query == '"puppy runs"'
@pytest.fixture
 def table_with_id(tmpdir_factory) -> Table:
    tmp_path = str(tmpdir_factory.mktemp("data"))
@@ -11,7 +11,6 @@ import lancedb
 import pyarrow as pa
 import pytest
 from lancedb._lancedb import LsmWriteSpec
 from lancedb.index import BTree
 SCHEMA = pa.schema(
    [
@@ -137,76 +136,3 @@ def test_lsm_write_spec_identity_and_writer_config_defaults():
    s = s.with_writer_config_defaults({"durable_write": "false"})
    assert s.writer_config_defaults == {"durable_write": "false"}
    assert "durable_write" in repr(s)
 def test_get_lsm_write_spec(tmp_path):
    _db, table = _make_table(tmp_path)
    table.set_unenforced_primary_key("id")
    # None when nothing is installed.
    assert table.get_lsm_write_spec() is None
    # A real scalar index is needed to name it as a maintained index.
    table.create_index("id", config=BTree())
    idx_name = table.list_indices()[0].name
    # Bucket spec round-trips, including maintained indexes and writer config
    # defaults.
    table.set_lsm_write_spec(
        LsmWriteSpec.bucket("id", 4)
        .with_maintained_indexes([idx_name])
        .with_writer_config_defaults({"durable_write": "false"})
    )
    spec = table.get_lsm_write_spec()
    assert spec is not None
    assert spec.spec_type == "bucket"
    assert spec.column == "id"
    assert spec.num_buckets == 4
    assert spec.maintained_indexes == [idx_name]
    assert spec.writer_config_defaults == {"durable_write": "false"}
    # After unset, None again.
    table.unset_lsm_write_spec()
    assert table.get_lsm_write_spec() is None
    # Identity round-trips (column recovered from the schema).
    table.set_lsm_write_spec(LsmWriteSpec.identity("id"))
    spec = table.get_lsm_write_spec()
    assert spec.spec_type == "identity"
    assert spec.column == "id"
    table.unset_lsm_write_spec()
    # Unsharded round-trips (no routing column).
    table.set_lsm_write_spec(LsmWriteSpec.unsharded())
    spec = table.get_lsm_write_spec()
    assert spec.spec_type == "unsharded"
    assert spec.column is None
@pytest.mark.asyncio
 async def test_async_get_lsm_write_spec(tmp_path):
    db = await lancedb.connect_async(
        tmp_path, read_consistency_interval=timedelta(seconds=0)
    )
    table = await db.create_table(
        "t",
        pa.RecordBatchReader.from_batches(SCHEMA, [_batch(["seed"], [0])]),
    )
    assert await table.get_lsm_write_spec() is None
    # A real scalar index is needed to name it as a maintained index.
    await table.create_index("id", config=BTree())
    idx_name = (await table.list_indices())[0].name
    await table.set_lsm_write_spec(
        LsmWriteSpec.bucket("id", 8).with_maintained_indexes([idx_name])
    )
    spec = await table.get_lsm_write_spec()
    assert spec is not None
    assert spec.spec_type == "bucket"
    assert spec.column == "id"
    assert spec.num_buckets == 8
    assert spec.maintained_indexes == [idx_name]
    await table.unset_lsm_write_spec()
    assert await table.get_lsm_write_spec() is None
@@ -935,41 +935,14 @@ def test_transform_fn(mem_db):
    try:
        import torch
-        # "torch" returns a list of per-row dicts. Default DataLoader collate
+        torch_result = list(
-        # stacks the per-row dicts back into a dict of batched tensors.
+            permutation.with_format("torch").iter(10, skip_last_batch=False)
        torch_perm = permutation.with_format("torch")
        torch_batch = list(torch_perm.iter(10, skip_last_batch=False))[0]
        assert isinstance(torch_batch, list)
        assert len(torch_batch) == 10
        assert isinstance(torch_batch[0], dict)
        assert set(torch_batch[0].keys()) == {"id", "value"}
        assert isinstance(torch_batch[0]["id"], torch.Tensor)
        assert torch_batch[0]["id"].dtype == torch.int64
        rows = torch_perm.__getitems__([0, 1, 2])
        assert isinstance(rows, list)
        assert len(rows) == 3
        assert isinstance(rows[0], dict)
        assert set(rows[0].keys()) == {"id", "value"}
        assert isinstance(rows[0]["id"], torch.Tensor)
        # "torch_row" returns a list of tensors, one per row.
        torch_rows = list(
            permutation.with_format("torch_row").iter(10, skip_last_batch=False)
        )[0]
-        assert isinstance(torch_rows, list)
+        assert isinstance(torch_result, list)
-        assert len(torch_rows) == 10
+        assert len(torch_result) == 10
-        assert isinstance(torch_rows[0], torch.Tensor)
+        assert isinstance(torch_result[0], torch.Tensor)
-        assert torch_rows[0].shape == (2,)
+        assert torch_result[0].shape == (2,)
-        assert torch_rows[0].dtype == torch.int64
+        assert torch_result[0].dtype == torch.int64
        # "torch_col" stacks columns into a single 2D tensor.
        torch_col = list(
            permutation.with_format("torch_col").iter(10, skip_last_batch=False)
        )[0]
        assert isinstance(torch_col, torch.Tensor)
        assert torch_col.shape == (2, 10)
        assert torch_col.dtype == torch.int64
    except ImportError:
        # Skip check if torch is not installed
        pass
@@ -11,7 +11,6 @@ import lancedb
 from lancedb.db import AsyncConnection
 from lancedb.embeddings.base import TextEmbeddingFunction
 from lancedb.embeddings.registry import get_registry, register
 from lancedb.expr import col
 from lancedb.index import FTS, IvfPq
 import lancedb.pydantic
 import numpy as np
@@ -64,71 +63,11 @@ def _blob_query_data():
    )
 def _create_blob_v2_query_table(db, name):
    schema = pa.schema(
        [
            pa.field("id", pa.int64()),
            pa.field("tag", pa.utf8()),
            pa.field("vector", pa.list_(pa.float32(), list_size=2)),
            lancedb.blob("blob"),
        ]
    )
    table = db.create_table(name, schema=schema)
    table.add(
        [
            {"id": 1, "tag": "drop", "vector": [1.0, 0.0], "blob": b"one"},
            {"id": 2, "tag": "keep", "vector": [2.0, 0.0], "blob": b"two"},
            {"id": 3, "tag": "keep", "vector": [3.0, 0.0], "blob": b"three"},
            {"id": 4, "tag": "keep", "vector": [4.0, 0.0], "blob": b"four"},
        ]
    )
    return table
 async def _create_blob_v2_query_table_async(db, name):
    schema = pa.schema(
        [
            pa.field("id", pa.int64()),
            pa.field("tag", pa.utf8()),
            pa.field("vector", pa.list_(pa.float32(), list_size=2)),
            lancedb.blob("blob"),
        ]
    )
    table = await db.create_table(name, schema=schema)
    await table.add(
        [
            {"id": 1, "tag": "drop", "vector": [1.0, 0.0], "blob": b"one"},
            {"id": 2, "tag": "keep", "vector": [2.0, 0.0], "blob": b"two"},
            {"id": 3, "tag": "keep", "vector": [3.0, 0.0], "blob": b"three"},
            {"id": 4, "tag": "keep", "vector": [4.0, 0.0], "blob": b"four"},
        ]
    )
    return table
 def _assert_lazy_blob(value, expected: bytes):
    assert hasattr(value, "readall")
    assert value.readall() == expected
 def _assert_blob_bytes_projection(df):
    assert df["id_alias"].tolist() == [3, 4]
    assert df["payload"].tolist() == [b"three", b"four"]
    assert df["double_id"].tolist() == [6, 8]
 def _blob_query_table(db, name, blob_schema):
    if blob_schema == "v1":
        return db.create_table(name, _blob_query_data())
    return _create_blob_v2_query_table(db, name)
 async def _blob_query_table_async(db, name, blob_schema):
    if blob_schema == "v1":
        return await db.create_table(name, _blob_query_data())
    return await _create_blob_v2_query_table_async(db, name)
@pytest.fixture(scope="module")
 def table(tmpdir_factory) -> lancedb.table.Table:
    tmp_path = str(tmpdir_factory.mktemp("data"))
@@ -296,11 +235,10 @@ def test_plain_scan_query_to_pandas_blob_modes(tmp_db, blob_mode):
        assert not hasattr(first, "readall")
-@pytest.mark.parametrize("blob_schema", ["v1", "v2"])
+def test_plain_scan_query_to_pandas_blob_projection(tmp_db):
 def test_plain_scan_query_to_pandas_blob_bytes_projection(tmp_db, blob_schema):
    pytest.importorskip("lance")
-    table = _blob_query_table(
+    table = tmp_db.create_table(
-        tmp_db, f"test_query_to_pandas_blob_{blob_schema}_bytes", blob_schema
+        "test_query_to_pandas_blob_projection", _blob_query_data()
    )
    df = (
@@ -312,8 +250,9 @@ def test_plain_scan_query_to_pandas_blob_bytes_projection(tmp_db, blob_schema):
        .to_pandas(blob_mode="bytes")
    )
-    _assert_blob_bytes_projection(df)
+    assert df["id_alias"].tolist() == [3, 4]
-    assert "_rowid" not in df.columns
+    assert df["payload"].tolist() == [b"three", b"four"]
    assert df["double_id"].tolist() == [6, 8]
@pytest.mark.parametrize("blob_mode", ["bytes", "descriptions"])
@@ -409,6 +348,18 @@ async def test_async_plain_scan_query_to_pandas_blob_projection(tmp_db_async):
    assert lazy_df["id"].tolist() == [1]
    _assert_lazy_blob(lazy_df["blob"].iloc[0], b"one")
    bytes_df = await (
        table.query()
        .where("id >= 2")
        .select({"id_alias": "id", "payload": "blob", "double_id": "id * 2"})
        .limit(2)
        .offset(1)
        .to_pandas(blob_mode="bytes")
    )
    assert bytes_df["id_alias"].tolist() == [3, 4]
    assert bytes_df["payload"].tolist() == [b"three", b"four"]
    assert bytes_df["double_id"].tolist() == [6, 8]
    desc_df = await (
        table.query()
        .where("id = 1")
@@ -420,31 +371,6 @@ async def test_async_plain_scan_query_to_pandas_blob_projection(tmp_db_async):
    assert not hasattr(first, "readall")
@pytest.mark.asyncio
@pytest.mark.parametrize("blob_schema", ["v1", "v2"])
 async def test_async_plain_scan_query_to_pandas_blob_bytes_projection(
    tmp_db_async, blob_schema
 ):
    pytest.importorskip("lance")
    table = await _blob_query_table_async(
        tmp_db_async,
        f"test_async_query_to_pandas_blob_{blob_schema}_bytes",
        blob_schema,
    )
    df = await (
        table.query()
        .where("id >= 2")
        .select({"id_alias": "id", "payload": "blob", "double_id": "id * 2"})
        .limit(2)
        .offset(1)
        .to_pandas(blob_mode="bytes")
    )
    _assert_blob_bytes_projection(df)
    assert "_rowid" not in df.columns
@pytest.mark.asyncio
@pytest.mark.parametrize("blob_mode", ["bytes", "descriptions"])
 async def test_async_plain_scan_query_to_pandas_blob_mode_does_not_collect_arrow(
@@ -576,18 +502,6 @@ def test_with_row_id(table: lancedb.table.Table):
    assert rs["_rowid"].to_pylist() == [0, 1]
 def test_blob_v2_query_omits_auto_row_id(tmp_db):
    table = _create_blob_v2_query_table(tmp_db, "test_blob_v2_omits_auto_rowid")
    query_obj = table.search().select(["id", "blob"]).limit(2).to_query_object()
    assert query_obj.with_row_id is None
    rs = table.search().select(["id", "blob"]).limit(2).to_arrow()
    assert "_rowid" not in rs.column_names
    assert rs["id"].to_pylist() == [1, 2]
 def test_where_repeated_combines_with_and(table: lancedb.table.Table):
    # Calling where() more than once should AND the filters together instead of
    # silently replacing the previous one (regression test for #2649).
@@ -2032,39 +1946,3 @@ def test_fast_search(tmp_path):
    # 2. Fast Search -> Should NOT include "LanceScan" (Uses Index)
    plan = table.search(q).fast_search().explain_plan(True)
    assert "LanceScan" not in plan
 def test_blob_v2_with_row_id_bytes_pandas(tmp_db):
    table = _create_blob_v2_query_table(tmp_db, "test_blob_v2_rowid_bytes_pandas")
    df = (
        table.search()
        .with_row_id(True)
        .select(["id", "blob"])
        .to_pandas(blob_mode="bytes")
    )
    assert "_rowid" in df.columns
    assert df["id"].tolist() == [1, 2, 3, 4]
    assert df["blob"].tolist() == [b"one", b"two", b"three", b"four"]
 def test_blob_v2_expr_projection_stash(tmp_db):
    table = _create_blob_v2_query_table(tmp_db, "test_blob_v2_expr_projection_stash")
    hits = table.search().select({"blob_alias": col("blob")}).limit(2).to_arrow()
    assert "_rowid" not in hits.column_names
    assert "_lance_row_id" in hits.schema.field("blob_alias").type.names
    blobs = table.fetch_blobs("blob", hits)
    assert [blobs[i].as_py() for i in range(len(blobs))] == [b"one", b"two"]
 def test_blob_v2_to_batches_row_id(tmp_db):
    table = _create_blob_v2_query_table(tmp_db, "test_blob_v2_to_batches_rowid")
    hits = table.search().select(["id", "blob"]).limit(2).to_batches().read_all()
    assert "_rowid" in hits.column_names
    blobs = table.fetch_blobs("blob", hits)
    assert [blobs[i].as_py() for i in range(len(blobs))] == [b"one", b"two"]
@@ -412,12 +412,10 @@ def test_remote_permutation_is_picklable():
            content_len = int(request.headers.get("Content-Length"))
            body = json.loads(request.rfile.read(content_len))
            if "filter" in body:
-                match = re.search(
+                match = re.search(r"_rowoffset in \((.*?)\)", body["filter"])
-                    r"_rowoffset\s+in\s+\((.*?)\)", body["filter"], re.IGNORECASE
+                offsets = [int(offset.strip()) for offset in match.group(1).split(",")]
                )
                offsets = [int(o.strip()) for o in match.group(1).split(",")]
            else:
-                offsets = list(range(len(rows)))
+                offsets = rows
            table = pa.table({"a": [rows[offset] for offset in offsets]})
            request.send_response(200)
@@ -23,7 +23,6 @@ from lancedb.rerankers import (
    AnswerdotaiRerankers,
    VoyageAIReranker,
    MRRReranker,
    WatsonxReranker,
 )
 from lancedb.table import LanceTable
@@ -728,19 +727,3 @@ def test_linear_combination_missing_fts_is_penalised():
        f"Document with FTS score (rowid 0, {scores[0]:.4f}) should beat "
        f"document with no FTS match (rowid 1, {scores[1]:.4f})"
    )
@pytest.mark.skipif(
    os.environ.get("WATSONX_API_KEY") is None
    or (
        os.environ.get("WATSONX_PROJECT_ID") is None
        and os.environ.get("WATSONX_SPACE_ID") is None
    ),
    reason="WATSONX_API_KEY and one of WATSONX_PROJECT_ID / "
    "WATSONX_SPACE_ID must be set",
 )
 def test_watsonx_reranker(tmp_path):
    pytest.importorskip("ibm_watsonx_ai")
    table, schema = get_test_table(tmp_path)
    reranker = WatsonxReranker()
    _run_test_reranker(reranker, table, "single player experience", None, schema)
@@ -7,8 +7,7 @@ Tests for S3 bucket names containing dots.
 Related issue: https://github.com/lancedb/lancedb/issues/1898
 These tests validate the early error checking for S3 bucket names with dots.
-When validation succeeds, eager namespace initialization may still fail later
+No actual S3 connection is made - validation happens before connection.
 because these tests intentionally do not provide real S3 credentials.
 """
 import pytest
@@ -21,13 +20,6 @@ BUCKET_WITH_DOTS_AND_AWS_REGION = ("s3://my.bucket.name", {"aws_region": "us-eas
 BUCKET_WITHOUT_DOTS = "s3://my-bucket/path"
 def assert_not_rejected_for_bucket_dots(connect):
    try:
        connect()
    except ValueError as err:
        assert "contains dots" not in str(err)
 class TestS3BucketWithDotsSync:
    """Tests for connect()."""
@@ -35,22 +27,19 @@ class TestS3BucketWithDotsSync:
        with pytest.raises(ValueError, match="contains dots"):
            lancedb.connect(BUCKET_WITH_DOTS)
-    def test_bucket_with_dots_and_region_is_not_rejected(self):
+    def test_bucket_with_dots_and_region_passes(self):
        uri, opts = BUCKET_WITH_DOTS_AND_REGION
-        assert_not_rejected_for_bucket_dots(
+        db = lancedb.connect(uri, storage_options=opts)
-            lambda: lancedb.connect(uri, storage_options=opts)
+        assert db is not None
        )
-    def test_bucket_with_dots_and_aws_region_is_not_rejected(self):
+    def test_bucket_with_dots_and_aws_region_passes(self):
        uri, opts = BUCKET_WITH_DOTS_AND_AWS_REGION
-        assert_not_rejected_for_bucket_dots(
+        db = lancedb.connect(uri, storage_options=opts)
-            lambda: lancedb.connect(uri, storage_options=opts)
+        assert db is not None
        )
-    def test_bucket_without_dots_is_not_rejected(self):
+    def test_bucket_without_dots_passes(self):
-        assert_not_rejected_for_bucket_dots(
+        db = lancedb.connect(BUCKET_WITHOUT_DOTS)
-            lambda: lancedb.connect(BUCKET_WITHOUT_DOTS)
+        assert db is not None
        )
 class TestS3BucketWithDotsAsync:
@@ -62,24 +51,18 @@ class TestS3BucketWithDotsAsync:
            await lancedb.connect_async(BUCKET_WITH_DOTS)
    @pytest.mark.asyncio
-    async def test_bucket_with_dots_and_region_is_not_rejected(self):
+    async def test_bucket_with_dots_and_region_passes(self):
        uri, opts = BUCKET_WITH_DOTS_AND_REGION
-        try:
+        db = await lancedb.connect_async(uri, storage_options=opts)
-            await lancedb.connect_async(uri, storage_options=opts)
+        assert db is not None
        except ValueError as err:
            assert "contains dots" not in str(err)
    @pytest.mark.asyncio
-    async def test_bucket_with_dots_and_aws_region_is_not_rejected(self):
+    async def test_bucket_with_dots_and_aws_region_passes(self):
        uri, opts = BUCKET_WITH_DOTS_AND_AWS_REGION
-        try:
+        db = await lancedb.connect_async(uri, storage_options=opts)
-            await lancedb.connect_async(uri, storage_options=opts)
+        assert db is not None
        except ValueError as err:
            assert "contains dots" not in str(err)
    @pytest.mark.asyncio
-    async def test_bucket_without_dots_is_not_rejected(self):
+    async def test_bucket_without_dots_passes(self):
-        try:
+        db = await lancedb.connect_async(BUCKET_WITHOUT_DOTS)
-            await lancedb.connect_async(BUCKET_WITHOUT_DOTS)
+        assert db is not None
        except ValueError as err:
            assert "contains dots" not in str(err)
@@ -45,32 +45,6 @@ def _blob_test_data():
    )
 def _blob_v2_table(db: DBConnection, name: str):
    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("blob")])
    table = db.create_table(name, schema=schema)
    table.add([{"id": 1, "blob": b"hello"}, {"id": 2, "blob": b"world"}])
    return table
 async def _blob_v2_table_async(db: AsyncConnection, name: str):
    schema = pa.schema([pa.field("id", pa.int64()), lancedb.blob("blob")])
    table = await db.create_table(name, schema=schema)
    await table.add([{"id": 1, "blob": b"hello"}, {"id": 2, "blob": b"world"}])
    return table
 def _blob_table(db: DBConnection, name: str, blob_schema: str):
    if blob_schema == "v1":
        return db.create_table(name, data=_blob_test_data())
    return _blob_v2_table(db, name)
 async def _blob_table_async(db: AsyncConnection, name: str, blob_schema: str):
    if blob_schema == "v1":
        return await db.create_table(name, data=_blob_test_data())
    return await _blob_v2_table_async(db, name)
 def _assert_lazy_blob(value, expected: bytes):
    assert hasattr(value, "readall")
    assert value.readall() == expected
@@ -133,18 +107,6 @@ def test_table_to_pandas_blob_modes(tmp_db: DBConnection, blob_mode):
        assert not hasattr(first, "readall")
@pytest.mark.parametrize("blob_schema", ["v1", "v2"])
 def test_table_to_pandas_blob_bytes(tmp_db: DBConnection, blob_schema):
    pytest.importorskip("lance")
    table = _blob_table(tmp_db, f"test_to_pandas_blob_{blob_schema}_bytes", blob_schema)
    df = table.to_pandas(blob_mode="bytes")
    assert list(df.columns) == ["id", "blob"]
    assert df["blob"].tolist() == [b"hello", b"world"]
    assert "_rowid" not in df.columns
 def test_table_to_pandas_kwargs(tmp_db: DBConnection):
    pd = pytest.importorskip("pandas")
    data = pa.table({"id": pa.array([1, 2], pa.int64())})
@@ -156,20 +118,15 @@ def test_table_to_pandas_kwargs(tmp_db: DBConnection):
@pytest.mark.asyncio
-@pytest.mark.parametrize("blob_schema", ["v1", "v2"])
+async def test_async_table_to_pandas_blob_bytes(tmp_db_async: AsyncConnection):
 async def test_async_table_to_pandas_blob_bytes(
    tmp_db_async: AsyncConnection, blob_schema
 ):
    pytest.importorskip("lance")
-    table = await _blob_table_async(
+    table = await tmp_db_async.create_table(
-        tmp_db_async, f"test_async_to_pandas_blob_{blob_schema}_bytes", blob_schema
+        "test_async_to_pandas_blob_bytes", data=_blob_test_data()
    )
    df = await table.to_pandas(blob_mode="bytes")
    assert list(df.columns) == ["id", "blob"]
    assert df["blob"].tolist() == [b"hello", b"world"]
    assert "_rowid" not in df.columns
@pytest.mark.asyncio
@@ -1611,23 +1568,16 @@ def test_create_with_nans(mem_db: DBConnection):
        "fill_test",
        data=[
            {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
            {"vector": [2.1, 4.1], "item": "foo", "price": 9.0},
            {"vector": [np.nan], "item": "bar", "price": 20.0},
-            {"vector": [np.nan, 5.0], "item": "bar", "price": 21.0},
+            {"vector": [np.nan, np.nan], "item": "bar", "price": 20.0},
            {"vector": [5], "item": "bar", "price": 22.0},
        ],
        on_bad_vectors="fill",
        fill_value=0.0,
    )
-    assert len(table) == 5
+    assert len(table) == 3
    arrow_tbl = table.search().where("item == 'bar'").to_arrow()
-    filled_vectors = {
+    v = arrow_tbl["vector"].to_pylist()[0]
-        row["price"]: row["vector"]
+    assert np.allclose(v, np.array([0.0, 0.0]))
        for row in arrow_tbl.select(["price", "vector"]).to_pylist()
    }
    assert np.allclose(filled_vectors[20.0], np.array([0.0, 0.0]))
    assert np.allclose(filled_vectors[21.0], np.array([0.0, 5.0]))
    assert np.allclose(filled_vectors[22.0], np.array([5.0, 0.0]))
 def test_add_with_nans(mem_db: DBConnection):
@@ -1670,21 +1620,15 @@ def test_add_with_nans(mem_db: DBConnection):
        data=[
            {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
            {"vector": [np.nan], "item": "bar", "price": 20.0},
-            {"vector": [np.nan, 5.0], "item": "bar", "price": 21.0},
+            {"vector": [np.nan, np.nan], "item": "bar", "price": 20.0},
            {"vector": [5], "item": "bar", "price": 22.0},
        ],
        on_bad_vectors="fill",
        fill_value=0.0,
    )
-    assert len(table) == 4
+    assert len(table) == 3
    arrow_tbl = table.search().where("item == 'bar'").to_arrow()
-    filled_vectors = {
+    v = arrow_tbl["vector"].to_pylist()[0]
-        row["price"]: row["vector"]
+    assert np.allclose(v, np.array([0.0, 0.0]))
        for row in arrow_tbl.select(["price", "vector"]).to_pylist()
    }
    assert np.allclose(filled_vectors[20.0], np.array([0.0, 0.0]))
    assert np.allclose(filled_vectors[21.0], np.array([0.0, 5.0]))
    assert np.allclose(filled_vectors[22.0], np.array([5.0, 0.0]))
 def test_add_with_empty_fixed_size_list_drops_bad_rows(mem_db: DBConnection):
@@ -1845,9 +1789,7 @@ def test_on_bad_vectors_fill_preserves_arrow_nested_vector_type(mem_db: DBConnec
        fill_value=0.0,
    )
-    vector = table.to_arrow()["vector"]
+    assert table.to_arrow()["vector"].to_pylist() == [[1.0, 2.0], [0.0, 0.0]]
    assert vector.type == pa.list_(pa.float32())
    assert vector.to_pylist() == [[1.0, 2.0], [0.0, 3.0]]
@pytest.mark.parametrize(
@@ -148,47 +148,15 @@ def test_permutation_dataloader(mem_db):
    for batch in dataloader:
        assert batch["a"].size(0) == 10
-    # "torch" produces a list of per-row dicts per batch. The default
+    permutation = permutation.with_format("torch")
-    # DataLoader collate stacks the per-row dicts back into a batched dict.
+    dataloader = torch.utils.data.DataLoader(permutation, batch_size=10, shuffle=True)
    torch_perm = permutation.with_format("torch")
    batch = next(torch_perm.iter(10, skip_last_batch=False))
    assert isinstance(batch, list)
    assert len(batch) == 10
    assert isinstance(batch[0], dict)
    assert isinstance(batch[0]["a"], torch.Tensor)
    rows = torch_perm.__getitems__([0, 1, 2])
    assert isinstance(rows, list)
    assert len(rows) == 3
    assert isinstance(rows[0], dict)
    assert isinstance(rows[0]["a"], torch.Tensor)
    dataloader = torch.utils.data.DataLoader(torch_perm, batch_size=10, shuffle=True)
    for batch in dataloader:
        assert isinstance(batch, dict)
        assert batch["a"].shape == (10,)
    # Spawn-based workers exercise the pickle round-trip path: the new
    # transform-as-list shape must survive pickling so workers produce the
    # same per-row dicts the parent does.
    spawn_loader = torch.utils.data.DataLoader(
        torch_perm,
        batch_size=10,
        num_workers=2,
        multiprocessing_context="spawn",
    )
    for batch in spawn_loader:
        assert isinstance(batch, dict)
        assert batch["a"].shape == (10,)
    # "torch_row" returns a list of row tensors. Works with the default
    # DataLoader collate (stacks rows into 2D).
    row_perm = permutation.with_format("torch_row")
    dataloader = torch.utils.data.DataLoader(row_perm, batch_size=10, shuffle=True)
    for batch in dataloader:
        assert batch.size(0) == 10
        assert batch.size(1) == 1
-    col_perm = permutation.with_format("torch_col")
+    permutation = permutation.with_format("torch_col")
    dataloader = torch.utils.data.DataLoader(
-        col_perm, collate_fn=lambda x: x, batch_size=10, shuffle=True
+        permutation, collate_fn=lambda x: x, batch_size=10, shuffle=True
    )
    for batch in dataloader:
        assert batch.size(0) == 1
@@ -13,7 +13,6 @@ from lancedb.embeddings.registry import EmbeddingFunctionRegistry
 from lancedb.table import (
    _append_vector_columns,
    _cast_to_target_schema,
    _fill_bad_vector_values,
    _handle_bad_vectors,
    _into_pyarrow_reader,
    _infer_target_schema,
@@ -26,42 +25,10 @@ import pandas as pd
 import polars as pl
 import pytest
 import lancedb
-from lancedb.util import flatten_columns, get_uri_scheme, join_uri, value_to_sql
+from lancedb.util import get_uri_scheme, join_uri, value_to_sql
 from utils import exception_output
 def _struct_table() -> pa.Table:
    return pa.table(
        {
            "id": [1, 2],
            "nested": pa.array([{"a": 1, "b": 2}, {"a": 3, "b": 4}]),
        }
    )
 def test_flatten_columns():
    tbl = _struct_table()
    # None / False mean "do not flatten": the struct column is preserved.
    # `False` is a regression guard: because bool is a subclass of int it used
    # to fall into the integer branch and raise ValueError (see issue).
    for no_flatten in (None, False):
        result = flatten_columns(tbl, no_flatten)
        assert result.column_names == ["id", "nested"]
    # True flattens all nested levels.
    flattened = flatten_columns(tbl, True)
    assert flattened.column_names == ["id", "nested.a", "nested.b"]
    # A positive integer flattens up to that depth.
    flattened = flatten_columns(tbl, 1)
    assert flattened.column_names == ["id", "nested.a", "nested.b"]
    # Non-positive integers are still rejected.
    with pytest.raises(ValueError):
        flatten_columns(tbl, 0)
 def test_normalize_uri():
    uris = [
        "relative/path",
@@ -288,9 +255,7 @@ def test_append_vector_columns():
@pytest.mark.parametrize("on_bad_vectors", ["error", "drop", "fill", "null"])
 def test_handle_bad_vectors_jagged(on_bad_vectors):
-    vector = pa.array(
+    vector = pa.array([[1.0, 2.0], [3.0], [4.0, 5.0]])
        [[1.0, 2.0], [3.0], [4.0, 5.0], [6.0, 7.0, 8.0], [None, 9.0], None]
    )
    schema = pa.schema({"vector": pa.list_(pa.float64())})
    data = pa.table({"vector": vector}, schema=schema)
@@ -316,54 +281,15 @@ def test_handle_bad_vectors_jagged(on_bad_vectors):
        ).read_all()
    if on_bad_vectors == "drop":
-        expected = pa.array([[1.0, 2.0], [4.0, 5.0], [None, 9.0]])
+        expected = pa.array([[1.0, 2.0], [4.0, 5.0]])
    elif on_bad_vectors == "fill":
-        expected = pa.array(
+        expected = pa.array([[1.0, 2.0], [42.0, 42.0], [4.0, 5.0]])
            [
                [1.0, 2.0],
                [3.0, 42.0],
                [4.0, 5.0],
                [6.0, 7.0],
                [None, 9.0],
                [42.0, 42.0],
            ]
        )
    elif on_bad_vectors == "null":
-        expected = pa.array([[1.0, 2.0], None, [4.0, 5.0], None, [None, 9.0], None])
+        expected = pa.array([[1.0, 2.0], None, [4.0, 5.0]])
    assert output["vector"].combine_chunks() == expected
@pytest.mark.parametrize(
    ("vector_type", "vectors", "expected"),
    [
        (
            pa.list_(pa.float64()),
            [[1.0, float("nan")], [2.0], None, [None, 3.0], [4.0, 5.0, 6.0]],
            [[1.0, 42.0], [2.0, 42.0], [42.0, 42.0], [None, 3.0], [4.0, 5.0]],
        ),
        (
            pa.large_list(pa.float64()),
            [[1.0, float("nan")], [2.0], None, [None, 3.0], [4.0, 5.0, 6.0]],
            [[1.0, 42.0], [2.0, 42.0], [42.0, 42.0], [None, 3.0], [4.0, 5.0]],
        ),
        (
            pa.list_(pa.float64(), 2),
            [[1.0, float("nan")], None, [None, 3.0]],
            [[1.0, 42.0], [42.0, 42.0], [None, 3.0]],
        ),
    ],
 )
 def test_fill_bad_vector_values_arrow_types(vector_type, vectors, expected):
    arr = pa.array([[0.0, 0.0], *vectors, [9.0, 9.0]], type=vector_type)
    arr = arr.slice(1, len(vectors))
    actual = _fill_bad_vector_values(arr, dim=2, fill_value=42.0)
    assert actual.type == vector_type
    assert actual.to_pylist() == expected
@pytest.mark.parametrize("on_bad_vectors", ["error", "drop", "fill", "null"])
 def test_handle_bad_vectors_nan(on_bad_vectors):
    vector = pa.array([[1.0, float("nan")], [3.0, 4.0]])
@@ -393,7 +319,7 @@ def test_handle_bad_vectors_nan(on_bad_vectors):
    if on_bad_vectors == "drop":
        expected = pa.array([[3.0, 4.0]])
    elif on_bad_vectors == "fill":
-        expected = pa.array([[1.0, 42.0], [3.0, 4.0]])
+        expected = pa.array([[42.0, 42.0], [3.0, 4.0]])
    elif on_bad_vectors == "null":
        expected = pa.array([None, [3.0, 4.0]])
@@ -7,14 +7,12 @@
 //! build type-safe filter / projection expressions that map directly to
 //! DataFusion [`Expr`] nodes, bypassing SQL string parsing.
 use std::ops::{Add, Div, Mul, Not, Sub};
 use arrow::{datatypes::DataType, pyarrow::PyArrowType};
 use datafusion_common::ScalarValue;
 use lancedb::expr::{
    DfExpr, col as ldb_col, contains, expr_cast, is_in, lit as df_lit, lower, upper,
 };
-use pyo3::types::{PyBytes, PyDate, PyDateTime};
+use pyo3::types::PyBytes;
 use pyo3::{Bound, PyAny, PyResult, exceptions::PyValueError, prelude::*, pyfunction};
 /// A type-safe DataFusion expression.
@@ -65,30 +63,30 @@ impl PyExpr {
        Self(self.0.clone().or(other.0.clone()))
    }
    /// Logical NOT.
    fn not_(&self) -> Self {
        use std::ops::Not;
        Self(self.0.clone().not())
    }
    // ── arithmetic ───────────────────────────────────────────────────────────
    /// Add expressions.
    fn add(&self, other: &Self) -> Self {
        use std::ops::Add;
        Self(self.0.clone().add(other.0.clone()))
    }
    /// Subtract expressions.
    fn sub(&self, other: &Self) -> Self {
        use std::ops::Sub;
        Self(self.0.clone().sub(other.0.clone()))
    }
    /// Multiply expressions.
    fn mul(&self, other: &Self) -> Self {
        use std::ops::Mul;
        Self(self.0.clone().mul(other.0.clone()))
    }
    /// Divide expressions.
    fn div(&self, other: &Self) -> Self {
        use std::ops::Div;
        Self(self.0.clone().div(other.0.clone()))
    }
@@ -155,8 +153,7 @@ pub fn expr_col(name: &str) -> PyExpr {
 /// Create a literal value expression.
 ///
-/// Supported Python types: `bool`, `int`, `float`, `str`, `bytes`, `date`,
+/// Supported Python types: `bool`, `int`, `float`, `str`, `bytes`.
 /// `datetime`, `Decimal`.
 #[pyfunction]
 pub fn expr_lit(value: Bound<'_, PyAny>) -> PyResult<PyExpr> {
    // bool must be checked before int because bool is a subclass of int in Python
@@ -166,19 +163,6 @@ pub fn expr_lit(value: Bound<'_, PyAny>) -> PyResult<PyExpr> {
    if let Ok(i) = value.extract::<i64>() {
        return Ok(PyExpr(df_lit(i)));
    }
    // Decimal must be checked before f64: Python's Decimal implements __float__,
    // so value.extract::<f64>() would succeed and silently truncate the value to
    // f64, losing precision. Build a Decimal128 scalar to preserve it instead.
    if value.get_type().name()? == "Decimal" {
        let s = value.call_method0("__str__")?.extract::<String>()?;
        // Parse the decimal string into an i128 value, precision, and scale.
        let (val, precision, scale) = parse_decimal(&s)?;
        return Ok(PyExpr(df_lit(ScalarValue::Decimal128(
            Some(val),
            precision,
            scale,
        ))));
    }
    if let Ok(f) = value.extract::<f64>() {
        return Ok(PyExpr(df_lit(f)));
    }
@@ -189,48 +173,12 @@ pub fn expr_lit(value: Bound<'_, PyAny>) -> PyResult<PyExpr> {
        let bytes = value.extract::<Vec<u8>>()?;
        return Ok(PyExpr(df_lit(ScalarValue::Binary(Some(bytes)))));
    }
    // datetime.datetime is a subclass of datetime.date, so it must be checked first.
    if let Ok(dt) = value.cast::<PyDateTime>() {
        let ts: f64 = dt.call_method0("timestamp")?.extract()?;
        let micros = (ts * 1_000_000.0).round() as i64;
        return Ok(PyExpr(df_lit(ScalarValue::TimestampMicrosecond(
            Some(micros),
            None,
        ))));
    }
    if let Ok(d) = value.cast::<PyDate>() {
        let ordinal: i32 = d.call_method0("toordinal")?.extract()?;
        let days = ordinal - 719163; // Unix epoch is 1970-01-01
        return Ok(PyExpr(df_lit(ScalarValue::Date32(Some(days)))));
    }
    Err(PyValueError::new_err(format!(
-        "unsupported literal type: {}. Supported: bool, int, float, str, bytes, date, datetime, Decimal",
+        "unsupported literal type: {}. Supported: bool, int, float, str, bytes",
        value.get_type().name()?
    )))
 }
 fn parse_decimal(s: &str) -> PyResult<(i128, u8, i8)> {
    let s = s.trim();
    let dot_pos = s.find('.');
    let scale = if let Some(pos) = dot_pos {
        (s.len() - pos - 1) as i8
    } else {
        0
    };
    let digits = s.replace('.', "");
    let val = digits
        .parse::<i128>()
        .map_err(|e| PyValueError::new_err(format!("failed to parse decimal digits: {}", e)))?;
    // Precision is total number of digits
    let precision = digits.trim_start_matches('-').len() as u8;
    Ok((val, precision, scale))
 }
 /// Call an arbitrary registered SQL function by name.
 ///
 /// See `lancedb::expr::func` for the list of supported function names.
@@ -15,8 +15,8 @@ use pyo3::{
 use query::{FTSQuery, HybridQuery, Query, VectorQuery};
 use session::Session;
 use table::{
-    AddColumnsResult, AddResult, AlterColumnsResult, DeleteResult, DropColumnsResult, FtsToken,
+    AddColumnsResult, AddResult, AlterColumnsResult, DeleteResult, DropColumnsResult, LsmWriteSpec,
-    LsmWriteSpec, MergeResult, PyBlobFile, Table, UpdateFieldMetadataResult, UpdateResult,
+    MergeResult, Table, UpdateFieldMetadataResult, UpdateResult,
 };
 pub mod arrow;
@@ -27,7 +27,6 @@ pub mod header;
 pub mod index;
 pub mod namespace;
 pub mod oauth;
 pub mod otel;
 pub mod permutation;
 pub mod query;
 pub mod runtime;
@@ -44,7 +43,6 @@ pub fn _lancedb(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
    m.add_class::<Connection>()?;
    m.add_class::<Session>()?;
    m.add_class::<Table>()?;
    m.add_class::<PyBlobFile>()?;
    m.add_class::<IndexConfig>()?;
    m.add_class::<Query>()?;
    m.add_class::<FTSQuery>()?;
@@ -60,23 +58,12 @@ pub fn _lancedb(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
    m.add_class::<DeleteResult>()?;
    m.add_class::<DropColumnsResult>()?;
    m.add_class::<UpdateResult>()?;
    m.add_class::<FtsToken>()?;
    m.add_class::<PyAsyncPermutationBuilder>()?;
    m.add_class::<PyPermutationReader>()?;
    m.add_class::<PyExpr>()?;
    // OpenTelemetry metrics bridge
    m.add_class::<otel::PyMetricPoint>()?;
    m.add_class::<otel::PyMetricDescription>()?;
    m.add_function(wrap_pyfunction!(
        otel::register_lancedb_metrics_recorder,
        m
    )?)?;
    m.add_function(wrap_pyfunction!(otel::lancedb_metrics_catalog, m)?)?;
    m.add_function(wrap_pyfunction!(otel::snapshot_lancedb_metrics, m)?)?;
    m.add_function(wrap_pyfunction!(connect, m)?)?;
    m.add_function(wrap_pyfunction!(connect_namespace, m)?)?;
    m.add_function(wrap_pyfunction!(connect_namespace_client, m)?)?;
    m.add_function(wrap_pyfunction!(table::tokenize, m)?)?;
    m.add_function(wrap_pyfunction!(permutation::async_permutation_builder, m)?)?;
    m.add_function(wrap_pyfunction!(util::validate_table_name, m)?)?;
    m.add_function(wrap_pyfunction!(query::fts_query_to_json, m)?)?;
@@ -1,96 +0,0 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 //! Python-facing wrappers over [`lancedb::metrics_otel`].
 //!
 //! The aggregation, catalog, and histogram bucketing all live in the LanceDB
 //! core crate; this module only converts the core snapshot types into PyO3
 //! classes and exposes the three entry points to Python, where
 //! `lancedb/otel.py` bridges them into the user's OpenTelemetry `MeterProvider`.
 use std::collections::HashMap;
 use lancedb::metrics_otel::{MetricPoint, MetricValue};
 use pyo3::prelude::*;
 /// One metric data point exposed to Python. For counters and gauges only
 /// `value` is set; for histograms `buckets` (cumulative `le` counts), `count`,
 /// and `sum` are set.
 #[pyclass(name = "MetricPoint", get_all)]
 pub struct PyMetricPoint {
    name: String,
    kind: String,
    attributes: HashMap<String, String>,
    value: Option<f64>,
    buckets: Option<Vec<(String, u64)>>,
    count: Option<u64>,
    sum: Option<f64>,
 }
 impl From<MetricPoint> for PyMetricPoint {
    fn from(point: MetricPoint) -> Self {
        let kind = point.kind.as_str().to_string();
        let (value, buckets, count, sum) = match point.value {
            MetricValue::Scalar(v) => (Some(v), None, None, None),
            MetricValue::Histogram {
                buckets,
                count,
                sum,
            } => (None, Some(buckets), Some(count), Some(sum)),
        };
        Self {
            name: point.name,
            kind,
            attributes: point.attributes,
            value,
            buckets,
            count,
            sum,
        }
    }
 }
 /// A described metric, used by the Python layer to create instruments up front.
 #[pyclass(name = "MetricDescription", get_all)]
 pub struct PyMetricDescription {
    name: String,
    kind: String,
    unit: Option<String>,
    description: String,
 }
 /// Install the LanceDB metrics recorder as the process-global `metrics` recorder.
 ///
 /// Returns `True` if the recorder is installed (now or previously). Returns
 /// `False` if a *different* recorder is already installed — `metrics` allows
 /// only one global recorder per process, so LanceDB cannot coexist with another.
 #[pyfunction]
 pub fn register_lancedb_metrics_recorder() -> bool {
    lancedb::metrics_otel::register_metrics_recorder()
 }
 /// The catalog of described LanceDB metrics. Empty until the recorder is installed.
 #[pyfunction]
 pub fn lancedb_metrics_catalog() -> Vec<PyMetricDescription> {
    lancedb::metrics_otel::metrics_catalog()
        .into_iter()
        .map(|desc| PyMetricDescription {
            name: desc.name,
            kind: desc.kind.as_str().to_string(),
            unit: desc.unit,
            description: desc.description,
        })
        .collect()
 }
 /// A point-in-time snapshot of every recorded metric. Empty until the recorder
 /// is installed.
 ///
 /// The read is lock-free but not O(1): it walks every registered series and
 /// allocates owned copies of their names and labels. The GIL is released across
 /// that work so a periodic collection doesn't stall other Python threads.
 #[pyfunction]
 pub fn snapshot_lancedb_metrics(py: Python<'_>) -> Vec<PyMetricPoint> {
    let points = py.detach(lancedb::metrics_otel::snapshot_metrics);
    points.into_iter().map(PyMetricPoint::from).collect()
 }
@@ -79,14 +79,13 @@ impl PyAsyncPermutationBuilder {
 #[pymethods]
 impl PyAsyncPermutationBuilder {
-    #[pyo3(signature = (*, ratios=None, counts=None, fixed=None, seed=None, clump_size=None, split_names=None))]
+    #[pyo3(signature = (*, ratios=None, counts=None, fixed=None, seed=None, split_names=None))]
    pub fn split_random(
        slf: PyRefMut<'_, Self>,
        ratios: Option<Vec<f64>>,
        counts: Option<Vec<u64>>,
        fixed: Option<u64>,
        seed: Option<u64>,
        clump_size: Option<u64>,
        split_names: Option<Vec<String>>,
    ) -> PyResult<Self> {
        // Check that exactly one split type is provided
@@ -112,14 +111,7 @@ impl PyAsyncPermutationBuilder {
        };
        slf.modify(|builder| {
-            builder.with_split_strategy(
+            builder.with_split_strategy(SplitStrategy::Random { seed, sizes }, split_names)
                SplitStrategy::Random {
                    seed,
                    sizes,
                    clump_size,
                },
                split_names,
            )
        })
    }
@@ -2,7 +2,7 @@
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 use std::{collections::HashMap, sync::Arc};
-use crate::runtime::{block_on, future_into_py};
+use crate::runtime::future_into_py;
 use crate::{
    connection::Connection,
    error::PythonErrorExt,
@@ -12,23 +12,19 @@ use crate::{
    table::scannable::PyScannable,
 };
 use arrow::{
    array::{Array, LargeBinaryArray},
    datatypes::{DataType, Schema},
    ffi_stream::ArrowArrayStreamReader,
    pyarrow::{FromPyArrow, PyArrowType, ToPyArrow},
 };
 use lancedb::blob::BlobFile;
 use lancedb::index::scalar::FtsIndexBuilder;
 use lancedb::table::{
-    AddDataMode, ColumnAlteration, Duration, FieldMetadataUpdate, FtsToken as LanceDbFtsToken,
+    AddDataMode, ColumnAlteration, Duration, FieldMetadataUpdate, NewColumnTransform,
-    NewColumnTransform, OptimizeAction, OptimizeOptions, Ref, Table as LanceDbTable,
+    OptimizeAction, OptimizeOptions, Ref, Table as LanceDbTable,
 };
 use lancedb::tokenize as lancedb_tokenize;
 use pyo3::{
    Bound, FromPyObject, Py, PyAny, PyRef, PyResult, Python,
    exceptions::{PyRuntimeError, PyValueError},
-    pyclass, pyfunction, pymethods,
+    pyclass, pymethods,
-    types::{IntoPyDict, PyAnyMethods, PyBytes, PyDict, PyDictMethods},
+    types::{IntoPyDict, PyAnyMethods, PyDict, PyDictMethods},
 };
 mod scannable;
@@ -326,12 +322,6 @@ impl From<LsmWriteSpec> for lancedb::table::LsmWriteSpec {
    }
 }
 impl From<lancedb::table::LsmWriteSpec> for LsmWriteSpec {
    fn from(inner: lancedb::table::LsmWriteSpec) -> Self {
        Self { inner }
    }
 }
 #[pyclass(get_all, from_py_object)]
 #[derive(Clone, Debug)]
 pub struct AddColumnsResult {
@@ -416,150 +406,6 @@ impl From<lancedb::table::DropColumnsResult> for DropColumnsResult {
    }
 }
 /// Lazy blob handle from ``Table.fetch_blob_files``.
 #[pyclass(name = "BlobFile")]
 pub struct PyBlobFile {
    inner: Arc<BlobFile>,
 }
 #[pymethods]
 impl PyBlobFile {
    fn read_bytes(self_: PyRef<'_, Self>) -> PyResult<Py<PyBytes>> {
        let inner = self_.inner.clone();
        let bytes = block_on(async move { inner.read().await })
            .map_err(|e| PyRuntimeError::new_err(format!("blob read failed: {e}")))?;
        Ok(PyBytes::new(self_.py(), bytes.as_ref()).unbind())
    }
    pub fn read(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner.clone();
        future_into_py(self_.py(), async move {
            let bytes = inner
                .read()
                .await
                .map_err(|e| PyRuntimeError::new_err(format!("blob read failed: {e}")))?;
            Python::attach(|py| Ok(PyBytes::new(py, bytes.as_ref()).unbind()))
        })
    }
    fn close(self_: PyRef<'_, Self>) -> PyResult<()> {
        let inner = self_.inner.clone();
        block_on(async move { inner.close().await })
            .map_err(|e| PyRuntimeError::new_err(format!("blob close failed: {e}")))
    }
    fn is_closed(self_: PyRef<'_, Self>) -> bool {
        let inner = self_.inner.clone();
        block_on(async move { inner.is_closed().await })
    }
    fn seek(self_: PyRef<'_, Self>, position: u64) -> PyResult<()> {
        let inner = self_.inner.clone();
        block_on(async move { inner.seek(position).await })
            .map_err(|e| PyRuntimeError::new_err(format!("blob seek failed: {e}")))
    }
    fn tell(self_: PyRef<'_, Self>) -> PyResult<u64> {
        let inner = self_.inner.clone();
        block_on(async move { inner.tell().await })
            .map_err(|e| PyRuntimeError::new_err(format!("blob tell failed: {e}")))
    }
    fn size(self_: PyRef<'_, Self>) -> u64 {
        self_.inner.size()
    }
    /// Read a blob-local byte range without moving the cursor.
    fn read_range(self_: PyRef<'_, Self>, offset: u64, length: usize) -> PyResult<Py<PyBytes>> {
        let end = offset
            .checked_add(length as u64)
            .ok_or_else(|| PyValueError::new_err("offset + length overflowed"))?;
        let inner = self_.inner.clone();
        let bytes = block_on(async move { inner.read_range(offset..end).await })
            .map_err(|e| PyRuntimeError::new_err(format!("blob read_range failed: {e}")))?;
        Ok(PyBytes::new(self_.py(), bytes.as_ref()).unbind())
    }
    fn read_up_to(self_: PyRef<'_, Self>, length: usize) -> PyResult<Py<PyBytes>> {
        let inner = self_.inner.clone();
        let bytes = block_on(async move { inner.read_up_to(length).await })
            .map_err(|e| PyRuntimeError::new_err(format!("blob read failed: {e}")))?;
        Ok(PyBytes::new(self_.py(), bytes.as_ref()).unbind())
    }
 }
 #[pyclass(get_all, from_py_object)]
 #[derive(Clone, Debug)]
 pub struct FtsToken {
    pub text: String,
    pub position: u32,
 }
 #[pymethods]
 impl FtsToken {
    pub fn __repr__(&self) -> String {
        format!("FtsToken(text={:?}, position={})", self.text, self.position)
    }
 }
 impl From<LanceDbFtsToken> for FtsToken {
    fn from(token: LanceDbFtsToken) -> Self {
        Self {
            text: token.text,
            position: token.position,
        }
    }
 }
 #[pyfunction(signature = (
    query,
    *,
    base_tokenizer = "simple".to_string(),
    language = "English".to_string(),
    max_token_length = Some(40),
    lower_case = true,
    stem = true,
    remove_stop_words = true,
    ascii_folding = true,
    ngram_min_length = 3,
    ngram_max_length = 3,
    prefix_only = false
 ))]
 #[allow(clippy::too_many_arguments)]
 pub fn tokenize(
    query: String,
    base_tokenizer: String,
    language: String,
    max_token_length: Option<u32>,
    lower_case: bool,
    stem: bool,
    remove_stop_words: bool,
    ascii_folding: bool,
    ngram_min_length: u32,
    ngram_max_length: u32,
    prefix_only: bool,
 ) -> PyResult<Vec<FtsToken>> {
    let params = FtsIndexBuilder::default()
        .base_tokenizer(base_tokenizer)
        .language(&language)
        .map_err(|_| {
            PyValueError::new_err(format!(
                "LanceDB does not support the requested language: '{}'",
                language
            ))
        })?
        .max_token_length(max_token_length.map(|value| value as usize))
        .lower_case(lower_case)
        .stem(stem)
        .remove_stop_words(remove_stop_words)
        .ascii_folding(ascii_folding)
        .ngram_min_length(ngram_min_length)
        .ngram_max_length(ngram_max_length)
        .ngram_prefix_only(prefix_only);
    let tokens = lancedb_tokenize(&query, &params).infer_error()?;
    Ok(tokens.into_iter().map(FtsToken::from).collect())
 }
 #[pyclass]
 pub struct Table {
    // We keep a copy of the name to use if the inner table is dropped
@@ -858,29 +704,6 @@ impl Table {
        })
    }
    #[pyo3(signature = (query, *, column=None, index_name=None))]
    pub fn tokenize(
        self_: PyRef<'_, Self>,
        query: String,
        column: Option<String>,
        index_name: Option<String>,
    ) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
            let tokens = match (column.as_deref(), index_name.as_deref()) {
                (Some(_), Some(_)) | (None, None) => {
                    return Err(PyValueError::new_err(
                        "Specify exactly one of 'column' or 'index_name'",
                    ));
                }
                (Some(column), None) => inner.tokenize_with_column(&query, column).await,
                (None, Some(index_name)) => inner.tokenize(&query, index_name).await,
            }
            .infer_error()?;
            Ok(tokens.into_iter().map(FtsToken::from).collect::<Vec<_>>())
        })
    }
    pub fn index_stats(self_: PyRef<'_, Self>, index_name: String) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
@@ -1072,55 +895,6 @@ impl Table {
        ))
    }
    /// Names of the blob v2 columns declared on this table, in declaration order.
    pub fn blob_columns(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
            inner.blob_columns().await.infer_error()
        })
    }
    /// Read blob bytes for `row_ids` from blob v2 column `column`.
    #[pyo3(signature = (column, row_ids))]
    pub fn fetch_blobs(
        self_: PyRef<'_, Self>,
        column: String,
        row_ids: Vec<u64>,
    ) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
            let blobs: LargeBinaryArray = inner
                .fetch_blobs(column.as_str(), &row_ids)
                .await
                .infer_error()?;
            Python::attach(|py| blobs.to_data().to_pyarrow(py).map(|obj| obj.unbind()))
        })
    }
    /// Open lazy blob handles for `row_ids` from blob v2 column `column`.
    #[pyo3(signature = (column, row_ids))]
    pub fn fetch_blob_files(
        self_: PyRef<'_, Self>,
        column: String,
        row_ids: Vec<u64>,
    ) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
            let handles = inner
                .fetch_blob_files(column.as_str(), &row_ids)
                .await
                .infer_error()?;
            Ok(handles
                .into_iter()
                .map(|handle| {
                    handle.map(|file| PyBlobFile {
                        inner: Arc::new(file),
                    })
                })
                .collect::<Vec<_>>())
        })
    }
    /// Optimize the on-disk data by compacting and pruning old data, for better performance.
    #[pyo3(signature = (cleanup_since_ms=None, delete_unverified=None))]
    pub fn optimize(
@@ -1255,14 +1029,6 @@ impl Table {
        })
    }
    pub fn get_lsm_write_spec(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
            let spec = inner.get_lsm_write_spec().await.infer_error()?;
            Ok(spec.map(LsmWriteSpec::from))
        })
    }
    pub fn close_lsm_writers(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
        let inner = self_.inner_ref()?.clone();
        future_into_py(self_.py(), async move {
@@ -3,14 +3,10 @@
 """Tests for the type-safe expression builder API."""
 from datetime import date, datetime, timedelta, timezone
 from decimal import Decimal
 import pyarrow as pa
 import pytest
-
+import pyarrow as pa
 import lancedb
-from lancedb.expr import Expr, col, func, lit
+from lancedb.expr import Expr, col, lit, func
 # ── unit tests for Expr construction ─────────────────────────────────────────
@@ -58,28 +54,6 @@ class TestExprConstruction:
        with pytest.raises(Exception):
            func("not_a_real_function", col("x"))
    def test_lit_date(self):
        e = lit(date(2024, 1, 1))
        assert isinstance(e, Expr)
    def test_lit_datetime(self):
        # Naive datetime
        e = lit(datetime(2024, 1, 1, 10, 0))
        assert isinstance(e, Expr)
    def test_lit_datetime_tz(self):
        # Timezone-aware datetime
        tz = timezone(timedelta(hours=5))
        dt = datetime(2024, 1, 1, 10, 0, tzinfo=tz)
        e = lit(dt)
        assert isinstance(e, Expr)
    def test_lit_decimal_precision(self):
        # High precision Decimal that would be rounded if converted to float
        d = Decimal("1.234567890123456789")
        e = lit(d)
        assert isinstance(e, Expr)
 class TestExprOperators:
    def test_eq_operator(self):
@@ -168,20 +142,6 @@ class TestExprOperators:
        assert isinstance(e, Expr)
        assert e.to_sql() == "(name = 'alice')"
    def test_reflexive_comparisons(self):
        # 10 < col("age") swaps to col("age") > 10
        assert (10 < col("age")).to_sql() == "(age > 10)"
        assert (10 <= col("age")).to_sql() == "(age >= 10)"
        assert (10 > col("age")).to_sql() == "(age < 10)"
        assert (10 >= col("age")).to_sql() == "(age <= 10)"
        assert (10 == col("age")).to_sql() == "(age = 10)"
        assert (10 != col("age")).to_sql() == "(age <> 10)"
    def test_reflexive_logical(self):
        # True & Expr calls Expr.__rand__(True)
        assert (True & (col("age") > 18)).to_sql() == "(true AND (age > 18))"
        assert (False | (col("age") > 18)).to_sql() == "(false OR (age > 18))"
 class TestExprBytesLiteral:
    def test_bytes_to_sql(self):
@@ -322,40 +282,6 @@ class TestExprRepr:
            {e: 1}
 class TestExprReflexive:
    def test_reflexive_eq(self):
        e = 1 == col("x")
        assert isinstance(e, Expr)
        assert e.to_sql() == "(x = 1)"
    def test_reflexive_ne(self):
        e = 1 != col("x")
        assert isinstance(e, Expr)
        assert e.to_sql() == "(x <> 1)"
    def test_reflexive_lt(self):
        # 1 < x  =>  (x > 1)
        e = 1 < col("x")
        assert isinstance(e, Expr)
        assert e.to_sql() == "(x > 1)"
    def test_reflexive_gt(self):
        # 1 > x  =>  (x < 1)
        e = 1 > col("x")
        assert isinstance(e, Expr)
        assert e.to_sql() == "(x < 1)"
    def test_reflexive_and(self):
        e = True & col("active")
        assert isinstance(e, Expr)
        assert e.to_sql() == "(true AND active)"
    def test_reflexive_or(self):
        e = False | col("inactive")
        assert isinstance(e, Expr)
        assert e.to_sql() == "(false OR inactive)"
 # ── integration tests: end-to-end query against a real table ─────────────────
@@ -506,72 +432,6 @@ class TestColNamingIntegration:
        assert sorted(result["upper_name"].to_pylist()) == ["ALICE", "BOB", "CHARLIE"]
@pytest.fixture
 def type_check_table(tmp_path):
    """Fixture that creates a table with Date32 and Decimal128 columns."""
    db = lancedb.connect(str(tmp_path))
    schema = pa.schema(
        [
            ("date", pa.date32()),
            ("decimal", pa.decimal128(10, 2)),
            ("binary", pa.binary()),
        ]
    )
    data = pa.table(
        {
            "date": [date(2024, 1, 1), date(2024, 1, 2)],
            "decimal": [Decimal("10.50"), Decimal("20.75")],
            "binary": [b"\x01", b"\x02"],
        },
        schema=schema,
    )
    return db.create_table("extended_types", data)
 class TestExtendedTypeIntegration:
    """Integration tests verifying that typed literals work correctly in filters."""
    def test_date_integration(self, type_check_table):
        """Verify that Date32 literals are correctly parsed and filtered."""
        result = (
            type_check_table.search()
            .where(col("date") == lit(date(2024, 1, 1)))
            .to_arrow()
        )
        assert result.num_rows == 1
        assert result["date"][0].as_py() == date(2024, 1, 1)
    def test_decimal_integration(self, tmp_path):
        """A Decimal literal must retain full 128-bit precision in a filter.
        1.234567890123456789 and 1.234567890123456790 differ only in the last
        digit and are indistinguishable once truncated to f64.  The filter
        therefore returns the single expected row only if ``lit(Decimal)``
        produces a true ``Decimal128`` scalar rather than being coerced to f64.
        """
        low = Decimal("1.234567890123456789")
        high = Decimal("1.234567890123456790")
        db = lancedb.connect(str(tmp_path / "decimal_precision"))
        schema = pa.schema([("val", pa.decimal128(19, 18))])
        table = db.create_table(
            "decimal_precision",
            pa.table({"val": [low, high]}, schema=schema),
        )
        result = table.search().where(col("val") < lit(high)).to_arrow()
        assert result.num_rows == 1
        assert result["val"][0].as_py() == low
    def test_binary_integration(self, type_check_table):
        """Verify that Binary literals are correctly filtered."""
        result = (
            type_check_table.search().where(col("binary") == lit(b"\x01")).to_arrow()
        )
        assert result.num_rows == 1
        assert result["binary"][0].as_py() == b"\x01"
 # ── bytes / binary column integration tests ───────────────────────────────────
@@ -1,101 +0,0 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import lancedb
 import pyarrow as pa
 import pytest
 # The metrics recorder is process-global and installed once, so the whole
 # bridge is exercised in a single test to avoid cross-test global-state coupling.
 def _metrics_by_name(reader):
    data = reader.get_metrics_data()
    result = {}
    for resource_metrics in data.resource_metrics:
        for scope_metrics in resource_metrics.scope_metrics:
            for metric in scope_metrics.metrics:
                result[metric.name] = metric
    return result
 def test_instrument_lancedb_metrics_exports_object_store_metrics(tmp_path):
    pytest.importorskip("opentelemetry.sdk.metrics")
    from lancedb.otel import instrument_lancedb_metrics
    from opentelemetry.sdk.metrics import MeterProvider
    from opentelemetry.sdk.metrics.export import InMemoryMetricReader
    reader = InMemoryMetricReader()
    provider = MeterProvider(metric_readers=[reader])
    assert instrument_lancedb_metrics(provider)
    # The catalog is populated once the recorder is installed.
    from lancedb._lancedb import lancedb_metrics_catalog
    catalog = {desc.name: desc for desc in lancedb_metrics_catalog()}
    # Every metric kind emitted by the object store must be described so it is
    # surfaced by the bridge (counter, histogram, and gauge).
    assert catalog["lance_object_store_requests_total"].kind == "counter"
    assert catalog["lance_object_store_request_duration_seconds"].kind == "histogram"
    assert catalog["lance_object_store_in_flight_requests"].kind == "gauge"
    assert catalog["lance_object_store_retryable_responses_total"].kind == "counter"
    # Generate object store activity on the local filesystem (scheme "file").
    db = lancedb.connect(str(tmp_path))
    table = db.create_table("t", pa.table({"id": pa.array(range(256))}))
    assert table.count_rows() == 256
    assert table.to_arrow().num_rows == 256
    metrics = _metrics_by_name(reader)
    requests = metrics["lance_object_store_requests_total"]
    points = list(requests.data.data_points)
    assert points, "expected at least one request data point"
    # Object store metrics are labelled by `operation` and `base` (the store
    # scheme, e.g. "file", by default).
    assert all("base" in p.attributes and "operation" in p.attributes for p in points)
    assert sum(p.value for p in points) > 0
    # Histograms are decomposed into bucket / count / sum observable counters.
    bucket = metrics["lance_object_store_request_duration_seconds_bucket"]
    bucket_points = list(bucket.data.data_points)
    assert bucket_points
    assert all("le" in p.attributes for p in bucket_points)
    # The implicit +Inf bucket must be present and is the cumulative maximum.
    assert any(p.attributes["le"] == "+Inf" for p in bucket_points)
    count = metrics["lance_object_store_request_duration_seconds_count"]
    assert sum(p.value for p in count.data.data_points) > 0
    # The `_sum` instrument must also be wired and report positive latency.
    duration_sum = metrics["lance_object_store_request_duration_seconds_sum"]
    assert sum(p.value for p in duration_sum.data.data_points) > 0
    # Unit handling: only `_sum` keeps the histogram's unit (seconds); `_bucket`
    # and `_count` observe cumulative counts and are unitless.
    assert duration_sum.unit == "s"
    assert bucket.unit == ""
    assert count.unit == ""
 def test_snapshot_empty_before_install_is_safe():
    # snapshot is callable regardless of installation state and never raises.
    from lancedb._lancedb import snapshot_lancedb_metrics
    assert isinstance(snapshot_lancedb_metrics(), list)
 def test_instrument_warns_when_recorder_unavailable(monkeypatch):
    # A foreign `metrics` recorder already installed -> register returns False;
    # instrument_lancedb_metrics must warn and return False without instrumenting.
    pytest.importorskip("opentelemetry.sdk.metrics")
    import lancedb.otel as otel
    from opentelemetry.sdk.metrics import MeterProvider
    from opentelemetry.sdk.metrics.export import InMemoryMetricReader
    monkeypatch.setattr(otel, "register_lancedb_metrics_recorder", lambda: False)
    reader = InMemoryMetricReader()
    provider = MeterProvider(metric_readers=[reader])
    with pytest.warns(UserWarning, match="recorder"):
        assert otel.instrument_lancedb_metrics(provider) is False
@@ -780,7 +780,7 @@ name = "cuda-bindings"
 version = "13.3.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "cuda-pathfinder", marker = "(python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
+    { name = "cuda-pathfinder" },
 ]
 wheels = [
    { url = "https://files.pythonhosted.org/packages/a9/21/8464d133752951c154feafb3b65c297e7d80f301183d220bec4c830f1441/cuda_bindings-13.3.1-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:120fcc53d57903df529c3486962c56528cba5b7d6c57c99537320ed9922c8b86", size = 6073403, upload-time = "2026-05-29T23:11:36.22Z" },
@@ -815,37 +815,37 @@ wheels = [
 [package.optional-dependencies]
 cublas = [
-    { name = "nvidia-cublas", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cublas", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 cudart = [
-    { name = "nvidia-cuda-runtime", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cuda-runtime", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 cufft = [
-    { name = "nvidia-cufft", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cufft", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 cufile = [
    { name = "nvidia-cufile", marker = "sys_platform == 'linux'" },
 ]
 cupti = [
-    { name = "nvidia-cuda-cupti", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cuda-cupti", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 curand = [
-    { name = "nvidia-curand", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-curand", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 cusolver = [
-    { name = "nvidia-cusolver", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cusolver", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 cusparse = [
-    { name = "nvidia-cusparse", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cusparse", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 nvjitlink = [
-    { name = "nvidia-nvjitlink", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-nvjitlink", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 nvrtc = [
-    { name = "nvidia-cuda-nvrtc", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-cuda-nvrtc", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 nvtx = [
-    { name = "nvidia-nvtx", marker = "(python_full_version < '3.14' and sys_platform == 'win32') or sys_platform == 'linux'" },
+    { name = "nvidia-nvtx", marker = "sys_platform == 'linux' or sys_platform == 'win32'" },
 ]
 [[package]]
@@ -1909,9 +1909,6 @@ embeddings = [
    { name = "sentencepiece" },
    { name = "torch" },
 ]
 otel = [
    { name = "opentelemetry-api" },
 ]
 pylance = [
    { name = "pylance" },
 ]
@@ -1926,7 +1923,6 @@ tests = [
    { name = "boto3" },
    { name = "datafusion" },
    { name = "duckdb" },
    { name = "opentelemetry-sdk" },
    { name = "pandas", version = "2.2.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.11'" },
    { name = "pandas", version = "2.3.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.11' and python_full_version < '3.14'" },
    { name = "pandas", version = "3.0.3", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.14'" },
@@ -1967,8 +1963,6 @@ requires-dist = [
    { name = "open-clip-torch", marker = "extra == 'clip'" },
    { name = "open-clip-torch", marker = "extra == 'embeddings'", specifier = ">=2.20.0" },
    { name = "openai", marker = "extra == 'embeddings'", specifier = ">=1.6.1" },
    { name = "opentelemetry-api", marker = "extra == 'otel'" },
    { name = "opentelemetry-sdk", marker = "extra == 'tests'", specifier = ">=1.30.0" },
    { name = "overrides", marker = "python_full_version < '3.12'", specifier = ">=0.7" },
    { name = "packaging", specifier = ">=23.0" },
    { name = "pandas", marker = "extra == 'tests'", specifier = ">=1.4" },
@@ -2000,7 +1994,7 @@ requires-dist = [
    { name = "transformers", marker = "extra == 'siglip'", specifier = ">=4.41.0" },
    { name = "typing-extensions", marker = "python_full_version < '3.11' and extra == 'dev'", specifier = ">=4.0.0" },
 ]
-provides-extras = ["azure", "clip", "dev", "docs", "embeddings", "otel", "pylance", "siglip", "tests"]
+provides-extras = ["azure", "clip", "dev", "docs", "embeddings", "pylance", "siglip", "tests"]
 [[package]]
 name = "lomond"
@@ -2781,7 +2775,7 @@ name = "nvidia-cudnn-cu13"
 version = "9.19.0.56"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas", marker = "(python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
+    { name = "nvidia-cublas" },
 ]
 wheels = [
    { url = "https://files.pythonhosted.org/packages/f1/84/26025437c1e6b61a707442184fa0c03d083b661adf3a3eecfd6d21677740/nvidia_cudnn_cu13-9.19.0.56-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:6ed29ffaee1176c612daf442e4dd6cfeb6a0caa43ddcbeb59da94953030b1be4", size = 433781201, upload-time = "2026-02-03T20:40:53.805Z" },
@@ -2793,7 +2787,7 @@ name = "nvidia-cufft"
 version = "12.0.0.61"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink", marker = "(python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
+    { name = "nvidia-nvjitlink" },
 ]
 wheels = [
    { url = "https://files.pythonhosted.org/packages/8b/ae/f417a75c0259e85c1d2f83ca4e960289a5f814ed0cea74d18c353d3e989d/nvidia_cufft-12.0.0.61-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:2708c852ef8cd89d1d2068bdbece0aa188813a0c934db3779b9b1faa8442e5f5", size = 214053554, upload-time = "2025-09-04T08:31:38.196Z" },
@@ -2823,9 +2817,9 @@ name = "nvidia-cusolver"
 version = "12.0.4.66"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-cublas", marker = "(python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
+    { name = "nvidia-cublas" },
-    { name = "nvidia-cusparse", marker = "(python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
+    { name = "nvidia-cusparse" },
-    { name = "nvidia-nvjitlink", marker = "(python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
+    { name = "nvidia-nvjitlink" },
 ]
 wheels = [
    { url = "https://files.pythonhosted.org/packages/c8/c3/b30c9e935fc01e3da443ec0116ed1b2a009bb867f5324d3f2d7e533e776b/nvidia_cusolver-12.0.4.66-py3-none-manylinux_2_27_aarch64.whl", hash = "sha256:02c2457eaa9e39de20f880f4bd8820e6a1cfb9f9a34f820eb12a155aa5bc92d2", size = 223467760, upload-time = "2025-09-04T08:33:04.222Z" },
@@ -2837,7 +2831,7 @@ name = "nvidia-cusparse"
 version = "12.6.3.3"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
-    { name = "nvidia-nvjitlink", marker = "(python_full_version < '3.14' and sys_platform == 'emscripten') or (python_full_version < '3.14' and sys_platform == 'win32') or (sys_platform != 'emscripten' and sys_platform != 'win32')" },
+    { name = "nvidia-nvjitlink" },
 ]
 wheels = [
    { url = "https://files.pythonhosted.org/packages/f8/94/5c26f33738ae35276672f12615a64bd008ed5be6d1ebcb23579285d960a9/nvidia_cusparse-12.6.3.3-py3-none-manylinux2014_aarch64.manylinux_2_17_aarch64.whl", hash = "sha256:80bcc4662f23f1054ee334a15c72b8940402975e0eab63178fc7e670aa59472c", size = 162155568, upload-time = "2025-09-04T08:33:42.864Z" },
@@ -2940,45 +2934,6 @@ wheels = [
    { url = "https://files.pythonhosted.org/packages/f6/46/180e14be801a75bc13f234cb1b594b232adeb9c84e60a9ab1832e8333591/openai-2.40.0-py3-none-any.whl", hash = "sha256:2b205637ff214477f9ce9ab035e9f494db0e3fa8f1e599008953735fbf6ff1ff", size = 1350935, upload-time = "2026-06-01T21:48:21.462Z" },
 ]
 [[package]]
 name = "opentelemetry-api"
 version = "1.43.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/ae/cc/e4c9584181f86494df0f6bdec1a4f3280c50db44704dc2a407e994fc87bb/opentelemetry_api-1.43.0.tar.gz", hash = "sha256:107d0d03857ea8fc7c5fcbbbd83f800c281f0d560553d61c1d675fccfd1761c1", size = 73476, upload-time = "2026-06-24T15:19:55.323Z" }
 wheels = [
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 ]
 [[package]]
 name = "opentelemetry-sdk"
 version = "1.43.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "opentelemetry-api" },
    { name = "opentelemetry-semantic-conventions" },
    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/3e/eb/5041074274ac0956b03637cc039d434569112468e875eddfcc9a0674ce06/opentelemetry_sdk-1.43.0.tar.gz", hash = "sha256:d8187c81c162df9913e4003dd6485f7390d9a24fc17026ec7387b8b8218b08e9", size = 254744, upload-time = "2026-06-24T15:20:08.467Z" }
 wheels = [
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 ]
 [[package]]
 name = "opentelemetry-semantic-conventions"
 version = "0.64b0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
    { name = "opentelemetry-api" },
    { name = "typing-extensions" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/5a/30/5f26df29509eccd86b99b481ac9ffa39da49ba9577cc69071c552ae30447/opentelemetry_semantic_conventions-0.64b0.tar.gz", hash = "sha256:72f76fb2d1582d9d033dd1fcd84532e961e6ff3d90d24ba6fabc72975a83864c", size = 148340, upload-time = "2026-06-24T15:20:09.267Z" }
 wheels = [
    { url = "https://files.pythonhosted.org/packages/f2/ca/23ba87a221b574a7c5a99d48849d80bfe8b047624681357e2b002e566187/opentelemetry_semantic_conventions-0.64b0-py3-none-any.whl", hash = "sha256:ea77e85e354b8f604ddbe5f3d9135216f982fa4d77e5859ac30f6d8a50505aa6", size = 203713, upload-time = "2026-06-24T15:19:53.339Z" },
 ]
 [[package]]
 name = "overrides"
 version = "7.7.0"
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.32.0-beta.1"
+version = "0.31.0-beta.6"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
@@ -44,12 +44,11 @@ lance-io = { workspace = true }
 lance-index = { workspace = true, features = ["tokenizer-jieba", "tokenizer-lindera"] }
 lance-table = { workspace = true }
 lance-linalg = { workspace = true }
 lance-testing = { workspace = true }
 lance-encoding = { workspace = true }
 lance-arrow = { workspace = true }
 lance-namespace = { workspace = true }
 lance-namespace-impls = { workspace = true }
 metrics = { workspace = true, optional = true }
 metrics-util = { workspace = true, optional = true }
 moka = { workspace = true }
 pin-project = { workspace = true }
 tokio = { version = "1.23", features = ["rt-multi-thread", "sync"] }
@@ -94,7 +93,6 @@ semver = { workspace = true }
 [dev-dependencies]
 anyhow = "1"
 lance-testing = { workspace = true }
 tempfile = "3.5.0"
 random_word = { version = "0.4.3", features = ["en"] }
 tokio = { version = "1.23", features = ["io-util", "macros", "net", "rt-multi-thread", "sync"] }
@@ -110,8 +108,6 @@ http-body = "1"                                        # Matching reqwest
 rstest = "0.23.0"
 test-log = "0.2"
 serial_test = "3"
 [target.'cfg(unix)'.dev-dependencies]
 pprof = { version = "0.14", features = ["flamegraph"] }
 [features]
@@ -130,12 +126,6 @@ azure = [
    "lance-namespace-impls/dir-azure",
    "lance-namespace-impls/credential-vendor-azure",
 ]
 cos = ["lance/tencent", "lance-io/tencent"]
 goosefs = [
    "lance/goosefs",
    "lance-io/goosefs",
    "lance-namespace-impls/dir-goosefs",
 ]
 huggingface = [
    "lance/huggingface",
    "lance-io/huggingface",
@@ -149,15 +139,6 @@ remote = [
    "lance-namespace-impls/rest",
    "lance-namespace-impls/rest-adapter",
 ]
 # Publish LanceDB's internal metrics (currently object store request counts,
 # bytes, latency, errors, and throttles) through the `metrics` crate facade,
 # and re-export the `metrics` crate as `lancedb::metrics`. Install any
 # `metrics`-compatible recorder to collect them.
 metrics = ["dep:metrics", "lance/metrics", "lance-io/metrics"]
 # Additional adapter on top of `metrics` that installs a process-global recorder
 # and exposes a pull-based snapshot/catalog API (see `lancedb::metrics_otel`)
 # for bridging metrics into OpenTelemetry or other pull-based exporters.
 metrics-otel = ["metrics", "dep:metrics-util"]
 fp16kernels = ["lance-linalg/fp16kernels"]
 s3-test = []
 bedrock = ["dep:aws-sdk-bedrockruntime"]
@@ -183,9 +164,6 @@ required-features = ["sentence-transformers"]
 name = "bedrock"
 required-features = ["bedrock"]
 [[example]]
 name = "bench_streaming_dataloader"
 [[example]]
 name = "simple"
@@ -1,272 +0,0 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 //! Benchmark + CPU profiler for the PermutationReader used by the elastic
 //! streaming dataloader.
 //!
 //! Normal sweep:
 //!   cargo run --release --example bench_streaming_dataloader
 //!
 //! Flamegraph (self-contained, no perf/dtrace needed):
 //!   BENCH_PROFILE=1 BENCH_CHUNK=64 cargo run --release \
 //!       --example bench_streaming_dataloader
 //!   # writes flamegraph.svg in the current directory
 //!
 //! Environment variables:
 //!   BENCH_NUM_ROWS   – total rows (default 49152 = 24 × 2048)
 //!   BENCH_NUM_SPLITS – number of splits (default 24)
 //!   BENCH_STEPS      – round-robin cycles per chunk-size trial (default 200)
 //!   BENCH_ROW_BYTES  – bytes of payload per row (default 4096)
 //!   BENCH_CHUNK      – restrict sweep to this single chunk size
 //!   BENCH_PROFILE    – if set to "1", capture a pprof flamegraph SVG
 use std::{sync::Arc, time::Instant};
 use arrow_array::{Int32Array, LargeBinaryArray, RecordBatch};
 use arrow_schema::{DataType, Field, Schema};
 use lancedb::{
    Result, Table,
    arrow::{SendableRecordBatchStream, SimpleRecordBatchStream},
    connect,
    dataloader::permutation::{
        builder::{PermutationBuilder, ShuffleStrategy},
        reader::PermutationReader,
        split::{SplitSizes, SplitStrategy},
    },
    query::Select,
 };
 fn env_usize(key: &str, default: usize) -> usize {
    std::env::var(key)
        .ok()
        .and_then(|v| v.parse().ok())
        .unwrap_or(default)
 }
 // ---------------------------------------------------------------------------
 // Table creation
 // ---------------------------------------------------------------------------
 async fn make_base_table(num_rows: usize, row_bytes: usize) -> Result<Table> {
    let schema = Arc::new(Schema::new(vec![
        Field::new("id", DataType::Int32, false),
        Field::new("payload", DataType::LargeBinary, false),
    ]));
    let payload = vec![0u8; row_bytes];
    let ids: Int32Array = (0..num_rows as i32).collect();
    let payloads: LargeBinaryArray = (0..num_rows).map(|_| Some(payload.as_slice())).collect();
    let batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(ids), Arc::new(payloads)])?;
    let stream: SendableRecordBatchStream = Box::pin(SimpleRecordBatchStream::new(
        futures::stream::once(std::future::ready(Ok(batch))),
        schema,
    ));
    let db = connect("memory:///").execute().await?;
    db.create_table("base", stream).execute().await
 }
 async fn make_permutation_table(base: &Table, num_splits: usize) -> Result<Table> {
    PermutationBuilder::new(base.clone())
        .with_split_strategy(
            SplitStrategy::Random {
                seed: Some(42),
                sizes: SplitSizes::Fixed(num_splits as u64),
                clump_size: None,
            },
            None,
        )
        .with_shuffle_strategy(ShuffleStrategy::Random {
            seed: Some(42),
            clump_size: None,
        })
        .build()
        .await
 }
 // ---------------------------------------------------------------------------
 // Round-robin hot loop (mirrors StreamingDataset.__iter__)
 // ---------------------------------------------------------------------------
 async fn run_hot_loop(
    readers: &[PermutationReader],
    chunk_size: usize,
    steps: usize,
 ) -> Result<(usize, f64)> {
    let n = readers.len();
    let split_sizes: Vec<usize> = readers.iter().map(|r| r.count_rows() as usize).collect();
    struct SplitBuf {
        batch: Option<RecordBatch>,
        row_in_batch: usize,
        consumed: usize,
    }
    let mut bufs: Vec<SplitBuf> = (0..n)
        .map(|_| SplitBuf {
            batch: None,
            row_in_batch: 0,
            consumed: 0,
        })
        .collect();
    // Pre-fill
    for i in 0..n {
        let fetch = chunk_size.min(split_sizes[i]);
        if fetch > 0 {
            let offsets: Vec<u64> = (0..fetch as u64).collect();
            bufs[i].batch = Some(readers[i].take_offsets(&offsets, Select::All).await?);
        }
    }
    let mut total_rows = 0usize;
    let t0 = Instant::now();
    'outer: for _step in 0..steps {
        for i in 0..n {
            if bufs[i].consumed >= split_sizes[i] {
                break 'outer;
            }
            let need_refill = bufs[i]
                .batch
                .as_ref()
                .map(|b| bufs[i].row_in_batch >= b.num_rows())
                .unwrap_or(true);
            if need_refill {
                let start = bufs[i].consumed as u64;
                let remaining = (split_sizes[i] - bufs[i].consumed) as u64;
                let fetch = chunk_size.min(remaining as usize);
                let offsets: Vec<u64> = (start..start + fetch as u64).collect();
                bufs[i].batch = Some(readers[i].take_offsets(&offsets, Select::All).await?);
                bufs[i].row_in_batch = 0;
            }
            bufs[i].row_in_batch += 1;
            bufs[i].consumed += 1;
            total_rows += 1;
        }
    }
    Ok((total_rows, t0.elapsed().as_secs_f64()))
 }
 // ---------------------------------------------------------------------------
 // Main
 // ---------------------------------------------------------------------------
 #[tokio::main]
 async fn main() -> Result<()> {
    let num_splits = env_usize("BENCH_NUM_SPLITS", 24);
    let num_rows = env_usize("BENCH_NUM_ROWS", num_splits * 2048);
    let steps = env_usize("BENCH_STEPS", 200);
    let row_bytes = env_usize("BENCH_ROW_BYTES", 4096);
    let single_chunk: Option<usize> = std::env::var("BENCH_CHUNK")
        .ok()
        .and_then(|v| v.parse().ok());
    let do_profile = std::env::var("BENCH_PROFILE")
        .map(|v| v == "1")
        .unwrap_or(false);
    assert_eq!(
        num_rows % num_splits,
        0,
        "NUM_ROWS must be divisible by NUM_SPLITS"
    );
    println!("Benchmark config:");
    println!(
        "  num_rows={}  num_splits={}  rows/split={}  steps={}  row_bytes={}",
        num_rows,
        num_splits,
        num_rows / num_splits,
        steps,
        row_bytes,
    );
    println!(
        "  ~{:.1} MB total",
        (num_rows * row_bytes) as f64 / (1024.0 * 1024.0)
    );
    println!();
    print!("Building base table... ");
    let _ = std::io::Write::flush(&mut std::io::stdout());
    let base = make_base_table(num_rows, row_bytes).await?;
    println!("done");
    print!("Building permutation table... ");
    let _ = std::io::Write::flush(&mut std::io::stdout());
    let perm = make_permutation_table(&base, num_splits).await?;
    println!("done");
    print!("Building {} PermutationReaders... ", num_splits);
    let _ = std::io::Write::flush(&mut std::io::stdout());
    let base_inner = base.base_table().clone();
    let perm_inner = perm.base_table().clone();
    let mut readers = Vec::with_capacity(num_splits);
    for split in 0..num_splits {
        readers.push(
            PermutationReader::try_from_tables(
                base_inner.clone(),
                perm_inner.clone(),
                split as u64,
            )
            .await?,
        );
    }
    println!("done ({} rows/split)", readers[0].count_rows());
    println!();
    let chunk_sizes: Vec<usize> = if let Some(c) = single_chunk {
        vec![c]
    } else {
        vec![1, 4, 16, 64, 256, 1024, 4096, 16384]
    };
    if do_profile {
        #[cfg(unix)]
        {
            let chunk = chunk_sizes[0];
            println!("Profiling chunk={chunk} for {steps} steps...");
            // Warm-up outside the profiler window
            let _ = run_hot_loop(&readers, chunk, 1).await?;
            let guard = pprof::ProfilerGuardBuilder::default()
                .frequency(1000)
                .build()
                .unwrap();
            let (rows, elapsed) = run_hot_loop(&readers, chunk, steps).await?;
            if let Ok(report) = guard.report().build() {
                let svg_path = "flamegraph.svg";
                let file = std::fs::File::create(svg_path).unwrap();
                report.flamegraph(file).unwrap();
                println!("Flamegraph written to {svg_path}");
            }
            let rows_per_sec = rows as f64 / elapsed;
            println!("chunk={chunk}  {rows} rows  {elapsed:.3}s  {rows_per_sec:.0} rows/s");
        }
        #[cfg(not(unix))]
        {
            println!("Flamegraph profiling (BENCH_PROFILE=1) is not supported on this platform.");
            println!("Run without BENCH_PROFILE to get throughput numbers.");
        }
    } else {
        println!(
            "{:>6}  {:>7}  {:>8}  {:>11}  {:>10}",
            "chunk", "rows", "elapsed", "rows/s", "ms/step"
        );
        println!("{}", "-".repeat(52));
        for &chunk in &chunk_sizes {
            let _ = run_hot_loop(&readers, chunk, 1).await?;
            let (rows, elapsed) = run_hot_loop(&readers, chunk, steps).await?;
            let rows_per_sec = rows as f64 / elapsed;
            let ms_per_step = elapsed / steps as f64 * 1000.0;
            println!(
                "{:>6}  {:>7}  {:>7.3}s  {:>11.0}  {:>9.1}ms",
                chunk, rows, elapsed, rows_per_sec, ms_per_step,
            );
        }
    }
    println!("\nDone.");
    Ok(())
 }
@@ -342,19 +342,6 @@ impl ListingDatabase {
        ))
    }
    fn storage_base_uri(uri: &str) -> String {
        let Ok(mut url) = url::Url::parse(uri) else {
            return uri.to_string();
        };
        url.set_query(None);
        let Some((storage_scheme, _commit_scheme)) = url.scheme().split_once('+') else {
            return url.to_string();
        };
        let storage_scheme = storage_scheme.to_string();
        let _ = url.set_scheme(&storage_scheme);
        url.to_string()
    }
    async fn prepare_namespace_root(
        uri: &str,
        storage_options: &HashMap<String, String>,
@@ -533,8 +520,6 @@ impl ListingDatabase {
                // will add a trailing '?' to the url
                url.set_query(None);
                let storage_base_uri = Self::storage_base_uri(url.as_str());
                let table_base_uri = if let Some(store) = engine {
                    static WARN_ONCE: std::sync::Once = std::sync::Once::new();
                    WARN_ONCE.call_once(|| {
@@ -547,6 +532,8 @@ impl ListingDatabase {
                    url.to_string()
                };
                let plain_uri = url.to_string();
                let session = request
                    .session
                    .clone()
@@ -563,13 +550,13 @@ impl ListingDatabase {
                };
                let (object_store, base_path) = ObjectStore::from_uri_and_params(
                    session.store_registry(),
-                    &storage_base_uri,
+                    &plain_uri,
                    &os_params,
                )
                .await?;
                if object_store.is_local() {
-                    Self::try_create_dir(&storage_base_uri).context(CreateDirSnafu {
+                    Self::try_create_dir(&plain_uri).context(CreateDirSnafu {
-                        path: storage_base_uri.clone(),
+                        path: plain_uri.clone(),
                    })?;
                }
@@ -583,7 +570,7 @@ impl ListingDatabase {
                };
                let namespace_database = Self::connect_namespace_database(
-                    &storage_base_uri,
+                    &table_base_uri,
                    options.storage_options.clone(),
                    request.namespace_client_properties.clone(),
                    request.read_consistency_interval,
@@ -1322,60 +1309,6 @@ mod tests {
        (tempdir, db)
    }
    #[tokio::test]
    async fn test_listing_database_root_ops_do_not_create_manifest() {
        let tempdir = tempdir().unwrap();
        let uri = tempdir.path().to_str().unwrap();
        let request = ConnectRequest {
            uri: uri.to_string(),
            #[cfg(feature = "remote")]
            client_config: Default::default(),
            options: Default::default(),
            namespace_client_properties: Default::default(),
            manifest_enabled: false,
            read_consistency_interval: None,
            session: None,
        };
        let db = ListingDatabase::connect_with_options(&request)
            .await
            .unwrap();
        assert!(!tempdir.path().join("__manifest").exists());
        let schema = Arc::new(Schema::new(vec![Field::new("id", DataType::Int32, false)]));
        db.create_table(CreateTableRequest {
            name: "root_table".to_string(),
            namespace_path: vec![],
            data: Box::new(RecordBatch::new_empty(schema)) as Box<dyn Scannable>,
            mode: CreateTableMode::Create,
            write_options: Default::default(),
            location: None,
            namespace_client: None,
        })
        .await
        .unwrap();
        db.open_table(OpenTableRequest {
            name: "root_table".to_string(),
            namespace_path: vec![],
            index_cache_size: None,
            lance_read_params: None,
            location: None,
            namespace_client: None,
            managed_versioning: None,
        })
        .await
        .unwrap();
        #[allow(deprecated)]
        let table_names = db.table_names(TableNamesRequest::default()).await.unwrap();
        assert_eq!(table_names, vec!["root_table".to_string()]);
        assert!(!tempdir.path().join("__manifest").exists());
    }
    #[tokio::test]
    async fn test_clone_table_basic() {
        let (_tempdir, db) = setup_database().await;
@@ -2350,24 +2283,6 @@ mod tests {
        assert_eq!(captured.as_deref(), Some("foo=bar"));
    }
    #[test]
    fn test_storage_base_uri_strips_commit_engine_scheme() {
        assert_eq!(
            ListingDatabase::storage_base_uri("s3+ddb://bucket/prefix?ddbTableName=commit_table"),
            "s3://bucket/prefix"
        );
        assert_eq!(
            ListingDatabase::storage_base_uri("s3://bucket/prefix?foo=bar"),
            "s3://bucket/prefix"
        );
        assert_eq!(
            ListingDatabase::storage_base_uri("/tmp/lancedb"),
            "/tmp/lancedb"
        );
    }
    /// Regression: connecting via a URL-style URI (which goes through
    /// `url::Url::parse` and the `query_pairs_mut()` path) must not
    /// append a trailing `?` to per-table URIs when the input URI has
@@ -391,7 +391,6 @@ mod tests {
                SplitStrategy::Random {
                    seed: Some(42),
                    sizes: SplitSizes::Percentages(vec![0.05, 0.30]),
                    clump_size: None,
                },
                None,
            )
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Lance Release	1545bd4b4f	Bump version: 0.34.0-beta.7 → 0.34.0	2026-07-02 15:59:10 +00:00
Lance Release	589331d86b	Bump version: 0.34.0-beta.6 → 0.34.0-beta.7	2026-07-02 15:59:09 +00:00
BubbleCal	56f856569c	feat: update lance dependency to v8.0.0	2026-07-02 23:00:26 +08:00