test: update python expectations for lance 9.1

test: relax hash split distribution assertions
chore: update lance dependency to v9.1.0-beta.2
2026-08-27 16:38:31 +00:00 · 2026-07-16 13:46:54 -07:00 · 2026-07-16 10:45:35 -07:00 · 2026-07-16 10:45:05 -07:00 · 2026-07-16 10:31:30 -07:00 · 2026-07-15 21:21:40 -07:00
164 changed files with 13618 additions and 1394 deletions
@@ -1,178 +0,0 @@
---
-name: lancedb-column-metadata
-description: Column metadata authoring for LanceDB tables via the REST API. This skill is required for tasks like writing field descriptions, setting tags on columns (field_type, model, project_id, version), classifying columns as embeddings vs labels vs eval metrics, or grouping versioned columns into logical families — because it has the API integration needed to read the schema and persist metadata back. Invoke whenever someone wants to document, annotate, tag, or classify what their table columns ARE. Trigger even without an explicit "LanceDB" mention, as long as the context is column-level documentation or tagging for an ML or vector database table.
-metadata:
-  short-description: Write column descriptions, tags, and logical groupings to a LanceDB table
---
-
-## Overview
-
-This skill authors column-level metadata for a LanceDB table. It connects to a LanceDB deployment over its REST API, inspects the table schema, generates appropriate metadata, and writes it back.
-
-## Step 0: Establish the connection
-
-Use the `lancedb-connect` skill (invoke it via the Skill tool) to resolve the base URL and auth headers (`x-api-key`, `x-lancedb-database`) for whichever deployment the user is working against — enterprise/self-hosted or a local dev server. Skip it only if the connection details are already established in the conversation.
-
-All examples below use `{base_url}` — substitute the resolved endpoint and include the resolved headers on every request.
-
-## Metadata keys
-
-All metadata uses namespaced keys:
-
-| Key | Purpose | Example value |
-|-----|---------|---------------|
-| `lancedb:description` | Human-readable explanation of what the column contains | `"CLIP ViT-L/14 image embedding, L2-normalized (768-dim)"` |
-| `lancedb:tag:<name>` | Flexible key-value tag; the suffix names the tag category | `lancedb:tag:field_type: "embedding"`, `lancedb:tag:model: "clip"`, `lancedb:tag:project_id: "foo"` |
-| `lancedb:logical-column` | Logical group/family this column belongs to | `"clip_features"` |
-
-Tags are open-ended — use whatever key suffix and value make sense given the user's intent. The tag suffix should describe *what is being classified* (e.g., `field_type`, `model`, `project_id`) and the value describes *how*.
-
-## Step 1: Resolve the table identifier
-
-You need:
- **Table name** (required) — e.g., `my_table` or `my_namespace.my_table`
- **Database name** — ask if not provided and not inferable from context; it goes in the `x-lancedb-database` header, never in the URL path
-
-The table identifier in the URL path is typically `table_name` for a top-level table, or `namespace$table_name` if the table lives in a namespace. The API accepts a `delimiter` query parameter to parse compound identifiers (default `$`).
-
-## Step 2: Describe the table
-
-```http
-POST {base_url}/v1/table/{table_id}/describe
-Content-Type: application/json
-
-{}
-```
-
-The response contains `schema.fields` — an array of field objects:
-
-```json
-{
-  "schema": {
-    "fields": [
-      {
-        "name": "clip_embedding_v3",
-        "type": { "type": "FixedSizeList", "fields": [...], "listSize": 768 },
-        "nullable": true,
-        "metadata": { "lancedb:description": "..." }
-      }
-    ]
-  }
-}
-```
-
-Each field has:
- `name` — field name
- `type` — Arrow data type (check `type.type` for the type string)
- `nullable` — boolean
- `metadata` — existing key-value metadata (read this before writing to avoid redundant updates)
-
-For struct/nested fields, recurse into `type.fields` and represent them as dot-notation paths (e.g., `parent.child`).
-
-If the user hasn't specified which columns to update, work with all columns.
-
-## Step 3: Generate metadata
-
-Decide what to generate based on the user's request.
-
-### Writing descriptions (`lancedb:description`)
-
-Base descriptions on:
- The column name and Arrow type (e.g., `FixedSizeList` of floats → likely an embedding)
- User-supplied context (upstream pipeline, sample values, domain knowledge)
- Name patterns: `_embedding`/`_vec`/`_embed` → vector; `_label`/`_class` → label; `_score`/`_eval`/`_metric` → evaluation metric
-
-Be specific and concise. Good: `"Sentence-BERT embedding of the query text (768-dim)."` Not: `"An embedding column."`
-
-### Tagging columns (`lancedb:tag:<name>`)
-
-Choose tag key names that match what the user asked to annotate. Common patterns:
-
- Semantic field type → `lancedb:tag:field_type: "embedding"` / `"text"` / `"image"` / `"label"` / `"eval"` / `"id"` / `"metadata"`
- Model or source → `lancedb:tag:model: "clip"` / `"bert"` / `"vit"`
- Project affiliation → `lancedb:tag:project_id: "<name>"`
- Version → `lancedb:tag:version: "v3"` (and `lancedb:tag:latest: "true"` for the newest)
-
-Use Arrow type as a hint: `FixedSizeList` + float → embedding; `Utf8`/`LargeUtf8` → text; `Binary` → image or blob.
-
-Multiple tags on the same column are fine — each is a separate key.
-
-### Grouping into logical columns (`lancedb:logical-column`)
-
-Look for naming patterns across columns:
- `clip_v1`, `clip_v2`, `clip_v3` → logical column `"clip"`, latest is `v3`
- `text_embed_20240101`, `text_embed_20240601` → logical column `"text_embed"`, latest is the most recent date suffix
-
-Write `lancedb:logical-column` on all members of a group. Mark the newest with `lancedb:tag:latest: "true"` (in addition to its version tag).
-
-## Step 4: Write the metadata
-
-```http
-POST {base_url}/v1/table/{table_id}/update_field_metadata
-Content-Type: application/json
-
-{
-  "updates": [
-    {
-      "path": "clip_v3",
-      "metadata": {
-        "lancedb:description": "CLIP ViT-L/14 image embedding, L2-normalized (1024-dim).",
-        "lancedb:tag:field_type": "embedding",
-        "lancedb:tag:model": "clip",
-        "lancedb:tag:version": "v3",
-        "lancedb:tag:latest": "true",
-        "lancedb:logical-column": "clip"
-      },
-      "replace": false
-    },
-    {
-      "path": "clip_v2",
-      "metadata": {
-        "lancedb:description": "CLIP ViT-B/32 image embedding (768-dim), superseded by v3.",
-        "lancedb:tag:field_type": "embedding",
-        "lancedb:tag:model": "clip",
-        "lancedb:tag:version": "v2",
-        "lancedb:logical-column": "clip"
-      },
-      "replace": false
-    }
-  ]
-}
-```
-
-Rules:
- **Use `"replace": false`** (merge) by default — this preserves existing metadata the user didn't ask to change
- Use `"replace": true` only if the user explicitly asks to overwrite all existing metadata on a column
- Set a value to `null` to delete a specific key
- Batch all updates in a single request when possible
-
-The response includes `version` (new table version) and `fields` (the updated metadata per field).
-
-## Step 5: Confirm
-
-Report back:
- Which columns were updated and what was written
- The new table version number
- Any columns skipped (e.g., already had up-to-date metadata)
-
---
-
-## Quick examples
-
-**"Write descriptions for all columns in the `product_embeddings` table"**
-1. POST `/v1/table/product_embeddings/describe` → get all fields
-2. Generate a `lancedb:description` for each column based on name + type
-3. POST `update_field_metadata` with descriptions
-4. Report
-
-**"Tag the columns in `model_outputs` with their field type and model"**
-1. Describe `model_outputs`
-2. For each field, classify by name + Arrow type → set `lancedb:tag:field_type` and `lancedb:tag:model` where applicable
-3. POST `update_field_metadata`
-4. Report
-
-**"Group the feature columns in `training_features` into logical families and mark the latest version"**
-1. Describe the table
-2. Find version patterns → assign `lancedb:logical-column` and `lancedb:tag:version`; mark newest with `lancedb:tag:latest: "true"`
-3. POST `update_field_metadata`
-4. Show the grouping
@@ -1,42 +0,0 @@
---
-name: lancedb-connect
-description: Resolve how to connect to a LanceDB deployment over the REST API — figure out the base URL, API key, and database header. Use this before making any REST requests to a LanceDB table, whenever the endpoint or auth setup is not already known. Also useful on its own when someone asks how to connect, authenticate, or curl their LanceDB instance.
-metadata:
-  short-description: Resolve the base URL and auth headers for a LanceDB deployment
---
-
-## Goal
-
-Produce two things every REST request needs:
-
-1. **Base URL** — the endpoint
-2. **Headers** — `x-api-key`, and usually `x-lancedb-database`
-
-## Resolution steps
-
-1. If the user already gave a URL and API key (or said which environment they're working against), use that.
-2. Otherwise, look for credentials already available in the environment:
-   - Env vars like `LANCEDB_URI` / `LANCEDB_HOST` / `LANCEDB_API_KEY`
-   - A LanceDB endpoint already running or port-forwarded locally (the REST default port is 2333, i.e. `http://localhost:2333`)
-3. If you didn't find both pieces, ask the user directly: **"What's your LanceDB endpoint's URL, and what's your API key?"** Also ask which database to use if it isn't obvious. Don't guess or probe further — the user knows their deployment.
-
-## Validating the connection
-
-Make a cheap authenticated request and check the status:
-
-```bash
-curl -s -w "\n%{http_code}" "{base_url}/v1/table/?limit=1" \
-  -H "x-api-key: <key>" \
-  -H "x-lancedb-database: <database>"
-```
-
- `200` — connection, key, and database header all good
- `401` — API key missing or wrong
- `400` mentioning a database header — this deployment expects `x-lancedb-database`
-
-## Non-REST equivalents
-
-If the caller would rather use the SDK or CLI than raw REST, the same credentials work:
-
- Python SDK: `lancedb.connect("db://<database>", api_key="<key>", host_override="<base_url>")`
- `lancedb` CLI: a `[profiles.<name>]` entry in `~/.lancedb/config.toml` with `http_server_url`, `api_key`, `database`
@@ -0,0 +1,81 @@
+---
+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`
+   - Column metadata authoring (both SDKs): `references/column_metadata.md`
+   - Branch operations (both SDKs): `references/branch_ops.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. Read `column_metadata.md` when the task is documenting, tagging, classifying, or grouping table columns (field descriptions, `lancedb:tag:*` tags, logical column families). Read `branch_ops.md` when the task involves branch lifecycle (list/create/delete), writing to a non-main branch, or verifying a change stayed off main.
+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.
@@ -0,0 +1,117 @@
+# Branch Operations
+
+Manage branches on a LanceDB table: list what exists, create new ones, delete stale ones, and direct read/write operations at a specific branch without touching main. Use for branch lifecycle tasks, experimental/isolated table versions, targeting an operation at a non-main branch, or confirming a mutation did not affect main.
+
+Works on local/OSS and remote Enterprise/Cloud tables.
+
+## The branch model (important)
+
+Branches are isolated, writable lines of history forked from another branch (or a specific version). Writes on a branch never affect `main`.
+
+There is **no global "switch branch" state** — you never repoint the whole table at a branch. Instead, **operations are scoped by which table handle you use**:
+
+- The handle you got from `open_table(name)` / `openTable(name)` targets `main`.
+- `branches.create(...)` and `branches.checkout(...)` return a **new table handle scoped to that branch**. Every read/write on that handle (add, update, `update_field_metadata`, `create_index`, search, …) lands on the branch.
+- The original main handle is unaffected — keep it around to verify isolation.
+
+`branches.list()` returns only non-main branches. Main always exists and is not listed.
+
+## Python
+
+`table.branches` is a property returning the branch manager; `table.current_branch()` tells you what a handle is scoped to (`None` = main).
+
+```python
+table = db.open_table("products")            # scoped to main
+
+# list — dict of name -> metadata (parent_branch, parent_version, ...); {} = only main
+table.branches.list()
+
+# create: forks from main by default and returns a handle scoped to the new branch
+exp = table.branches.create("experiment-reindex")
+exp = table.branches.create("exp2", from_ref="main", from_version=None)  # optional fork point
+
+# checkout an existing branch -> branch-scoped handle
+wip = table.branches.checkout("wip-branch")
+# with version= it pins to that version (read-only detached view); omit to track latest, writable
+
+# operate on the branch simply by using its handle
+wip.update_field_metadata(
+    {"path": "category", "metadata": {"lancedb:description": "Product category label."}}
+)
+wip.create_scalar_index("category")
+
+# delete: removes only the branch pointer; main and row data remain intact
+table.branches.delete("stale-2024")
+
+# alternatively, open a branch handle directly from the connection
+wip = db.open_table("products", branch="wip-branch")
+
+exp.current_branch()    # "experiment-reindex"
+table.current_branch()  # None (main)
+```
+
+Async: same shape — `table.branches` returns `AsyncBranches`; `await table.branches.create(...)` etc.
+
+## TypeScript
+
+`table.branches()` is an **async method** returning the `Branches` manager; `table.currentBranch()` returns the scoped branch or `null` for main.
+
+```typescript
+const table = await db.openTable("products"); // scoped to main
+const branches = await table.branches();
+
+// list — Record<string, BranchContents>; {} = only main
+await branches.list();
+
+// create: forks from main by default, returns a Table scoped to the new branch
+const exp = await branches.create("experiment-reindex");
+const exp2 = await branches.create("exp2", "main" /* fromRef */, undefined /* fromVersion */);
+
+// checkout an existing branch -> branch-scoped Table
+const wip = await branches.checkout("wip-branch");
+// with a version arg it pins (read-only detached view); omit to track latest, writable
+
+// operate on the branch simply by using its handle
+await wip.updateFieldMetadata([
+  { path: "category", metadata: { "lancedb:description": "Product category label." } },
+]);
+await wip.createIndex("category");
+
+// delete: removes only the branch pointer; main and row data remain intact
+await branches.delete("stale-2024");
+
+// alternatively, open a branch handle directly from the connection
+const wip2 = await db.openTable("products", { branch: "wip-branch" });
+
+exp.currentBranch();   // "experiment-reindex"
+table.currentBranch(); // null (main)
+```
+
+## Verifying isolation
+
+After writing to a branch, confirm the change did NOT land on main by reading through both handles:
+
+```python
+wip = table.branches.checkout("wip-branch")
+wip.update_field_metadata({"path": "category", "metadata": {"lancedb:description": "..."}})
+
+assert b"lancedb:description" in (wip.schema.field("category").metadata or {})
+assert b"lancedb:description" not in (table.schema.field("category").metadata or {})  # main untouched
+```
+
+Two handles on the same branch see each other's writes (e.g. `table.branches.create("exp")` and `db.open_table(name, branch="exp")`); main stays isolated.
+
+## Quick reference
+
+| Goal | Python | TypeScript |
+|------|--------|------------|
+| List branches (non-main) | `table.branches.list()` | `await (await table.branches()).list()` |
+| Create branch (off main) | `table.branches.create(name)` → branch handle | `await branches.create(name)` → branch `Table` |
+| Create from a fork point | `table.branches.create(name, from_ref=..., from_version=...)` | `await branches.create(name, fromRef, fromVersion)` |
+| Get a branch handle | `table.branches.checkout(name)` or `db.open_table(t, branch=name)` | `await branches.checkout(name)` or `await db.openTable(t, { branch: name })` |
+| Pin to a branch version (read-only) | `table.branches.checkout(name, version=v)` | `await branches.checkout(name, v)` |
+| Delete branch | `table.branches.delete(name)` | `await branches.delete(name)` |
+| Which branch is this handle on? | `table.current_branch()` (`None` = main) | `table.currentBranch()` (`null` = main) |
+| Target main | use the original (non-branch) handle | use the original (non-branch) handle |
+
+Branch names must be non-empty; empty names raise a validation error.
@@ -0,0 +1,183 @@
+# Column Metadata Authoring
+
+Write column-level descriptions, tags, and logical groupings onto a LanceDB table's schema. Use this when the user wants to document, annotate, tag, or classify what their table columns ARE (embeddings vs labels vs eval metrics, model provenance, version families, etc.).
+
+Works on local/OSS and remote Enterprise/Cloud tables alike — read the schema through the table handle, write through `update_field_metadata` (Python) / `updateFieldMetadata` (TypeScript).
+
+## Metadata key conventions
+
+All metadata uses namespaced keys:
+
+| Key | Purpose | Example value |
+|-----|---------|---------------|
+| `lancedb:description` | Human-readable explanation of what the column contains | `"CLIP ViT-L/14 image embedding, L2-normalized (768-dim)"` |
+| `lancedb:tag:<name>` | Flexible key-value tag; the suffix names the tag category | `lancedb:tag:field_type: "embedding"`, `lancedb:tag:model: "clip"`, `lancedb:tag:project_id: "foo"` |
+| `lancedb:logical-column` | Logical group/family this column belongs to | `"clip_features"` |
+
+Tags are open-ended — use whatever key suffix and value make sense given the user's intent. The tag suffix should describe *what is being classified* (e.g., `field_type`, `model`, `project_id`) and the value describes *how*. Multiple tags on the same column are fine — each is a separate key. All values are strings.
+
+## Step 1: Read the schema and existing metadata
+
+Read existing metadata before writing, to avoid redundant updates.
+
+Python — `table.schema` (sync property; async: `await table.schema()`) returns a `pyarrow.Schema`. **Arrow field metadata is bytes-keyed in Python**:
+
+```python
+schema = table.schema
+for field in schema:
+    meta = field.metadata or {}  # dict[bytes, bytes], e.g. {b"lancedb:description": b"..."}
+    print(field.name, field.type, field.nullable, meta)
+```
+
+TypeScript — `await table.schema()` returns an Arrow `Schema`; field metadata is a `Map<string, string>`:
+
+```typescript
+const schema = await table.schema();
+for (const field of schema.fields) {
+  console.log(field.name, field.type, field.nullable, field.metadata); // Map
+  // field.metadata.get("lancedb:description")
+}
+```
+
+For struct/nested fields, recurse into the field's children and address them as dot-paths (e.g., `parent.child`).
+
+If the user hasn't specified which columns to update, work with all columns.
+
+## Step 2: Generate metadata
+
+Decide what to generate based on the user's request.
+
+### Descriptions (`lancedb:description`)
+
+Base descriptions on:
+- The column name and Arrow type (e.g., `FixedSizeList` of floats → likely an embedding)
+- User-supplied context (upstream pipeline, sample values, domain knowledge)
+- Name patterns: `_embedding`/`_vec`/`_embed` → vector; `_label`/`_class` → label; `_score`/`_eval`/`_metric` → evaluation metric
+
+Be specific and concise. Good: `"Sentence-BERT embedding of the query text (768-dim)."` Not: `"An embedding column."`
+
+### Tags (`lancedb:tag:<name>`)
+
+Choose tag key names that match what the user asked to annotate. Common patterns:
+
+- Semantic field type → `lancedb:tag:field_type: "embedding"` / `"text"` / `"image"` / `"label"` / `"eval"` / `"id"` / `"metadata"`
+- Model or source → `lancedb:tag:model: "clip"` / `"bert"` / `"vit"`
+- Project affiliation → `lancedb:tag:project_id: "<name>"`
+- Version → `lancedb:tag:version: "v3"` (and `lancedb:tag:latest: "true"` for the newest)
+
+Use Arrow type as a hint: `FixedSizeList` + float → embedding; `Utf8`/`LargeUtf8` → text; `Binary` → image or blob.
+
+### Logical groupings (`lancedb:logical-column`)
+
+Look for naming patterns across columns:
+- `clip_v1`, `clip_v2`, `clip_v3` → logical column `"clip"`, latest is `v3`
+- `text_embed_20240101`, `text_embed_20240601` → logical column `"text_embed"`, latest is the most recent date suffix
+
+Write `lancedb:logical-column` on all members of a group. Mark the newest with `lancedb:tag:latest: "true"` (in addition to its version tag).
+
+## Step 3: Write the metadata
+
+Each update names a field by dot-path and carries a metadata map. Semantics (identical in both SDKs):
+
+- **Merge by default** (`replace` omitted/false) — preserves existing metadata the user didn't ask to change
+- `replace: true` swaps the field's entire metadata map — only if the user explicitly asks to overwrite
+- A value of `None`/`null` deletes that specific key
+- Batch all field updates into a single call when possible
+- Returns the new table version
+
+Python (sync and async take one dict per field, as varargs):
+
+```python
+res = table.update_field_metadata(
+    {
+        "path": "clip_v3",
+        "metadata": {
+            "lancedb:description": "CLIP ViT-L/14 image embedding, L2-normalized (1024-dim).",
+            "lancedb:tag:field_type": "embedding",
+            "lancedb:tag:model": "clip",
+            "lancedb:tag:version": "v3",
+            "lancedb:tag:latest": "true",
+            "lancedb:logical-column": "clip",
+        },
+    },
+    {
+        "path": "clip_v2",
+        "metadata": {
+            "lancedb:description": "CLIP ViT-B/32 image embedding (768-dim), superseded by v3.",
+            "lancedb:tag:field_type": "embedding",
+            "lancedb:tag:model": "clip",
+            "lancedb:tag:version": "v2",
+            "lancedb:logical-column": "clip",
+        },
+    },
+)
+print(res.version)  # new table version
+
+# merge semantics: add a key, delete one via None, keep the rest
+table.update_field_metadata(
+    {"path": "clip_v2", "metadata": {"lancedb:tag:archived": "true", "lancedb:tag:latest": None}}
+)
+```
+
+(`replace_field_metadata` is deprecated — use `update_field_metadata`.)
+
+TypeScript (takes an array of `FieldMetadataUpdate`):
+
+```typescript
+const res = await table.updateFieldMetadata([
+  {
+    path: "clip_v3",
+    metadata: {
+      "lancedb:description": "CLIP ViT-L/14 image embedding, L2-normalized (1024-dim).",
+      "lancedb:tag:field_type": "embedding",
+      "lancedb:tag:model": "clip",
+      "lancedb:tag:version": "v3",
+      "lancedb:tag:latest": "true",
+      "lancedb:logical-column": "clip",
+    },
+  },
+  {
+    path: "clip_v2",
+    metadata: {
+      "lancedb:description": "CLIP ViT-B/32 image embedding (768-dim), superseded by v3.",
+      "lancedb:tag:field_type": "embedding",
+      "lancedb:tag:model": "clip",
+      "lancedb:tag:version": "v2",
+      "lancedb:logical-column": "clip",
+    },
+  },
+]);
+console.log(res.version); // new table version
+
+// merge semantics: add a key, delete one via null, keep the rest
+await table.updateFieldMetadata([
+  { path: "clip_v2", metadata: { "lancedb:tag:archived": "true", "lancedb:tag:latest": null } },
+]);
+```
+
+## Step 4: Confirm
+
+Report back:
+- Which columns were updated and what was written
+- The new table version number (from the result)
+- Any columns skipped (e.g., already had up-to-date metadata)
+
+## Quick examples
+
+**"Write descriptions for all columns in the `product_embeddings` table"**
+1. Read `table.schema` → all fields + existing metadata
+2. Generate a `lancedb:description` for each column based on name + type
+3. One `update_field_metadata` call with all descriptions
+4. Report
+
+**"Tag the columns in `model_outputs` with their field type and model"**
+1. Read the schema
+2. For each field, classify by name + Arrow type → set `lancedb:tag:field_type` and `lancedb:tag:model` where applicable
+3. Write in one batched call
+4. Report
+
+**"Group the feature columns in `training_features` into logical families and mark the latest version"**
+1. Read the schema
+2. Find version patterns → assign `lancedb:logical-column` and `lancedb:tag:version`; mark newest with `lancedb:tag:latest: "true"`
+3. Write in one batched call
+4. Show the grouping
@@ -0,0 +1,131 @@
+# 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.
+
+## Column (Field) Metadata
+
+```python
+schema = table.schema                       # sync property; async: await table.schema()
+meta = schema.field("category").metadata    # dict[bytes, bytes] — Arrow metadata is bytes-keyed
+res = table.update_field_metadata(          # varargs: one dict per field; works local + remote
+    {"path": "category", "metadata": {"lancedb:description": "...", "lancedb:tag:field_type": "label"}}
+)
+res.version  # new table version
+```
+
+Merges by default; a `None` value deletes that key; `"replace": True` swaps the whole map. Nested fields use dot-paths (`"a.b.c"`). `replace_field_metadata` is deprecated. See `references/column_metadata.md` for key conventions (`lancedb:description`, `lancedb:tag:<name>`, `lancedb:logical-column`) and the authoring workflow.
+
+## Branches
+
+```python
+table.branches.list()                          # non-main branches; {} = only main
+exp = table.branches.create("exp")             # fork off main -> handle scoped to the branch
+wip = table.branches.checkout("wip")           # existing branch -> scoped handle (version= pins read-only)
+wip = db.open_table("t", branch="wip")         # or open scoped directly
+table.branches.delete("stale")                 # removes only the branch pointer
+table.current_branch()                         # None = main
+```
+
+There is no global switch — scoping is per table handle: any read/write on a branch handle lands on that branch; the original handle keeps targeting main. See `references/branch_ops.md` for the model and isolation checks.
+
+## 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.
@@ -0,0 +1,173 @@
+# 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.
@@ -0,0 +1,131 @@
+# 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.
@@ -0,0 +1,105 @@
+# 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.
+
+## Column (Field) Metadata
+
+```typescript
+const schema = await table.schema();
+const meta = schema.fields.find((f) => f.name === "category")?.metadata; // Map<string, string>
+const res = await table.updateFieldMetadata([
+  { path: "category", metadata: { "lancedb:description": "...", "lancedb:tag:field_type": "label" } },
+]);
+res.version; // new table version
+```
+
+Merges by default; a `null` value deletes that key; `replace: true` swaps the whole map. Nested fields use dot-paths (`"a.b.c"`). See `references/column_metadata.md` for key conventions (`lancedb:description`, `lancedb:tag:<name>`, `lancedb:logical-column`) and the authoring workflow.
+
+## Branches
+
+```typescript
+const branches = await table.branches();       // async manager
+await branches.list();                         // non-main branches; {} = only main
+const exp = await branches.create("exp");      // fork off main -> Table scoped to the branch
+const wip = await branches.checkout("wip");    // existing branch -> scoped Table (version arg pins read-only)
+const wip2 = await db.openTable("t", { branch: "wip" }); // or open scoped directly
+await branches.delete("stale");                // removes only the branch pointer
+table.currentBranch();                         // null = main
+```
+
+There is no global switch — scoping is per table handle: any read/write on a branch handle lands on that branch; the original handle keeps targeting main. See `references/branch_ops.md` for the model and isolation checks.
+
+## 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.
@@ -0,0 +1,100 @@
+# 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.
@@ -0,0 +1,78 @@
+# 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.
@@ -0,0 +1,135 @@
+#!/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 = "0.31.0-beta.3"
+current_version = "0.32.0-beta.2"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
@@ -34,15 +34,16 @@ 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/'"
+        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc"
        target: x86_64-unknown-linux-gnu
        manylinux: ${{ inputs.manylinux }}
        args: ${{ inputs.args }}
        before-script-linux: |
          set -e
-          curl -L https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-$(uname -m).zip > /tmp/protoc.zip \
-            && unzip /tmp/protoc.zip -d /usr/local \
-            && rm /tmp/protoc.zip
+          curl -fsSL https://github.com/protocolbuffers/protobuf/releases/download/v24.4/protoc-24.4-linux-x86_64.zip -o /tmp/protoc.zip
+          unzip /tmp/protoc.zip -d /usr/local
+          rm /tmp/protoc.zip
+          /usr/local/bin/protoc --version
    - name: Build Arm Manylinux Wheel
      if: ${{ inputs.arm-build == 'true' }}
      uses: PyO3/maturin-action@v1
@@ -50,13 +51,14 @@ 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/'"
+        docker-options: "-e PIP_EXTRA_INDEX_URL='https://pypi.fury.io/lance-format/ https://pypi.fury.io/lancedb/' -e PROTOC=/usr/local/bin/protoc"
        target: aarch64-unknown-linux-gnu
        manylinux: ${{ inputs.manylinux }}
        args: ${{ inputs.args }}
        before-script-linux: |
          set -e
-          yum install -y clang \
-            && 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 \
-            && rm /tmp/protoc.zip
+          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
+          unzip /tmp/protoc.zip -d /usr/local
+          rm /tmp/protoc.zip
+          /usr/local/bin/protoc --version
@@ -25,7 +25,7 @@ jobs:
    # Only runs on tags that matches the make-release action
    if: startsWith(github.ref, 'refs/tags/v')
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - uses: Swatinem/rust-cache@v2
        with:
          workspaces: rust
@@ -47,7 +47,7 @@ jobs:
      contents: read
      issues: write
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - uses: ./.github/actions/create-failure-issue
        with:
          job-results: ${{ toJSON(needs) }}
@@ -36,14 +36,14 @@ jobs:
          echo "guidelines = ${{ inputs.guidelines }}"

      - name: Checkout Repo
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
        with:
          ref: ${{ inputs.branch }}
          fetch-depth: 0
          persist-credentials: true

      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          # pnpm 11 (used by the nodejs install step below) requires
          # Node >= 22.13; use 24 since 22 hits EOL in October.
@@ -82,7 +82,7 @@ jobs:
          cache: maven

      - name: Setup pnpm
-        uses: pnpm/action-setup@v4
+        uses: pnpm/action-setup@v6
        with:
          version: 11.1.1
      - name: Install Node.js dependencies for TypeScript bindings
@@ -30,13 +30,13 @@ jobs:
          echo "tag = ${{ inputs.tag || 'latest' }}"

      - name: Checkout Repo LanceDB
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
        with:
          fetch-depth: 0
          persist-credentials: true

      - name: Set up Node.js
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: 20

@@ -27,7 +27,7 @@ jobs:
    name: Verify PR title / description conforms to semantic-release
    runs-on: ubuntu-latest
    steps:
-      - uses: actions/setup-node@v4
+      - uses: actions/setup-node@v6
        with:
          node-version: "18"
      # These rules are disabled because Github will always ensure there
@@ -35,7 +35,7 @@ jobs:
    runs-on: ubuntu-24.04
    steps:
      - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
      - name: Install dependencies needed for ubuntu
        run: |
          sudo apt install -y protobuf-compiler libssl-dev
@@ -53,7 +53,7 @@ jobs:
          python -m pip install --extra-index-url https://pypi.fury.io/lance-format/ --extra-index-url https://pypi.fury.io/lancedb/ -e .
          python -m pip install --extra-index-url https://pypi.fury.io/lance-format/ --extra-index-url https://pypi.fury.io/lancedb/ -r ../docs/requirements.txt
      - name: Set up node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: 20
          cache: 'npm'
@@ -32,7 +32,7 @@ jobs:
        working-directory: ./java
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
      - name: Set up Java 8
        uses: actions/setup-java@v4
        with:
@@ -73,7 +73,7 @@ jobs:
      contents: read
      issues: write
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - uses: ./.github/actions/create-failure-issue
        with:
          job-results: ${{ toJSON(needs) }}
@@ -36,7 +36,7 @@ jobs:
        working-directory: ./java
    steps:
      - name: Checkout repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
      - name: Set up Java 17
        uses: actions/setup-java@v4
        with:
@@ -19,7 +19,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Check out code
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
      - name: Install license-header-checker
        working-directory: /tmp
        run: |
@@ -49,7 +49,7 @@ jobs:
    steps:
      - name: Output Inputs
        run: echo "${{ toJSON(github.event.inputs) }}"
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -38,14 +38,14 @@ jobs:
      CC: gcc-12
      CXX: g++-12
    steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
      with:
        fetch-depth: 0
        lfs: true
-    - uses: pnpm/action-setup@v4
+    - uses: pnpm/action-setup@v6
      with:
        version: 11.1.1
-    - uses: actions/setup-node@v4
+    - uses: actions/setup-node@v6
      with:
        # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
        # in October. The library itself still supports Node >= 18
@@ -86,14 +86,14 @@ jobs:
        shell: bash
        working-directory: nodejs
    steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
      with:
        fetch-depth: 0
        lfs: true
-    - uses: pnpm/action-setup@v4
+    - uses: pnpm/action-setup@v6
      with:
        version: 11.1.1
-    - uses: actions/setup-node@v4
+    - uses: actions/setup-node@v6
      name: Setup Node.js 24 for build
      with:
        # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
@@ -130,7 +130,7 @@ jobs:
          echo "Run 'pnpm run docs', fix any warnings, and commit the changes."
          exit 1
        fi
-    - uses: actions/setup-node@v4
+    - uses: actions/setup-node@v6
      name: Setup Node.js ${{ matrix.node-version }} for test
      with:
        node-version: ${{ matrix.node-version }}
@@ -166,14 +166,14 @@ jobs:
        shell: bash
        working-directory: nodejs
    steps:
-    - uses: actions/checkout@v4
+    - uses: actions/checkout@v6
      with:
        fetch-depth: 0
        lfs: true
-    - uses: pnpm/action-setup@v4
+    - uses: pnpm/action-setup@v6
      with:
        version: 11.1.1
-    - uses: actions/setup-node@v4
+    - uses: actions/setup-node@v6
      with:
        # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
        # in October.
@@ -32,7 +32,7 @@ jobs:
    permissions:
      contents: write
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -103,7 +103,7 @@ jobs:
            features: fp16kernels
            pre_build: brew install protobuf
          - target: x86_64-pc-windows-msvc
-            host: windows-latest
+            host: windows-2025-8x-x64
            features: ","
            pre_build: |-
              choco install --no-progress protoc ninja nasm
@@ -111,12 +111,21 @@ jobs:
              # There is an issue where choco doesn't add nasm to the path
              export PATH="$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-latest
+            host: windows-2025-8x-x64
            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
@@ -170,13 +179,13 @@ jobs:
      run:
        working-directory: nodejs
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - name: Setup pnpm
-        uses: pnpm/action-setup@v4
+        uses: pnpm/action-setup@v6
        with:
          version: 11.1.1
      - name: Setup node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
          # in October.
@@ -190,7 +199,7 @@ jobs:
          toolchain: stable
          targets: ${{ matrix.settings.target }}
      - name: Cache cargo
-        uses: actions/cache@v4
+        uses: actions/cache@v5
        with:
          path: |
            ~/.cargo/registry/index/
@@ -244,7 +253,7 @@ jobs:
        if: ${{ !matrix.settings.docker }}
        shell: bash
      - name: Upload artifact
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
        with:
          name: lancedb-${{ matrix.settings.target }}
          path: nodejs/dist/*.node
@@ -256,7 +265,7 @@ jobs:
        run: pnpm tsc
      - name: Upload Generic Artifacts
        if: ${{ matrix.settings.target == 'aarch64-apple-darwin' }}
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v7
        with:
          name: nodejs-dist
          path: |
@@ -287,13 +296,13 @@ jobs:
        shell: bash
        working-directory: nodejs
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - name: Setup pnpm
-        uses: pnpm/action-setup@v4
+        uses: pnpm/action-setup@v6
        with:
          version: 11.1.1
      - name: Setup Node.js 24 for install
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          # pnpm 11 requires Node >= 22.13; use 24 since 22 hits EOL
          # in October.
@@ -303,18 +312,18 @@ jobs:
      - name: Install dependencies
        run: pnpm install --frozen-lockfile
      - name: Setup Node.js ${{ matrix.node }} for test
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: ${{ matrix.node }}
      - name: Download artifacts
-        uses: actions/download-artifact@v4
+        uses: actions/download-artifact@v8
        with:
          name: lancedb-${{ matrix.settings.target }}
          path: nodejs/dist/
          # For testing purposes:
          # run-id: 13982782871
          # github-token: ${{ secrets.GITHUB_TOKEN }} # token with actions:read permissions on target repo
-      - uses: actions/download-artifact@v4
+      - uses: actions/download-artifact@v8
        with:
          name: nodejs-dist
          path: nodejs/dist
@@ -339,13 +348,13 @@ jobs:
    needs:
      - test-lancedb
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - name: Setup pnpm
-        uses: pnpm/action-setup@v4
+        uses: pnpm/action-setup@v6
        with:
          version: 11.1.1
      - name: Setup node
-        uses: actions/setup-node@v4
+        uses: actions/setup-node@v6
        with:
          node-version: 24
          cache: pnpm
@@ -353,14 +362,14 @@ jobs:
          registry-url: "https://registry.npmjs.org"
      - name: Install dependencies
        run: pnpm install --frozen-lockfile
-      - uses: actions/download-artifact@v4
+      - uses: actions/download-artifact@v8
        with:
          name: nodejs-dist
          path: nodejs/dist
          # For testing purposes:
          # run-id: 13982782871
          # github-token: ${{ secrets.GITHUB_TOKEN }} # token with actions:read permissions on target repo
-      - uses: actions/download-artifact@v4
+      - uses: actions/download-artifact@v8
        name: Download arch-specific binaries
        with:
          pattern: lancedb-*
@@ -398,7 +407,7 @@ jobs:
      contents: read
      issues: write
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - uses: ./.github/actions/create-failure-issue
        with:
          job-results: ${{ toJSON(needs) }}
@@ -41,7 +41,7 @@ jobs:
        shell: bash
        working-directory: python
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -66,7 +66,7 @@ jobs:
        shell: bash
        working-directory: python
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -95,7 +95,7 @@ jobs:
        shell: bash
        working-directory: python
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -126,7 +126,7 @@ jobs:
        shell: bash
        working-directory: python
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -160,7 +160,7 @@ jobs:
        shell: bash
        working-directory: python
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -189,7 +189,7 @@ jobs:
        shell: bash
        working-directory: python
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -212,7 +212,7 @@ jobs:
        shell: bash
        working-directory: python
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -40,7 +40,7 @@ jobs:
      CC: clang-18
      CXX: clang++-18
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -65,7 +65,7 @@ jobs:
    timeout-minutes: 10
    runs-on: ubuntu-24.04
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - uses: EmbarkStudios/cargo-deny-action@v2
        with:
          command: check advisories bans licenses sources
@@ -78,7 +78,7 @@ jobs:
      CC: clang
      CXX: clang++
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      # Building without a lock file often requires the latest Rust version since downstream
      # dependencies may have updated their minimum Rust version.
      - uses: actions-rust-lang/setup-rust-toolchain@v1
@@ -113,7 +113,7 @@ jobs:
      CXX: clang++-18
      GH_TOKEN: ${{ secrets.SOPHON_READ_TOKEN }}
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -125,10 +125,26 @@ jobs:
      - uses: rui314/setup-mold@v1
      - name: Make Swap
        run: |
-          sudo fallocate -l 16G /swapfile
-          sudo chmod 600 /swapfile
-          sudo mkswap /swapfile
-          sudo swapon /swapfile
+          swapfile=/swapfile
+          min_swap_bytes=$((15 * 1024 * 1024 * 1024))
+          active_swap_bytes="$(sudo swapon --show=NAME,SIZE --bytes --noheadings | awk '$1 == "/swapfile" { print $2 }')"
+          if [ -n "$active_swap_bytes" ]; then
+            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
@@ -152,7 +168,7 @@ jobs:
        shell: bash
        working-directory: rust
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          fetch-depth: 0
          lfs: true
@@ -181,7 +197,7 @@ jobs:
      run:
        working-directory: rust/lancedb
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
      - name: Set target
        run: rustup target add ${{ matrix.target }}
      - uses: Swatinem/rust-cache@v2
@@ -210,7 +226,7 @@ jobs:
      CC: clang-18
      CXX: clang++-18
    steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v6
        with:
          submodules: true
      - name: Install dependencies
@@ -11,7 +11,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
        with:
          ref: main
          persist-credentials: false
@@ -11,7 +11,7 @@ jobs:
    runs-on: ubuntu-latest
    steps:
      - name: Checkout
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
        with:
          ref: main
          persist-credentials: false
@@ -13,24 +13,25 @@ categories = ["database-implementations"]
 rust-version = "1.91.0"

 [workspace.dependencies]
-lance = { "version" = "=9.0.0-beta.8", default-features = false, "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-core = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-datagen = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-file = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-io = { "version" = "=9.0.0-beta.8", default-features = false, "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-index = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-linalg = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-namespace-impls = { "version" = "=9.0.0-beta.8", default-features = false, "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-table = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-testing = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-datafusion = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-encoding = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
-lance-arrow = { "version" = "=9.0.0-beta.8", "tag" = "v9.0.0-beta.8", "git" = "https://github.com/lance-format/lance.git" }
+lance = { "version" = "=9.1.0-beta.2", default-features = false, "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-core = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-datagen = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-file = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-io = { "version" = "=9.1.0-beta.2", default-features = false, "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-index = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-linalg = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-namespace-impls = { "version" = "=9.1.0-beta.2", default-features = false, "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-table = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-testing = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-datafusion = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-encoding = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
+lance-arrow = { "version" = "=9.1.0-beta.2", "tag" = "v9.1.0-beta.2", "git" = "https://github.com/lance-format/lance.git" }
 ahash = "0.8"
 # Note that this one does not include pyarrow
 arrow = { version = "58.0.0", optional = false }
 arrow-array = "58.0.0"
+arrow-buffer = "58.0.0"
 arrow-data = "58.0.0"
 arrow-ipc = "58.0.0"
 arrow-ord = "58.0.0"
@@ -38,21 +39,23 @@ arrow-schema = "58.0.0"
 arrow-select = "58.0.0"
 arrow-cast = "58.0.0"
 async-trait = "0"
-datafusion = { version = "53.0.0", default-features = false }
-datafusion-catalog = "53.0.0"
-datafusion-common = { version = "53.0.0", default-features = false }
-datafusion-execution = "53.0.0"
-datafusion-expr = "53.0.0"
-datafusion-functions = "53.0.0"
-datafusion-physical-plan = "53.0.0"
-datafusion-physical-expr = "53.0.0"
-datafusion-sql = "53.0.0"
+datafusion = { version = "54.0.0", default-features = false }
+datafusion-catalog = "54.0.0"
+datafusion-common = { version = "54.0.0", default-features = false }
+datafusion-execution = "54.0.0"
+datafusion-expr = "54.0.0"
+datafusion-functions = "54.0.0"
+datafusion-physical-plan = "54.0.0"
+datafusion-physical-expr = "54.0.0"
+datafusion-sql = "54.0.0"
 env_logger = "0.11"
 half = { "version" = "2.7.1", default-features = false, features = [
    "num-traits",
 ] }
 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"
@@ -51,18 +51,6 @@ 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.
@@ -80,18 +68,6 @@ 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.
@@ -108,17 +84,23 @@ 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>0.31.0-beta.3</version>
+    <version>0.32.0-beta.2</version>
 </dependency>
 ```

@@ -33,7 +33,7 @@ protected inner: Query | Promise<Query>;
 ### analyzePlan()

 ```ts
-analyzePlan(): Promise<string>
+analyzePlan(distributedMetrics?): Promise<string>
 ```

 Executes the query and returns the physical query plan annotated with runtime metrics.
@@ -41,6 +41,12 @@ Executes the query and returns the physical query plan annotated with runtime me
 This is useful for debugging and performance analysis, as it shows how the query was executed
 and includes metrics such as elapsed time, rows processed, and I/O statistics.

+#### Parameters
+
+* **distributedMetrics?**: [`AnalyzePlanDistributedMetrics`](../type-aliases/AnalyzePlanDistributedMetrics.md)
+    How distributed worker metrics are displayed for remote query plans.
+    Defaults to `"aggregate"`.
+
 #### Returns

 `Promise`&lt;`string`&gt;
@@ -518,6 +524,9 @@ x > 5 OR y = 'test'

 Filtering performance can often be improved by creating a scalar index
 on the filter column(s).
+
+Calling this multiple times combines the filters with a logical AND rather
+than replacing the previous filter.
 ```

 #### Inherited from
@@ -38,7 +38,7 @@ protected inner: NativeQueryType | Promise<NativeQueryType>;
 ### analyzePlan()

 ```ts
-analyzePlan(): Promise<string>
+analyzePlan(distributedMetrics?): Promise<string>
 ```

 Executes the query and returns the physical query plan annotated with runtime metrics.
@@ -46,6 +46,12 @@ Executes the query and returns the physical query plan annotated with runtime me
 This is useful for debugging and performance analysis, as it shows how the query was executed
 and includes metrics such as elapsed time, rows processed, and I/O statistics.

+#### Parameters
+
+* **distributedMetrics?**: [`AnalyzePlanDistributedMetrics`](../type-aliases/AnalyzePlanDistributedMetrics.md)
+    How distributed worker metrics are displayed for remote query plans.
+    Defaults to `"aggregate"`.
+
 #### Returns

 `Promise`&lt;`string`&gt;
@@ -398,6 +398,26 @@ 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
@@ -914,6 +934,32 @@ 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
@@ -29,7 +29,7 @@ protected inner: TakeQuery | Promise<TakeQuery>;
 ### analyzePlan()

 ```ts
-analyzePlan(): Promise<string>
+analyzePlan(distributedMetrics?): Promise<string>
 ```

 Executes the query and returns the physical query plan annotated with runtime metrics.
@@ -37,6 +37,12 @@ Executes the query and returns the physical query plan annotated with runtime me
 This is useful for debugging and performance analysis, as it shows how the query was executed
 and includes metrics such as elapsed time, rows processed, and I/O statistics.

+#### Parameters
+
+* **distributedMetrics?**: [`AnalyzePlanDistributedMetrics`](../type-aliases/AnalyzePlanDistributedMetrics.md)
+    How distributed worker metrics are displayed for remote query plans.
+    Defaults to `"aggregate"`.
+
 #### Returns

 `Promise`&lt;`string`&gt;
@@ -51,7 +51,7 @@ addQueryVector(vector): VectorQuery
 ### analyzePlan()

 ```ts
-analyzePlan(): Promise<string>
+analyzePlan(distributedMetrics?): Promise<string>
 ```

 Executes the query and returns the physical query plan annotated with runtime metrics.
@@ -59,6 +59,12 @@ Executes the query and returns the physical query plan annotated with runtime me
 This is useful for debugging and performance analysis, as it shows how the query was executed
 and includes metrics such as elapsed time, rows processed, and I/O statistics.

+#### Parameters
+
+* **distributedMetrics?**: [`AnalyzePlanDistributedMetrics`](../type-aliases/AnalyzePlanDistributedMetrics.md)
+    How distributed worker metrics are displayed for remote query plans.
+    Defaults to `"aggregate"`.
+
 #### Returns

 `Promise`&lt;`string`&gt;
@@ -767,6 +773,9 @@ x > 5 OR y = 'test'

 Filtering performance can often be improved by creating a scalar index
 on the filter column(s).
+
+Calling this multiple times combines the filters with a logical AND rather
+than replacing the previous filter.
 ```

 #### Inherited from
@@ -0,0 +1,29 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / OAuthFlowType
+
+# Enumeration: OAuthFlowType
+
+OAuth authentication flow types.
+
+## Enumeration Members
+
+### AzureManagedIdentity
+
+```ts
+AzureManagedIdentity: "azure_managed_identity";
+```
+
+Azure Managed Identity via IMDS.
+
+***
+
+### ClientCredentials
+
+```ts
+ClientCredentials: "client_credentials";
+```
+
+Client Credentials grant (service-to-service / M2M).
@@ -0,0 +1,42 @@
+[**@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.
@@ -0,0 +1,26 @@
+[**@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;
@@ -12,6 +12,7 @@
 ## Enumerations

 - [FullTextQueryType](enumerations/FullTextQueryType.md)
+- [OAuthFlowType](enumerations/OAuthFlowType.md)
 - [Occur](enumerations/Occur.md)
 - [Operator](enumerations/Operator.md)

@@ -71,6 +72,7 @@
 - [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)
@@ -85,6 +87,8 @@
 - [ListNamespacesResponse](interfaces/ListNamespacesResponse.md)
 - [LsmWriteSpec](interfaces/LsmWriteSpec.md)
 - [MergeResult](interfaces/MergeResult.md)
+- [NativeOAuthConfig](interfaces/NativeOAuthConfig.md)
+- [OAuthConfig](interfaces/OAuthConfig.md)
 - [OpenTableOptions](interfaces/OpenTableOptions.md)
 - [OptimizeOptions](interfaces/OptimizeOptions.md)
 - [OptimizeStats](interfaces/OptimizeStats.md)
@@ -104,6 +108,7 @@
 - [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)
@@ -113,6 +118,8 @@

 ## Type Aliases

+- [AnalyzePlanDistributedMetrics](type-aliases/AnalyzePlanDistributedMetrics.md)
+- [BaseTokenizer](type-aliases/BaseTokenizer.md)
 - [Data](type-aliases/Data.md)
 - [DataLike](type-aliases/DataLike.md)
 - [FieldLike](type-aliases/FieldLike.md)
@@ -122,12 +129,15 @@
 - [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)
@@ -64,6 +64,19 @@ client used by manifest-enabled native connections.

 ***

+### oauthConfig?
+
+```ts
+optional oauthConfig: NativeOAuthConfig;
+```
+
+(For LanceDB cloud only): OAuth configuration for IdP-based
+authentication (e.g., Azure Entra ID). When set, token acquisition
+and refresh are handled entirely in Rust. TypeScript users should pass
+the public `OAuthConfig` type exported from `@lancedb/lancedb`.
+
+***
+
 ### readConsistencyInterval?

 ```ts
@@ -23,7 +23,7 @@ whether to remove punctuation
 ### baseTokenizer?

 ```ts
-optional baseTokenizer: "raw" | "simple" | "whitespace" | "ngram";
+optional baseTokenizer: BaseTokenizer;
 ```

 The tokenizer to use when building the index.
@@ -37,6 +37,10 @@ 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?
@@ -0,0 +1,29 @@
+[**@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.
@@ -0,0 +1,88 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / NativeOAuthConfig
+
+# Interface: NativeOAuthConfig
+
+OAuth configuration for LanceDB authentication.
+
+This is the generated napi-rs binding shape. TypeScript users should prefer
+the public `OAuthConfig` type exported from `@lancedb/lancedb`.
+
+All token acquisition and refresh is handled in the Rust layer.
+
+## Properties
+
+### clientId
+
+```ts
+clientId: string;
+```
+
+Application / Client ID.
+
+***
+
+### clientSecret?
+
+```ts
+optional clientSecret: string;
+```
+
+Client secret (required for client_credentials).
+
+***
+
+### flow?
+
+```ts
+optional flow: string;
+```
+
+Authentication flow: "client_credentials" or "azure_managed_identity"
+
+***
+
+### issuerUrl
+
+```ts
+issuerUrl: string;
+```
+
+OIDC issuer URL or OAuth authority URL.
+For Azure: `https://login.microsoftonline.com/{tenant_id}/v2.0`
+
+***
+
+### managedIdentityClientId?
+
+```ts
+optional managedIdentityClientId: string;
+```
+
+Client ID for user-assigned managed identity (azure_managed_identity).
+
+***
+
+### refreshBufferSecs?
+
+```ts
+optional refreshBufferSecs: number;
+```
+
+Seconds before expiry to trigger proactive refresh (default: 300).
+Keep this well below the token TTL; if it is greater than or equal to
+the TTL, each request refreshes the token.
+
+***
+
+### scopes
+
+```ts
+scopes: string[];
+```
+
+OAuth scopes to request. For Azure managed identity, exactly one scope
+or resource is required. For example: `["api://{app_id}/.default"]`
@@ -0,0 +1,111 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / OAuthConfig
+
+# Interface: OAuthConfig
+
+OAuth configuration for LanceDB authentication.
+
+This is the public TypeScript OAuth configuration type. The generated
+`NativeOAuthConfig` type has the same runtime shape but is an implementation
+detail of the napi-rs binding.
+
+All token acquisition and refresh is handled in the Rust layer.
+This config is passed through to Rust via napi-rs.
+
+## Examples
+
+```typescript
+const config: OAuthConfig = {
+  issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
+  clientId: "app-id",
+  clientSecret: "secret",
+  scopes: ["api://lancedb-api/.default"],
+};
+```
+
+```typescript
+const config: OAuthConfig = {
+  issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
+  clientId: "app-id",
+  scopes: ["api://lancedb-api/.default"],
+  flow: OAuthFlowType.AzureManagedIdentity,
+};
+```
+
+## Properties
+
+### clientId
+
+```ts
+clientId: string;
+```
+
+Application / Client ID.
+
+***
+
+### clientSecret?
+
+```ts
+optional clientSecret: string;
+```
+
+Client secret (required for ClientCredentials).
+
+***
+
+### flow?
+
+```ts
+optional flow: OAuthFlowType;
+```
+
+Authentication flow (default: ClientCredentials).
+
+***
+
+### issuerUrl
+
+```ts
+issuerUrl: string;
+```
+
+OIDC issuer URL or OAuth authority URL.
+For Azure: `https://login.microsoftonline.com/{tenant_id}/v2.0`
+
+***
+
+### managedIdentityClientId?
+
+```ts
+optional managedIdentityClientId: string;
+```
+
+Client ID for user-assigned managed identity (AzureManagedIdentity).
+
+***
+
+### refreshBufferSecs?
+
+```ts
+optional refreshBufferSecs: number;
+```
+
+Seconds before expiry to trigger proactive refresh (default: 300).
+Keep this well below the token TTL; if it is greater than or equal to
+the TTL, each request refreshes the token.
+
+***
+
+### scopes
+
+```ts
+scopes: string[];
+```
+
+OAuth scopes to request.
+For Azure managed identity, exactly one scope or resource is required.
+For example: `["api://{app_id}/.default"]`
@@ -8,6 +8,14 @@

 ## Properties

+### clumpSize?
+
+```ts
+optional clumpSize: number;
+```
+
+***
+
 ### counts?

 ```ts
@@ -0,0 +1,109 @@
+[**@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.
@@ -0,0 +1,11 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / AnalyzePlanDistributedMetrics
+
+# Type Alias: AnalyzePlanDistributedMetrics
+
+```ts
+type AnalyzePlanDistributedMetrics: "aggregate" | "per_worker" | "full";
+```
@@ -0,0 +1,19 @@
+[**@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}`;
+```
@@ -0,0 +1,11 @@
+[**@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>0.31.0-beta.3</version>
+      <version>0.32.0-beta.2</version>
      <relativePath>../pom.xml</relativePath>
    </parent>

@@ -6,7 +6,7 @@

    <groupId>com.lancedb</groupId>
    <artifactId>lancedb-parent</artifactId>
-    <version>0.31.0-beta.3</version>
+    <version>0.32.0-beta.2</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.8</lance-core.version>
+        <lance-core.version>9.1.0-beta.2</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 = "0.31.0-beta.3"
+version = "0.32.0-beta.2"
 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"]
+default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/goosefs", "lancedb/metrics-otel"]
 fp16kernels = ["lancedb/fp16kernels"]
 remote = ["lancedb/remote"]
@@ -0,0 +1,114 @@
+// 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();
+  });
+});
@@ -215,6 +215,20 @@ describe("Query orderBy", () => {
    expect(results[2].score).toBeCloseTo(4.1, 0.001);
  });

+  it("should combine repeated where clauses with AND", async () => {
+    const results = await table
+      .query()
+      .where("score > 1.0")
+      .where("score < 3.0")
+      .orderBy({ columnName: "score" })
+      .toArray();
+    // Only rows matching both predicates should be returned, rather than the
+    // second where() silently replacing the first.
+    expect(results.length).toBe(2);
+    expect(results[0].score).toBeCloseTo(1.2, 0.001);
+    expect(results[1].score).toBeCloseTo(2.8, 0.001);
+  });
+
  it("should support method chaining with limit", async () => {
    const results = await table
      .query()
@@ -16,6 +16,7 @@ import {
  PhraseQuery,
  Table,
  connect,
+  tokenize,
 } from "../lancedb";
 import {
  Table as ArrowTable,
@@ -2307,6 +2308,75 @@ 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 }];
@@ -2705,8 +2775,13 @@ describe("when calling analyzePlan", () => {
      .fill(1)
      .map(() => Math.random());
    const plan = await table.query().nearestTo(queryVec).analyzePlan();
-    console.log("Query Plan:\n", plan); // <--- Print the plan
    expect(plan).toMatch("AnalyzeExec");
+
+    const fullPlan = await table
+      .query()
+      .nearestTo(queryVec)
+      .analyzePlan("full");
+    expect(fullPlan).toMatch("AnalyzeExec");
  });
 });

@@ -2992,6 +3067,56 @@ 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", () => {
@@ -13,13 +13,21 @@ 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,
@@ -52,6 +60,7 @@ export {
  SplitHashOptions,
  SplitSequentialOptions,
  ShuffleOptions,
+  OAuthConfig as NativeOAuthConfig,
 } from "./native.js";

 export {
@@ -84,6 +93,7 @@ export {
  QueryBase,
  VectorQuery,
  TakeQuery,
+  AnalyzePlanDistributedMetrics,
  QueryExecutionOptions,
  ColumnOrdering,
  FullTextSearchOptions,
@@ -108,6 +118,7 @@ export {
  HnswPqOptions,
  HnswSqOptions,
  FtsOptions,
+  BaseTokenizer,
 } from "./indices";

 export {
@@ -118,6 +129,8 @@ export {
  OptimizeOptions,
  Version,
  WriteProgress,
+  FtsToken,
+  TokenizeTableOptions,
  LsmWriteSpec,
  ColumnAlteration,
  FieldMetadataUpdate,
@@ -130,6 +143,8 @@ export {
  TokenResponse,
 } from "./header";

+export { OAuthConfig, OAuthFlowType } from "./oauth";
+
 export { MergeInsertBuilder, WriteExecutionOptions } from "./merge";

 export * as embedding from "./embedding";
@@ -147,6 +162,68 @@ 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.
 *
@@ -486,6 +486,16 @@ 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
 */
@@ -509,8 +519,12 @@ 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?: "simple" | "whitespace" | "raw" | "ngram";
+  baseTokenizer?: BaseTokenizer;

  /**
   * language for stemming and stop words
@@ -0,0 +1,76 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+/**
+ * OAuth authentication flow types.
+ */
+export enum OAuthFlowType {
+  /** Client Credentials grant (service-to-service / M2M). */
+  ClientCredentials = "client_credentials",
+  /** Azure Managed Identity via IMDS. */
+  AzureManagedIdentity = "azure_managed_identity",
+}
+
+/**
+ * OAuth configuration for LanceDB authentication.
+ *
+ * This is the public TypeScript OAuth configuration type. The generated
+ * `NativeOAuthConfig` type has the same runtime shape but is an implementation
+ * detail of the napi-rs binding.
+ *
+ * All token acquisition and refresh is handled in the Rust layer.
+ * This config is passed through to Rust via napi-rs.
+ *
+ * @example Client Credentials (service-to-service):
+ * ```typescript
+ * const config: OAuthConfig = {
+ *   issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
+ *   clientId: "app-id",
+ *   clientSecret: "secret",
+ *   scopes: ["api://lancedb-api/.default"],
+ * };
+ * ```
+ *
+ * @example Azure Managed Identity:
+ * ```typescript
+ * const config: OAuthConfig = {
+ *   issuerUrl: "https://login.microsoftonline.com/{tenant}/v2.0",
+ *   clientId: "app-id",
+ *   scopes: ["api://lancedb-api/.default"],
+ *   flow: OAuthFlowType.AzureManagedIdentity,
+ * };
+ * ```
+ */
+export interface OAuthConfig {
+  /**
+   * OIDC issuer URL or OAuth authority URL.
+   * For Azure: `https://login.microsoftonline.com/{tenant_id}/v2.0`
+   */
+  issuerUrl: string;
+
+  /** Application / Client ID. */
+  clientId: string;
+
+  /**
+   * OAuth scopes to request.
+   * For Azure managed identity, exactly one scope or resource is required.
+   * For example: `["api://{app_id}/.default"]`
+   */
+  scopes: string[];
+
+  /** Authentication flow (default: ClientCredentials). */
+  flow?: OAuthFlowType;
+
+  /** Client secret (required for ClientCredentials). */
+  clientSecret?: string;
+
+  /** Client ID for user-assigned managed identity (AzureManagedIdentity). */
+  managedIdentityClientId?: string;
+
+  /**
+   * Seconds before expiry to trigger proactive refresh (default: 300).
+   * Keep this well below the token TTL; if it is greater than or equal to
+   * the TTL, each request refreshes the token.
+   */
+  refreshBufferSecs?: number;
+}
@@ -0,0 +1,137 @@
+// 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;
+}
@@ -79,6 +79,8 @@ export interface QueryExecutionOptions {
  timeoutMs?: number;
 }

+export type AnalyzePlanDistributedMetrics = "aggregate" | "per_worker" | "full";
+
 export interface ColumnOrdering {
  columnName: string;
  ascending?: boolean;
@@ -311,13 +313,20 @@ export class QueryBase<
   *        KNNVectorDistance: metric=l2, metrics=[output_rows=1, elapsed_compute=114.333µs, output_batches=1]
   *         LanceScan: uri=/path/to/data, projection=[vector], row_id=true, row_addr=false, ordered=false, metrics=[output_rows=1, elapsed_compute=103.626µs, bytes_read=549, iops=2, requests=2]
   *
+   * @param distributedMetrics - How distributed worker metrics are displayed for remote query plans.
+   * Defaults to `"aggregate"`.
   * @returns A query execution plan with runtime metrics for each step.
   */
-  async analyzePlan(): Promise<string> {
+  async analyzePlan(
+    distributedMetrics?: AnalyzePlanDistributedMetrics,
+  ): Promise<string> {
+    const distributedMetricsMode = distributedMetrics ?? "aggregate";
    if (this.inner instanceof Promise) {
-      return this.inner.then((inner) => inner.analyzePlan());
+      return this.inner.then((inner) =>
+        inner.analyzePlan(distributedMetricsMode),
+      );
    } else {
-      return this.inner.analyzePlan();
+      return this.inner.analyzePlan(distributedMetricsMode);
    }
  }

@@ -362,6 +371,9 @@ export class StandardQueryBase<
   *
   * Filtering performance can often be improved by creating a scalar index
   * on the filter column(s).
+   *
+   * Calling this multiple times combines the filters with a logical AND rather
+   * than replacing the previous filter.
   */
  where(predicate: string): this {
    this.doCall((inner: NativeQueryType) => inner.onlyIf(predicate));
@@ -158,6 +158,26 @@ 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`.
@@ -585,6 +605,17 @@ 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.
   *
@@ -705,6 +736,19 @@ 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>;

@@ -1091,6 +1135,15 @@ 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();
  }
@@ -1153,6 +1206,17 @@ 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": "0.31.0-beta.3",
+	"version": "0.32.0-beta.2",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.31.0-beta.3",
+	"version": "0.32.0-beta.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.31.0-beta.3",
+	"version": "0.32.0-beta.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.31.0-beta.3",
+	"version": "0.32.0-beta.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.31.0-beta.3",
+	"version": "0.32.0-beta.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",
@@ -1,6 +1,6 @@
 {
  "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.31.0-beta.3",
+  "version": "0.32.0-beta.2",
  "os": [
    "win32"
  ],
@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.31.0-beta.3",
+	"version": "0.32.0-beta.2",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",
@@ -1,12 +1,12 @@
 {
  "name": "@lancedb/lancedb",
-  "version": "0.31.0-beta.3",
+  "version": "0.32.0-beta.2",
  "lockfileVersion": 3,
  "requires": true,
  "packages": {
    "": {
      "name": "@lancedb/lancedb",
-      "version": "0.31.0-beta.3",
+      "version": "0.32.0-beta.2",
      "cpu": [
        "x64",
        "arm64"
@@ -18,6 +18,7 @@
        "win32"
      ],
      "dependencies": {
+        "@opentelemetry/api": "^1.9.0",
        "reflect-metadata": "^0.2.2"
      },
      "devDependencies": {
@@ -27,6 +28,7 @@
        "@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",
@@ -4148,6 +4150,75 @@
        "@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": "0.31.0-beta.3",
+  "version": "0.32.0-beta.2",
  "main": "dist/index.js",
  "exports": {
    ".": "./dist/index.js",
@@ -44,6 +44,7 @@
    "@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",
@@ -92,6 +93,7 @@
    "version": "napi version"
  },
  "dependencies": {
+    "@opentelemetry/api": "^1.9.0",
    "reflect-metadata": "^0.2.2"
  },
  "optionalDependencies": {
@@ -8,6 +8,9 @@ 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
@@ -33,6 +36,9 @@ 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
@@ -1307,6 +1313,32 @@ 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==}

@@ -4925,6 +4957,27 @@ 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

@@ -112,6 +112,12 @@ impl Connection {

        builder = builder.client_config(rust_config);

+        if let Some(oauth_config) = options.oauth_config {
+            let config: lancedb::remote::oauth::OAuthConfig =
+                oauth_config.try_into().default_error()?;
+            builder = builder.oauth_config(config);
+        }
+
        if let Some(api_key) = options.api_key {
            builder = builder.api_key(&api_key);
        }
@@ -9,8 +9,11 @@ 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]
@@ -30,6 +33,65 @@ 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)]
@@ -12,6 +12,7 @@ mod header;
 mod index;
 mod iterator;
 pub mod merge;
+pub mod otel;
 pub mod permutation;
 mod query;
 pub mod remote;
@@ -65,6 +66,11 @@ pub struct ConnectionOptions {
    /// (For LanceDB cloud only): the host to use for LanceDB cloud. Used
    /// for testing purposes.
    pub host_override: Option<String>,
+    /// (For LanceDB cloud only): OAuth configuration for IdP-based
+    /// authentication (e.g., Azure Entra ID). When set, token acquisition
+    /// and refresh are handled entirely in Rust. TypeScript users should pass
+    /// the public `OAuthConfig` type exported from `@lancedb/lancedb`.
+    pub oauth_config: Option<remote::OAuthConfig>,
 }

 #[napi(object)]
@@ -3,7 +3,7 @@

 use std::time::Duration;

-use lancedb::{arrow::IntoArrow, ipc::ipc_file_to_batches, table::merge::MergeInsertBuilder};
+use lancedb::{ipc::ipc_file_to_batches, table::merge::MergeInsertBuilder};
 use napi::bindgen_prelude::*;
 use napi_derive::napi;

@@ -66,11 +66,9 @@ impl NativeMergeInsertBuilder {

    #[napi(catch_unwind)]
    pub async fn execute(&self, buf: Buffer) -> napi::Result<MergeResult> {
-        let data = ipc_file_to_batches(buf.to_vec())
-            .and_then(IntoArrow::into_arrow)
-            .map_err(|e| {
-                napi::Error::from_reason(format!("Failed to read IPC file: {}", convert_error(&e)))
-            })?;
+        let data = ipc_file_to_batches(buf.to_vec()).map_err(|e| {
+            napi::Error::from_reason(format!("Failed to read IPC file: {}", convert_error(&e)))
+        })?;

        let this = self.clone();

@@ -0,0 +1,119 @@
+// 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()
+}
@@ -16,6 +16,7 @@ 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>>,
 }

@@ -125,10 +126,15 @@ 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 { seed, sizes },
+                SplitStrategy::Random {
+                    seed,
+                    sizes,
+                    clump_size,
+                },
                options.split_names.clone(),
            )
        })
@@ -19,6 +19,7 @@ use lancedb::index::scalar::{
    BooleanQuery, BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, Occur,
    Operator, PhraseQuery,
 };
+use lancedb::query::AnalyzePlanDistributedMetrics;
 use lancedb::query::ExecutableQuery;
 use lancedb::query::Query as LanceDbQuery;
 use lancedb::query::QueryBase;
@@ -47,6 +48,28 @@ impl From<ColumnOrdering> for LanceDbColumnOrdering {
    }
 }

+fn analyze_plan_options(
+    distributed_metrics: Option<String>,
+) -> napi::Result<QueryExecutionOptions> {
+    let analyze_plan_distributed_metrics =
+        match distributed_metrics.as_deref().unwrap_or("aggregate") {
+            "aggregate" => AnalyzePlanDistributedMetrics::Aggregate,
+            "per_worker" => AnalyzePlanDistributedMetrics::PerWorker,
+            "full" => AnalyzePlanDistributedMetrics::Full,
+            mode => {
+                return Err(napi::Error::from_reason(format!(
+                    "Invalid distributedMetrics value '{}'. Expected one of: \
+                 'aggregate', 'per_worker', 'full'",
+                    mode
+                )));
+            }
+        };
+
+    let mut options = QueryExecutionOptions::default();
+    options.analyze_plan_distributed_metrics = analyze_plan_distributed_metrics;
+    Ok(options)
+}
+
 fn bytes_to_arrow_array(data: Uint8Array, dtype: String) -> napi::Result<Arc<dyn Array>> {
    let buf = arrow_buffer::Buffer::from(data.to_vec());
    let num_bytes = buf.len();
@@ -200,13 +223,17 @@ impl Query {
    }

    #[napi(catch_unwind)]
-    pub async fn analyze_plan(&self) -> napi::Result<String> {
-        self.inner.analyze_plan().await.map_err(|e| {
-            napi::Error::from_reason(format!(
-                "Failed to execute analyze plan: {}",
-                convert_error(&e)
-            ))
-        })
+    pub async fn analyze_plan(&self, distributed_metrics: Option<String>) -> napi::Result<String> {
+        let options = analyze_plan_options(distributed_metrics)?;
+        self.inner
+            .analyze_plan_with_options(options)
+            .await
+            .map_err(|e| {
+                napi::Error::from_reason(format!(
+                    "Failed to execute analyze plan: {}",
+                    convert_error(&e)
+                ))
+            })
    }
 }

@@ -412,13 +439,17 @@ impl VectorQuery {
    }

    #[napi(catch_unwind)]
-    pub async fn analyze_plan(&self) -> napi::Result<String> {
-        self.inner.analyze_plan().await.map_err(|e| {
-            napi::Error::from_reason(format!(
-                "Failed to execute analyze plan: {}",
-                convert_error(&e)
-            ))
-        })
+    pub async fn analyze_plan(&self, distributed_metrics: Option<String>) -> napi::Result<String> {
+        let options = analyze_plan_options(distributed_metrics)?;
+        self.inner
+            .analyze_plan_with_options(options)
+            .await
+            .map_err(|e| {
+                napi::Error::from_reason(format!(
+                    "Failed to execute analyze plan: {}",
+                    convert_error(&e)
+                ))
+            })
    }
 }

@@ -491,13 +522,17 @@ impl TakeQuery {
    }

    #[napi(catch_unwind)]
-    pub async fn analyze_plan(&self) -> napi::Result<String> {
-        self.inner.analyze_plan().await.map_err(|e| {
-            napi::Error::from_reason(format!(
-                "Failed to execute analyze plan: {}",
-                convert_error(&e)
-            ))
-        })
+    pub async fn analyze_plan(&self, distributed_metrics: Option<String>) -> napi::Result<String> {
+        let options = analyze_plan_options(distributed_metrics)?;
+        self.inner
+            .analyze_plan_with_options(options)
+            .await
+            .map_err(|e| {
+                napi::Error::from_reason(format!(
+                    "Failed to execute analyze plan: {}",
+                    convert_error(&e)
+                ))
+            })
    }
 }

@@ -3,6 +3,7 @@

 use std::collections::HashMap;

+use lancedb::error::Error;
 use napi_derive::*;

 /// Timeout configuration for remote HTTP client.
@@ -140,6 +141,84 @@ impl From<TlsConfig> for lancedb::remote::TlsConfig {
    }
 }

+/// OAuth configuration for LanceDB authentication.
+///
+/// This is the generated napi-rs binding shape. TypeScript users should prefer
+/// the public `OAuthConfig` type exported from `@lancedb/lancedb`.
+///
+/// All token acquisition and refresh is handled in the Rust layer.
+#[napi(object)]
+#[derive(Clone)]
+pub struct OAuthConfig {
+    /// OIDC issuer URL or OAuth authority URL.
+    /// For Azure: `https://login.microsoftonline.com/{tenant_id}/v2.0`
+    pub issuer_url: String,
+    /// Application / Client ID.
+    pub client_id: String,
+    /// OAuth scopes to request. For Azure managed identity, exactly one scope
+    /// or resource is required. For example: `["api://{app_id}/.default"]`
+    pub scopes: Vec<String>,
+    /// Authentication flow: "client_credentials" or "azure_managed_identity"
+    pub flow: Option<String>,
+    /// Client secret (required for client_credentials).
+    pub client_secret: Option<String>,
+    /// Client ID for user-assigned managed identity (azure_managed_identity).
+    pub managed_identity_client_id: Option<String>,
+    /// Seconds before expiry to trigger proactive refresh (default: 300).
+    /// Keep this well below the token TTL; if it is greater than or equal to
+    /// the TTL, each request refreshes the token.
+    pub refresh_buffer_secs: Option<u32>,
+}
+
+impl std::fmt::Debug for OAuthConfig {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.debug_struct("OAuthConfig")
+            .field("issuer_url", &self.issuer_url)
+            .field("client_id", &self.client_id)
+            .field("scopes", &self.scopes)
+            .field("flow", &self.flow)
+            .field(
+                "client_secret",
+                &self.client_secret.as_deref().map(|_| "<redacted>"),
+            )
+            .field(
+                "managed_identity_client_id",
+                &self.managed_identity_client_id,
+            )
+            .field("refresh_buffer_secs", &self.refresh_buffer_secs)
+            .finish()
+    }
+}
+
+impl TryFrom<OAuthConfig> for lancedb::remote::oauth::OAuthConfig {
+    type Error = Error;
+
+    fn try_from(config: OAuthConfig) -> Result<Self, Self::Error> {
+        use lancedb::remote::oauth::OAuthFlow;
+
+        let flow = match config.flow.as_deref().unwrap_or("client_credentials") {
+            "client_credentials" => OAuthFlow::ClientCredentials,
+            "azure_managed_identity" => OAuthFlow::AzureManagedIdentity {
+                client_id: config.managed_identity_client_id,
+            },
+            other => {
+                return Err(Error::InvalidInput {
+                    message: format!("Unknown OAuth flow type: {other}"),
+                });
+            }
+        };
+
+        Ok(Self {
+            issuer_url: config.issuer_url,
+            client_id: config.client_id,
+            client_secret: config.client_secret,
+            scopes: config.scopes,
+            flow,
+            refresh_buffer_secs: config.refresh_buffer_secs.map(|v| v as u64),
+        })
+    }
+}
+
 impl From<ClientConfig> for lancedb::remote::ClientConfig {
    fn from(config: ClientConfig) -> Self {
        Self {
@@ -156,3 +235,45 @@ impl From<ClientConfig> for lancedb::remote::ClientConfig {
        }
    }
 }
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+
+    #[test]
+    fn test_unknown_oauth_flow_returns_invalid_input() {
+        let config = OAuthConfig {
+            issuer_url: "https://issuer.example.com".to_string(),
+            client_id: "client-id".to_string(),
+            scopes: vec!["scope".to_string()],
+            flow: Some("typo".to_string()),
+            client_secret: None,
+            managed_identity_client_id: None,
+            refresh_buffer_secs: None,
+        };
+
+        let err = lancedb::remote::oauth::OAuthConfig::try_from(config).unwrap_err();
+        assert!(matches!(
+            err,
+            Error::InvalidInput { message }
+                if message == "Unknown OAuth flow type: typo"
+        ));
+    }
+
+    #[test]
+    fn test_oauth_config_debug_redacts_client_secret() {
+        let config = OAuthConfig {
+            issuer_url: "https://issuer.example.com".to_string(),
+            client_id: "client-id".to_string(),
+            scopes: vec!["scope".to_string()],
+            flow: Some("client_credentials".to_string()),
+            client_secret: Some("super-secret".to_string()),
+            managed_identity_client_id: None,
+            refresh_buffer_secs: None,
+        };
+
+        let debug = format!("{config:?}");
+        assert!(!debug.contains("super-secret"));
+        assert!(debug.contains("client_secret: Some(\"<redacted>\")"));
+    }
+}
@@ -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, NewColumnTransform, OptimizeAction,
-    OptimizeOptions, Ref, Table as LanceDbTable,
+    FieldMetadataUpdate as LanceFieldMetadataUpdate, FtsToken as LanceDbFtsToken,
+    NewColumnTransform, OptimizeAction, OptimizeOptions, Ref, Table as LanceDbTable,
 };
 use napi::bindgen_prelude::*;
 use napi::threadsafe_function::{ThreadsafeFunction, ThreadsafeFunctionCallMode};
@@ -411,6 +411,16 @@ 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()
@@ -564,6 +574,27 @@ 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()?;
@@ -671,6 +702,24 @@ 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`.
 ///
@@ -728,6 +777,47 @@ 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 = "0.34.0-beta.4"
+current_version = "0.35.0-beta.2"
 parse = """(?x)
    (?P<major>0|[1-9]\\d*)\\.
    (?P<minor>0|[1-9]\\d*)\\.
@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.34.0-beta.4"
+version = "0.35.0-beta.2"
 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"]
+default = ["remote", "lancedb/aws", "lancedb/gcs", "lancedb/azure", "lancedb/dynamodb", "lancedb/oss", "lancedb/huggingface", "lancedb/cos", "lancedb/goosefs", "lancedb/metrics-otel"]
 fp16kernels = ["lancedb/fp16kernels"]
 remote = ["lancedb/remote"]
@@ -0,0 +1,135 @@
+#!/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()
@@ -47,6 +47,10 @@ 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",
@@ -57,10 +61,12 @@ tests = [
    "duckdb>=0.9.0",
    "pytz>=2023.3",
    "polars>=0.19, <=1.3.0",
+    "pyarrow<25",
    "pyarrow-stubs>=16.0",
-    "pylance>=5.0.0b5",
+    "pylance==9.0.0rc1",
    "requests>=2.31.0",
-    "datafusion>=52,<53",
+    "datafusion>=54,<55",
+    "opentelemetry-sdk>=1.30.0",
 ]
 dev = [
    "ruff>=0.3.0",
@@ -6,19 +6,22 @@ import importlib.metadata
 import os
 from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
-from typing import Dict, Optional, Union, Any, List
+from typing import Dict, Optional, Union, Any, List, Iterable

 __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 vector
+from .schema import blob, vector, BlobType
 from .table import AsyncTable, Table
+from .types import BaseTokenizerType
 from ._lancedb import Session
 from .namespace import (
    connect_namespace,
@@ -89,6 +92,8 @@ def connect(
        If presented, connect to LanceDB cloud.
        Otherwise, connect to a database on file system or cloud storage.
        Can be set via environment variable `LANCEDB_API_KEY`.
+        OAuth configuration is currently supported only by ``connect_async``;
+        synchronous LanceDB Cloud connections require an API key.
    region: str, default "us-east-1"
        The region to use for LanceDB Cloud.
    host_override: str, optional
@@ -147,8 +152,14 @@ def connect(

    For object storage, use a URI prefix:

-    >>> db = lancedb.connect("s3://my-bucket/lancedb",
-    ...                      storage_options={"aws_access_key_id": "***"})
+    >>> db = lancedb.connect(  # doctest: +SKIP
+    ...     "s3://my-bucket/lancedb",
+    ...     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:

@@ -238,6 +249,40 @@ 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_"


@@ -340,6 +385,7 @@ async def connect_async(
    session: Optional[Session] = None,
    manifest_enabled: bool = False,
    namespace_client_properties: Optional[Dict[str, str]] = None,
+    oauth_config=None,
 ) -> AsyncConnection:
    """Connect to a LanceDB database.

@@ -389,6 +435,10 @@ async def connect_async(
    namespace_client_properties : dict, optional
        Additional directory namespace client properties to use with
        ``manifest_enabled=True``.
+    oauth_config : OAuthConfig, optional
+        OAuth configuration for LanceDB Cloud/Enterprise. This is supported by
+        ``connect_async`` only; synchronous ``connect`` uses API key
+        authentication for ``db://`` URIs.

    Examples
    --------
@@ -435,6 +485,7 @@ async def connect_async(
            session,
            manifest_enabled,
            namespace_client_properties,
+            oauth_config,
        )
    )

@@ -442,17 +493,21 @@ 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",
@@ -0,0 +1,420 @@
+# 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]
@@ -1,4 +1,5 @@
-from datetime import datetime, timedelta
+from datetime import date, datetime, timedelta
+from decimal import Decimal
 from typing import Dict, List, Optional, Tuple, Any, TypedDict, Union, Literal

 import pyarrow as pa
@@ -24,10 +25,45 @@ 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
+AnalyzePlanDistributedMetrics = Literal["aggregate", "per_worker", "full"]
+
+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)."""
@@ -53,7 +89,9 @@ class PyExpr:
    def to_sql(self) -> str: ...

 def expr_col(name: str) -> PyExpr: ...
-def expr_lit(value: Union[bool, int, float, str, bytes]) -> PyExpr: ...
+def expr_lit(
+    value: Union[bool, int, float, str, bytes, date, datetime, Decimal],
+) -> PyExpr: ...
 def expr_func(name: str, args: List[PyExpr]) -> PyExpr: ...

 class Session:
@@ -159,6 +197,17 @@ 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: ...
@@ -205,6 +254,13 @@ 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: ...
@@ -226,6 +282,7 @@ 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: ...
@@ -235,6 +292,13 @@ 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:
@@ -280,6 +344,24 @@ async def connect(
    session: Optional[Session],
    manifest_enabled: bool = False,
    namespace_client_properties: Optional[Dict[str, str]] = None,
+    oauth_config: Optional[Any] = None,
+) -> Connection: ...
+def connect_namespace(
+    namespace_client_impl: str,
+    namespace_client_properties: Dict[str, str],
+    read_consistency_interval: Optional[float] = None,
+    storage_options: Optional[Dict[str, str]] = None,
+    session: Optional[Session] = None,
+    namespace_client_pushdown_operations: Optional[List[str]] = None,
+) -> Connection: ...
+def connect_namespace_client(
+    namespace_client: Any,
+    read_consistency_interval: Optional[float] = None,
+    storage_options: Optional[Dict[str, str]] = None,
+    session: Optional[Session] = None,
+    namespace_client_pushdown_operations: Optional[List[str]] = None,
+    namespace_client_impl: Optional[str] = None,
+    namespace_client_properties: Optional[Dict[str, str]] = None,
 ) -> Connection: ...

 class RecordBatchStream:
@@ -312,7 +394,9 @@ class Query:
        self, max_batch_length: Optional[int], timeout: Optional[timedelta]
    ) -> RecordBatchStream: ...
    async def explain_plan(self, verbose: Optional[bool]) -> str: ...
-    async def analyze_plan(self) -> str: ...
+    async def analyze_plan(
+        self, distributed_metrics: Optional[AnalyzePlanDistributedMetrics] = None
+    ) -> str: ...
    def to_query_request(self) -> PyQueryRequest: ...

 class TakeQuery:
@@ -320,6 +404,10 @@ class TakeQuery:
    def with_row_id(self): ...
    async def output_schema(self) -> pa.Schema: ...
    async def execute(self) -> RecordBatchStream: ...
+    async def explain_plan(self, verbose: Optional[bool]) -> str: ...
+    async def analyze_plan(
+        self, distributed_metrics: Optional[AnalyzePlanDistributedMetrics] = None
+    ) -> str: ...
    def to_query_request(self) -> PyQueryRequest: ...

 class FTSQuery:
@@ -340,6 +428,10 @@ class FTSQuery:
    async def execute(
        self, max_batch_length: Optional[int], timeout: Optional[timedelta]
    ) -> RecordBatchStream: ...
+    async def explain_plan(self, verbose: Optional[bool]) -> str: ...
+    async def analyze_plan(
+        self, distributed_metrics: Optional[AnalyzePlanDistributedMetrics] = None
+    ) -> str: ...
    def to_query_request(self) -> PyQueryRequest: ...

 class VectorQuery:
@@ -362,6 +454,10 @@ class VectorQuery:
    def bypass_vector_index(self): ...
    def nearest_to_text(self, query: dict) -> HybridQuery: ...
    def order_by(self, ordering: Optional[List[ColumnOrdering]]): ...
+    async def explain_plan(self, verbose: Optional[bool]) -> str: ...
+    async def analyze_plan(
+        self, distributed_metrics: Optional[AnalyzePlanDistributedMetrics] = None
+    ) -> str: ...
    def to_query_request(self) -> PyQueryRequest: ...

 class HybridQuery:
@@ -452,6 +548,10 @@ 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, Union
+from typing import List, Optional, Union

 import numpy as np

@@ -15,6 +15,8 @@ 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):
@@ -81,6 +83,7 @@ class GeminiText(TextEmbeddingFunction):
    """

    name: str = "gemini-embedding-001"
+    dim: Optional[int] = None
    query_task_type: str = "retrieval_query"
    source_task_type: str = "retrieval_document"

@@ -93,6 +96,8 @@ class GeminiText(TextEmbeddingFunction):
        model_config["ignored_types"] = (cached_property,)

    def ndims(self):
+        if self.dim:
+            return self.dim
        # TODO: fix hardcoding
        return 768

@@ -133,22 +138,22 @@ class GeminiText(TextEmbeddingFunction):
                contents.append({"parts": [{"text": text}]})

        # Build config
-        config_kwargs = {}
+        config_kwargs = {"output_dimensionality": self.ndims()}
        if task_type:
            config_kwargs["task_type"] = task_type.upper()  # API expects uppercase

-        # Call embed_content for each content
+        config = types.EmbedContentConfig(**config_kwargs) if config_kwargs else None
+
+        # Call embed_content in groups of at most EMBEDDING_BATCH_SIZE docs at a time
        embeddings = []
-        for content in contents:
-            config = (
-                types.EmbedContentConfig(**config_kwargs) if config_kwargs else None
-            )
+        for i in range(0, len(contents), EMBEDDING_BATCH_SIZE):
+            chunk = contents[i : i + EMBEDDING_BATCH_SIZE]
            response = self.client.models.embed_content(
                model=self.name,
-                contents=content,
+                contents=chunk,
                config=config,
            )
-            embeddings.append(response.embeddings[0].values)
+            embeddings.extend([np.array(e.values) for e in response.embeddings])

        return embeddings

@@ -160,5 +165,13 @@ class GeminiText(TextEmbeddingFunction):
            api_key_not_found_help("google")

        from google import genai as genai_module
+        from lancedb import __version__

-        return genai_module.Client(api_key=os.environ.get("GOOGLE_API_KEY"))
+        return genai_module.Client(
+            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, List, Optional, Sequence, Union
+from typing import TYPE_CHECKING, Any, List, Optional, Sequence, Union

 import numpy as np

@@ -56,6 +56,16 @@ 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")
@@ -19,6 +19,8 @@ operators::

 from __future__ import annotations

+from datetime import date, datetime
+from decimal import Decimal
 from typing import Iterable, Union

 import pyarrow as pa
@@ -63,7 +65,7 @@ def _coerce(value: "ExprLike") -> "Expr":


 # Type alias used in annotations.
-ExprLike = Union["Expr", bool, int, float, str, bytes]
+ExprLike = Union["Expr", bool, int, float, str, bytes, date, datetime, Decimal]


 class Expr:
@@ -118,10 +120,18 @@ 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_())
@@ -266,13 +276,14 @@ def col(name: str) -> Expr:
    return Expr(expr_col(name))


-def lit(value: Union[bool, int, float, str, bytes]) -> Expr:
+def lit(value: Union[bool, int, float, str, bytes, date, datetime, Decimal]) -> Expr:
    """Create a literal (constant) value expression.

    Parameters
    ----------
    value:
-        A Python ``bool``, ``int``, ``float``, ``str``, or ``bytes``.
+        A Python ``bool``, ``int``, ``float``, ``str``, ``bytes``, ``date``,
+        ``datetime``, or ``Decimal``.

    Examples
    --------
@@ -280,6 +291,9 @@ def lit(value: Union[bool, int, float, str, bytes]) -> Expr:
    >>> 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))


@@ -127,6 +127,8 @@ 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"
@@ -51,6 +51,15 @@ class LanceMergeInsertBuilder(object):
        If there are multiple matches then the behavior is undefined.
        Currently this causes multiple copies of the row to be created
        but that behavior is subject to change.
+
+        Parameters
+        ----------
+        where: Optional[str], default None
+            An optional filter to limit which rows are updated. Column
+            references in this expression must be prefixed with "target."
+            to refer to the existing table data. For example, to only
+            update rows where the existing color is red, use:
+            ``where="target.color = 'red'"``
        """
        self._when_matched_update_all = True
        self._when_matched_update_all_condition = where
@@ -38,15 +38,13 @@ from lance_namespace_urllib3_client.models.query_table_request_vector import (
    QueryTableRequestVector,
 )
 from lance_namespace_urllib3_client.models.string_fts_query import StringFtsQuery
-from lance_namespace.errors import TableNotFoundError
-from lancedb._lancedb import connect_namespace_client as _connect_namespace_client
+from lance_namespace.errors import NamespaceNotEmptyError, TableNotFoundError
+from lancedb._lancedb import (
+    connect_namespace as _connect_namespace,
+    connect_namespace_client as _connect_namespace_client,
+)
 from lancedb.background_loop import LOOP
 from lancedb.db import AsyncConnection, DBConnection
-from lancedb.namespace_utils import (
-    _normalize_create_namespace_mode,
-    _normalize_drop_namespace_mode,
-    _normalize_drop_namespace_behavior,
-)
 from lance_namespace import (
    LanceNamespace,
    connect as namespace_connect,
@@ -55,13 +53,6 @@ from lance_namespace import (
    DropNamespaceResponse,
    ListNamespacesResponse,
    ListTablesResponse,
-    ListTablesRequest,
-    DescribeNamespaceRequest,
-    DropTableRequest,
-    RenameTableRequest,
-    ListNamespacesRequest,
-    CreateNamespaceRequest,
-    DropNamespaceRequest,
 )
 from lancedb.table import AsyncTable, LanceTable, Table
 from lancedb.util import validate_table_name
@@ -386,6 +377,10 @@ def _builds_namespace_natively(
    return namespace_client_impl == "rest" and bool(namespace_client_properties)


+def _supports_native_namespace(namespace_client_impl: str) -> bool:
+    return namespace_client_impl in {"dir", "rest"}
+
+
 class LanceNamespaceDBConnection(DBConnection):
    """
    A LanceDB connection that uses a namespace for table management.
@@ -396,7 +391,7 @@ class LanceNamespaceDBConnection(DBConnection):

    def __init__(
        self,
-        namespace_client: LanceNamespace,
+        namespace_client: Optional[LanceNamespace] = None,
        *,
        read_consistency_interval: Optional[timedelta] = None,
        storage_options: Optional[Dict[str, str]] = None,
@@ -404,6 +399,7 @@ class LanceNamespaceDBConnection(DBConnection):
        namespace_client_pushdown_operations: Optional[List[str]] = None,
        namespace_client_impl: Optional[str] = None,
        namespace_client_properties: Optional[Dict[str, str]] = None,
+        _inner: Optional[AsyncConnection] = None,
    ):
        """
        Initialize a namespace-based LanceDB connection.
@@ -445,30 +441,36 @@ class LanceNamespaceDBConnection(DBConnection):
        )
        self._namespace_client_impl = namespace_client_impl
        self._namespace_client_properties = namespace_client_properties
-        # When the namespace client is built natively (see Rust
-        # ``build_namespace_natively``), the underlying Rust table performs
-        # QueryTable pushdown through the read-freshness context provider, which
-        # the pure-Python ``query_table`` path bypasses.
-        self._route_pushdown_to_rust = _builds_namespace_natively(
+        # When the namespace connection or client is built natively in Rust, the
+        # underlying Rust table performs QueryTable pushdown through the
+        # read-freshness context provider, which the pure-Python ``query_table``
+        # path bypasses.
+        self._route_pushdown_to_rust = _inner is not None or _builds_namespace_natively(
            namespace_client_impl, namespace_client_properties
        )
-        self._inner = AsyncConnection(
-            _connect_namespace_client(
-                namespace_client,
-                read_consistency_interval=(
-                    read_consistency_interval.total_seconds()
-                    if read_consistency_interval is not None
-                    else None
-                ),
-                storage_options=self.storage_options or None,
-                session=session,
-                namespace_client_pushdown_operations=(
-                    list(self._namespace_client_pushdown_operations)
-                ),
-                namespace_client_impl=namespace_client_impl,
-                namespace_client_properties=namespace_client_properties,
+        if _inner is not None:
+            self._inner = _inner
+        else:
+            if namespace_client is None:
+                raise ValueError("namespace_client is required without a native _inner")
+            self._inner = AsyncConnection(
+                _connect_namespace_client(
+                    namespace_client,
+                    read_consistency_interval=(
+                        read_consistency_interval.total_seconds()
+                        if read_consistency_interval is not None
+                        else None
+                    ),
+                    storage_options=self.storage_options or None,
+                    session=session,
+                    namespace_client_pushdown_operations=(
+                        list(self._namespace_client_pushdown_operations)
+                    ),
+                    namespace_client_impl=namespace_client_impl,
+                    namespace_client_properties=namespace_client_properties,
+                )
            )
-        )
+        self._uri = self._inner.uri

    @override
    def serialize(self) -> str:
@@ -514,11 +516,11 @@ class LanceNamespaceDBConnection(DBConnection):
        )
        if namespace_path is None:
            namespace_path = []
-        request = ListTablesRequest(
-            id=namespace_path, page_token=page_token, limit=limit
+        return LOOP.run(
+            self._inner.table_names(
+                namespace_path=namespace_path, start_after=page_token, limit=limit
+            )
        )
-        response = self._namespace_client.list_tables(request)
-        return response.tables if response.tables else []

    @override
    def create_table(
@@ -589,8 +591,8 @@ class LanceNamespaceDBConnection(DBConnection):
                    index_cache_size=index_cache_size,
                )
            )
-        except RuntimeError as e:
-            if "Table not found" in str(e):
+        except (RuntimeError, ValueError) as e:
+            if "Table not found" in str(e) or "was not found" in str(e):
                table_id = namespace_path + [name]
                raise TableNotFoundError(f"Table not found: {'$'.join(table_id)}")
            raise
@@ -612,12 +614,9 @@ class LanceNamespaceDBConnection(DBConnection):

    @override
    def drop_table(self, name: str, namespace_path: Optional[List[str]] = None):
-        # Use namespace drop_table directly
        if namespace_path is None:
            namespace_path = []
-        table_id = namespace_path + [name]
-        request = DropTableRequest(id=table_id)
-        self._namespace_client.drop_table(request)
+        LOOP.run(self._inner.drop_table(name, namespace_path=namespace_path))

    @override
    def rename_table(
@@ -631,14 +630,19 @@ class LanceNamespaceDBConnection(DBConnection):
            cur_namespace_path = []
        if new_namespace_path is None:
            new_namespace_path = []
-        cur_table_id = cur_namespace_path + [cur_name]
-        new_namespace_id = new_namespace_path if new_namespace_path else None
-        request = RenameTableRequest(
-            id=cur_table_id,
-            new_table_name=new_name,
-            new_namespace_id=new_namespace_id,
-        )
-        self._namespace_client.rename_table(request)
+        try:
+            LOOP.run(
+                self._inner.rename_table(
+                    cur_name,
+                    new_name,
+                    cur_namespace_path=cur_namespace_path,
+                    new_namespace_path=new_namespace_path,
+                )
+            )
+        except RuntimeError as e:
+            if "rename_table not implemented" in str(e):
+                raise NotImplementedError("rename_table not implemented") from e
+            raise

    @override
    def drop_database(self):
@@ -650,8 +654,7 @@ class LanceNamespaceDBConnection(DBConnection):
    def drop_all_tables(self, namespace_path: Optional[List[str]] = None):
        if namespace_path is None:
            namespace_path = []
-        for table_name in self.table_names(namespace_path=namespace_path):
-            self.drop_table(table_name, namespace_path=namespace_path)
+        LOOP.run(self._inner.drop_all_tables(namespace_path=namespace_path))

    @override
    def list_namespaces(
@@ -681,13 +684,10 @@ class LanceNamespaceDBConnection(DBConnection):
        """
        if namespace_path is None:
            namespace_path = []
-        request = ListNamespacesRequest(
-            id=namespace_path, page_token=page_token, limit=limit
-        )
-        response = self._namespace_client.list_namespaces(request)
-        return ListNamespacesResponse(
-            namespaces=response.namespaces if response.namespaces else [],
-            page_token=response.page_token,
+        return LOOP.run(
+            self._inner.list_namespaces(
+                namespace_path=namespace_path, page_token=page_token, limit=limit
+            )
        )

    @override
@@ -715,14 +715,12 @@ class LanceNamespaceDBConnection(DBConnection):
        CreateNamespaceResponse
            Response containing the properties of the created namespace.
        """
-        request = CreateNamespaceRequest(
-            id=namespace_path,
-            mode=_normalize_create_namespace_mode(mode),
-            properties=properties,
-        )
-        response = self._namespace_client.create_namespace(request)
-        return CreateNamespaceResponse(
-            properties=response.properties if hasattr(response, "properties") else None
+        return LOOP.run(
+            self._inner.create_namespace(
+                namespace_path=namespace_path,
+                mode=mode,
+                properties=properties,
+            )
        )

    @override
@@ -750,20 +748,18 @@ class LanceNamespaceDBConnection(DBConnection):
        DropNamespaceResponse
            Response containing properties and transaction_id if applicable.
        """
-        request = DropNamespaceRequest(
-            id=namespace_path,
-            mode=_normalize_drop_namespace_mode(mode),
-            behavior=_normalize_drop_namespace_behavior(behavior),
-        )
-        response = self._namespace_client.drop_namespace(request)
-        return DropNamespaceResponse(
-            properties=(
-                response.properties if hasattr(response, "properties") else None
-            ),
-            transaction_id=(
-                response.transaction_id if hasattr(response, "transaction_id") else None
-            ),
-        )
+        try:
+            return LOOP.run(
+                self._inner.drop_namespace(
+                    namespace_path=namespace_path,
+                    mode=mode,
+                    behavior=behavior,
+                )
+            )
+        except RuntimeError as e:
+            if "Namespace not empty" in str(e):
+                raise NamespaceNotEmptyError(str(e)) from e
+            raise

    @override
    def describe_namespace(
@@ -782,11 +778,7 @@ class LanceNamespaceDBConnection(DBConnection):
        DescribeNamespaceResponse
            Response containing the namespace properties.
        """
-        request = DescribeNamespaceRequest(id=namespace_path)
-        response = self._namespace_client.describe_namespace(request)
-        return DescribeNamespaceResponse(
-            properties=response.properties if hasattr(response, "properties") else None
-        )
+        return LOOP.run(self._inner.describe_namespace(namespace_path))

    @override
    def list_tables(
@@ -816,13 +808,10 @@ class LanceNamespaceDBConnection(DBConnection):
        """
        if namespace_path is None:
            namespace_path = []
-        request = ListTablesRequest(
-            id=namespace_path, page_token=page_token, limit=limit
-        )
-        response = self._namespace_client.list_tables(request)
-        return ListTablesResponse(
-            tables=response.tables if response.tables else [],
-            page_token=response.page_token,
+        return LOOP.run(
+            self._inner.list_tables(
+                namespace_path=namespace_path, page_token=page_token, limit=limit
+            )
        )

    def _lance_table_from_uri(
@@ -878,6 +867,18 @@ class LanceNamespaceDBConnection(DBConnection):
        LanceNamespace
            The namespace client for this connection.
        """
+        if self._namespace_client is None:
+            if (
+                self._namespace_client_impl is None
+                or self._namespace_client_properties is None
+            ):
+                raise ValueError(
+                    "Cannot construct a Python namespace client without "
+                    "namespace implementation properties"
+                )
+            self._namespace_client = namespace_connect(
+                self._namespace_client_impl, self._namespace_client_properties
+            )
        return self._namespace_client


@@ -891,7 +892,7 @@ class AsyncLanceNamespaceDBConnection:

    def __init__(
        self,
-        namespace_client: LanceNamespace,
+        namespace_client: Optional[LanceNamespace] = None,
        *,
        read_consistency_interval: Optional[timedelta] = None,
        storage_options: Optional[Dict[str, str]] = None,
@@ -899,6 +900,7 @@ class AsyncLanceNamespaceDBConnection:
        namespace_client_pushdown_operations: Optional[List[str]] = None,
        namespace_client_impl: Optional[str] = None,
        namespace_client_properties: Optional[Dict[str, str]] = None,
+        _inner: Optional[AsyncConnection] = None,
    ):
        """
        Initialize an async namespace-based LanceDB connection.
@@ -940,29 +942,35 @@ class AsyncLanceNamespaceDBConnection:
        )
        self._namespace_client_impl = namespace_client_impl
        self._namespace_client_properties = namespace_client_properties
-        # See LanceNamespaceDBConnection: when built natively the Rust table runs
-        # QueryTable pushdown through the read-freshness provider, so defer to it
-        # rather than the urllib3 client (which omits x-lancedb-min-timestamp).
-        self._route_pushdown_to_rust = _builds_namespace_natively(
+        # See LanceNamespaceDBConnection: when Rust owns the namespace
+        # connection/client, its table performs QueryTable pushdown through the
+        # read-freshness provider, so defer to it rather than the urllib3 client
+        # path (which omits x-lancedb-min-timestamp).
+        self._route_pushdown_to_rust = _inner is not None or _builds_namespace_natively(
            namespace_client_impl, namespace_client_properties
        )
-        self._inner = AsyncConnection(
-            _connect_namespace_client(
-                namespace_client,
-                read_consistency_interval=(
-                    read_consistency_interval.total_seconds()
-                    if read_consistency_interval is not None
-                    else None
-                ),
-                storage_options=self.storage_options or None,
-                session=session,
-                namespace_client_pushdown_operations=(
-                    list(self._namespace_client_pushdown_operations)
-                ),
-                namespace_client_impl=namespace_client_impl,
-                namespace_client_properties=namespace_client_properties,
+        if _inner is not None:
+            self._inner = _inner
+        else:
+            if namespace_client is None:
+                raise ValueError("namespace_client is required without a native _inner")
+            self._inner = AsyncConnection(
+                _connect_namespace_client(
+                    namespace_client,
+                    read_consistency_interval=(
+                        read_consistency_interval.total_seconds()
+                        if read_consistency_interval is not None
+                        else None
+                    ),
+                    storage_options=self.storage_options or None,
+                    session=session,
+                    namespace_client_pushdown_operations=(
+                        list(self._namespace_client_pushdown_operations)
+                    ),
+                    namespace_client_impl=namespace_client_impl,
+                    namespace_client_properties=namespace_client_properties,
+                )
            )
-        )

    async def table_names(
        self,
@@ -986,11 +994,9 @@ class AsyncLanceNamespaceDBConnection:
        )
        if namespace_path is None:
            namespace_path = []
-        request = ListTablesRequest(
-            id=namespace_path, page_token=page_token, limit=limit
+        return await self._inner.table_names(
+            namespace_path=namespace_path, start_after=page_token, limit=limit
        )
-        response = self._namespace_client.list_tables(request)
-        return response.tables if response.tables else []

    async def create_table(
        self,
@@ -1053,8 +1059,8 @@ class AsyncLanceNamespaceDBConnection:
                storage_options=storage_options,
                index_cache_size=index_cache_size,
            )
-        except RuntimeError as e:
-            if "Table not found" in str(e):
+        except (RuntimeError, ValueError) as e:
+            if "Table not found" in str(e) or "was not found" in str(e):
                table_id = namespace_path + [name]
                raise TableNotFoundError(f"Table not found: {'$'.join(table_id)}")
            raise
@@ -1075,9 +1081,7 @@ class AsyncLanceNamespaceDBConnection:
        """Drop a table from the namespace."""
        if namespace_path is None:
            namespace_path = []
-        table_id = namespace_path + [name]
-        request = DropTableRequest(id=table_id)
-        self._namespace_client.drop_table(request)
+        await self._inner.drop_table(name, namespace_path=namespace_path)

    async def rename_table(
        self,
@@ -1091,14 +1095,17 @@ class AsyncLanceNamespaceDBConnection:
            cur_namespace_path = []
        if new_namespace_path is None:
            new_namespace_path = []
-        cur_table_id = cur_namespace_path + [cur_name]
-        new_namespace_id = new_namespace_path if new_namespace_path else None
-        request = RenameTableRequest(
-            id=cur_table_id,
-            new_table_name=new_name,
-            new_namespace_id=new_namespace_id,
-        )
-        self._namespace_client.rename_table(request)
+        try:
+            await self._inner.rename_table(
+                cur_name,
+                new_name,
+                cur_namespace_path=cur_namespace_path,
+                new_namespace_path=new_namespace_path,
+            )
+        except RuntimeError as e:
+            if "rename_table not implemented" in str(e):
+                raise NotImplementedError("rename_table not implemented") from e
+            raise

    async def drop_database(self):
        """Deprecated method."""
@@ -1110,9 +1117,7 @@ class AsyncLanceNamespaceDBConnection:
        """Drop all tables in the namespace."""
        if namespace_path is None:
            namespace_path = []
-        table_names = await self.table_names(namespace_path=namespace_path)
-        for table_name in table_names:
-            await self.drop_table(table_name, namespace_path=namespace_path)
+        await self._inner.drop_all_tables(namespace_path=namespace_path)

    async def list_namespaces(
        self,
@@ -1141,13 +1146,8 @@ class AsyncLanceNamespaceDBConnection:
        """
        if namespace_path is None:
            namespace_path = []
-        request = ListNamespacesRequest(
-            id=namespace_path, page_token=page_token, limit=limit
-        )
-        response = self._namespace_client.list_namespaces(request)
-        return ListNamespacesResponse(
-            namespaces=response.namespaces if response.namespaces else [],
-            page_token=response.page_token,
+        return await self._inner.list_namespaces(
+            namespace_path=namespace_path, page_token=page_token, limit=limit
        )

    async def create_namespace(
@@ -1174,15 +1174,11 @@ class AsyncLanceNamespaceDBConnection:
        CreateNamespaceResponse
            Response containing the properties of the created namespace.
        """
-        request = CreateNamespaceRequest(
-            id=namespace_path,
-            mode=_normalize_create_namespace_mode(mode),
+        return await self._inner.create_namespace(
+            namespace_path=namespace_path,
+            mode=mode,
            properties=properties,
        )
-        response = self._namespace_client.create_namespace(request)
-        return CreateNamespaceResponse(
-            properties=response.properties if hasattr(response, "properties") else None
-        )

    async def drop_namespace(
        self,
@@ -1208,20 +1204,16 @@ class AsyncLanceNamespaceDBConnection:
        DropNamespaceResponse
            Response containing properties and transaction_id if applicable.
        """
-        request = DropNamespaceRequest(
-            id=namespace_path,
-            mode=_normalize_drop_namespace_mode(mode),
-            behavior=_normalize_drop_namespace_behavior(behavior),
-        )
-        response = self._namespace_client.drop_namespace(request)
-        return DropNamespaceResponse(
-            properties=(
-                response.properties if hasattr(response, "properties") else None
-            ),
-            transaction_id=(
-                response.transaction_id if hasattr(response, "transaction_id") else None
-            ),
-        )
+        try:
+            return await self._inner.drop_namespace(
+                namespace_path=namespace_path,
+                mode=mode,
+                behavior=behavior,
+            )
+        except RuntimeError as e:
+            if "Namespace not empty" in str(e):
+                raise NamespaceNotEmptyError(str(e)) from e
+            raise

    async def describe_namespace(
        self, namespace_path: List[str]
@@ -1239,11 +1231,7 @@ class AsyncLanceNamespaceDBConnection:
        DescribeNamespaceResponse
            Response containing the namespace properties.
        """
-        request = DescribeNamespaceRequest(id=namespace_path)
-        response = self._namespace_client.describe_namespace(request)
-        return DescribeNamespaceResponse(
-            properties=response.properties if hasattr(response, "properties") else None
-        )
+        return await self._inner.describe_namespace(namespace_path)

    async def list_tables(
        self,
@@ -1272,13 +1260,8 @@ class AsyncLanceNamespaceDBConnection:
        """
        if namespace_path is None:
            namespace_path = []
-        request = ListTablesRequest(
-            id=namespace_path, page_token=page_token, limit=limit
-        )
-        response = self._namespace_client.list_tables(request)
-        return ListTablesResponse(
-            tables=response.tables if response.tables else [],
-            page_token=response.page_token,
+        return await self._inner.list_tables(
+            namespace_path=namespace_path, page_token=page_token, limit=limit
        )

    async def namespace_client(self) -> LanceNamespace:
@@ -1292,6 +1275,18 @@ class AsyncLanceNamespaceDBConnection:
        LanceNamespace
            The namespace client for this connection.
        """
+        if self._namespace_client is None:
+            if (
+                self._namespace_client_impl is None
+                or self._namespace_client_properties is None
+            ):
+                raise ValueError(
+                    "Cannot construct a Python namespace client without "
+                    "namespace implementation properties"
+                )
+            self._namespace_client = namespace_connect(
+                self._namespace_client_impl, self._namespace_client_properties
+            )
        return self._namespace_client


@@ -1342,6 +1337,32 @@ def connect_namespace(
    LanceNamespaceDBConnection
        A namespace-based connection to LanceDB
    """
+    if _supports_native_namespace(namespace_client_impl):
+        inner = AsyncConnection(
+            _connect_namespace(
+                namespace_client_impl,
+                namespace_client_properties,
+                read_consistency_interval=(
+                    read_consistency_interval.total_seconds()
+                    if read_consistency_interval is not None
+                    else None
+                ),
+                storage_options=storage_options,
+                session=session,
+                namespace_client_pushdown_operations=namespace_client_pushdown_operations,
+            )
+        )
+        return LanceNamespaceDBConnection(
+            namespace_client=None,
+            read_consistency_interval=read_consistency_interval,
+            storage_options=storage_options,
+            session=session,
+            namespace_client_pushdown_operations=namespace_client_pushdown_operations,
+            namespace_client_impl=namespace_client_impl,
+            namespace_client_properties=namespace_client_properties,
+            _inner=inner,
+        )
+
    namespace_client = namespace_connect(
        namespace_client_impl, namespace_client_properties
    )
@@ -1417,6 +1438,32 @@ def connect_namespace_async(
    ...     tables = await db.table_names()
    ...     table = await db.create_table("my_table", schema=schema)
    """
+    if _supports_native_namespace(namespace_client_impl):
+        inner = AsyncConnection(
+            _connect_namespace(
+                namespace_client_impl,
+                namespace_client_properties,
+                read_consistency_interval=(
+                    read_consistency_interval.total_seconds()
+                    if read_consistency_interval is not None
+                    else None
+                ),
+                storage_options=storage_options,
+                session=session,
+                namespace_client_pushdown_operations=namespace_client_pushdown_operations,
+            )
+        )
+        return AsyncLanceNamespaceDBConnection(
+            namespace_client=None,
+            read_consistency_interval=read_consistency_interval,
+            storage_options=storage_options,
+            session=session,
+            namespace_client_pushdown_operations=namespace_client_pushdown_operations,
+            namespace_client_impl=namespace_client_impl,
+            namespace_client_properties=namespace_client_properties,
+            _inner=inner,
+        )
+
    namespace_client = namespace_connect(
        namespace_client_impl, namespace_client_properties
    )
@@ -0,0 +1,170 @@
+# 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_rows
+from .util import batch_to_tensor, batch_to_tensor_dict, batch_to_tensor_rows
 from typing import Any, Callable, Iterator, Literal, Optional, TYPE_CHECKING, Union

 if TYPE_CHECKING:
@@ -48,6 +48,14 @@ class PermutationBuilder:
        By default, the permutation builder will create a single split that contains all
        rows in the same order as the base table.
        """
+        if not hasattr(table, "_inner"):
+            raise TypeError(
+                f"PermutationBuilder requires a local LanceTable, "
+                f"got {type(table).__name__}. "
+                "The permutation API is not supported on remote tables. "
+                "Remote tables connect to LanceDB Cloud or Enterprise and do not have "
+                "direct access to the underlying Lance dataset needed for permutations."
+            )
        self._async = async_permutation_builder(table)

    def split_random(
@@ -57,6 +65,7 @@ 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":
        """
@@ -79,6 +88,9 @@ 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.
        """
@@ -87,6 +99,7 @@ class PermutationBuilder:
            counts=counts,
            fixed=fixed,
            seed=seed,
+            clump_size=clump_size,
            split_names=split_names,
        )
        return self
@@ -933,6 +946,7 @@ class Permutation:
            "pandas",
            "arrow",
            "torch",
+            "torch_row",
            "torch_col",
            "polars",
        ],
@@ -948,15 +962,19 @@ 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 tensors, one per row
+        - "torch" - the batch will be a list of per-row dicts mapping column
+          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_col will be zero-copy but will only support a subset of data
-        types (numeric types).
+        Conversion to torch and torch_col will be zero-copy but will only support a
+        subset of data 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
@@ -977,6 +995,8 @@ 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)
@@ -15,10 +15,12 @@ from typing import (
    List,
    Literal,
    Optional,
+    Protocol,
    Tuple,
    Type,
    TypeVar,
    Union,
+    runtime_checkable,
 )

 import deprecation
@@ -39,15 +41,21 @@ 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
-
-BlobMode = Literal["lazy", "bytes", "descriptions"]
-
-_BLOB_MODE_TO_HANDLING = {
-    "lazy": "blobs_descriptions",
-    "bytes": "all_binary",
-    "descriptions": "blobs_descriptions",
-}
+from ._blob import (
+    BLOB_MODE_TO_HANDLING,
+    FetchBlobsAsync,
+    FetchBlobsSync,
+    blob_auto_row_id_for_scan,
+    blob_v2_projection_sources,
+    finalize_blob_query_table,
+    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
@@ -71,27 +79,25 @@ if TYPE_CHECKING:
        from typing_extensions import Self

 T = TypeVar("T", bound="LanceModel")
+AnalyzePlanDistributedMetrics = Literal["aggregate", "per_worker", "full"]


-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}")
+@runtime_checkable
+class _LanceScanner(Protocol):
+    projected_schema: pa.Schema | None
+    schema: pa.Schema | None

+    def to_pandas(self, blob_mode: BlobMode | None = ..., **kwargs) -> pd.DataFrame: ...

-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_pyarrow(self): ...

+    def to_table(self) -> pa.Table: ...

-def _schema_has_blob_field(schema: pa.Schema) -> bool:
-    return any(_field_is_blob(field) for field in schema)
+    def to_reader(self): ...


 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:
@@ -119,13 +125,28 @@ def _filter_to_sql(filter: Optional[Union[str, Expr]]) -> Optional[str]:
    return filter


-def _projection_to_scanner_kwargs(
-    columns: Optional[
-        Union[
-            List[str], List[Tuple[str, Union[str, Expr]]], Dict[str, Union[str, Expr]]
-        ]
-    ],
-) -> Dict[str, Any]:
+def _combine_where(
+    existing: Optional[Union[str, Expr]], new: Union[str, Expr]
+) -> Union[str, Expr]:
+    """Combine a new filter with an existing one using a logical AND.
+
+    Calling ``where`` more than once composes the filters with AND instead of
+    replacing the previous filter. Two :class:`~lancedb.expr.Expr` filters are
+    combined as an expression; otherwise both filters are lowered to SQL strings
+    and combined as SQL.
+    """
+    if existing is None:
+        return new
+    existing_is_expr = isinstance(existing, Expr)
+    new_is_expr = isinstance(new, Expr)
+    if existing_is_expr and new_is_expr:
+        return existing & new
+    existing_sql = existing.to_sql() if existing_is_expr else existing
+    new_sql = new.to_sql() if new_is_expr else new
+    return f"({existing_sql}) AND ({new_sql})"
+
+
+def _projection_to_scanner_kwargs(columns: QueryProjection) -> Dict[str, Any]:
    if columns is None:
        return {}
    if isinstance(columns, list):
@@ -150,7 +171,11 @@ def _projection_to_scanner_kwargs(


 def _scanner_kwargs_for_query(
-    query: Query, blob_mode: BlobMode, dataset: Optional[Any] = None
+    query: Query,
+    blob_mode: BlobMode,
+    dataset: Optional[Any] = None,
+    *,
+    with_row_id: Optional[bool] = None,
 ) -> Dict[str, Any]:
    fragments = _scanner_fragments_for_query(query, dataset)
    kwargs = {
@@ -158,10 +183,10 @@ def _scanner_kwargs_for_query(
        "filter": _filter_to_sql(query.filter),
        "limit": query.limit,
        "offset": query.offset,
-        "with_row_id": query.with_row_id,
+        "with_row_id": with_row_id if with_row_id is not None else 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}
@@ -194,11 +219,11 @@ 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 _field_is_blob(field) or field.name not in df.columns:
+        if not is_blob_like_field(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"):
@@ -208,7 +233,7 @@ def _ensure_lazy_blob_frame(
    return df


-def _scanner_to_table(scanner: Any) -> pa.Table:
+def _scanner_to_table(scanner: _LanceScanner) -> pa.Table:
    if hasattr(scanner, "to_pyarrow"):
        reader = scanner.to_pyarrow()
        return reader.read_all()
@@ -218,7 +243,9 @@ def _scanner_to_table(scanner: Any) -> pa.Table:
    return reader.read_all()


-def _scanner_to_pandas(scanner: Any, blob_mode: BlobMode, **kwargs) -> "pd.DataFrame":
+def _scanner_to_pandas(
+    scanner: _LanceScanner, blob_mode: BlobMode, **kwargs
+) -> pd.DataFrame:
    schema = getattr(scanner, "projected_schema", None)
    if schema is None:
        schema = getattr(scanner, "schema", None)
@@ -239,13 +266,71 @@ def _scanner_to_pandas(scanner: Any, blob_mode: BlobMode, **kwargs) -> "pd.DataF
        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,
@@ -653,7 +738,7 @@ class Query(pydantic.BaseModel):
    distance_type: Optional[str] = None

    # which columns to return in the results (dict values may be str or Expr)
-    columns: Optional[Union[List[str], Dict[str, Union[str, Expr]]]] = None
+    columns: QueryProjection = None

    # minimum number of IVF partitions to search
    #
@@ -937,7 +1022,7 @@ 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()
@@ -996,6 +1081,11 @@ 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
@@ -1148,8 +1238,13 @@ class LanceQueryBuilder(ABC):
        -------
        LanceQueryBuilder
            The LanceQueryBuilder object.
+
+        Notes
+        -----
+        Calling this multiple times combines the filters with a logical AND
+        rather than replacing the previous filter.
        """
-        self._where = where
+        self._where = _combine_where(self._where, where)
        self._postfilter = not prefilter
        return self

@@ -1169,6 +1264,42 @@ 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.

@@ -1242,7 +1373,9 @@ class LanceQueryBuilder(ABC):
        self._order_by = ordering
        return self

-    def analyze_plan(self) -> str:
+    def analyze_plan(
+        self, distributed_metrics: AnalyzePlanDistributedMetrics = "aggregate"
+    ) -> str:
        """
        Run the query and return its execution plan with runtime metrics.

@@ -1280,12 +1413,22 @@ class LanceQueryBuilder(ABC):
                      fragments_scanned=..., ranges_scanned=1, rows_scanned=1,
                      bytes_read=..., iops=..., requests=..., task_wait_time=...]

+        Parameters
+        ----------
+        distributed_metrics : Literal["aggregate", "per_worker", "full"]
+            Defaults to "aggregate".
+            How distributed worker metrics are displayed for remote query plans.
+            "aggregate" preserves the legacy summary, "per_worker" shows each
+            worker separately, and "full" includes both.
+
        Returns
        -------
        plan : str
            The physical query execution plan with runtime metrics.
        """
-        return self._table._analyze_plan(self.to_query_object())
+        return self._table._analyze_plan(
+            self.to_query_object(), distributed_metrics=distributed_metrics
+        )

    def vector(self, vector: Union[np.ndarray, list]) -> Self:
        """Set the vector to search for.
@@ -1345,13 +1488,29 @@ class LanceQueryBuilder(ABC):
            return None

        dataset = self._table.to_lance()
-        scanner = dataset.scanner(
-            **_scanner_kwargs_for_query(query, blob_mode, dataset)
+        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(
+                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:
@@ -1599,7 +1758,9 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
            The maximum time to wait for the query to complete.
            If None, wait indefinitely.
        """
-        return self.to_batches(timeout=timeout).read_all()
+        return self._finalize_blob_query_table(
+            self.to_batches(timeout=timeout).read_all()
+        )

    def to_query_object(self) -> Query:
        """
@@ -1659,7 +1820,7 @@ 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.to_query_object()
+        query = self._query_for_scan()
        result_set = self._table._execute_query(
            query, batch_size=batch_size, timeout=timeout
        )
@@ -1693,8 +1854,13 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
        -------
        LanceQueryBuilder
            The LanceQueryBuilder object.
+
+        Notes
+        -----
+        Calling this multiple times combines the filters with a logical AND
+        rather than replacing the previous filter.
        """
-        self._where = where
+        self._where = _combine_where(self._where, where)
        if prefilter is not None:
            self._postfilter = not prefilter
        return self
@@ -1798,8 +1964,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
        Parameters
        ----------
        phrase_query: bool, default True
-            If True, then the query will be wrapped in quotes and
-            double quotes replaced by single quotes.
+            If True, then an unquoted string query will be wrapped in quotes.

        Returns
        -------
@@ -1809,6 +1974,21 @@ 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
@@ -1833,7 +2013,7 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
            fragments=self._fragments,
            fragment_ids=self._fragment_ids,
            full_text_query=FullTextSearchQuery(
-                query=self._query, columns=self._fts_columns
+                query=self._query_with_phrase_semantics(), columns=self._fts_columns
            ),
            offset=self._offset,
            fast_search=self._fast_search,
@@ -1851,22 +2031,13 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
        self._table._ensure_no_legacy_fts_index()

-        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()
+        query = self._query_for_scan()
        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 results
+        return self._finalize_blob_query_table(results)

    def to_batches(
        self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
@@ -1894,7 +2065,9 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):

 class LanceEmptyQueryBuilder(LanceQueryBuilder):
    def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
-        return self.to_batches(timeout=timeout).read_all()
+        return self._finalize_blob_query_table(
+            self.to_batches(timeout=timeout).read_all()
+        )

    def to_query_object(self) -> Query:
        return Query(
@@ -1916,7 +2089,7 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
    def to_batches(
        self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
    ) -> pa.RecordBatchReader:
-        query = self.to_query_object()
+        query = self._query_for_scan()
        return self._table._execute_query(query, batch_size=batch_size, timeout=timeout)

    def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
@@ -1988,14 +2161,13 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):

        return vector_query, text_query

-    def phrase_query(self, phrase_query: bool = None) -> LanceHybridQueryBuilder:
+    def phrase_query(self, phrase_query: bool = True) -> LanceHybridQueryBuilder:
        """Set whether to use phrase query.

        Parameters
        ----------
        phrase_query: bool, default True
-            If True, then the query will be wrapped in quotes and
-            double quotes replaced by single quotes.
+            If True, then an unquoted string query will be wrapped in quotes.

        Returns
        -------
@@ -2020,15 +2192,25 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
            fts_results = fts_future.result()
            vector_results = vector_future.result()

-        return self._combine_hybrid_results(
+        results = 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=self._with_row_id,
+            with_row_ids=True,
        )
+        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(
@@ -2412,9 +2594,17 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
        indented_fts = "\n".join("  " + line for line in fts_plan.splitlines())
        return f"{reranker_label}\n  {indented_vector}\n  {indented_fts}"

-    def analyze_plan(self):
+    def analyze_plan(
+        self, distributed_metrics: AnalyzePlanDistributedMetrics = "aggregate"
+    ) -> str:
        """Execute the query and display with runtime metrics.

+        Parameters
+        ----------
+        distributed_metrics : Literal["aggregate", "per_worker", "full"]
+            Defaults to "aggregate".
+            How distributed worker metrics are displayed for remote query plans.
+
        Returns
        -------
        plan : str
@@ -2422,9 +2612,19 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
        self._create_query_builders()

        results = ["Vector Search Plan:"]
-        results.append(self._table._analyze_plan(self._vector_query.to_query_object()))
+        results.append(
+            self._table._analyze_plan(
+                self._vector_query.to_query_object(),
+                distributed_metrics=distributed_metrics,
+            )
+        )
        results.append("FTS Search Plan:")
-        results.append(self._table._analyze_plan(self._fts_query.to_query_object()))
+        results.append(
+            self._table._analyze_plan(
+                self._fts_query.to_query_object(),
+                distributed_metrics=distributed_metrics,
+            )
+        )
        return "\n".join(results)

    def _create_query_builders(self):
@@ -2469,7 +2669,7 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
            self._vector_query.ef(self._ef)
        if self._bypass_vector_index:
            self._vector_query.bypass_vector_index()
-        if self._lower_bound or self._upper_bound:
+        if self._lower_bound is not None or self._upper_bound is not None:
            self._vector_query.distance_range(
                lower_bound=self._lower_bound, upper_bound=self._upper_bound
            )
@@ -2499,6 +2699,9 @@ 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:
        """
@@ -2508,11 +2711,46 @@ 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.
@@ -2565,6 +2803,7 @@ class AsyncQueryBase(object):
        """
        Include the _rowid column in the results.
        """
+        self._with_row_id = True
        self._inner.with_row_id()
        return self

@@ -2611,6 +2850,7 @@ 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
@@ -2641,8 +2881,8 @@ class AsyncQueryBase(object):
            complete within the specified time, an error will be raised.
        """
        batch_iter = await self.to_batches(timeout=timeout)
-        return pa.Table.from_batches(
-            await batch_iter.read_all(), schema=batch_iter.schema
+        return self._finalize_blob_query_table(
+            pa.Table.from_batches(await batch_iter.read_all(), schema=batch_iter.schema)
        )

    async def to_list(self, timeout: Optional[timedelta] = None) -> List[dict]:
@@ -2709,7 +2949,7 @@ 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):
@@ -2750,14 +2990,36 @@ 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(query, blob_mode, dataset)
+            **_scanner_kwargs_for_query(
+                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,
@@ -2849,14 +3111,22 @@ class AsyncQueryBase(object):
        """  # noqa: E501
        return await self._inner.explain_plan(verbose)

-    async def analyze_plan(self):
+    async def analyze_plan(
+        self, distributed_metrics: AnalyzePlanDistributedMetrics = "aggregate"
+    ) -> str:
        """Execute the query and display with runtime metrics.

+        Parameters
+        ----------
+        distributed_metrics : Literal["aggregate", "per_worker", "full"]
+            Defaults to "aggregate".
+            How distributed worker metrics are displayed for remote query plans.
+
        Returns
        -------
        plan : str
        """
-        return await self._inner.analyze_plan()
+        return await self._inner.analyze_plan(distributed_metrics)


 class AsyncStandardQuery(AsyncQueryBase):
@@ -2894,6 +3164,9 @@ class AsyncStandardQuery(AsyncQueryBase):

        Filtering performance can often be improved by creating a scalar index
        on the filter column(s).
+
+        Calling this multiple times combines the filters with a logical AND
+        rather than replacing the previous filter.
        """
        if isinstance(predicate, Expr):
            self._inner.where_expr(predicate._inner)
@@ -3539,9 +3812,24 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
        fts_query = AsyncFTSQuery(self._inner.to_fts_query(), self._table)
        vec_query = AsyncVectorQuery(self._inner.to_vector_query(), self._table)

-        # save the row ID choice that was made on the query builder and force it
-        # to actually fetch the row ids because we need this for reranking
-        with_row_ids = self._inner.get_with_row_id()
+        req = fts_query._inner.to_query_request()
+        blob_auto_row_id = False
+        blob_paths: tuple[str, ...] = ()
+        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()

@@ -3557,8 +3845,14 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
            fts_query=fts_query.get_query(),
            reranker=self._reranker,
            limit=self._inner.get_limit(),
-            with_row_ids=with_row_ids,
+            with_row_ids=True,
        )
+        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)

@@ -3581,18 +3875,18 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
        >>> asyncio.run(doctest_example()) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
        RRFReranker(K=60)
            ProjectionExec: expr=[vector@0 as vector, text@3 as text, _distance@2 as _distance]
-              Take: columns="vector, _rowid, _distance, (text)"
-                CoalesceBatchesExec: target_batch_size=1024
-                  GlobalLimitExec: skip=0, fetch=10
-                    FilterExec: _distance@2 IS NOT NULL
-                      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST, _rowid@1 ASC NULLS LAST], preserve_partitioning=[false]
-                        KNNVectorDistance: metric=l2
-                          LanceRead: uri=..., projection=[vector], ...
+            Take: columns="vector, _rowid, _distance, (text)"
+              CoalesceBatchesExec: target_batch_size=1024
+                GlobalLimitExec: skip=0, fetch=10
+                  FilterExec: _distance@2 IS NOT NULL
+                    SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST, _rowid@1 ASC NULLS LAST], preserve_partitioning=[false]
+                      KNNVectorDistance: metric=l2
+                        LanceRead: uri=..., projection=[vector], ...
            ProjectionExec: expr=[vector@2 as vector, text@3 as text, _score@1 as _score]
-              Take: columns="_rowid, _score, (vector), (text)"
-                CoalesceBatchesExec: target_batch_size=1024
-                  GlobalLimitExec: skip=0, fetch=10
-                    MatchQuery: column=text, query=hello
+            Take: columns="_rowid, _score, (vector), (text)"
+              CoalesceBatchesExec: target_batch_size=1024
+                GlobalLimitExec: skip=0, fetch=10
+                  MatchQuery: column=text, query=[hello]

        Parameters
        ----------
@@ -3611,7 +3905,9 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
        indented_fts = "\n".join("  " + line for line in fts_plan.splitlines())
        return f"{self._reranker}\n  {indented_vector}\n  {indented_fts}"

-    async def analyze_plan(self):
+    async def analyze_plan(
+        self, distributed_metrics: AnalyzePlanDistributedMetrics = "aggregate"
+    ) -> str:
        """
        Execute the query and return the physical execution plan with runtime metrics.

@@ -3620,14 +3916,24 @@ class AsyncHybridQuery(AsyncStandardQuery, AsyncVectorQueryBase):
        elapsed time, I/O stats, and more. It’s useful for debugging and
        performance analysis.

+        Parameters
+        ----------
+        distributed_metrics : Literal["aggregate", "per_worker", "full"]
+            Defaults to "aggregate".
+            How distributed worker metrics are displayed for remote query plans.
+
        Returns
        -------
        plan : str
        """
        results = ["Vector Search Query:"]
-        results.append(await self._inner.to_vector_query().analyze_plan())
+        results.append(
+            await self._inner.to_vector_query().analyze_plan(distributed_metrics)
+        )
        results.append("FTS Search Query:")
-        results.append(await self._inner.to_fts_query().analyze_plan())
+        results.append(
+            await self._inner.to_fts_query().analyze_plan(distributed_metrics)
+        )

        return "\n".join(results)

@@ -3911,14 +4217,22 @@ class BaseQueryBuilder(object):
        """  # noqa: E501
        return LOOP.run(self._inner.explain_plan(verbose))

-    def analyze_plan(self):
+    def analyze_plan(
+        self, distributed_metrics: AnalyzePlanDistributedMetrics = "aggregate"
+    ) -> str:
        """Execute the query and display with runtime metrics.

+        Parameters
+        ----------
+        distributed_metrics : Literal["aggregate", "per_worker", "full"]
+            Defaults to "aggregate".
+            How distributed worker metrics are displayed for remote query plans.
+
        Returns
        -------
        plan : str
        """
-        return LOOP.run(self._inner.analyze_plan())
+        return LOOP.run(self._inner.analyze_plan(distributed_metrics))


 class LanceTakeQueryBuilder(BaseQueryBuilder):
@@ -9,6 +9,7 @@ from typing import List, Optional
 from lancedb import __version__

 from .header import HeaderProvider
+from .oauth import OAuthConfig, OAuthFlowType

 __all__ = [
    "TimeoutConfig",
@@ -16,6 +17,8 @@ __all__ = [
    "TlsConfig",
    "ClientConfig",
    "HeaderProvider",
+    "OAuthConfig",
+    "OAuthFlowType",
 ]


--- a/Show More
+++ b/Show More