fix small details

Closes #2191


<!-- This is an auto-generated comment: release notes by coderabbit.ai
-->

## Summary by CodeRabbit

- **Chores**
- Updated the required version of the pyarrow package to version 16 or
higher.
- Adjusted automated testing workflows to install pyarrow version 16 for
compatibility checks.

<!-- end of auto-generated comment: release notes by coderabbit.ai -->

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.18.1"
+current_version = "0.19.1-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/docs.yml

@@ -18,17 +18,24 @@ concurrency:
   group: "pages"
   cancel-in-progress: true
 
+env:
+  # This reduces the disk space needed for the build
+  RUSTFLAGS: "-C debuginfo=0"
+  # according to: https://matklad.github.io/2021/09/04/fast-rust-builds.html
+  # CI builds are faster with incremental disabled.
+  CARGO_INCREMENTAL: "0"
+
 jobs:
   # Single deploy job since we're just deploying
   build:
     environment:
       name: github-pages
       url: ${{ steps.deployment.outputs.page_url }}
-    runs-on: buildjet-8vcpu-ubuntu-2204
+    runs-on: ubuntu-24.04
     steps:
       - name: Checkout
         uses: actions/checkout@v4
-      - name: Install dependecies needed for ubuntu
+      - name: Install dependencies needed for ubuntu
         run: |
           sudo apt install -y protobuf-compiler libssl-dev
           rustup update && rustup default
@@ -38,6 +45,7 @@ jobs:
           python-version: "3.10"
           cache: "pip"
           cache-dependency-path: "docs/requirements.txt"
+      - uses: Swatinem/rust-cache@v2
       - name: Build Python
         working-directory: python
         run: |
@@ -49,7 +57,6 @@ jobs:
           node-version: 20
           cache: 'npm'
           cache-dependency-path: node/package-lock.json
-      - uses: Swatinem/rust-cache@v2
       - name: Install node dependencies
         working-directory: node
         run: |

.github/workflows/java-publish.yml

@@ -43,7 +43,7 @@ jobs:
       - uses: Swatinem/rust-cache@v2
       - uses: actions-rust-lang/setup-rust-toolchain@v1
         with:
-          toolchain: "1.79.0"
+          toolchain: "1.81.0"
           cache-workspaces: "./java/core/lancedb-jni"
           # Disable full debug symbol generation to speed up CI build and keep memory down
           # "1" means line tables only, which is useful for panic tracebacks.
@@ -97,7 +97,7 @@ jobs:
       - name: Dry run
         if: github.event_name == 'pull_request'
         run: |
-          mvn --batch-mode -DskipTests package
+          mvn --batch-mode -DskipTests -Drust.release.build=true package
       - name: Set github
         run: |
           git config --global user.email "LanceDB Github Runner"
@@ -108,7 +108,7 @@ jobs:
           echo "use-agent" >> ~/.gnupg/gpg.conf
           echo "pinentry-mode loopback" >> ~/.gnupg/gpg.conf
           export GPG_TTY=$(tty)
-          mvn --batch-mode -DskipTests -DpushChanges=false -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} deploy -P deploy-to-ossrh
+          mvn --batch-mode -DskipTests -Drust.release.build=true -DpushChanges=false -Dgpg.passphrase=${{ secrets.GPG_PASSPHRASE }} deploy -P deploy-to-ossrh
         env:
           SONATYPE_USER: ${{ secrets.SONATYPE_USER }}
           SONATYPE_TOKEN: ${{ secrets.SONATYPE_TOKEN }}

.github/workflows/npm-publish.yml

@@ -18,6 +18,7 @@ on:
     # This should trigger a dry run (we skip the final publish step)
     paths:
       - .github/workflows/npm-publish.yml
+      - Cargo.toml # Change in dependency frequently breaks builds
 
 concurrency:
   group: ${{ github.workflow }}-${{ github.ref }}
@@ -130,29 +131,24 @@ jobs:
               set -e &&
               apt-get update &&
               apt-get install -y protobuf-compiler pkg-config
-
+          - target: x86_64-unknown-linux-musl
-          # TODO: re-enable x64 musl builds. I could not figure out why, but it
+            # This one seems to need some extra memory
-          # consistently made GHA runners non-responsive at the end of build. Example:
+            host: ubuntu-2404-8x-x64
-          # https://github.com/lancedb/lancedb/actions/runs/13980431071/job/39144319470?pr=2250
+            # https://github.com/napi-rs/napi-rs/blob/main/alpine.Dockerfile
-
+            docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-alpine
-          # - target: x86_64-unknown-linux-musl
+            features: fp16kernels
-          #   # This one seems to need some extra memory
+            pre_build: |-
-          #   host: ubuntu-2404-8x-x64
+              set -e &&
-          #   # https://github.com/napi-rs/napi-rs/blob/main/alpine.Dockerfile
+              apk add protobuf-dev curl &&
-          #   docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-alpine
+              ln -s /usr/lib/gcc/x86_64-alpine-linux-musl/14.2.0/crtbeginS.o /usr/lib/crtbeginS.o &&
-          #   features: ","
+              ln -s /usr/lib/libgcc_s.so /usr/lib/libgcc.so &&
-          #   pre_build: |-
+              CC=gcc &&
-          #     set -e &&
+              CXX=g++
-          #     apk add protobuf-dev curl &&
-          #     ln -s /usr/lib/gcc/x86_64-alpine-linux-musl/14.2.0/crtbeginS.o /usr/lib/crtbeginS.o &&
-          #     ln -s /usr/lib/libgcc_s.so /usr/lib/libgcc.so
-
           - target: aarch64-unknown-linux-gnu
             host: ubuntu-2404-8x-x64
             # https://github.com/napi-rs/napi-rs/blob/main/debian-aarch64.Dockerfile
             docker: ghcr.io/napi-rs/napi-rs/nodejs-rust:lts-debian-aarch64
-            # TODO: enable fp16kernels after https://github.com/lancedb/lance/pull/3559
+            features: "fp16kernels"
-            features: ","
             pre_build: |-
               set -e &&
               apt-get update &&
@@ -170,8 +166,8 @@ jobs:
               set -e &&
               apk add protobuf-dev &&
               rustup target add aarch64-unknown-linux-musl &&
-              export CC="/aarch64-linux-musl-cross/bin/aarch64-linux-musl-gcc" &&
+              export CC_aarch64_unknown_linux_musl=aarch64-linux-musl-gcc &&
-              export CXX="/aarch64-linux-musl-cross/bin/aarch64-linux-musl-g++"
+              export CXX_aarch64_unknown_linux_musl=aarch64-linux-musl-g++
     name: build - ${{ matrix.settings.target }}
     runs-on: ${{ matrix.settings.host }}
     defaults:
@@ -331,6 +327,7 @@ jobs:
           node-version: 20
           cache: npm
           cache-dependency-path: nodejs/package-lock.json
+          registry-url: "https://registry.npmjs.org"
       - name: Install dependencies
         run: npm ci
       - uses: actions/download-artifact@v4
@@ -534,6 +531,12 @@ jobs:
           for filename in *.tgz; do
             npm publish $PUBLISH_ARGS $filename
           done
+      - name: Deprecate
+        env:
+          NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
+        # We need to deprecate the old package to avoid confusion.
+        # Each time we publish a new version, it gets undeprecated.
+        run: npm deprecate vectordb "Use @lancedb/lancedb instead."
       - name: Notify Slack Action
         uses: ravsamhq/notify-slack-action@2.3.0
         if: ${{ always() }}

.github/workflows/pypi-publish.yml

@@ -4,6 +4,11 @@ on:
   push:
     tags:
       - 'python-v*'
+  pull_request:
+    # This should trigger a dry run (we skip the final publish step)
+    paths:
+      - .github/workflows/pypi-publish.yml
+      - Cargo.toml # Change in dependency frequently breaks builds
 
 jobs:
   linux:
@@ -46,6 +51,7 @@ jobs:
           arm-build: ${{ matrix.config.platform == 'aarch64' }}
           manylinux: ${{ matrix.config.manylinux }}
       - uses: ./.github/workflows/upload_wheel
+        if: startsWith(github.ref, 'refs/tags/python-v')
         with:
           pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
           fury_token: ${{ secrets.FURY_TOKEN }}
@@ -75,6 +81,7 @@ jobs:
           python-minor-version: 8
           args: "--release --strip --target ${{ matrix.config.target }} --features fp16kernels"
       - uses: ./.github/workflows/upload_wheel
+        if: startsWith(github.ref, 'refs/tags/python-v')
         with:
           pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
           fury_token: ${{ secrets.FURY_TOKEN }}
@@ -96,10 +103,12 @@ jobs:
           args: "--release --strip"
           vcpkg_token: ${{ secrets.VCPKG_GITHUB_PACKAGES }}
       - uses: ./.github/workflows/upload_wheel
+        if: startsWith(github.ref, 'refs/tags/python-v')
         with:
           pypi_token: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
           fury_token: ${{ secrets.FURY_TOKEN }}
   gh-release:
+    if: startsWith(github.ref, 'refs/tags/python-v')
     runs-on: ubuntu-latest
     permissions:
       contents: write

.github/workflows/python.yml

@@ -136,9 +136,9 @@ jobs:
       - uses: ./.github/workflows/run_tests
         with:
           integration: true
-      - name: Test without pylance
+      - name: Test without pylance or pandas
         run: |
-          pip uninstall -y pylance
+          pip uninstall -y pylance pandas
           pytest -vv python/tests/test_table.py
       # Make sure wheels are not included in the Rust cache
       - name: Delete wheels
@@ -228,6 +228,7 @@ jobs:
       - name: Install lancedb
         run: |
           pip install "pydantic<2"
+          pip install pyarrow==16
           pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests]
           pip install tantivy
       - name: Run tests

Cargo.toml

@@ -21,16 +21,14 @@ categories = ["database-implementations"]
 rust-version = "1.78.0"
 
 [workspace.dependencies]
-lance = { "version" = "=0.25.0", "features" = [
+lance = { "version" = "=0.27.0", "features" = ["dynamodb"], tag = "v0.27.0-beta.2", git="https://github.com/lancedb/lance.git" }
-    "dynamodb",
+lance-io = { version = "=0.27.0", tag = "v0.27.0-beta.2", git="https://github.com/lancedb/lance.git" }
-], tag = "v0.25.0-beta.5", git = "https://github.com/lancedb/lance.git" }
+lance-index = { version = "=0.27.0", tag = "v0.27.0-beta.2", git="https://github.com/lancedb/lance.git" }
-lance-io = { version = "=0.25.0", tag = "v0.25.0-beta.5", git = "https://github.com/lancedb/lance.git" }
+lance-linalg = { version = "=0.27.0", tag = "v0.27.0-beta.2", git="https://github.com/lancedb/lance.git" }
-lance-index = { version = "=0.25.0", tag = "v0.25.0-beta.5", git = "https://github.com/lancedb/lance.git" }
+lance-table = { version = "=0.27.0", tag = "v0.27.0-beta.2", git="https://github.com/lancedb/lance.git" }
-lance-linalg = { version = "=0.25.0", tag = "v0.25.0-beta.5", git = "https://github.com/lancedb/lance.git" }
+lance-testing = { version = "=0.27.0", tag = "v0.27.0-beta.2", git="https://github.com/lancedb/lance.git" }
-lance-table = { version = "=0.25.0", tag = "v0.25.0-beta.5", git = "https://github.com/lancedb/lance.git" }
+lance-datafusion = { version = "=0.27.0", tag = "v0.27.0-beta.2", git="https://github.com/lancedb/lance.git" }
-lance-testing = { version = "=0.25.0", tag = "v0.25.0-beta.5", git = "https://github.com/lancedb/lance.git" }
+lance-encoding = { version = "=0.27.0", tag = "v0.27.0-beta.2", git="https://github.com/lancedb/lance.git" }
-lance-datafusion = { version = "=0.25.0", tag = "v0.25.0-beta.5", git = "https://github.com/lancedb/lance.git" }
-lance-encoding = { version = "=0.25.0", tag = "v0.25.0-beta.5", git = "https://github.com/lancedb/lance.git" }
 # Note that this one does not include pyarrow
 arrow = { version = "54.1", optional = false }
 arrow-array = "54.1"
@@ -41,12 +39,12 @@ arrow-schema = "54.1"
 arrow-arith = "54.1"
 arrow-cast = "54.1"
 async-trait = "0"
-datafusion = { version = "45.0", default-features = false }
+datafusion = { version = "46.0", default-features = false }
-datafusion-catalog = "45.0"
+datafusion-catalog = "46.0"
-datafusion-common = { version = "45.0", default-features = false }
+datafusion-common = { version = "46.0", default-features = false }
-datafusion-execution = "45.0"
+datafusion-execution = "46.0"
-datafusion-expr = "45.0"
+datafusion-expr = "46.0"
-datafusion-physical-plan = "45.0"
+datafusion-physical-plan = "46.0"
 env_logger = "0.11"
 half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",

README.md

@@ -1,9 +1,17 @@
+<a href="https://cloud.lancedb.com" target="_blank">
+  <img src="https://github.com/user-attachments/assets/92dad0a2-2a37-4ce1-b783-0d1b4f30a00c" alt="LanceDB Cloud Public Beta" width="100%" style="max-width: 100%;">
+</a>
+
 <div align="center">
 <p align="center">
 
-<img width="275" alt="LanceDB Logo" src="https://github.com/lancedb/lancedb/assets/5846846/37d7c7ad-c2fd-4f56-9f16-fffb0d17c73a">
+<picture>
+  <source media="(prefers-color-scheme: dark)" srcset="https://github.com/user-attachments/assets/ac270358-333e-4bea-a132-acefaa94040e">
+  <source media="(prefers-color-scheme: light)" srcset="https://github.com/user-attachments/assets/b864d814-0d29-4784-8fd9-807297c758c0">
+  <img alt="LanceDB Logo" src="https://github.com/user-attachments/assets/b864d814-0d29-4784-8fd9-807297c758c0" width=300>
+</picture>
 
-**Developer-friendly, database for multimodal AI**
+**Search More, Manage Less**
 
 <a href='https://github.com/lancedb/vectordb-recipes/tree/main' target="_blank"><img alt='LanceDB' src='https://img.shields.io/badge/VectorDB_Recipes-100000?style=for-the-badge&logo=LanceDB&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
 <a href='https://lancedb.github.io/lancedb/' target="_blank"><img alt='lancdb' src='https://img.shields.io/badge/DOCS-100000?style=for-the-badge&logo=lancdb&logoColor=white&labelColor=645cfb&color=645cfb'/></a>

docs/README.md

@@ -2,7 +2,7 @@
 
 LanceDB docs are deployed to https://lancedb.github.io/lancedb/.
 
-Docs is built and deployed automatically by [Github Actions](.github/workflows/docs.yml)
+Docs is built and deployed automatically by [Github Actions](../.github/workflows/docs.yml)
 whenever a commit is pushed to the `main` branch. So it is possible for the docs to show
 unreleased features.
 

docs/mkdocs.yml

@@ -105,7 +105,8 @@ markdown_extensions:
 nav:
   - Home:
       - LanceDB: index.md
-      - 🏃🏼‍♂️ Quick start: basic.md
+      - 👉 Quickstart: quickstart.md
+      - 🏃🏼‍♂️ Basic Usage: basic.md
       - 📚 Concepts:
           - Vector search: concepts/vector_search.md
           - Indexing:
@@ -237,7 +238,9 @@ nav:
           - 👾 JavaScript (lancedb): js/globals.md
           - 🦀 Rust: https://docs.rs/lancedb/latest/lancedb/
 
-  - Quick start: basic.md
+  - Getting Started:
+      - Quickstart: quickstart.md
+      - Basic Usage: basic.md
   - Concepts:
       - Vector search: concepts/vector_search.md
       - Indexing:

docs/src/basic.md

@@ -1,4 +1,4 @@
-# Quick start
+# Basic Usage
 
 !!! info "LanceDB can be run in a number of ways:"
 

docs/src/cloud/index.md

@@ -2,7 +2,7 @@
 
 LanceDB Cloud is a SaaS (software-as-a-service) solution that runs serverless in the cloud, clearly separating storage from compute. It's designed to be highly scalable without breaking the bank. LanceDB Cloud is currently in private beta with general availability coming soon, but you can apply for early access with the private beta release by signing up below.
 
-[Try out LanceDB Cloud](https://noteforms.com/forms/lancedb-mailing-list-cloud-kty1o5?notionforms=1&utm_source=notionforms){ .md-button .md-button--primary }
+[Try out LanceDB Cloud (Public Beta)](https://cloud.lancedb.com){ .md-button .md-button--primary }
 
 ## Architecture
 

docs/src/guides/storage.md

@@ -342,7 +342,7 @@ For **read and write access**, LanceDB will need a policy such as:
             "Action": [
               "s3:PutObject",
               "s3:GetObject",
-              "s3:DeleteObject",
+              "s3:DeleteObject"
             ],
             "Resource": "arn:aws:s3:::<bucket>/<prefix>/*"
         },
@@ -374,7 +374,7 @@ For **read-only access**, LanceDB will need a policy such as:
         {
             "Effect": "Allow",
             "Action": [
-              "s3:GetObject",
+              "s3:GetObject"
             ],
             "Resource": "arn:aws:s3:::<bucket>/<prefix>/*"
         },

docs/src/guides/tables.md

@@ -765,7 +765,10 @@ This can be used to update zero to all rows depending on how many rows match the
         ];
         const tbl = await db.createTable("my_table", data)
 
-        await tbl.update({vector: [10, 10]}, { where: "x = 2"})
+        await tbl.update({ 
+            values: { vector: [10, 10] },
+            where: "x = 2"
+        });
         ```
 
     === "vectordb (deprecated)"
@@ -784,7 +787,10 @@ This can be used to update zero to all rows depending on how many rows match the
         ];
         const tbl = await db.createTable("my_table", data)
 
-        await tbl.update({ where: "x = 2", values: {vector: [10, 10]} })
+        await tbl.update({ 
+            where: "x = 2", 
+            values: { vector: [10, 10] } 
+        });
         ```
 
 #### Updating using a sql query
@@ -942,28 +948,6 @@ rewriting the column, which can be a heavy operation.
     ```
     **API Reference:** [lancedb.Table.alterColumns](../js/classes/Table.md/#altercolumns)
 
-You can even cast the a vector column to a different dimension:
-
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-        --8<-- "python/python/tests/docs/test_basic.py:alter_columns_vector"
-        ```
-    === "Async API"
-
-        ```python
-        --8<-- "python/python/tests/docs/test_guide_tables.py:import-pyarrow"
-        --8<-- "python/python/tests/docs/test_basic.py:alter_columns_async_vector"
-        ```
-=== "Typescript"
-
-    ```typescript
-    --8<-- "nodejs/examples/basic.test.ts:alter_columns_vector"
-    ```
-
 ### Dropping columns
 
 You can drop columns from the table with the `drop_columns` method. This will

docs/src/index.md

@@ -23,7 +23,7 @@ LanceDB **OSS** is an **open-source**, batteries-included embedded vector databa
 
 LanceDB **Cloud** is a SaaS (software-as-a-service) solution that runs serverless in the cloud, making the storage clearly separated from compute. It's designed to be cost-effective and highly scalable without breaking the bank. LanceDB Cloud is currently in private beta with general availability coming soon, but you can apply for early access with the private beta release by signing up below.
 
-[Try out LanceDB Cloud](https://noteforms.com/forms/lancedb-mailing-list-cloud-kty1o5?notionforms=1&utm_source=notionforms){ .md-button .md-button--primary }
+[Try out LanceDB Cloud (Public Beta) Now](https://cloud.lancedb.com){ .md-button .md-button--primary }
 
 ## Why use LanceDB?
 

docs/src/js/classes/BoostQuery.md

@@ -0,0 +1,67 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / BoostQuery
+
+# Class: BoostQuery
+
+Represents a full-text query interface.
+This interface defines the structure and behavior for full-text queries,
+including methods to retrieve the query type and convert the query to a dictionary format.
+
+## Implements
+
+- [`FullTextQuery`](../interfaces/FullTextQuery.md)
+
+## Constructors
+
+### new BoostQuery()
+
+```ts
+new BoostQuery(
+   positive,
+   negative,
+   options?): BoostQuery
+```
+
+Creates an instance of BoostQuery.
+The boost returns documents that match the positive query,
+but penalizes those that match the negative query.
+the penalty is controlled by the `negativeBoost` parameter.
+
+#### Parameters
+
+* **positive**: [`FullTextQuery`](../interfaces/FullTextQuery.md)
+    The positive query that boosts the relevance score.
+
+* **negative**: [`FullTextQuery`](../interfaces/FullTextQuery.md)
+    The negative query that reduces the relevance score.
+
+* **options?**
+    Optional parameters for the boost query.
+    - `negativeBoost`: The boost factor for the negative query (default is 0.0).
+
+* **options.negativeBoost?**: `number`
+
+#### Returns
+
+[`BoostQuery`](BoostQuery.md)
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+The type of the full-text query.
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)

docs/src/js/classes/MatchQuery.md

@@ -0,0 +1,70 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / MatchQuery
+
+# Class: MatchQuery
+
+Represents a full-text query interface.
+This interface defines the structure and behavior for full-text queries,
+including methods to retrieve the query type and convert the query to a dictionary format.
+
+## Implements
+
+- [`FullTextQuery`](../interfaces/FullTextQuery.md)
+
+## Constructors
+
+### new MatchQuery()
+
+```ts
+new MatchQuery(
+   query,
+   column,
+   options?): MatchQuery
+```
+
+Creates an instance of MatchQuery.
+
+#### Parameters
+
+* **query**: `string`
+    The text query to search for.
+
+* **column**: `string`
+    The name of the column to search within.
+
+* **options?**
+    Optional parameters for the match query.
+    - `boost`: The boost factor for the query (default is 1.0).
+    - `fuzziness`: The fuzziness level for the query (default is 0).
+    - `maxExpansions`: The maximum number of terms to consider for fuzzy matching (default is 50).
+
+* **options.boost?**: `number`
+
+* **options.fuzziness?**: `number`
+
+* **options.maxExpansions?**: `number`
+
+#### Returns
+
+[`MatchQuery`](MatchQuery.md)
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+The type of the full-text query.
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)

docs/src/js/classes/MergeInsertBuilder.md

@@ -33,20 +33,20 @@ Construct a MergeInsertBuilder. __Internal use only.__
 ### execute()
 
 ```ts
-execute(data): Promise<void>
+execute(data): Promise<MergeStats>
 ```
 
 Executes the merge insert operation
 
-Nothing is returned but the `Table` is updated
-
 #### Parameters
 
 * **data**: [`Data`](../type-aliases/Data.md)
 
 #### Returns
 
-`Promise`&lt;`void`&gt;
+`Promise`&lt;[`MergeStats`](../interfaces/MergeStats.md)&gt;
+
+Statistics about the merge operation: counts of inserted, updated, and deleted rows
 
 ***
 

docs/src/js/classes/MultiMatchQuery.md

@@ -0,0 +1,64 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / MultiMatchQuery
+
+# Class: MultiMatchQuery
+
+Represents a full-text query interface.
+This interface defines the structure and behavior for full-text queries,
+including methods to retrieve the query type and convert the query to a dictionary format.
+
+## Implements
+
+- [`FullTextQuery`](../interfaces/FullTextQuery.md)
+
+## Constructors
+
+### new MultiMatchQuery()
+
+```ts
+new MultiMatchQuery(
+   query,
+   columns,
+   options?): MultiMatchQuery
+```
+
+Creates an instance of MultiMatchQuery.
+
+#### Parameters
+
+* **query**: `string`
+    The text query to search for across multiple columns.
+
+* **columns**: `string`[]
+    An array of column names to search within.
+
+* **options?**
+    Optional parameters for the multi-match query.
+    - `boosts`: An array of boost factors for each column (default is 1.0 for all).
+
+* **options.boosts?**: `number`[]
+
+#### Returns
+
+[`MultiMatchQuery`](MultiMatchQuery.md)
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+The type of the full-text query.
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)

docs/src/js/classes/PhraseQuery.md

@@ -0,0 +1,55 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / PhraseQuery
+
+# Class: PhraseQuery
+
+Represents a full-text query interface.
+This interface defines the structure and behavior for full-text queries,
+including methods to retrieve the query type and convert the query to a dictionary format.
+
+## Implements
+
+- [`FullTextQuery`](../interfaces/FullTextQuery.md)
+
+## Constructors
+
+### new PhraseQuery()
+
+```ts
+new PhraseQuery(query, column): PhraseQuery
+```
+
+Creates an instance of `PhraseQuery`.
+
+#### Parameters
+
+* **query**: `string`
+    The phrase to search for in the specified column.
+
+* **column**: `string`
+    The name of the column to search within.
+
+#### Returns
+
+[`PhraseQuery`](PhraseQuery.md)
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+The type of the full-text query.
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)
+
+#### Implementation of
+
+[`FullTextQuery`](../interfaces/FullTextQuery.md).[`queryType`](../interfaces/FullTextQuery.md#querytype)

docs/src/js/classes/Query.md

@@ -30,6 +30,53 @@ protected inner: Query | Promise<Query>;
 
 ## Methods
 
+### analyzePlan()
+
+```ts
+analyzePlan(): Promise<string>
+```
+
+Executes the query and returns the physical query plan annotated with runtime metrics.
+
+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.
+
+#### Returns
+
+`Promise`&lt;`string`&gt;
+
+A query execution plan with runtime metrics for each step.
+
+#### Example
+
+```ts
+import * as lancedb from "@lancedb/lancedb"
+
+const db = await lancedb.connect("./.lancedb");
+const table = await db.createTable("my_table", [
+  { vector: [1.1, 0.9], id: "1" },
+]);
+
+const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
+
+Example output (with runtime metrics inlined):
+AnalyzeExec verbose=true, metrics=[]
+ ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
+  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
+   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
+    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
+     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
+      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
+       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]
+```
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`analyzePlan`](QueryBase.md#analyzeplan)
+
+***
+
 ### execute()
 
 ```ts
@@ -159,7 +206,7 @@ fullTextSearch(query, options?): this
 
 #### Parameters
 
-* **query**: `string`
+* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
 
 * **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;
 
@@ -262,7 +309,7 @@ nearestToText(query, columns?): Query
 
 #### Parameters
 
-* **query**: `string`
+* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
 
 * **columns?**: `string`[]
 

docs/src/js/classes/QueryBase.md

@@ -36,6 +36,49 @@ protected inner: NativeQueryType | Promise<NativeQueryType>;
 
 ## Methods
 
+### analyzePlan()
+
+```ts
+analyzePlan(): Promise<string>
+```
+
+Executes the query and returns the physical query plan annotated with runtime metrics.
+
+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.
+
+#### Returns
+
+`Promise`&lt;`string`&gt;
+
+A query execution plan with runtime metrics for each step.
+
+#### Example
+
+```ts
+import * as lancedb from "@lancedb/lancedb"
+
+const db = await lancedb.connect("./.lancedb");
+const table = await db.createTable("my_table", [
+  { vector: [1.1, 0.9], id: "1" },
+]);
+
+const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
+
+Example output (with runtime metrics inlined):
+AnalyzeExec verbose=true, metrics=[]
+ ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
+  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
+   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
+    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
+     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
+      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
+       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]
+```
+
+***
+
 ### execute()
 
 ```ts
@@ -149,7 +192,7 @@ fullTextSearch(query, options?): this
 
 #### Parameters
 
-* **query**: `string`
+* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
 
 * **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;
 

docs/src/js/classes/Table.md

@@ -117,8 +117,8 @@ wish to return to standard mode, call `checkoutLatest`.
 
 #### Parameters
 
-* **version**: `number`
+* **version**: `string` \| `number`
-    The version to checkout
+    The version to checkout, could be version number or tag
 
 #### Returns
 
@@ -454,6 +454,28 @@ Modeled after ``VACUUM`` in PostgreSQL.
 
 ***
 
+### prewarmIndex()
+
+```ts
+abstract prewarmIndex(name): Promise<void>
+```
+
+Prewarm an index in the table.
+
+#### Parameters
+
+* **name**: `string`
+    The name of the index.
+    This will load the index into memory.  This may reduce the cold-start time for
+    future queries.  If the index does not fit in the cache then this call may be
+    wasteful.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
 ### query()
 
 ```ts
@@ -575,7 +597,7 @@ of the given query
 
 #### Parameters
 
-* **query**: `string` \| [`IntoVector`](../type-aliases/IntoVector.md)
+* **query**: `string` \| [`IntoVector`](../type-aliases/IntoVector.md) \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
     the query, a vector or string
 
 * **queryType?**: `string`
@@ -593,6 +615,50 @@ of the given query
 
 ***
 
+### stats()
+
+```ts
+abstract stats(): Promise<TableStatistics>
+```
+
+Returns table and fragment statistics
+
+#### Returns
+
+`Promise`&lt;[`TableStatistics`](../interfaces/TableStatistics.md)&gt;
+
+The table and fragment statistics
+
+***
+
+### tags()
+
+```ts
+abstract tags(): Promise<Tags>
+```
+
+Get a tags manager for this table.
+
+Tags allow you to label specific versions of a table with a human-readable name.
+The returned tags manager can be used to list, create, update, or delete tags.
+
+#### Returns
+
+`Promise`&lt;[`Tags`](Tags.md)&gt;
+
+A tags manager for this table
+
+#### Example
+
+```typescript
+const tagsManager = await table.tags();
+await tagsManager.create("v1", 1);
+const tags = await tagsManager.list();
+console.log(tags); // { "v1": { version: 1, manifestSize: ... } }
+```
+
+***
+
 ### toArrow()
 
 ```ts
@@ -731,3 +797,26 @@ Retrieve the version of the table
 #### Returns
 
 `Promise`&lt;`number`&gt;
+
+***
+
+### waitForIndex()
+
+```ts
+abstract waitForIndex(indexNames, timeoutSeconds): Promise<void>
+```
+
+Waits for asynchronous indexing to complete on the table.
+
+#### Parameters
+
+* **indexNames**: `string`[]
+    The name of the indices to wait for
+
+* **timeoutSeconds**: `number`
+    The number of seconds to wait before timing out
+    This will raise an error if the indices are not created and fully indexed within the timeout.
+
+#### Returns
+
+`Promise`&lt;`void`&gt;

docs/src/js/classes/TagContents.md

@@ -0,0 +1,35 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / TagContents
+
+# Class: TagContents
+
+## Constructors
+
+### new TagContents()
+
+```ts
+new TagContents(): TagContents
+```
+
+#### Returns
+
+[`TagContents`](TagContents.md)
+
+## Properties
+
+### manifestSize
+
+```ts
+manifestSize: number;
+```
+
+***
+
+### version
+
+```ts
+version: number;
+```

docs/src/js/classes/Tags.md

@@ -0,0 +1,99 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / Tags
+
+# Class: Tags
+
+## Constructors
+
+### new Tags()
+
+```ts
+new Tags(): Tags
+```
+
+#### Returns
+
+[`Tags`](Tags.md)
+
+## Methods
+
+### create()
+
+```ts
+create(tag, version): Promise<void>
+```
+
+#### Parameters
+
+* **tag**: `string`
+
+* **version**: `number`
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
+### delete()
+
+```ts
+delete(tag): Promise<void>
+```
+
+#### Parameters
+
+* **tag**: `string`
+
+#### Returns
+
+`Promise`&lt;`void`&gt;
+
+***
+
+### getVersion()
+
+```ts
+getVersion(tag): Promise<number>
+```
+
+#### Parameters
+
+* **tag**: `string`
+
+#### Returns
+
+`Promise`&lt;`number`&gt;
+
+***
+
+### list()
+
+```ts
+list(): Promise<Record<string, TagContents>>
+```
+
+#### Returns
+
+`Promise`&lt;`Record`&lt;`string`, [`TagContents`](TagContents.md)&gt;&gt;
+
+***
+
+### update()
+
+```ts
+update(tag, version): Promise<void>
+```
+
+#### Parameters
+
+* **tag**: `string`
+
+* **version**: `number`
+
+#### Returns
+
+`Promise`&lt;`void`&gt;

docs/src/js/classes/VectorQuery.md

@@ -48,6 +48,53 @@ addQueryVector(vector): VectorQuery
 
 ***
 
+### analyzePlan()
+
+```ts
+analyzePlan(): Promise<string>
+```
+
+Executes the query and returns the physical query plan annotated with runtime metrics.
+
+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.
+
+#### Returns
+
+`Promise`&lt;`string`&gt;
+
+A query execution plan with runtime metrics for each step.
+
+#### Example
+
+```ts
+import * as lancedb from "@lancedb/lancedb"
+
+const db = await lancedb.connect("./.lancedb");
+const table = await db.createTable("my_table", [
+  { vector: [1.1, 0.9], id: "1" },
+]);
+
+const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
+
+Example output (with runtime metrics inlined):
+AnalyzeExec verbose=true, metrics=[]
+ ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
+  Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
+   CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
+    GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
+     FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
+      SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
+       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]
+```
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`analyzePlan`](QueryBase.md#analyzeplan)
+
+***
+
 ### bypassVectorIndex()
 
 ```ts
@@ -300,7 +347,7 @@ fullTextSearch(query, options?): this
 
 #### Parameters
 
-* **query**: `string`
+* **query**: `string` \| [`FullTextQuery`](../interfaces/FullTextQuery.md)
 
 * **options?**: `Partial`&lt;[`FullTextSearchOptions`](../interfaces/FullTextSearchOptions.md)&gt;
 

docs/src/js/enumerations/FullTextQueryType.md

@@ -0,0 +1,46 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / FullTextQueryType
+
+# Enumeration: FullTextQueryType
+
+Enum representing the types of full-text queries supported.
+
+- `Match`: Performs a full-text search for terms in the query string.
+- `MatchPhrase`: Searches for an exact phrase match in the text.
+- `Boost`: Boosts the relevance score of specific terms in the query.
+- `MultiMatch`: Searches across multiple fields for the query terms.
+
+## Enumeration Members
+
+### Boost
+
+```ts
+Boost: "boost";
+```
+
+***
+
+### Match
+
+```ts
+Match: "match";
+```
+
+***
+
+### MatchPhrase
+
+```ts
+MatchPhrase: "match_phrase";
+```
+
+***
+
+### MultiMatch
+
+```ts
+MultiMatch: "multi_match";
+```

docs/src/js/globals.md

@@ -9,16 +9,26 @@
 - [embedding](namespaces/embedding/README.md)
 - [rerankers](namespaces/rerankers/README.md)
 
+## Enumerations
+
+- [FullTextQueryType](enumerations/FullTextQueryType.md)
+
 ## Classes
 
+- [BoostQuery](classes/BoostQuery.md)
 - [Connection](classes/Connection.md)
 - [Index](classes/Index.md)
 - [MakeArrowTableOptions](classes/MakeArrowTableOptions.md)
+- [MatchQuery](classes/MatchQuery.md)
 - [MergeInsertBuilder](classes/MergeInsertBuilder.md)
+- [MultiMatchQuery](classes/MultiMatchQuery.md)
+- [PhraseQuery](classes/PhraseQuery.md)
 - [Query](classes/Query.md)
 - [QueryBase](classes/QueryBase.md)
 - [RecordBatchIterator](classes/RecordBatchIterator.md)
 - [Table](classes/Table.md)
+- [TagContents](classes/TagContents.md)
+- [Tags](classes/Tags.md)
 - [VectorColumnOptions](classes/VectorColumnOptions.md)
 - [VectorQuery](classes/VectorQuery.md)
 
@@ -32,7 +42,10 @@
 - [ConnectionOptions](interfaces/ConnectionOptions.md)
 - [CreateTableOptions](interfaces/CreateTableOptions.md)
 - [ExecutableQuery](interfaces/ExecutableQuery.md)
+- [FragmentStatistics](interfaces/FragmentStatistics.md)
+- [FragmentSummaryStats](interfaces/FragmentSummaryStats.md)
 - [FtsOptions](interfaces/FtsOptions.md)
+- [FullTextQuery](interfaces/FullTextQuery.md)
 - [FullTextSearchOptions](interfaces/FullTextSearchOptions.md)
 - [HnswPqOptions](interfaces/HnswPqOptions.md)
 - [HnswSqOptions](interfaces/HnswSqOptions.md)
@@ -41,6 +54,7 @@
 - [IndexStatistics](interfaces/IndexStatistics.md)
 - [IvfFlatOptions](interfaces/IvfFlatOptions.md)
 - [IvfPqOptions](interfaces/IvfPqOptions.md)
+- [MergeStats](interfaces/MergeStats.md)
 - [OpenTableOptions](interfaces/OpenTableOptions.md)
 - [OptimizeOptions](interfaces/OptimizeOptions.md)
 - [OptimizeStats](interfaces/OptimizeStats.md)
@@ -48,6 +62,7 @@
 - [RemovalStats](interfaces/RemovalStats.md)
 - [RetryConfig](interfaces/RetryConfig.md)
 - [TableNamesOptions](interfaces/TableNamesOptions.md)
+- [TableStatistics](interfaces/TableStatistics.md)
 - [TimeoutConfig](interfaces/TimeoutConfig.md)
 - [UpdateOptions](interfaces/UpdateOptions.md)
 - [Version](interfaces/Version.md)

docs/src/js/interfaces/FragmentStatistics.md

@@ -0,0 +1,37 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / FragmentStatistics
+
+# Interface: FragmentStatistics
+
+## Properties
+
+### lengths
+
+```ts
+lengths: FragmentSummaryStats;
+```
+
+Statistics on the number of rows in the table fragments
+
+***
+
+### numFragments
+
+```ts
+numFragments: number;
+```
+
+The number of fragments in the table
+
+***
+
+### numSmallFragments
+
+```ts
+numSmallFragments: number;
+```
+
+The number of uncompacted fragments in the table

docs/src/js/interfaces/FragmentSummaryStats.md

@@ -0,0 +1,77 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / FragmentSummaryStats
+
+# Interface: FragmentSummaryStats
+
+## Properties
+
+### max
+
+```ts
+max: number;
+```
+
+The number of rows in the fragment with the most rows
+
+***
+
+### mean
+
+```ts
+mean: number;
+```
+
+The mean number of rows in the fragments
+
+***
+
+### min
+
+```ts
+min: number;
+```
+
+The number of rows in the fragment with the fewest rows
+
+***
+
+### p25
+
+```ts
+p25: number;
+```
+
+The 25th percentile of number of rows in the fragments
+
+***
+
+### p50
+
+```ts
+p50: number;
+```
+
+The 50th percentile of number of rows in the fragments
+
+***
+
+### p75
+
+```ts
+p75: number;
+```
+
+The 75th percentile of number of rows in the fragments
+
+***
+
+### p99
+
+```ts
+p99: number;
+```
+
+The 99th percentile of number of rows in the fragments

docs/src/js/interfaces/FullTextQuery.md

@@ -0,0 +1,25 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / FullTextQuery
+
+# Interface: FullTextQuery
+
+Represents a full-text query interface.
+This interface defines the structure and behavior for full-text queries,
+including methods to retrieve the query type and convert the query to a dictionary format.
+
+## Methods
+
+### queryType()
+
+```ts
+queryType(): FullTextQueryType
+```
+
+The type of the full-text query.
+
+#### Returns
+
+[`FullTextQueryType`](../enumerations/FullTextQueryType.md)

docs/src/js/interfaces/IndexOptions.md

@@ -39,3 +39,11 @@ and the same name, then an error will be returned.  This is true even if
 that index is out of date.
 
 The default is true
+
+***
+
+### waitTimeoutSeconds?
+
+```ts
+optional waitTimeoutSeconds: number;
+```

docs/src/js/interfaces/MergeStats.md

@@ -0,0 +1,31 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / MergeStats
+
+# Interface: MergeStats
+
+## Properties
+
+### numDeletedRows
+
+```ts
+numDeletedRows: bigint;
+```
+
+***
+
+### numInsertedRows
+
+```ts
+numInsertedRows: bigint;
+```
+
+***
+
+### numUpdatedRows
+
+```ts
+numUpdatedRows: bigint;
+```

docs/src/js/interfaces/QueryExecutionOptions.md

@@ -20,3 +20,13 @@ The maximum number of rows to return in a single batch
 
 Batches may have fewer rows if the underlying data is stored
 in smaller chunks.
+
+***
+
+### timeoutMs?
+
+```ts
+optional timeoutMs: number;
+```
+
+Timeout for query execution in milliseconds

docs/src/js/interfaces/TableStatistics.md

@@ -0,0 +1,47 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / TableStatistics
+
+# Interface: TableStatistics
+
+## Properties
+
+### fragmentStats
+
+```ts
+fragmentStats: FragmentStatistics;
+```
+
+Statistics on table fragments
+
+***
+
+### numIndices
+
+```ts
+numIndices: number;
+```
+
+The number of indices in the table
+
+***
+
+### numRows
+
+```ts
+numRows: number;
+```
+
+The number of rows in the table
+
+***
+
+### totalBytes
+
+```ts
+totalBytes: number;
+```
+
+The total number of bytes in the table

docs/src/quickstart.md

@@ -0,0 +1,101 @@
+
+# Getting Started with LanceDB: A Minimal Vector Search Tutorial
+
+Let's set up a LanceDB database, insert vector data, and perform a simple vector search. We'll use simple character classes like "knight" and "rogue" to illustrate semantic relevance.
+
+## 1. Install Dependencies
+
+Before starting, make sure you have the necessary packages:
+
+```bash
+pip install lancedb pandas numpy
+```
+
+## 2. Import Required Libraries
+
+```python
+import lancedb
+import pandas as pd
+import numpy as np
+```
+
+## 3. Connect to LanceDB
+
+You can use a local directory to store your database:
+
+```python
+db = lancedb.connect("./lancedb")
+```
+
+## 4. Create Sample Data
+
+Add sample text data and corresponding 4D vectors:
+
+```python
+data = pd.DataFrame([
+    {"id": "1", "vector": [1.0, 0.0, 0.0, 0.0], "text": "knight"},
+    {"id": "2", "vector": [0.9, 0.1, 0.0, 0.0], "text": "warrior"},
+    {"id": "3", "vector": [0.0, 1.0, 0.0, 0.0], "text": "rogue"},
+    {"id": "4", "vector": [0.0, 0.9, 0.1, 0.0], "text": "thief"},
+    {"id": "5", "vector": [0.5, 0.5, 0.0, 0.0], "text": "ranger"},
+])
+```
+
+## 5. Create a Table in LanceDB
+
+```python
+table = db.create_table("rpg_classes", data=data, mode="overwrite")
+```
+
+Let's see how the table looks:
+```python
+print(data)
+```
+
+| id | vector | text |
+|----|--------|------|
+| 1 | [1.0, 0.0, 0.0, 0.0] | knight |
+| 2 | [0.9, 0.1, 0.0, 0.0] | warrior |
+| 3 | [0.0, 1.0, 0.0, 0.0] | rogue |
+| 4 | [0.0, 0.9, 0.1, 0.0] | thief |
+| 5 | [0.5, 0.5, 0.0, 0.0] | ranger |
+
+
+
+## 6. Perform a Vector Search
+
+Search for the most similar character classes to our query vector:
+
+```python
+# Query as if we are searching for "rogue"
+results = table.search([0.95, 0.05, 0.0, 0.0]).limit(3).to_df()
+print(results)
+```
+
+This will return the top 3 closest classes to the vector, effectively showing how LanceDB can be used for semantic search.
+
+|  id  |          vector         |   text   | _distance |
+|------|------------------------|----------|-----------|
+|  3   | [0.0, 1.0, 0.0, 0.0]  |  rogue   |   0.00   |
+|  4   | [0.0, 0.9, 0.1, 0.0]  | thief |   0.02   |
+|  5   | [0.5, 0.5, 0.0, 0.0]  |  ranger  |   0.50   |
+
+Let's try searching for "knight"
+
+```python
+query_vector = [1.0, 0.0, 0.0, 0.0]
+results = table.search(query_vector).limit(3).to_pandas()
+print(results)
+```
+
+|  id  |          vector         |   text   | _distance |
+|------|------------------------|----------|-----------|
+|  1   | [1.0, 0.0, 0.0, 0.0]  |  knight  |   0.00   |
+|  2   | [0.9, 0.1, 0.0, 0.0]  | warrior  |   0.02   |
+|  5   | [0.5, 0.5, 0.0, 0.0]  |  ranger  |   0.50   |
+
+## Next Steps
+
+That's it - you just conducted vector search!
+
+For more beginner tips, check out the [Basic Usage](basic.md) guide.

docs/src/troubleshooting.md

@@ -8,6 +8,10 @@ For trouble shooting, the best place to ask is in our Discord, under the relevan
 language channel. By asking in the language-specific channel, it makes it more
 likely that someone who knows the answer will see your question.
 
+## Common issues
+
+* Multiprocessing with `fork` is not supported. You should use `spawn` instead.
+
 ## Enabling logging
 
 To provide more information, especially for LanceDB Cloud related issues, enable
@@ -31,3 +35,9 @@ print the resolved query plan. You can use the `explain_plan` method to do this:
 * Python Sync: [LanceQueryBuilder.explain_plan][lancedb.query.LanceQueryBuilder.explain_plan]
 * Python Async: [AsyncQueryBase.explain_plan][lancedb.query.AsyncQueryBase.explain_plan]
 * Node @lancedb/lancedb: [LanceQueryBuilder.explainPlan](/lancedb/js/classes/QueryBase/#explainplan)
+
+To understand how a query was actually executed—including metrics like execution time, number of rows processed, I/O stats, and more—use the analyze_plan method. This executes the query and returns a physical execution plan annotated with runtime metrics, making it especially helpful for performance tuning and debugging.
+
+* Python Sync: [LanceQueryBuilder.analyze_plan][lancedb.query.LanceQueryBuilder.analyze_plan]
+* Python Async: [AsyncQueryBase.analyze_plan][lancedb.query.AsyncQueryBase.analyze_plan]
+* Node @lancedb/lancedb: [LanceQueryBuilder.analyzePlan](/lancedb/js/classes/QueryBase/#analyzePlan)

java/.gitignore

@@ -0,0 +1,3 @@
+*.iml
+.java-version
+

java/core/pom.xml

@@ -8,13 +8,16 @@
     <parent>
         <groupId>com.lancedb</groupId>
         <artifactId>lancedb-parent</artifactId>
-        <version>0.18.1-final.0</version>
+        <version>0.19.1-beta.1</version>
         <relativePath>../pom.xml</relativePath>
     </parent>
 
     <artifactId>lancedb-core</artifactId>
     <name>LanceDB Core</name>
     <packaging>jar</packaging>
+    <properties>
+        <rust.release.build>false</rust.release.build>
+    </properties>
 
     <dependencies>
         <dependency>
@@ -68,7 +71,7 @@
                                 </goals>
                                 <configuration>
                                     <path>lancedb-jni</path>
-                                    <release>true</release>
+                                    <release>${rust.release.build}</release>
                                     <!-- Copy native libraries to target/classes for runtime access -->
                                     <copyTo>${project.build.directory}/classes/nativelib</copyTo>
                                     <copyWithPlatformDir>true</copyWithPlatformDir>

java/core/src/main/java/com/lancedb/lancedb/Connection.java

@@ -1,16 +1,25 @@
-// SPDX-License-Identifier: Apache-2.0
+/*
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+ * Licensed under the Apache License, Version 2.0 (the "License");
-
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
 package com.lancedb.lancedb;
 
 import io.questdb.jar.jni.JarJniLoader;
+
 import java.io.Closeable;
 import java.util.List;
 import java.util.Optional;
 
-/**
+/** Represents LanceDB database. */
- * Represents LanceDB database.
- */
 public class Connection implements Closeable {
   static {
     JarJniLoader.loadLib(Connection.class, "/nativelib", "lancedb_jni");
@@ -18,14 +27,11 @@ public class Connection implements Closeable {
 
   private long nativeConnectionHandle;
 
-  /**
+  /** Connect to a LanceDB instance. */
-   * Connect to a LanceDB instance.
-   */
   public static native Connection connect(String uri);
 
   /**
-   * Get the names of all tables in the database. The names are sorted in
+   * Get the names of all tables in the database. The names are sorted in ascending order.
-   * ascending order.
    *
    * @return the table names
    */
@@ -34,8 +40,7 @@ public class Connection implements Closeable {
   }
 
   /**
-   * Get the names of filtered tables in the database. The names are sorted in
+   * Get the names of filtered tables in the database. The names are sorted in ascending order.
-   * ascending order.
    *
    * @param limit The number of results to return.
    * @return the table names
@@ -45,12 +50,11 @@ public class Connection implements Closeable {
   }
 
   /**
-   * Get the names of filtered tables in the database. The names are sorted in
+   * Get the names of filtered tables in the database. The names are sorted in ascending order.
-   * ascending order.
    *
    * @param startAfter If present, only return names that come lexicographically after the supplied
-   *                   value. This can be combined with limit to implement pagination
+   *     value. This can be combined with limit to implement pagination by setting this to the last
-   *                   by setting this to the last table name from the previous page.
+   *     table name from the previous page.
    * @return the table names
    */
   public List<String> tableNames(String startAfter) {
@@ -58,12 +62,11 @@ public class Connection implements Closeable {
   }
 
   /**
-   * Get the names of filtered tables in the database. The names are sorted in
+   * Get the names of filtered tables in the database. The names are sorted in ascending order.
-   * ascending order.
    *
    * @param startAfter If present, only return names that come lexicographically after the supplied
-   *                   value. This can be combined with limit to implement pagination
+   *     value. This can be combined with limit to implement pagination by setting this to the last
-   *                   by setting this to the last table name from the previous page.
+   *     table name from the previous page.
    * @param limit The number of results to return.
    * @return the table names
    */
@@ -72,22 +75,19 @@ public class Connection implements Closeable {
   }
 
   /**
-   * Get the names of filtered tables in the database. The names are sorted in
+   * Get the names of filtered tables in the database. The names are sorted in ascending order.
-   * ascending order.
    *
    * @param startAfter If present, only return names that come lexicographically after the supplied
-   *                   value. This can be combined with limit to implement pagination
+   *     value. This can be combined with limit to implement pagination by setting this to the last
-   *                   by setting this to the last table name from the previous page.
+   *     table name from the previous page.
    * @param limit The number of results to return.
    * @return the table names
    */
-  public native List<String> tableNames(
+  public native List<String> tableNames(Optional<String> startAfter, Optional<Integer> limit);
-      Optional<String> startAfter, Optional<Integer> limit);
 
   /**
-   * Closes this connection and releases any system resources associated with it. If
+   * Closes this connection and releases any system resources associated with it. If the connection
-   * the connection is
+   * is already closed, then invoking this method has no effect.
-   * already closed, then invoking this method has no effect.
    */
   @Override
   public void close() {
@@ -98,8 +98,7 @@ public class Connection implements Closeable {
   }
 
   /**
-   * Native method to release the Lance connection resources associated with the
+   * Native method to release the Lance connection resources associated with the given handle.
-   * given handle.
    *
    * @param handle The native handle to the connection resource.
    */

java/core/src/test/java/com/lancedb/lancedb/ConnectionTest.java

@@ -1,27 +1,35 @@
-// SPDX-License-Identifier: Apache-2.0
+/*
-// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
 package com.lancedb.lancedb;
 
-import static org.junit.jupiter.api.Assertions.assertEquals;
-import static org.junit.jupiter.api.Assertions.assertTrue;
-
-import java.nio.file.Path;
-import java.util.List;
-import java.net.URL;
 import org.junit.jupiter.api.BeforeAll;
 import org.junit.jupiter.api.Test;
 import org.junit.jupiter.api.io.TempDir;
 
+import java.net.URL;
+import java.nio.file.Path;
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
 public class ConnectionTest {
   private static final String[] TABLE_NAMES = {
-      "dataset_version",
+    "dataset_version", "new_empty_dataset", "test", "write_stream"
-      "new_empty_dataset",
-      "test",
-      "write_stream"
   };
 
-  @TempDir
+  @TempDir static Path tempDir; // Temporary directory for the tests
-  static Path tempDir; // Temporary directory for the tests
   private static URL lanceDbURL;
 
   @BeforeAll
@@ -53,18 +61,21 @@ public class ConnectionTest {
   @Test
   void tableNamesStartAfter() {
     try (Connection conn = Connection.connect(lanceDbURL.toString())) {
-      assertTableNamesStartAfter(conn, TABLE_NAMES[0], 3, TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
+      assertTableNamesStartAfter(
+          conn, TABLE_NAMES[0], 3, TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
       assertTableNamesStartAfter(conn, TABLE_NAMES[1], 2, TABLE_NAMES[2], TABLE_NAMES[3]);
       assertTableNamesStartAfter(conn, TABLE_NAMES[2], 1, TABLE_NAMES[3]);
       assertTableNamesStartAfter(conn, TABLE_NAMES[3], 0);
-      assertTableNamesStartAfter(conn, "a_dataset", 4, TABLE_NAMES[0], TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
+      assertTableNamesStartAfter(
+          conn, "a_dataset", 4, TABLE_NAMES[0], TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
       assertTableNamesStartAfter(conn, "o_dataset", 2, TABLE_NAMES[2], TABLE_NAMES[3]);
       assertTableNamesStartAfter(conn, "v_dataset", 1, TABLE_NAMES[3]);
       assertTableNamesStartAfter(conn, "z_dataset", 0);
     }
   }
 
-  private void assertTableNamesStartAfter(Connection conn, String startAfter, int expectedSize, String... expectedNames) {
+  private void assertTableNamesStartAfter(
+      Connection conn, String startAfter, int expectedSize, String... expectedNames) {
     List<String> tableNames = conn.tableNames(startAfter);
     assertEquals(expectedSize, tableNames.size());
     for (int i = 0; i < expectedNames.length; i++) {

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.18.1-final.0</version>
+    <version>0.19.1-beta.1</version>
     <packaging>pom</packaging>
 
     <name>LanceDB Parent</name>
@@ -29,6 +29,25 @@
     <properties>
         <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
         <arrow.version>15.0.0</arrow.version>
+        <spotless.skip>false</spotless.skip>
+        <spotless.version>2.30.0</spotless.version>
+        <spotless.java.googlejavaformat.version>1.7</spotless.java.googlejavaformat.version>
+        <spotless.delimiter>package</spotless.delimiter>
+        <spotless.license.header>
+            /*
+            * Licensed under the Apache License, Version 2.0 (the "License");
+            * you may not use this file except in compliance with the License.
+            * You may obtain a copy of the License at
+            *
+            *     http://www.apache.org/licenses/LICENSE-2.0
+            *
+            * Unless required by applicable law or agreed to in writing, software
+            * distributed under the License is distributed on an "AS IS" BASIS,
+            * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+            * See the License for the specific language governing permissions and
+            * limitations under the License.
+            */
+        </spotless.license.header>
     </properties>
 
     <modules>
@@ -127,7 +146,8 @@
                 <configuration>
                     <configLocation>google_checks.xml</configLocation>
                     <consoleOutput>true</consoleOutput>
-                    <failsOnError>true</failsOnError>
+                    <failsOnError>false</failsOnError>
+                    <failOnViolation>false</failOnViolation>
                     <violationSeverity>warning</violationSeverity>
                     <linkXRef>false</linkXRef>
                 </configuration>
@@ -141,6 +161,10 @@
                     </execution>
                 </executions>
             </plugin>
+            <plugin>
+                <groupId>com.diffplug.spotless</groupId>
+                <artifactId>spotless-maven-plugin</artifactId>
+            </plugin>
         </plugins>
         <pluginManagement>
             <plugins>
@@ -166,7 +190,6 @@
                     <artifactId>maven-surefire-plugin</artifactId>
                     <version>3.2.5</version>
                     <configuration>
-                        <argLine>--add-opens=java.base/java.nio=ALL-UNNAMED</argLine>
                         <forkNode
                             implementation="org.apache.maven.plugin.surefire.extensions.SurefireForkNodeFactory" />
                         <useSystemClassLoader>false</useSystemClassLoader>
@@ -180,6 +203,54 @@
                     <artifactId>maven-install-plugin</artifactId>
                     <version>2.5.2</version>
                 </plugin>
+                <plugin>
+                    <groupId>com.diffplug.spotless</groupId>
+                    <artifactId>spotless-maven-plugin</artifactId>
+                    <version>${spotless.version}</version>
+                    <configuration>
+                        <skip>${spotless.skip}</skip>
+                        <upToDateChecking>
+                            <enabled>true</enabled>
+                        </upToDateChecking>
+                        <java>
+                            <includes>
+                                <include>src/main/java/**/*.java</include>
+                                <include>src/test/java/**/*.java</include>
+                            </includes>
+                            <googleJavaFormat>
+                                <version>${spotless.java.googlejavaformat.version}</version>
+                                <style>GOOGLE</style>
+                            </googleJavaFormat>
+
+                            <importOrder>
+                                <order>com.lancedb.lance,,javax,java,\#</order>
+                            </importOrder>
+
+                            <removeUnusedImports />
+                        </java>
+                        <scala>
+                            <includes>
+                                <include>src/main/scala/**/*.scala</include>
+                                <include>src/main/scala-*/**/*.scala</include>
+                                <include>src/test/scala/**/*.scala</include>
+                                <include>src/test/scala-*/**/*.scala</include>
+                            </includes>
+                        </scala>
+                        <licenseHeader>
+                            <content>${spotless.license.header}</content>
+                            <delimiter>${spotless.delimiter}</delimiter>
+                        </licenseHeader>
+                    </configuration>
+                    <executions>
+                        <execution>
+                            <id>spotless-check</id>
+                            <phase>validate</phase>
+                            <goals>
+                                <goal>apply</goal>
+                            </goals>
+                        </execution>
+                    </executions>
+                </plugin>
             </plugins>
         </pluginManagement>
     </build>

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.18.1",
+  "version": "0.19.1-beta.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.18.1",
+      "version": "0.19.1-beta.1",
       "cpu": [
         "x64",
         "arm64"
@@ -52,11 +52,11 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.18.1",
+        "@lancedb/vectordb-darwin-arm64": "0.19.1-beta.1",
-        "@lancedb/vectordb-darwin-x64": "0.18.1",
+        "@lancedb/vectordb-darwin-x64": "0.19.1-beta.1",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.18.1",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.19.1-beta.1",
-        "@lancedb/vectordb-linux-x64-gnu": "0.18.1",
+        "@lancedb/vectordb-linux-x64-gnu": "0.19.1-beta.1",
-        "@lancedb/vectordb-win32-x64-msvc": "0.18.1"
+        "@lancedb/vectordb-win32-x64-msvc": "0.19.1-beta.1"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",
@@ -327,9 +327,9 @@
       }
     },
     "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.18.1",
+      "version": "0.19.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.18.1.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.19.1-beta.1.tgz",
-      "integrity": "sha512-LAuaIfANAingnc4yxZ142kidY6KDkTzWDVidfG4847MO/eKk029A8zbhxkMHdSyTe1cNTBr7fYDfc7/LtHrhFQ==",
+      "integrity": "sha512-Epvel0pF5TM6MtIWQ2KhqezqSSHTL3Wr7a2rGAwz6X/XY23i6DbMPpPs0HyeIDzDrhxNfE3cz3S+SiCA6xpR0g==",
       "cpu": [
         "arm64"
       ],
@@ -340,9 +340,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.18.1",
+      "version": "0.19.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.18.1.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.19.1-beta.1.tgz",
-      "integrity": "sha512-6weTuzYZNv0z5vX7D6TSjDG47anw9NQ6cqxXTiZp2u99qaiuKj1EVxsRrDNIknspqKQ+YKwOLKwRum5VuLnHkQ==",
+      "integrity": "sha512-hOiUSlIoISbiXytp46hToi/r6sF5pImAsfbzCsIq8ExDV4TPa8fjbhcIT80vxxOwc2mpSSK4HsVJYod95RSbEQ==",
       "cpu": [
         "x64"
       ],
@@ -353,9 +353,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.18.1",
+      "version": "0.19.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.18.1.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.19.1-beta.1.tgz",
-      "integrity": "sha512-MowuP2eVNx9p0Fw6qi9O20liKxUy3c3YugOVs0eo8h1fpbSlXx2bIA40JgyzIjiX0QICIt9IzLCideIZduPc1A==",
+      "integrity": "sha512-/1JhGVDEngwrlM8o2TNW8G6nJ9U/VgHKAORmj/cTA7O30helJIoo9jfvUAUy+vZ4VoEwRXQbMI+gaYTg0l3MTg==",
       "cpu": [
         "arm64"
       ],
@@ -366,9 +366,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.18.1",
+      "version": "0.19.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.18.1.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.19.1-beta.1.tgz",
-      "integrity": "sha512-E9Qtdstr5aTMuRzi4WdKAvQKrNaXuCdiUYph2D4tanawftn3cZVHjbQu63nRphmBKAqzuJ4vcIsYLnFbzM/CtA==",
+      "integrity": "sha512-zNRGSSUt8nTJMmll4NdxhQjwxR8Rezq3T4dsRoiDts5ienMam5HFjYiZ3FkDZQo16rgq2BcbFuH1G8u1chywlg==",
       "cpu": [
         "x64"
       ],
@@ -379,9 +379,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.18.1",
+      "version": "0.19.1-beta.1",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.18.1.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.19.1-beta.1.tgz",
-      "integrity": "sha512-ILXHvq2s28U6I6mv1UK9LRuXGMXKTidztlRGDUeSCLBKQy033xOUnBBIOMQiS1IDsFMcJTGLTvr5nPemKI2NyQ==",
+      "integrity": "sha512-yV550AJGlsIFdm1KoHQPJ1TZx121ZXCIdebBtBZj3wOObIhyB/i0kZAtGvwjkmr7EYyfzt1EHZzbjSGVdehIAA==",
       "cpu": [
         "x64"
       ],
@@ -1184,9 +1184,10 @@
       }
     },
     "node_modules/axios": {
-      "version": "1.7.7",
+      "version": "1.8.4",
-      "resolved": "https://registry.npmjs.org/axios/-/axios-1.7.7.tgz",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.8.4.tgz",
-      "integrity": "sha512-S4kL7XrjgBmvdGut0sN3yJxqYzrDOnivkBiN0OFs6hLiUam3UPvswUo0kqGyhqUZGEOytHyumEdXsAkgCOUf3Q==",
+      "integrity": "sha512-eBSYY4Y68NNlHbHBMdeDmKNtDgXWhQsJcGqzO3iLUM0GraQFSS9cVgPX5I9b3lbdFKyYoAEGAZF1DwhTaljNAw==",
+      "license": "MIT",
       "dependencies": {
         "follow-redirects": "^1.15.6",
         "form-data": "^4.0.0",

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.18.1",
+  "version": "0.19.1-beta.1",
   "description": " Serverless, low-latency vector database for AI applications",
   "private": false,
   "main": "dist/index.js",
@@ -89,10 +89,10 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-x64": "0.18.1",
+    "@lancedb/vectordb-darwin-x64": "0.19.1-beta.1",
-    "@lancedb/vectordb-darwin-arm64": "0.18.1",
+    "@lancedb/vectordb-darwin-arm64": "0.19.1-beta.1",
-    "@lancedb/vectordb-linux-x64-gnu": "0.18.1",
+    "@lancedb/vectordb-linux-x64-gnu": "0.19.1-beta.1",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.18.1",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.19.1-beta.1",
-    "@lancedb/vectordb-win32-x64-msvc": "0.18.1"
+    "@lancedb/vectordb-win32-x64-msvc": "0.19.1-beta.1"
   }
 }

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.18.1"
+version = "0.19.1-beta.1"
 license.workspace = true
 description.workspace = true
 repository.workspace = true
@@ -28,6 +28,9 @@ napi-derive = "2.16.4"
 lzma-sys = { version = "*", features = ["static"] }
 log.workspace = true
 
+# Workaround for build failure until we can fix it.
+aws-lc-sys = "=0.28.0"
+
 [build-dependencies]
 napi-build = "2.1"
 

nodejs/__test__/arrow.test.ts

@@ -374,6 +374,71 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
         expect(table2.numRows).toBe(4);
         expect(table2.schema).toEqual(schema);
       });
+
+      it("should correctly retain values in nested struct fields", async function () {
+        // Define test data with nested struct
+        const testData = [
+          {
+            id: "doc1",
+            vector: [1, 2, 3],
+            metadata: {
+              filePath: "/path/to/file1.ts",
+              startLine: 10,
+              endLine: 20,
+              text: "function test() { return true; }",
+            },
+          },
+          {
+            id: "doc2",
+            vector: [4, 5, 6],
+            metadata: {
+              filePath: "/path/to/file2.ts",
+              startLine: 30,
+              endLine: 40,
+              text: "function test2() { return false; }",
+            },
+          },
+        ];
+
+        // Create Arrow table from the data
+        const table = makeArrowTable(testData);
+
+        // Verify schema has the nested struct fields
+        const metadataField = table.schema.fields.find(
+          (f) => f.name === "metadata",
+        );
+        expect(metadataField).toBeDefined();
+        // biome-ignore lint/suspicious/noExplicitAny: accessing fields in different Arrow versions
+        const childNames = metadataField?.type.children.map((c: any) => c.name);
+        expect(childNames).toEqual([
+          "filePath",
+          "startLine",
+          "endLine",
+          "text",
+        ]);
+
+        // Convert to buffer and back (simulating storage and retrieval)
+        const buf = await fromTableToBuffer(table);
+        const retrievedTable = tableFromIPC(buf);
+
+        // Verify the retrieved table has the same structure
+        const rows = [];
+        for (let i = 0; i < retrievedTable.numRows; i++) {
+          rows.push(retrievedTable.get(i));
+        }
+
+        // Check values in the first row
+        const firstRow = rows[0];
+        expect(firstRow.id).toBe("doc1");
+        expect(firstRow.vector.toJSON()).toEqual([1, 2, 3]);
+
+        // Verify metadata values are preserved (this is where the bug is)
+        expect(firstRow.metadata).toBeDefined();
+        expect(firstRow.metadata.filePath).toBe("/path/to/file1.ts");
+        expect(firstRow.metadata.startLine).toBe(10);
+        expect(firstRow.metadata.endLine).toBe(20);
+        expect(firstRow.metadata.text).toBe("function test() { return true; }");
+      });
     });
 
     class DummyEmbedding extends EmbeddingFunction<string> {

nodejs/__test__/table.test.ts

@@ -10,7 +10,7 @@ import * as arrow16 from "apache-arrow-16";
 import * as arrow17 from "apache-arrow-17";
 import * as arrow18 from "apache-arrow-18";
 
-import { Table, connect } from "../lancedb";
+import { MatchQuery, PhraseQuery, Table, connect } from "../lancedb";
 import {
   Table as ArrowTable,
   Field,
@@ -33,6 +33,7 @@ import {
   register,
 } from "../lancedb/embedding";
 import { Index } from "../lancedb/indices";
+import { instanceOfFullTextQuery } from "../lancedb/query";
 
 describe.each([arrow15, arrow16, arrow17, arrow18])(
   "Given a table",
@@ -70,6 +71,29 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       await expect(table.countRows()).resolves.toBe(3);
     });
 
+    it("should show table stats", async () => {
+      await table.add([{ id: 1 }, { id: 2 }]);
+      await table.add([{ id: 1 }]);
+      await expect(table.stats()).resolves.toEqual({
+        fragmentStats: {
+          lengths: {
+            max: 2,
+            mean: 1,
+            min: 1,
+            p25: 1,
+            p50: 2,
+            p75: 2,
+            p99: 2,
+          },
+          numFragments: 2,
+          numSmallFragments: 2,
+        },
+        numIndices: 0,
+        numRows: 3,
+        totalBytes: 24,
+      });
+    });
+
     it("should overwrite data if asked", async () => {
       await table.add([{ id: 1 }, { id: 2 }]);
       await table.add([{ id: 1 }], { mode: "overwrite" });
@@ -314,11 +338,16 @@ describe("merge insert", () => {
       { a: 3, b: "y" },
       { a: 4, b: "z" },
     ];
-    await table
+    const stats = await table
       .mergeInsert("a")
       .whenMatchedUpdateAll()
       .whenNotMatchedInsertAll()
       .execute(newData);
+
+    expect(stats.numInsertedRows).toBe(1n);
+    expect(stats.numUpdatedRows).toBe(2n);
+    expect(stats.numDeletedRows).toBe(0n);
+
     const expected = [
       { a: 1, b: "a" },
       { a: 2, b: "x" },
@@ -506,6 +535,15 @@ describe("When creating an index", () => {
     expect(indices2.length).toBe(0);
   });
 
+  it("should wait for index readiness", async () => {
+    // Create an index and then wait for it to be ready
+    await tbl.createIndex("vec");
+    const indices = await tbl.listIndices();
+    expect(indices.length).toBeGreaterThan(0);
+    const idxName = indices[0].name;
+    await expect(tbl.waitForIndex([idxName], 5)).resolves.toBeUndefined();
+  });
+
   it("should search with distance range", async () => {
     await tbl.createIndex("vec");
 
@@ -633,6 +671,23 @@ describe("When creating an index", () => {
     expect(plan2).not.toMatch("LanceScan");
   });
 
+  it("should be able to run analyze plan", async () => {
+    await tbl.createIndex("vec");
+    await tbl.add([
+      {
+        id: 300,
+        vec: Array(32)
+          .fill(1)
+          .map(() => Math.random()),
+        tags: [],
+      },
+    ]);
+
+    const plan = await tbl.query().nearestTo(queryVec).analyzePlan();
+    expect(plan).toMatch("AnalyzeExec");
+    expect(plan).toMatch("metrics=");
+  });
+
   it("should be able to query with row id", async () => {
     const results = await tbl
       .query()
@@ -806,6 +861,7 @@ describe("When creating an index", () => {
     // Only build index over v1
     await tbl.createIndex("vec", {
       config: Index.ivfPq({ numPartitions: 2, numSubVectors: 2 }),
+      waitTimeoutSeconds: 30,
     });
 
     const rst = await tbl
@@ -850,6 +906,44 @@ describe("When creating an index", () => {
   });
 });
 
+describe("When querying a table", () => {
+  let tmpDir: tmp.DirResult;
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+  afterEach(() => tmpDir.removeCallback());
+
+  it("should throw an error when timeout is reached", async () => {
+    const db = await connect(tmpDir.name);
+    const data = makeArrowTable([
+      { text: "a", vector: [0.1, 0.2] },
+      { text: "b", vector: [0.3, 0.4] },
+    ]);
+    const table = await db.createTable("test", data);
+    await table.createIndex("text", { config: Index.fts() });
+
+    await expect(
+      table.query().where("text != 'a'").toArray({ timeoutMs: 0 }),
+    ).rejects.toThrow("Query timeout");
+
+    await expect(
+      table.query().nearestTo([0.0, 0.0]).toArrow({ timeoutMs: 0 }),
+    ).rejects.toThrow("Query timeout");
+
+    await expect(
+      table.search("a", "fts").toArray({ timeoutMs: 0 }),
+    ).rejects.toThrow("Query timeout");
+
+    await expect(
+      table
+        .query()
+        .nearestToText("a")
+        .nearestTo([0.0, 0.0])
+        .toArrow({ timeoutMs: 0 }),
+    ).rejects.toThrow("Query timeout");
+  });
+});
+
 describe("Read consistency interval", () => {
   let tmpDir: tmp.DirResult;
   beforeEach(() => {
@@ -1112,6 +1206,73 @@ describe("when dealing with versioning", () => {
   });
 });
 
+describe("when dealing with tags", () => {
+  let tmpDir: tmp.DirResult;
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+  afterEach(() => {
+    tmpDir.removeCallback();
+  });
+
+  it("can manage tags", async () => {
+    const conn = await connect(tmpDir.name, {
+      readConsistencyInterval: 0,
+    });
+
+    const table = await conn.createTable("my_table", [
+      { id: 1n, vector: [0.1, 0.2] },
+    ]);
+    expect(await table.version()).toBe(1);
+
+    await table.add([{ id: 2n, vector: [0.3, 0.4] }]);
+    expect(await table.version()).toBe(2);
+
+    const tagsManager = await table.tags();
+
+    const initialTags = await tagsManager.list();
+    expect(Object.keys(initialTags).length).toBe(0);
+
+    const tag1 = "tag1";
+    await tagsManager.create(tag1, 1);
+    expect(await tagsManager.getVersion(tag1)).toBe(1);
+
+    const tagsAfterFirst = await tagsManager.list();
+    expect(Object.keys(tagsAfterFirst).length).toBe(1);
+    expect(tagsAfterFirst).toHaveProperty(tag1);
+    expect(tagsAfterFirst[tag1].version).toBe(1);
+
+    await tagsManager.create("tag2", 2);
+    expect(await tagsManager.getVersion("tag2")).toBe(2);
+
+    const tagsAfterSecond = await tagsManager.list();
+    expect(Object.keys(tagsAfterSecond).length).toBe(2);
+    expect(tagsAfterSecond).toHaveProperty(tag1);
+    expect(tagsAfterSecond[tag1].version).toBe(1);
+    expect(tagsAfterSecond).toHaveProperty("tag2");
+    expect(tagsAfterSecond["tag2"].version).toBe(2);
+
+    await table.add([{ id: 3n, vector: [0.5, 0.6] }]);
+    await tagsManager.update(tag1, 3);
+    expect(await tagsManager.getVersion(tag1)).toBe(3);
+
+    await tagsManager.delete("tag2");
+    const tagsAfterDelete = await tagsManager.list();
+    expect(Object.keys(tagsAfterDelete).length).toBe(1);
+    expect(tagsAfterDelete).toHaveProperty(tag1);
+    expect(tagsAfterDelete[tag1].version).toBe(3);
+
+    await table.add([{ id: 4n, vector: [0.7, 0.8] }]);
+    expect(await table.version()).toBe(4);
+
+    await table.checkout(tag1);
+    expect(await table.version()).toBe(3);
+
+    await table.checkoutLatest();
+    expect(await table.version()).toBe(4);
+  });
+});
+
 describe("when optimizing a dataset", () => {
   let tmpDir: tmp.DirResult;
   let table: Table;
@@ -1247,6 +1408,56 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
 
       const results = await table.search("hello").toArray();
       expect(results[0].text).toBe(data[0].text);
+
+      const query = new MatchQuery("goodbye", "text");
+      expect(instanceOfFullTextQuery(query)).toBe(true);
+      const results2 = await table
+        .search(new MatchQuery("goodbye", "text"))
+        .toArray();
+      expect(results2[0].text).toBe(data[1].text);
+    });
+
+    test("prewarm full text search index", async () => {
+      const db = await connect(tmpDir.name);
+      const data = [
+        { text: ["lance database", "the", "search"], vector: [0.1, 0.2, 0.3] },
+        { text: ["lance database"], vector: [0.4, 0.5, 0.6] },
+        { text: ["lance", "search"], vector: [0.7, 0.8, 0.9] },
+        { text: ["database", "search"], vector: [1.0, 1.1, 1.2] },
+        { text: ["unrelated", "doc"], vector: [1.3, 1.4, 1.5] },
+      ];
+      const table = await db.createTable("test", data);
+      await table.createIndex("text", {
+        config: Index.fts(),
+      });
+
+      // For the moment, we just confirm we can call prewarmIndex without error
+      // and still search it afterwards
+      await table.prewarmIndex("text_idx");
+
+      const results = await table.search("lance").toArray();
+      expect(results.length).toBe(3);
+    });
+
+    test("full text index on list", async () => {
+      const db = await connect(tmpDir.name);
+      const data = [
+        { text: ["lance database", "the", "search"], vector: [0.1, 0.2, 0.3] },
+        { text: ["lance database"], vector: [0.4, 0.5, 0.6] },
+        { text: ["lance", "search"], vector: [0.7, 0.8, 0.9] },
+        { text: ["database", "search"], vector: [1.0, 1.1, 1.2] },
+        { text: ["unrelated", "doc"], vector: [1.3, 1.4, 1.5] },
+      ];
+      const table = await db.createTable("test", data);
+      await table.createIndex("text", {
+        config: Index.fts(),
+      });
+
+      const results = await table.search("lance").toArray();
+      expect(results.length).toBe(3);
+
+      const results2 = await table.search('"lance database"').toArray();
+      expect(results2.length).toBe(2);
     });
 
     test("full text search without positions", async () => {
@@ -1299,6 +1510,43 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
       expect(results.length).toBe(2);
       const phraseResults = await table.search('"hello world"').toArray();
       expect(phraseResults.length).toBe(1);
+      const phraseResults2 = await table
+        .search(new PhraseQuery("hello world", "text"))
+        .toArray();
+      expect(phraseResults2.length).toBe(1);
+    });
+
+    test("full text search fuzzy query", async () => {
+      const db = await connect(tmpDir.name);
+      const data = [
+        { text: "fa", vector: [0.1, 0.2, 0.3] },
+        { text: "fo", vector: [0.4, 0.5, 0.6] },
+        { text: "fob", vector: [0.4, 0.5, 0.6] },
+        { text: "focus", vector: [0.4, 0.5, 0.6] },
+        { text: "foo", vector: [0.4, 0.5, 0.6] },
+        { text: "food", vector: [0.4, 0.5, 0.6] },
+        { text: "foul", vector: [0.4, 0.5, 0.6] },
+      ];
+      const table = await db.createTable("test", data);
+      await table.createIndex("text", {
+        config: Index.fts(),
+      });
+
+      const results = await table
+        .search(new MatchQuery("foo", "text"))
+        .toArray();
+      expect(results.length).toBe(1);
+      expect(results[0].text).toBe("foo");
+
+      const fuzzyResults = await table
+        .search(new MatchQuery("foo", "text", { fuzziness: 1 }))
+        .toArray();
+      expect(fuzzyResults.length).toBe(4);
+      const resultSet = new Set(fuzzyResults.map((r) => r.text));
+      expect(resultSet.has("foo")).toBe(true);
+      expect(resultSet.has("fob")).toBe(true);
+      expect(resultSet.has("fo")).toBe(true);
+      expect(resultSet.has("food")).toBe(true);
     });
 
     test.each([
@@ -1346,6 +1594,30 @@ describe("when calling explainPlan", () => {
   });
 });
 
+describe("when calling analyzePlan", () => {
+  let tmpDir: tmp.DirResult;
+  let table: Table;
+  let queryVec: number[];
+  beforeEach(async () => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    const con = await connect(tmpDir.name);
+    table = await con.createTable("vectors", [{ id: 1, vector: [1.1, 0.9] }]);
+  });
+
+  afterEach(() => {
+    tmpDir.removeCallback();
+  });
+
+  it("retrieves runtime metrics", async () => {
+    queryVec = Array(2)
+      .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");
+  });
+});
+
 describe("column name options", () => {
   let tmpDir: tmp.DirResult;
   let table: Table;

nodejs/lancedb/arrow.ts

@@ -639,8 +639,9 @@ function transposeData(
 ): Vector {
   if (field.type instanceof Struct) {
     const childFields = field.type.children;
+    const fullPath = [...path, field.name];
     const childVectors = childFields.map((child) => {
-      return transposeData(data, child, [...path, child.name]);
+      return transposeData(data, child, fullPath);
     });
     const structData = makeData({
       type: field.type,
@@ -652,7 +653,14 @@ function transposeData(
     const values = data.map((datum) => {
       let current: unknown = datum;
       for (const key of valuesPath) {
-        if (isObject(current) && Object.hasOwn(current, key)) {
+        if (current == null) {
+          return null;
+        }
+
+        if (
+          isObject(current) &&
+          (Object.hasOwn(current, key) || key in current)
+        ) {
           current = current[key];
         } else {
           return null;

nodejs/lancedb/index.ts

@@ -23,6 +23,12 @@ export {
   OptimizeStats,
   CompactionStats,
   RemovalStats,
+  TableStatistics,
+  FragmentStatistics,
+  FragmentSummaryStats,
+  Tags,
+  TagContents,
+  MergeStats,
 } from "./native.js";
 
 export {
@@ -47,6 +53,12 @@ export {
   QueryExecutionOptions,
   FullTextSearchOptions,
   RecordBatchIterator,
+  FullTextQuery,
+  MatchQuery,
+  PhraseQuery,
+  BoostQuery,
+  MultiMatchQuery,
+  FullTextQueryType,
 } from "./query";
 
 export {

nodejs/lancedb/indices.ts

@@ -681,4 +681,6 @@ export interface IndexOptions {
    * The default is true
    */
   replace?: boolean;
+
+  waitTimeoutSeconds?: number;
 }

nodejs/lancedb/merge.ts

@@ -1,7 +1,7 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import { Data, Schema, fromDataToBuffer } from "./arrow";
-import { NativeMergeInsertBuilder } from "./native";
+import { MergeStats, NativeMergeInsertBuilder } from "./native";
 
 /** A builder used to create and run a merge insert operation */
 export class MergeInsertBuilder {
@@ -73,9 +73,9 @@ export class MergeInsertBuilder {
   /**
    * Executes the merge insert operation
    *
-   * Nothing is returned but the `Table` is updated
+   * @returns Statistics about the merge operation: counts of inserted, updated, and deleted rows
    */
-  async execute(data: Data): Promise<void> {
+  async execute(data: Data): Promise<MergeStats> {
     let schema: Schema;
     if (this.#schema instanceof Promise) {
       schema = await this.#schema;
@@ -84,6 +84,6 @@ export class MergeInsertBuilder {
       schema = this.#schema;
     }
     const buffer = await fromDataToBuffer(data, undefined, schema);
-    await this.#native.execute(buffer);
+    return await this.#native.execute(buffer);
   }
 }

nodejs/lancedb/query.ts

@@ -11,12 +11,14 @@ import {
 } from "./arrow";
 import { type IvfPqOptions } from "./indices";
 import {
+  JsFullTextQuery,
   RecordBatchIterator as NativeBatchIterator,
   Query as NativeQuery,
   Table as NativeTable,
   VectorQuery as NativeVectorQuery,
 } from "./native";
 import { Reranker } from "./rerankers";
+
 export class RecordBatchIterator implements AsyncIterator<RecordBatch> {
   private promisedInner?: Promise<NativeBatchIterator>;
   private inner?: NativeBatchIterator;
@@ -62,7 +64,7 @@ class RecordBatchIterable<
   // biome-ignore lint/suspicious/noExplicitAny: skip
   [Symbol.asyncIterator](): AsyncIterator<RecordBatch<any>, any, undefined> {
     return new RecordBatchIterator(
-      this.inner.execute(this.options?.maxBatchLength),
+      this.inner.execute(this.options?.maxBatchLength, this.options?.timeoutMs),
     );
   }
 }
@@ -78,6 +80,11 @@ export interface QueryExecutionOptions {
    * in smaller chunks.
    */
   maxBatchLength?: number;
+
+  /**
+   * Timeout for query execution in milliseconds
+   */
+  timeoutMs?: number;
 }
 
 /**
@@ -152,7 +159,7 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
   }
 
   fullTextSearch(
-    query: string,
+    query: string | FullTextQuery,
     options?: Partial<FullTextSearchOptions>,
   ): this {
     let columns: string[] | null = null;
@@ -164,9 +171,16 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
       }
     }
 
-    this.doCall((inner: NativeQueryType) =>
+    this.doCall((inner: NativeQueryType) => {
-      inner.fullTextSearch(query, columns),
+      if (typeof query === "string") {
-    );
+        inner.fullTextSearch({
+          query: query,
+          columns: columns,
+        });
+      } else {
+        inner.fullTextSearch({ query: query.inner });
+      }
+    });
     return this;
   }
 
@@ -273,9 +287,11 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
     options?: Partial<QueryExecutionOptions>,
   ): Promise<NativeBatchIterator> {
     if (this.inner instanceof Promise) {
-      return this.inner.then((inner) => inner.execute(options?.maxBatchLength));
+      return this.inner.then((inner) =>
+        inner.execute(options?.maxBatchLength, options?.timeoutMs),
+      );
     } else {
-      return this.inner.execute(options?.maxBatchLength);
+      return this.inner.execute(options?.maxBatchLength, options?.timeoutMs);
     }
   }
 
@@ -348,6 +364,43 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
       return this.inner.explainPlan(verbose);
     }
   }
+
+  /**
+   * Executes the query and returns the physical query plan annotated with runtime metrics.
+   *
+   * 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.
+   *
+   * @example
+   * import * as lancedb from "@lancedb/lancedb"
+   *
+   * const db = await lancedb.connect("./.lancedb");
+   * const table = await db.createTable("my_table", [
+   *   { vector: [1.1, 0.9], id: "1" },
+   * ]);
+   *
+   * const plan = await table.query().nearestTo([0.5, 0.2]).analyzePlan();
+   *
+   * Example output (with runtime metrics inlined):
+   * AnalyzeExec verbose=true, metrics=[]
+   *  ProjectionExec: expr=[id@3 as id, vector@0 as vector, _distance@2 as _distance], metrics=[output_rows=1, elapsed_compute=3.292µs]
+   *   Take: columns="vector, _rowid, _distance, (id)", metrics=[output_rows=1, elapsed_compute=66.001µs, batches_processed=1, bytes_read=8, iops=1, requests=1]
+   *    CoalesceBatchesExec: target_batch_size=1024, metrics=[output_rows=1, elapsed_compute=3.333µs]
+   *     GlobalLimitExec: skip=0, fetch=10, metrics=[output_rows=1, elapsed_compute=167ns]
+   *      FilterExec: _distance@2 IS NOT NULL, metrics=[output_rows=1, elapsed_compute=8.542µs]
+   *       SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], metrics=[output_rows=1, elapsed_compute=63.25µs, row_replacements=1]
+   *        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]
+   *
+   * @returns A query execution plan with runtime metrics for each step.
+   */
+  async analyzePlan(): Promise<string> {
+    if (this.inner instanceof Promise) {
+      return this.inner.then((inner) => inner.analyzePlan());
+    } else {
+      return this.inner.analyzePlan();
+    }
+  }
 }
 
 /**
@@ -681,8 +734,177 @@ export class Query extends QueryBase<NativeQuery> {
     }
   }
 
-  nearestToText(query: string, columns?: string[]): Query {
+  nearestToText(query: string | FullTextQuery, columns?: string[]): Query {
-    this.doCall((inner) => inner.fullTextSearch(query, columns));
+    this.doCall((inner) => {
+      if (typeof query === "string") {
+        inner.fullTextSearch({
+          query: query,
+          columns: columns,
+        });
+      } else {
+        inner.fullTextSearch({ query: query.inner });
+      }
+    });
     return this;
   }
 }
+
+/**
+ * Enum representing the types of full-text queries supported.
+ *
+ * - `Match`: Performs a full-text search for terms in the query string.
+ * - `MatchPhrase`: Searches for an exact phrase match in the text.
+ * - `Boost`: Boosts the relevance score of specific terms in the query.
+ * - `MultiMatch`: Searches across multiple fields for the query terms.
+ */
+export enum FullTextQueryType {
+  Match = "match",
+  MatchPhrase = "match_phrase",
+  Boost = "boost",
+  MultiMatch = "multi_match",
+}
+
+/**
+ * Represents a full-text query interface.
+ * This interface defines the structure and behavior for full-text queries,
+ * including methods to retrieve the query type and convert the query to a dictionary format.
+ */
+export interface FullTextQuery {
+  /**
+   * Returns the inner query object.
+   * This is the underlying query object used by the database engine.
+   * @ignore
+   */
+  inner: JsFullTextQuery;
+
+  /**
+   * The type of the full-text query.
+   */
+  queryType(): FullTextQueryType;
+}
+
+// biome-ignore lint/suspicious/noExplicitAny: we want any here
+export function instanceOfFullTextQuery(obj: any): obj is FullTextQuery {
+  return obj != null && obj.inner instanceof JsFullTextQuery;
+}
+
+export class MatchQuery implements FullTextQuery {
+  /** @ignore */
+  public readonly inner: JsFullTextQuery;
+  /**
+   * Creates an instance of MatchQuery.
+   *
+   * @param query - The text query to search for.
+   * @param column - The name of the column to search within.
+   * @param options - Optional parameters for the match query.
+   *   - `boost`: The boost factor for the query (default is 1.0).
+   *   - `fuzziness`: The fuzziness level for the query (default is 0).
+   *   - `maxExpansions`: The maximum number of terms to consider for fuzzy matching (default is 50).
+   */
+  constructor(
+    query: string,
+    column: string,
+    options?: {
+      boost?: number;
+      fuzziness?: number;
+      maxExpansions?: number;
+    },
+  ) {
+    let fuzziness = options?.fuzziness;
+    if (fuzziness === undefined) {
+      fuzziness = 0;
+    }
+    this.inner = JsFullTextQuery.matchQuery(
+      query,
+      column,
+      options?.boost ?? 1.0,
+      fuzziness,
+      options?.maxExpansions ?? 50,
+    );
+  }
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.Match;
+  }
+}
+
+export class PhraseQuery implements FullTextQuery {
+  /** @ignore */
+  public readonly inner: JsFullTextQuery;
+  /**
+   * Creates an instance of `PhraseQuery`.
+   *
+   * @param query - The phrase to search for in the specified column.
+   * @param column - The name of the column to search within.
+   */
+  constructor(query: string, column: string) {
+    this.inner = JsFullTextQuery.phraseQuery(query, column);
+  }
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.MatchPhrase;
+  }
+}
+
+export class BoostQuery implements FullTextQuery {
+  /** @ignore */
+  public readonly inner: JsFullTextQuery;
+  /**
+   * Creates an instance of BoostQuery.
+   * The boost returns documents that match the positive query,
+   * but penalizes those that match the negative query.
+   * the penalty is controlled by the `negativeBoost` parameter.
+   *
+   * @param positive - The positive query that boosts the relevance score.
+   * @param negative - The negative query that reduces the relevance score.
+   * @param options - Optional parameters for the boost query.
+   *  - `negativeBoost`: The boost factor for the negative query (default is 0.0).
+   */
+  constructor(
+    positive: FullTextQuery,
+    negative: FullTextQuery,
+    options?: {
+      negativeBoost?: number;
+    },
+  ) {
+    this.inner = JsFullTextQuery.boostQuery(
+      positive.inner,
+      negative.inner,
+      options?.negativeBoost,
+    );
+  }
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.Boost;
+  }
+}
+
+export class MultiMatchQuery implements FullTextQuery {
+  /** @ignore */
+  public readonly inner: JsFullTextQuery;
+  /**
+   * Creates an instance of MultiMatchQuery.
+   *
+   * @param query - The text query to search for across multiple columns.
+   * @param columns - An array of column names to search within.
+   * @param options - Optional parameters for the multi-match query.
+   *  - `boosts`: An array of boost factors for each column (default is 1.0 for all).
+   */
+  constructor(
+    query: string,
+    columns: string[],
+    options?: {
+      boosts?: number[];
+    },
+  ) {
+    this.inner = JsFullTextQuery.multiMatchQuery(
+      query,
+      columns,
+      options?.boosts,
+    );
+  }
+
+  queryType(): FullTextQueryType {
+    return FullTextQueryType.MultiMatch;
+  }
+}

nodejs/lancedb/table.ts

@@ -20,9 +20,16 @@ import {
   IndexConfig,
   IndexStatistics,
   OptimizeStats,
+  TableStatistics,
+  Tags,
   Table as _NativeTable,
 } from "./native";
-import { Query, VectorQuery } from "./query";
+import {
+  FullTextQuery,
+  Query,
+  VectorQuery,
+  instanceOfFullTextQuery,
+} from "./query";
 import { sanitizeType } from "./sanitize";
 import { IntoSql, toSQL } from "./util";
 export { IndexConfig } from "./native";
@@ -230,6 +237,30 @@ export abstract class Table {
    */
   abstract dropIndex(name: string): Promise<void>;
 
+  /**
+   * Prewarm an index in the table.
+   *
+   * @param name The name of the index.
+   *
+   * This will load the index into memory.  This may reduce the cold-start time for
+   * future queries.  If the index does not fit in the cache then this call may be
+   * wasteful.
+   */
+  abstract prewarmIndex(name: string): Promise<void>;
+
+  /**
+   * Waits for asynchronous indexing to complete on the table.
+   *
+   * @param indexNames The name of the indices to wait for
+   * @param timeoutSeconds The number of seconds to wait before timing out
+   *
+   * This will raise an error if the indices are not created and fully indexed within the timeout.
+   */
+  abstract waitForIndex(
+    indexNames: string[],
+    timeoutSeconds: number,
+  ): Promise<void>;
+
   /**
    * Create a {@link Query} Builder.
    *
@@ -294,7 +325,7 @@ export abstract class Table {
    * if the query is a string and no embedding function is defined, it will be treated as a full text search query
    */
   abstract search(
-    query: string | IntoVector,
+    query: string | IntoVector | FullTextQuery,
     queryType?: string,
     ftsColumns?: string | string[],
   ): VectorQuery | Query;
@@ -345,7 +376,7 @@ export abstract class Table {
    *
    * Calling this method will set the table into time-travel mode. If you
    * wish to return to standard mode, call `checkoutLatest`.
-   * @param {number} version The version to checkout
+   * @param {number | string} version The version to checkout, could be version number or tag
    * @example
    * ```typescript
    * import * as lancedb from "@lancedb/lancedb"
@@ -361,7 +392,8 @@ export abstract class Table {
    * console.log(await table.version()); // 2
    * ```
    */
-  abstract checkout(version: number): Promise<void>;
+  abstract checkout(version: number | string): Promise<void>;
+
   /**
    * Checkout the latest version of the table. _This is an in-place operation._
    *
@@ -375,6 +407,23 @@ export abstract class Table {
    */
   abstract listVersions(): Promise<Version[]>;
 
+  /**
+   * Get a tags manager for this table.
+   *
+   * Tags allow you to label specific versions of a table with a human-readable name.
+   * The returned tags manager can be used to list, create, update, or delete tags.
+   *
+   * @returns {Tags} A tags manager for this table
+   * @example
+   * ```typescript
+   * const tagsManager = await table.tags();
+   * await tagsManager.create("v1", 1);
+   * const tags = await tagsManager.list();
+   * console.log(tags); // { "v1": { version: 1, manifestSize: ... } }
+   * ```
+   */
+  abstract tags(): Promise<Tags>;
+
   /**
    * Restore the table to the currently checked out version
    *
@@ -434,6 +483,13 @@ export abstract class Table {
    * Use {@link Table.listIndices} to find the names of the indices.
    */
   abstract indexStats(name: string): Promise<IndexStatistics | undefined>;
+
+  /** Returns table and fragment statistics
+   *
+   * @returns {TableStatistics} The table and fragment statistics
+   *
+   */
+  abstract stats(): Promise<TableStatistics>;
 }
 
 export class LocalTable extends Table {
@@ -553,23 +609,39 @@ export class LocalTable extends Table {
     // Bit of a hack to get around the fact that TS has no package-scope.
     // biome-ignore lint/suspicious/noExplicitAny: skip
     const nativeIndex = (options?.config as any)?.inner;
-    await this.inner.createIndex(nativeIndex, column, options?.replace);
+    await this.inner.createIndex(
+      nativeIndex,
+      column,
+      options?.replace,
+      options?.waitTimeoutSeconds,
+    );
   }
 
   async dropIndex(name: string): Promise<void> {
     await this.inner.dropIndex(name);
   }
 
+  async prewarmIndex(name: string): Promise<void> {
+    await this.inner.prewarmIndex(name);
+  }
+
+  async waitForIndex(
+    indexNames: string[],
+    timeoutSeconds: number,
+  ): Promise<void> {
+    await this.inner.waitForIndex(indexNames, timeoutSeconds);
+  }
+
   query(): Query {
     return new Query(this.inner);
   }
 
   search(
-    query: string | IntoVector,
+    query: string | IntoVector | FullTextQuery,
     queryType: string = "auto",
     ftsColumns?: string | string[],
   ): VectorQuery | Query {
-    if (typeof query !== "string") {
+    if (typeof query !== "string" && !instanceOfFullTextQuery(query)) {
       if (queryType === "fts") {
         throw new Error("Cannot perform full text search on a vector query");
       }
@@ -585,7 +657,10 @@ export class LocalTable extends Table {
 
     // The query type is auto or vector
     // fall back to full text search if no embedding functions are defined and the query is a string
-    if (queryType === "auto" && getRegistry().length() === 0) {
+    if (
+      queryType === "auto" &&
+      (getRegistry().length() === 0 || instanceOfFullTextQuery(query))
+    ) {
       return this.query().fullTextSearch(query, {
         columns: ftsColumns,
       });
@@ -651,8 +726,11 @@ export class LocalTable extends Table {
     return await this.inner.version();
   }
 
-  async checkout(version: number): Promise<void> {
+  async checkout(version: number | string): Promise<void> {
-    await this.inner.checkout(version);
+    if (typeof version === "string") {
+      return this.inner.checkoutTag(version);
+    }
+    return this.inner.checkout(version);
   }
 
   async checkoutLatest(): Promise<void> {
@@ -671,6 +749,10 @@ export class LocalTable extends Table {
     await this.inner.restore();
   }
 
+  async tags(): Promise<Tags> {
+    return await this.inner.tags();
+  }
+
   async optimize(options?: Partial<OptimizeOptions>): Promise<OptimizeStats> {
     let cleanupOlderThanMs;
     if (
@@ -701,6 +783,11 @@ export class LocalTable extends Table {
     }
     return stats;
   }
+
+  async stats(): Promise<TableStatistics> {
+    return await this.inner.stats();
+  }
+
   mergeInsert(on: string | string[]): MergeInsertBuilder {
     on = Array.isArray(on) ? on : [on];
     return new MergeInsertBuilder(this.inner.mergeInsert(on), this.schema());

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.18.1",
+	"version": "0.19.1-beta.1",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.18.1",
+	"version": "0.19.1-beta.1",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

nodejs/npm/linux-arm64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.18.1",
+	"version": "0.19.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-arm64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.18.1",
+	"version": "0.19.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",

nodejs/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.18.1",
+	"version": "0.19.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/linux-x64-musl/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.18.1",
+	"version": "0.19.1-beta.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",

nodejs/npm/win32-arm64-msvc/package.json

@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.18.1",
+  "version": "0.19.1-beta.1",
   "os": [
     "win32"
   ],

nodejs/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.18.1",
+	"version": "0.19.1-beta.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.18.1",
+  "version": "0.19.1-beta.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.18.1",
+      "version": "0.19.1-beta.1",
       "cpu": [
         "x64",
         "arm64"
@@ -2304,89 +2304,20 @@
       }
     },
     "node_modules/@babel/code-frame": {
-      "version": "7.23.5",
+      "version": "7.26.2",
-      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.23.5.tgz",
+      "resolved": "https://registry.npmjs.org/@babel/code-frame/-/code-frame-7.26.2.tgz",
-      "integrity": "sha512-CgH3s1a96LipHCmSUmYFPwY7MNx8C3avkq7i4Wl3cfa662ldtUe4VM1TPXX70pfmrlWTb6jLqTYrZyT2ZTJBgA==",
+      "integrity": "sha512-RJlIHRueQgwWitWgF8OdFYGZX328Ax5BCemNGlqHfplnRT9ESi8JkFlvaVYbS+UubVY6dpv87Fs2u5M29iNFVQ==",
       "dev": true,
+      "license": "MIT",
       "dependencies": {
-        "@babel/highlight": "^7.23.4",
+        "@babel/helper-validator-identifier": "^7.25.9",
-        "chalk": "^2.4.2"
+        "js-tokens": "^4.0.0",
+        "picocolors": "^1.0.0"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
-    "node_modules/@babel/code-frame/node_modules/ansi-styles": {
-      "version": "3.2.1",
-      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-3.2.1.tgz",
-      "integrity": "sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA==",
-      "dev": true,
-      "dependencies": {
-        "color-convert": "^1.9.0"
-      },
-      "engines": {
-        "node": ">=4"
-      }
-    },
-    "node_modules/@babel/code-frame/node_modules/chalk": {
-      "version": "2.4.2",
-      "resolved": "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz",
-      "integrity": "sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==",
-      "dev": true,
-      "dependencies": {
-        "ansi-styles": "^3.2.1",
-        "escape-string-regexp": "^1.0.5",
-        "supports-color": "^5.3.0"
-      },
-      "engines": {
-        "node": ">=4"
-      }
-    },
-    "node_modules/@babel/code-frame/node_modules/color-convert": {
-      "version": "1.9.3",
-      "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-1.9.3.tgz",
-      "integrity": "sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==",
-      "dev": true,
-      "dependencies": {
-        "color-name": "1.1.3"
-      }
-    },
-    "node_modules/@babel/code-frame/node_modules/color-name": {
-      "version": "1.1.3",
-      "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.3.tgz",
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-      "dev": true
-    },
-    "node_modules/@babel/code-frame/node_modules/escape-string-regexp": {
-      "version": "1.0.5",
-      "resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz",
-      "integrity": "sha512-vbRorB5FUQWvla16U8R/qgaFIya2qGzwDrNmCZuYKrbdSUMG6I1ZCGQRefkRVhuOkIGVne7BQ35DSfo1qvJqFg==",
-      "dev": true,
-      "engines": {
-        "node": ">=0.8.0"
-      }
-    },
-    "node_modules/@babel/code-frame/node_modules/has-flag": {
-      "version": "3.0.0",
-      "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz",
-      "integrity": "sha512-sKJf1+ceQBr4SMkvQnBDNDtf4TXpVhVGateu0t918bl30FnbE2m4vNLX+VWe/dpjlb+HugGYzW7uQXH98HPEYw==",
-      "dev": true,
-      "engines": {
-        "node": ">=4"
-      }
-    },
-    "node_modules/@babel/code-frame/node_modules/supports-color": {
-      "version": "5.5.0",
-      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz",
-      "integrity": "sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==",
-      "dev": true,
-      "dependencies": {
-        "has-flag": "^3.0.0"
-      },
-      "engines": {
-        "node": ">=4"
-      }
-    },
     "node_modules/@babel/compat-data": {
       "version": "7.23.5",
       "resolved": "https://registry.npmjs.org/@babel/compat-data/-/compat-data-7.23.5.tgz",
@@ -2589,19 +2520,21 @@
       }
     },
     "node_modules/@babel/helper-string-parser": {
-      "version": "7.23.4",
+      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.23.4.tgz",
+      "resolved": "https://registry.npmjs.org/@babel/helper-string-parser/-/helper-string-parser-7.25.9.tgz",
-      "integrity": "sha512-803gmbQdqwdf4olxrX4AJyFBV/RTr3rSmOj0rKwesmzlfhYNDEs+/iOcznzpNWlJlIlTJC2QfPFcHB6DlzdVLQ==",
+      "integrity": "sha512-4A/SCr/2KLd5jrtOMFzaKjVtAei3+2r/NChoBNoZ3EyP/+GlhoaEGoWOZUmFmoITP7zOJyHIMm+DYRd8o3PvHA==",
       "dev": true,
+      "license": "MIT",
       "engines": {
         "node": ">=6.9.0"
       }
     },
     "node_modules/@babel/helper-validator-identifier": {
-      "version": "7.22.20",
+      "version": "7.25.9",
-      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.22.20.tgz",
+      "resolved": "https://registry.npmjs.org/@babel/helper-validator-identifier/-/helper-validator-identifier-7.25.9.tgz",
-      "integrity": "sha512-Y4OZ+ytlatR8AI+8KZfKuL5urKp7qey08ha31L8b3BwewJAoJamTzyvxPR/5D+KkdJCGPq/+8TukHBlY10FX9A==",
+      "integrity": "sha512-Ed61U6XJc3CVRfkERJWDz4dJwKe7iLmmJsbOGu9wSloNSFttHV0I8g6UAgb7qnK5ly5bGLPd4oXZlxCdANBOWQ==",
       "dev": true,
+      "license": "MIT",
       "engines": {
         "node": ">=6.9.0"
       }
@@ -2616,109 +2549,28 @@
       }
     },
     "node_modules/@babel/helpers": {
-      "version": "7.23.8",
+      "version": "7.27.0",
-      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.23.8.tgz",
+      "resolved": "https://registry.npmjs.org/@babel/helpers/-/helpers-7.27.0.tgz",
-      "integrity": "sha512-KDqYz4PiOWvDFrdHLPhKtCThtIcKVy6avWD2oG4GEvyQ+XDZwHD4YQd+H2vNMnq2rkdxsDkU82T+Vk8U/WXHRQ==",
+      "integrity": "sha512-U5eyP/CTFPuNE3qk+WZMxFkp/4zUzdceQlfzf7DdGdhp+Fezd7HD+i8Y24ZuTMKX3wQBld449jijbGq6OdGNQg==",
       "dev": true,
+      "license": "MIT",
       "dependencies": {
-        "@babel/template": "^7.22.15",
+        "@babel/template": "^7.27.0",
-        "@babel/traverse": "^7.23.7",
+        "@babel/types": "^7.27.0"
-        "@babel/types": "^7.23.6"
       },
       "engines": {
         "node": ">=6.9.0"
       }
     },
-    "node_modules/@babel/highlight": {
-      "version": "7.23.4",
-      "resolved": "https://registry.npmjs.org/@babel/highlight/-/highlight-7.23.4.tgz",
-      "integrity": "sha512-acGdbYSfp2WheJoJm/EBBBLh/ID8KDc64ISZ9DYtBmC8/Q204PZJLHyzeB5qMzJ5trcOkybd78M4x2KWsUq++A==",
-      "dev": true,
-      "dependencies": {
-        "@babel/helper-validator-identifier": "^7.22.20",
-        "chalk": "^2.4.2",
-        "js-tokens": "^4.0.0"
-      },
-      "engines": {
-        "node": ">=6.9.0"
-      }
-    },
-    "node_modules/@babel/highlight/node_modules/ansi-styles": {
-      "version": "3.2.1",
-      "resolved": "https://registry.npmjs.org/ansi-styles/-/ansi-styles-3.2.1.tgz",
-      "integrity": "sha512-VT0ZI6kZRdTh8YyJw3SMbYm/u+NqfsAxEpWO0Pf9sq8/e94WxxOpPKx9FR1FlyCtOVDNOQ+8ntlqFxiRc+r5qA==",
-      "dev": true,
-      "dependencies": {
-        "color-convert": "^1.9.0"
-      },
-      "engines": {
-        "node": ">=4"
-      }
-    },
-    "node_modules/@babel/highlight/node_modules/chalk": {
-      "version": "2.4.2",
-      "resolved": "https://registry.npmjs.org/chalk/-/chalk-2.4.2.tgz",
-      "integrity": "sha512-Mti+f9lpJNcwF4tWV8/OrTTtF1gZi+f8FqlyAdouralcFWFQWF2+NgCHShjkCb+IFBLq9buZwE1xckQU4peSuQ==",
-      "dev": true,
-      "dependencies": {
-        "ansi-styles": "^3.2.1",
-        "escape-string-regexp": "^1.0.5",
-        "supports-color": "^5.3.0"
-      },
-      "engines": {
-        "node": ">=4"
-      }
-    },
-    "node_modules/@babel/highlight/node_modules/color-convert": {
-      "version": "1.9.3",
-      "resolved": "https://registry.npmjs.org/color-convert/-/color-convert-1.9.3.tgz",
-      "integrity": "sha512-QfAUtd+vFdAtFQcC8CCyYt1fYWxSqAiK2cSD6zDB8N3cpsEBAvRxp9zOGg6G/SHHJYAT88/az/IuDGALsNVbGg==",
-      "dev": true,
-      "dependencies": {
-        "color-name": "1.1.3"
-      }
-    },
-    "node_modules/@babel/highlight/node_modules/color-name": {
-      "version": "1.1.3",
-      "resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.3.tgz",
-      "integrity": "sha512-72fSenhMw2HZMTVHeCA9KCmpEIbzWiQsjN+BHcBbS9vr1mtt+vJjPdksIBNUmKAW8TFUDPJK5SUU3QhE9NEXDw==",
-      "dev": true
-    },
-    "node_modules/@babel/highlight/node_modules/escape-string-regexp": {
-      "version": "1.0.5",
-      "resolved": "https://registry.npmjs.org/escape-string-regexp/-/escape-string-regexp-1.0.5.tgz",
-      "integrity": "sha512-vbRorB5FUQWvla16U8R/qgaFIya2qGzwDrNmCZuYKrbdSUMG6I1ZCGQRefkRVhuOkIGVne7BQ35DSfo1qvJqFg==",
-      "dev": true,
-      "engines": {
-        "node": ">=0.8.0"
-      }
-    },
-    "node_modules/@babel/highlight/node_modules/has-flag": {
-      "version": "3.0.0",
-      "resolved": "https://registry.npmjs.org/has-flag/-/has-flag-3.0.0.tgz",
-      "integrity": "sha512-sKJf1+ceQBr4SMkvQnBDNDtf4TXpVhVGateu0t918bl30FnbE2m4vNLX+VWe/dpjlb+HugGYzW7uQXH98HPEYw==",
-      "dev": true,
-      "engines": {
-        "node": ">=4"
-      }
-    },
-    "node_modules/@babel/highlight/node_modules/supports-color": {
-      "version": "5.5.0",
-      "resolved": "https://registry.npmjs.org/supports-color/-/supports-color-5.5.0.tgz",
-      "integrity": "sha512-QjVjwdXIt408MIiAqCX4oUKsgU2EqAGzs2Ppkm4aQYbjm+ZEWEcW4SfFNTr4uMNZma0ey4f5lgLrkB0aX0QMow==",
-      "dev": true,
-      "dependencies": {
-        "has-flag": "^3.0.0"
-      },
-      "engines": {
-        "node": ">=4"
-      }
-    },
     "node_modules/@babel/parser": {
-      "version": "7.23.6",
+      "version": "7.27.0",
-      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.23.6.tgz",
+      "resolved": "https://registry.npmjs.org/@babel/parser/-/parser-7.27.0.tgz",
-      "integrity": "sha512-Z2uID7YJ7oNvAI20O9X0bblw7Qqs8Q2hFy0R9tAfnfLkp5MW0UH9eUvnDSnFwKZ0AvgS1ucqR4KzvVHgnke1VQ==",
+      "integrity": "sha512-iaepho73/2Pz7w2eMS0Q5f83+0RKI7i4xmiYeBmDzfRVbQtTOG7Ts0S4HzJVsTMGI9keU8rNfuZr8DKfSt7Yyg==",
       "dev": true,
+      "license": "MIT",
+      "dependencies": {
+        "@babel/types": "^7.27.0"
+      },
       "bin": {
         "parser": "bin/babel-parser.js"
       },
@@ -2904,14 +2756,15 @@
       }
     },
     "node_modules/@babel/template": {
-      "version": "7.22.15",
+      "version": "7.27.0",
-      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.22.15.tgz",
+      "resolved": "https://registry.npmjs.org/@babel/template/-/template-7.27.0.tgz",
-      "integrity": "sha512-QPErUVm4uyJa60rkI73qneDacvdvzxshT3kksGqlGWYdOTIUOwJ7RDUL8sGqslY1uXWSL6xMFKEXDS3ox2uF0w==",
+      "integrity": "sha512-2ncevenBqXI6qRMukPlXwHKHchC7RyMuu4xv5JBXRfOGVcTy1mXCD12qrp7Jsoxll1EV3+9sE4GugBVRjT2jFA==",
       "dev": true,
+      "license": "MIT",
       "dependencies": {
-        "@babel/code-frame": "^7.22.13",
+        "@babel/code-frame": "^7.26.2",
-        "@babel/parser": "^7.22.15",
+        "@babel/parser": "^7.27.0",
-        "@babel/types": "^7.22.15"
+        "@babel/types": "^7.27.0"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -2948,14 +2801,14 @@
       }
     },
     "node_modules/@babel/types": {
-      "version": "7.23.6",
+      "version": "7.27.0",
-      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.23.6.tgz",
+      "resolved": "https://registry.npmjs.org/@babel/types/-/types-7.27.0.tgz",
-      "integrity": "sha512-+uarb83brBzPKN38NX1MkB6vb6+mwvR6amUulqAE7ccQw1pEl+bCia9TbdG1lsnFP7lZySvUn37CHyXQdfTwzg==",
+      "integrity": "sha512-H45s8fVLYjbhFH62dIJ3WtmJ6RSPt/3DRO0ZcT2SUiYiQyz3BLVb9ADEnLl91m74aQPS3AzzeajZHYOalWe3bg==",
       "dev": true,
+      "license": "MIT",
       "dependencies": {
-        "@babel/helper-string-parser": "^7.23.4",
+        "@babel/helper-string-parser": "^7.25.9",
-        "@babel/helper-validator-identifier": "^7.22.20",
+        "@babel/helper-validator-identifier": "^7.25.9"
-        "to-fast-properties": "^2.0.0"
       },
       "engines": {
         "node": ">=6.9.0"
@@ -5550,10 +5403,11 @@
       "devOptional": true
     },
     "node_modules/axios": {
-      "version": "1.7.7",
+      "version": "1.8.4",
-      "resolved": "https://registry.npmjs.org/axios/-/axios-1.7.7.tgz",
+      "resolved": "https://registry.npmjs.org/axios/-/axios-1.8.4.tgz",
-      "integrity": "sha512-S4kL7XrjgBmvdGut0sN3yJxqYzrDOnivkBiN0OFs6hLiUam3UPvswUo0kqGyhqUZGEOytHyumEdXsAkgCOUf3Q==",
+      "integrity": "sha512-eBSYY4Y68NNlHbHBMdeDmKNtDgXWhQsJcGqzO3iLUM0GraQFSS9cVgPX5I9b3lbdFKyYoAEGAZF1DwhTaljNAw==",
       "dev": true,
+      "license": "MIT",
       "dependencies": {
         "follow-redirects": "^1.15.6",
         "form-data": "^4.0.0",
@@ -7869,7 +7723,8 @@
       "version": "4.0.0",
       "resolved": "https://registry.npmjs.org/js-tokens/-/js-tokens-4.0.0.tgz",
       "integrity": "sha512-RdJUflcE3cUzKiMqQgsCu06FPu9UdIJO0beYbPhHN4k6apgJtifcoCtT9bcxOpYBtpD2kCM6Sbzg4CausW/PKQ==",
-      "dev": true
+      "dev": true,
+      "license": "MIT"
     },
     "node_modules/js-yaml": {
       "version": "3.14.1",
@@ -9360,15 +9215,6 @@
       "integrity": "sha512-3f0uOEAQwIqGuWW2MVzYg8fV/QNnc/IpuJNG837rLuczAaLVHslWHZQj4IGiEl5Hs3kkbhwL9Ab7Hrsmuj+Smw==",
       "dev": true
     },
-    "node_modules/to-fast-properties": {
-      "version": "2.0.0",
-      "resolved": "https://registry.npmjs.org/to-fast-properties/-/to-fast-properties-2.0.0.tgz",
-      "integrity": "sha512-/OaKK0xYrs3DmxRYqL/yDc+FxFUVYhDlXMhRmv3z915w2HF1tnN1omB354j8VUGO/hbRzyD6Y3sA7v7GS/ceog==",
-      "dev": true,
-      "engines": {
-        "node": ">=4"
-      }
-    },
     "node_modules/to-regex-range": {
       "version": "5.0.1",
       "resolved": "https://registry.npmjs.org/to-regex-range/-/to-regex-range-5.0.1.tgz",

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.18.1",
+  "version": "0.19.1-beta.1",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",
@@ -29,6 +29,7 @@
         "aarch64-apple-darwin",
         "x86_64-unknown-linux-gnu",
         "aarch64-unknown-linux-gnu",
+        "x86_64-unknown-linux-musl",
         "aarch64-unknown-linux-musl",
         "x86_64-pc-windows-msvc",
         "aarch64-pc-windows-msvc"

nodejs/src/merge.rs

@@ -37,7 +37,7 @@ impl NativeMergeInsertBuilder {
     }
 
     #[napi(catch_unwind)]
-    pub async fn execute(&self, buf: Buffer) -> napi::Result<()> {
+    pub async fn execute(&self, buf: Buffer) -> napi::Result<MergeStats> {
         let data = ipc_file_to_batches(buf.to_vec())
             .and_then(IntoArrow::into_arrow)
             .map_err(|e| {
@@ -46,12 +46,14 @@ impl NativeMergeInsertBuilder {
 
         let this = self.clone();
 
-        this.inner.execute(data).await.map_err(|e| {
+        let stats = this.inner.execute(data).await.map_err(|e| {
             napi::Error::from_reason(format!(
                 "Failed to execute merge insert: {}",
                 convert_error(&e)
             ))
-        })
+        })?;
+
+        Ok(stats.into())
     }
 }
 
@@ -60,3 +62,20 @@ impl From<MergeInsertBuilder> for NativeMergeInsertBuilder {
         Self { inner }
     }
 }
+
+#[napi(object)]
+pub struct MergeStats {
+    pub num_inserted_rows: BigInt,
+    pub num_updated_rows: BigInt,
+    pub num_deleted_rows: BigInt,
+}
+
+impl From<lancedb::table::MergeStats> for MergeStats {
+    fn from(stats: lancedb::table::MergeStats) -> Self {
+        Self {
+            num_inserted_rows: stats.num_inserted_rows.into(),
+            num_updated_rows: stats.num_updated_rows.into(),
+            num_deleted_rows: stats.num_deleted_rows.into(),
+        }
+    }
+}

nodejs/src/query.rs

@@ -3,7 +3,9 @@
 
 use std::sync::Arc;
 
-use lancedb::index::scalar::FullTextSearchQuery;
+use lancedb::index::scalar::{
+    BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, PhraseQuery,
+};
 use lancedb::query::ExecutableQuery;
 use lancedb::query::Query as LanceDbQuery;
 use lancedb::query::QueryBase;
@@ -38,9 +40,10 @@ impl Query {
     }
 
     #[napi]
-    pub fn full_text_search(&mut self, query: String, columns: Option<Vec<String>>) {
+    pub fn full_text_search(&mut self, query: napi::JsObject) -> napi::Result<()> {
-        let query = FullTextSearchQuery::new(query).columns(columns);
+        let query = parse_fts_query(query)?;
         self.inner = self.inner.clone().full_text_search(query);
+        Ok(())
     }
 
     #[napi]
@@ -87,11 +90,15 @@ impl Query {
     pub async fn execute(
         &self,
         max_batch_length: Option<u32>,
+        timeout_ms: Option<u32>,
     ) -> napi::Result<RecordBatchIterator> {
         let mut execution_opts = QueryExecutionOptions::default();
         if let Some(max_batch_length) = max_batch_length {
             execution_opts.max_batch_length = max_batch_length;
         }
+        if let Some(timeout_ms) = timeout_ms {
+            execution_opts.timeout = Some(std::time::Duration::from_millis(timeout_ms as u64))
+        }
         let inner_stream = self
             .inner
             .execute_with_options(execution_opts)
@@ -114,6 +121,16 @@ 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)
+            ))
+        })
+    }
 }
 
 #[napi]
@@ -185,9 +202,10 @@ impl VectorQuery {
     }
 
     #[napi]
-    pub fn full_text_search(&mut self, query: String, columns: Option<Vec<String>>) {
+    pub fn full_text_search(&mut self, query: napi::JsObject) -> napi::Result<()> {
-        let query = FullTextSearchQuery::new(query).columns(columns);
+        let query = parse_fts_query(query)?;
         self.inner = self.inner.clone().full_text_search(query);
+        Ok(())
     }
 
     #[napi]
@@ -232,11 +250,15 @@ impl VectorQuery {
     pub async fn execute(
         &self,
         max_batch_length: Option<u32>,
+        timeout_ms: Option<u32>,
     ) -> napi::Result<RecordBatchIterator> {
         let mut execution_opts = QueryExecutionOptions::default();
         if let Some(max_batch_length) = max_batch_length {
             execution_opts.max_batch_length = max_batch_length;
         }
+        if let Some(timeout_ms) = timeout_ms {
+            execution_opts.timeout = Some(std::time::Duration::from_millis(timeout_ms as u64))
+        }
         let inner_stream = self
             .inner
             .execute_with_options(execution_opts)
@@ -259,4 +281,127 @@ 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)
+            ))
+        })
+    }
+}
+
+#[napi]
+#[derive(Debug, Clone)]
+pub struct JsFullTextQuery {
+    pub(crate) inner: FtsQuery,
+}
+
+#[napi]
+impl JsFullTextQuery {
+    #[napi(factory)]
+    pub fn match_query(
+        query: String,
+        column: String,
+        boost: f64,
+        fuzziness: Option<u32>,
+        max_expansions: u32,
+    ) -> napi::Result<Self> {
+        Ok(Self {
+            inner: MatchQuery::new(query)
+                .with_column(Some(column))
+                .with_boost(boost as f32)
+                .with_fuzziness(fuzziness)
+                .with_max_expansions(max_expansions as usize)
+                .into(),
+        })
+    }
+
+    #[napi(factory)]
+    pub fn phrase_query(query: String, column: String) -> napi::Result<Self> {
+        Ok(Self {
+            inner: PhraseQuery::new(query).with_column(Some(column)).into(),
+        })
+    }
+
+    #[napi(factory)]
+    #[allow(clippy::use_self)] // NAPI doesn't allow Self here but clippy reports it
+    pub fn boost_query(
+        positive: &JsFullTextQuery,
+        negative: &JsFullTextQuery,
+        negative_boost: Option<f64>,
+    ) -> napi::Result<Self> {
+        Ok(Self {
+            inner: BoostQuery::new(
+                positive.inner.clone(),
+                negative.inner.clone(),
+                negative_boost.map(|v| v as f32),
+            )
+            .into(),
+        })
+    }
+
+    #[napi(factory)]
+    pub fn multi_match_query(
+        query: String,
+        columns: Vec<String>,
+        boosts: Option<Vec<f64>>,
+    ) -> napi::Result<Self> {
+        let q = match boosts {
+            Some(boosts) => MultiMatchQuery::try_new(query, columns)
+                .and_then(|q| q.try_with_boosts(boosts.into_iter().map(|v| v as f32).collect())),
+            None => MultiMatchQuery::try_new(query, columns),
+        }
+        .map_err(|e| {
+            napi::Error::from_reason(format!("Failed to create multi match query: {}", e))
+        })?;
+
+        Ok(Self { inner: q.into() })
+    }
+}
+
+fn parse_fts_query(query: napi::JsObject) -> napi::Result<FullTextSearchQuery> {
+    if let Ok(Some(query)) = query.get::<_, &JsFullTextQuery>("query") {
+        Ok(FullTextSearchQuery::new_query(query.inner.clone()))
+    } else if let Ok(Some(query_text)) = query.get::<_, String>("query") {
+        let mut query_text = query_text;
+        let columns = query.get::<_, Option<Vec<String>>>("columns")?.flatten();
+
+        let is_phrase =
+            query_text.len() >= 2 && query_text.starts_with('"') && query_text.ends_with('"');
+        let is_multi_match = columns.as_ref().map(|cols| cols.len() > 1).unwrap_or(false);
+
+        if is_phrase {
+            // Remove the surrounding quotes for phrase queries
+            query_text = query_text[1..query_text.len() - 1].to_string();
+        }
+
+        let query: FtsQuery = match (is_phrase, is_multi_match) {
+            (false, _) => MatchQuery::new(query_text).into(),
+            (true, false) => PhraseQuery::new(query_text).into(),
+            (true, true) => {
+                return Err(napi::Error::from_reason(
+                    "Phrase queries cannot be used with multiple columns.",
+                ));
+            }
+        };
+        let mut query = FullTextSearchQuery::new_query(query);
+        if let Some(cols) = columns {
+            if !cols.is_empty() {
+                query = query.with_columns(&cols).map_err(|e| {
+                    napi::Error::from_reason(format!(
+                        "Failed to set full text search columns: {}",
+                        e
+                    ))
+                })?;
+            }
+        }
+        Ok(query)
+    } else {
+        Err(napi::Error::from_reason(
+            "Invalid full text search query object".to_string(),
+        ))
+    }
 }

nodejs/src/table.rs

@@ -111,6 +111,7 @@ impl Table {
         index: Option<&Index>,
         column: String,
         replace: Option<bool>,
+        wait_timeout_s: Option<i64>,
     ) -> napi::Result<()> {
         let lancedb_index = if let Some(index) = index {
             index.consume()?
@@ -121,6 +122,10 @@ impl Table {
         if let Some(replace) = replace {
             builder = builder.replace(replace);
         }
+        if let Some(timeout) = wait_timeout_s {
+            builder =
+                builder.wait_timeout(std::time::Duration::from_secs(timeout.try_into().unwrap()));
+        }
         builder.execute().await.default_error()
     }
 
@@ -132,6 +137,32 @@ impl Table {
             .default_error()
     }
 
+    #[napi(catch_unwind)]
+    pub async fn prewarm_index(&self, index_name: String) -> napi::Result<()> {
+        self.inner_ref()?
+            .prewarm_index(&index_name)
+            .await
+            .default_error()
+    }
+
+    #[napi(catch_unwind)]
+    pub async fn wait_for_index(&self, index_names: Vec<String>, timeout_s: i64) -> Result<()> {
+        let timeout = std::time::Duration::from_secs(timeout_s.try_into().unwrap());
+        let index_names: Vec<&str> = index_names.iter().map(|s| s.as_str()).collect();
+        let slice: &[&str] = &index_names;
+
+        self.inner_ref()?
+            .wait_for_index(slice, timeout)
+            .await
+            .default_error()
+    }
+
+    #[napi(catch_unwind)]
+    pub async fn stats(&self) -> Result<TableStatistics> {
+        let stats = self.inner_ref()?.stats().await.default_error()?;
+        Ok(stats.into())
+    }
+
     #[napi(catch_unwind)]
     pub async fn update(
         &self,
@@ -224,6 +255,14 @@ impl Table {
             .default_error()
     }
 
+    #[napi(catch_unwind)]
+    pub async fn checkout_tag(&self, tag: String) -> napi::Result<()> {
+        self.inner_ref()?
+            .checkout_tag(tag.as_str())
+            .await
+            .default_error()
+    }
+
     #[napi(catch_unwind)]
     pub async fn checkout_latest(&self) -> napi::Result<()> {
         self.inner_ref()?.checkout_latest().await.default_error()
@@ -256,6 +295,13 @@ impl Table {
         self.inner_ref()?.restore().await.default_error()
     }
 
+    #[napi(catch_unwind)]
+    pub async fn tags(&self) -> napi::Result<Tags> {
+        Ok(Tags {
+            inner: self.inner_ref()?.clone(),
+        })
+    }
+
     #[napi(catch_unwind)]
     pub async fn optimize(
         &self,
@@ -515,9 +561,158 @@ impl From<lancedb::index::IndexStatistics> for IndexStatistics {
     }
 }
 
+#[napi(object)]
+pub struct TableStatistics {
+    /// The total number of bytes in the table
+    pub total_bytes: i64,
+
+    /// The number of rows in the table
+    pub num_rows: i64,
+
+    /// The number of indices in the table
+    pub num_indices: i64,
+
+    /// Statistics on table fragments
+    pub fragment_stats: FragmentStatistics,
+}
+
+#[napi(object)]
+pub struct FragmentStatistics {
+    /// The number of fragments in the table
+    pub num_fragments: i64,
+
+    /// The number of uncompacted fragments in the table
+    pub num_small_fragments: i64,
+
+    /// Statistics on the number of rows in the table fragments
+    pub lengths: FragmentSummaryStats,
+}
+
+#[napi(object)]
+pub struct FragmentSummaryStats {
+    /// The number of rows in the fragment with the fewest rows
+    pub min: i64,
+
+    /// The number of rows in the fragment with the most rows
+    pub max: i64,
+
+    /// The mean number of rows in the fragments
+    pub mean: i64,
+
+    /// The 25th percentile of number of rows in the fragments
+    pub p25: i64,
+
+    /// The 50th percentile of number of rows in the fragments
+    pub p50: i64,
+
+    /// The 75th percentile of number of rows in the fragments
+    pub p75: i64,
+
+    /// The 99th percentile of number of rows in the fragments
+    pub p99: i64,
+}
+
+impl From<lancedb::table::TableStatistics> for TableStatistics {
+    fn from(v: lancedb::table::TableStatistics) -> Self {
+        Self {
+            total_bytes: v.total_bytes as i64,
+            num_rows: v.num_rows as i64,
+            num_indices: v.num_indices as i64,
+            fragment_stats: FragmentStatistics {
+                num_fragments: v.fragment_stats.num_fragments as i64,
+                num_small_fragments: v.fragment_stats.num_small_fragments as i64,
+                lengths: FragmentSummaryStats {
+                    min: v.fragment_stats.lengths.min as i64,
+                    max: v.fragment_stats.lengths.max as i64,
+                    mean: v.fragment_stats.lengths.mean as i64,
+                    p25: v.fragment_stats.lengths.p25 as i64,
+                    p50: v.fragment_stats.lengths.p50 as i64,
+                    p75: v.fragment_stats.lengths.p75 as i64,
+                    p99: v.fragment_stats.lengths.p99 as i64,
+                },
+            },
+        }
+    }
+}
+
 #[napi(object)]
 pub struct Version {
     pub version: i64,
     pub timestamp: i64,
     pub metadata: HashMap<String, String>,
 }
+
+#[napi]
+pub struct TagContents {
+    pub version: i64,
+    pub manifest_size: i64,
+}
+
+#[napi]
+pub struct Tags {
+    inner: LanceDbTable,
+}
+
+#[napi]
+impl Tags {
+    #[napi]
+    pub async fn list(&self) -> napi::Result<HashMap<String, TagContents>> {
+        let rust_tags = self.inner.tags().await.default_error()?;
+        let tag_list = rust_tags.as_ref().list().await.default_error()?;
+        let tag_contents = tag_list
+            .into_iter()
+            .map(|(k, v)| {
+                (
+                    k,
+                    TagContents {
+                        version: v.version as i64,
+                        manifest_size: v.manifest_size as i64,
+                    },
+                )
+            })
+            .collect();
+
+        Ok(tag_contents)
+    }
+
+    #[napi]
+    pub async fn get_version(&self, tag: String) -> napi::Result<i64> {
+        let rust_tags = self.inner.tags().await.default_error()?;
+        rust_tags
+            .as_ref()
+            .get_version(tag.as_str())
+            .await
+            .map(|v| v as i64)
+            .default_error()
+    }
+
+    #[napi]
+    pub async unsafe fn create(&mut self, tag: String, version: i64) -> napi::Result<()> {
+        let mut rust_tags = self.inner.tags().await.default_error()?;
+        rust_tags
+            .as_mut()
+            .create(tag.as_str(), version as u64)
+            .await
+            .default_error()
+    }
+
+    #[napi]
+    pub async unsafe fn delete(&mut self, tag: String) -> napi::Result<()> {
+        let mut rust_tags = self.inner.tags().await.default_error()?;
+        rust_tags
+            .as_mut()
+            .delete(tag.as_str())
+            .await
+            .default_error()
+    }
+
+    #[napi]
+    pub async unsafe fn update(&mut self, tag: String, version: i64) -> napi::Result<()> {
+        let mut rust_tags = self.inner.tags().await.default_error()?;
+        rust_tags
+            .as_mut()
+            .update(tag.as_str(), version as u64)
+            .await
+            .default_error()
+    }
+}

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.21.2-beta.0"
+current_version = "0.22.1-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.21.2-beta.0"
+version = "0.22.1-beta.1"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/pyproject.toml

@@ -4,11 +4,12 @@ name = "lancedb"
 dynamic = ["version"]
 dependencies = [
     "deprecation",
-    "tqdm>=4.27.0",
+    "numpy",
-    "pyarrow>=14",
-    "pydantic>=1.10",
-    "packaging",
     "overrides>=0.7",
+    "packaging",
+    "pyarrow>=16",
+    "pydantic>=1.10",
+    "tqdm>=4.27.0",
 ]
 description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
@@ -42,6 +43,9 @@ classifiers = [
 repository = "https://github.com/lancedb/lancedb"
 
 [project.optional-dependencies]
+pylance = [
+    "pylance>=0.25",
+]
 tests = [
     "aiohttp",
     "boto3",
@@ -54,7 +58,8 @@ tests = [
     "polars>=0.19, <=1.3.0",
     "tantivy",
     "pyarrow-stubs",
-    "pylance>=0.23.2",
+    "pylance>=0.25",
+    "requests",
 ]
 dev = [
     "ruff",
@@ -72,6 +77,7 @@ embeddings = [
     "pillow",
     "open-clip-torch",
     "cohere",
+    "colpali-engine>=0.3.10",
     "huggingface_hub",
     "InstructorEmbedding",
     "google.generativeai",

python/python/lancedb/__init__.py

@@ -7,6 +7,7 @@ import os
 from concurrent.futures import ThreadPoolExecutor
 from datetime import timedelta
 from typing import Dict, Optional, Union, Any
+import warnings
 
 __version__ = importlib.metadata.version("lancedb")
 
@@ -213,3 +214,13 @@ __all__ = [
     "RemoteDBConnection",
     "__version__",
 ]
+
+
+def __warn_on_fork():
+    warnings.warn(
+        "lance is not fork-safe. If you are using multiprocessing, use spawn instead.",
+    )
+
+
+if hasattr(os, "register_at_fork"):
+    os.register_at_fork(before=__warn_on_fork)

python/python/lancedb/_lancedb.pyi

@@ -1,4 +1,5 @@
-from typing import Dict, List, Optional, Tuple, Any, Union, Literal
+from datetime import timedelta
+from typing import Dict, List, Optional, Tuple, Any, TypedDict, Union, Literal
 
 import pyarrow as pa
 
@@ -46,12 +47,13 @@ class Table:
     ): ...
     async def list_versions(self) -> List[Dict[str, Any]]: ...
     async def version(self) -> int: ...
-    async def checkout(self, version: int): ...
+    async def checkout(self, version: Union[int, str]): ...
     async def checkout_latest(self): ...
-    async def restore(self): ...
+    async def restore(self, version: Optional[int] = None): ...
     async def list_indices(self) -> list[IndexConfig]: ...
     async def delete(self, filter: str): ...
     async def add_columns(self, columns: list[tuple[str, str]]) -> None: ...
+    async def add_columns_with_schema(self, schema: pa.Schema) -> None: ...
     async def alter_columns(self, columns: list[dict[str, Any]]) -> None: ...
     async def optimize(
         self,
@@ -59,9 +61,18 @@ class Table:
         cleanup_since_ms: Optional[int] = None,
         delete_unverified: Optional[bool] = None,
     ) -> OptimizeStats: ...
+    @property
+    def tags(self) -> Tags: ...
     def query(self) -> Query: ...
     def vector_search(self) -> VectorQuery: ...
 
+class Tags:
+    async def list(self) -> Dict[str, Tag]: ...
+    async def get_version(self, tag: str) -> int: ...
+    async def create(self, tag: str, version: int): ...
+    async def delete(self, tag: str): ...
+    async def update(self, tag: str, version: int): ...
+
 class IndexConfig:
     index_type: str
     columns: List[str]
@@ -93,7 +104,11 @@ class Query:
     def postfilter(self): ...
     def nearest_to(self, query_vec: pa.Array) -> VectorQuery: ...
     def nearest_to_text(self, query: dict) -> FTSQuery: ...
-    async def execute(self, max_batch_length: Optional[int]) -> RecordBatchStream: ...
+    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) -> str: ...
     def to_query_request(self) -> PyQueryRequest: ...
 
 class FTSQuery:
@@ -107,8 +122,9 @@ class FTSQuery:
     def get_query(self) -> str: ...
     def add_query_vector(self, query_vec: pa.Array) -> None: ...
     def nearest_to(self, query_vec: pa.Array) -> HybridQuery: ...
-    async def execute(self, max_batch_length: Optional[int]) -> RecordBatchStream: ...
+    async def execute(
-    async def explain_plan(self) -> str: ...
+        self, max_batch_length: Optional[int], timeout: Optional[timedelta]
+    ) -> RecordBatchStream: ...
     def to_query_request(self) -> PyQueryRequest: ...
 
 class VectorQuery:
@@ -188,3 +204,7 @@ class RemovalStats:
 class OptimizeStats:
     compaction: CompactionStats
     prune: RemovalStats
+
+class Tag(TypedDict):
+    version: int
+    manifest_size: int

python/python/lancedb/common.py

@@ -9,7 +9,7 @@ import numpy as np
 import pyarrow as pa
 import pyarrow.dataset
 
-from .dependencies import pandas as pd
+from .dependencies import _check_for_pandas, pandas as pd
 
 DATA = Union[List[dict], "pd.DataFrame", pa.Table, Iterable[pa.RecordBatch]]
 VEC = Union[list, np.ndarray, pa.Array, pa.ChunkedArray]
@@ -63,7 +63,7 @@ def data_to_reader(
     data: DATA, schema: Optional[pa.Schema] = None
 ) -> pa.RecordBatchReader:
     """Convert various types of input into a RecordBatchReader"""
-    if pd is not None and isinstance(data, pd.DataFrame):
+    if _check_for_pandas(data) and isinstance(data, pd.DataFrame):
         return pa.Table.from_pandas(data, schema=schema).to_reader()
     elif isinstance(data, pa.Table):
         return data.to_reader()

python/python/lancedb/embeddings/__init__.py

@@ -19,3 +19,4 @@ from .imagebind import ImageBindEmbeddings
 from .jinaai import JinaEmbeddings
 from .watsonx import WatsonxEmbeddings
 from .voyageai import VoyageAIEmbeddingFunction
+from .colpali import ColPaliEmbeddings

python/python/lancedb/embeddings/colpali.py

@@ -0,0 +1,255 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+
+from functools import lru_cache
+from typing import List, Union, Optional, Any
+import numpy as np
+import io
+
+from ..util import attempt_import_or_raise
+from .base import EmbeddingFunction
+from .registry import register
+from .utils import TEXT, IMAGES, is_flash_attn_2_available
+
+
+@register("colpali")
+class ColPaliEmbeddings(EmbeddingFunction):
+    """
+    An embedding function that uses the ColPali engine for
+    multimodal multi-vector embeddings.
+
+    This embedding function supports ColQwen2.5 models, producing multivector outputs
+    for both text and image inputs. The output embeddings are lists of vectors, each
+    vector being 128-dimensional by default, represented as List[List[float]].
+
+    Parameters
+    ----------
+    model_name : str
+        The name of the model to use (e.g., "Metric-AI/ColQwen2.5-3b-multilingual-v1.0")
+    device : str
+        The device for inference (default "cuda:0").
+    dtype : str
+        Data type for model weights (default "bfloat16").
+    use_token_pooling : bool
+        Whether to use token pooling to reduce embedding size (default True).
+    pool_factor : int
+        Factor to reduce sequence length if token pooling is enabled (default 2).
+    quantization_config : Optional[BitsAndBytesConfig]
+        Quantization configuration for the model. (default None, bitsandbytes needed)
+    batch_size : int
+        Batch size for processing inputs (default 2).
+    """
+
+    model_name: str = "Metric-AI/ColQwen2.5-3b-multilingual-v1.0"
+    device: str = "auto"
+    dtype: str = "bfloat16"
+    use_token_pooling: bool = True
+    pool_factor: int = 2
+    quantization_config: Optional[Any] = None
+    batch_size: int = 2
+
+    _model = None
+    _processor = None
+    _token_pooler = None
+    _vector_dim = None
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        (
+            self._model,
+            self._processor,
+            self._token_pooler,
+        ) = self._load_model(
+            self.model_name,
+            self.dtype,
+            self.device,
+            self.use_token_pooling,
+            self.quantization_config,
+        )
+
+    @staticmethod
+    @lru_cache(maxsize=1)
+    def _load_model(
+        model_name: str,
+        dtype: str,
+        device: str,
+        use_token_pooling: bool,
+        quantization_config: Optional[Any],
+    ):
+        """
+        Initialize and cache the ColPali model, processor, and token pooler.
+        """
+        torch = attempt_import_or_raise("torch", "torch")
+        transformers = attempt_import_or_raise("transformers", "transformers")
+        colpali_engine = attempt_import_or_raise("colpali_engine", "colpali_engine")
+        from colpali_engine.compression.token_pooling import HierarchicalTokenPooler
+
+        if quantization_config is not None:
+            if not isinstance(quantization_config, transformers.BitsAndBytesConfig):
+                raise ValueError("quantization_config must be a BitsAndBytesConfig")
+
+        if dtype == "bfloat16":
+            torch_dtype = torch.bfloat16
+        elif dtype == "float16":
+            torch_dtype = torch.float16
+        elif dtype == "float64":
+            torch_dtype = torch.float64
+        else:
+            torch_dtype = torch.float32
+
+        model = colpali_engine.models.ColQwen2_5.from_pretrained(
+            model_name,
+            torch_dtype=torch_dtype,
+            device_map=device,
+            quantization_config=quantization_config
+            if quantization_config is not None
+            else None,
+            attn_implementation="flash_attention_2"
+            if is_flash_attn_2_available()
+            else None,
+        ).eval()
+        processor = colpali_engine.models.ColQwen2_5_Processor.from_pretrained(
+            model_name
+        )
+        token_pooler = HierarchicalTokenPooler() if use_token_pooling else None
+        return model, processor, token_pooler
+
+    def ndims(self):
+        """
+        Return the dimension of a vector in the multivector output (e.g., 128).
+        """
+        torch = attempt_import_or_raise("torch", "torch")
+        if self._vector_dim is None:
+            dummy_query = "test"
+            batch_queries = self._processor.process_queries([dummy_query]).to(
+                self._model.device
+            )
+            with torch.no_grad():
+                query_embeddings = self._model(**batch_queries)
+
+            if self.use_token_pooling and self._token_pooler is not None:
+                query_embeddings = self._token_pooler.pool_embeddings(
+                    query_embeddings,
+                    pool_factor=self.pool_factor,
+                    padding=True,
+                    padding_side=self._processor.tokenizer.padding_side,
+                )
+
+            self._vector_dim = query_embeddings[0].shape[-1]
+        return self._vector_dim
+
+    def _process_embeddings(self, embeddings):
+        """
+        Format model embeddings into List[List[float]].
+        Use token pooling if enabled.
+        """
+        torch = attempt_import_or_raise("torch", "torch")
+        if self.use_token_pooling and self._token_pooler is not None:
+            embeddings = self._token_pooler.pool_embeddings(
+                embeddings,
+                pool_factor=self.pool_factor,
+                padding=True,
+                padding_side=self._processor.tokenizer.padding_side,
+            )
+
+        if isinstance(embeddings, torch.Tensor):
+            tensors = embeddings.detach().cpu()
+            if tensors.dtype == torch.bfloat16:
+                tensors = tensors.to(torch.float32)
+            return (
+                tensors.numpy()
+                .astype(np.float64 if self.dtype == "float64" else np.float32)
+                .tolist()
+            )
+        return []
+
+    def generate_text_embeddings(self, text: TEXT) -> List[List[List[float]]]:
+        """
+        Generate embeddings for text input.
+        """
+        torch = attempt_import_or_raise("torch", "torch")
+        text = self.sanitize_input(text)
+        all_embeddings = []
+
+        for i in range(0, len(text), self.batch_size):
+            batch_text = text[i : i + self.batch_size]
+            batch_queries = self._processor.process_queries(batch_text).to(
+                self._model.device
+            )
+            with torch.no_grad():
+                query_embeddings = self._model(**batch_queries)
+            all_embeddings.extend(self._process_embeddings(query_embeddings))
+        return all_embeddings
+
+    def _prepare_images(self, images: IMAGES) -> List:
+        """
+        Convert image inputs to PIL Images.
+        """
+        PIL = attempt_import_or_raise("PIL", "pillow")
+        requests = attempt_import_or_raise("requests", "requests")
+        images = self.sanitize_input(images)
+        pil_images = []
+        try:
+            for image in images:
+                if isinstance(image, str):
+                    if image.startswith(("http://", "https://")):
+                        response = requests.get(image, timeout=10)
+                        response.raise_for_status()
+                        pil_images.append(PIL.Image.open(io.BytesIO(response.content)))
+                    else:
+                        with PIL.Image.open(image) as im:
+                            pil_images.append(im.copy())
+                elif isinstance(image, bytes):
+                    pil_images.append(PIL.Image.open(io.BytesIO(image)))
+                else:
+                    # Assume it's a PIL Image; will raise if invalid
+                    pil_images.append(image)
+        except Exception as e:
+            raise ValueError(f"Failed to process image: {e}")
+
+        return pil_images
+
+    def generate_image_embeddings(self, images: IMAGES) -> List[List[List[float]]]:
+        """
+        Generate embeddings for a batch of images.
+        """
+        torch = attempt_import_or_raise("torch", "torch")
+        pil_images = self._prepare_images(images)
+        all_embeddings = []
+
+        for i in range(0, len(pil_images), self.batch_size):
+            batch_images = pil_images[i : i + self.batch_size]
+            batch_images = self._processor.process_images(batch_images).to(
+                self._model.device
+            )
+            with torch.no_grad():
+                image_embeddings = self._model(**batch_images)
+            all_embeddings.extend(self._process_embeddings(image_embeddings))
+        return all_embeddings
+
+    def compute_query_embeddings(
+        self, query: Union[str, IMAGES], *args, **kwargs
+    ) -> List[List[List[float]]]:
+        """
+        Compute embeddings for a single user query (text only).
+        """
+        if not isinstance(query, str):
+            raise ValueError(
+                "Query must be a string, image to image search is not supported"
+            )
+        return self.generate_text_embeddings([query])
+
+    def compute_source_embeddings(
+        self, images: IMAGES, *args, **kwargs
+    ) -> List[List[List[float]]]:
+        """
+        Compute embeddings for a batch of source images.
+
+        Parameters
+        ----------
+        images : Union[str, bytes, List, pa.Array, pa.ChunkedArray, np.ndarray]
+            Batch of images (paths, URLs, bytes, or PIL Images).
+        """
+        images = self.sanitize_input(images)
+        return self.generate_image_embeddings(images)

python/python/lancedb/embeddings/utils.py

@@ -18,6 +18,7 @@ import numpy as np
 import pyarrow as pa
 
 from ..dependencies import pandas as pd
+from ..util import attempt_import_or_raise
 
 
 # ruff: noqa: PERF203
@@ -275,3 +276,12 @@ def url_retrieve(url: str):
 def api_key_not_found_help(provider):
     logging.error("Could not find API key for %s", provider)
     raise ValueError(f"Please set the {provider.upper()}_API_KEY environment variable.")
+
+
+def is_flash_attn_2_available():
+    try:
+        attempt_import_or_raise("flash_attn", "flash_attn")
+
+        return True
+    except ImportError:
+        return False

python/python/lancedb/embeddings/voyageai.py

@@ -1,9 +1,12 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
+import base64
-
 import os
-from typing import ClassVar, TYPE_CHECKING, List, Union
+from typing import ClassVar, TYPE_CHECKING, List, Union, Any
+
+from pathlib import Path
+from urllib.parse import urlparse
+from io import BytesIO
 
 import numpy as np
 import pyarrow as pa
@@ -11,12 +14,100 @@ import pyarrow as pa
 from ..util import attempt_import_or_raise
 from .base import EmbeddingFunction
 from .registry import register
-from .utils import api_key_not_found_help, IMAGES
+from .utils import api_key_not_found_help, IMAGES, TEXT
 
 if TYPE_CHECKING:
     import PIL
 
 
+def is_valid_url(text):
+    try:
+        parsed = urlparse(text)
+        return bool(parsed.scheme) and bool(parsed.netloc)
+    except Exception:
+        return False
+
+
+def transform_input(input_data: Union[str, bytes, Path]):
+    PIL = attempt_import_or_raise("PIL", "pillow")
+    if isinstance(input_data, str):
+        if is_valid_url(input_data):
+            content = {"type": "image_url", "image_url": input_data}
+        else:
+            content = {"type": "text", "text": input_data}
+    elif isinstance(input_data, PIL.Image.Image):
+        buffered = BytesIO()
+        input_data.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        content = {
+            "type": "image_base64",
+            "image_base64": "data:image/jpeg;base64," + img_str,
+        }
+    elif isinstance(input_data, bytes):
+        img = PIL.Image.open(BytesIO(input_data))
+        buffered = BytesIO()
+        img.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        content = {
+            "type": "image_base64",
+            "image_base64": "data:image/jpeg;base64," + img_str,
+        }
+    elif isinstance(input_data, Path):
+        img = PIL.Image.open(input_data)
+        buffered = BytesIO()
+        img.save(buffered, format="JPEG")
+        img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+        content = {
+            "type": "image_base64",
+            "image_base64": "data:image/jpeg;base64," + img_str,
+        }
+    else:
+        raise ValueError("Each input should be either str, bytes, Path or Image.")
+
+    return {"content": [content]}
+
+
+def sanitize_multimodal_input(inputs: Union[TEXT, IMAGES]) -> List[Any]:
+    """
+    Sanitize the input to the embedding function.
+    """
+    PIL = attempt_import_or_raise("PIL", "pillow")
+    if isinstance(inputs, (str, bytes, Path, PIL.Image.Image)):
+        inputs = [inputs]
+    elif isinstance(inputs, pa.Array):
+        inputs = inputs.to_pylist()
+    elif isinstance(inputs, pa.ChunkedArray):
+        inputs = inputs.combine_chunks().to_pylist()
+    else:
+        raise ValueError(
+            f"Input type {type(inputs)} not allowed with multimodal model."
+        )
+
+    if not all(isinstance(x, (str, bytes, Path, PIL.Image.Image)) for x in inputs):
+        raise ValueError("Each input should be either str, bytes, Path or Image.")
+
+    return [transform_input(i) for i in inputs]
+
+
+def sanitize_text_input(inputs: TEXT) -> List[str]:
+    """
+    Sanitize the input to the embedding function.
+    """
+    if isinstance(inputs, str):
+        inputs = [inputs]
+    elif isinstance(inputs, pa.Array):
+        inputs = inputs.to_pylist()
+    elif isinstance(inputs, pa.ChunkedArray):
+        inputs = inputs.combine_chunks().to_pylist()
+    else:
+        raise ValueError(f"Input type {type(inputs)} not allowed with text model.")
+
+    if not all(isinstance(x, str) for x in inputs):
+        raise ValueError("Each input should be str.")
+
+    return inputs
+
+
 @register("voyageai")
 class VoyageAIEmbeddingFunction(EmbeddingFunction):
     """
@@ -74,6 +165,11 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
     ]
     multimodal_embedding_models: list = ["voyage-multimodal-3"]
 
+    def _is_multimodal_model(self, model_name: str):
+        return (
+            model_name in self.multimodal_embedding_models or "multimodal" in model_name
+        )
+
     def ndims(self):
         if self.name == "voyage-3-lite":
             return 512
@@ -85,55 +181,12 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
             "voyage-finance-2",
             "voyage-multilingual-2",
             "voyage-law-2",
+            "voyage-multimodal-3",
         ]:
             return 1024
         else:
             raise ValueError(f"Model {self.name} not supported")
 
-    def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
-        """
-        Sanitize the input to the embedding function.
-        """
-        if isinstance(images, (str, bytes)):
-            images = [images]
-        elif isinstance(images, pa.Array):
-            images = images.to_pylist()
-        elif isinstance(images, pa.ChunkedArray):
-            images = images.combine_chunks().to_pylist()
-        return images
-
-    def generate_text_embeddings(self, text: str, **kwargs) -> np.ndarray:
-        """
-        Get the embeddings for the given texts
-
-        Parameters
-        ----------
-        texts: list[str] or np.ndarray (of str)
-            The texts to embed
-        input_type: Optional[str]
-
-        truncation: Optional[bool]
-        """
-        client = VoyageAIEmbeddingFunction._get_client()
-        if self.name in self.text_embedding_models:
-            rs = client.embed(texts=[text], model=self.name, **kwargs)
-        elif self.name in self.multimodal_embedding_models:
-            rs = client.multimodal_embed(inputs=[[text]], model=self.name, **kwargs)
-        else:
-            raise ValueError(
-                f"Model {self.name} not supported to generate text embeddings"
-            )
-
-        return rs.embeddings[0]
-
-    def generate_image_embedding(
-        self, image: "PIL.Image.Image", **kwargs
-    ) -> np.ndarray:
-        rs = VoyageAIEmbeddingFunction._get_client().multimodal_embed(
-            inputs=[[image]], model=self.name, **kwargs
-        )
-        return rs.embeddings[0]
-
     def compute_query_embeddings(
         self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
     ) -> List[np.ndarray]:
@@ -144,23 +197,52 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
         ----------
         query : Union[str, PIL.Image.Image]
             The query to embed. A query can be either text or an image.
+
+        Returns
+        -------
+            List[np.array]: the list of embeddings
         """
-        if isinstance(query, str):
+        client = VoyageAIEmbeddingFunction._get_client()
-            return [self.generate_text_embeddings(query, input_type="query")]
+        if self._is_multimodal_model(self.name):
+            result = client.multimodal_embed(
+                inputs=[[query]], model=self.name, input_type="query", **kwargs
+            )
         else:
-            PIL = attempt_import_or_raise("PIL", "pillow")
+            result = client.embed(
-            if isinstance(query, PIL.Image.Image):
+                texts=[query], model=self.name, input_type="query", **kwargs
-                return [self.generate_image_embedding(query, input_type="query")]
+            )
-            else:
+
-                raise TypeError("Only text PIL images supported as query")
+        return [result.embeddings[0]]
 
     def compute_source_embeddings(
-        self, images: IMAGES, *args, **kwargs
+        self, inputs: Union[TEXT, IMAGES], *args, **kwargs
     ) -> List[np.array]:
-        images = self.sanitize_input(images)
+        """
-        return [
+        Compute the embeddings for the inputs
-            self.generate_image_embedding(img, input_type="document") for img in images
+
-        ]
+        Parameters
+        ----------
+        inputs : Union[TEXT, IMAGES]
+            The inputs to embed. The input can be either str, bytes, Path (to an image),
+            PIL.Image or list of these.
+
+        Returns
+        -------
+            List[np.array]: the list of embeddings
+        """
+        client = VoyageAIEmbeddingFunction._get_client()
+        if self._is_multimodal_model(self.name):
+            inputs = sanitize_multimodal_input(inputs)
+            result = client.multimodal_embed(
+                inputs=inputs, model=self.name, input_type="document", **kwargs
+            )
+        else:
+            inputs = sanitize_text_input(inputs)
+            result = client.embed(
+                texts=inputs, model=self.name, input_type="document", **kwargs
+            )
+
+        return result.embeddings
 
     @staticmethod
     def _get_client():

python/python/lancedb/pydantic.py

@@ -152,6 +152,104 @@ def Vector(
     return FixedSizeList
 
 
+def MultiVector(
+    dim: int, value_type: pa.DataType = pa.float32(), nullable: bool = True
+) -> Type:
+    """Pydantic MultiVector Type for multi-vector embeddings.
+
+    This type represents a list of vectors, each with the same dimension.
+    Useful for models that produce multiple embeddings per input, like ColPali.
+
+    Parameters
+    ----------
+    dim : int
+        The dimension of each vector in the multi-vector.
+    value_type : pyarrow.DataType, optional
+        The value type of the vectors, by default pa.float32()
+    nullable : bool, optional
+        Whether the multi-vector is nullable, by default it is True.
+
+    Examples
+    --------
+
+    >>> import pydantic
+    >>> from lancedb.pydantic import MultiVector
+    ...
+    >>> class MyModel(pydantic.BaseModel):
+    ...     id: int
+    ...     text: str
+    ...     embeddings: MultiVector(128)  # List of 128-dimensional vectors
+    >>> schema = pydantic_to_schema(MyModel)
+    >>> assert schema == pa.schema([
+    ...     pa.field("id", pa.int64(), False),
+    ...     pa.field("text", pa.utf8(), False),
+    ...     pa.field("embeddings", pa.list_(pa.list_(pa.float32(), 128)))
+    ... ])
+    """
+
+    class MultiVectorList(list, FixedSizeListMixin):
+        def __repr__(self):
+            return f"MultiVector(dim={dim})"
+
+        @staticmethod
+        def nullable() -> bool:
+            return nullable
+
+        @staticmethod
+        def dim() -> int:
+            return dim
+
+        @staticmethod
+        def value_arrow_type() -> pa.DataType:
+            return value_type
+
+        @staticmethod
+        def is_multi_vector() -> bool:
+            return True
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, _source_type: Any, _handler: pydantic.GetCoreSchemaHandler
+        ) -> CoreSchema:
+            return core_schema.no_info_after_validator_function(
+                cls,
+                core_schema.list_schema(
+                    items_schema=core_schema.list_schema(
+                        min_length=dim,
+                        max_length=dim,
+                        items_schema=core_schema.float_schema(),
+                    ),
+                ),
+            )
+
+        @classmethod
+        def __get_validators__(cls) -> Generator[Callable, None, None]:
+            yield cls.validate
+
+        # For pydantic v1
+        @classmethod
+        def validate(cls, v):
+            if not isinstance(v, (list, range)):
+                raise TypeError("A list of vectors is needed")
+            for vec in v:
+                if not isinstance(vec, (list, range, np.ndarray)) or len(vec) != dim:
+                    raise TypeError(f"Each vector must be a list of {dim} numbers")
+            return cls(v)
+
+        if PYDANTIC_VERSION.major < 2:
+
+            @classmethod
+            def __modify_schema__(cls, field_schema: Dict[str, Any]):
+                field_schema["items"] = {
+                    "type": "array",
+                    "items": {"type": "number"},
+                    "minItems": dim,
+                    "maxItems": dim,
+                }
+
+    return MultiVectorList
+
+
 def _py_type_to_arrow_type(py_type: Type[Any], field: FieldInfo) -> pa.DataType:
     """Convert a field with native Python type to Arrow data type.
 
@@ -206,6 +304,9 @@ def _pydantic_type_to_arrow_type(tp: Any, field: FieldInfo) -> pa.DataType:
             fields = _pydantic_model_to_fields(tp)
             return pa.struct(fields)
         if issubclass(tp, FixedSizeListMixin):
+            if getattr(tp, "is_multi_vector", lambda: False)():
+                return pa.list_(pa.list_(tp.value_arrow_type(), tp.dim()))
+            # For regular Vector
             return pa.list_(tp.value_arrow_type(), tp.dim())
     return _py_type_to_arrow_type(tp, field)
 

python/python/lancedb/remote/table.py

@@ -18,7 +18,7 @@ from lancedb.merge import LanceMergeInsertBuilder
 from lancedb.embeddings import EmbeddingFunctionRegistry
 
 from ..query import LanceVectorQueryBuilder, LanceQueryBuilder
-from ..table import AsyncTable, IndexStatistics, Query, Table
+from ..table import AsyncTable, IndexStatistics, Query, Table, Tags
 
 
 class RemoteTable(Table):
@@ -54,6 +54,10 @@ class RemoteTable(Table):
         """Get the current version of the table"""
         return LOOP.run(self._table.version())
 
+    @property
+    def tags(self) -> Tags:
+        return Tags(self._table)
+
     @cached_property
     def embedding_functions(self) -> Dict[str, EmbeddingFunctionConfig]:
         """
@@ -81,12 +85,15 @@ class RemoteTable(Table):
         """to_pandas() is not yet supported on LanceDB cloud."""
         return NotImplementedError("to_pandas() is not yet supported on LanceDB cloud.")
 
-    def checkout(self, version: int):
+    def checkout(self, version: Union[int, str]):
         return LOOP.run(self._table.checkout(version))
 
     def checkout_latest(self):
         return LOOP.run(self._table.checkout_latest())
 
+    def restore(self, version: Optional[int] = None):
+        return LOOP.run(self._table.restore(version))
+
     def list_indices(self) -> Iterable[IndexConfig]:
         """List all the indices on the table"""
         return LOOP.run(self._table.list_indices())
@@ -101,6 +108,7 @@ class RemoteTable(Table):
         index_type: Literal["BTREE", "BITMAP", "LABEL_LIST", "scalar"] = "scalar",
         *,
         replace: bool = False,
+        wait_timeout: timedelta = None,
     ):
         """Creates a scalar index
         Parameters
@@ -123,13 +131,18 @@ class RemoteTable(Table):
         else:
             raise ValueError(f"Unknown index type: {index_type}")
 
-        LOOP.run(self._table.create_index(column, config=config, replace=replace))
+        LOOP.run(
+            self._table.create_index(
+                column, config=config, replace=replace, wait_timeout=wait_timeout
+            )
+        )
 
     def create_fts_index(
         self,
         column: str,
         *,
         replace: bool = False,
+        wait_timeout: timedelta = None,
         with_position: bool = True,
         # tokenizer configs:
         base_tokenizer: str = "simple",
@@ -150,7 +163,11 @@ class RemoteTable(Table):
             remove_stop_words=remove_stop_words,
             ascii_folding=ascii_folding,
         )
-        LOOP.run(self._table.create_index(column, config=config, replace=replace))
+        LOOP.run(
+            self._table.create_index(
+                column, config=config, replace=replace, wait_timeout=wait_timeout
+            )
+        )
 
     def create_index(
         self,
@@ -162,6 +179,7 @@ class RemoteTable(Table):
         replace: Optional[bool] = None,
         accelerator: Optional[str] = None,
         index_type="vector",
+        wait_timeout: Optional[timedelta] = None,
     ):
         """Create an index on the table.
         Currently, the only parameters that matter are
@@ -233,7 +251,11 @@ class RemoteTable(Table):
                 " 'IVF_FLAT', 'IVF_PQ', 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
             )
 
-        LOOP.run(self._table.create_index(vector_column_name, config=config))
+        LOOP.run(
+            self._table.create_index(
+                vector_column_name, config=config, wait_timeout=wait_timeout
+            )
+        )
 
     def add(
         self,
@@ -352,9 +374,15 @@ class RemoteTable(Table):
         )
 
     def _execute_query(
-        self, query: Query, batch_size: Optional[int] = None
+        self,
+        query: Query,
+        *,
+        batch_size: Optional[int] = None,
+        timeout: Optional[timedelta] = None,
     ) -> pa.RecordBatchReader:
-        async_iter = LOOP.run(self._table._execute_query(query, batch_size=batch_size))
+        async_iter = LOOP.run(
+            self._table._execute_query(query, batch_size=batch_size, timeout=timeout)
+        )
 
         def iter_sync():
             try:
@@ -365,6 +393,12 @@ class RemoteTable(Table):
 
         return pa.RecordBatchReader.from_batches(async_iter.schema, iter_sync())
 
+    def _explain_plan(self, query: Query, verbose: Optional[bool] = False) -> str:
+        return LOOP.run(self._table._explain_plan(query, verbose))
+
+    def _analyze_plan(self, query: Query) -> str:
+        return LOOP.run(self._table._analyze_plan(query))
+
     def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
         """Returns a [`LanceMergeInsertBuilder`][lancedb.merge.LanceMergeInsertBuilder]
         that can be used to create a "merge insert" operation.
@@ -539,6 +573,14 @@ class RemoteTable(Table):
     def drop_index(self, index_name: str):
         return LOOP.run(self._table.drop_index(index_name))
 
+    def wait_for_index(
+        self, index_names: Iterable[str], timeout: timedelta = timedelta(seconds=300)
+    ):
+        return LOOP.run(self._table.wait_for_index(index_names, timeout))
+
+    def stats(self):
+        return LOOP.run(self._table.stats())
+
     def uses_v2_manifest_paths(self) -> bool:
         raise NotImplementedError(
             "uses_v2_manifest_paths() is not supported on the LanceDB Cloud"

python/python/lancedb/rerankers/answerdotai.py

@@ -42,9 +42,14 @@ class AnswerdotaiRerankers(Reranker):
         rerankers = attempt_import_or_raise(
             "rerankers"
         )  # import here for faster ops later
-        self.reranker = rerankers.Reranker(model_name, model_type, **kwargs)
+        self.reranker = rerankers.Reranker(
+            model_name=model_name, model_type=model_type, **kwargs
+        )
 
     def _rerank(self, result_set: pa.Table, query: str):
+        result_set = self._handle_empty_results(result_set)
+        if len(result_set) == 0:
+            return result_set
         docs = result_set[self.column].to_pylist()
         doc_ids = list(range(len(docs)))
         result = self.reranker.rank(query, docs, doc_ids=doc_ids)
@@ -81,7 +86,6 @@ class AnswerdotaiRerankers(Reranker):
         vector_results = self._rerank(vector_results, query)
         if self.score == "relevance":
             vector_results = vector_results.drop_columns(["_distance"])
-
         vector_results = vector_results.sort_by([("_relevance_score", "descending")])
         return vector_results
 
@@ -89,7 +93,5 @@ class AnswerdotaiRerankers(Reranker):
         fts_results = self._rerank(fts_results, query)
         if self.score == "relevance":
             fts_results = fts_results.drop_columns(["_score"])
-
         fts_results = fts_results.sort_by([("_relevance_score", "descending")])
-
         return fts_results

python/python/lancedb/rerankers/base.py

@@ -65,6 +65,16 @@ class Reranker(ABC):
             f"{self.__class__.__name__} does not implement rerank_vector"
         )
 
+    def _handle_empty_results(self, results: pa.Table):
+        """
+        Helper method to handle empty FTS results consistently
+        """
+        if len(results) > 0:
+            return results
+        return results.append_column(
+            "_relevance_score", pa.array([], type=pa.float32())
+        )
+
     def rerank_fts(
         self,
         query: str,

python/python/lancedb/rerankers/cohere.py

@@ -62,6 +62,9 @@ class CohereReranker(Reranker):
         return cohere.Client(os.environ.get("COHERE_API_KEY") or self.api_key)
 
     def _rerank(self, result_set: pa.Table, query: str):
+        result_set = self._handle_empty_results(result_set)
+        if len(result_set) == 0:
+            return result_set
         docs = result_set[self.column].to_pylist()
         response = self._client.rerank(
             query=query,
@@ -99,24 +102,14 @@ class CohereReranker(Reranker):
             )
         return combined_results
 
-    def rerank_vector(
+    def rerank_vector(self, query: str, vector_results: pa.Table):
-        self,
+        vector_results = self._rerank(vector_results, query)
-        query: str,
-        vector_results: pa.Table,
-    ):
-        result_set = self._rerank(vector_results, query)
         if self.score == "relevance":
-            result_set = result_set.drop_columns(["_distance"])
+            vector_results = vector_results.drop_columns(["_distance"])
+        return vector_results
 
-        return result_set
+    def rerank_fts(self, query: str, fts_results: pa.Table):
-
+        fts_results = self._rerank(fts_results, query)
-    def rerank_fts(
-        self,
-        query: str,
-        fts_results: pa.Table,
-    ):
-        result_set = self._rerank(fts_results, query)
         if self.score == "relevance":
-            result_set = result_set.drop_columns(["_score"])
+            fts_results = fts_results.drop_columns(["_score"])
-
+        return fts_results
-        return result_set

python/python/lancedb/rerankers/cross_encoder.py

@@ -63,6 +63,9 @@ class CrossEncoderReranker(Reranker):
         return cross_encoder
 
     def _rerank(self, result_set: pa.Table, query: str):
+        result_set = self._handle_empty_results(result_set)
+        if len(result_set) == 0:
+            return result_set
         passages = result_set[self.column].to_pylist()
         cross_inp = [[query, passage] for passage in passages]
         cross_scores = self.model.predict(cross_inp)
@@ -93,11 +96,7 @@ class CrossEncoderReranker(Reranker):
 
         return combined_results
 
-    def rerank_vector(
+    def rerank_vector(self, query: str, vector_results: pa.Table):
-        self,
-        query: str,
-        vector_results: pa.Table,
-    ):
         vector_results = self._rerank(vector_results, query)
         if self.score == "relevance":
             vector_results = vector_results.drop_columns(["_distance"])
@@ -105,11 +104,7 @@ class CrossEncoderReranker(Reranker):
         vector_results = vector_results.sort_by([("_relevance_score", "descending")])
         return vector_results
 
-    def rerank_fts(
+    def rerank_fts(self, query: str, fts_results: pa.Table):
-        self,
-        query: str,
-        fts_results: pa.Table,
-    ):
         fts_results = self._rerank(fts_results, query)
         if self.score == "relevance":
             fts_results = fts_results.drop_columns(["_score"])

python/python/lancedb/rerankers/jinaai.py

@@ -62,6 +62,9 @@ class JinaReranker(Reranker):
         return self._session
 
     def _rerank(self, result_set: pa.Table, query: str):
+        result_set = self._handle_empty_results(result_set)
+        if len(result_set) == 0:
+            return result_set
         docs = result_set[self.column].to_pylist()
         response = self._client.post(  # type: ignore
             API_URL,
@@ -104,24 +107,14 @@ class JinaReranker(Reranker):
             )
         return combined_results
 
-    def rerank_vector(
+    def rerank_vector(self, query: str, vector_results: pa.Table):
-        self,
+        vector_results = self._rerank(vector_results, query)
-        query: str,
-        vector_results: pa.Table,
-    ):
-        result_set = self._rerank(vector_results, query)
         if self.score == "relevance":
-            result_set = result_set.drop_columns(["_distance"])
+            vector_results = vector_results.drop_columns(["_distance"])
+        return vector_results
 
-        return result_set
+    def rerank_fts(self, query: str, fts_results: pa.Table):
-
+        fts_results = self._rerank(fts_results, query)
-    def rerank_fts(
-        self,
-        query: str,
-        fts_results: pa.Table,
-    ):
-        result_set = self._rerank(fts_results, query)
         if self.score == "relevance":
-            result_set = result_set.drop_columns(["_score"])
+            fts_results = fts_results.drop_columns(["_score"])
-
+        return fts_results
-        return result_set

python/python/lancedb/rerankers/openai.py

@@ -44,6 +44,9 @@ class OpenaiReranker(Reranker):
         self.api_key = api_key
 
     def _rerank(self, result_set: pa.Table, query: str):
+        result_set = self._handle_empty_results(result_set)
+        if len(result_set) == 0:
+            return result_set
         docs = result_set[self.column].to_pylist()
         response = self._client.chat.completions.create(
             model=self.model_name,
@@ -104,18 +107,14 @@ class OpenaiReranker(Reranker):
         vector_results = self._rerank(vector_results, query)
         if self.score == "relevance":
             vector_results = vector_results.drop_columns(["_distance"])
-
         vector_results = vector_results.sort_by([("_relevance_score", "descending")])
-
         return vector_results
 
     def rerank_fts(self, query: str, fts_results: pa.Table):
         fts_results = self._rerank(fts_results, query)
         if self.score == "relevance":
             fts_results = fts_results.drop_columns(["_score"])
-
         fts_results = fts_results.sort_by([("_relevance_score", "descending")])
-
         return fts_results
 
     @cached_property

python/python/lancedb/rerankers/voyageai.py

@@ -63,6 +63,9 @@ class VoyageAIReranker(Reranker):
         )
 
     def _rerank(self, result_set: pa.Table, query: str):
+        result_set = self._handle_empty_results(result_set)
+        if len(result_set) == 0:
+            return result_set
         docs = result_set[self.column].to_pylist()
         response = self._client.rerank(
             query=query,
@@ -101,24 +104,14 @@ class VoyageAIReranker(Reranker):
             )
         return combined_results
 
-    def rerank_vector(
+    def rerank_vector(self, query: str, vector_results: pa.Table):
-        self,
+        vector_results = self._rerank(vector_results, query)
-        query: str,
-        vector_results: pa.Table,
-    ):
-        result_set = self._rerank(vector_results, query)
         if self.score == "relevance":
-            result_set = result_set.drop_columns(["_distance"])
+            vector_results = vector_results.drop_columns(["_distance"])
+        return vector_results
 
-        return result_set
+    def rerank_fts(self, query: str, fts_results: pa.Table):
-
+        fts_results = self._rerank(fts_results, query)
-    def rerank_fts(
-        self,
-        query: str,
-        fts_results: pa.Table,
-    ):
-        result_set = self._rerank(fts_results, query)
         if self.score == "relevance":
-            result_set = result_set.drop_columns(["_score"])
+            fts_results = fts_results.drop_columns(["_score"])
-
+        return fts_results
-        return result_set

python/python/lancedb/table.py

@@ -52,6 +52,7 @@ from .query import (
     AsyncHybridQuery,
     AsyncQuery,
     AsyncVectorQuery,
+    FullTextQuery,
     LanceEmptyQueryBuilder,
     LanceFtsQueryBuilder,
     LanceHybridQueryBuilder,
@@ -76,6 +77,7 @@ if TYPE_CHECKING:
         OptimizeStats,
         CleanupStats,
         CompactionStats,
+        Tag,
     )
     from .db import LanceDBConnection
     from .index import IndexConfig
@@ -581,6 +583,35 @@ class Table(ABC):
         """
         raise NotImplementedError
 
+    @property
+    @abstractmethod
+    def tags(self) -> Tags:
+        """Tag management for the table.
+
+        Similar to Git, tags are a way to add metadata to a specific version of the
+        table.
+
+        .. warning::
+
+            Tagged versions are exempted from the :py:meth:`cleanup_old_versions()`
+            process.
+
+            To remove a version that has been tagged, you must first
+            :py:meth:`~Tags.delete` the associated tag.
+
+        Examples
+        --------
+
+        .. code-block:: python
+
+            table = db.open_table("my_table")
+            table.tags.create("v2-prod-20250203", 10)
+
+            tags = table.tags.list()
+
+        """
+        raise NotImplementedError
+
     @property
     @abstractmethod
     def embedding_functions(self) -> Dict[str, EmbeddingFunctionConfig]:
@@ -630,6 +661,7 @@ class Table(ABC):
         index_cache_size: Optional[int] = None,
         *,
         index_type: VectorIndexType = "IVF_PQ",
+        wait_timeout: Optional[timedelta] = None,
         num_bits: int = 8,
         max_iterations: int = 50,
         sample_rate: int = 256,
@@ -665,6 +697,8 @@ class Table(ABC):
         num_bits: int
             The number of bits to encode sub-vectors. Only used with the IVF_PQ index.
             Only 4 and 8 are supported.
+        wait_timeout: timedelta, optional
+            The timeout to wait if indexing is asynchronous.
         """
         raise NotImplementedError
 
@@ -688,6 +722,30 @@ class Table(ABC):
         """
         raise NotImplementedError
 
+    def wait_for_index(
+        self, index_names: Iterable[str], timeout: timedelta = timedelta(seconds=300)
+    ) -> None:
+        """
+        Wait for indexing to complete for the given index names.
+        This will poll the table until all the indices are fully indexed,
+        or raise a timeout exception if the timeout is reached.
+
+        Parameters
+        ----------
+        index_names: str
+            The name of the indices to poll
+        timeout: timedelta
+            Timeout to wait for asynchronous indexing. The default is 5 minutes.
+        """
+        raise NotImplementedError
+
+    @abstractmethod
+    def stats(self) -> TableStatistics:
+        """
+        Retrieve table and fragment statistics.
+        """
+        raise NotImplementedError
+
     @abstractmethod
     def create_scalar_index(
         self,
@@ -695,6 +753,7 @@ class Table(ABC):
         *,
         replace: bool = True,
         index_type: ScalarIndexType = "BTREE",
+        wait_timeout: Optional[timedelta] = None,
     ):
         """Create a scalar index on a column.
 
@@ -707,7 +766,8 @@ class Table(ABC):
             Replace the existing index if it exists.
         index_type: Literal["BTREE", "BITMAP", "LABEL_LIST"], default "BTREE"
             The type of index to create.
-
+        wait_timeout: timedelta, optional
+            The timeout to wait if indexing is asynchronous.
         Examples
         --------
 
@@ -766,6 +826,7 @@ class Table(ABC):
         stem: bool = False,
         remove_stop_words: bool = False,
         ascii_folding: bool = False,
+        wait_timeout: Optional[timedelta] = None,
     ):
         """Create a full-text search index on the table.
 
@@ -821,6 +882,8 @@ class Table(ABC):
         ascii_folding : bool, default False
             Whether to fold ASCII characters. This converts accented characters to
             their ASCII equivalent. For example, "café" would be converted to "cafe".
+        wait_timeout: timedelta, optional
+            The timeout to wait if indexing is asynchronous.
         """
         raise NotImplementedError
 
@@ -899,10 +962,12 @@ class Table(ABC):
         >>> table = db.create_table("my_table", data)
         >>> new_data = pa.table({"a": [2, 3, 4], "b": ["x", "y", "z"]})
         >>> # Perform a "upsert" operation
-        >>> table.merge_insert("a")             \\
+        >>> stats = table.merge_insert("a")     \\
         ...      .when_matched_update_all()     \\
         ...      .when_not_matched_insert_all() \\
         ...      .execute(new_data)
+        >>> stats
+        {'num_inserted_rows': 1, 'num_updated_rows': 2, 'num_deleted_rows': 0}
         >>> # The order of new rows is non-deterministic since we use
         >>> # a hash-join as part of this operation and so we sort here
         >>> table.to_arrow().sort_by("a").to_pandas()
@@ -919,7 +984,9 @@ class Table(ABC):
     @abstractmethod
     def search(
         self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
         vector_column_name: Optional[str] = None,
         query_type: QueryType = "auto",
         ordering_field_name: Optional[str] = None,
@@ -1004,9 +1071,19 @@ class Table(ABC):
 
     @abstractmethod
     def _execute_query(
-        self, query: Query, batch_size: Optional[int] = None
+        self,
+        query: Query,
+        *,
+        batch_size: Optional[int] = None,
+        timeout: Optional[timedelta] = None,
     ) -> pa.RecordBatchReader: ...
 
+    @abstractmethod
+    def _explain_plan(self, query: Query, verbose: Optional[bool] = False) -> str: ...
+
+    @abstractmethod
+    def _analyze_plan(self, query: Query) -> str: ...
+
     @abstractmethod
     def _do_merge(
         self,
@@ -1262,16 +1339,21 @@ class Table(ABC):
         """
 
     @abstractmethod
-    def add_columns(self, transforms: Dict[str, str]):
+    def add_columns(
+        self, transforms: Dict[str, str] | pa.Field | List[pa.Field] | pa.Schema
+    ):
         """
         Add new columns with defined values.
 
         Parameters
         ----------
-        transforms: Dict[str, str]
+        transforms: Dict[str, str], pa.Field, List[pa.Field], pa.Schema
             A map of column name to a SQL expression to use to calculate the
             value of the new column. These expressions will be evaluated for
             each row in the table, and can reference existing columns.
+            Alternatively, a pyarrow Field or Schema can be provided to add
+            new columns with the specified data types. The new columns will
+            be initialized with null values.
         """
 
     @abstractmethod
@@ -1311,7 +1393,7 @@ class Table(ABC):
         """
 
     @abstractmethod
-    def checkout(self, version: int):
+    def checkout(self, version: Union[int, str]):
         """
         Checks out a specific version of the Table
 
@@ -1326,6 +1408,12 @@ class Table(ABC):
         Any operation that modifies the table will fail while the table is in a checked
         out state.
 
+        Parameters
+        ----------
+        version: int | str,
+            The version to check out. A version number (`int`) or a tag
+            (`str`) can be provided.
+
         To return the table to a normal state use `[Self::checkout_latest]`
         """
 
@@ -1339,6 +1427,21 @@ class Table(ABC):
         It can also be used to undo a `[Self::checkout]` operation
         """
 
+    @abstractmethod
+    def restore(self, version: Optional[int] = None):
+        """Restore a version of the table. This is an in-place operation.
+
+        This creates a new version where the data is equivalent to the
+        specified previous version. Data is not copied (as of python-v0.2.1).
+
+        Parameters
+        ----------
+        version : int, default None
+            The version to restore. If unspecified then restores the currently
+            checked out version. If the currently checked out version is the
+            latest version then this is a no-op.
+        """
+
     @abstractmethod
     def list_versions(self) -> List[Dict[str, Any]]:
         """List all versions of the table"""
@@ -1480,7 +1583,45 @@ class LanceTable(Table):
         """Get the current version of the table"""
         return LOOP.run(self._table.version())
 
-    def checkout(self, version: int):
+    @property
+    def tags(self) -> Tags:
+        """Tag management for the table.
+
+        Similar to Git, tags are a way to add metadata to a specific version of the
+        table.
+
+        .. warning::
+
+            Tagged versions are exempted from the :py:meth:`cleanup_old_versions()`
+            process.
+
+            To remove a version that has been tagged, you must first
+            :py:meth:`~Tags.delete` the associated tag.
+
+        Returns
+        -------
+        Tags
+            The tag manager for managing tags for the table.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> db = lancedb.connect("./.lancedb")
+        >>> table = db.create_table("my_table",
+        ...    [{"vector": [1.1, 0.9], "type": "vector"}])
+        >>> table.tags.create("v1", table.version)
+        >>> table.add([{"vector": [0.5, 0.2], "type": "vector"}])
+        >>> tags = table.tags.list()
+        >>> print(tags["v1"]["version"])
+        1
+        >>> table.checkout("v1")
+        >>> table.to_pandas()
+               vector    type
+        0  [1.1, 0.9]  vector
+        """
+        return Tags(self._table)
+
+    def checkout(self, version: Union[int, str]):
         """Checkout a version of the table. This is an in-place operation.
 
         This allows viewing previous versions of the table. If you wish to
@@ -1492,8 +1633,9 @@ class LanceTable(Table):
 
         Parameters
         ----------
-        version : int
+        version: int | str,
-            The version to checkout.
+            The version to check out. A version number (`int`) or a tag
+            (`str`) can be provided.
 
         Examples
         --------
@@ -1712,8 +1854,40 @@ class LanceTable(Table):
         )
 
     def drop_index(self, name: str) -> None:
+        """
+        Drops an index from the table
+
+        Parameters
+        ----------
+        name: str
+            The name of the index to drop
+        """
         return LOOP.run(self._table.drop_index(name))
 
+    def prewarm_index(self, name: str) -> None:
+        """
+        Prewarms an index in the table
+
+        This loads the entire index into memory
+
+        If the index does not fit into the available cache this call
+        may be wasteful
+
+        Parameters
+        ----------
+        name: str
+            The name of the index to prewarm
+        """
+        return LOOP.run(self._table.prewarm_index(name))
+
+    def wait_for_index(
+        self, index_names: Iterable[str], timeout: timedelta = timedelta(seconds=300)
+    ) -> None:
+        return LOOP.run(self._table.wait_for_index(index_names, timeout))
+
+    def stats(self) -> TableStatistics:
+        return LOOP.run(self._table.stats())
+
     def create_scalar_index(
         self,
         column: str,
@@ -2013,7 +2187,9 @@ class LanceTable(Table):
     @overload
     def search(
         self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
         vector_column_name: Optional[str] = None,
         query_type: Literal["hybrid"] = "hybrid",
         ordering_field_name: Optional[str] = None,
@@ -2032,7 +2208,9 @@ class LanceTable(Table):
 
     def search(
         self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
         vector_column_name: Optional[str] = None,
         query_type: QueryType = "auto",
         ordering_field_name: Optional[str] = None,
@@ -2104,6 +2282,8 @@ class LanceTable(Table):
             and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
+        if isinstance(query, FullTextQuery):
+            query_type = "fts"
         vector_column_name = infer_vector_column_name(
             schema=self.schema,
             query_type=query_type,
@@ -2279,9 +2459,15 @@ class LanceTable(Table):
         LOOP.run(self._table.update(values, where=where, updates_sql=values_sql))
 
     def _execute_query(
-        self, query: Query, batch_size: Optional[int] = None
+        self,
+        query: Query,
+        *,
+        batch_size: Optional[int] = None,
+        timeout: Optional[timedelta] = None,
     ) -> pa.RecordBatchReader:
-        async_iter = LOOP.run(self._table._execute_query(query, batch_size))
+        async_iter = LOOP.run(
+            self._table._execute_query(query, batch_size=batch_size, timeout=timeout)
+        )
 
         def iter_sync():
             try:
@@ -2292,8 +2478,11 @@ class LanceTable(Table):
 
         return pa.RecordBatchReader.from_batches(async_iter.schema, iter_sync())
 
-    def _explain_plan(self, query: Query) -> str:
+    def _explain_plan(self, query: Query, verbose: Optional[bool] = False) -> str:
-        return LOOP.run(self._table._explain_plan(query))
+        return LOOP.run(self._table._explain_plan(query, verbose))
+
+    def _analyze_plan(self, query: Query) -> str:
+        return LOOP.run(self._table._analyze_plan(query))
 
     def _do_merge(
         self,
@@ -2302,7 +2491,9 @@ class LanceTable(Table):
         on_bad_vectors: OnBadVectorsType,
         fill_value: float,
     ):
-        LOOP.run(self._table._do_merge(merge, new_data, on_bad_vectors, fill_value))
+        return LOOP.run(
+            self._table._do_merge(merge, new_data, on_bad_vectors, fill_value)
+        )
 
     @deprecation.deprecated(
         deprecated_in="0.21.0",
@@ -2442,7 +2633,9 @@ class LanceTable(Table):
         """
         return LOOP.run(self._table.index_stats(index_name))
 
-    def add_columns(self, transforms: Dict[str, str]):
+    def add_columns(
+        self, transforms: Dict[str, str] | pa.field | List[pa.field] | pa.Schema
+    ):
         LOOP.run(self._table.add_columns(transforms))
 
     def alter_columns(self, *alterations: Iterable[Dict[str, str]]):
@@ -2890,6 +3083,7 @@ class AsyncTable:
         config: Optional[
             Union[IvfFlat, IvfPq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS]
         ] = None,
+        wait_timeout: Optional[timedelta] = None,
     ):
         """Create an index to speed up queries
 
@@ -2914,6 +3108,8 @@ class AsyncTable:
             For advanced configuration you can specify the type of index you would
             like to create.   You can also specify index-specific parameters when
             creating an index object.
+        wait_timeout: timedelta, optional
+            The timeout to wait if indexing is asynchronous.
         """
         if config is not None:
             if not isinstance(
@@ -2924,7 +3120,9 @@ class AsyncTable:
                     " Bitmap, LabelList, or FTS"
                 )
         try:
-            await self._inner.create_index(column, index=config, replace=replace)
+            await self._inner.create_index(
+                column, index=config, replace=replace, wait_timeout=wait_timeout
+            )
         except ValueError as e:
             if "not support the requested language" in str(e):
                 supported_langs = ", ".join(lang_mapping.values())
@@ -2952,6 +3150,46 @@ class AsyncTable:
         """
         await self._inner.drop_index(name)
 
+    async def prewarm_index(self, name: str) -> None:
+        """
+        Prewarm an index in the table.
+
+        Parameters
+        ----------
+        name: str
+            The name of the index to prewarm
+
+        Notes
+        -----
+        This will load the index into memory.  This may reduce the cold-start time for
+        future queries.  If the index does not fit in the cache then this call may be
+        wasteful.
+        """
+        await self._inner.prewarm_index(name)
+
+    async def wait_for_index(
+        self, index_names: Iterable[str], timeout: timedelta = timedelta(seconds=300)
+    ) -> None:
+        """
+        Wait for indexing to complete for the given index names.
+        This will poll the table until all the indices are fully indexed,
+        or raise a timeout exception if the timeout is reached.
+
+        Parameters
+        ----------
+        index_names: str
+            The name of the indices to poll
+        timeout: timedelta
+            Timeout to wait for asynchronous indexing. The default is 5 minutes.
+        """
+        await self._inner.wait_for_index(index_names, timeout)
+
+    async def stats(self) -> TableStatistics:
+        """
+        Retrieve table and fragment statistics.
+        """
+        return await self._inner.stats()
+
     async def add(
         self,
         data: DATA,
@@ -3043,10 +3281,12 @@ class AsyncTable:
         >>> table = db.create_table("my_table", data)
         >>> new_data = pa.table({"a": [2, 3, 4], "b": ["x", "y", "z"]})
         >>> # Perform a "upsert" operation
-        >>> table.merge_insert("a")             \\
+        >>> stats = table.merge_insert("a")     \\
         ...      .when_matched_update_all()     \\
         ...      .when_not_matched_insert_all() \\
         ...      .execute(new_data)
+        >>> stats
+        {'num_inserted_rows': 1, 'num_updated_rows': 2, 'num_deleted_rows': 0}
         >>> # The order of new rows is non-deterministic since we use
         >>> # a hash-join as part of this operation and so we sort here
         >>> table.to_arrow().sort_by("a").to_pandas()
@@ -3103,7 +3343,9 @@ class AsyncTable:
     @overload
     async def search(
         self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
         vector_column_name: Optional[str] = None,
         query_type: Literal["vector"] = ...,
         ordering_field_name: Optional[str] = None,
@@ -3112,7 +3354,9 @@ class AsyncTable:
 
     async def search(
         self,
-        query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]] = None,
+        query: Optional[
+            Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]
+        ] = None,
         vector_column_name: Optional[str] = None,
         query_type: QueryType = "auto",
         ordering_field_name: Optional[str] = None,
@@ -3171,8 +3415,10 @@ class AsyncTable:
         async def get_embedding_func(
             vector_column_name: Optional[str],
             query_type: QueryType,
-            query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple]],
+            query: Optional[Union[VEC, str, "PIL.Image.Image", Tuple, FullTextQuery]],
         ) -> Tuple[str, EmbeddingFunctionConfig]:
+            if isinstance(query, FullTextQuery):
+                query_type = "fts"
             schema = await self.schema()
             vector_column_name = infer_vector_column_name(
                 schema=schema,
@@ -3222,6 +3468,8 @@ class AsyncTable:
             if is_embedding(query):
                 vector_query = query
                 query_type = "vector"
+            elif isinstance(query, FullTextQuery):
+                query_type = "fts"
             elif isinstance(query, str):
                 try:
                     (
@@ -3342,13 +3590,15 @@ class AsyncTable:
             async_query = async_query.nearest_to_text(
                 query.full_text_query.query, query.full_text_query.columns
             )
-            if query.full_text_query.limit is not None:
-                async_query = async_query.limit(query.full_text_query.limit)
 
         return async_query
 
     async def _execute_query(
-        self, query: Query, batch_size: Optional[int] = None
+        self,
+        query: Query,
+        *,
+        batch_size: Optional[int] = None,
+        timeout: Optional[timedelta] = None,
     ) -> pa.RecordBatchReader:
         # The sync table calls into this method, so we need to map the
         # query to the async version of the query and run that here. This is only
@@ -3356,12 +3606,19 @@ class AsyncTable:
 
         async_query = self._sync_query_to_async(query)
 
-        return await async_query.to_batches(max_batch_length=batch_size)
+        return await async_query.to_batches(
+            max_batch_length=batch_size, timeout=timeout
+        )
 
-    async def _explain_plan(self, query: Query) -> str:
+    async def _explain_plan(self, query: Query, verbose: Optional[bool]) -> str:
         # This method is used by the sync table
         async_query = self._sync_query_to_async(query)
-        return await async_query.explain_plan()
+        return await async_query.explain_plan(verbose)
+
+    async def _analyze_plan(self, query: Query) -> str:
+        # This method is used by the sync table
+        async_query = self._sync_query_to_async(query)
+        return await async_query.analyze_plan()
 
     async def _do_merge(
         self,
@@ -3385,7 +3642,7 @@ class AsyncTable:
         )
         if isinstance(data, pa.Table):
             data = pa.RecordBatchReader.from_batches(data.schema, data.to_batches())
-        await self._inner.execute_merge_insert(
+        return await self._inner.execute_merge_insert(
             data,
             dict(
                 on=merge._on,
@@ -3501,7 +3758,9 @@ class AsyncTable:
 
         return await self._inner.update(updates_sql, where)
 
-    async def add_columns(self, transforms: dict[str, str]):
+    async def add_columns(
+        self, transforms: dict[str, str] | pa.field | List[pa.field] | pa.Schema
+    ):
         """
         Add new columns with defined values.
 
@@ -3511,7 +3770,18 @@ class AsyncTable:
             A map of column name to a SQL expression to use to calculate the
             value of the new column. These expressions will be evaluated for
             each row in the table, and can reference existing columns.
+            Alternatively, you can pass a pyarrow field or schema to add
+            new columns with NULLs.
         """
+        if isinstance(transforms, pa.Field):
+            transforms = [transforms]
+        if isinstance(transforms, list) and all(
+            {isinstance(f, pa.Field) for f in transforms}
+        ):
+            transforms = pa.schema(transforms)
+        if isinstance(transforms, pa.Schema):
+            await self._inner.add_columns_with_schema(transforms)
+        else:
             await self._inner.add_columns(list(transforms.items()))
 
     async def alter_columns(self, *alterations: Iterable[dict[str, Any]]):
@@ -3573,7 +3843,7 @@ class AsyncTable:
 
         return versions
 
-    async def checkout(self, version: int):
+    async def checkout(self, version: int | str):
         """
         Checks out a specific version of the Table
 
@@ -3588,6 +3858,12 @@ class AsyncTable:
         Any operation that modifies the table will fail while the table is in a checked
         out state.
 
+        Parameters
+        ----------
+        version: int | str,
+            The version to check out. A version number (`int`) or a tag
+            (`str`) can be provided.
+
         To return the table to a normal state use `[Self::checkout_latest]`
         """
         try:
@@ -3610,7 +3886,7 @@ class AsyncTable:
         """
         await self._inner.checkout_latest()
 
-    async def restore(self):
+    async def restore(self, version: Optional[int] = None):
         """
         Restore the table to the currently checked out version
 
@@ -3623,7 +3899,25 @@ class AsyncTable:
         Once the operation concludes the table will no longer be in a checked
         out state and the read_consistency_interval, if any, will apply.
         """
-        await self._inner.restore()
+        await self._inner.restore(version)
+
+    @property
+    def tags(self) -> AsyncTags:
+        """Tag management for the dataset.
+
+        Similar to Git, tags are a way to add metadata to a specific version of the
+        dataset.
+
+        .. warning::
+
+            Tagged versions are exempted from the
+            :py:meth:`optimize(cleanup_older_than)` process.
+
+            To remove a version that has been tagged, you must first
+            :py:meth:`~Tags.delete` the associated tag.
+
+        """
+        return AsyncTags(self._inner)
 
     async def optimize(
         self,
@@ -3794,3 +4088,217 @@ class IndexStatistics:
     # a dictionary instead of a class.
     def __getitem__(self, key):
         return getattr(self, key)
+
+
+@dataclass
+class TableStatistics:
+    """
+    Statistics about a table and fragments.
+
+    Attributes
+    ----------
+    total_bytes: int
+        The total number of bytes in the table.
+    num_rows: int
+        The total number of rows in the table.
+    num_indices: int
+        The total number of indices in the table.
+    fragment_stats: FragmentStatistics
+        Statistics about fragments in the table.
+    """
+
+    total_bytes: int
+    num_rows: int
+    num_indices: int
+    fragment_stats: FragmentStatistics
+
+
+@dataclass
+class FragmentStatistics:
+    """
+    Statistics about fragments.
+
+    Attributes
+    ----------
+    num_fragments: int
+        The total number of fragments in the table.
+    num_small_fragments: int
+        The total number of small fragments in the table.
+        Small fragments have low row counts and may need to be compacted.
+    lengths: FragmentSummaryStats
+        Statistics about the number of rows in the table fragments.
+    """
+
+    num_fragments: int
+    num_small_fragments: int
+    lengths: FragmentSummaryStats
+
+
+@dataclass
+class FragmentSummaryStats:
+    """
+    Statistics about fragments sizes
+
+    Attributes
+    ----------
+    min: int
+        The number of rows in the fragment with the fewest rows.
+    max: int
+        The number of rows in the fragment with the most rows.
+    mean: int
+        The mean number of rows in the fragments.
+    p25: int
+        The 25th percentile of number of rows in the fragments.
+    p50: int
+        The 50th percentile of number of rows in the fragments.
+    p75: int
+        The 75th percentile of number of rows in the fragments.
+    p99: int
+        The 99th percentile of number of rows in the fragments.
+    """
+
+    min: int
+    max: int
+    mean: int
+    p25: int
+    p50: int
+    p75: int
+    p99: int
+
+
+class Tags:
+    """
+    Table tag manager.
+    """
+
+    def __init__(self, table):
+        self._table = table
+
+    def list(self) -> Dict[str, Tag]:
+        """
+        List all table tags.
+
+        Returns
+        -------
+        dict[str, Tag]
+            A dictionary mapping tag names to version numbers.
+        """
+        return LOOP.run(self._table.tags.list())
+
+    def get_version(self, tag: str) -> int:
+        """
+        Get the version of a tag.
+
+        Parameters
+        ----------
+        tag: str,
+            The name of the tag to get the version for.
+        """
+        return LOOP.run(self._table.tags.get_version(tag))
+
+    def create(self, tag: str, version: int) -> None:
+        """
+        Create a tag for a given table version.
+
+        Parameters
+        ----------
+        tag: str,
+            The name of the tag to create. This name must be unique among all tag
+            names for the table.
+        version: int,
+            The table version to tag.
+        """
+        LOOP.run(self._table.tags.create(tag, version))
+
+    def delete(self, tag: str) -> None:
+        """
+        Delete tag from the table.
+
+        Parameters
+        ----------
+        tag: str,
+            The name of the tag to delete.
+        """
+        LOOP.run(self._table.tags.delete(tag))
+
+    def update(self, tag: str, version: int) -> None:
+        """
+        Update tag to a new version.
+
+        Parameters
+        ----------
+        tag: str,
+            The name of the tag to update.
+        version: int,
+            The new table version to tag.
+        """
+        LOOP.run(self._table.tags.update(tag, version))
+
+
+class AsyncTags:
+    """
+    Async table tag manager.
+    """
+
+    def __init__(self, table):
+        self._table = table
+
+    async def list(self) -> Dict[str, Tag]:
+        """
+        List all table tags.
+
+        Returns
+        -------
+        dict[str, Tag]
+            A dictionary mapping tag names to version numbers.
+        """
+        return await self._table.tags.list()
+
+    async def get_version(self, tag: str) -> int:
+        """
+        Get the version of a tag.
+
+        Parameters
+        ----------
+        tag: str,
+            The name of the tag to get the version for.
+        """
+        return await self._table.tags.get_version(tag)
+
+    async def create(self, tag: str, version: int) -> None:
+        """
+        Create a tag for a given table version.
+
+        Parameters
+        ----------
+        tag: str,
+            The name of the tag to create. This name must be unique among all tag
+            names for the table.
+        version: int,
+            The table version to tag.
+        """
+        await self._table.tags.create(tag, version)
+
+    async def delete(self, tag: str) -> None:
+        """
+        Delete tag from the table.
+
+        Parameters
+        ----------
+        tag: str,
+            The name of the tag to delete.
+        """
+        await self._table.tags.delete(tag)
+
+    async def update(self, tag: str, version: int) -> None:
+        """
+        Update tag to a new version.
+
+        Parameters
+        ----------
+        tag: str,
+            The name of the tag to update.
+        version: int,
+            The new table version to tag.
+        """
+        await self._table.tags.update(tag, version)

python/python/lancedb/util.py

@@ -253,9 +253,14 @@ def infer_vector_column_name(
     query: Optional[Any],  # inferred later in query builder
     vector_column_name: Optional[str],
 ):
-    if (vector_column_name is None and query is not None and query_type != "fts") or (
+    if vector_column_name is not None:
-        vector_column_name is None and query_type == "hybrid"
+        return vector_column_name
-    ):
+
+    if query_type == "fts":
+        # FTS queries do not require a vector column
+        return None
+
+    if query is not None or query_type == "hybrid":
         try:
             vector_column_name = inf_vector_column_query(schema)
         except Exception as e:

python/python/tests/docs/test_guide_tables.py

@@ -562,7 +562,7 @@ async def test_table_async():
     async_db = await lancedb.connect_async(uri, read_consistency_interval=timedelta(0))
     async_tbl = await async_db.open_table("test_table_async")
     # --8<-- [end:table_async_strong_consistency]
-    # --8<-- [start:table_async_ventual_consistency]
+    # --8<-- [start:table_async_eventual_consistency]
     uri = "data/sample-lancedb"
     async_db = await lancedb.connect_async(
         uri, read_consistency_interval=timedelta(seconds=5)

python/python/tests/docs/test_merge_insert.py

@@ -18,15 +18,19 @@ def test_upsert(mem_db):
         {"id": 1, "name": "Bobby"},
         {"id": 2, "name": "Charlie"},
     ]
-    (
+    stats = (
         table.merge_insert("id")
         .when_matched_update_all()
         .when_not_matched_insert_all()
         .execute(new_users)
     )
     table.count_rows()  # 3
+    stats  # {'num_inserted_rows': 1, 'num_updated_rows': 1, 'num_deleted_rows': 0}
     # --8<-- [end:upsert_basic]
     assert table.count_rows() == 3
+    assert stats["num_inserted_rows"] == 1
+    assert stats["num_updated_rows"] == 1
+    assert stats["num_deleted_rows"] == 0
 
 
 @pytest.mark.asyncio
@@ -44,15 +48,19 @@ async def test_upsert_async(mem_db_async):
         {"id": 1, "name": "Bobby"},
         {"id": 2, "name": "Charlie"},
     ]
-    await (
+    stats = await (
         table.merge_insert("id")
         .when_matched_update_all()
         .when_not_matched_insert_all()
         .execute(new_users)
     )
     await table.count_rows()  # 3
+    stats  # {'num_inserted_rows': 1, 'num_updated_rows': 1, 'num_deleted_rows': 0}
     # --8<-- [end:upsert_basic_async]
     assert await table.count_rows() == 3
+    assert stats["num_inserted_rows"] == 1
+    assert stats["num_updated_rows"] == 1
+    assert stats["num_deleted_rows"] == 0
 
 
 def test_insert_if_not_exists(mem_db):
@@ -69,10 +77,16 @@ def test_insert_if_not_exists(mem_db):
         {"domain": "google.com", "name": "Google"},
         {"domain": "facebook.com", "name": "Facebook"},
     ]
-    (table.merge_insert("domain").when_not_matched_insert_all().execute(new_domains))
+    stats = (
+        table.merge_insert("domain").when_not_matched_insert_all().execute(new_domains)
+    )
     table.count_rows()  # 3
+    stats  # {'num_inserted_rows': 1, 'num_updated_rows': 0, 'num_deleted_rows': 0}
     # --8<-- [end:insert_if_not_exists]
     assert table.count_rows() == 3
+    assert stats["num_inserted_rows"] == 1
+    assert stats["num_updated_rows"] == 0
+    assert stats["num_deleted_rows"] == 0
 
 
 @pytest.mark.asyncio
@@ -90,12 +104,16 @@ async def test_insert_if_not_exists_async(mem_db_async):
         {"domain": "google.com", "name": "Google"},
         {"domain": "facebook.com", "name": "Facebook"},
     ]
-    await (
+    stats = await (
         table.merge_insert("domain").when_not_matched_insert_all().execute(new_domains)
     )
     await table.count_rows()  # 3
+    stats  # {'num_inserted_rows': 1, 'num_updated_rows': 0, 'num_deleted_rows': 0}
     # --8<-- [end:insert_if_not_exists_async]
     assert await table.count_rows() == 3
+    assert stats["num_inserted_rows"] == 1
+    assert stats["num_updated_rows"] == 0
+    assert stats["num_deleted_rows"] == 0
 
 
 def test_replace_range(mem_db):
@@ -113,7 +131,7 @@ def test_replace_range(mem_db):
     new_chunks = [
         {"doc_id": 1, "chunk_id": 0, "text": "Baz"},
     ]
-    (
+    stats = (
         table.merge_insert(["doc_id", "chunk_id"])
         .when_matched_update_all()
         .when_not_matched_insert_all()
@@ -121,8 +139,12 @@ def test_replace_range(mem_db):
         .execute(new_chunks)
     )
     table.count_rows("doc_id = 1")  # 1
+    stats  # {'num_inserted_rows': 0, 'num_updated_rows': 1, 'num_deleted_rows': 1}
     # --8<-- [end:replace_range]
     assert table.count_rows("doc_id = 1") == 1
+    assert stats["num_inserted_rows"] == 0
+    assert stats["num_updated_rows"] == 1
+    assert stats["num_deleted_rows"] == 1
 
 
 @pytest.mark.asyncio
@@ -141,7 +163,7 @@ async def test_replace_range_async(mem_db_async):
     new_chunks = [
         {"doc_id": 1, "chunk_id": 0, "text": "Baz"},
     ]
-    await (
+    stats = await (
         table.merge_insert(["doc_id", "chunk_id"])
         .when_matched_update_all()
         .when_not_matched_insert_all()
@@ -149,5 +171,9 @@ async def test_replace_range_async(mem_db_async):
         .execute(new_chunks)
     )
     await table.count_rows("doc_id = 1")  # 1
+    stats  # {'num_inserted_rows': 0, 'num_updated_rows': 1, 'num_deleted_rows': 1}
     # --8<-- [end:replace_range_async]
     assert await table.count_rows("doc_id = 1") == 1
+    assert stats["num_inserted_rows"] == 0
+    assert stats["num_updated_rows"] == 1
+    assert stats["num_deleted_rows"] == 1

python/python/tests/docs/test_search.py

@@ -6,7 +6,9 @@ import lancedb
 
 # --8<-- [end:import-lancedb]
 # --8<-- [start:import-numpy]
+from lancedb.query import BoostQuery, MatchQuery
 import numpy as np
+import pyarrow as pa
 
 # --8<-- [end:import-numpy]
 # --8<-- [start:import-datetime]
@@ -154,6 +156,84 @@ async def test_vector_search_async():
     # --8<-- [end:search_result_async_as_list]
 
 
+def test_fts_fuzzy_query():
+    uri = "data/fuzzy-example"
+    db = lancedb.connect(uri)
+
+    table = db.create_table(
+        "my_table_fts_fuzzy",
+        data=pa.table(
+            {
+                "text": [
+                    "fa",
+                    "fo",  # spellchecker:disable-line
+                    "fob",
+                    "focus",
+                    "foo",
+                    "food",
+                    "foul",
+                ]
+            }
+        ),
+        mode="overwrite",
+    )
+    table.create_fts_index("text", use_tantivy=False, replace=True)
+
+    results = table.search(MatchQuery("foo", "text", fuzziness=1)).to_pandas()
+    assert len(results) == 4
+    assert set(results["text"].to_list()) == {
+        "foo",
+        "fo",  # 1 deletion # spellchecker:disable-line
+        "fob",  # 1 substitution
+        "food",  # 1 insertion
+    }
+
+
+def test_fts_boost_query():
+    uri = "data/boost-example"
+    db = lancedb.connect(uri)
+
+    table = db.create_table(
+        "my_table_fts_boost",
+        data=pa.table(
+            {
+                "title": [
+                    "The Hidden Gems of Travel",
+                    "Exploring Nature's Wonders",
+                    "Cultural Treasures Unveiled",
+                    "The Nightlife Chronicles",
+                    "Scenic Escapes and Challenges",
+                ],
+                "desc": [
+                    "A vibrant city with occasional traffic jams.",
+                    "Beautiful landscapes but overpriced tourist spots.",
+                    "Rich cultural heritage but humid summers.",
+                    "Bustling nightlife but noisy streets.",
+                    "Scenic views but limited public transport options.",
+                ],
+            }
+        ),
+        mode="overwrite",
+    )
+    table.create_fts_index("desc", use_tantivy=False, replace=True)
+
+    results = table.search(
+        BoostQuery(
+            MatchQuery("beautiful, cultural, nightlife", "desc"),
+            MatchQuery("bad traffic jams, overpriced", "desc"),
+        ),
+    ).to_pandas()
+
+    # we will hit 3 results because the positive query has 3 hits
+    assert len(results) == 3
+    # the one containing "overpriced" will be negatively boosted,
+    # so it will be the last one
+    assert (
+        results["desc"].to_list()[2]
+        == "Beautiful landscapes but overpriced tourist spots."
+    )
+
+
 def test_fts_native():
     # --8<-- [start:basic_fts]
     uri = "data/sample-lancedb"

python/python/tests/test_embeddings_slow.py

@@ -11,7 +11,8 @@ import pandas as pd
 import pyarrow as pa
 import pytest
 from lancedb.embeddings import get_registry
-from lancedb.pydantic import LanceModel, Vector
+from lancedb.pydantic import LanceModel, Vector, MultiVector
+import requests
 
 # These are integration tests for embedding functions.
 # They are slow because they require downloading models
@@ -516,3 +517,125 @@ def test_voyageai_embedding_function():
 
     tbl.add(df)
     assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(
+    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
+)
+def test_voyageai_multimodal_embedding_function():
+    voyageai = (
+        get_registry().get("voyageai").create(name="voyage-multimodal-3", max_retries=0)
+    )
+
+    class Images(LanceModel):
+        label: str
+        image_uri: str = voyageai.SourceField()  # image uri as the source
+        image_bytes: bytes = voyageai.SourceField()  # image bytes as the source
+        vector: Vector(voyageai.ndims()) = voyageai.VectorField()  # vector column
+        vec_from_bytes: Vector(voyageai.ndims()) = (
+            voyageai.VectorField()
+        )  # Another vector column
+
+    db = lancedb.connect("~/lancedb")
+    table = db.create_table("test", schema=Images, mode="overwrite")
+    labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+    ]
+    # get each uri as bytes
+    image_bytes = [requests.get(uri).content for uri in uris]
+    table.add(
+        pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
+    )
+    assert len(table.to_pandas()["vector"][0]) == voyageai.ndims()
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(
+    os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
+)
+def test_voyageai_multimodal_embedding_text_function():
+    voyageai = (
+        get_registry().get("voyageai").create(name="voyage-multimodal-3", max_retries=0)
+    )
+
+    class TextModel(LanceModel):
+        text: str = voyageai.SourceField()
+        vector: Vector(voyageai.ndims()) = voyageai.VectorField()
+
+    df = pd.DataFrame({"text": ["hello world", "goodbye world"]})
+    db = lancedb.connect("~/lancedb")
+    tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+    tbl.add(df)
+    assert len(tbl.to_pandas()["vector"][0]) == voyageai.ndims()
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(
+    importlib.util.find_spec("colpali_engine") is None,
+    reason="colpali_engine not installed",
+)
+def test_colpali(tmp_path):
+    import requests
+    from lancedb.pydantic import LanceModel
+
+    db = lancedb.connect(tmp_path)
+    registry = get_registry()
+    func = registry.get("colpali").create()
+
+    class MediaItems(LanceModel):
+        text: str
+        image_uri: str = func.SourceField()
+        image_bytes: bytes = func.SourceField()
+        image_vectors: MultiVector(func.ndims()) = (
+            func.VectorField()
+        )  # Multivector image embeddings
+
+    table = db.create_table("media", schema=MediaItems)
+
+    texts = [
+        "a cute cat playing with yarn",
+        "a puppy in a flower field",
+        "a red sports car on the highway",
+        "a vintage bicycle leaning against a wall",
+        "a plate of delicious pasta",
+        "fresh fruit salad in a bowl",
+    ]
+
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+        "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+        "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+        "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+        "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+        "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+    ]
+
+    # Get images as bytes
+    image_bytes = [requests.get(uri).content for uri in uris]
+
+    table.add(
+        pd.DataFrame({"text": texts, "image_uri": uris, "image_bytes": image_bytes})
+    )
+
+    # Test text-to-image search
+    image_results = (
+        table.search("fluffy companion", vector_column_name="image_vectors")
+        .limit(1)
+        .to_pydantic(MediaItems)[0]
+    )
+    assert "cat" in image_results.text.lower() or "puppy" in image_results.text.lower()
+
+    # Verify multivector dimensions
+    first_row = table.to_arrow().to_pylist()[0]
+    assert len(first_row["image_vectors"]) > 1, "Should have multiple image vectors"
+    assert len(first_row["image_vectors"][0]) == func.ndims(), (
+        "Vector dimension mismatch"
+    )

python/python/tests/test_fts.py

@@ -20,7 +20,9 @@ from unittest import mock
 import lancedb as ldb
 from lancedb.db import DBConnection
 from lancedb.index import FTS
+from lancedb.query import BoostQuery, MatchQuery, MultiMatchQuery, PhraseQuery
 import numpy as np
+import pyarrow as pa
 import pandas as pd
 import pytest
 from utils import exception_output
@@ -178,11 +180,47 @@ def test_search_fts(table, use_tantivy):
     results = table.search("puppy").select(["id", "text"]).to_list()
     assert len(results) == 10
 
+    if not use_tantivy:
+        # Test with a query
+        results = (
+            table.search(MatchQuery("puppy", "text"))
+            .select(["id", "text"])
+            .limit(5)
+            .to_list()
+        )
+        assert len(results) == 5
+
+        # Test boost query
+        results = (
+            table.search(
+                BoostQuery(
+                    MatchQuery("puppy", "text"),
+                    MatchQuery("runs", "text"),
+                )
+            )
+            .select(["id", "text"])
+            .limit(5)
+            .to_list()
+        )
+        assert len(results) == 5
+
+        # Test multi match query
+        table.create_fts_index("text2", use_tantivy=use_tantivy)
+        results = (
+            table.search(MultiMatchQuery("puppy", ["text", "text2"]))
+            .select(["id", "text"])
+            .limit(5)
+            .to_list()
+        )
+        assert len(results) == 5
+        assert len(results[0]) == 3  # id, text, _score
+
 
 @pytest.mark.asyncio
 async def test_fts_select_async(async_table):
     tbl = await async_table
     await tbl.create_index("text", config=FTS())
+    await tbl.create_index("text2", config=FTS())
     results = (
         await tbl.query()
         .nearest_to_text("puppy")
@@ -193,6 +231,54 @@ async def test_fts_select_async(async_table):
     assert len(results) == 5
     assert len(results[0]) == 3  # id, text, _score
 
+    # Test with FullTextQuery
+    results = (
+        await tbl.query()
+        .nearest_to_text(MatchQuery("puppy", "text"))
+        .select(["id", "text"])
+        .limit(5)
+        .to_list()
+    )
+    assert len(results) == 5
+    assert len(results[0]) == 3  # id, text, _score
+
+    # Test with BoostQuery
+    results = (
+        await tbl.query()
+        .nearest_to_text(
+            BoostQuery(
+                MatchQuery("puppy", "text"),
+                MatchQuery("runs", "text"),
+            )
+        )
+        .select(["id", "text"])
+        .limit(5)
+        .to_list()
+    )
+    assert len(results) == 5
+    assert len(results[0]) == 3  # id, text, _score
+
+    # Test with MultiMatchQuery
+    results = (
+        await tbl.query()
+        .nearest_to_text(MultiMatchQuery("puppy", ["text", "text2"]))
+        .select(["id", "text"])
+        .limit(5)
+        .to_list()
+    )
+    assert len(results) == 5
+    assert len(results[0]) == 3  # id, text, _score
+
+    # Test with search() API
+    results = (
+        await (await tbl.search(MatchQuery("puppy", "text")))
+        .select(["id", "text"])
+        .limit(5)
+        .to_list()
+    )
+    assert len(results) == 5
+    assert len(results[0]) == 3  # id, text, _score
+
 
 def test_search_fts_phrase_query(table):
     table.create_fts_index("text", use_tantivy=False, with_position=False)
@@ -207,6 +293,13 @@ def test_search_fts_phrase_query(table):
     assert len(results) > len(phrase_results)
     assert len(phrase_results) > 0
 
+    # Test with a query
+    phrase_results = (
+        table.search(PhraseQuery("puppy runs", "text")).limit(100).to_list()
+    )
+    assert len(results) > len(phrase_results)
+    assert len(phrase_results) > 0
+
 
 @pytest.mark.asyncio
 async def test_search_fts_phrase_query_async(async_table):
@@ -227,6 +320,16 @@ async def test_search_fts_phrase_query_async(async_table):
     assert len(results) > len(phrase_results)
     assert len(phrase_results) > 0
 
+    # Test with a query
+    phrase_results = (
+        await async_table.query()
+        .nearest_to_text(PhraseQuery("puppy runs", "text"))
+        .limit(100)
+        .to_list()
+    )
+    assert len(results) > len(phrase_results)
+    assert len(phrase_results) > 0
+
 
 def test_search_fts_specify_column(table):
     table.create_fts_index("text", use_tantivy=False)
@@ -524,3 +627,32 @@ def test_language(mem_db: DBConnection):
     # Stop words -> no results
     results = table.search("la", query_type="fts").limit(5).to_list()
     assert len(results) == 0
+
+
+def test_fts_on_list(mem_db: DBConnection):
+    data = pa.table(
+        {
+            "text": [
+                ["lance database", "the", "search"],
+                ["lance database"],
+                ["lance", "search"],
+                ["database", "search"],
+                ["unrelated", "doc"],
+            ],
+            "vector": [
+                [1.0, 2.0, 3.0],
+                [4.0, 5.0, 6.0],
+                [7.0, 8.0, 9.0],
+                [10.0, 11.0, 12.0],
+                [13.0, 14.0, 15.0],
+            ],
+        }
+    )
+    table = mem_db.create_table("test", data=data)
+    table.create_fts_index("text", use_tantivy=False)
+
+    res = table.search("lance").limit(5).to_list()
+    assert len(res) == 3
+
+    res = table.search(PhraseQuery("lance database", "text")).limit(5).to_list()
+    assert len(res) == 2

python/python/tests/test_hybrid_query.py

@@ -4,13 +4,32 @@
 import lancedb
 
 from lancedb.query import LanceHybridQueryBuilder
+from lancedb.rerankers.rrf import RRFReranker
 import pyarrow as pa
 import pyarrow.compute as pc
 import pytest
 import pytest_asyncio
 
 from lancedb.index import FTS
-from lancedb.table import AsyncTable
+from lancedb.table import AsyncTable, Table
+
+
+@pytest.fixture
+def sync_table(tmpdir_factory) -> Table:
+    tmp_path = str(tmpdir_factory.mktemp("data"))
+    db = lancedb.connect(tmp_path)
+    data = pa.table(
+        {
+            "text": pa.array(["a", "b", "cat", "dog"]),
+            "vector": pa.array(
+                [[0.1, 0.1], [2, 2], [-0.1, -0.1], [0.5, -0.5]],
+                type=pa.list_(pa.float32(), list_size=2),
+            ),
+        }
+    )
+    table = db.create_table("test", data)
+    table.create_fts_index("text", with_position=False, use_tantivy=False)
+    return table
 
 
 @pytest_asyncio.fixture
@@ -102,6 +121,42 @@ async def test_async_hybrid_query_default_limit(table: AsyncTable):
     assert texts.count("a") == 1
 
 
+def test_hybrid_query_distance_range(sync_table: Table):
+    reranker = RRFReranker(return_score="all")
+    result = (
+        sync_table.search(query_type="hybrid")
+        .vector([0.0, 0.4])
+        .text("cat and dog")
+        .distance_range(lower_bound=0.2, upper_bound=0.5)
+        .rerank(reranker)
+        .limit(2)
+        .to_arrow()
+    )
+    assert len(result) == 2
+    print(result)
+    for dist in result["_distance"]:
+        if dist.is_valid:
+            assert 0.2 <= dist.as_py() <= 0.5
+
+
+@pytest.mark.asyncio
+async def test_hybrid_query_distance_range_async(table: AsyncTable):
+    reranker = RRFReranker(return_score="all")
+    result = await (
+        table.query()
+        .nearest_to([0.0, 0.4])
+        .nearest_to_text("cat and dog")
+        .distance_range(lower_bound=0.2, upper_bound=0.5)
+        .rerank(reranker)
+        .limit(2)
+        .to_arrow()
+    )
+    assert len(result) == 2
+    for dist in result["_distance"]:
+        if dist.is_valid:
+            assert 0.2 <= dist.as_py() <= 0.5
+
+
 @pytest.mark.asyncio
 async def test_explain_plan(table: AsyncTable):
     plan = await (
@@ -114,6 +169,16 @@ async def test_explain_plan(table: AsyncTable):
     assert "LanceScan" in plan
 
 
+@pytest.mark.asyncio
+async def test_analyze_plan(table: AsyncTable):
+    res = await (
+        table.query().nearest_to_text("dog").nearest_to([0.1, 0.1]).analyze_plan()
+    )
+
+    assert "AnalyzeExec" in res
+    assert "metrics=" in res
+
+
 def test_normalize_scores():
     cases = [
         (pa.array([0.1, 0.4]), pa.array([0.0, 1.0])),

python/python/tests/test_index.py

@@ -8,7 +8,7 @@ import pyarrow as pa
 import pytest
 import pytest_asyncio
 from lancedb import AsyncConnection, AsyncTable, connect_async
-from lancedb.index import BTree, IvfFlat, IvfPq, Bitmap, LabelList, HnswPq, HnswSq
+from lancedb.index import BTree, IvfFlat, IvfPq, Bitmap, LabelList, HnswPq, HnswSq, FTS
 
 
 @pytest_asyncio.fixture
@@ -31,6 +31,7 @@ async def some_table(db_async):
         {
             "id": list(range(NROWS)),
             "vector": sample_fixed_size_list_array(NROWS, DIM),
+            "fsb": pa.array([bytes([i]) for i in range(NROWS)], pa.binary(1)),
             "tags": [
                 [f"tag{random.randint(0, 8)}" for _ in range(2)] for _ in range(NROWS)
             ],
@@ -85,6 +86,16 @@ async def test_create_scalar_index(some_table: AsyncTable):
     assert len(indices) == 0
 
 
+@pytest.mark.asyncio
+async def test_create_fixed_size_binary_index(some_table: AsyncTable):
+    await some_table.create_index("fsb", config=BTree())
+    indices = await some_table.list_indices()
+    assert str(indices) == '[Index(BTree, columns=["fsb"], name="fsb_idx")]'
+    assert len(indices) == 1
+    assert indices[0].index_type == "BTree"
+    assert indices[0].columns == ["fsb"]
+
+
 @pytest.mark.asyncio
 async def test_create_bitmap_index(some_table: AsyncTable):
     await some_table.create_index("id", config=Bitmap())
@@ -108,6 +119,18 @@ async def test_create_label_list_index(some_table: AsyncTable):
     assert str(indices) == '[Index(LabelList, columns=["tags"], name="tags_idx")]'
 
 
+@pytest.mark.asyncio
+async def test_full_text_search_index(some_table: AsyncTable):
+    await some_table.create_index("tags", config=FTS(with_position=False))
+    indices = await some_table.list_indices()
+    assert str(indices) == '[Index(FTS, columns=["tags"], name="tags_idx")]'
+
+    await some_table.prewarm_index("tags_idx")
+
+    res = await (await some_table.search("tag0")).to_arrow()
+    assert res.num_rows > 0
+
+
 @pytest.mark.asyncio
 async def test_create_vector_index(some_table: AsyncTable):
     # Can create

python/python/tests/test_pydantic.py

@@ -9,7 +9,13 @@ from typing import List, Optional, Tuple
 import pyarrow as pa
 import pydantic
 import pytest
-from lancedb.pydantic import PYDANTIC_VERSION, LanceModel, Vector, pydantic_to_schema
+from lancedb.pydantic import (
+    PYDANTIC_VERSION,
+    LanceModel,
+    Vector,
+    pydantic_to_schema,
+    MultiVector,
+)
 from pydantic import BaseModel
 from pydantic import Field
 
@@ -354,3 +360,55 @@ def test_optional_nested_model():
             ),
         ]
     )
+
+
+def test_multi_vector():
+    class TestModel(pydantic.BaseModel):
+        vec: MultiVector(8)
+
+    schema = pydantic_to_schema(TestModel)
+    assert schema == pa.schema(
+        [pa.field("vec", pa.list_(pa.list_(pa.float32(), 8)), True)]
+    )
+
+    with pytest.raises(pydantic.ValidationError):
+        TestModel(vec=[[1.0] * 7])
+
+    with pytest.raises(pydantic.ValidationError):
+        TestModel(vec=[[1.0] * 9])
+
+    TestModel(vec=[[1.0] * 8])
+    TestModel(vec=[[1.0] * 8, [2.0] * 8])
+
+    TestModel(vec=[])
+
+
+def test_multi_vector_nullable():
+    class NullableModel(pydantic.BaseModel):
+        vec: MultiVector(16, nullable=False)
+
+    schema = pydantic_to_schema(NullableModel)
+    assert schema == pa.schema(
+        [pa.field("vec", pa.list_(pa.list_(pa.float32(), 16)), False)]
+    )
+
+    class DefaultModel(pydantic.BaseModel):
+        vec: MultiVector(16)
+
+    schema = pydantic_to_schema(DefaultModel)
+    assert schema == pa.schema(
+        [pa.field("vec", pa.list_(pa.list_(pa.float32(), 16)), True)]
+    )
+
+
+def test_multi_vector_in_lance_model():
+    class TestModel(LanceModel):
+        id: int
+        vectors: MultiVector(16) = Field(default=[[0.0] * 16])
+
+    schema = pydantic_to_schema(TestModel)
+    assert schema == TestModel.to_arrow_schema()
+    assert TestModel.field_names() == ["id", "vectors"]
+
+    t = TestModel(id=1)
+    assert t.vectors == [[0.0] * 16]

python/python/tests/test_query.py

@@ -257,7 +257,9 @@ async def test_distance_range_with_new_rows_async():
         }
     )
     table = await conn.create_table("test", data)
-    table.create_index("vector", config=IvfPq(num_partitions=1, num_sub_vectors=2))
+    await table.create_index(
+        "vector", config=IvfPq(num_partitions=1, num_sub_vectors=2)
+    )
 
     q = [0, 0]
     rs = await table.query().nearest_to(q).to_arrow()
@@ -511,7 +513,8 @@ def test_query_builder_with_different_vector_column():
             columns=["b"],
             vector_column="foo_vector",
         ),
-        None,
+        batch_size=None,
+        timeout=None,
     )
 
 
@@ -702,6 +705,20 @@ async def test_fast_search_async(tmp_path):
     assert "LanceScan" not in plan
 
 
+def test_analyze_plan(table):
+    q = LanceVectorQueryBuilder(table, [0, 0], "vector")
+    res = q.analyze_plan()
+    assert "AnalyzeExec" in res
+    assert "metrics=" in res
+
+
+@pytest.mark.asyncio
+async def test_analyze_plan_async(table_async: AsyncTable):
+    res = await table_async.query().nearest_to(pa.array([1, 2])).analyze_plan()
+    assert "AnalyzeExec" in res
+    assert "metrics=" in res
+
+
 def test_explain_plan(table):
     q = LanceVectorQueryBuilder(table, [0, 0], "vector")
     plan = q.explain_plan(verbose=True)
@@ -1062,3 +1079,67 @@ async def test_query_serialization_async(table_async: AsyncTable):
         full_text_query=FullTextSearchQuery(columns=[], query="foo"),
         with_row_id=False,
     )
+
+
+def test_query_timeout(tmp_path):
+    # Use local directory instead of memory:// to add a bit of latency to
+    # operations so a timeout of zero will trigger exceptions.
+    db = lancedb.connect(tmp_path)
+    data = pa.table(
+        {
+            "text": ["a", "b"],
+            "vector": pa.FixedSizeListArray.from_arrays(
+                pc.random(4).cast(pa.float32()), 2
+            ),
+        }
+    )
+    table = db.create_table("test", data)
+    table.create_fts_index("text", use_tantivy=False)
+
+    with pytest.raises(Exception, match="Query timeout"):
+        table.search().where("text = 'a'").to_list(timeout=timedelta(0))
+
+    with pytest.raises(Exception, match="Query timeout"):
+        table.search([0.0, 0.0]).to_arrow(timeout=timedelta(0))
+
+    with pytest.raises(Exception, match="Query timeout"):
+        table.search("a", query_type="fts").to_pandas(timeout=timedelta(0))
+
+    with pytest.raises(Exception, match="Query timeout"):
+        table.search(query_type="hybrid").vector([0.0, 0.0]).text("a").to_arrow(
+            timeout=timedelta(0)
+        )
+
+
+@pytest.mark.asyncio
+async def test_query_timeout_async(tmp_path):
+    db = await lancedb.connect_async(tmp_path)
+    data = pa.table(
+        {
+            "text": ["a", "b"],
+            "vector": pa.FixedSizeListArray.from_arrays(
+                pc.random(4).cast(pa.float32()), 2
+            ),
+        }
+    )
+    table = await db.create_table("test", data)
+    await table.create_index("text", config=FTS())
+
+    with pytest.raises(Exception, match="Query timeout"):
+        await table.query().where("text != 'a'").to_list(timeout=timedelta(0))
+
+    with pytest.raises(Exception, match="Query timeout"):
+        await table.vector_search([0.0, 0.0]).to_arrow(timeout=timedelta(0))
+
+    with pytest.raises(Exception, match="Query timeout"):
+        await (await table.search("a", query_type="fts")).to_pandas(
+            timeout=timedelta(0)
+        )
+
+    with pytest.raises(Exception, match="Query timeout"):
+        await (
+            table.query()
+            .nearest_to_text("a")
+            .nearest_to([0.0, 0.0])
+            .to_list(timeout=timedelta(0))
+        )

python/python/tests/test_remote_db.py

@@ -1,6 +1,6 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
-
+import re
 from concurrent.futures import ThreadPoolExecutor
 import contextlib
 from datetime import timedelta
@@ -235,6 +235,10 @@ def test_table_add_in_threadpool():
 
 def test_table_create_indices():
     def handler(request):
+        index_stats = dict(
+            index_type="IVF_PQ", num_indexed_rows=1000, num_unindexed_rows=0
+        )
+
         if request.path == "/v1/table/test/create_index/":
             request.send_response(200)
             request.end_headers()
@@ -258,6 +262,47 @@ def test_table_create_indices():
                 )
             )
             request.wfile.write(payload.encode())
+        elif request.path == "/v1/table/test/index/list/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(
+                dict(
+                    indexes=[
+                        {
+                            "index_name": "id_idx",
+                            "columns": ["id"],
+                        },
+                        {
+                            "index_name": "text_idx",
+                            "columns": ["text"],
+                        },
+                        {
+                            "index_name": "vector_idx",
+                            "columns": ["vector"],
+                        },
+                    ]
+                )
+            )
+            request.wfile.write(payload.encode())
+        elif request.path == "/v1/table/test/index/id_idx/stats/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(index_stats)
+            request.wfile.write(payload.encode())
+        elif request.path == "/v1/table/test/index/text_idx/stats/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(index_stats)
+            request.wfile.write(payload.encode())
+        elif request.path == "/v1/table/test/index/vector_idx/stats/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(index_stats)
+            request.wfile.write(payload.encode())
         elif "/drop/" in request.path:
             request.send_response(200)
             request.end_headers()
@@ -269,14 +314,125 @@ def test_table_create_indices():
         # Parameters are well-tested through local and async tests.
         # This is a smoke-test.
         table = db.create_table("test", [{"id": 1}])
-        table.create_scalar_index("id")
+        table.create_scalar_index("id", wait_timeout=timedelta(seconds=2))
-        table.create_fts_index("text")
+        table.create_fts_index("text", wait_timeout=timedelta(seconds=2))
-        table.create_scalar_index("vector")
+        table.create_index(
+            vector_column_name="vector", wait_timeout=timedelta(seconds=10)
+        )
+        table.wait_for_index(["id_idx"], timedelta(seconds=2))
+        table.wait_for_index(["text_idx", "vector_idx"], timedelta(seconds=2))
         table.drop_index("vector_idx")
         table.drop_index("id_idx")
         table.drop_index("text_idx")
 
 
+def test_table_wait_for_index_timeout():
+    def handler(request):
+        index_stats = dict(
+            index_type="BTREE", num_indexed_rows=1000, num_unindexed_rows=1
+        )
+
+        if request.path == "/v1/table/test/create/?mode=create":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"{}")
+        elif request.path == "/v1/table/test/describe/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(
+                dict(
+                    version=1,
+                    schema=dict(
+                        fields=[
+                            dict(name="id", type={"type": "int64"}, nullable=False),
+                        ]
+                    ),
+                )
+            )
+            request.wfile.write(payload.encode())
+        elif request.path == "/v1/table/test/index/list/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(
+                dict(
+                    indexes=[
+                        {
+                            "index_name": "id_idx",
+                            "columns": ["id"],
+                        },
+                    ]
+                )
+            )
+            request.wfile.write(payload.encode())
+        elif request.path == "/v1/table/test/index/id_idx/stats/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(index_stats)
+            print(f"{index_stats=}")
+            request.wfile.write(payload.encode())
+        else:
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.create_table("test", [{"id": 1}])
+        with pytest.raises(
+            RuntimeError,
+            match=re.escape(
+                'Timeout error: timed out waiting for indices: ["id_idx"] after 1s'
+            ),
+        ):
+            table.wait_for_index(["id_idx"], timedelta(seconds=1))
+
+
+def test_stats():
+    stats = {
+        "total_bytes": 38,
+        "num_rows": 2,
+        "num_indices": 0,
+        "fragment_stats": {
+            "num_fragments": 1,
+            "num_small_fragments": 1,
+            "lengths": {
+                "min": 2,
+                "max": 2,
+                "mean": 2,
+                "p25": 2,
+                "p50": 2,
+                "p75": 2,
+                "p99": 2,
+            },
+        },
+    }
+
+    def handler(request):
+        if request.path == "/v1/table/test/create/?mode=create":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            request.wfile.write(b"{}")
+        elif request.path == "/v1/table/test/stats/":
+            request.send_response(200)
+            request.send_header("Content-Type", "application/json")
+            request.end_headers()
+            payload = json.dumps(stats)
+            request.wfile.write(payload.encode())
+        else:
+            print(request.path)
+            request.send_response(404)
+            request.end_headers()
+
+    with mock_lancedb_connection(handler) as db:
+        table = db.create_table("test", [{"id": 1}])
+        res = table.stats()
+        print(f"{res=}")
+        assert res == stats
+
+
 @contextlib.contextmanager
 def query_test_table(query_handler, *, server_version=Version("0.1.0")):
     def handler(request):
@@ -444,6 +600,16 @@ def test_query_sync_fts():
             "prefilter": True,
             "with_row_id": True,
             "version": None,
+        } or body == {
+            "full_text_query": {
+                "query": "puppy",
+                "columns": ["description", "name"],
+            },
+            "k": 42,
+            "vector": [],
+            "prefilter": True,
+            "with_row_id": True,
+            "version": None,
         }
 
         return pa.table({"id": [1, 2, 3]})

python/python/tests/test_rerankers.py

@@ -457,3 +457,45 @@ def test_voyageai_reranker(tmp_path, use_tantivy):
     reranker = VoyageAIReranker(model_name="rerank-2")
     table, schema = get_test_table(tmp_path, use_tantivy)
     _run_test_reranker(reranker, table, "single player experience", None, schema)
+
+
+def test_empty_result_reranker():
+    pytest.importorskip("sentence_transformers")
+    db = lancedb.connect("memory://")
+
+    # Define schema
+    schema = pa.schema(
+        [
+            ("id", pa.int64()),
+            ("text", pa.string()),
+            ("vector", pa.list_(pa.float32(), 128)),  # 128-dimensional vector
+        ]
+    )
+
+    # Create empty table with schema
+    empty_table = db.create_table("empty_table", schema=schema, mode="overwrite")
+    empty_table.create_fts_index("text", use_tantivy=False, replace=True)
+    for reranker in [
+        CrossEncoderReranker(),
+        # ColbertReranker(),
+        # AnswerdotaiRerankers(),
+        # OpenaiReranker(),
+        # JinaReranker(),
+        # VoyageAIReranker(model_name="rerank-2"),
+    ]:
+        results = (
+            empty_table.search(list(range(128)))
+            .limit(3)
+            .rerank(reranker, "query")
+            .to_arrow()
+        )
+        # check if empty set contains _relevance_score column
+        assert "_relevance_score" in results.column_names
+        assert len(results) == 0
+
+        results = (
+            empty_table.search("query", query_type="fts")
+            .limit(3)
+            .rerank(reranker)
+            .to_arrow()
+        )