Bump version: 0.25.3-beta.6 → 0.25.3

Closes #2767

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.22.2-beta.2"
+current_version = "0.22.3-beta.5"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/codex-update-lance-dependency.yml

@@ -0,0 +1,107 @@
+name: Codex Update Lance Dependency
+
+on:
+  workflow_call:
+    inputs:
+      tag:
+        description: "Tag name from Lance"
+        required: true
+        type: string
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: "Tag name from Lance"
+        required: true
+        type: string
+
+permissions:
+  contents: write
+  pull-requests: write
+  actions: read
+
+jobs:
+  update:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Show inputs
+        run: |
+          echo "tag = ${{ inputs.tag }}"
+
+      - name: Checkout Repo LanceDB
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
+          persist-credentials: true
+
+      - name: Set up Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: 20
+
+      - name: Install Codex CLI
+        run: npm install -g @openai/codex
+
+      - name: Install Rust toolchain
+        uses: dtolnay/rust-toolchain@stable
+        with:
+          toolchain: stable
+          components: clippy, rustfmt
+
+      - name: Install system dependencies
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y protobuf-compiler libssl-dev
+
+      - name: Install cargo-info
+        run: cargo install cargo-info
+
+      - name: Install Python dependencies
+        run: python3 -m pip install --upgrade pip packaging
+
+      - name: Configure git user
+        run: |
+          git config user.name "lancedb automation"
+          git config user.email "robot@lancedb.com"
+
+      - name: Configure Codex authentication
+        env:
+          CODEX_TOKEN_B64: ${{ secrets.CODEX_TOKEN }}
+        run: |
+          if [ -z "${CODEX_TOKEN_B64}" ]; then
+            echo "Repository secret CODEX_TOKEN is not defined; skipping Codex execution."
+            exit 1
+          fi
+          mkdir -p ~/.codex
+          echo "${CODEX_TOKEN_B64}" | base64 --decode > ~/.codex/auth.json
+
+      - name: Run Codex to update Lance dependency
+        env:
+          TAG: ${{ inputs.tag }}
+          GITHUB_TOKEN: ${{ secrets.ROBOT_TOKEN }}
+          GH_TOKEN: ${{ secrets.ROBOT_TOKEN }}
+        run: |
+          set -euo pipefail
+          VERSION="${TAG#refs/tags/}"
+          VERSION="${VERSION#v}"
+          BRANCH_NAME="codex/update-lance-${VERSION//[^a-zA-Z0-9]/-}"
+          cat <<EOF >/tmp/codex-prompt.txt
+          You are running inside the lancedb repository on a GitHub Actions runner. Update the Lance dependency to version ${VERSION} and prepare a pull request for maintainers to review.
+
+          Follow these steps exactly:
+          1. Use script "ci/set_lance_version.py" to update Lance dependencies. The script already refreshes Cargo metadata, so allow it to finish even if it takes time.
+          2. Run "cargo clippy --workspace --tests --all-features -- -D warnings". If diagnostics appear, fix them yourself and rerun clippy until it exits cleanly. Do not skip any warnings.
+          3. After clippy succeeds, run "cargo fmt --all" to format the workspace.
+          4. Ensure the repository is clean except for intentional changes. Inspect "git status --short" and "git diff" to confirm the dependency update and any required fixes.
+          5. Create and switch to a new branch named "${BRANCH_NAME}" (replace any duplicated hyphens if necessary).
+          6. Stage all relevant files with "git add -A". Commit using the message "chore: update lance dependency to v${VERSION}".
+          7. Push the branch to origin. If the branch already exists, force-push your changes.
+          8. env "GH_TOKEN" is available, use "gh" tools for github related operations like creating pull request.
+          9. Create a pull request targeting "main" with title "chore: update lance dependency to v${VERSION}". In the body, summarize the dependency bump, clippy/fmt verification, and link the triggering tag (${TAG}).
+          10. After creating the PR, display the PR URL, "git status --short", and a concise summary of the commands run and their results.
+
+          Constraints:
+          - Use bash commands; avoid modifying GitHub workflow files other than through the scripted task above.
+          - Do not merge the PR.
+          - If any command fails, diagnose and fix the issue instead of aborting.
+          EOF
+          codex --config shell_environment_policy.ignore_default_excludes=true exec --dangerously-bypass-approvals-and-sandbox "$(cat /tmp/codex-prompt.txt)"

AGENTS.md

@@ -0,0 +1,101 @@
+LanceDB is a database designed for retrieval, including vector, full-text, and hybrid search.
+It is a wrapper around Lance. There are two backends: local (in-process like SQLite) and
+remote (against LanceDB Cloud).
+
+The core of LanceDB is written in Rust. There are bindings in Python, Typescript, and Java.
+
+Project layout:
+
+* `rust/lancedb`: The LanceDB core Rust implementation.
+* `python`: The Python bindings, using PyO3.
+* `nodejs`: The Typescript bindings, using napi-rs
+* `java`: The Java bindings
+
+Common commands:
+
+* Check for compiler errors: `cargo check --quiet --features remote --tests --examples`
+* Run tests: `cargo test --quiet --features remote --tests`
+* Run specific test: `cargo test --quiet --features remote -p <package_name> --test <test_name>`
+* Lint: `cargo clippy --quiet --features remote --tests --examples`
+* Format: `cargo fmt --all`
+
+Before committing changes, run formatting.
+
+## Coding tips
+
+* When writing Rust doctests for things that require a connection or table reference,
+  write them as a function instead of a fully executable test. This allows type checking
+  to run but avoids needing a full test environment. For example:
+    ```rust
+    /// ```
+    /// use lance_index::scalar::FullTextSearchQuery;
+    /// use lancedb::query::{QueryBase, ExecutableQuery};
+    ///
+    /// # use lancedb::Table;
+    /// # async fn query(table: &Table) -> Result<(), Box<dyn std::error::Error>> {
+    /// let results = table.query()
+    ///     .full_text_search(FullTextSearchQuery::new("hello world".into()))
+    ///     .execute()
+    ///     .await?;
+    /// # Ok(())
+    /// # }
+    /// ```
+    ```
+
+## Example plan: adding a new method on Table
+
+Adding a new method involves first adding it to the Rust core, then exposing it
+in the Python and TypeScript bindings. There are both local and remote tables.
+Remote tables are implemented via a HTTP API and require the `remote` cargo
+feature flag to be enabled. Python has both sync and async methods.
+
+Rust core changes:
+
+1. Add method on `Table` struct in `rust/lancedb/src/table.rs` (calls `BaseTable` trait).
+2. Add method to `BaseTable` trait in `rust/lancedb/src/table.rs`.
+3. Implement new trait method on `NativeTable` in `rust/lancedb/src/table.rs`.
+    * Test with unit test in `rust/lancedb/src/table.rs`.
+4. Implement new trait method on `RemoteTable` in `rust/lancedb/src/remote/table.rs`.
+    * Test with unit test in `rust/lancedb/src/remote/table.rs` against mocked endpoint.
+
+Python bindings changes:
+
+1. Add PyO3 method binding in `python/src/table.rs`. Run `make develop` to compile bindings.
+2. Add types for PyO3 method in `python/python/lancedb/_lancedb.pyi`.
+3. Add method to `AsyncTable` class in `python/python/lancedb/table.py`.
+4. Add abstract method to `Table` abstract base class in `python/python/lancedb/table.py`.
+5. Add concrete sync method to `LanceTable` class in `python/python/lancedb/table.py`.
+    * Should use `LOOP.run()` to call the corresponding `AsyncTable` method.
+6. Add concrete sync method to `RemoteTable` class in `python/python/lancedb/remote/table.py`.
+7. Add unit test in `python/tests/test_table.py`.
+
+TypeScript bindings changes:
+
+1. Add napi-rs method binding on `Table` in `nodejs/src/table.rs`.
+2. Run `npm run build` to generate TypeScript definitions.
+3. Add typescript method on abstract class `Table` in `nodejs/src/table.ts`.
+4. Add concrete method on `LocalTable` class in `nodejs/src/native_table.ts`.
+    * Note: despite the name, this class is also used for remote tables.
+5. Add test in `nodejs/__test__/table.test.ts`.
+6. Run `npm run docs` to generate TypeScript documentation.
+
+## Review Guidelines
+
+Please consider the following when reviewing code contributions.
+
+### Rust API design
+* Design public APIs so they can be evolved easily in the future without breaking
+  changes. Often this means using builder patterns or options structs instead of
+  long argument lists.
+* For public APIs, prefer inputs that use `Into<T>` or `AsRef<T>` traits to allow
+  more flexible inputs. For example, use `name: Into<String>` instead of `name: String`,
+  so we don't have to write `func("my_string".to_string())`.
+
+### Testing
+* Ensure all new public APIs have documentation and examples.
+* Ensure that all bugfixes and features have corresponding tests. **We do not merge
+  code without tests.**
+
+### Documentation
+* New features must include updates to the rust documentation comments. Link to
+  relevant structs and methods to increase the value of documentation.

CLAUDE.md

@@ -1,80 +0,0 @@
-LanceDB is a database designed for retrieval, including vector, full-text, and hybrid search.
-It is a wrapper around Lance. There are two backends: local (in-process like SQLite) and
-remote (against LanceDB Cloud).
-
-The core of LanceDB is written in Rust. There are bindings in Python, Typescript, and Java.
-
-Project layout:
-
-* `rust/lancedb`: The LanceDB core Rust implementation.
-* `python`: The Python bindings, using PyO3.
-* `nodejs`: The Typescript bindings, using napi-rs
-* `java`: The Java bindings
-
-Common commands:
-
-* Check for compiler errors: `cargo check --quiet --features remote --tests --examples`
-* Run tests: `cargo test --quiet --features remote --tests`
-* Run specific test: `cargo test --quiet --features remote -p <package_name> --test <test_name>`
-* Lint: `cargo clippy --quiet --features remote --tests --examples`
-* Format: `cargo fmt --all`
-
-Before committing changes, run formatting.
-
-## Coding tips
-
-* When writing Rust doctests for things that require a connection or table reference,
-  write them as a function instead of a fully executable test. This allows type checking
-  to run but avoids needing a full test environment. For example:
-    ```rust
-    /// ```
-    /// use lance_index::scalar::FullTextSearchQuery;
-    /// use lancedb::query::{QueryBase, ExecutableQuery};
-    ///
-    /// # use lancedb::Table;
-    /// # async fn query(table: &Table) -> Result<(), Box<dyn std::error::Error>> {
-    /// let results = table.query()
-    ///     .full_text_search(FullTextSearchQuery::new("hello world".into()))
-    ///     .execute()
-    ///     .await?;
-    /// # Ok(())
-    /// # }
-    /// ```
-    ```
-
-## Example plan: adding a new method on Table
-
-Adding a new method involves first adding it to the Rust core, then exposing it
-in the Python and TypeScript bindings. There are both local and remote tables.
-Remote tables are implemented via a HTTP API and require the `remote` cargo
-feature flag to be enabled. Python has both sync and async methods.
-
-Rust core changes:
-
-1. Add method on `Table` struct in `rust/lancedb/src/table.rs` (calls `BaseTable` trait).
-2. Add method to `BaseTable` trait in `rust/lancedb/src/table.rs`.
-3. Implement new trait method on `NativeTable` in `rust/lancedb/src/table.rs`.
-    * Test with unit test in `rust/lancedb/src/table.rs`.
-4. Implement new trait method on `RemoteTable` in `rust/lancedb/src/remote/table.rs`.
-    * Test with unit test in `rust/lancedb/src/remote/table.rs` against mocked endpoint.
-
-Python bindings changes:
-
-1. Add PyO3 method binding in `python/src/table.rs`. Run `make develop` to compile bindings.
-2. Add types for PyO3 method in `python/python/lancedb/_lancedb.pyi`.
-3. Add method to `AsyncTable` class in `python/python/lancedb/table.py`.
-4. Add abstract method to `Table` abstract base class in `python/python/lancedb/table.py`.
-5. Add concrete sync method to `LanceTable` class in `python/python/lancedb/table.py`.
-    * Should use `LOOP.run()` to call the corresponding `AsyncTable` method.
-6. Add concrete sync method to `RemoteTable` class in `python/python/lancedb/remote/table.py`.
-7. Add unit test in `python/tests/test_table.py`.
-
-TypeScript bindings changes:
-
-1. Add napi-rs method binding on `Table` in `nodejs/src/table.rs`.
-2. Run `npm run build` to generate TypeScript definitions.
-3. Add typescript method on abstract class `Table` in `nodejs/src/table.ts`.
-4. Add concrete method on `LocalTable` class in `nodejs/src/native_table.ts`.
-    * Note: despite the name, this class is also used for remote tables.
-5. Add test in `nodejs/__test__/table.test.ts`.
-6. Run `npm run docs` to generate TypeScript documentation.

CLAUDE.md

@@ -0,0 +1 @@
+AGENTS.md

Cargo.lock

@@ -72,12 +72,6 @@ version = "0.2.21"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "683d7910e743518b0e34f1186f92494becacb047c7b6bf616c96772180fef923"
 
-[[package]]
-name = "android-tzdata"
-version = "0.1.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "e999941b234f3131b00bc13c22d06e8c5ff726d1b6318ac7eb276997bbb4fef0"
-
 [[package]]
 name = "android_system_properties"
 version = "0.1.5"
@@ -1474,17 +1468,16 @@ checksum = "613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724"
 
 [[package]]
 name = "chrono"
-version = "0.4.41"
+version = "0.4.42"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c469d952047f47f91b68d1cba3f10d63c11d73e4636f24f08daf0278abf01c4d"
+checksum = "145052bdd345b87320e369255277e3fb5152762ad123a901ef5c262dd38fe8d2"
 dependencies = [
- "android-tzdata",
  "iana-time-zone",
  "js-sys",
  "num-traits",
  "serde",
  "wasm-bindgen",
- "windows-link 0.1.3",
+ "windows-link 0.2.1",
 ]
 
 [[package]]
@@ -1573,7 +1566,7 @@ checksum = "e0d05af1e006a2407bedef5af410552494ce5be9090444dbbcb57258c1af3d56"
 dependencies = [
  "crossterm 0.27.0",
  "crossterm 0.28.1",
- "strum 0.26.3",
+ "strum",
  "strum_macros 0.26.4",
  "unicode-width",
 ]
@@ -2940,18 +2933,6 @@ version = "0.2.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "f8eb564c5c7423d25c886fb561d1e4ee69f72354d16918afa32c08811f6b6a55"
 
-[[package]]
-name = "fastbloom"
-version = "0.14.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "18c1ddb9231d8554c2d6bdf4cfaabf0c59251658c68b6c95cd52dd0c513a912a"
-dependencies = [
- "getrandom 0.3.3",
- "libm",
- "rand 0.9.2",
- "siphasher",
-]
-
 [[package]]
 name = "fastdivide"
 version = "0.4.2"
@@ -3051,9 +3032,9 @@ checksum = "42703706b716c37f96a77aea830392ad231f44c9e9a67872fa5548707e11b11c"
 
 [[package]]
 name = "fsst"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "480fc4f47567da549ab44bb2f37f6db1570c9eff7200e50334b69fa1daa74339"
+checksum = "1d2475ce218217196b161b025598f77e2b405d5e729f7c37bfff145f5df00a41"
 dependencies = [
  "arrow-array",
  "rand 0.9.2",
@@ -3850,7 +3831,7 @@ dependencies = [
  "js-sys",
  "log",
  "wasm-bindgen",
- "windows-core 0.62.2",
+ "windows-core 0.61.2",
 ]
 
 [[package]]
@@ -4237,9 +4218,9 @@ dependencies = [
 
 [[package]]
 name = "lance"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "5e2d2472f58d01894bc5f0a9f9d28dfca4649c9e28faf467c47e87f788ef322b"
+checksum = "a2f0ca022d0424d991933a62d2898864cf5621873962bd84e65e7d1f023f9c36"
 dependencies = [
  "arrow",
  "arrow-arith",
@@ -4278,6 +4259,7 @@ dependencies = [
  "lance-index",
  "lance-io",
  "lance-linalg",
+ "lance-namespace",
  "lance-table",
  "log",
  "moka",
@@ -4288,6 +4270,7 @@ dependencies = [
  "prost-types",
  "rand 0.9.2",
  "roaring",
+ "semver",
  "serde",
  "serde_json",
  "snafu",
@@ -4301,9 +4284,9 @@ dependencies = [
 
 [[package]]
 name = "lance-arrow"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "a2abba8770c4217fbdc8b517cdfb7183639b02dc5c2bcad1e7c69ffdcf4fbe1a"
+checksum = "7552f8d528775bf0ab21e1f75dcb70bdb2a828eeae58024a803b5a4655fd9a11"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -4321,9 +4304,9 @@ dependencies = [
 
 [[package]]
 name = "lance-bitpacking"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "efb7af69bff8d8499999684f961b0a4dc6e159065c773041545d19bc158f0814"
+checksum = "a2ea14583cc6fa0bb190bcc2d3bc364b0aa545b345702976025f810e4740e8ce"
 dependencies = [
  "arrayref",
  "paste",
@@ -4332,9 +4315,9 @@ dependencies = [
 
 [[package]]
 name = "lance-core"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "356a5df5f9cd7cb4aedaf78a4e346190ae50ba574b828316caed7d1df3b6dcd8"
+checksum = "69c752dedd207384892006c40930f898d6634e05e3d489e89763abfe4b9307e7"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
@@ -4370,9 +4353,9 @@ dependencies = [
 
 [[package]]
 name = "lance-datafusion"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b8e8ec07021bdaba6a441563d8fbcb0431350aae6842910ae3622557765f218f"
+checksum = "21e1e98ca6e5cd337bdda2d9fb66063f295c0c2852d2bc6831366fea833ee608"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -4381,6 +4364,7 @@ dependencies = [
  "arrow-schema",
  "arrow-select",
  "async-trait",
+ "chrono",
  "datafusion",
  "datafusion-common",
  "datafusion-functions",
@@ -4400,9 +4384,9 @@ dependencies = [
 
 [[package]]
 name = "lance-datagen"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "d4fe98730cd5297dc68b22f6ad7e1e27cf34e2db05586b64d3540ca74a519a61"
+checksum = "483c643fc2806ed1a2766edf4d180511bbd1d549bcc60373e33f4785c6185891"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -4419,9 +4403,9 @@ dependencies = [
 
 [[package]]
 name = "lance-encoding"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "ef073d419cc00ef41dd95cb25203b333118b224151ae397145530b1d559769c9"
+checksum = "a199d1fa3487529c5ffc433fbd1721231330b9350c2ff9b0c7b7dbdb98f0806a"
 dependencies = [
  "arrow-arith",
  "arrow-array",
@@ -4449,7 +4433,7 @@ dependencies = [
  "prost-types",
  "rand 0.9.2",
  "snafu",
- "strum 0.25.0",
+ "strum",
  "tokio",
  "tracing",
  "xxhash-rust",
@@ -4458,9 +4442,9 @@ dependencies = [
 
 [[package]]
 name = "lance-file"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0e34aba3a41f119188da997730560e4a6915ee5a38b672bbf721fdc99121aa1e"
+checksum = "b57def2279465232cf5a8cd996300c632442e368745768bbed661c7f0a35334b"
 dependencies = [
  "arrow-arith",
  "arrow-array",
@@ -4485,7 +4469,6 @@ dependencies = [
  "prost",
  "prost-build",
  "prost-types",
- "roaring",
  "snafu",
  "tokio",
  "tracing",
@@ -4493,9 +4476,9 @@ dependencies = [
 
 [[package]]
 name = "lance-index"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c5f480f801c8efb41a6dedc48a5cacff6044a10f82c6f9764b8dac7194a7754e"
+checksum = "a75938c61e986aef8c615dc44c92e4c19e393160a59e2b57402ccfe08c5e63af"
 dependencies = [
  "arrow",
  "arrow-arith",
@@ -4517,7 +4500,6 @@ dependencies = [
  "datafusion-sql",
  "deepsize",
  "dirs",
- "fastbloom",
  "fst",
  "futures",
  "half",
@@ -4557,9 +4539,9 @@ dependencies = [
 
 [[package]]
 name = "lance-io"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "0708125c74965b2b7e5e0c4fe2d8e6bd8346a7031484f8844cf06c08bfa29a72"
+checksum = "fa6c3b5b28570d6c951206c5b043f1b35c936928af14fca6f2ac25b0097e4c32"
 dependencies = [
  "arrow",
  "arrow-arith",
@@ -4580,6 +4562,7 @@ dependencies = [
  "futures",
  "lance-arrow",
  "lance-core",
+ "lance-namespace",
  "log",
  "object_store",
  "object_store_opendal",
@@ -4598,44 +4581,55 @@ dependencies = [
 
 [[package]]
 name = "lance-linalg"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "da9d1c22deed92420a1869e4b89188ccecc7e1aee2ea4e5bca92eae861511d60"
+checksum = "b3cbc7e85a89ff9cb3a4627559dea3fd1c1fb16c0d8bc46ede75eefef51eec06"
 dependencies = [
  "arrow-array",
  "arrow-buffer",
- "arrow-ord",
  "arrow-schema",
- "bitvec",
  "cc",
  "deepsize",
- "futures",
  "half",
  "lance-arrow",
  "lance-core",
- "log",
  "num-traits",
  "rand 0.9.2",
- "rayon",
- "tokio",
- "tracing",
 ]
 
 [[package]]
 name = "lance-namespace"
-version = "0.0.18"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7c0629165b5d85ff305f2de8833dcee507e899b36b098864c59f14f3b8b8e62d"
+checksum = "897dd6726816515bb70a698ce7cda44670dca5761637696d7905b45f405a8cd9"
 dependencies = [
  "arrow",
  "async-trait",
  "bytes",
- "lance",
+ "lance-core",
  "lance-namespace-reqwest-client",
- "opendal",
+ "snafu",
+]
+
+[[package]]
+name = "lance-namespace-impls"
+version = "0.39.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "5e3cfcd3ba369de2719abf6fb6233f69cda639eb5cbcb328487a790e745ab988"
+dependencies = [
+ "arrow",
+ "arrow-ipc",
+ "arrow-schema",
+ "async-trait",
+ "bytes",
+ "lance",
+ "lance-core",
+ "lance-io",
+ "lance-namespace",
+ "object_store",
  "reqwest",
  "serde_json",
- "thiserror 1.0.69",
+ "snafu",
  "url",
 ]
 
@@ -4654,9 +4648,9 @@ dependencies = [
 
 [[package]]
 name = "lance-table"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "805e6c64efbb3295f74714668c9033121ffdfa6c868f067024e65ade700b8b8b"
+checksum = "c8facc13760ba034b6c38767b16adba85e44cbcbea8124dc0c63c43865c60630"
 dependencies = [
  "arrow",
  "arrow-array",
@@ -4683,6 +4677,7 @@ dependencies = [
  "rand 0.9.2",
  "rangemap",
  "roaring",
+ "semver",
  "serde",
  "serde_json",
  "snafu",
@@ -4694,9 +4689,9 @@ dependencies = [
 
 [[package]]
 name = "lance-testing"
-version = "0.38.2"
+version = "0.39.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "8ac735b5eb153a6ac841ce0206e4c30df941610c812cc89c8ae20006f8d0b018"
+checksum = "b05052ef86188d6ae6339bdd9f2c5d77190e8ad1158f3dc8a42fa91bde9e5246"
 dependencies = [
  "arrow-array",
  "arrow-schema",
@@ -4707,8 +4702,9 @@ dependencies = [
 
 [[package]]
 name = "lancedb"
-version = "0.22.2-beta.2"
+version = "0.22.3-beta.5"
 dependencies = [
+ "ahash",
  "anyhow",
  "arrow",
  "arrow-array",
@@ -4717,6 +4713,7 @@ dependencies = [
  "arrow-ipc",
  "arrow-ord",
  "arrow-schema",
+ "arrow-select",
  "async-openai",
  "async-trait",
  "aws-config",
@@ -4725,13 +4722,11 @@ dependencies = [
  "aws-sdk-kms",
  "aws-sdk-s3",
  "aws-smithy-runtime",
- "bytemuck_derive",
  "bytes",
  "candle-core",
  "candle-nn",
  "candle-transformers",
  "chrono",
- "crunchy",
  "datafusion",
  "datafusion-catalog",
  "datafusion-common",
@@ -4744,12 +4739,17 @@ dependencies = [
  "http 1.3.1",
  "http-body 1.0.1",
  "lance",
+ "lance-arrow",
+ "lance-core",
  "lance-datafusion",
+ "lance-datagen",
  "lance-encoding",
+ "lance-file",
  "lance-index",
  "lance-io",
  "lance-linalg",
  "lance-namespace",
+ "lance-namespace-impls",
  "lance-table",
  "lance-testing",
  "lazy_static",
@@ -4771,6 +4771,7 @@ dependencies = [
  "serde_with",
  "snafu",
  "tempfile",
+ "test-log",
  "tokenizers",
  "tokio",
  "url",
@@ -4796,7 +4797,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-nodejs"
-version = "0.22.2-beta.2"
+version = "0.22.3-beta.5"
 dependencies = [
  "arrow-array",
  "arrow-ipc",
@@ -4816,7 +4817,7 @@ dependencies = [
 
 [[package]]
 name = "lancedb-python"
-version = "0.25.2-beta.2"
+version = "0.25.3-beta.5"
 dependencies = [
  "arrow",
  "async-trait",
@@ -5176,12 +5177,9 @@ dependencies = [
 
 [[package]]
 name = "mock_instant"
-version = "0.3.2"
+version = "0.6.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "9366861eb2a2c436c20b12c8dbec5f798cea6b47ad99216be0282942e2c81ea0"
-dependencies = [
- "once_cell",
-]
+checksum = "dce6dd36094cac388f119d2e9dc82dc730ef91c32a6222170d630e5414b956e6"
 
 [[package]]
 name = "moka"
@@ -7909,20 +7907,14 @@ version = "0.11.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f"
 
-[[package]]
-name = "strum"
-version = "0.25.0"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "290d54ea6f91c969195bdbcd7442c8c2a2ba87da8bf60a7ee86a235d4bc1e125"
-dependencies = [
- "strum_macros 0.25.3",
-]
-
 [[package]]
 name = "strum"
 version = "0.26.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "8fec0f0aef304996cf250b31b5a10dee7980c85da9d759361292b8bca5a18f06"
+dependencies = [
+ "strum_macros 0.26.4",
+]
 
 [[package]]
 name = "strum_macros"
@@ -8244,6 +8236,28 @@ dependencies = [
  "windows-sys 0.61.2",
 ]
 
+[[package]]
+name = "test-log"
+version = "0.2.18"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "1e33b98a582ea0be1168eba097538ee8dd4bbe0f2b01b22ac92ea30054e5be7b"
+dependencies = [
+ "env_logger",
+ "test-log-macros",
+ "tracing-subscriber",
+]
+
+[[package]]
+name = "test-log-macros"
+version = "0.2.18"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "451b374529930d7601b1eef8d32bc79ae870b6079b069401709c2a8bf9e75f36"
+dependencies = [
+ "proc-macro2",
+ "quote",
+ "syn 2.0.106",
+]
+
 [[package]]
 name = "thiserror"
 version = "1.0.69"
@@ -9061,21 +9075,8 @@ dependencies = [
  "windows-implement",
  "windows-interface",
  "windows-link 0.1.3",
- "windows-result 0.3.4",
- "windows-strings 0.4.2",
-]
-
-[[package]]
-name = "windows-core"
-version = "0.62.2"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "b8e83a14d34d0623b51dce9581199302a221863196a1dde71a7663a4c2be9deb"
-dependencies = [
- "windows-implement",
- "windows-interface",
- "windows-link 0.2.1",
- "windows-result 0.4.1",
- "windows-strings 0.5.1",
+ "windows-result",
+ "windows-strings",
 ]
 
 [[package]]
@@ -9140,8 +9141,8 @@ source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "5b8a9ed28765efc97bbc954883f4e6796c33a06546ebafacbabee9696967499e"
 dependencies = [
  "windows-link 0.1.3",
- "windows-result 0.3.4",
- "windows-strings 0.4.2",
+ "windows-result",
+ "windows-strings",
 ]
 
 [[package]]
@@ -9153,15 +9154,6 @@ dependencies = [
  "windows-link 0.1.3",
 ]
 
-[[package]]
-name = "windows-result"
-version = "0.4.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7781fa89eaf60850ac3d2da7af8e5242a5ea78d1a11c49bf2910bb5a73853eb5"
-dependencies = [
- "windows-link 0.2.1",
-]
-
 [[package]]
 name = "windows-strings"
 version = "0.4.2"
@@ -9171,15 +9163,6 @@ dependencies = [
  "windows-link 0.1.3",
 ]
 
-[[package]]
-name = "windows-strings"
-version = "0.5.1"
-source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "7837d08f69c77cf6b07689544538e017c1bfcf57e34b4c0ff58e6c2cd3b37091"
-dependencies = [
- "windows-link 0.2.1",
-]
-
 [[package]]
 name = "windows-sys"
 version = "0.45.0"

Cargo.toml

@@ -15,15 +15,21 @@ categories = ["database-implementations"]
 rust-version = "1.78.0"
 
 [workspace.dependencies]
-lance = { "version" = "=0.38.2", default-features = false, "features" = ["dynamodb"] }
-lance-io = { "version" = "=0.38.2", default-features = false }
-lance-index = "=0.38.2"
-lance-linalg = "=0.38.2"
-lance-table = "=0.38.2"
-lance-testing = "=0.38.2"
-lance-datafusion = "=0.38.2"
-lance-encoding = "=0.38.2"
-lance-namespace = "0.0.18"
+lance = { "version" = "=0.39.0", default-features = false }
+lance-core = "=0.39.0"
+lance-datagen = "=0.39.0"
+lance-file = "=0.39.0"
+lance-io = { "version" = "=0.39.0", default-features = false }
+lance-index = "=0.39.0"
+lance-linalg = "=0.39.0"
+lance-namespace = "=0.39.0"
+lance-namespace-impls = { "version" = "=0.39.0", "features" = ["dir-aws", "dir-gcp", "dir-azure", "dir-oss", "rest"] }
+lance-table = "=0.39.0"
+lance-testing = "=0.39.0"
+lance-datafusion = "=0.39.0"
+lance-encoding = "=0.39.0"
+lance-arrow = "=0.39.0"
+ahash = "0.8"
 # Note that this one does not include pyarrow
 arrow = { version = "56.2", optional = false }
 arrow-array = "56.2"
@@ -31,6 +37,7 @@ arrow-data = "56.2"
 arrow-ipc = "56.2"
 arrow-ord = "56.2"
 arrow-schema = "56.2"
+arrow-select = "56.2"
 arrow-cast = "56.2"
 async-trait = "0"
 datafusion = { version = "50.1", default-features = false }
@@ -48,27 +55,11 @@ log = "0.4"
 moka = { version = "0.12", features = ["future"] }
 object_store = "0.12.0"
 pin-project = "1.0.7"
+rand = "0.9"
 snafu = "0.8"
 url = "2"
 num-traits = "0.2"
 regex = "1.10"
 lazy_static = "1"
 semver = "1.0.25"
-crunchy = "0.2.4"
-# Temporary pins to work around downstream issues
-# https://github.com/apache/arrow-rs/commit/2fddf85afcd20110ce783ed5b4cdeb82293da30b
-chrono = "=0.4.41"
-# Workaround for: https://github.com/Lokathor/bytemuck/issues/306
-bytemuck_derive = ">=1.8.1, <1.9.0"
-
-# This is only needed when we reference preview releases of lance
-# [patch.crates-io]
-# # Force to use the same lance version as the rest of the project to avoid duplicate dependencies
-# lance = { "version" = "=0.38.0", "tag" = "v0.38.0", "git" = "https://github.com/lancedb/lance.git" }
-# lance-io = { "version" = "=0.38.0", "tag" = "v0.38.0", "git" = "https://github.com/lancedb/lance.git" }
-# lance-index = { "version" = "=0.38.0", "tag" = "v0.38.0", "git" = "https://github.com/lancedb/lance.git" }
-# lance-linalg = { "version" = "=0.38.0", "tag" = "v0.38.0", "git" = "https://github.com/lancedb/lance.git" }
-# lance-table = { "version" = "=0.38.0", "tag" = "v0.38.0", "git" = "https://github.com/lancedb/lance.git" }
-# lance-testing = { "version" = "=0.38.0", "tag" = "v0.38.0", "git" = "https://github.com/lancedb/lance.git" }
-# lance-datafusion = { "version" = "=0.38.0", "tag" = "v0.38.0", "git" = "https://github.com/lancedb/lance.git" }
-# lance-encoding = { "version" = "=0.38.0", "tag" = "v0.38.0", "git" = "https://github.com/lancedb/lance.git" }
+chrono = "0.4"

ci/set_lance_version.py

@@ -55,7 +55,7 @@ def extract_features(line: str) -> list:
     match = re.search(r'"features"\s*=\s*\[\s*(.*?)\s*\]', line, re.DOTALL)
     if match:
         features_str = match.group(1)
-        return [f.strip('"') for f in features_str.split(",") if len(f) > 0]
+        return [f.strip().strip('"') for f in features_str.split(",") if f.strip()]
     return []
 
 
@@ -117,7 +117,7 @@ def update_cargo_toml(line_updater):
     lance_line = ""
     is_parsing_lance_line = False
     for line in lines:
-        if line.startswith("lance") and not line.startswith("lance-namespace"):
+        if line.startswith("lance"):
             # Check if this is a single-line or multi-line entry
             # Single-line entries either:
             # 1. End with } (complete inline table)
@@ -183,10 +183,8 @@ def set_preview_version(version: str):
 
     def line_updater(line: str) -> str:
         package_name = line.split("=", maxsplit=1)[0].strip()
-        base_version = version.split("-")[0]  # Get the base version without beta suffix
-
         # Build config in desired order: version, default-features, features, tag, git
-        config = {"version": f"={base_version}"}
+        config = {"version": f"={version}"}
 
         if extract_default_features(line):
             config["default-features"] = False

docs/src/embeddings/available_embedding_models/multimodal_embedding_functions/voyageai_multimodal_embedding.md

@@ -0,0 +1,97 @@
+# VoyageAI Embeddings : Multimodal
+
+VoyageAI embeddings can also be used to embed both text and image data, only some of the models support image data and you can check the list
+under [https://docs.voyageai.com/docs/multimodal-embeddings](https://docs.voyageai.com/docs/multimodal-embeddings)
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value           | Description                               |
+|---|---|-------------------------|-------------------------------------------|
+| `name` | `str` | `"voyage-multimodal-3"` | The model ID of the VoyageAI model to use |
+
+Usage Example:
+
+```python
+import base64
+import os
+from io import BytesIO
+
+import requests
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import get_registry
+import pandas as pd
+
+os.environ['VOYAGE_API_KEY'] = 'YOUR_VOYAGE_API_KEY'
+
+db = lancedb.connect(".lancedb")
+func = get_registry().get("voyageai").create(name="voyage-multimodal-3")
+
+
+def image_to_base64(image_bytes: bytes):
+    buffered = BytesIO(image_bytes)
+    img_str = base64.b64encode(buffered.getvalue())
+    return img_str.decode("utf-8")
+
+
+class Images(LanceModel):
+    label: str
+    image_uri: str = func.SourceField()  # image uri as the source
+    image_bytes: str = func.SourceField()  # image bytes base64 encoded as the source
+    vector: Vector(func.ndims()) = func.VectorField()  # vector column
+    vec_from_bytes: Vector(func.ndims()) = func.VectorField()  # Another vector column
+
+
+if "images" in db.table_names():
+    db.drop_table("images")
+table = db.create_table("images", schema=Images)
+labels = ["cat", "cat", "dog", "dog", "horse", "horse"]
+uris = [
+    "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+    "http://farm1.staticflickr.com/134/332220238_da527d8140_z.jpg",
+    "http://farm9.staticflickr.com/8387/8602747737_2e5c2a45d4_z.jpg",
+    "http://farm5.staticflickr.com/4092/5017326486_1f46057f5f_z.jpg",
+    "http://farm9.staticflickr.com/8216/8434969557_d37882c42d_z.jpg",
+    "http://farm6.staticflickr.com/5142/5835678453_4f3a4edb45_z.jpg",
+]
+# get each uri as bytes
+images_bytes = [image_to_base64(requests.get(uri).content) for uri in uris]
+table.add(
+    pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": images_bytes})
+)
+```
+Now we can search using text from both the default vector column and the custom vector column
+```python
+
+# text search
+actual = table.search("man's best friend", "vec_from_bytes").limit(1).to_pydantic(Images)[0]
+print(actual.label) # prints "dog"
+
+frombytes = (
+    table.search("man's best friend", vector_column_name="vec_from_bytes")
+    .limit(1)
+    .to_pydantic(Images)[0]
+)
+print(frombytes.label)
+
+```
+
+Because we're using a multi-modal embedding function, we can also search using images
+
+```python
+# image search
+query_image_uri = "http://farm1.staticflickr.com/200/467715466_ed4a31801f_z.jpg"
+image_bytes = requests.get(query_image_uri).content
+query_image = Image.open(BytesIO(image_bytes))
+actual = table.search(query_image, "vec_from_bytes").limit(1).to_pydantic(Images)[0]
+print(actual.label == "dog")
+
+# image search using a custom vector column
+other = (
+    table.search(query_image, vector_column_name="vec_from_bytes")
+    .limit(1)
+    .to_pydantic(Images)[0]
+)
+print(actual.label)
+
+```

docs/src/guides/storage.md

@@ -397,117 +397,6 @@ For **read-only access**, LanceDB will need a policy such as:
 }
 ```
 
-#### DynamoDB Commit Store for concurrent writes
-
-By default, S3 does not support concurrent writes. Having two or more processes
-writing to the same table at the same time can lead to data corruption. This is
-because S3, unlike other object stores, does not have any atomic put or copy
-operation.
-
-To enable concurrent writes, you can configure LanceDB to use a DynamoDB table
-as a commit store. This table will be used to coordinate writes between
-different processes. To enable this feature, you must modify your connection
-URI to use the `s3+ddb` scheme and add a query parameter `ddbTableName` with the
-name of the table to use.
-
-=== "Python"
-
-    === "Sync API"
-
-        ```python
-        import lancedb
-        db = lancedb.connect(
-            "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-        )
-        ```
-    === "Async API"
-
-        ```python
-        import lancedb
-        async_db = await lancedb.connect_async(
-            "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-        )    
-        ```
-
-=== "JavaScript"
-
-    ```javascript
-    const lancedb = require("lancedb");
-
-    const db = await lancedb.connect(
-        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-    );
-    ```
-
-The DynamoDB table must be created with the following schema:
-
-- Hash key: `base_uri` (string)
-- Range key: `version` (number)
-
-You can create this programmatically with:
-
-=== "Python"
-
-    <!-- skip-test -->
-    ```python
-    import boto3
-
-    dynamodb = boto3.client("dynamodb")
-    table = dynamodb.create_table(
-        TableName=table_name,
-        KeySchema=[
-            {"AttributeName": "base_uri", "KeyType": "HASH"},
-            {"AttributeName": "version", "KeyType": "RANGE"},
-        ],
-        AttributeDefinitions=[
-            {"AttributeName": "base_uri", "AttributeType": "S"},
-            {"AttributeName": "version", "AttributeType": "N"},
-        ],
-        ProvisionedThroughput={"ReadCapacityUnits": 1, "WriteCapacityUnits": 1},
-    )
-    ```
-
-=== "JavaScript"
-
-    <!-- skip-test -->
-    ```javascript
-    import {
-      CreateTableCommand,
-      DynamoDBClient,
-    } from "@aws-sdk/client-dynamodb";
-
-    const dynamodb = new DynamoDBClient({
-      region: CONFIG.awsRegion,
-      credentials: {
-        accessKeyId: CONFIG.awsAccessKeyId,
-        secretAccessKey: CONFIG.awsSecretAccessKey,
-      },
-      endpoint: CONFIG.awsEndpoint,
-    });
-    const command = new CreateTableCommand({
-      TableName: table_name,
-      AttributeDefinitions: [
-        {
-          AttributeName: "base_uri",
-          AttributeType: "S",
-        },
-        {
-          AttributeName: "version",
-          AttributeType: "N",
-        },
-      ],
-      KeySchema: [
-        { AttributeName: "base_uri", KeyType: "HASH" },
-        { AttributeName: "version", KeyType: "RANGE" },
-      ],
-      ProvisionedThroughput: {
-        ReadCapacityUnits: 1,
-        WriteCapacityUnits: 1,
-      },
-    });
-    await client.send(command);
-    ```
-
 
 #### S3-compatible stores
 

docs/src/js/classes/Index.md

@@ -194,6 +194,37 @@ currently is also a memory intensive operation.
 
 ***
 
+### ivfRq()
+
+```ts
+static ivfRq(options?): Index
+```
+
+Create an IvfRq index
+
+IVF-RQ (RabitQ Quantization) compresses vectors using RabitQ quantization
+and organizes them into IVF partitions.
+
+The compression scheme is called RabitQ quantization. Each dimension is quantized into a small number of bits.
+The parameters `num_bits` and `num_partitions` control this process, providing a tradeoff
+between index size (and thus search speed) and index accuracy.
+
+The partitioning process is called IVF and the `num_partitions` parameter controls how
+many groups to create.
+
+Note that training an IVF RQ index on a large dataset is a slow operation and
+currently is also a memory intensive operation.
+
+#### Parameters
+
+* **options?**: `Partial`<[`IvfRqOptions`](../interfaces/IvfRqOptions.md)>
+
+#### Returns
+
+[`Index`](Index.md)
+
+***
+
 ### labelList()
 
 ```ts

docs/src/js/classes/PermutationBuilder.md

@@ -0,0 +1,250 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / PermutationBuilder
+
+# Class: PermutationBuilder
+
+A PermutationBuilder for creating data permutations with splits, shuffling, and filtering.
+
+This class provides a TypeScript wrapper around the native Rust PermutationBuilder,
+offering methods to configure data splits, shuffling, and filtering before executing
+the permutation to create a new table.
+
+## Methods
+
+### execute()
+
+```ts
+execute(): Promise<Table>
+```
+
+Execute the permutation and create the destination table.
+
+#### Returns
+
+`Promise`&lt;[`Table`](Table.md)&gt;
+
+A Promise that resolves to the new Table instance
+
+#### Example
+
+```ts
+const permutationTable = await builder.execute();
+console.log(`Created table: ${permutationTable.name}`);
+```
+
+***
+
+### filter()
+
+```ts
+filter(filter): PermutationBuilder
+```
+
+Configure filtering for the permutation.
+
+#### Parameters
+
+* **filter**: `string`
+    SQL filter expression
+
+#### Returns
+
+[`PermutationBuilder`](PermutationBuilder.md)
+
+A new PermutationBuilder instance
+
+#### Example
+
+```ts
+builder.filter("age > 18 AND status = 'active'");
+```
+
+***
+
+### persist()
+
+```ts
+persist(connection, tableName): PermutationBuilder
+```
+
+Configure the permutation to be persisted.
+
+#### Parameters
+
+* **connection**: [`Connection`](Connection.md)
+    The connection to persist the permutation to
+
+* **tableName**: `string`
+    The name of the table to create
+
+#### Returns
+
+[`PermutationBuilder`](PermutationBuilder.md)
+
+A new PermutationBuilder instance
+
+#### Example
+
+```ts
+builder.persist(connection, "permutation_table");
+```
+
+***
+
+### shuffle()
+
+```ts
+shuffle(options): PermutationBuilder
+```
+
+Configure shuffling for the permutation.
+
+#### Parameters
+
+* **options**: [`ShuffleOptions`](../interfaces/ShuffleOptions.md)
+    Configuration for shuffling
+
+#### Returns
+
+[`PermutationBuilder`](PermutationBuilder.md)
+
+A new PermutationBuilder instance
+
+#### Example
+
+```ts
+// Basic shuffle
+builder.shuffle({ seed: 42 });
+
+// Shuffle with clump size
+builder.shuffle({ seed: 42, clumpSize: 10 });
+```
+
+***
+
+### splitCalculated()
+
+```ts
+splitCalculated(options): PermutationBuilder
+```
+
+Configure calculated splits for the permutation.
+
+#### Parameters
+
+* **options**: [`SplitCalculatedOptions`](../interfaces/SplitCalculatedOptions.md)
+    Configuration for calculated splitting
+
+#### Returns
+
+[`PermutationBuilder`](PermutationBuilder.md)
+
+A new PermutationBuilder instance
+
+#### Example
+
+```ts
+builder.splitCalculated("user_id % 3");
+```
+
+***
+
+### splitHash()
+
+```ts
+splitHash(options): PermutationBuilder
+```
+
+Configure hash-based splits for the permutation.
+
+#### Parameters
+
+* **options**: [`SplitHashOptions`](../interfaces/SplitHashOptions.md)
+    Configuration for hash-based splitting
+
+#### Returns
+
+[`PermutationBuilder`](PermutationBuilder.md)
+
+A new PermutationBuilder instance
+
+#### Example
+
+```ts
+builder.splitHash({
+  columns: ["user_id"],
+  splitWeights: [70, 30],
+  discardWeight: 0
+});
+```
+
+***
+
+### splitRandom()
+
+```ts
+splitRandom(options): PermutationBuilder
+```
+
+Configure random splits for the permutation.
+
+#### Parameters
+
+* **options**: [`SplitRandomOptions`](../interfaces/SplitRandomOptions.md)
+    Configuration for random splitting
+
+#### Returns
+
+[`PermutationBuilder`](PermutationBuilder.md)
+
+A new PermutationBuilder instance
+
+#### Example
+
+```ts
+// Split by ratios
+builder.splitRandom({ ratios: [0.7, 0.3], seed: 42 });
+
+// Split by counts
+builder.splitRandom({ counts: [1000, 500], seed: 42 });
+
+// Split with fixed size
+builder.splitRandom({ fixed: 100, seed: 42 });
+```
+
+***
+
+### splitSequential()
+
+```ts
+splitSequential(options): PermutationBuilder
+```
+
+Configure sequential splits for the permutation.
+
+#### Parameters
+
+* **options**: [`SplitSequentialOptions`](../interfaces/SplitSequentialOptions.md)
+    Configuration for sequential splitting
+
+#### Returns
+
+[`PermutationBuilder`](PermutationBuilder.md)
+
+A new PermutationBuilder instance
+
+#### Example
+
+```ts
+// Split by ratios
+builder.splitSequential({ ratios: [0.8, 0.2] });
+
+// Split by counts
+builder.splitSequential({ counts: [800, 200] });
+
+// Split with fixed size
+builder.splitSequential({ fixed: 1000 });
+```

docs/src/js/classes/Query.md

@@ -80,7 +80,7 @@ AnalyzeExec verbose=true, metrics=[]
 ### execute()
 
 ```ts
-protected execute(options?): RecordBatchIterator
+protected execute(options?): AsyncGenerator<RecordBatch<any>, void, unknown>
 ```
 
 Execute the query and return the results as an
@@ -91,7 +91,7 @@ Execute the query and return the results as an
 
 #### Returns
 
-[`RecordBatchIterator`](RecordBatchIterator.md)
+`AsyncGenerator`&lt;`RecordBatch`&lt;`any`&gt;, `void`, `unknown`&gt;
 
 #### See
 
@@ -343,6 +343,29 @@ This is useful for pagination.
 
 ***
 
+### outputSchema()
+
+```ts
+outputSchema(): Promise<Schema<any>>
+```
+
+Returns the schema of the output that will be returned by this query.
+
+This can be used to inspect the types and names of the columns that will be
+returned by the query before executing it.
+
+#### Returns
+
+`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+
+An Arrow Schema describing the output columns.
+
+#### Inherited from
+
+`StandardQueryBase.outputSchema`
+
+***
+
 ### select()
 
 ```ts

docs/src/js/classes/QueryBase.md

@@ -81,7 +81,7 @@ AnalyzeExec verbose=true, metrics=[]
 ### execute()
 
 ```ts
-protected execute(options?): RecordBatchIterator
+protected execute(options?): AsyncGenerator<RecordBatch<any>, void, unknown>
 ```
 
 Execute the query and return the results as an
@@ -92,7 +92,7 @@ Execute the query and return the results as an
 
 #### Returns
 
-[`RecordBatchIterator`](RecordBatchIterator.md)
+`AsyncGenerator`&lt;`RecordBatch`&lt;`any`&gt;, `void`, `unknown`&gt;
 
 #### See
 
@@ -140,6 +140,25 @@ const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
 
 ***
 
+### outputSchema()
+
+```ts
+outputSchema(): Promise<Schema<any>>
+```
+
+Returns the schema of the output that will be returned by this query.
+
+This can be used to inspect the types and names of the columns that will be
+returned by the query before executing it.
+
+#### Returns
+
+`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+
+An Arrow Schema describing the output columns.
+
+***
+
 ### select()
 
 ```ts

docs/src/js/classes/RecordBatchIterator.md

@@ -1,43 +0,0 @@
-[**@lancedb/lancedb**](../README.md) • **Docs**
-
-***
-
-[@lancedb/lancedb](../globals.md) / RecordBatchIterator
-
-# Class: RecordBatchIterator
-
-## Implements
-
-- `AsyncIterator`<`RecordBatch`>
-
-## Constructors
-
-### new RecordBatchIterator()
-
-```ts
-new RecordBatchIterator(promise?): RecordBatchIterator
-```
-
-#### Parameters
-
-* **promise?**: `Promise`<`RecordBatchIterator`>
-
-#### Returns
-
-[`RecordBatchIterator`](RecordBatchIterator.md)
-
-## Methods
-
-### next()
-
-```ts
-next(): Promise<IteratorResult<RecordBatch<any>, any>>
-```
-
-#### Returns
-
-`Promise`&lt;`IteratorResult`&lt;`RecordBatch`&lt;`any`&gt;, `any`&gt;&gt;
-
-#### Implementation of
-
-`AsyncIterator.next`

docs/src/js/classes/TakeQuery.md

@@ -76,7 +76,7 @@ AnalyzeExec verbose=true, metrics=[]
 ### execute()
 
 ```ts
-protected execute(options?): RecordBatchIterator
+protected execute(options?): AsyncGenerator<RecordBatch<any>, void, unknown>
 ```
 
 Execute the query and return the results as an
@@ -87,7 +87,7 @@ Execute the query and return the results as an
 
 #### Returns
 
-[`RecordBatchIterator`](RecordBatchIterator.md)
+`AsyncGenerator`&lt;`RecordBatch`&lt;`any`&gt;, `void`, `unknown`&gt;
 
 #### See
 
@@ -143,6 +143,29 @@ const plan = await table.query().nearestTo([0.5, 0.2]).explainPlan();
 
 ***
 
+### outputSchema()
+
+```ts
+outputSchema(): Promise<Schema<any>>
+```
+
+Returns the schema of the output that will be returned by this query.
+
+This can be used to inspect the types and names of the columns that will be
+returned by the query before executing it.
+
+#### Returns
+
+`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+
+An Arrow Schema describing the output columns.
+
+#### Inherited from
+
+[`QueryBase`](QueryBase.md).[`outputSchema`](QueryBase.md#outputschema)
+
+***
+
 ### select()
 
 ```ts

docs/src/js/classes/VectorQuery.md

@@ -221,7 +221,7 @@ also increase the latency of your query. The default value is 1.5*limit.
 ### execute()
 
 ```ts
-protected execute(options?): RecordBatchIterator
+protected execute(options?): AsyncGenerator<RecordBatch<any>, void, unknown>
 ```
 
 Execute the query and return the results as an
@@ -232,7 +232,7 @@ Execute the query and return the results as an
 
 #### Returns
 
-[`RecordBatchIterator`](RecordBatchIterator.md)
+`AsyncGenerator`&lt;`RecordBatch`&lt;`any`&gt;, `void`, `unknown`&gt;
 
 #### See
 
@@ -498,6 +498,29 @@ This is useful for pagination.
 
 ***
 
+### outputSchema()
+
+```ts
+outputSchema(): Promise<Schema<any>>
+```
+
+Returns the schema of the output that will be returned by this query.
+
+This can be used to inspect the types and names of the columns that will be
+returned by the query before executing it.
+
+#### Returns
+
+`Promise`&lt;`Schema`&lt;`any`&gt;&gt;
+
+An Arrow Schema describing the output columns.
+
+#### Inherited from
+
+`StandardQueryBase.outputSchema`
+
+***
+
 ### postfilter()
 
 ```ts

docs/src/js/functions/RecordBatchIterator.md

@@ -0,0 +1,19 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / RecordBatchIterator
+
+# Function: RecordBatchIterator()
+
+```ts
+function RecordBatchIterator(promisedInner): AsyncGenerator<RecordBatch<any>, void, unknown>
+```
+
+## Parameters
+
+* **promisedInner**: `Promise`&lt;`RecordBatchIterator`&gt;
+
+## Returns
+
+`AsyncGenerator`&lt;`RecordBatch`&lt;`any`&gt;, `void`, `unknown`&gt;

docs/src/js/functions/permutationBuilder.md

@@ -0,0 +1,34 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / permutationBuilder
+
+# Function: permutationBuilder()
+
+```ts
+function permutationBuilder(table): PermutationBuilder
+```
+
+Create a permutation builder for the given table.
+
+## Parameters
+
+* **table**: [`Table`](../classes/Table.md)
+    The source table to create a permutation from
+
+## Returns
+
+[`PermutationBuilder`](../classes/PermutationBuilder.md)
+
+A PermutationBuilder instance
+
+## Example
+
+```ts
+const builder = permutationBuilder(sourceTable, "training_data")
+  .splitRandom({ ratios: [0.8, 0.2], seed: 42 })
+  .shuffle({ seed: 123 });
+
+const trainingTable = await builder.execute();
+```

docs/src/js/globals.md

@@ -28,10 +28,10 @@
 - [MultiMatchQuery](classes/MultiMatchQuery.md)
 - [NativeJsHeaderProvider](classes/NativeJsHeaderProvider.md)
 - [OAuthHeaderProvider](classes/OAuthHeaderProvider.md)
+- [PermutationBuilder](classes/PermutationBuilder.md)
 - [PhraseQuery](classes/PhraseQuery.md)
 - [Query](classes/Query.md)
 - [QueryBase](classes/QueryBase.md)
-- [RecordBatchIterator](classes/RecordBatchIterator.md)
 - [Session](classes/Session.md)
 - [StaticHeaderProvider](classes/StaticHeaderProvider.md)
 - [Table](classes/Table.md)
@@ -68,6 +68,7 @@
 - [IndexStatistics](interfaces/IndexStatistics.md)
 - [IvfFlatOptions](interfaces/IvfFlatOptions.md)
 - [IvfPqOptions](interfaces/IvfPqOptions.md)
+- [IvfRqOptions](interfaces/IvfRqOptions.md)
 - [MergeResult](interfaces/MergeResult.md)
 - [OpenTableOptions](interfaces/OpenTableOptions.md)
 - [OptimizeOptions](interfaces/OptimizeOptions.md)
@@ -75,6 +76,11 @@
 - [QueryExecutionOptions](interfaces/QueryExecutionOptions.md)
 - [RemovalStats](interfaces/RemovalStats.md)
 - [RetryConfig](interfaces/RetryConfig.md)
+- [ShuffleOptions](interfaces/ShuffleOptions.md)
+- [SplitCalculatedOptions](interfaces/SplitCalculatedOptions.md)
+- [SplitHashOptions](interfaces/SplitHashOptions.md)
+- [SplitRandomOptions](interfaces/SplitRandomOptions.md)
+- [SplitSequentialOptions](interfaces/SplitSequentialOptions.md)
 - [TableNamesOptions](interfaces/TableNamesOptions.md)
 - [TableStatistics](interfaces/TableStatistics.md)
 - [TimeoutConfig](interfaces/TimeoutConfig.md)
@@ -99,6 +105,8 @@
 
 ## Functions
 
+- [RecordBatchIterator](functions/RecordBatchIterator.md)
 - [connect](functions/connect.md)
 - [makeArrowTable](functions/makeArrowTable.md)
 - [packBits](functions/packBits.md)
+- [permutationBuilder](functions/permutationBuilder.md)

docs/src/js/interfaces/IvfRqOptions.md

@@ -0,0 +1,101 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / IvfRqOptions
+
+# Interface: IvfRqOptions
+
+## Properties
+
+### distanceType?
+
+```ts
+optional distanceType: "l2" | "cosine" | "dot";
+```
+
+Distance type to use to build the index.
+
+Default value is "l2".
+
+This is used when training the index to calculate the IVF partitions
+(vectors are grouped in partitions with similar vectors according to this
+distance type) and during quantization.
+
+The distance type used to train an index MUST match the distance type used
+to search the index. Failure to do so will yield inaccurate results.
+
+The following distance types are available:
+
+"l2" - Euclidean distance.
+"cosine" - Cosine distance.
+"dot" - Dot product.
+
+***
+
+### maxIterations?
+
+```ts
+optional maxIterations: number;
+```
+
+Max iterations to train IVF kmeans.
+
+When training an IVF index we use kmeans to calculate the partitions. This parameter
+controls how many iterations of kmeans to run.
+
+The default value is 50.
+
+***
+
+### numBits?
+
+```ts
+optional numBits: number;
+```
+
+Number of bits per dimension for residual quantization.
+
+This value controls how much each residual component is compressed. The more
+bits, the more accurate the index will be but the slower search. Typical values
+are small integers; the default is 1 bit per dimension.
+
+***
+
+### numPartitions?
+
+```ts
+optional numPartitions: number;
+```
+
+The number of IVF partitions to create.
+
+This value should generally scale with the number of rows in the dataset.
+By default the number of partitions is the square root of the number of
+rows.
+
+If this value is too large then the first part of the search (picking the
+right partition) will be slow. If this value is too small then the second
+part of the search (searching within a partition) will be slow.
+
+***
+
+### sampleRate?
+
+```ts
+optional sampleRate: number;
+```
+
+The number of vectors, per partition, to sample when training IVF kmeans.
+
+When an IVF index is trained, we need to calculate partitions. These are groups
+of vectors that are similar to each other. To do this we use an algorithm called kmeans.
+
+Running kmeans on a large dataset can be slow. To speed this up we run kmeans on a
+random sample of the data. This parameter controls the size of the sample. The total
+number of vectors used to train the index is `sample_rate * num_partitions`.
+
+Increasing this value might improve the quality of the index but in most cases the
+default should be sufficient.
+
+The default value is 256.

docs/src/js/interfaces/ShuffleOptions.md

@@ -0,0 +1,23 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / ShuffleOptions
+
+# Interface: ShuffleOptions
+
+## Properties
+
+### clumpSize?
+
+```ts
+optional clumpSize: number;
+```
+
+***
+
+### seed?
+
+```ts
+optional seed: number;
+```

docs/src/js/interfaces/SplitCalculatedOptions.md

@@ -0,0 +1,23 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / SplitCalculatedOptions
+
+# Interface: SplitCalculatedOptions
+
+## Properties
+
+### calculation
+
+```ts
+calculation: string;
+```
+
+***
+
+### splitNames?
+
+```ts
+optional splitNames: string[];
+```

docs/src/js/interfaces/SplitHashOptions.md

@@ -0,0 +1,39 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / SplitHashOptions
+
+# Interface: SplitHashOptions
+
+## Properties
+
+### columns
+
+```ts
+columns: string[];
+```
+
+***
+
+### discardWeight?
+
+```ts
+optional discardWeight: number;
+```
+
+***
+
+### splitNames?
+
+```ts
+optional splitNames: string[];
+```
+
+***
+
+### splitWeights
+
+```ts
+splitWeights: number[];
+```

docs/src/js/interfaces/SplitRandomOptions.md

@@ -0,0 +1,47 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / SplitRandomOptions
+
+# Interface: SplitRandomOptions
+
+## Properties
+
+### counts?
+
+```ts
+optional counts: number[];
+```
+
+***
+
+### fixed?
+
+```ts
+optional fixed: number;
+```
+
+***
+
+### ratios?
+
+```ts
+optional ratios: number[];
+```
+
+***
+
+### seed?
+
+```ts
+optional seed: number;
+```
+
+***
+
+### splitNames?
+
+```ts
+optional splitNames: string[];
+```

docs/src/js/interfaces/SplitSequentialOptions.md

@@ -0,0 +1,39 @@
+[**@lancedb/lancedb**](../README.md) • **Docs**
+
+***
+
+[@lancedb/lancedb](../globals.md) / SplitSequentialOptions
+
+# Interface: SplitSequentialOptions
+
+## Properties
+
+### counts?
+
+```ts
+optional counts: number[];
+```
+
+***
+
+### fixed?
+
+```ts
+optional fixed: number;
+```
+
+***
+
+### ratios?
+
+```ts
+optional ratios: number[];
+```
+
+***
+
+### splitNames?
+
+```ts
+optional splitNames: string[];
+```

java/core/lancedb-jni/src/error.rs

@@ -51,8 +51,11 @@ pub enum Error {
     DatasetAlreadyExists { uri: String, location: Location },
     #[snafu(display("Table '{name}' already exists"))]
     TableAlreadyExists { name: String },
-    #[snafu(display("Table '{name}' was not found"))]
-    TableNotFound { name: String },
+    #[snafu(display("Table '{name}' was not found: {source}"))]
+    TableNotFound {
+        name: String,
+        source: Box<dyn std::error::Error + Send + Sync>,
+    },
     #[snafu(display("Invalid table name '{name}': {reason}"))]
     InvalidTableName { name: String, reason: String },
     #[snafu(display("Embedding function '{name}' was not found: {reason}, {location}"))]
@@ -191,7 +194,7 @@ impl From<lancedb::Error> for Error {
                 message,
                 location: std::panic::Location::caller().to_snafu_location(),
             },
-            lancedb::Error::TableNotFound { name } => Self::TableNotFound { name },
+            lancedb::Error::TableNotFound { name, source } => Self::TableNotFound { name, source },
             lancedb::Error::TableAlreadyExists { name } => Self::TableAlreadyExists { name },
             lancedb::Error::EmbeddingFunctionNotFound { name, reason } => {
                 Self::EmbeddingFunctionNotFound {

java/core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
         <groupId>com.lancedb</groupId>
         <artifactId>lancedb-parent</artifactId>
-        <version>0.22.2-beta.2</version>
+        <version>0.22.3-beta.5</version>
         <relativePath>../pom.xml</relativePath>
     </parent>
 

java/lance-namespace/pom.xml

@@ -8,7 +8,7 @@
     <parent>
         <groupId>com.lancedb</groupId>
         <artifactId>lancedb-parent</artifactId>
-        <version>0.22.2-beta.2</version>
+        <version>0.22.3-beta.5</version>
         <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.22.2-beta.2</version>
+    <version>0.22.3-beta.5</version>
     <packaging>pom</packaging>
     <name>${project.artifactId}</name>
     <description>LanceDB Java SDK Parent POM</description>

nodejs/AGENTS.md

@@ -0,0 +1,13 @@
+These are the typescript bindings of LanceDB.
+The core Rust library is in the `../rust/lancedb` directory, the rust binding
+code is in the `src/` directory and the typescript bindings are in
+the `lancedb/` directory.
+
+Whenever you change the Rust code, you will need to recompile: `npm run build`.
+
+Common commands:
+* Build: `npm run build`
+* Lint: `npm run lint`
+* Fix lints: `npm run lint-fix`
+* Test: `npm test`
+* Run single test file: `npm test __test__/arrow.test.ts`

nodejs/CLAUDE.md

@@ -1,13 +0,0 @@
-These are the typescript bindings of LanceDB.
-The core Rust library is in the `../rust/lancedb` directory, the rust binding
-code is in the `src/` directory and the typescript bindings are in
-the `lancedb/` directory.
-
-Whenever you change the Rust code, you will need to recompile: `npm run build`.
-
-Common commands:
-* Build: `npm run build`
-* Lint: `npm run lint`
-* Fix lints: `npm run lint-fix`
-* Test: `npm test`
-* Run single test file: `npm test __test__/arrow.test.ts`

nodejs/CLAUDE.md

@@ -0,0 +1 @@
+AGENTS.md

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.22.2-beta.2"
+version = "0.22.3-beta.5"
 license.workspace = true
 description.workspace = true
 repository.workspace = true

nodejs/__test__/permutation.test.ts

@@ -0,0 +1,371 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import * as tmp from "tmp";
+import { Table, connect, permutationBuilder } from "../lancedb";
+import { makeArrowTable } from "../lancedb/arrow";
+
+describe("PermutationBuilder", () => {
+  let tmpDir: tmp.DirResult;
+  let table: Table;
+
+  beforeEach(async () => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    const db = await connect(tmpDir.name);
+
+    // Create test data
+    const data = makeArrowTable(
+      [
+        { id: 1, value: 10 },
+        { id: 2, value: 20 },
+        { id: 3, value: 30 },
+        { id: 4, value: 40 },
+        { id: 5, value: 50 },
+        { id: 6, value: 60 },
+        { id: 7, value: 70 },
+        { id: 8, value: 80 },
+        { id: 9, value: 90 },
+        { id: 10, value: 100 },
+      ],
+      { vectorColumns: {} },
+    );
+
+    table = await db.createTable("test_table", data);
+  });
+
+  afterEach(() => {
+    tmpDir.removeCallback();
+  });
+
+  test("should create permutation builder", () => {
+    const builder = permutationBuilder(table);
+    expect(builder).toBeDefined();
+  });
+
+  test("should execute basic permutation", async () => {
+    const builder = permutationBuilder(table);
+    const permutationTable = await builder.execute();
+
+    expect(permutationTable).toBeDefined();
+
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+  });
+
+  test("should create permutation with random splits", async () => {
+    const builder = permutationBuilder(table).splitRandom({
+      ratios: [1.0],
+      seed: 42,
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+  });
+
+  test("should create permutation with percentage splits", async () => {
+    const builder = permutationBuilder(table).splitRandom({
+      ratios: [0.3, 0.7],
+      seed: 42,
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Check split distribution
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+  });
+
+  test("should create permutation with count splits", async () => {
+    const builder = permutationBuilder(table).splitRandom({
+      counts: [3, 7],
+      seed: 42,
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Check split distribution
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBe(3);
+    expect(split1Count).toBe(7);
+  });
+
+  test("should create permutation with hash splits", async () => {
+    const builder = permutationBuilder(table).splitHash({
+      columns: ["id"],
+      splitWeights: [50, 50],
+      discardWeight: 0,
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Check that splits exist
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+  });
+
+  test("should create permutation with sequential splits", async () => {
+    const builder = permutationBuilder(table).splitSequential({
+      ratios: [0.5, 0.5],
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Check split distribution - sequential should give exactly 5 and 5
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBe(5);
+    expect(split1Count).toBe(5);
+  });
+
+  test("should create permutation with calculated splits", async () => {
+    const builder = permutationBuilder(table).splitCalculated({
+      calculation: "id % 2",
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Check split distribution
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+  });
+
+  test("should create permutation with shuffle", async () => {
+    const builder = permutationBuilder(table).shuffle({
+      seed: 42,
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+  });
+
+  test("should create permutation with shuffle and clump size", async () => {
+    const builder = permutationBuilder(table).shuffle({
+      seed: 42,
+      clumpSize: 2,
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+  });
+
+  test("should create permutation with filter", async () => {
+    const builder = permutationBuilder(table).filter("value > 50");
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(5); // Values 60, 70, 80, 90, 100
+  });
+
+  test("should chain multiple operations", async () => {
+    const builder = permutationBuilder(table)
+      .filter("value <= 80")
+      .splitRandom({ ratios: [0.5, 0.5], seed: 42 })
+      .shuffle({ seed: 123 });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(8); // Values 10, 20, 30, 40, 50, 60, 70, 80
+
+    // Check split distribution
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(8);
+  });
+
+  test("should throw error for invalid split arguments", () => {
+    const builder = permutationBuilder(table);
+
+    // Test no arguments provided
+    expect(() => builder.splitRandom({})).toThrow(
+      "Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+    );
+
+    // Test multiple arguments provided
+    expect(() =>
+      builder.splitRandom({ ratios: [0.5, 0.5], counts: [3, 7], seed: 42 }),
+    ).toThrow("Exactly one of 'ratios', 'counts', or 'fixed' must be provided");
+  });
+
+  test("should throw error when builder is consumed", async () => {
+    const builder = permutationBuilder(table);
+
+    // Execute once
+    await builder.execute();
+
+    // Should throw error on second execution
+    await expect(builder.execute()).rejects.toThrow("Builder already consumed");
+  });
+
+  test("should accept custom split names with random splits", async () => {
+    const builder = permutationBuilder(table).splitRandom({
+      ratios: [0.3, 0.7],
+      seed: 42,
+      splitNames: ["train", "test"],
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Split names are provided but split_id is still numeric (0, 1, etc.)
+    // The names are metadata that can be used by higher-level APIs
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+  });
+
+  test("should accept custom split names with hash splits", async () => {
+    const builder = permutationBuilder(table).splitHash({
+      columns: ["id"],
+      splitWeights: [50, 50],
+      discardWeight: 0,
+      splitNames: ["set_a", "set_b"],
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Split names are provided but split_id is still numeric
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+  });
+
+  test("should accept custom split names with sequential splits", async () => {
+    const builder = permutationBuilder(table).splitSequential({
+      ratios: [0.5, 0.5],
+      splitNames: ["first", "second"],
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Split names are provided but split_id is still numeric
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBe(5);
+    expect(split1Count).toBe(5);
+  });
+
+  test("should accept custom split names with calculated splits", async () => {
+    const builder = permutationBuilder(table).splitCalculated({
+      calculation: "id % 2",
+      splitNames: ["even", "odd"],
+    });
+
+    const permutationTable = await builder.execute();
+    const rowCount = await permutationTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Split names are provided but split_id is still numeric
+    const split0Count = await permutationTable.countRows("split_id = 0");
+    const split1Count = await permutationTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+  });
+
+  test("should persist permutation to a new table", async () => {
+    const db = await connect(tmpDir.name);
+    const builder = permutationBuilder(table)
+      .splitRandom({
+        ratios: [0.7, 0.3],
+        seed: 42,
+        splitNames: ["train", "validation"],
+      })
+      .persist(db, "my_permutation");
+
+    // Execute the builder which will persist the table
+    const permutationTable = await builder.execute();
+
+    // Verify the persisted table exists and can be opened
+    const persistedTable = await db.openTable("my_permutation");
+    expect(persistedTable).toBeDefined();
+
+    // Verify the persisted table has the correct number of rows
+    const rowCount = await persistedTable.countRows();
+    expect(rowCount).toBe(10);
+
+    // Verify splits exist (numeric split_id values)
+    const split0Count = await persistedTable.countRows("split_id = 0");
+    const split1Count = await persistedTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(10);
+
+    // Verify the table returned by execute is the same as the persisted one
+    const executedRowCount = await permutationTable.countRows();
+    expect(executedRowCount).toBe(10);
+  });
+
+  test("should persist permutation with multiple operations", async () => {
+    const db = await connect(tmpDir.name);
+    const builder = permutationBuilder(table)
+      .filter("value > 30")
+      .splitRandom({ ratios: [0.5, 0.5], seed: 123, splitNames: ["a", "b"] })
+      .shuffle({ seed: 456 })
+      .persist(db, "filtered_permutation");
+
+    // Execute the builder
+    const permutationTable = await builder.execute();
+
+    // Verify the persisted table
+    const persistedTable = await db.openTable("filtered_permutation");
+    const rowCount = await persistedTable.countRows();
+    expect(rowCount).toBe(7); // Values 40, 50, 60, 70, 80, 90, 100
+
+    // Verify splits exist (numeric split_id values)
+    const split0Count = await persistedTable.countRows("split_id = 0");
+    const split1Count = await persistedTable.countRows("split_id = 1");
+
+    expect(split0Count).toBeGreaterThan(0);
+    expect(split1Count).toBeGreaterThan(0);
+    expect(split0Count + split1Count).toBe(7);
+
+    // Verify the executed table matches
+    const executedRowCount = await permutationTable.countRows();
+    expect(executedRowCount).toBe(7);
+  });
+});

nodejs/__test__/query.test.ts

@@ -0,0 +1,111 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import * as tmp from "tmp";
+
+import { type Table, connect } from "../lancedb";
+import {
+  Field,
+  FixedSizeList,
+  Float32,
+  Int64,
+  Schema,
+  Utf8,
+  makeArrowTable,
+} from "../lancedb/arrow";
+import { Index } from "../lancedb/indices";
+
+describe("Query outputSchema", () => {
+  let tmpDir: tmp.DirResult;
+  let table: Table;
+
+  beforeEach(async () => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    const db = await connect(tmpDir.name);
+
+    // Create table with explicit schema to ensure proper types
+    const schema = new Schema([
+      new Field("a", new Int64(), true),
+      new Field("text", new Utf8(), true),
+      new Field(
+        "vec",
+        new FixedSizeList(2, new Field("item", new Float32())),
+        true,
+      ),
+    ]);
+
+    const data = makeArrowTable(
+      [
+        { a: 1n, text: "foo", vec: [1, 2] },
+        { a: 2n, text: "bar", vec: [3, 4] },
+        { a: 3n, text: "baz", vec: [5, 6] },
+      ],
+      { schema },
+    );
+    table = await db.createTable("test", data);
+  });
+
+  afterEach(() => {
+    tmpDir.removeCallback();
+  });
+
+  it("should return schema for plain query", async () => {
+    const schema = await table.query().outputSchema();
+
+    expect(schema.fields.length).toBe(3);
+    expect(schema.fields.map((f) => f.name)).toEqual(["a", "text", "vec"]);
+    expect(schema.fields[0].type.toString()).toBe("Int64");
+    expect(schema.fields[1].type.toString()).toBe("Utf8");
+  });
+
+  it("should return schema with dynamic projection", async () => {
+    const schema = await table.query().select({ bl: "a * 2" }).outputSchema();
+
+    expect(schema.fields.length).toBe(1);
+    expect(schema.fields[0].name).toBe("bl");
+    expect(schema.fields[0].type.toString()).toBe("Int64");
+  });
+
+  it("should return schema for vector search with _distance column", async () => {
+    const schema = await table
+      .vectorSearch([1, 2])
+      .select(["a"])
+      .outputSchema();
+
+    expect(schema.fields.length).toBe(2);
+    expect(schema.fields.map((f) => f.name)).toEqual(["a", "_distance"]);
+    expect(schema.fields[0].type.toString()).toBe("Int64");
+    expect(schema.fields[1].type.toString()).toBe("Float32");
+  });
+
+  it("should return schema for FTS search", async () => {
+    await table.createIndex("text", { config: Index.fts() });
+
+    const schema = await table
+      .search("foo", "fts")
+      .select(["a"])
+      .outputSchema();
+
+    // FTS search includes _score column in addition to selected columns
+    expect(schema.fields.length).toBe(2);
+    expect(schema.fields.map((f) => f.name)).toContain("a");
+    expect(schema.fields.map((f) => f.name)).toContain("_score");
+    const aField = schema.fields.find((f) => f.name === "a");
+    expect(aField?.type.toString()).toBe("Int64");
+  });
+
+  it("should return schema for take query", async () => {
+    const schema = await table.takeOffsets([0]).select(["text"]).outputSchema();
+
+    expect(schema.fields.length).toBe(1);
+    expect(schema.fields[0].name).toBe("text");
+    expect(schema.fields[0].type.toString()).toBe("Utf8");
+  });
+
+  it("should return full schema when no select is specified", async () => {
+    const schema = await table.query().outputSchema();
+
+    // Should return all columns
+    expect(schema.fields.length).toBe(3);
+  });
+});

nodejs/__test__/table.test.ts

@@ -861,6 +861,15 @@ describe("When creating an index", () => {
     });
   });
 
+  it("should be able to create IVF_RQ", async () => {
+    await tbl.createIndex("vec", {
+      config: Index.ivfRq({
+        numPartitions: 10,
+        numBits: 1,
+      }),
+    });
+  });
+
   it("should allow me to replace (or not) an existing index", async () => {
     await tbl.createIndex("id");
     // Default is replace=true

nodejs/lancedb/index.ts

@@ -43,6 +43,11 @@ export {
   DeleteResult,
   DropColumnsResult,
   UpdateResult,
+  SplitCalculatedOptions,
+  SplitRandomOptions,
+  SplitHashOptions,
+  SplitSequentialOptions,
+  ShuffleOptions,
 } from "./native.js";
 
 export {
@@ -85,6 +90,7 @@ export {
   Index,
   IndexOptions,
   IvfPqOptions,
+  IvfRqOptions,
   IvfFlatOptions,
   HnswPqOptions,
   HnswSqOptions,
@@ -110,6 +116,7 @@ export {
 export { MergeInsertBuilder, WriteExecutionOptions } from "./merge";
 
 export * as embedding from "./embedding";
+export { permutationBuilder, PermutationBuilder } from "./permutation";
 export * as rerankers from "./rerankers";
 export {
   SchemaLike,

nodejs/lancedb/indices.ts

@@ -112,6 +112,77 @@ export interface IvfPqOptions {
   sampleRate?: number;
 }
 
+export interface IvfRqOptions {
+  /**
+   * The number of IVF partitions to create.
+   *
+   * This value should generally scale with the number of rows in the dataset.
+   * By default the number of partitions is the square root of the number of
+   * rows.
+   *
+   * If this value is too large then the first part of the search (picking the
+   * right partition) will be slow. If this value is too small then the second
+   * part of the search (searching within a partition) will be slow.
+   */
+  numPartitions?: number;
+
+  /**
+   * Number of bits per dimension for residual quantization.
+   *
+   * This value controls how much each residual component is compressed. The more
+   * bits, the more accurate the index will be but the slower search. Typical values
+   * are small integers; the default is 1 bit per dimension.
+   */
+  numBits?: number;
+
+  /**
+   * Distance type to use to build the index.
+   *
+   * Default value is "l2".
+   *
+   * This is used when training the index to calculate the IVF partitions
+   * (vectors are grouped in partitions with similar vectors according to this
+   * distance type) and during quantization.
+   *
+   * The distance type used to train an index MUST match the distance type used
+   * to search the index. Failure to do so will yield inaccurate results.
+   *
+   * The following distance types are available:
+   *
+   * "l2" - Euclidean distance.
+   * "cosine" - Cosine distance.
+   * "dot" - Dot product.
+   */
+  distanceType?: "l2" | "cosine" | "dot";
+
+  /**
+   * Max iterations to train IVF kmeans.
+   *
+   * When training an IVF index we use kmeans to calculate the partitions. This parameter
+   * controls how many iterations of kmeans to run.
+   *
+   * The default value is 50.
+   */
+  maxIterations?: number;
+
+  /**
+   * The number of vectors, per partition, to sample when training IVF kmeans.
+   *
+   * When an IVF index is trained, we need to calculate partitions. These are groups
+   * of vectors that are similar to each other. To do this we use an algorithm called kmeans.
+   *
+   * Running kmeans on a large dataset can be slow. To speed this up we run kmeans on a
+   * random sample of the data. This parameter controls the size of the sample. The total
+   * number of vectors used to train the index is `sample_rate * num_partitions`.
+   *
+   * Increasing this value might improve the quality of the index but in most cases the
+   * default should be sufficient.
+   *
+   * The default value is 256.
+   */
+  sampleRate?: number;
+}
+
 /**
  * Options to create an `HNSW_PQ` index
  */
@@ -523,6 +594,35 @@ export class Index {
         options?.distanceType,
         options?.numPartitions,
         options?.numSubVectors,
+        options?.numBits,
+        options?.maxIterations,
+        options?.sampleRate,
+      ),
+    );
+  }
+
+  /**
+   * Create an IvfRq index
+   *
+   * IVF-RQ (RabitQ Quantization) compresses vectors using RabitQ quantization
+   * and organizes them into IVF partitions.
+   *
+   * The compression scheme is called RabitQ quantization. Each dimension is quantized into a small number of bits.
+   * The parameters `num_bits` and `num_partitions` control this process, providing a tradeoff
+   * between index size (and thus search speed) and index accuracy.
+   *
+   * The partitioning process is called IVF and the `num_partitions` parameter controls how
+   * many groups to create.
+   *
+   * Note that training an IVF RQ index on a large dataset is a slow operation and
+   * currently is also a memory intensive operation.
+   */
+  static ivfRq(options?: Partial<IvfRqOptions>) {
+    return new Index(
+      LanceDbIndex.ivfRq(
+        options?.distanceType,
+        options?.numPartitions,
+        options?.numBits,
         options?.maxIterations,
         options?.sampleRate,
       ),

nodejs/lancedb/permutation.ts

@@ -0,0 +1,202 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import { Connection, LocalConnection } from "./connection.js";
+import {
+  PermutationBuilder as NativePermutationBuilder,
+  Table as NativeTable,
+  ShuffleOptions,
+  SplitCalculatedOptions,
+  SplitHashOptions,
+  SplitRandomOptions,
+  SplitSequentialOptions,
+  permutationBuilder as nativePermutationBuilder,
+} from "./native.js";
+import { LocalTable, Table } from "./table";
+
+/**
+ * A PermutationBuilder for creating data permutations with splits, shuffling, and filtering.
+ *
+ * This class provides a TypeScript wrapper around the native Rust PermutationBuilder,
+ * offering methods to configure data splits, shuffling, and filtering before executing
+ * the permutation to create a new table.
+ */
+export class PermutationBuilder {
+  private inner: NativePermutationBuilder;
+
+  /**
+   * @hidden
+   */
+  constructor(inner: NativePermutationBuilder) {
+    this.inner = inner;
+  }
+
+  /**
+   * Configure the permutation to be persisted.
+   *
+   * @param connection - The connection to persist the permutation to
+   * @param tableName - The name of the table to create
+   * @returns A new PermutationBuilder instance
+   * @example
+   * ```ts
+   * builder.persist(connection, "permutation_table");
+   * ```
+   */
+  persist(connection: Connection, tableName: string): PermutationBuilder {
+    const localConnection = connection as LocalConnection;
+    const newInner = this.inner.persist(localConnection.inner, tableName);
+    return new PermutationBuilder(newInner);
+  }
+
+  /**
+   * Configure random splits for the permutation.
+   *
+   * @param options - Configuration for random splitting
+   * @returns A new PermutationBuilder instance
+   * @example
+   * ```ts
+   * // Split by ratios
+   * builder.splitRandom({ ratios: [0.7, 0.3], seed: 42 });
+   *
+   * // Split by counts
+   * builder.splitRandom({ counts: [1000, 500], seed: 42 });
+   *
+   * // Split with fixed size
+   * builder.splitRandom({ fixed: 100, seed: 42 });
+   * ```
+   */
+  splitRandom(options: SplitRandomOptions): PermutationBuilder {
+    const newInner = this.inner.splitRandom(options);
+    return new PermutationBuilder(newInner);
+  }
+
+  /**
+   * Configure hash-based splits for the permutation.
+   *
+   * @param options - Configuration for hash-based splitting
+   * @returns A new PermutationBuilder instance
+   * @example
+   * ```ts
+   * builder.splitHash({
+   *   columns: ["user_id"],
+   *   splitWeights: [70, 30],
+   *   discardWeight: 0
+   * });
+   * ```
+   */
+  splitHash(options: SplitHashOptions): PermutationBuilder {
+    const newInner = this.inner.splitHash(options);
+    return new PermutationBuilder(newInner);
+  }
+
+  /**
+   * Configure sequential splits for the permutation.
+   *
+   * @param options - Configuration for sequential splitting
+   * @returns A new PermutationBuilder instance
+   * @example
+   * ```ts
+   * // Split by ratios
+   * builder.splitSequential({ ratios: [0.8, 0.2] });
+   *
+   * // Split by counts
+   * builder.splitSequential({ counts: [800, 200] });
+   *
+   * // Split with fixed size
+   * builder.splitSequential({ fixed: 1000 });
+   * ```
+   */
+  splitSequential(options: SplitSequentialOptions): PermutationBuilder {
+    const newInner = this.inner.splitSequential(options);
+    return new PermutationBuilder(newInner);
+  }
+
+  /**
+   * Configure calculated splits for the permutation.
+   *
+   * @param options - Configuration for calculated splitting
+   * @returns A new PermutationBuilder instance
+   * @example
+   * ```ts
+   * builder.splitCalculated("user_id % 3");
+   * ```
+   */
+  splitCalculated(options: SplitCalculatedOptions): PermutationBuilder {
+    const newInner = this.inner.splitCalculated(options);
+    return new PermutationBuilder(newInner);
+  }
+
+  /**
+   * Configure shuffling for the permutation.
+   *
+   * @param options - Configuration for shuffling
+   * @returns A new PermutationBuilder instance
+   * @example
+   * ```ts
+   * // Basic shuffle
+   * builder.shuffle({ seed: 42 });
+   *
+   * // Shuffle with clump size
+   * builder.shuffle({ seed: 42, clumpSize: 10 });
+   * ```
+   */
+  shuffle(options: ShuffleOptions): PermutationBuilder {
+    const newInner = this.inner.shuffle(options);
+    return new PermutationBuilder(newInner);
+  }
+
+  /**
+   * Configure filtering for the permutation.
+   *
+   * @param filter - SQL filter expression
+   * @returns A new PermutationBuilder instance
+   * @example
+   * ```ts
+   * builder.filter("age > 18 AND status = 'active'");
+   * ```
+   */
+  filter(filter: string): PermutationBuilder {
+    const newInner = this.inner.filter(filter);
+    return new PermutationBuilder(newInner);
+  }
+
+  /**
+   * Execute the permutation and create the destination table.
+   *
+   * @returns A Promise that resolves to the new Table instance
+   * @example
+   * ```ts
+   * const permutationTable = await builder.execute();
+   * console.log(`Created table: ${permutationTable.name}`);
+   * ```
+   */
+  async execute(): Promise<Table> {
+    const nativeTable: NativeTable = await this.inner.execute();
+    return new LocalTable(nativeTable);
+  }
+}
+
+/**
+ * Create a permutation builder for the given table.
+ *
+ * @param table - The source table to create a permutation from
+ * @returns A PermutationBuilder instance
+ * @example
+ * ```ts
+ * const builder = permutationBuilder(sourceTable, "training_data")
+ *   .splitRandom({ ratios: [0.8, 0.2], seed: 42 })
+ *   .shuffle({ seed: 123 });
+ *
+ * const trainingTable = await builder.execute();
+ * ```
+ */
+export function permutationBuilder(table: Table): PermutationBuilder {
+  // Extract the inner native table from the TypeScript wrapper
+  const localTable = table as LocalTable;
+  // Access inner through type assertion since it's private
+  const nativeBuilder = nativePermutationBuilder(
+    // biome-ignore lint/suspicious/noExplicitAny: need access to private variable
+    (localTable as any).inner,
+  );
+  return new PermutationBuilder(nativeBuilder);
+}

nodejs/lancedb/query.ts

@@ -20,35 +20,25 @@ import {
 } from "./native";
 import { Reranker } from "./rerankers";
 
-export class RecordBatchIterator implements AsyncIterator<RecordBatch> {
-  private promisedInner?: Promise<NativeBatchIterator>;
-  private inner?: NativeBatchIterator;
+export async function* RecordBatchIterator(
+  promisedInner: Promise<NativeBatchIterator>,
+) {
+  const inner = await promisedInner;
 
-  constructor(promise?: Promise<NativeBatchIterator>) {
-    // TODO: check promise reliably so we dont need to pass two arguments.
-    this.promisedInner = promise;
+  if (inner === undefined) {
+    throw new Error("Invalid iterator state");
   }
 
-  // biome-ignore lint/suspicious/noExplicitAny: skip
-  async next(): Promise<IteratorResult<RecordBatch<any>>> {
-    if (this.inner === undefined) {
-      this.inner = await this.promisedInner;
-    }
-    if (this.inner === undefined) {
-      throw new Error("Invalid iterator state state");
-    }
-    const n = await this.inner.next();
-    if (n == null) {
-      return Promise.resolve({ done: true, value: null });
-    }
-    const tbl = tableFromIPC(n);
-    if (tbl.batches.length != 1) {
+  for (let buffer = await inner.next(); buffer; buffer = await inner.next()) {
+    const { batches } = tableFromIPC(buffer);
+
+    if (batches.length !== 1) {
       throw new Error("Expected only one batch");
     }
-    return Promise.resolve({ done: false, value: tbl.batches[0] });
+
+    yield batches[0];
   }
 }
-/* eslint-enable */
 
 class RecordBatchIterable<
   NativeQueryType extends NativeQuery | NativeVectorQuery | NativeTakeQuery,
@@ -64,7 +54,7 @@ class RecordBatchIterable<
 
   // biome-ignore lint/suspicious/noExplicitAny: skip
   [Symbol.asyncIterator](): AsyncIterator<RecordBatch<any>, any, undefined> {
-    return new RecordBatchIterator(
+    return RecordBatchIterator(
       this.inner.execute(this.options?.maxBatchLength, this.options?.timeoutMs),
     );
   }
@@ -231,10 +221,8 @@ export class QueryBase<
    * single query)
    *
    */
-  protected execute(
-    options?: Partial<QueryExecutionOptions>,
-  ): RecordBatchIterator {
-    return new RecordBatchIterator(this.nativeExecute(options));
+  protected execute(options?: Partial<QueryExecutionOptions>) {
+    return RecordBatchIterator(this.nativeExecute(options));
   }
 
   /**
@@ -242,8 +230,7 @@ export class QueryBase<
    */
   // biome-ignore lint/suspicious/noExplicitAny: skip
   [Symbol.asyncIterator](): AsyncIterator<RecordBatch<any>> {
-    const promise = this.nativeExecute();
-    return new RecordBatchIterator(promise);
+    return RecordBatchIterator(this.nativeExecute());
   }
 
   /** Collect the results as an Arrow @see {@link ArrowTable}. */
@@ -326,6 +313,25 @@ export class QueryBase<
       return this.inner.analyzePlan();
     }
   }
+
+  /**
+   * Returns the schema of the output that will be returned by this query.
+   *
+   * This can be used to inspect the types and names of the columns that will be
+   * returned by the query before executing it.
+   *
+   * @returns An Arrow Schema describing the output columns.
+   */
+  async outputSchema(): Promise<import("./arrow").Schema> {
+    let schemaBuffer: Buffer;
+    if (this.inner instanceof Promise) {
+      schemaBuffer = await this.inner.then((inner) => inner.outputSchema());
+    } else {
+      schemaBuffer = await this.inner.outputSchema();
+    }
+    const schema = tableFromIPC(schemaBuffer).schema;
+    return schema;
+  }
 }
 
 export class StandardQueryBase<

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.22.2-beta.2",
+	"version": "0.22.3-beta.5",
 	"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": "0.22.2-beta.2",
+	"version": "0.22.3-beta.5",
 	"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": "0.22.2-beta.2",
+	"version": "0.22.3-beta.5",
 	"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": "0.22.2-beta.2",
+	"version": "0.22.3-beta.5",
 	"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": "0.22.2-beta.2",
+	"version": "0.22.3-beta.5",
 	"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": "0.22.2-beta.2",
+	"version": "0.22.3-beta.5",
 	"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": "0.22.2-beta.2",
+  "version": "0.22.3-beta.5",
   "os": [
     "win32"
   ],

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.22.2-beta.2",
+	"version": "0.22.3-beta.5",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.22.2-beta.2",
+  "version": "0.22.3-beta.5",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.22.2-beta.2",
+      "version": "0.22.3-beta.5",
       "cpu": [
         "x64",
         "arm64"

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.22.2-beta.2",
+  "version": "0.22.3-beta.5",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

nodejs/src/connection.rs

@@ -4,7 +4,7 @@
 use std::collections::HashMap;
 use std::sync::Arc;
 
-use lancedb::database::CreateTableMode;
+use lancedb::database::{CreateTableMode, Database};
 use napi::bindgen_prelude::*;
 use napi_derive::*;
 
@@ -41,6 +41,10 @@ impl Connection {
             _ => Err(napi::Error::from_reason(format!("Invalid mode {}", mode))),
         }
     }
+
+    pub fn database(&self) -> napi::Result<Arc<dyn Database>> {
+        Ok(self.get_inner()?.database().clone())
+    }
 }
 
 #[napi]

nodejs/src/index.rs

@@ -6,6 +6,7 @@ use std::sync::Mutex;
 use lancedb::index::scalar::{BTreeIndexBuilder, FtsIndexBuilder};
 use lancedb::index::vector::{
     IvfFlatIndexBuilder, IvfHnswPqIndexBuilder, IvfHnswSqIndexBuilder, IvfPqIndexBuilder,
+    IvfRqIndexBuilder,
 };
 use lancedb::index::Index as LanceDbIndex;
 use napi_derive::napi;
@@ -65,6 +66,36 @@ impl Index {
         })
     }
 
+    #[napi(factory)]
+    pub fn ivf_rq(
+        distance_type: Option<String>,
+        num_partitions: Option<u32>,
+        num_bits: Option<u32>,
+        max_iterations: Option<u32>,
+        sample_rate: Option<u32>,
+    ) -> napi::Result<Self> {
+        let mut ivf_rq_builder = IvfRqIndexBuilder::default();
+        if let Some(distance_type) = distance_type {
+            let distance_type = parse_distance_type(distance_type)?;
+            ivf_rq_builder = ivf_rq_builder.distance_type(distance_type);
+        }
+        if let Some(num_partitions) = num_partitions {
+            ivf_rq_builder = ivf_rq_builder.num_partitions(num_partitions);
+        }
+        if let Some(num_bits) = num_bits {
+            ivf_rq_builder = ivf_rq_builder.num_bits(num_bits);
+        }
+        if let Some(max_iterations) = max_iterations {
+            ivf_rq_builder = ivf_rq_builder.max_iterations(max_iterations);
+        }
+        if let Some(sample_rate) = sample_rate {
+            ivf_rq_builder = ivf_rq_builder.sample_rate(sample_rate);
+        }
+        Ok(Self {
+            inner: Mutex::new(Some(LanceDbIndex::IvfRq(ivf_rq_builder))),
+        })
+    }
+
     #[napi(factory)]
     pub fn ivf_flat(
         distance_type: Option<String>,

nodejs/src/lib.rs

@@ -12,6 +12,7 @@ mod header;
 mod index;
 mod iterator;
 pub mod merge;
+pub mod permutation;
 mod query;
 pub mod remote;
 mod rerankers;

nodejs/src/permutation.rs

@@ -0,0 +1,248 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::sync::{Arc, Mutex};
+
+use crate::{error::NapiErrorExt, table::Table};
+use lancedb::dataloader::{
+    permutation::builder::{PermutationBuilder as LancePermutationBuilder, ShuffleStrategy},
+    permutation::split::{SplitSizes, SplitStrategy},
+};
+use napi_derive::napi;
+
+#[napi(object)]
+pub struct SplitRandomOptions {
+    pub ratios: Option<Vec<f64>>,
+    pub counts: Option<Vec<i64>>,
+    pub fixed: Option<i64>,
+    pub seed: Option<i64>,
+    pub split_names: Option<Vec<String>>,
+}
+
+#[napi(object)]
+pub struct SplitHashOptions {
+    pub columns: Vec<String>,
+    pub split_weights: Vec<i64>,
+    pub discard_weight: Option<i64>,
+    pub split_names: Option<Vec<String>>,
+}
+
+#[napi(object)]
+pub struct SplitSequentialOptions {
+    pub ratios: Option<Vec<f64>>,
+    pub counts: Option<Vec<i64>>,
+    pub fixed: Option<i64>,
+    pub split_names: Option<Vec<String>>,
+}
+
+#[napi(object)]
+pub struct SplitCalculatedOptions {
+    pub calculation: String,
+    pub split_names: Option<Vec<String>>,
+}
+
+#[napi(object)]
+pub struct ShuffleOptions {
+    pub seed: Option<i64>,
+    pub clump_size: Option<i64>,
+}
+
+pub struct PermutationBuilderState {
+    pub builder: Option<LancePermutationBuilder>,
+}
+
+#[napi]
+pub struct PermutationBuilder {
+    state: Arc<Mutex<PermutationBuilderState>>,
+}
+
+impl PermutationBuilder {
+    pub fn new(builder: LancePermutationBuilder) -> Self {
+        Self {
+            state: Arc::new(Mutex::new(PermutationBuilderState {
+                builder: Some(builder),
+            })),
+        }
+    }
+}
+
+impl PermutationBuilder {
+    fn modify(
+        &self,
+        func: impl FnOnce(LancePermutationBuilder) -> LancePermutationBuilder,
+    ) -> napi::Result<Self> {
+        let mut state = self.state.lock().unwrap();
+        let builder = state
+            .builder
+            .take()
+            .ok_or_else(|| napi::Error::from_reason("Builder already consumed"))?;
+        state.builder = Some(func(builder));
+        Ok(Self {
+            state: self.state.clone(),
+        })
+    }
+}
+
+#[napi]
+impl PermutationBuilder {
+    #[napi]
+    pub fn persist(
+        &self,
+        connection: &crate::connection::Connection,
+        table_name: String,
+    ) -> napi::Result<Self> {
+        let database = connection.database()?;
+        self.modify(|builder| builder.persist(database, table_name))
+    }
+
+    /// Configure random splits
+    #[napi]
+    pub fn split_random(&self, options: SplitRandomOptions) -> napi::Result<Self> {
+        // Check that exactly one split type is provided
+        let split_args_count = [
+            options.ratios.is_some(),
+            options.counts.is_some(),
+            options.fixed.is_some(),
+        ]
+        .iter()
+        .filter(|&&x| x)
+        .count();
+
+        if split_args_count != 1 {
+            return Err(napi::Error::from_reason(
+                "Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+            ));
+        }
+
+        let sizes = if let Some(ratios) = options.ratios {
+            SplitSizes::Percentages(ratios)
+        } else if let Some(counts) = options.counts {
+            SplitSizes::Counts(counts.into_iter().map(|c| c as u64).collect())
+        } else if let Some(fixed) = options.fixed {
+            SplitSizes::Fixed(fixed as u64)
+        } else {
+            unreachable!("One of the split arguments must be provided");
+        };
+
+        let seed = options.seed.map(|s| s as u64);
+
+        self.modify(|builder| {
+            builder.with_split_strategy(
+                SplitStrategy::Random { seed, sizes },
+                options.split_names.clone(),
+            )
+        })
+    }
+
+    /// Configure hash-based splits
+    #[napi]
+    pub fn split_hash(&self, options: SplitHashOptions) -> napi::Result<Self> {
+        let split_weights = options
+            .split_weights
+            .into_iter()
+            .map(|w| w as u64)
+            .collect();
+        let discard_weight = options.discard_weight.unwrap_or(0) as u64;
+
+        self.modify(move |builder| {
+            builder.with_split_strategy(
+                SplitStrategy::Hash {
+                    columns: options.columns,
+                    split_weights,
+                    discard_weight,
+                },
+                options.split_names,
+            )
+        })
+    }
+
+    /// Configure sequential splits
+    #[napi]
+    pub fn split_sequential(&self, options: SplitSequentialOptions) -> napi::Result<Self> {
+        // Check that exactly one split type is provided
+        let split_args_count = [
+            options.ratios.is_some(),
+            options.counts.is_some(),
+            options.fixed.is_some(),
+        ]
+        .iter()
+        .filter(|&&x| x)
+        .count();
+
+        if split_args_count != 1 {
+            return Err(napi::Error::from_reason(
+                "Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+            ));
+        }
+
+        let sizes = if let Some(ratios) = options.ratios {
+            SplitSizes::Percentages(ratios)
+        } else if let Some(counts) = options.counts {
+            SplitSizes::Counts(counts.into_iter().map(|c| c as u64).collect())
+        } else if let Some(fixed) = options.fixed {
+            SplitSizes::Fixed(fixed as u64)
+        } else {
+            unreachable!("One of the split arguments must be provided");
+        };
+
+        self.modify(move |builder| {
+            builder.with_split_strategy(SplitStrategy::Sequential { sizes }, options.split_names)
+        })
+    }
+
+    /// Configure calculated splits
+    #[napi]
+    pub fn split_calculated(&self, options: SplitCalculatedOptions) -> napi::Result<Self> {
+        self.modify(move |builder| {
+            builder.with_split_strategy(
+                SplitStrategy::Calculated {
+                    calculation: options.calculation,
+                },
+                options.split_names,
+            )
+        })
+    }
+
+    /// Configure shuffling
+    #[napi]
+    pub fn shuffle(&self, options: ShuffleOptions) -> napi::Result<Self> {
+        let seed = options.seed.map(|s| s as u64);
+        let clump_size = options.clump_size.map(|c| c as u64);
+
+        self.modify(|builder| {
+            builder.with_shuffle_strategy(ShuffleStrategy::Random { seed, clump_size })
+        })
+    }
+
+    /// Configure filtering
+    #[napi]
+    pub fn filter(&self, filter: String) -> napi::Result<Self> {
+        self.modify(|builder| builder.with_filter(filter))
+    }
+
+    /// Execute the permutation builder and create the table
+    #[napi]
+    pub async fn execute(&self) -> napi::Result<Table> {
+        let builder = {
+            let mut state = self.state.lock().unwrap();
+            state
+                .builder
+                .take()
+                .ok_or_else(|| napi::Error::from_reason("Builder already consumed"))?
+        };
+
+        let table = builder.build().await.default_error()?;
+        Ok(Table::new(table))
+    }
+}
+
+/// Create a permutation builder for the given table
+#[napi]
+pub fn permutation_builder(table: &crate::table::Table) -> napi::Result<PermutationBuilder> {
+    use lancedb::dataloader::permutation::builder::PermutationBuilder as LancePermutationBuilder;
+
+    let inner_table = table.inner_ref()?.clone();
+    let inner_builder = LancePermutationBuilder::new(inner_table);
+
+    Ok(PermutationBuilder::new(inner_builder))
+}

nodejs/src/query.rs

@@ -22,7 +22,7 @@ use crate::error::NapiErrorExt;
 use crate::iterator::RecordBatchIterator;
 use crate::rerankers::Reranker;
 use crate::rerankers::RerankerCallbacks;
-use crate::util::parse_distance_type;
+use crate::util::{parse_distance_type, schema_to_buffer};
 
 #[napi]
 pub struct Query {
@@ -88,6 +88,12 @@ impl Query {
         self.inner = self.inner.clone().with_row_id();
     }
 
+    #[napi(catch_unwind)]
+    pub async fn output_schema(&self) -> napi::Result<Buffer> {
+        let schema = self.inner.output_schema().await.default_error()?;
+        schema_to_buffer(&schema)
+    }
+
     #[napi(catch_unwind)]
     pub async fn execute(
         &self,
@@ -273,6 +279,12 @@ impl VectorQuery {
             .rerank(Arc::new(Reranker::new(callbacks)));
     }
 
+    #[napi(catch_unwind)]
+    pub async fn output_schema(&self) -> napi::Result<Buffer> {
+        let schema = self.inner.output_schema().await.default_error()?;
+        schema_to_buffer(&schema)
+    }
+
     #[napi(catch_unwind)]
     pub async fn execute(
         &self,
@@ -346,6 +358,12 @@ impl TakeQuery {
         self.inner = self.inner.clone().with_row_id();
     }
 
+    #[napi(catch_unwind)]
+    pub async fn output_schema(&self) -> napi::Result<Buffer> {
+        let schema = self.inner.output_schema().await.default_error()?;
+        schema_to_buffer(&schema)
+    }
+
     #[napi(catch_unwind)]
     pub async fn execute(
         &self,

nodejs/src/table.rs

@@ -3,7 +3,6 @@
 
 use std::collections::HashMap;
 
-use arrow_ipc::writer::FileWriter;
 use lancedb::ipc::ipc_file_to_batches;
 use lancedb::table::{
     AddDataMode, ColumnAlteration as LanceColumnAlteration, Duration, NewColumnTransform,
@@ -16,6 +15,7 @@ use crate::error::NapiErrorExt;
 use crate::index::Index;
 use crate::merge::NativeMergeInsertBuilder;
 use crate::query::{Query, TakeQuery, VectorQuery};
+use crate::util::schema_to_buffer;
 
 #[napi]
 pub struct Table {
@@ -26,7 +26,7 @@ pub struct Table {
 }
 
 impl Table {
-    fn inner_ref(&self) -> napi::Result<&LanceDbTable> {
+    pub(crate) fn inner_ref(&self) -> napi::Result<&LanceDbTable> {
         self.inner
             .as_ref()
             .ok_or_else(|| napi::Error::from_reason(format!("Table {} is closed", self.name)))
@@ -64,14 +64,7 @@ impl Table {
     #[napi(catch_unwind)]
     pub async fn schema(&self) -> napi::Result<Buffer> {
         let schema = self.inner_ref()?.schema().await.default_error()?;
-        let mut writer = FileWriter::try_new(vec![], &schema)
-            .map_err(|e| napi::Error::from_reason(format!("Failed to create IPC file: {}", e)))?;
-        writer
-            .finish()
-            .map_err(|e| napi::Error::from_reason(format!("Failed to finish IPC file: {}", e)))?;
-        Ok(Buffer::from(writer.into_inner().map_err(|e| {
-            napi::Error::from_reason(format!("Failed to get IPC file: {}", e))
-        })?))
+        schema_to_buffer(&schema)
     }
 
     #[napi(catch_unwind)]

nodejs/src/util.rs

@@ -1,7 +1,10 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
+use arrow_ipc::writer::FileWriter;
+use arrow_schema::Schema;
 use lancedb::DistanceType;
+use napi::bindgen_prelude::Buffer;
 
 pub fn parse_distance_type(distance_type: impl AsRef<str>) -> napi::Result<DistanceType> {
     match distance_type.as_ref().to_lowercase().as_str() {
@@ -15,3 +18,15 @@ pub fn parse_distance_type(distance_type: impl AsRef<str>) -> napi::Result<Dista
         ))),
     }
 }
+
+/// Convert an Arrow Schema to an Arrow IPC file buffer
+pub fn schema_to_buffer(schema: &Schema) -> napi::Result<Buffer> {
+    let mut writer = FileWriter::try_new(vec![], schema)
+        .map_err(|e| napi::Error::from_reason(format!("Failed to create IPC file: {}", e)))?;
+    writer
+        .finish()
+        .map_err(|e| napi::Error::from_reason(format!("Failed to finish IPC file: {}", e)))?;
+    Ok(Buffer::from(writer.into_inner().map_err(|e| {
+        napi::Error::from_reason(format!("Failed to get IPC file: {}", e))
+    })?))
+}

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.25.2"
+current_version = "0.25.3"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.
@@ -24,6 +24,19 @@ commit = true
 message = "Bump version: {current_version} → {new_version}"
 commit_args = ""
 
+# Update Cargo.lock after version bump
+pre_commit_hooks = [
+  """
+    cd python && cargo update -p lancedb-python
+    if git diff --quiet ../Cargo.lock; then
+        echo "Cargo.lock unchanged"
+    else
+        git add ../Cargo.lock
+        echo "Updated and staged Cargo.lock"
+    fi
+    """,
+]
+
 [tool.bumpversion.parts.pre_l]
 values = ["beta", "final"]
 optional_value = "final"

python/AGENTS.md

@@ -0,0 +1,19 @@
+These are the Python bindings of LanceDB.
+The core Rust library is in the `../rust/lancedb` directory, the rust binding
+code is in the `src/` directory and the Python bindings are in the `lancedb/` directory.
+
+Common commands:
+
+* Build: `make develop`
+* Format: `make format`
+* Lint: `make check`
+* Fix lints: `make fix`
+* Test: `make test`
+* Doc test: `make doctest`
+
+Before committing changes, run lints and then formatting.
+
+When you change the Rust code, you will need to recompile the Python bindings: `make develop`.
+
+When you export new types from Rust to Python, you must manually update `python/lancedb/_lancedb.pyi`
+with the corresponding type hints. You can run `pyright` to check for type errors in the Python code.

python/CLAUDE.md

@@ -1,19 +0,0 @@
-These are the Python bindings of LanceDB.
-The core Rust library is in the `../rust/lancedb` directory, the rust binding
-code is in the `src/` directory and the Python bindings are in the `lancedb/` directory.
-
-Common commands:
-
-* Build: `make develop`
-* Format: `make format`
-* Lint: `make check`
-* Fix lints: `make fix`
-* Test: `make test`
-* Doc test: `make doctest`
-
-Before committing changes, run lints and then formatting.
-
-When you change the Rust code, you will need to recompile the Python bindings: `make develop`.
-
-When you export new types from Rust to Python, you must manually update `python/lancedb/_lancedb.pyi`
-with the corresponding type hints. You can run `pyright` to check for type errors in the Python code.

python/CLAUDE.md

@@ -0,0 +1 @@
+AGENTS.md

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.25.2"
+version = "0.25.3"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/python/lancedb/__init__.py

@@ -17,7 +17,7 @@ from .db import AsyncConnection, DBConnection, LanceDBConnection
 from .remote import ClientConfig
 from .remote.db import RemoteDBConnection
 from .schema import vector
-from .table import AsyncTable
+from .table import AsyncTable, Table
 from ._lancedb import Session
 from .namespace import connect_namespace, LanceNamespaceDBConnection
 
@@ -233,6 +233,7 @@ __all__ = [
     "LanceNamespaceDBConnection",
     "RemoteDBConnection",
     "Session",
+    "Table",
     "__version__",
 ]
 

python/python/lancedb/_lancedb.pyi

@@ -123,6 +123,8 @@ class Table:
     @property
     def tags(self) -> Tags: ...
     def query(self) -> Query: ...
+    def take_offsets(self, offsets: list[int]) -> TakeQuery: ...
+    def take_row_ids(self, row_ids: list[int]) -> TakeQuery: ...
     def vector_search(self) -> VectorQuery: ...
 
 class Tags:
@@ -165,6 +167,7 @@ class Query:
     def postfilter(self): ...
     def nearest_to(self, query_vec: pa.Array) -> VectorQuery: ...
     def nearest_to_text(self, query: dict) -> FTSQuery: ...
+    async def output_schema(self) -> pa.Schema: ...
     async def execute(
         self, max_batch_length: Optional[int], timeout: Optional[timedelta]
     ) -> RecordBatchStream: ...
@@ -172,6 +175,13 @@ class Query:
     async def analyze_plan(self) -> str: ...
     def to_query_request(self) -> PyQueryRequest: ...
 
+class TakeQuery:
+    def select(self, columns: List[str]): ...
+    def with_row_id(self): ...
+    async def output_schema(self) -> pa.Schema: ...
+    async def execute(self) -> RecordBatchStream: ...
+    def to_query_request(self) -> PyQueryRequest: ...
+
 class FTSQuery:
     def where(self, filter: str): ...
     def select(self, columns: List[str]): ...
@@ -183,12 +193,14 @@ 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 output_schema(self) -> pa.Schema: ...
     async def execute(
         self, max_batch_length: Optional[int], timeout: Optional[timedelta]
     ) -> RecordBatchStream: ...
     def to_query_request(self) -> PyQueryRequest: ...
 
 class VectorQuery:
+    async def output_schema(self) -> pa.Schema: ...
     async def execute(self) -> RecordBatchStream: ...
     def where(self, filter: str): ...
     def select(self, columns: List[str]): ...
@@ -296,3 +308,38 @@ class AlterColumnsResult:
 
 class DropColumnsResult:
     version: int
+
+class AsyncPermutationBuilder:
+    def select(self, projections: Dict[str, str]) -> "AsyncPermutationBuilder": ...
+    def split_random(
+        self,
+        *,
+        ratios: Optional[List[float]] = None,
+        counts: Optional[List[int]] = None,
+        fixed: Optional[int] = None,
+        seed: Optional[int] = None,
+    ) -> "AsyncPermutationBuilder": ...
+    def split_hash(
+        self, columns: List[str], split_weights: List[int], *, discard_weight: int = 0
+    ) -> "AsyncPermutationBuilder": ...
+    def split_sequential(
+        self,
+        *,
+        ratios: Optional[List[float]] = None,
+        counts: Optional[List[int]] = None,
+        fixed: Optional[int] = None,
+    ) -> "AsyncPermutationBuilder": ...
+    def split_calculated(self, calculation: str) -> "AsyncPermutationBuilder": ...
+    def shuffle(
+        self, seed: Optional[int], clump_size: Optional[int]
+    ) -> "AsyncPermutationBuilder": ...
+    def filter(self, filter: str) -> "AsyncPermutationBuilder": ...
+    async def execute(self) -> Table: ...
+
+def async_permutation_builder(
+    table: Table, dest_table_name: str
+) -> AsyncPermutationBuilder: ...
+def fts_query_to_json(query: Any) -> str: ...
+
+class PermutationReader:
+    def __init__(self, base_table: Table, permutation_table: Table): ...

python/python/lancedb/db.py

@@ -5,6 +5,7 @@
 from __future__ import annotations
 
 from abc import abstractmethod
+from datetime import timedelta
 from pathlib import Path
 import sys
 from typing import TYPE_CHECKING, Dict, Iterable, List, Literal, Optional, Union
@@ -40,7 +41,6 @@ import deprecation
 if TYPE_CHECKING:
     import pyarrow as pa
     from .pydantic import LanceModel
-    from datetime import timedelta
 
     from ._lancedb import Connection as LanceDbConnection
     from .common import DATA, URI
@@ -452,7 +452,12 @@ class LanceDBConnection(DBConnection):
         read_consistency_interval: Optional[timedelta] = None,
         storage_options: Optional[Dict[str, str]] = None,
         session: Optional[Session] = None,
+        _inner: Optional[LanceDbConnection] = None,
     ):
+        if _inner is not None:
+            self._conn = _inner
+            return
+
         if not isinstance(uri, Path):
             scheme = get_uri_scheme(uri)
         is_local = isinstance(uri, Path) or scheme == "file"
@@ -461,11 +466,6 @@ class LanceDBConnection(DBConnection):
                 uri = Path(uri)
             uri = uri.expanduser().absolute()
             Path(uri).mkdir(parents=True, exist_ok=True)
-        self._uri = str(uri)
-        self._entered = False
-        self.read_consistency_interval = read_consistency_interval
-        self.storage_options = storage_options
-        self.session = session
 
         if read_consistency_interval is not None:
             read_consistency_interval_secs = read_consistency_interval.total_seconds()
@@ -484,10 +484,32 @@ class LanceDBConnection(DBConnection):
                 session,
             )
 
+        # TODO: It would be nice if we didn't store self.storage_options but it is
+        # currently used by the LanceTable.to_lance method.  This doesn't _really_
+        # work because some paths like LanceDBConnection.from_inner will lose the
+        # storage_options.  Also, this class really shouldn't be holding any state
+        # beyond _conn.
+        self.storage_options = storage_options
         self._conn = AsyncConnection(LOOP.run(do_connect()))
 
+    @property
+    def read_consistency_interval(self) -> Optional[timedelta]:
+        return LOOP.run(self._conn.get_read_consistency_interval())
+
+    @property
+    def session(self) -> Optional[Session]:
+        return self._conn.session
+
+    @property
+    def uri(self) -> str:
+        return self._conn.uri
+
+    @classmethod
+    def from_inner(cls, inner: LanceDbConnection):
+        return cls(None, _inner=inner)
+
     def __repr__(self) -> str:
-        val = f"{self.__class__.__name__}(uri={self._uri!r}"
+        val = f"{self.__class__.__name__}(uri={self._conn.uri!r}"
         if self.read_consistency_interval is not None:
             val += f", read_consistency_interval={repr(self.read_consistency_interval)}"
         val += ")"
@@ -497,6 +519,10 @@ class LanceDBConnection(DBConnection):
         conn = AsyncConnection(await lancedb_connect(self.uri))
         return await conn.table_names(start_after=start_after, limit=limit)
 
+    @property
+    def _inner(self) -> LanceDbConnection:
+        return self._conn._inner
+
     @override
     def list_namespaces(
         self,
@@ -856,6 +882,13 @@ class AsyncConnection(object):
     def uri(self) -> str:
         return self._inner.uri
 
+    async def get_read_consistency_interval(self) -> Optional[timedelta]:
+        interval_secs = await self._inner.get_read_consistency_interval()
+        if interval_secs is not None:
+            return timedelta(seconds=interval_secs)
+        else:
+            return None
+
     async def list_namespaces(
         self,
         namespace: List[str] = [],

python/python/lancedb/embeddings/colpali.py

@@ -3,9 +3,11 @@
 
 
 from functools import lru_cache
-from typing import List, Union, Optional, Any
+from logging import warning
+from typing import List, Union, Optional, Any, Callable
 import numpy as np
 import io
+import warnings
 
 from ..util import attempt_import_or_raise
 from .base import EmbeddingFunction
@@ -19,35 +21,52 @@ 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]].
+    This embedding function supports ColPali models, producing multivector outputs
+    for both text and image inputs.
 
     Parameters
     ----------
     model_name : str
         The name of the model to use (e.g., "Metric-AI/ColQwen2.5-3b-multilingual-v1.0")
+        Supports models based on these engines:
+        - ColPali: "vidore/colpali-v1.3" and others
+        - ColQwen2.5: "Metric-AI/ColQwen2.5-3b-multilingual-v1.0" and others
+        - ColQwen2: "vidore/colqwen2-v1.0" and others
+        - ColSmol: "vidore/colSmol-256M" and others
+
     device : str
-        The device for inference (default "cuda:0").
+        The device for inference (default "auto").
     dtype : str
         Data type for model weights (default "bfloat16").
     use_token_pooling : bool
-        Whether to use token pooling to reduce embedding size (default True).
+        DEPRECATED. Whether to use token pooling. Use `pooling_strategy` instead.
+    pooling_strategy : str, optional
+        The token pooling strategy to use, by default "hierarchical".
+        - "hierarchical": Progressively pools tokens to reduce sequence length.
+        - "lambda": A simpler pooling that uses a custom `pooling_func`.
+    pooling_func: typing.Callable, optional
+        A function to use for pooling when `pooling_strategy` is "lambda".
     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).
+    offload_folder: str, optional
+        Folder to offload model weights if using CPU offloading (default None). This is
+        useful for large models that do not fit in memory.
     """
 
     model_name: str = "Metric-AI/ColQwen2.5-3b-multilingual-v1.0"
     device: str = "auto"
     dtype: str = "bfloat16"
     use_token_pooling: bool = True
+    pooling_strategy: Optional[str] = "hierarchical"
+    pooling_func: Optional[Any] = None
     pool_factor: int = 2
     quantization_config: Optional[Any] = None
     batch_size: int = 2
+    offload_folder: Optional[str] = None
 
     _model = None
     _processor = None
@@ -56,15 +75,43 @@ class ColPaliEmbeddings(EmbeddingFunction):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
+        torch = attempt_import_or_raise("torch", "torch")
+
+        if not self.use_token_pooling:
+            warnings.warn(
+                "use_token_pooling is deprecated, use pooling_strategy=None instead",
+                DeprecationWarning,
+            )
+            self.pooling_strategy = None
+
+        if self.pooling_strategy == "lambda" and self.pooling_func is None:
+            raise ValueError(
+                "pooling_func must be provided when pooling_strategy is 'lambda'"
+            )
+
+        device = self.device
+        if device == "auto":
+            if torch.cuda.is_available():
+                device = "cuda"
+            elif torch.backends.mps.is_available():
+                device = "mps"
+            else:
+                device = "cpu"
+
+        dtype = self.dtype
+        if device == "mps" and dtype == "bfloat16":
+            dtype = "float32"  # Avoid NaNs on MPS
+
         (
             self._model,
             self._processor,
             self._token_pooler,
         ) = self._load_model(
             self.model_name,
-            self.dtype,
-            self.device,
-            self.use_token_pooling,
+            dtype,
+            device,
+            self.pooling_strategy,
+            self.pooling_func,
             self.quantization_config,
         )
 
@@ -74,16 +121,26 @@ class ColPaliEmbeddings(EmbeddingFunction):
         model_name: str,
         dtype: str,
         device: str,
-        use_token_pooling: bool,
+        pooling_strategy: Optional[str],
+        pooling_func: Optional[Callable],
         quantization_config: Optional[Any],
     ):
         """
         Initialize and cache the ColPali model, processor, and token pooler.
         """
+        if device.startswith("mps"):
+            # warn some torch ops in late interaction architecture result in nans on mps
+            warning(
+                "MPS device detected. Some operations may result in NaNs. "
+                "If you encounter issues, consider using 'cpu' or 'cuda' devices."
+            )
         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
+        from colpali_engine.compression.token_pooling import (
+            HierarchicalTokenPooler,
+            LambdaTokenPooler,
+        )
 
         if quantization_config is not None:
             if not isinstance(quantization_config, transformers.BitsAndBytesConfig):
@@ -98,21 +155,45 @@ class ColPaliEmbeddings(EmbeddingFunction):
         else:
             torch_dtype = torch.float32
 
-        model = colpali_engine.models.ColQwen2_5.from_pretrained(
+        model_class, processor_class = None, None
+        model_name_lower = model_name.lower()
+        if "colqwen2.5" in model_name_lower:
+            model_class = colpali_engine.models.ColQwen2_5
+            processor_class = colpali_engine.models.ColQwen2_5_Processor
+        elif "colsmol" in model_name_lower or "colidefics3" in model_name_lower:
+            model_class = colpali_engine.models.ColIdefics3
+            processor_class = colpali_engine.models.ColIdefics3Processor
+        elif "colqwen" in model_name_lower:
+            model_class = colpali_engine.models.ColQwen2
+            processor_class = colpali_engine.models.ColQwen2Processor
+        elif "colpali" in model_name_lower:
+            model_class = colpali_engine.models.ColPali
+            processor_class = colpali_engine.models.ColPaliProcessor
+
+        if model_class is None:
+            raise ValueError(f"Unsupported model: {model_name}")
+
+        model = model_class.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,
+            low_cpu_mem_usage=True,
         ).eval()
-        processor = colpali_engine.models.ColQwen2_5_Processor.from_pretrained(
-            model_name
-        )
-        token_pooler = HierarchicalTokenPooler() if use_token_pooling else None
+        model = model.to(device)
+        model = model.to(torch_dtype)  # Force cast after moving to device
+        processor = processor_class.from_pretrained(model_name)
+
+        token_pooler = None
+        if pooling_strategy == "hierarchical":
+            token_pooler = HierarchicalTokenPooler()
+        elif pooling_strategy == "lambda":
+            token_pooler = LambdaTokenPooler(pool_func=pooling_func)
+
         return model, processor, token_pooler
 
     def ndims(self):
@@ -128,7 +209,7 @@ class ColPaliEmbeddings(EmbeddingFunction):
             with torch.no_grad():
                 query_embeddings = self._model(**batch_queries)
 
-            if self.use_token_pooling and self._token_pooler is not None:
+            if self.pooling_strategy and self._token_pooler is not None:
                 query_embeddings = self._token_pooler.pool_embeddings(
                     query_embeddings,
                     pool_factor=self.pool_factor,
@@ -145,13 +226,20 @@ class ColPaliEmbeddings(EmbeddingFunction):
         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 self.pooling_strategy and self._token_pooler is not None:
+            if self.pooling_strategy == "hierarchical":
+                embeddings = self._token_pooler.pool_embeddings(
+                    embeddings,
+                    pool_factor=self.pool_factor,
+                    padding=True,
+                    padding_side=self._processor.tokenizer.padding_side,
+                )
+            elif self.pooling_strategy == "lambda":
+                embeddings = self._token_pooler.pool_embeddings(
+                    embeddings,
+                    padding=True,
+                    padding_side=self._processor.tokenizer.padding_side,
+                )
 
         if isinstance(embeddings, torch.Tensor):
             tensors = embeddings.detach().cpu()
@@ -179,6 +267,7 @@ class ColPaliEmbeddings(EmbeddingFunction):
             )
             with torch.no_grad():
                 query_embeddings = self._model(**batch_queries)
+            query_embeddings = torch.nan_to_num(query_embeddings)
             all_embeddings.extend(self._process_embeddings(query_embeddings))
         return all_embeddings
 
@@ -225,6 +314,7 @@ class ColPaliEmbeddings(EmbeddingFunction):
             )
             with torch.no_grad():
                 image_embeddings = self._model(**batch_images)
+            image_embeddings = torch.nan_to_num(image_embeddings)
             all_embeddings.extend(self._process_embeddings(image_embeddings))
         return all_embeddings
 

python/python/lancedb/embeddings/voyageai.py

@@ -2,7 +2,7 @@
 # SPDX-FileCopyrightText: Copyright The LanceDB Authors
 import base64
 import os
-from typing import ClassVar, TYPE_CHECKING, List, Union, Any
+from typing import ClassVar, TYPE_CHECKING, List, Union, Any, Generator
 
 from pathlib import Path
 from urllib.parse import urlparse
@@ -19,6 +19,23 @@ from .utils import api_key_not_found_help, IMAGES, TEXT
 if TYPE_CHECKING:
     import PIL
 
+# Token limits for different VoyageAI models
+VOYAGE_TOTAL_TOKEN_LIMITS = {
+    "voyage-context-3": 32_000,
+    "voyage-3.5-lite": 1_000_000,
+    "voyage-3.5": 320_000,
+    "voyage-3-lite": 120_000,
+    "voyage-3": 120_000,
+    "voyage-multimodal-3": 120_000,
+    "voyage-finance-2": 120_000,
+    "voyage-multilingual-2": 120_000,
+    "voyage-law-2": 120_000,
+    "voyage-code-2": 120_000,
+}
+
+# Batch size for embedding requests (max number of items per batch)
+BATCH_SIZE = 1000
+
 
 def is_valid_url(text):
     try:
@@ -120,6 +137,9 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
     name: str
         The name of the model to use. List of acceptable models:
 
+            * voyage-context-3
+            * voyage-3.5
+            * voyage-3.5-lite
             * voyage-3
             * voyage-3-lite
             * voyage-multimodal-3
@@ -157,25 +177,35 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
     name: str
     client: ClassVar = None
     text_embedding_models: list = [
+        "voyage-3.5",
+        "voyage-3.5-lite",
         "voyage-3",
         "voyage-3-lite",
         "voyage-finance-2",
+        "voyage-multilingual-2",
         "voyage-law-2",
         "voyage-code-2",
     ]
     multimodal_embedding_models: list = ["voyage-multimodal-3"]
+    contextual_embedding_models: list = ["voyage-context-3"]
 
     def _is_multimodal_model(self, model_name: str):
         return (
             model_name in self.multimodal_embedding_models or "multimodal" in model_name
         )
 
+    def _is_contextual_model(self, model_name: str):
+        return model_name in self.contextual_embedding_models or "context" in model_name
+
     def ndims(self):
         if self.name == "voyage-3-lite":
             return 512
         elif self.name == "voyage-code-2":
             return 1536
         elif self.name in [
+            "voyage-context-3",
+            "voyage-3.5",
+            "voyage-3.5-lite",
             "voyage-3",
             "voyage-multimodal-3",
             "voyage-finance-2",
@@ -207,6 +237,11 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
             result = client.multimodal_embed(
                 inputs=[[query]], model=self.name, input_type="query", **kwargs
             )
+        elif self._is_contextual_model(self.name):
+            result = client.contextualized_embed(
+                inputs=[[query]], model=self.name, input_type="query", **kwargs
+            )
+            result = result.results[0]
         else:
             result = client.embed(
                 texts=[query], model=self.name, input_type="query", **kwargs
@@ -231,18 +266,164 @@ class VoyageAIEmbeddingFunction(EmbeddingFunction):
             List[np.array]: the list of embeddings
         """
         client = VoyageAIEmbeddingFunction._get_client()
+
+        # For multimodal models, check if inputs contain images
         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
+            sanitized = sanitize_multimodal_input(inputs)
+            has_images = any(
+                inp["content"][0].get("type") != "text" for inp in sanitized
             )
+            if has_images:
+                # Use non-batched API for images
+                result = client.multimodal_embed(
+                    inputs=sanitized, model=self.name, input_type="document", **kwargs
+                )
+                return result.embeddings
+            # Extract texts for batching
+            inputs = [inp["content"][0]["text"] for inp in sanitized]
         else:
             inputs = sanitize_text_input(inputs)
-            result = client.embed(
-                texts=inputs, model=self.name, input_type="document", **kwargs
-            )
 
-        return result.embeddings
+        # Use batching for all text inputs
+        return self._embed_with_batching(
+            client, inputs, input_type="document", **kwargs
+        )
+
+    def _build_batches(
+        self, client, texts: List[str]
+    ) -> Generator[List[str], None, None]:
+        """
+        Generate batches of texts based on token limits using a generator.
+
+        Parameters
+        ----------
+        client : voyageai.Client
+            The VoyageAI client instance.
+        texts : List[str]
+            List of texts to batch.
+
+        Yields
+        ------
+            List[str]: Batches of texts.
+        """
+        if not texts:
+            return
+
+        max_tokens_per_batch = VOYAGE_TOTAL_TOKEN_LIMITS.get(self.name, 120_000)
+        current_batch: List[str] = []
+        current_batch_tokens = 0
+
+        # Tokenize all texts in one API call
+        token_lists = client.tokenize(texts, model=self.name)
+        token_counts = [len(token_list) for token_list in token_lists]
+
+        for i, text in enumerate(texts):
+            n_tokens = token_counts[i]
+
+            # Check if adding this text would exceed limits
+            if current_batch and (
+                len(current_batch) >= BATCH_SIZE
+                or (current_batch_tokens + n_tokens > max_tokens_per_batch)
+            ):
+                # Yield the current batch and start a new one
+                yield current_batch
+                current_batch = []
+                current_batch_tokens = 0
+
+            current_batch.append(text)
+            current_batch_tokens += n_tokens
+
+        # Yield the last batch (always has at least one text)
+        if current_batch:
+            yield current_batch
+
+    def _get_embed_function(
+        self, client, input_type: str = "document", **kwargs
+    ) -> callable:
+        """
+        Get the appropriate embedding function based on model type.
+
+        Parameters
+        ----------
+        client : voyageai.Client
+            The VoyageAI client instance.
+        input_type : str
+            Either "query" or "document"
+        **kwargs
+            Additional arguments to pass to the embedding API
+
+        Returns
+        -------
+            callable: A function that takes a batch of texts and returns embeddings.
+        """
+        if self._is_multimodal_model(self.name):
+
+            def embed_batch(batch: List[str]) -> List[np.array]:
+                batch_inputs = sanitize_multimodal_input(batch)
+                result = client.multimodal_embed(
+                    inputs=batch_inputs,
+                    model=self.name,
+                    input_type=input_type,
+                    **kwargs,
+                )
+                return result.embeddings
+
+            return embed_batch
+
+        elif self._is_contextual_model(self.name):
+
+            def embed_batch(batch: List[str]) -> List[np.array]:
+                result = client.contextualized_embed(
+                    inputs=[batch], model=self.name, input_type=input_type, **kwargs
+                )
+                return result.results[0].embeddings
+
+            return embed_batch
+
+        else:
+
+            def embed_batch(batch: List[str]) -> List[np.array]:
+                result = client.embed(
+                    texts=batch, model=self.name, input_type=input_type, **kwargs
+                )
+                return result.embeddings
+
+            return embed_batch
+
+    def _embed_with_batching(
+        self, client, texts: List[str], input_type: str = "document", **kwargs
+    ) -> List[np.array]:
+        """
+        Embed texts with automatic batching based on token limits.
+
+        Parameters
+        ----------
+        client : voyageai.Client
+            The VoyageAI client instance.
+        texts : List[str]
+            List of texts to embed.
+        input_type : str
+            Either "query" or "document"
+        **kwargs
+            Additional arguments to pass to the embedding API
+
+        Returns
+        -------
+            List[np.array]: List of embeddings.
+        """
+        if not texts:
+            return []
+
+        # Get the appropriate embedding function for this model type
+        embed_fn = self._get_embed_function(client, input_type=input_type, **kwargs)
+
+        # Process each batch
+        all_embeddings = []
+        for batch in self._build_batches(client, texts):
+            batch_embeddings = embed_fn(batch)
+            all_embeddings.extend(batch_embeddings)
+
+        return all_embeddings
 
     @staticmethod
     def _get_client():

python/python/lancedb/index.py

@@ -605,9 +605,53 @@ class IvfPq:
     target_partition_size: Optional[int] = None
 
 
+@dataclass
+class IvfRq:
+    """Describes an IVF RQ Index
+
+    IVF-RQ (Residual Quantization) stores a compressed copy of each vector using
+    residual quantization and organizes them into IVF partitions. Parameters
+    largely mirror IVF-PQ for consistency.
+
+    Attributes
+    ----------
+    distance_type: str, default "l2"
+        Distance metric used to train the index and for quantization.
+
+        The following distance types are available:
+
+        "l2" - Euclidean distance.
+        "cosine" - Cosine distance.
+        "dot" - Dot product.
+
+    num_partitions: int, default sqrt(num_rows)
+        Number of IVF partitions to create.
+
+    num_bits: int, default 1
+        Number of bits to encode each dimension.
+
+    max_iterations: int, default 50
+        Max iterations to train kmeans when computing IVF partitions.
+
+    sample_rate: int, default 256
+        Controls the number of training vectors: sample_rate * num_partitions.
+
+    target_partition_size, default is 8192
+        Target size of each partition.
+    """
+
+    distance_type: Literal["l2", "cosine", "dot"] = "l2"
+    num_partitions: Optional[int] = None
+    num_bits: int = 1
+    max_iterations: int = 50
+    sample_rate: int = 256
+    target_partition_size: Optional[int] = None
+
+
 __all__ = [
     "BTree",
     "IvfPq",
+    "IvfRq",
     "IvfFlat",
     "HnswPq",
     "HnswSq",

python/python/lancedb/permutation.py

@@ -0,0 +1,821 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+from deprecation import deprecated
+from lancedb import AsyncConnection, DBConnection
+import pyarrow as pa
+import json
+
+from ._lancedb import async_permutation_builder, PermutationReader
+from .table import LanceTable
+from .background_loop import LOOP
+from .util import batch_to_tensor
+from typing import Any, Callable, Iterator, Literal, Optional, TYPE_CHECKING, Union
+
+if TYPE_CHECKING:
+    from lancedb.dependencies import pandas as pd, numpy as np, polars as pl
+
+
+class PermutationBuilder:
+    """
+    A utility for creating a "permutation table" which is a table that defines an
+    ordering on a base table.
+
+    The permutation table does not store the actual data.  It only stores row
+    ids and split ids to define the ordering.  The [Permutation] class can be used to
+    read the data from the base table in the order defined by the permutation table.
+
+    Permutations can split, shuffle, and filter the data in the base table.
+
+    A filter limits the rows that are included in the permutation.
+    Splits divide the data into subsets (for example, a test/train split, or K
+    different splits for cross-validation).
+    Shuffling randomizes the order of the rows in the permutation.
+
+    Splits can optionally be named.  If names are provided it will enable them to
+    be referenced by name in the future.  If names are not provided then they can only
+    be referenced by their ordinal index.  There is no requirement to name every split.
+
+    By default, the permutation will be stored in memory and will be lost when the
+    program exits.  To persist the permutation (for very large datasets or to share
+    the permutation across multiple workers) use the [persist](#persist) method to
+    create a permanent table.
+    """
+
+    def __init__(self, table: LanceTable):
+        """
+        Creates a new permutation builder for the given table.
+
+        By default, the permutation builder will create a single split that contains all
+        rows in the same order as the base table.
+        """
+        self._async = async_permutation_builder(table)
+
+    def persist(
+        self, database: Union[DBConnection, AsyncConnection], table_name: str
+    ) -> "PermutationBuilder":
+        """
+        Persist the permutation to the given database.
+        """
+        self._async.persist(database, table_name)
+        return self
+
+    def split_random(
+        self,
+        *,
+        ratios: Optional[list[float]] = None,
+        counts: Optional[list[int]] = None,
+        fixed: Optional[int] = None,
+        seed: Optional[int] = None,
+        split_names: Optional[list[str]] = None,
+    ) -> "PermutationBuilder":
+        """
+        Configure random splits for the permutation.
+
+        One of ratios, counts, or fixed must be provided.
+
+        If ratios are provided, they will be used to determine the relative size of each
+        split. For example, if ratios are [0.3, 0.7] then the first split will contain
+        30% of the rows and the second split will contain 70% of the rows.
+
+        If counts are provided, they will be used to determine the absolute number of
+        rows in each split. For example, if counts are [100, 200] then the first split
+        will contain 100 rows and the second split will contain 200 rows.
+
+        If fixed is provided, it will be used to determine the number of splits.
+        For example, if fixed is 3 then the permutation will be split evenly into 3
+        splits.
+
+        Rows will be randomly assigned to splits.  The optional seed can be provided to
+        make the assignment deterministic.
+
+        The optional split_names can be provided to name the splits.  If not provided,
+        the splits can only be referenced by their index.
+        """
+        self._async.split_random(
+            ratios=ratios,
+            counts=counts,
+            fixed=fixed,
+            seed=seed,
+            split_names=split_names,
+        )
+        return self
+
+    def split_hash(
+        self,
+        columns: list[str],
+        split_weights: list[int],
+        *,
+        discard_weight: Optional[int] = None,
+        split_names: Optional[list[str]] = None,
+    ) -> "PermutationBuilder":
+        """
+        Configure hash-based splits for the permutation.
+
+        First, a hash will be calculated over the specified columns.  The splits weights
+        are then used to determine how many rows to assign to each split.  For example,
+        if split weights are [1, 2] then the first split will contain 1/3 of the rows
+        and the second split will contain 2/3 of the rows.
+
+        The optional discard weight can be provided to determine what percentage of rows
+        should be discarded.  For example, if split weights are [1, 2] and discard
+        weight is 1 then 25% of the rows will be discarded.
+
+        Hash-based splits are useful if you want the split to be more or less random but
+        you don't want the split assignments to change if rows are added or removed
+        from the table.
+
+        The optional split_names can be provided to name the splits.  If not provided,
+        the splits can only be referenced by their index.
+        """
+        self._async.split_hash(
+            columns,
+            split_weights,
+            discard_weight=discard_weight,
+            split_names=split_names,
+        )
+        return self
+
+    def split_sequential(
+        self,
+        *,
+        ratios: Optional[list[float]] = None,
+        counts: Optional[list[int]] = None,
+        fixed: Optional[int] = None,
+        split_names: Optional[list[str]] = None,
+    ) -> "PermutationBuilder":
+        """
+        Configure sequential splits for the permutation.
+
+        One of ratios, counts, or fixed must be provided.
+
+        If ratios are provided, they will be used to determine the relative size of each
+        split. For example, if ratios are [0.3, 0.7] then the first split will contain
+        30% of the rows and the second split will contain 70% of the rows.
+
+        If counts are provided, they will be used to determine the absolute number of
+        rows in each split. For example, if counts are [100, 200] then the first split
+        will contain 100 rows and the second split will contain 200 rows.
+
+        If fixed is provided, it will be used to determine the number of splits.
+        For example, if fixed is 3 then the permutation will be split evenly into 3
+        splits.
+
+        Rows will be assigned to splits sequentially.  The first N1 rows are assigned to
+        split 1, the next N2 rows are assigned to split 2, etc.
+
+        The optional split_names can be provided to name the splits.  If not provided,
+        the splits can only be referenced by their index.
+        """
+        self._async.split_sequential(
+            ratios=ratios, counts=counts, fixed=fixed, split_names=split_names
+        )
+        return self
+
+    def split_calculated(
+        self, calculation: str, split_names: Optional[list[str]] = None
+    ) -> "PermutationBuilder":
+        """
+        Use pre-calculated splits for the permutation.
+
+        The calculation should be an SQL statement that returns an integer value between
+        0 and the number of splits - 1.  For example, if you have 3 splits then the
+        calculation should return 0 for the first split, 1 for the second split, and 2
+        for the third split.
+
+        This can be used to implement any kind of user-defined split strategy.
+
+        The optional split_names can be provided to name the splits.  If not provided,
+        the splits can only be referenced by their index.
+        """
+        self._async.split_calculated(calculation, split_names=split_names)
+        return self
+
+    def shuffle(
+        self, *, seed: Optional[int] = None, clump_size: Optional[int] = None
+    ) -> "PermutationBuilder":
+        """
+        Randomly shuffle the rows in the permutation.
+
+        An optional seed can be provided to make the shuffle deterministic.
+
+        If a clump size is provided, then data will be shuffled as small "clumps"
+        of contiguous rows.  This allows for a balance between randomization and
+        I/O performance.  It can be useful when reading from cloud storage.
+        """
+        self._async.shuffle(seed=seed, clump_size=clump_size)
+        return self
+
+    def filter(self, filter: str) -> "PermutationBuilder":
+        """
+        Configure a filter for the permutation.
+
+        The filter should be an SQL statement that returns a boolean value for each row.
+        Only rows where the filter is true will be included in the permutation.
+        """
+        self._async.filter(filter)
+        return self
+
+    def execute(self) -> LanceTable:
+        """
+        Execute the configuration and create the permutation table.
+        """
+
+        async def do_execute():
+            inner_tbl = await self._async.execute()
+            return LanceTable.from_inner(inner_tbl)
+
+        return LOOP.run(do_execute())
+
+
+def permutation_builder(table: LanceTable) -> PermutationBuilder:
+    return PermutationBuilder(table)
+
+
+class Permutations:
+    """
+    A collection of permutations indexed by name or ordinal index.
+
+    Splits are defined when the permutation is created.  Splits can always be referenced
+    by their ordinal index.  If names were provided when the permutation was created
+    then they can also be referenced by name.
+
+    Each permutation or "split" is a view of a portion of the base table.  For more
+    details see [Permutation].
+
+    Attributes
+    ----------
+    base_table: LanceTable
+        The base table that the permutations are based on.
+    permutation_table: LanceTable
+        The permutation table that defines the splits.
+    split_names: list[str]
+        The names of the splits.
+    split_dict: dict[str, int]
+        A dictionary mapping split names to their ordinal index.
+
+    Examples
+    --------
+    >>> # Initial data
+    >>> import lancedb
+    >>> db = lancedb.connect("memory:///")
+    >>> tbl = db.create_table("tbl", data=[{"x": x} for x in range(1000)])
+    >>> # Create a permutation
+    >>> perm_tbl = (
+    ...    permutation_builder(tbl)
+    ...    .split_random(ratios=[0.95, 0.05], split_names=["train", "test"])
+    ...    .shuffle()
+    ...    .execute()
+    ... )
+    >>> # Read the permutations
+    >>> permutations = Permutations(tbl, perm_tbl)
+    >>> permutations["train"]
+    <lancedb.permutation.Permutation ...>
+    >>> permutations[0]
+    <lancedb.permutation.Permutation ...>
+    >>> permutations.split_names
+    ['train', 'test']
+    >>> permutations.split_dict
+    {'train': 0, 'test': 1}
+    """
+
+    def __init__(self, base_table: LanceTable, permutation_table: LanceTable):
+        self.base_table = base_table
+        self.permutation_table = permutation_table
+
+        if permutation_table.schema.metadata is not None:
+            split_names = permutation_table.schema.metadata.get(
+                b"split_names", None
+            ).decode("utf-8")
+            if split_names is not None:
+                self.split_names = json.loads(split_names)
+                self.split_dict = {
+                    name: idx for idx, name in enumerate(self.split_names)
+                }
+            else:
+                # No split names are defined in the permutation table
+                self.split_names = []
+                self.split_dict = {}
+        else:
+            # No metadata is defined in the permutation table
+            self.split_names = []
+            self.split_dict = {}
+
+    def get_by_name(self, name: str) -> "Permutation":
+        """
+        Get a permutation by name.
+
+        If no split named `name` is found then an error will be raised.
+        """
+        idx = self.split_dict.get(name, None)
+        if idx is None:
+            raise ValueError(f"No split named `{name}` found")
+        return self.get_by_index(idx)
+
+    def get_by_index(self, index: int) -> "Permutation":
+        """
+        Get a permutation by index.
+        """
+        return Permutation.from_tables(self.base_table, self.permutation_table, index)
+
+    def __getitem__(self, name: Union[str, int]) -> "Permutation":
+        if isinstance(name, str):
+            return self.get_by_name(name)
+        elif isinstance(name, int):
+            return self.get_by_index(name)
+        else:
+            raise TypeError(f"Invalid split name or index: {name}")
+
+
+class Transforms:
+    """
+    Namespace for common transformation functions
+    """
+
+    @staticmethod
+    def arrow2python(batch: pa.RecordBatch) -> dict[str, list[Any]]:
+        return batch.to_pydict()
+
+    @staticmethod
+    def arrow2arrow(batch: pa.RecordBatch) -> pa.RecordBatch:
+        return batch
+
+    @staticmethod
+    def arrow2numpy(batch: pa.RecordBatch) -> "np.ndarray":
+        return batch.to_pandas().to_numpy()
+
+    @staticmethod
+    def arrow2pandas(batch: pa.RecordBatch) -> "pd.DataFrame":
+        return batch.to_pandas()
+
+    @staticmethod
+    def arrow2polars() -> "pl.DataFrame":
+        import polars as pl
+
+        def impl(batch: pa.RecordBatch) -> pl.DataFrame:
+            return pl.from_arrow(batch)
+
+        return impl
+
+
+# HuggingFace uses 10 which is pretty small
+DEFAULT_BATCH_SIZE = 100
+
+
+class Permutation:
+    """
+    A Permutation is a view of a dataset that can be used as input to model training
+    and evaluation.
+
+    A Permutation fulfills the pytorch Dataset contract and is loosely modeled after the
+    huggingface Dataset so it should be easy to use with existing code.
+
+    A permutation is not a "materialized view" or copy of the underlying data.  It is
+    calculated on the fly from the base table.  As a result, it is truly "lazy" and does
+    not require materializing the entire dataset in memory.
+    """
+
+    def __init__(
+        self,
+        reader: PermutationReader,
+        selection: dict[str, str],
+        batch_size: int,
+        transform_fn: Callable[pa.RecordBatch, Any],
+    ):
+        """
+        Internal constructor.  Use [from_tables](#from_tables) instead.
+        """
+        assert reader is not None, "reader is required"
+        assert selection is not None, "selection is required"
+        self.reader = reader
+        self.selection = selection
+        self.transform_fn = transform_fn
+        self.batch_size = batch_size
+
+    def _with_selection(self, selection: dict[str, str]) -> "Permutation":
+        """
+        Creates a new permutation with the given selection
+
+        Does not validation of the selection and it replaces it entirely.  This is not
+        intended for public use.
+        """
+        return Permutation(self.reader, selection, self.batch_size, self.transform_fn)
+
+    def _with_reader(self, reader: PermutationReader) -> "Permutation":
+        """
+        Creates a new permutation with the given reader
+
+        This is an internal method and should not be used directly.
+        """
+        return Permutation(reader, self.selection, self.batch_size, self.transform_fn)
+
+    def with_batch_size(self, batch_size: int) -> "Permutation":
+        """
+        Creates a new permutation with the given batch size
+        """
+        return Permutation(self.reader, self.selection, batch_size, self.transform_fn)
+
+    @classmethod
+    def identity(cls, table: LanceTable) -> "Permutation":
+        """
+        Creates an identity permutation for the given table.
+        """
+        return Permutation.from_tables(table, None, None)
+
+    @classmethod
+    def from_tables(
+        cls,
+        base_table: LanceTable,
+        permutation_table: Optional[LanceTable] = None,
+        split: Optional[Union[str, int]] = None,
+    ) -> "Permutation":
+        """
+        Creates a permutation from the given base table and permutation table.
+
+        A permutation table identifies which rows, and in what order, the data should
+        be read from the base table.  For more details see the [PermutationBuilder]
+        class.
+
+        If no permutation table is provided, then the identity permutation will be
+        created.  An identity permutation is a permutation that reads all rows in the
+        base table in the order they are stored.
+
+        The split parameter identifies which split to use.  If no split is provided
+        then the first split will be used.
+        """
+        assert base_table is not None, "base_table is required"
+        if split is not None:
+            if permutation_table is None:
+                raise ValueError(
+                    "Cannot create a permutation on split `{split}`"
+                    " because no permutation table is provided"
+                )
+            if isinstance(split, str):
+                if permutation_table.schema.metadata is None:
+                    raise ValueError(
+                        f"Cannot create a permutation on split `{split}`"
+                        " because no split names are defined in the permutation table"
+                    )
+                split_names = permutation_table.schema.metadata.get(
+                    b"split_names", None
+                ).decode("utf-8")
+                if split_names is None:
+                    raise ValueError(
+                        f"Cannot create a permutation on split `{split}`"
+                        " because no split names are defined in the permutation table"
+                    )
+                split_names = json.loads(split_names)
+                try:
+                    split = split_names.index(split)
+                except ValueError:
+                    raise ValueError(
+                        f"Cannot create a permutation on split `{split}`"
+                        f" because split `{split}` is not defined in the "
+                        "permutation table"
+                    )
+            elif isinstance(split, int):
+                split = split
+            else:
+                raise TypeError(f"Invalid split: {split}")
+        else:
+            split = 0
+
+        async def do_from_tables():
+            reader = await PermutationReader.from_tables(
+                base_table, permutation_table, split
+            )
+            schema = await reader.output_schema(None)
+            initial_selection = {name: name for name in schema.names}
+            return cls(
+                reader, initial_selection, DEFAULT_BATCH_SIZE, Transforms.arrow2python
+            )
+
+        return LOOP.run(do_from_tables())
+
+    @property
+    def schema(self) -> pa.Schema:
+        async def do_output_schema():
+            return await self.reader.output_schema(self.selection)
+
+        return LOOP.run(do_output_schema())
+
+    @property
+    def num_columns(self) -> int:
+        """
+        The number of columns in the permutation
+        """
+        return len(self.schema)
+
+    @property
+    def num_rows(self) -> int:
+        """
+        The number of rows in the permutation
+        """
+        return self.reader.count_rows()
+
+    @property
+    def column_names(self) -> list[str]:
+        """
+        The names of the columns in the permutation
+        """
+        return self.schema.names
+
+    @property
+    def shape(self) -> tuple[int, int]:
+        """
+        The shape of the permutation
+
+        This will return self.num_rows, self.num_columns
+        """
+        return self.num_rows, self.num_columns
+
+    def __len__(self) -> int:
+        """
+        The number of rows in the permutation
+
+        This is an alias for [num_rows][lancedb.permutation.Permutation.num_rows]
+        """
+        return self.num_rows
+
+    def unique(self, _column: str) -> list[Any]:
+        """
+        Get the unique values in the given column
+        """
+        raise Exception("unique is not yet implemented")
+
+    def flatten(self) -> "Permutation":
+        """
+        Flatten the permutation
+
+        Each column with a struct type will be flattened into multiple columns.
+
+        This flattening operation happens at read time as a post-processing step
+        so this call is cheap and no data is copied or modified in the underlying
+        dataset.
+        """
+        raise Exception("flatten is not yet implemented")
+
+    def remove_columns(self, columns: list[str]) -> "Permutation":
+        """
+        Remove the given columns from the permutation
+
+        Note: this does not actually modify the underlying dataset.  It only changes
+        which columns are visible from this permutation.  Also, this does not introduce
+        a post-processing step.  Instead, we simply do not read those columns in the
+        first place.
+
+        If any of the provided columns does not exist in the current permutation then it
+        will be ignored (no error is raised for missing columns)
+
+        Returns a new permutation with the given columns removed.  This does not modify
+        self.
+        """
+        assert columns is not None, "columns is required"
+
+        new_selection = {
+            name: value for name, value in self.selection.items() if name not in columns
+        }
+
+        if len(new_selection) == 0:
+            raise ValueError("Cannot remove all columns")
+
+        return self._with_selection(new_selection)
+
+    def rename_column(self, old_name: str, new_name: str) -> "Permutation":
+        """
+        Rename a column in the permutation
+
+        If there is no column named old_name then an error will be raised
+        If there is already a column named new_name then an error will be raised
+
+        Note: this does not actually modify the underlying dataset.  It only changes
+        the name of the column that is visible from this permutation.  This is a
+        post-processing step but done at the batch level and so it is very cheap.
+        No data will be copied.
+        """
+        assert old_name is not None, "old_name is required"
+        assert new_name is not None, "new_name is required"
+        if old_name not in self.selection:
+            raise ValueError(
+                f"Cannot rename column `{old_name}` because it does not exist"
+            )
+        if new_name in self.selection:
+            raise ValueError(
+                f"Cannot rename column `{old_name}` to `{new_name}` because a column "
+                "with that name already exists"
+            )
+        new_selection = self.selection.copy()
+        new_selection[new_name] = new_selection[old_name]
+        del new_selection[old_name]
+        return self._with_selection(new_selection)
+
+    def rename_columns(self, column_map: dict[str, str]) -> "Permutation":
+        """
+        Rename the given columns in the permutation
+
+        If any of the columns do not exist then an error will be raised
+        If any of the new names already exist then an error will be raised
+
+        Note: this does not actually modify the underlying dataset.  It only changes
+        the name of the column that is visible from this permutation.  This is a
+        post-processing step but done at the batch level and so it is very cheap.
+        No data will be copied.
+        """
+        assert column_map is not None, "column_map is required"
+
+        new_permutation = self
+        for old_name, new_name in column_map.items():
+            new_permutation = new_permutation.rename_column(old_name, new_name)
+        return new_permutation
+
+    def select_columns(self, columns: list[str]) -> "Permutation":
+        """
+        Select the given columns from the permutation
+
+        This method refines the current selection, potentially removing columns.  It
+        will not add back columns that were previously removed.
+
+        If any of the columns do not exist then an error will be raised
+
+        This does not introduce a post-processing step.  It simply reduces the amount
+        of data we read.
+        """
+        assert columns is not None, "columns is required"
+        if len(columns) == 0:
+            raise ValueError("Must select at least one column")
+
+        new_selection = {}
+        for name in columns:
+            value = self.selection.get(name, None)
+            if value is None:
+                raise ValueError(
+                    f"Cannot select column `{name}` because it does not exist"
+                )
+            new_selection[name] = value
+        return self._with_selection(new_selection)
+
+    def __iter__(self) -> Iterator[dict[str, Any]]:
+        """
+        Iterate over the permutation
+        """
+        return self.iter(self.batch_size, skip_last_batch=True)
+
+    def iter(
+        self, batch_size: int, skip_last_batch: bool = False
+    ) -> Iterator[dict[str, Any]]:
+        """
+        Iterate over the permutation in batches
+
+        If skip_last_batch is True, the last batch will be skipped if it is not a
+        multiple of batch_size.
+        """
+
+        async def get_iter():
+            return await self.reader.read(self.selection, batch_size=batch_size)
+
+        async_iter = LOOP.run(get_iter())
+
+        async def get_next():
+            return await async_iter.__anext__()
+
+        try:
+            while True:
+                batch = LOOP.run(get_next())
+                if batch.num_rows == batch_size or not skip_last_batch:
+                    yield self.transform_fn(batch)
+        except StopAsyncIteration:
+            return
+
+    def with_format(
+        self, format: Literal["numpy", "python", "pandas", "arrow", "torch", "polars"]
+    ) -> "Permutation":
+        """
+        Set the format for batches
+
+        If this method is not called, the "python" format will be used.
+
+        The format can be one of:
+        - "numpy" - the batch will be a dict of numpy arrays (one per column)
+        - "python" - the batch will be a dict of lists (one per column)
+        - "pandas" - the batch will be a pandas DataFrame
+        - "arrow" - the batch will be a pyarrow RecordBatch
+        - "torch" - the batch will be a two dimensional torch tensor
+        - "polars" - the batch will be a polars DataFrame
+
+        Conversion may or may not involve a data copy.  Lance uses Arrow internally
+        and so it is able to zero-copy to the arrow and polars.
+
+        Conversion to torch will be zero-copy but will only support a subset of data
+        types (numeric types).
+
+        Conversion to numpy and/or pandas will typically be zero-copy for numeric
+        types.  Conversion of strings, lists, and structs will require creating python
+        objects and this is not zero-copy.
+
+        For custom formatting, use [with_transform](#with_transform) which overrides
+        this method.
+        """
+        assert format is not None, "format is required"
+        if format == "python":
+            return self.with_transform(Transforms.arrow2python)
+        elif format == "numpy":
+            return self.with_transform(Transforms.arrow2numpy)
+        elif format == "pandas":
+            return self.with_transform(Transforms.arrow2pandas)
+        elif format == "arrow":
+            return self.with_transform(Transforms.arrow2arrow)
+        elif format == "torch":
+            return self.with_transform(batch_to_tensor)
+        elif format == "polars":
+            return self.with_transform(Transforms.arrow2polars())
+        else:
+            raise ValueError(f"Invalid format: {format}")
+
+    def with_transform(self, transform: Callable[pa.RecordBatch, Any]) -> "Permutation":
+        """
+        Set a custom transform for the permutation
+
+        The transform is a callable that will be invoked with each record batch.  The
+        return value will be used as the batch for iteration.
+
+        Note: transforms are not invoked in parallel.  This method is not a good place
+        for expensive operations such as image decoding.
+        """
+        assert transform is not None, "transform is required"
+        return Permutation(self.reader, self.selection, self.batch_size, transform)
+
+    def __getitem__(self, index: int) -> Any:
+        """
+        Return a single row from the permutation
+
+        The output will always be a python dictionary regardless of the format.
+
+        This method is mostly useful for debugging and exploration.  For actual
+        processing use [iter](#iter) or a torch data loader to perform batched
+        processing.
+        """
+        pass
+
+    @deprecated(details="Use with_skip instead")
+    def skip(self, skip: int) -> "Permutation":
+        """
+        Skip the first `skip` rows of the permutation
+
+        Note: this method returns a new permutation and does not modify `self`
+        It is provided for compatibility with the huggingface Dataset API.
+
+        Use [with_skip](#with_skip) instead to avoid confusion.
+        """
+        return self.with_skip(skip)
+
+    def with_skip(self, skip: int) -> "Permutation":
+        """
+        Skip the first `skip` rows of the permutation
+        """
+
+        async def do_with_skip():
+            reader = await self.reader.with_offset(skip)
+            return self._with_reader(reader)
+
+        return LOOP.run(do_with_skip())
+
+    @deprecated(details="Use with_take instead")
+    def take(self, limit: int) -> "Permutation":
+        """
+        Limit the permutation to `limit` rows (following any `skip`)
+
+        Note: this method returns a new permutation and does not modify `self`
+        It is provided for compatibility with the huggingface Dataset API.
+
+        Use [with_take](#with_take) instead to avoid confusion.
+        """
+        return self.with_take(limit)
+
+    def with_take(self, limit: int) -> "Permutation":
+        """
+        Limit the permutation to `limit` rows (following any `skip`)
+        """
+
+        async def do_with_take():
+            reader = await self.reader.with_limit(limit)
+            return self._with_reader(reader)
+
+        return LOOP.run(do_with_take())
+
+    @deprecated(details="Use with_repeat instead")
+    def repeat(self, times: int) -> "Permutation":
+        """
+        Repeat the permutation `times` times
+
+        Note: this method returns a new permutation and does not modify `self`
+        It is provided for compatibility with the huggingface Dataset API.
+
+        Use [with_repeat](#with_repeat) instead to avoid confusion.
+        """
+        return self.with_repeat(times)
+
+    def with_repeat(self, times: int) -> "Permutation":
+        """
+        Repeat the permutation `times` times
+        """
+        raise Exception("with_repeat is not yet implemented")

python/python/lancedb/query.py

@@ -37,7 +37,7 @@ from .rerankers.base import Reranker
 from .rerankers.rrf import RRFReranker
 from .rerankers.util import check_reranker_result
 from .util import flatten_columns
-
+from lancedb._lancedb import fts_query_to_json
 from typing_extensions import Annotated
 
 if TYPE_CHECKING:
@@ -124,6 +124,24 @@ class FullTextQuery(ABC):
         """
         pass
 
+    def to_json(self) -> str:
+        """
+        Convert the query to a JSON string.
+
+        Returns
+        -------
+        str
+            A JSON string representation of the query.
+
+        Examples
+        --------
+        >>> from lancedb.query import MatchQuery
+        >>> query = MatchQuery("puppy", "text", fuzziness=2)
+        >>> query.to_json()
+        '{"match":{"column":"text","terms":"puppy","boost":1.0,"fuzziness":2,"max_expansions":50,"operator":"Or","prefix_length":0}}'
+        """
+        return fts_query_to_json(self)
+
     def __and__(self, other: "FullTextQuery") -> "FullTextQuery":
         """
         Combine two queries with a logical AND operation.
@@ -288,6 +306,8 @@ class BooleanQuery(FullTextQuery):
     ----------
     queries : list[tuple(Occur, FullTextQuery)]
         The list of queries with their occurrence requirements.
+        Each tuple contains an Occur value (MUST, SHOULD, or MUST_NOT)
+        and a FullTextQuery to apply.
     """
 
     queries: list[tuple[Occur, FullTextQuery]]
@@ -1237,6 +1257,14 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._refine_factor = refine_factor
         return self
 
+    def output_schema(self) -> pa.Schema:
+        """
+        Return the output schema for the query
+
+        This does not execute the query.
+        """
+        return self._table._output_schema(self.to_query_object())
+
     def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
         """
         Execute the query and return the results as an
@@ -1452,6 +1480,14 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
             offset=self._offset,
         )
 
+    def output_schema(self) -> pa.Schema:
+        """
+        Return the output schema for the query
+
+        This does not execute the query.
+        """
+        return self._table._output_schema(self.to_query_object())
+
     def to_arrow(self, *, timeout: Optional[timedelta] = None) -> pa.Table:
         path, fs, exist = self._table._get_fts_index_path()
         if exist:
@@ -1595,6 +1631,10 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
             offset=self._offset,
         )
 
+    def output_schema(self) -> pa.Schema:
+        query = self.to_query_object()
+        return self._table._output_schema(query)
+
     def to_batches(
         self, /, batch_size: Optional[int] = None, timeout: Optional[timedelta] = None
     ) -> pa.RecordBatchReader:
@@ -2238,6 +2278,14 @@ class AsyncQueryBase(object):
             )
         )
 
+    async def output_schema(self) -> pa.Schema:
+        """
+        Return the output schema for the query
+
+        This does not execute the query.
+        """
+        return await self._inner.output_schema()
+
     async def to_arrow(self, timeout: Optional[timedelta] = None) -> pa.Table:
         """
         Execute the query and collect the results into an Apache Arrow Table.
@@ -3193,6 +3241,14 @@ class BaseQueryBuilder(object):
         self._inner.with_row_id()
         return self
 
+    def output_schema(self) -> pa.Schema:
+        """
+        Return the output schema for the query
+
+        This does not execute the query.
+        """
+        return LOOP.run(self._inner.output_schema())
+
     def to_batches(
         self,
         *,

python/python/lancedb/remote/table.py

@@ -436,6 +436,9 @@ class RemoteTable(Table):
     def _analyze_plan(self, query: Query) -> str:
         return LOOP.run(self._table._analyze_plan(query))
 
+    def _output_schema(self, query: Query) -> pa.Schema:
+        return LOOP.run(self._table._output_schema(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.

python/python/lancedb/rerankers/voyageai.py

@@ -21,6 +21,8 @@ class VoyageAIReranker(Reranker):
     ----------
     model_name : str, default "rerank-english-v2.0"
         The name of the cross encoder model to use. Available voyageai models are:
+        - rerank-2.5
+        - rerank-2.5-lite
         - rerank-2
         - rerank-2-lite
     column : str, default "text"

python/python/lancedb/table.py

@@ -44,7 +44,7 @@ import numpy as np
 
 from .common import DATA, VEC, VECTOR_COLUMN_NAME
 from .embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
-from .index import BTree, IvfFlat, IvfPq, Bitmap, LabelList, HnswPq, HnswSq, FTS
+from .index import BTree, IvfFlat, IvfPq, Bitmap, IvfRq, LabelList, HnswPq, HnswSq, FTS
 from .merge import LanceMergeInsertBuilder
 from .pydantic import LanceModel, model_to_dict
 from .query import (
@@ -74,6 +74,7 @@ from .index import lang_mapping
 
 
 if TYPE_CHECKING:
+    from .db import LanceDBConnection
     from ._lancedb import (
         Table as LanceDBTable,
         OptimizeStats,
@@ -88,7 +89,6 @@ if TYPE_CHECKING:
         MergeResult,
         UpdateResult,
     )
-    from .db import LanceDBConnection
     from .index import IndexConfig
     import pandas
     import PIL
@@ -1248,6 +1248,9 @@ class Table(ABC):
     @abstractmethod
     def _analyze_plan(self, query: Query) -> str: ...
 
+    @abstractmethod
+    def _output_schema(self, query: Query) -> pa.Schema: ...
+
     @abstractmethod
     def _do_merge(
         self,
@@ -1707,22 +1710,38 @@ class LanceTable(Table):
         namespace: List[str] = [],
         storage_options: Optional[Dict[str, str]] = None,
         index_cache_size: Optional[int] = None,
+        _async: AsyncTable = None,
     ):
         self._conn = connection
         self._namespace = namespace
-        self._table = LOOP.run(
-            connection._conn.open_table(
-                name,
-                namespace=namespace,
-                storage_options=storage_options,
-                index_cache_size=index_cache_size,
+        if _async is not None:
+            self._table = _async
+        else:
+            self._table = LOOP.run(
+                connection._conn.open_table(
+                    name,
+                    namespace=namespace,
+                    storage_options=storage_options,
+                    index_cache_size=index_cache_size,
+                )
             )
-        )
 
     @property
     def name(self) -> str:
         return self._table.name
 
+    @classmethod
+    def from_inner(cls, tbl: LanceDBTable):
+        from .db import LanceDBConnection
+
+        async_tbl = AsyncTable(tbl)
+        conn = LanceDBConnection.from_inner(tbl.database())
+        return cls(
+            conn,
+            async_tbl.name,
+            _async=async_tbl,
+        )
+
     @classmethod
     def open(cls, db, name, *, namespace: List[str] = [], **kwargs):
         tbl = cls(db, name, namespace=namespace, **kwargs)
@@ -1991,7 +2010,7 @@ class LanceTable(Table):
         index_cache_size: Optional[int] = None,
         num_bits: int = 8,
         index_type: Literal[
-            "IVF_FLAT", "IVF_PQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ"
+            "IVF_FLAT", "IVF_PQ", "IVF_RQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ"
         ] = "IVF_PQ",
         max_iterations: int = 50,
         sample_rate: int = 256,
@@ -2039,6 +2058,15 @@ class LanceTable(Table):
                 sample_rate=sample_rate,
                 target_partition_size=target_partition_size,
             )
+        elif index_type == "IVF_RQ":
+            config = IvfRq(
+                distance_type=metric,
+                num_partitions=num_partitions,
+                num_bits=num_bits,
+                max_iterations=max_iterations,
+                sample_rate=sample_rate,
+                target_partition_size=target_partition_size,
+            )
         elif index_type == "IVF_HNSW_PQ":
             config = HnswPq(
                 distance_type=metric,
@@ -2736,6 +2764,9 @@ class LanceTable(Table):
     def _analyze_plan(self, query: Query) -> str:
         return LOOP.run(self._table._analyze_plan(query))
 
+    def _output_schema(self, query: Query) -> pa.Schema:
+        return LOOP.run(self._table._output_schema(query))
+
     def _do_merge(
         self,
         merge: LanceMergeInsertBuilder,
@@ -2747,6 +2778,10 @@ class LanceTable(Table):
             self._table._do_merge(merge, new_data, on_bad_vectors, fill_value)
         )
 
+    @property
+    def _inner(self) -> LanceDBTable:
+        return self._table._inner
+
     @deprecation.deprecated(
         deprecated_in="0.21.0",
         current_version=__version__,
@@ -3330,7 +3365,7 @@ class AsyncTable:
         *,
         replace: Optional[bool] = None,
         config: Optional[
-            Union[IvfFlat, IvfPq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS]
+            Union[IvfFlat, IvfPq, IvfRq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS]
         ] = None,
         wait_timeout: Optional[timedelta] = None,
         name: Optional[str] = None,
@@ -3369,11 +3404,12 @@ class AsyncTable:
         """
         if config is not None:
             if not isinstance(
-                config, (IvfFlat, IvfPq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS)
+                config,
+                (IvfFlat, IvfPq, IvfRq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS),
             ):
                 raise TypeError(
-                    "config must be an instance of IvfPq, HnswPq, HnswSq, BTree,"
-                    " Bitmap, LabelList, or FTS"
+                    "config must be an instance of IvfPq, IvfRq, HnswPq, HnswSq, BTree,"
+                    " Bitmap, LabelList, or FTS, but got " + str(type(config))
                 )
         try:
             await self._inner.create_index(
@@ -3888,6 +3924,10 @@ class AsyncTable:
         async_query = self._sync_query_to_async(query)
         return await async_query.analyze_plan()
 
+    async def _output_schema(self, query: Query) -> pa.Schema:
+        async_query = self._sync_query_to_async(query)
+        return await async_query.output_schema()
+
     async def _do_merge(
         self,
         merge: LanceMergeInsertBuilder,

python/python/lancedb/types.py

@@ -18,10 +18,17 @@ AddMode = Literal["append", "overwrite"]
 CreateMode = Literal["create", "overwrite"]
 
 # Index type literals
-VectorIndexType = Literal["IVF_FLAT", "IVF_PQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ"]
+VectorIndexType = Literal["IVF_FLAT", "IVF_PQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ", "IVF_RQ"]
 ScalarIndexType = Literal["BTREE", "BITMAP", "LABEL_LIST"]
 IndexType = Literal[
-    "IVF_PQ", "IVF_HNSW_PQ", "IVF_HNSW_SQ", "FTS", "BTREE", "BITMAP", "LABEL_LIST"
+    "IVF_PQ",
+    "IVF_HNSW_PQ",
+    "IVF_HNSW_SQ",
+    "FTS",
+    "BTREE",
+    "BITMAP",
+    "LABEL_LIST",
+    "IVF_RQ",
 ]
 
 # Tokenizer literals

python/python/lancedb/util.py

@@ -366,3 +366,56 @@ def add_note(base_exception: BaseException, note: str):
         )
     else:
         raise ValueError("Cannot add note to exception")
+
+
+def tbl_to_tensor(tbl: pa.Table):
+    """
+    Convert a PyArrow Table to a PyTorch Tensor.
+
+    Each column is converted to a tensor (using zero-copy via DLPack)
+    and the columns are then stacked into a single tensor.
+
+    Fails if torch is not installed.
+    Fails if any column is more than one chunk.
+    Fails if a column's data type is not supported by PyTorch.
+
+    Parameters
+    ----------
+    tbl : pa.Table or pa.RecordBatch
+        The table or record batch to convert to a tensor.
+
+    Returns
+    -------
+    torch.Tensor: The tensor containing the columns of the table.
+    """
+    torch = attempt_import_or_raise("torch", "torch")
+
+    def to_tensor(col: pa.ChunkedArray):
+        if col.num_chunks > 1:
+            raise Exception("Single batch was too large to fit into a one-chunk table")
+        return torch.from_dlpack(col.chunk(0))
+
+    return torch.stack([to_tensor(tbl.column(i)) for i in range(tbl.num_columns)])
+
+
+def batch_to_tensor(batch: pa.RecordBatch):
+    """
+    Convert a PyArrow RecordBatch to a PyTorch Tensor.
+
+    Each column is converted to a tensor (using zero-copy via DLPack)
+    and the columns are then stacked into a single tensor.
+
+    Fails if torch is not installed.
+    Fails if a column's data type is not supported by PyTorch.
+
+    Parameters
+    ----------
+    batch : pa.RecordBatch
+        The record batch to convert to a tensor.
+
+    Returns
+    -------
+    torch.Tensor: The tensor containing the columns of the record batch.
+    """
+    torch = attempt_import_or_raise("torch", "torch")
+    return torch.stack([torch.from_dlpack(col) for col in batch.columns])

python/python/tests/test_embeddings_slow.py

@@ -532,6 +532,27 @@ def test_voyageai_embedding_function():
     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_embedding_function_contextual_model():
+    voyageai = (
+        get_registry().get("voyageai").create(name="voyage-context-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(
     os.environ.get("VOYAGE_API_KEY") is None, reason="VOYAGE_API_KEY not set"
@@ -656,6 +677,106 @@ def test_colpali(tmp_path):
     )
 
 
+@pytest.mark.slow
+@pytest.mark.skipif(
+    importlib.util.find_spec("colpali_engine") is None,
+    reason="colpali_engine not installed",
+)
+@pytest.mark.parametrize(
+    "model_name",
+    [
+        "vidore/colSmol-256M",
+        "vidore/colqwen2.5-v0.2",
+        "vidore/colpali-v1.3",
+        "vidore/colqwen2-v1.0",
+    ],
+)
+def test_colpali_models(tmp_path, model_name):
+    import requests
+    from lancedb.pydantic import LanceModel
+
+    db = lancedb.connect(tmp_path)
+    registry = get_registry()
+    func = registry.get("colpali").create(model_name=model_name)
+
+    class MediaItems(LanceModel):
+        text: str
+        image_uri: str = func.SourceField()
+        image_bytes: bytes = func.SourceField()
+        image_vectors: MultiVector(func.ndims()) = func.VectorField()
+
+    table = db.create_table(f"media_{model_name.replace('/', '_')}", schema=MediaItems)
+
+    texts = [
+        "a cute cat playing with yarn",
+    ]
+
+    uris = [
+        "http://farm1.staticflickr.com/53/167798175_7c7845bbbd_z.jpg",
+    ]
+
+    image_bytes = [requests.get(uri).content for uri in uris]
+
+    table.add(
+        pd.DataFrame({"text": texts, "image_uri": uris, "image_bytes": image_bytes})
+    )
+
+    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()
+
+    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"
+    )
+
+
+@pytest.mark.slow
+@pytest.mark.skipif(
+    importlib.util.find_spec("colpali_engine") is None,
+    reason="colpali_engine not installed",
+)
+def test_colpali_pooling(tmp_path):
+    registry = get_registry()
+    model_name = "vidore/colSmol-256M"
+    test_sentence = "a test sentence for pooling"
+
+    # 1. Get embeddings with no pooling
+    func_no_pool = registry.get("colpali").create(
+        model_name=model_name, pooling_strategy=None
+    )
+    unpooled_embeddings = func_no_pool.generate_text_embeddings([test_sentence])[0]
+    original_length = len(unpooled_embeddings)
+    assert original_length > 1
+
+    # 2. Test hierarchical pooling
+    func_hierarchical = registry.get("colpali").create(
+        model_name=model_name, pooling_strategy="hierarchical", pool_factor=2
+    )
+    hierarchical_embeddings = func_hierarchical.generate_text_embeddings(
+        [test_sentence]
+    )[0]
+    expected_hierarchical_length = (original_length + 1) // 2
+    assert len(hierarchical_embeddings) == expected_hierarchical_length
+
+    # 3. Test lambda pooling
+    def simple_pool_func(tensor):
+        return tensor[::2]
+
+    func_lambda = registry.get("colpali").create(
+        model_name=model_name,
+        pooling_strategy="lambda",
+        pooling_func=simple_pool_func,
+    )
+    lambda_embeddings = func_lambda.generate_text_embeddings([test_sentence])[0]
+    expected_lambda_length = (original_length + 1) // 2
+    assert len(lambda_embeddings) == expected_lambda_length
+
+
 @pytest.mark.slow
 def test_siglip(tmp_path, test_images, query_image_bytes):
     from PIL import Image

python/python/tests/test_fts.py

@@ -20,7 +20,14 @@ 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
+from lancedb.query import (
+    BoostQuery,
+    MatchQuery,
+    MultiMatchQuery,
+    PhraseQuery,
+    BooleanQuery,
+    Occur,
+)
 import numpy as np
 import pyarrow as pa
 import pandas as pd
@@ -727,3 +734,146 @@ def test_fts_ngram(mem_db: DBConnection):
     results = table.search("la", query_type="fts").limit(10).to_list()
     assert len(results) == 2
     assert set(r["text"] for r in results) == {"lance database", "lance is cool"}
+
+
+def test_fts_query_to_json():
+    """Test that FTS query to_json() produces valid JSON strings with exact format."""
+
+    # Test MatchQuery - basic
+    match_query = MatchQuery("hello world", "text")
+    json_str = match_query.to_json()
+    expected = (
+        '{"match":{"column":"text","terms":"hello world","boost":1.0,'
+        '"fuzziness":0,"max_expansions":50,"operator":"Or","prefix_length":0}}'
+    )
+    assert json_str == expected
+
+    # Test MatchQuery with options
+    match_query = MatchQuery("puppy", "text", fuzziness=2, boost=1.5, prefix_length=3)
+    json_str = match_query.to_json()
+    expected = (
+        '{"match":{"column":"text","terms":"puppy","boost":1.5,"fuzziness":2,'
+        '"max_expansions":50,"operator":"Or","prefix_length":3}}'
+    )
+    assert json_str == expected
+
+    # Test PhraseQuery
+    phrase_query = PhraseQuery("quick brown fox", "title")
+    json_str = phrase_query.to_json()
+    expected = '{"phrase":{"column":"title","terms":"quick brown fox","slop":0}}'
+    assert json_str == expected
+
+    # Test PhraseQuery with slop
+    phrase_query = PhraseQuery("quick brown", "title", slop=2)
+    json_str = phrase_query.to_json()
+    expected = '{"phrase":{"column":"title","terms":"quick brown","slop":2}}'
+    assert json_str == expected
+
+    # Test BooleanQuery with MUST
+    must_query = BooleanQuery(
+        [
+            (Occur.MUST, MatchQuery("puppy", "text")),
+            (Occur.MUST, MatchQuery("runs", "text")),
+        ]
+    )
+    json_str = must_query.to_json()
+    expected = (
+        '{"boolean":{"should":[],"must":[{"match":{"column":"text","terms":"puppy",'
+        '"boost":1.0,"fuzziness":0,"max_expansions":50,"operator":"Or",'
+        '"prefix_length":0}},{"match":{"column":"text","terms":"runs","boost":1.0,'
+        '"fuzziness":0,"max_expansions":50,"operator":"Or","prefix_length":0}}],'
+        '"must_not":[]}}'
+    )
+    assert json_str == expected
+
+    # Test BooleanQuery with SHOULD
+    should_query = BooleanQuery(
+        [
+            (Occur.SHOULD, MatchQuery("cat", "text")),
+            (Occur.SHOULD, MatchQuery("dog", "text")),
+        ]
+    )
+    json_str = should_query.to_json()
+    expected = (
+        '{"boolean":{"should":[{"match":{"column":"text","terms":"cat","boost":1.0,'
+        '"fuzziness":0,"max_expansions":50,"operator":"Or","prefix_length":0}},'
+        '{"match":{"column":"text","terms":"dog","boost":1.0,"fuzziness":0,'
+        '"max_expansions":50,"operator":"Or","prefix_length":0}}],"must":[],'
+        '"must_not":[]}}'
+    )
+    assert json_str == expected
+
+    # Test BooleanQuery with MUST_NOT
+    must_not_query = BooleanQuery(
+        [
+            (Occur.MUST, MatchQuery("puppy", "text")),
+            (Occur.MUST_NOT, MatchQuery("training", "text")),
+        ]
+    )
+    json_str = must_not_query.to_json()
+    expected = (
+        '{"boolean":{"should":[],"must":[{"match":{"column":"text","terms":"puppy",'
+        '"boost":1.0,"fuzziness":0,"max_expansions":50,"operator":"Or",'
+        '"prefix_length":0}}],"must_not":[{"match":{"column":"text",'
+        '"terms":"training","boost":1.0,"fuzziness":0,"max_expansions":50,'
+        '"operator":"Or","prefix_length":0}}]}}'
+    )
+    assert json_str == expected
+
+    # Test BoostQuery
+    positive = MatchQuery("puppy", "text")
+    negative = MatchQuery("training", "text")
+    boost_query = BoostQuery(positive, negative, negative_boost=0.3)
+    json_str = boost_query.to_json()
+    expected = (
+        '{"boost":{"positive":{"match":{"column":"text","terms":"puppy",'
+        '"boost":1.0,"fuzziness":0,"max_expansions":50,"operator":"Or",'
+        '"prefix_length":0}},"negative":{"match":{"column":"text",'
+        '"terms":"training","boost":1.0,"fuzziness":0,"max_expansions":50,'
+        '"operator":"Or","prefix_length":0}},"negative_boost":0.3}}'
+    )
+    assert json_str == expected
+
+    # Test MultiMatchQuery
+    multi_match = MultiMatchQuery("python", ["tags", "title"])
+    json_str = multi_match.to_json()
+    expected = (
+        '{"multi_match":{"query":"python","columns":["tags","title"],'
+        '"boost":[1.0,1.0]}}'
+    )
+    assert json_str == expected
+
+    # Test complex nested BooleanQuery
+    inner1 = BooleanQuery(
+        [
+            (Occur.MUST, MatchQuery("python", "tags")),
+            (Occur.MUST, MatchQuery("tutorial", "title")),
+        ]
+    )
+    inner2 = BooleanQuery(
+        [
+            (Occur.MUST, MatchQuery("rust", "tags")),
+            (Occur.MUST, MatchQuery("guide", "title")),
+        ]
+    )
+    complex_query = BooleanQuery(
+        [
+            (Occur.SHOULD, inner1),
+            (Occur.SHOULD, inner2),
+        ]
+    )
+    json_str = complex_query.to_json()
+    expected = (
+        '{"boolean":{"should":[{"boolean":{"should":[],"must":[{"match":'
+        '{"column":"tags","terms":"python","boost":1.0,"fuzziness":0,'
+        '"max_expansions":50,"operator":"Or","prefix_length":0}},{"match":'
+        '{"column":"title","terms":"tutorial","boost":1.0,"fuzziness":0,'
+        '"max_expansions":50,"operator":"Or","prefix_length":0}}],"must_not":[]}}'
+        ',{"boolean":{"should":[],"must":[{"match":{"column":"tags",'
+        '"terms":"rust","boost":1.0,"fuzziness":0,"max_expansions":50,'
+        '"operator":"Or","prefix_length":0}},{"match":{"column":"title",'
+        '"terms":"guide","boost":1.0,"fuzziness":0,"max_expansions":50,'
+        '"operator":"Or","prefix_length":0}}],"must_not":[]}}],"must":[],'
+        '"must_not":[]}}'
+    )
+    assert json_str == expected

python/python/tests/test_index.py

@@ -8,7 +8,17 @@ 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, FTS
+from lancedb.index import (
+    BTree,
+    IvfFlat,
+    IvfPq,
+    IvfRq,
+    Bitmap,
+    LabelList,
+    HnswPq,
+    HnswSq,
+    FTS,
+)
 
 
 @pytest_asyncio.fixture
@@ -195,6 +205,16 @@ async def test_create_4bit_ivfpq_index(some_table: AsyncTable):
     assert stats.loss >= 0.0
 
 
+@pytest.mark.asyncio
+async def test_create_ivfrq_index(some_table: AsyncTable):
+    await some_table.create_index("vector", config=IvfRq(num_bits=1))
+    indices = await some_table.list_indices()
+    assert len(indices) == 1
+    assert indices[0].index_type == "IvfRq"
+    assert indices[0].columns == ["vector"]
+    assert indices[0].name == "vector_idx"
+
+
 @pytest.mark.asyncio
 async def test_create_hnswpq_index(some_table: AsyncTable):
     await some_table.create_index("vector", config=HnswPq(num_partitions=10))

python/python/tests/test_namespace.py

@@ -59,6 +59,14 @@ class TempNamespace(LanceNamespace):
             root
         ]  # Reference to shared namespaces
 
+    def namespace_id(self) -> str:
+        """Return a human-readable unique identifier for this namespace instance.
+
+        Returns:
+            A unique identifier string based on the root directory
+        """
+        return f"TempNamespace {{ root: '{self.config.root}' }}"
+
     def list_tables(self, request: ListTablesRequest) -> ListTablesResponse:
         """List all tables in the namespace."""
         if not request.id:

python/python/tests/test_permutation.py

@@ -0,0 +1,943 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import pyarrow as pa
+import math
+import pytest
+
+from lancedb import DBConnection, Table, connect
+from lancedb.permutation import Permutation, Permutations, permutation_builder
+
+
+def test_permutation_persistence(tmp_path):
+    db = connect(tmp_path)
+    tbl = db.create_table("test_table", pa.table({"x": range(100), "y": range(100)}))
+
+    permutation_tbl = (
+        permutation_builder(tbl).shuffle().persist(db, "test_permutation").execute()
+    )
+    assert permutation_tbl.count_rows() == 100
+
+    re_open = db.open_table("test_permutation")
+    assert re_open.count_rows() == 100
+
+    assert permutation_tbl.to_arrow() == re_open.to_arrow()
+
+
+def test_split_random_ratios(mem_db):
+    """Test random splitting with ratios."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"x": range(100), "y": range(100)})
+    )
+    permutation_tbl = permutation_builder(tbl).split_random(ratios=[0.3, 0.7]).execute()
+
+    # Check that the table was created and has data
+    assert permutation_tbl.count_rows() == 100
+
+    # Check that split_id column exists and has correct values
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    split_ids = data["split_id"]
+    assert set(split_ids) == {0, 1}
+
+    # Check approximate split sizes (allowing for rounding)
+    split_0_count = split_ids.count(0)
+    split_1_count = split_ids.count(1)
+    assert 25 <= split_0_count <= 35  # ~30% ± tolerance
+    assert 65 <= split_1_count <= 75  # ~70% ± tolerance
+
+
+def test_split_random_counts(mem_db):
+    """Test random splitting with absolute counts."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"x": range(100), "y": range(100)})
+    )
+    permutation_tbl = permutation_builder(tbl).split_random(counts=[20, 30]).execute()
+
+    # Check that we have exactly the requested counts
+    assert permutation_tbl.count_rows() == 50
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    split_ids = data["split_id"]
+    assert split_ids.count(0) == 20
+    assert split_ids.count(1) == 30
+
+
+def test_split_random_fixed(mem_db):
+    """Test random splitting with fixed number of splits."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"x": range(100), "y": range(100)})
+    )
+    permutation_tbl = permutation_builder(tbl).split_random(fixed=4).execute()
+
+    # Check that we have 4 splits with 25 rows each
+    assert permutation_tbl.count_rows() == 100
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    split_ids = data["split_id"]
+    assert set(split_ids) == {0, 1, 2, 3}
+
+    for split_id in range(4):
+        assert split_ids.count(split_id) == 25
+
+
+def test_split_random_with_seed(mem_db):
+    """Test that seeded random splits are reproducible."""
+    tbl = mem_db.create_table("test_table", pa.table({"x": range(50), "y": range(50)}))
+
+    # Create two identical permutations with same seed
+    perm1 = permutation_builder(tbl).split_random(ratios=[0.6, 0.4], seed=42).execute()
+
+    perm2 = permutation_builder(tbl).split_random(ratios=[0.6, 0.4], seed=42).execute()
+
+    # Results should be identical
+    data1 = perm1.search(None).to_arrow().to_pydict()
+    data2 = perm2.search(None).to_arrow().to_pydict()
+
+    assert data1["row_id"] == data2["row_id"]
+    assert data1["split_id"] == data2["split_id"]
+
+
+def test_split_hash(mem_db):
+    """Test hash-based splitting."""
+    tbl = mem_db.create_table(
+        "test_table",
+        pa.table(
+            {
+                "id": range(100),
+                "category": (["A", "B", "C"] * 34)[:100],  # Repeating pattern
+                "value": range(100),
+            }
+        ),
+    )
+
+    permutation_tbl = (
+        permutation_builder(tbl)
+        .split_hash(["category"], [1, 1], discard_weight=0)
+        .execute()
+    )
+
+    # Should have all 100 rows (no discard)
+    assert permutation_tbl.count_rows() == 100
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    split_ids = data["split_id"]
+    assert set(split_ids) == {0, 1}
+
+    # Verify that each split has roughly 50 rows (allowing for hash variance)
+    split_0_count = split_ids.count(0)
+    split_1_count = split_ids.count(1)
+    assert 30 <= split_0_count <= 70  # ~50 ± 20 tolerance for hash distribution
+    assert 30 <= split_1_count <= 70  # ~50 ± 20 tolerance for hash distribution
+
+    # Hash splits should be deterministic - same category should go to same split
+    # Let's verify by creating another permutation and checking consistency
+    perm2 = (
+        permutation_builder(tbl)
+        .split_hash(["category"], [1, 1], discard_weight=0)
+        .execute()
+    )
+
+    data2 = perm2.search(None).to_arrow().to_pydict()
+    assert data["split_id"] == data2["split_id"]  # Should be identical
+
+
+def test_split_hash_with_discard(mem_db):
+    """Test hash-based splitting with discard weight."""
+    tbl = mem_db.create_table(
+        "test_table",
+        pa.table({"id": range(100), "category": ["A", "B"] * 50, "value": range(100)}),
+    )
+
+    permutation_tbl = (
+        permutation_builder(tbl)
+        .split_hash(["category"], [1, 1], discard_weight=2)  # Should discard ~50%
+        .execute()
+    )
+
+    # Should have fewer than 100 rows due to discard
+    row_count = permutation_tbl.count_rows()
+    assert row_count < 100
+    assert row_count > 0  # But not empty
+
+
+def test_split_sequential(mem_db):
+    """Test sequential splitting."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"x": range(100), "y": range(100)})
+    )
+
+    permutation_tbl = (
+        permutation_builder(tbl).split_sequential(counts=[30, 40]).execute()
+    )
+
+    assert permutation_tbl.count_rows() == 70
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    row_ids = data["row_id"]
+    split_ids = data["split_id"]
+
+    # Sequential should maintain order
+    assert row_ids == sorted(row_ids)
+
+    # First 30 should be split 0, next 40 should be split 1
+    assert split_ids[:30] == [0] * 30
+    assert split_ids[30:] == [1] * 40
+
+
+def test_split_calculated(mem_db):
+    """Test calculated splitting."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(100), "value": range(100)})
+    )
+
+    permutation_tbl = (
+        permutation_builder(tbl)
+        .split_calculated("id % 3")  # Split based on id modulo 3
+        .execute()
+    )
+
+    assert permutation_tbl.count_rows() == 100
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    row_ids = data["row_id"]
+    split_ids = data["split_id"]
+
+    # Verify the calculation: each row's split_id should equal row_id % 3
+    for i, (row_id, split_id) in enumerate(zip(row_ids, split_ids)):
+        assert split_id == row_id % 3
+
+
+def test_split_error_cases(mem_db):
+    """Test error handling for invalid split parameters."""
+    tbl = mem_db.create_table("test_table", pa.table({"x": range(10), "y": range(10)}))
+
+    # Test split_random with no parameters
+    with pytest.raises(
+        ValueError,
+        match="Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+    ):
+        permutation_builder(tbl).split_random().execute()
+
+    # Test split_random with multiple parameters
+    with pytest.raises(
+        ValueError,
+        match="Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+    ):
+        permutation_builder(tbl).split_random(
+            ratios=[0.5, 0.5], counts=[5, 5]
+        ).execute()
+
+    # Test split_sequential with no parameters
+    with pytest.raises(
+        ValueError,
+        match="Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+    ):
+        permutation_builder(tbl).split_sequential().execute()
+
+    # Test split_sequential with multiple parameters
+    with pytest.raises(
+        ValueError,
+        match="Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+    ):
+        permutation_builder(tbl).split_sequential(ratios=[0.5, 0.5], fixed=2).execute()
+
+
+def test_shuffle_no_seed(mem_db):
+    """Test shuffling without a seed."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(100), "value": range(100)})
+    )
+
+    # Create a permutation with shuffling (no seed)
+    permutation_tbl = permutation_builder(tbl).shuffle().execute()
+
+    assert permutation_tbl.count_rows() == 100
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    row_ids = data["row_id"]
+
+    # Row IDs should not be in sequential order due to shuffling
+    # This is probabilistic but with 100 rows, it's extremely unlikely they'd stay
+    # in order
+    assert row_ids != list(range(100))
+
+
+def test_shuffle_with_seed(mem_db):
+    """Test that shuffling with a seed is reproducible."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(50), "value": range(50)})
+    )
+
+    # Create two identical permutations with same shuffle seed
+    perm1 = permutation_builder(tbl).shuffle(seed=42).execute()
+
+    perm2 = permutation_builder(tbl).shuffle(seed=42).execute()
+
+    # Results should be identical due to same seed
+    data1 = perm1.search(None).to_arrow().to_pydict()
+    data2 = perm2.search(None).to_arrow().to_pydict()
+
+    assert data1["row_id"] == data2["row_id"]
+    assert data1["split_id"] == data2["split_id"]
+
+
+def test_shuffle_with_clump_size(mem_db):
+    """Test shuffling with clump size."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(100), "value": range(100)})
+    )
+
+    # Create a permutation with shuffling using clumps
+    permutation_tbl = (
+        permutation_builder(tbl)
+        .shuffle(clump_size=10)  # 10-row clumps
+        .execute()
+    )
+
+    assert permutation_tbl.count_rows() == 100
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    row_ids = data["row_id"]
+
+    for i in range(10):
+        start = row_ids[i * 10]
+        assert row_ids[i * 10 : (i + 1) * 10] == list(range(start, start + 10))
+
+
+def test_shuffle_different_seeds(mem_db):
+    """Test that different seeds produce different shuffle orders."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(50), "value": range(50)})
+    )
+
+    # Create two permutations with different shuffle seeds
+    perm1 = permutation_builder(tbl).split_random(fixed=2).shuffle(seed=42).execute()
+
+    perm2 = permutation_builder(tbl).split_random(fixed=2).shuffle(seed=123).execute()
+
+    # Results should be different due to different seeds
+    data1 = perm1.search(None).to_arrow().to_pydict()
+    data2 = perm2.search(None).to_arrow().to_pydict()
+
+    # Row order should be different
+    assert data1["row_id"] != data2["row_id"]
+
+
+def test_shuffle_combined_with_splits(mem_db):
+    """Test shuffling combined with different split strategies."""
+    tbl = mem_db.create_table(
+        "test_table",
+        pa.table(
+            {
+                "id": range(100),
+                "category": (["A", "B", "C"] * 34)[:100],
+                "value": range(100),
+            }
+        ),
+    )
+
+    # Test shuffle with random splits
+    perm_random = (
+        permutation_builder(tbl)
+        .split_random(ratios=[0.6, 0.4], seed=42)
+        .shuffle(seed=123, clump_size=None)
+        .execute()
+    )
+
+    # Test shuffle with hash splits
+    perm_hash = (
+        permutation_builder(tbl)
+        .split_hash(["category"], [1, 1], discard_weight=0)
+        .shuffle(seed=456, clump_size=5)
+        .execute()
+    )
+
+    # Test shuffle with sequential splits
+    perm_sequential = (
+        permutation_builder(tbl)
+        .split_sequential(counts=[40, 35])
+        .shuffle(seed=789, clump_size=None)
+        .execute()
+    )
+
+    # Verify all permutations work and have expected properties
+    assert perm_random.count_rows() == 100
+    assert perm_hash.count_rows() == 100
+    assert perm_sequential.count_rows() == 75
+
+    # Verify shuffle affected the order
+    data_random = perm_random.search(None).to_arrow().to_pydict()
+    data_sequential = perm_sequential.search(None).to_arrow().to_pydict()
+
+    assert data_random["row_id"] != list(range(100))
+    assert data_sequential["row_id"] != list(range(75))
+
+
+def test_no_shuffle_maintains_order(mem_db):
+    """Test that not calling shuffle maintains the original order."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(50), "value": range(50)})
+    )
+
+    # Create permutation without shuffle (should maintain some order)
+    permutation_tbl = (
+        permutation_builder(tbl)
+        .split_sequential(counts=[25, 25])  # Sequential maintains order
+        .execute()
+    )
+
+    assert permutation_tbl.count_rows() == 50
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    row_ids = data["row_id"]
+
+    # With sequential splits and no shuffle, should maintain order
+    assert row_ids == list(range(50))
+
+
+def test_filter_basic(mem_db):
+    """Test basic filtering functionality."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(100), "value": range(100, 200)})
+    )
+
+    # Filter to only include rows where id < 50
+    permutation_tbl = permutation_builder(tbl).filter("id < 50").execute()
+
+    assert permutation_tbl.count_rows() == 50
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    row_ids = data["row_id"]
+
+    # All row_ids should be less than 50
+    assert all(row_id < 50 for row_id in row_ids)
+
+
+def test_filter_with_splits(mem_db):
+    """Test filtering combined with split strategies."""
+    tbl = mem_db.create_table(
+        "test_table",
+        pa.table(
+            {
+                "id": range(100),
+                "category": (["A", "B", "C"] * 34)[:100],
+                "value": range(100),
+            }
+        ),
+    )
+
+    # Filter to only category A and B, then split
+    permutation_tbl = (
+        permutation_builder(tbl)
+        .filter("category IN ('A', 'B')")
+        .split_random(ratios=[0.5, 0.5])
+        .execute()
+    )
+
+    # Should have fewer than 100 rows due to filtering
+    row_count = permutation_tbl.count_rows()
+    assert row_count == 67
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    categories = data["category"]
+
+    # All categories should be A or B
+    assert all(cat in ["A", "B"] for cat in categories)
+
+
+def test_filter_with_shuffle(mem_db):
+    """Test filtering combined with shuffling."""
+    tbl = mem_db.create_table(
+        "test_table",
+        pa.table(
+            {
+                "id": range(100),
+                "category": (["A", "B", "C", "D"] * 25)[:100],
+                "value": range(100),
+            }
+        ),
+    )
+
+    # Filter and shuffle
+    permutation_tbl = (
+        permutation_builder(tbl)
+        .filter("category IN ('A', 'C')")
+        .shuffle(seed=42)
+        .execute()
+    )
+
+    row_count = permutation_tbl.count_rows()
+    assert row_count == 50  # Should have 50 rows (A and C categories)
+
+    data = permutation_tbl.search(None).to_arrow().to_pydict()
+    row_ids = data["row_id"]
+
+    assert row_ids != sorted(row_ids)
+
+
+def test_filter_empty_result(mem_db):
+    """Test filtering that results in empty set."""
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+
+    # Filter that matches nothing
+    permutation_tbl = (
+        permutation_builder(tbl)
+        .filter("value > 100")  # No values > 100 in our data
+        .execute()
+    )
+
+    assert permutation_tbl.count_rows() == 0
+
+
+@pytest.fixture
+def mem_db() -> DBConnection:
+    return connect("memory:///")
+
+
+@pytest.fixture
+def some_table(mem_db: DBConnection) -> Table:
+    data = pa.table(
+        {
+            "id": range(1000),
+            "value": range(1000),
+        }
+    )
+    return mem_db.create_table("some_table", data)
+
+
+def test_no_split_names(some_table: Table):
+    perm_tbl = (
+        permutation_builder(some_table).split_sequential(counts=[500, 500]).execute()
+    )
+    permutations = Permutations(some_table, perm_tbl)
+    assert permutations.split_names == []
+    assert permutations.split_dict == {}
+    assert permutations[0].num_rows == 500
+    assert permutations[1].num_rows == 500
+
+
+@pytest.fixture
+def some_perm_table(some_table: Table) -> Table:
+    return (
+        permutation_builder(some_table)
+        .split_random(ratios=[0.95, 0.05], seed=42, split_names=["train", "test"])
+        .shuffle(seed=42)
+        .execute()
+    )
+
+
+def test_nonexistent_split(some_table: Table, some_perm_table: Table):
+    # Reference by name and name does not exist
+    with pytest.raises(ValueError, match="split `nonexistent` is not defined"):
+        Permutation.from_tables(some_table, some_perm_table, "nonexistent")
+
+    # Reference by ordinal and there are no rows
+    with pytest.raises(ValueError, match="No rows found"):
+        Permutation.from_tables(some_table, some_perm_table, 5)
+
+
+def test_permutations(some_table: Table, some_perm_table: Table):
+    permutations = Permutations(some_table, some_perm_table)
+    assert permutations.split_names == ["train", "test"]
+    assert permutations.split_dict == {"train": 0, "test": 1}
+    assert permutations["train"].num_rows == 950
+    assert permutations[0].num_rows == 950
+    assert permutations["test"].num_rows == 50
+    assert permutations[1].num_rows == 50
+
+    with pytest.raises(ValueError, match="No split named `nonexistent` found"):
+        permutations["nonexistent"]
+    with pytest.raises(ValueError, match="No rows found"):
+        permutations[5]
+
+
+@pytest.fixture
+def some_permutation(some_table: Table, some_perm_table: Table) -> Permutation:
+    return Permutation.from_tables(some_table, some_perm_table)
+
+
+def test_num_rows(some_permutation: Permutation):
+    assert some_permutation.num_rows == 950
+
+
+def test_num_columns(some_permutation: Permutation):
+    assert some_permutation.num_columns == 2
+
+
+def test_column_names(some_permutation: Permutation):
+    assert some_permutation.column_names == ["id", "value"]
+
+
+def test_shape(some_permutation: Permutation):
+    assert some_permutation.shape == (950, 2)
+
+
+def test_schema(some_permutation: Permutation):
+    assert some_permutation.schema == pa.schema(
+        [("id", pa.int64()), ("value", pa.int64())]
+    )
+
+
+def test_limit_offset(some_permutation: Permutation):
+    assert some_permutation.with_take(100).num_rows == 100
+    assert some_permutation.with_skip(100).num_rows == 850
+    assert some_permutation.with_take(100).with_skip(100).num_rows == 100
+
+    with pytest.raises(Exception):
+        some_permutation.with_take(1000000).num_rows
+    with pytest.raises(Exception):
+        some_permutation.with_skip(1000000).num_rows
+    with pytest.raises(Exception):
+        some_permutation.with_take(500).with_skip(500).num_rows
+    with pytest.raises(Exception):
+        some_permutation.with_skip(500).with_take(500).num_rows
+
+
+def test_remove_columns(some_permutation: Permutation):
+    assert some_permutation.remove_columns(["value"]).schema == pa.schema(
+        [("id", pa.int64())]
+    )
+    # Should not modify the original permutation
+    assert some_permutation.schema.names == ["id", "value"]
+    # Cannot remove all columns
+    with pytest.raises(ValueError, match="Cannot remove all columns"):
+        some_permutation.remove_columns(["id", "value"])
+
+
+def test_rename_column(some_permutation: Permutation):
+    assert some_permutation.rename_column("value", "new_value").schema == pa.schema(
+        [("id", pa.int64()), ("new_value", pa.int64())]
+    )
+    # Should not modify the original permutation
+    assert some_permutation.schema.names == ["id", "value"]
+    # Cannot rename to an existing column
+    with pytest.raises(
+        ValueError,
+        match="a column with that name already exists",
+    ):
+        some_permutation.rename_column("value", "id")
+    # Cannot rename a non-existent column
+    with pytest.raises(
+        ValueError,
+        match="does not exist",
+    ):
+        some_permutation.rename_column("non_existent", "new_value")
+
+
+def test_rename_columns(some_permutation: Permutation):
+    assert some_permutation.rename_columns({"value": "new_value"}).schema == pa.schema(
+        [("id", pa.int64()), ("new_value", pa.int64())]
+    )
+    # Should not modify the original permutation
+    assert some_permutation.schema.names == ["id", "value"]
+    # Cannot rename to an existing column
+    with pytest.raises(ValueError, match="a column with that name already exists"):
+        some_permutation.rename_columns({"value": "id"})
+
+
+def test_select_columns(some_permutation: Permutation):
+    assert some_permutation.select_columns(["id"]).schema == pa.schema(
+        [("id", pa.int64())]
+    )
+    # Should not modify the original permutation
+    assert some_permutation.schema.names == ["id", "value"]
+    # Cannot select a non-existent column
+    with pytest.raises(ValueError, match="does not exist"):
+        some_permutation.select_columns(["non_existent"])
+    # Empty selection is not allowed
+    with pytest.raises(ValueError, match="select at least one column"):
+        some_permutation.select_columns([])
+
+
+def test_iter_basic(some_permutation: Permutation):
+    """Test basic iteration with custom batch size."""
+    batch_size = 100
+    batches = list(some_permutation.iter(batch_size, skip_last_batch=False))
+
+    # Check that we got the expected number of batches
+    expected_batches = (950 + batch_size - 1) // batch_size  # ceiling division
+    assert len(batches) == expected_batches
+
+    # Check that all batches are dicts (default python format)
+    assert all(isinstance(batch, dict) for batch in batches)
+
+    # Check that batches have the correct structure
+    for batch in batches:
+        assert "id" in batch
+        assert "value" in batch
+        assert isinstance(batch["id"], list)
+        assert isinstance(batch["value"], list)
+
+    # Check that all batches except the last have the correct size
+    for batch in batches[:-1]:
+        assert len(batch["id"]) == batch_size
+        assert len(batch["value"]) == batch_size
+
+    # Last batch might be smaller
+    assert len(batches[-1]["id"]) <= batch_size
+
+
+def test_iter_skip_last_batch(some_permutation: Permutation):
+    """Test iteration with skip_last_batch=True."""
+    batch_size = 300
+    batches_with_skip = list(some_permutation.iter(batch_size, skip_last_batch=True))
+    batches_without_skip = list(
+        some_permutation.iter(batch_size, skip_last_batch=False)
+    )
+
+    # With skip_last_batch=True, we should have fewer batches if the last one is partial
+    num_full_batches = 950 // batch_size
+    assert len(batches_with_skip) == num_full_batches
+
+    # Without skip_last_batch, we should have one more batch if there's a remainder
+    if 950 % batch_size != 0:
+        assert len(batches_without_skip) == num_full_batches + 1
+        # Last batch should be smaller
+        assert len(batches_without_skip[-1]["id"]) == 950 % batch_size
+
+    # All batches with skip_last_batch should be full size
+    for batch in batches_with_skip:
+        assert len(batch["id"]) == batch_size
+
+
+def test_iter_different_batch_sizes(some_permutation: Permutation):
+    """Test iteration with different batch sizes."""
+
+    # Test with small batch size
+    small_batches = list(some_permutation.iter(100, skip_last_batch=False))
+    assert len(small_batches) == 10  # ceiling(950 / 100)
+
+    # Test with large batch size
+    large_batches = list(some_permutation.iter(400, skip_last_batch=False))
+    assert len(large_batches) == 3  # ceiling(950 / 400)
+
+    # Test with batch size equal to total rows
+    single_batch = list(some_permutation.iter(950, skip_last_batch=False))
+    assert len(single_batch) == 1
+    assert len(single_batch[0]["id"]) == 950
+
+    # Test with batch size larger than total rows
+    oversized_batch = list(some_permutation.iter(10000, skip_last_batch=False))
+    assert len(oversized_batch) == 1
+    assert len(oversized_batch[0]["id"]) == 950
+
+
+def test_dunder_iter(some_permutation: Permutation):
+    """Test the __iter__ method."""
+    # __iter__ should use DEFAULT_BATCH_SIZE (100) and skip_last_batch=True
+    batches = list(some_permutation)
+
+    # With DEFAULT_BATCH_SIZE=100 and skip_last_batch=True, we should get 9 batches
+    assert len(batches) == 9  # ceiling(950 / 100)
+
+    # All batches should be full size
+    for batch in batches:
+        assert len(batch["id"]) == 100
+        assert len(batch["value"]) == 100
+
+    some_permutation = some_permutation.with_batch_size(400)
+    batches = list(some_permutation)
+    assert len(batches) == 2  # floor(950 / 400) since skip_last_batch=True
+    for batch in batches:
+        assert len(batch["id"]) == 400
+        assert len(batch["value"]) == 400
+
+
+def test_iter_with_different_formats(some_permutation: Permutation):
+    """Test iteration with different output formats."""
+    batch_size = 100
+
+    # Test with arrow format
+    arrow_perm = some_permutation.with_format("arrow")
+    arrow_batches = list(arrow_perm.iter(batch_size, skip_last_batch=False))
+    assert all(isinstance(batch, pa.RecordBatch) for batch in arrow_batches)
+
+    # Test with python format (default)
+    python_perm = some_permutation.with_format("python")
+    python_batches = list(python_perm.iter(batch_size, skip_last_batch=False))
+    assert all(isinstance(batch, dict) for batch in python_batches)
+
+    # Test with pandas format
+    pandas_perm = some_permutation.with_format("pandas")
+    pandas_batches = list(pandas_perm.iter(batch_size, skip_last_batch=False))
+    # Import pandas to check the type
+    import pandas as pd
+
+    assert all(isinstance(batch, pd.DataFrame) for batch in pandas_batches)
+
+
+def test_iter_with_column_selection(some_permutation: Permutation):
+    """Test iteration after column selection."""
+    # Select only the id column
+    id_only = some_permutation.select_columns(["id"])
+    batches = list(id_only.iter(100, skip_last_batch=False))
+
+    # Check that batches only contain the id column
+    for batch in batches:
+        assert "id" in batch
+        assert "value" not in batch
+
+
+def test_iter_with_column_rename(some_permutation: Permutation):
+    """Test iteration after renaming columns."""
+    renamed = some_permutation.rename_column("value", "data")
+    batches = list(renamed.iter(100, skip_last_batch=False))
+
+    # Check that batches have the renamed column
+    for batch in batches:
+        assert "id" in batch
+        assert "data" in batch
+        assert "value" not in batch
+
+
+def test_iter_with_limit_offset(some_permutation: Permutation):
+    """Test iteration with limit and offset."""
+    # Test with offset
+    offset_perm = some_permutation.with_skip(100)
+    offset_batches = list(offset_perm.iter(100, skip_last_batch=False))
+    # Should have 850 rows (950 - 100)
+    expected_batches = math.ceil(850 / 100)
+    assert len(offset_batches) == expected_batches
+
+    # Test with limit
+    limit_perm = some_permutation.with_take(500)
+    limit_batches = list(limit_perm.iter(100, skip_last_batch=False))
+    # Should have 5 batches (500 / 100)
+    assert len(limit_batches) == 5
+
+    no_skip = some_permutation.iter(101, skip_last_batch=False)
+    row_100 = next(no_skip)["id"][100]
+
+    # Test with both limit and offset
+    limited_perm = some_permutation.with_skip(100).with_take(300)
+    limited_batches = list(limited_perm.iter(100, skip_last_batch=False))
+    # Should have 3 batches (300 / 100)
+    assert len(limited_batches) == 3
+    assert limited_batches[0]["id"][0] == row_100
+
+
+def test_iter_empty_permutation(mem_db):
+    """Test iteration over an empty permutation."""
+    # Create a table and filter it to be empty
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+    permutation_tbl = permutation_builder(tbl).filter("value > 100").execute()
+    with pytest.raises(ValueError, match="No rows found"):
+        Permutation.from_tables(tbl, permutation_tbl)
+
+
+def test_iter_single_row(mem_db):
+    """Test iteration over a permutation with a single row."""
+    tbl = mem_db.create_table("test_table", pa.table({"id": [42], "value": [100]}))
+    permutation_tbl = permutation_builder(tbl).execute()
+    perm = Permutation.from_tables(tbl, permutation_tbl)
+
+    # With skip_last_batch=False, should get one batch
+    batches = list(perm.iter(10, skip_last_batch=False))
+    assert len(batches) == 1
+    assert len(batches[0]["id"]) == 1
+
+    # With skip_last_batch=True, should skip the single row (since it's < batch_size)
+    batches_skip = list(perm.iter(10, skip_last_batch=True))
+    assert len(batches_skip) == 0
+
+
+def test_identity_permutation(mem_db):
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+    permutation = Permutation.identity(tbl)
+
+    assert permutation.num_rows == 10
+    assert permutation.num_columns == 2
+
+    batches = list(permutation.iter(10, skip_last_batch=False))
+    assert len(batches) == 1
+    assert len(batches[0]["id"]) == 10
+    assert len(batches[0]["value"]) == 10
+
+    permutation = permutation.remove_columns(["value"])
+    assert permutation.num_columns == 1
+    assert permutation.schema == pa.schema([("id", pa.int64())])
+    assert permutation.column_names == ["id"]
+    assert permutation.shape == (10, 1)
+
+
+def test_transform_fn(mem_db):
+    import numpy as np
+    import pandas as pd
+    import polars as pl
+
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+    permutation = Permutation.identity(tbl)
+
+    np_result = list(permutation.with_format("numpy").iter(10, skip_last_batch=False))[
+        0
+    ]
+    assert np_result.shape == (10, 2)
+    assert np_result.dtype == np.int64
+    assert isinstance(np_result, np.ndarray)
+
+    pd_result = list(permutation.with_format("pandas").iter(10, skip_last_batch=False))[
+        0
+    ]
+    assert pd_result.shape == (10, 2)
+    assert pd_result.dtypes.tolist() == [np.int64, np.int64]
+    assert isinstance(pd_result, pd.DataFrame)
+
+    pl_result = list(permutation.with_format("polars").iter(10, skip_last_batch=False))[
+        0
+    ]
+    assert pl_result.shape == (10, 2)
+    assert pl_result.dtypes == [pl.Int64, pl.Int64]
+    assert isinstance(pl_result, pl.DataFrame)
+
+    py_result = list(permutation.with_format("python").iter(10, skip_last_batch=False))[
+        0
+    ]
+    assert len(py_result) == 2
+    assert len(py_result["id"]) == 10
+    assert len(py_result["value"]) == 10
+    assert isinstance(py_result, dict)
+
+    try:
+        import torch
+
+        torch_result = list(
+            permutation.with_format("torch").iter(10, skip_last_batch=False)
+        )[0]
+        assert torch_result.shape == (2, 10)
+        assert torch_result.dtype == torch.int64
+        assert isinstance(torch_result, torch.Tensor)
+    except ImportError:
+        # Skip check if torch is not installed
+        pass
+
+    arrow_result = list(
+        permutation.with_format("arrow").iter(10, skip_last_batch=False)
+    )[0]
+    assert arrow_result.shape == (10, 2)
+    assert arrow_result.schema == pa.schema([("id", pa.int64()), ("value", pa.int64())])
+    assert isinstance(arrow_result, pa.RecordBatch)
+
+
+def test_custom_transform(mem_db):
+    tbl = mem_db.create_table(
+        "test_table", pa.table({"id": range(10), "value": range(10)})
+    )
+    permutation = Permutation.identity(tbl)
+
+    def transform(batch: pa.RecordBatch) -> pa.RecordBatch:
+        return batch.select(["id"])
+
+    transformed = permutation.with_transform(transform)
+    batches = list(transformed.iter(10, skip_last_batch=False))
+    assert len(batches) == 1
+    batch = batches[0]
+
+    assert batch == pa.record_batch([range(10)], ["id"])

python/python/tests/test_query.py

@@ -1298,6 +1298,79 @@ async def test_query_serialization_async(table_async: AsyncTable):
     )
 
 
+def test_query_schema(tmp_path):
+    db = lancedb.connect(tmp_path)
+    tbl = db.create_table(
+        "test",
+        pa.table(
+            {
+                "a": [1, 2, 3],
+                "text": ["a", "b", "c"],
+                "vec": pa.array(
+                    [[1, 2], [3, 4], [5, 6]], pa.list_(pa.float32(), list_size=2)
+                ),
+            }
+        ),
+    )
+
+    assert tbl.search(None).output_schema() == pa.schema(
+        {
+            "a": pa.int64(),
+            "text": pa.string(),
+            "vec": pa.list_(pa.float32(), list_size=2),
+        }
+    )
+    assert tbl.search(None).select({"bl": "a * 2"}).output_schema() == pa.schema(
+        {"bl": pa.int64()}
+    )
+    assert tbl.search([1, 2]).select(["a"]).output_schema() == pa.schema(
+        {"a": pa.int64(), "_distance": pa.float32()}
+    )
+    assert tbl.search("blah").select(["a"]).output_schema() == pa.schema(
+        {"a": pa.int64()}
+    )
+    assert tbl.take_offsets([0]).select(["text"]).output_schema() == pa.schema(
+        {"text": pa.string()}
+    )
+
+
+@pytest.mark.asyncio
+async def test_query_schema_async(tmp_path):
+    db = await lancedb.connect_async(tmp_path)
+    tbl = await db.create_table(
+        "test",
+        pa.table(
+            {
+                "a": [1, 2, 3],
+                "text": ["a", "b", "c"],
+                "vec": pa.array(
+                    [[1, 2], [3, 4], [5, 6]], pa.list_(pa.float32(), list_size=2)
+                ),
+            }
+        ),
+    )
+
+    assert await tbl.query().output_schema() == pa.schema(
+        {
+            "a": pa.int64(),
+            "text": pa.string(),
+            "vec": pa.list_(pa.float32(), list_size=2),
+        }
+    )
+    assert await tbl.query().select({"bl": "a * 2"}).output_schema() == pa.schema(
+        {"bl": pa.int64()}
+    )
+    assert await tbl.vector_search([1, 2]).select(["a"]).output_schema() == pa.schema(
+        {"a": pa.int64(), "_distance": pa.float32()}
+    )
+    assert await (await tbl.search("blah")).select(["a"]).output_schema() == pa.schema(
+        {"a": pa.int64()}
+    )
+    assert await tbl.take_offsets([0]).select(["text"]).output_schema() == pa.schema(
+        {"text": pa.string()}
+    )
+
+
 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.

python/python/tests/test_rerankers.py

@@ -484,7 +484,7 @@ def test_jina_reranker(tmp_path, use_tantivy):
 @pytest.mark.parametrize("use_tantivy", [True, False])
 def test_voyageai_reranker(tmp_path, use_tantivy):
     pytest.importorskip("voyageai")
-    reranker = VoyageAIReranker(model_name="rerank-2")
+    reranker = VoyageAIReranker(model_name="rerank-2.5")
     table, schema = get_test_table(tmp_path, use_tantivy)
     _run_test_reranker(reranker, table, "single player experience", None, schema)
 

python/python/tests/test_torch.py

@@ -3,19 +3,11 @@
 
 import pyarrow as pa
 import pytest
+from lancedb.util import tbl_to_tensor
 
 torch = pytest.importorskip("torch")
 
 
-def tbl_to_tensor(tbl):
-    def to_tensor(col: pa.ChunkedArray):
-        if col.num_chunks > 1:
-            raise Exception("Single batch was too large to fit into a one-chunk table")
-        return torch.from_dlpack(col.chunk(0))
-
-    return torch.stack([to_tensor(tbl.column(i)) for i in range(tbl.num_columns)])
-
-
 def test_table_dataloader(mem_db):
     table = mem_db.create_table("test_table", pa.table({"a": range(1000)}))
     dataloader = torch.utils.data.DataLoader(

python/src/connection.rs

@@ -4,7 +4,10 @@
 use std::{collections::HashMap, sync::Arc, time::Duration};
 
 use arrow::{datatypes::Schema, ffi_stream::ArrowArrayStreamReader, pyarrow::FromPyArrow};
-use lancedb::{connection::Connection as LanceConnection, database::CreateTableMode};
+use lancedb::{
+    connection::Connection as LanceConnection,
+    database::{CreateTableMode, Database, ReadConsistency},
+};
 use pyo3::{
     exceptions::{PyRuntimeError, PyValueError},
     pyclass, pyfunction, pymethods, Bound, FromPyObject, Py, PyAny, PyRef, PyResult, Python,
@@ -23,7 +26,7 @@ impl Connection {
         Self { inner: Some(inner) }
     }
 
-    fn get_inner(&self) -> PyResult<&LanceConnection> {
+    pub(crate) fn get_inner(&self) -> PyResult<&LanceConnection> {
         self.inner
             .as_ref()
             .ok_or_else(|| PyRuntimeError::new_err("Connection is closed"))
@@ -39,6 +42,10 @@ impl Connection {
             _ => Err(PyValueError::new_err(format!("Invalid mode {}", mode))),
         }
     }
+
+    pub fn database(&self) -> PyResult<Arc<dyn Database>> {
+        Ok(self.get_inner()?.database().clone())
+    }
 }
 
 #[pymethods]
@@ -63,6 +70,18 @@ impl Connection {
         self.get_inner().map(|inner| inner.uri().to_string())
     }
 
+    #[pyo3(signature = ())]
+    pub fn get_read_consistency_interval(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.get_inner()?.clone();
+        future_into_py(self_.py(), async move {
+            Ok(match inner.read_consistency().await.infer_error()? {
+                ReadConsistency::Manual => None,
+                ReadConsistency::Eventual(duration) => Some(duration.as_secs_f64()),
+                ReadConsistency::Strong => Some(0.0_f64),
+            })
+        })
+    }
+
     #[pyo3(signature = (namespace=vec![], start_after=None, limit=None))]
     pub fn table_names(
         self_: PyRef<'_, Self>,

python/src/index.rs

@@ -1,7 +1,7 @@
 // SPDX-License-Identifier: Apache-2.0
 // SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
-use lancedb::index::vector::IvfFlatIndexBuilder;
+use lancedb::index::vector::{IvfFlatIndexBuilder, IvfRqIndexBuilder};
 use lancedb::index::{
     scalar::{BTreeIndexBuilder, FtsIndexBuilder},
     vector::{IvfHnswPqIndexBuilder, IvfHnswSqIndexBuilder, IvfPqIndexBuilder},
@@ -87,6 +87,22 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                 }
                 Ok(LanceDbIndex::IvfPq(ivf_pq_builder))
             },
+            "IvfRq" => {
+                let params = source.extract::<IvfRqParams>()?;
+                let distance_type = parse_distance_type(params.distance_type)?;
+                let mut ivf_rq_builder = IvfRqIndexBuilder::default()
+                    .distance_type(distance_type)
+                    .max_iterations(params.max_iterations)
+                    .sample_rate(params.sample_rate)
+                    .num_bits(params.num_bits);
+                if let Some(num_partitions) = params.num_partitions {
+                    ivf_rq_builder = ivf_rq_builder.num_partitions(num_partitions);
+                }
+                if let Some(target_partition_size) = params.target_partition_size {
+                    ivf_rq_builder = ivf_rq_builder.target_partition_size(target_partition_size);
+                }
+                Ok(LanceDbIndex::IvfRq(ivf_rq_builder))
+            },
             "HnswPq" => {
                 let params = source.extract::<IvfHnswPqParams>()?;
                 let distance_type = parse_distance_type(params.distance_type)?;
@@ -170,6 +186,16 @@ struct IvfPqParams {
     target_partition_size: Option<u32>,
 }
 
+#[derive(FromPyObject)]
+struct IvfRqParams {
+    distance_type: String,
+    num_partitions: Option<u32>,
+    num_bits: u32,
+    max_iterations: u32,
+    sample_rate: u32,
+    target_partition_size: Option<u32>,
+}
+
 #[derive(FromPyObject)]
 struct IvfHnswPqParams {
     distance_type: String,

python/src/lib.rs

@@ -5,6 +5,7 @@ use arrow::RecordBatchStream;
 use connection::{connect, Connection};
 use env_logger::Env;
 use index::IndexConfig;
+use permutation::{PyAsyncPermutationBuilder, PyPermutationReader};
 use pyo3::{
     pymodule,
     types::{PyModule, PyModuleMethods},
@@ -22,6 +23,7 @@ pub mod connection;
 pub mod error;
 pub mod header;
 pub mod index;
+pub mod permutation;
 pub mod query;
 pub mod session;
 pub mod table;
@@ -49,8 +51,12 @@ pub fn _lancedb(_py: Python, m: &Bound<'_, PyModule>) -> PyResult<()> {
     m.add_class::<DeleteResult>()?;
     m.add_class::<DropColumnsResult>()?;
     m.add_class::<UpdateResult>()?;
+    m.add_class::<PyAsyncPermutationBuilder>()?;
+    m.add_class::<PyPermutationReader>()?;
     m.add_function(wrap_pyfunction!(connect, m)?)?;
+    m.add_function(wrap_pyfunction!(permutation::async_permutation_builder, m)?)?;
     m.add_function(wrap_pyfunction!(util::validate_table_name, m)?)?;
+    m.add_function(wrap_pyfunction!(query::fts_query_to_json, m)?)?;
     m.add("__version__", env!("CARGO_PKG_VERSION"))?;
     Ok(())
 }

python/src/permutation.rs

@@ -0,0 +1,331 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::sync::{Arc, Mutex};
+
+use crate::{
+    arrow::RecordBatchStream, connection::Connection, error::PythonErrorExt, table::Table,
+};
+use arrow::pyarrow::ToPyArrow;
+use lancedb::{
+    dataloader::permutation::{
+        builder::{PermutationBuilder as LancePermutationBuilder, ShuffleStrategy},
+        reader::PermutationReader,
+        split::{SplitSizes, SplitStrategy},
+    },
+    query::Select,
+};
+use pyo3::{
+    exceptions::PyRuntimeError,
+    pyclass, pymethods,
+    types::{PyAnyMethods, PyDict, PyDictMethods, PyType},
+    Bound, PyAny, PyRef, PyRefMut, PyResult, Python,
+};
+use pyo3_async_runtimes::tokio::future_into_py;
+
+/// Create a permutation builder for the given table
+#[pyo3::pyfunction]
+pub fn async_permutation_builder(table: Bound<'_, PyAny>) -> PyResult<PyAsyncPermutationBuilder> {
+    let table = table.getattr("_inner")?.downcast_into::<Table>()?;
+    let inner_table = table.borrow().inner_ref()?.clone();
+    let inner_builder = LancePermutationBuilder::new(inner_table);
+
+    Ok(PyAsyncPermutationBuilder {
+        state: Arc::new(Mutex::new(PyAsyncPermutationBuilderState {
+            builder: Some(inner_builder),
+        })),
+    })
+}
+
+struct PyAsyncPermutationBuilderState {
+    builder: Option<LancePermutationBuilder>,
+}
+
+#[pyclass(name = "AsyncPermutationBuilder")]
+pub struct PyAsyncPermutationBuilder {
+    state: Arc<Mutex<PyAsyncPermutationBuilderState>>,
+}
+
+impl PyAsyncPermutationBuilder {
+    fn modify(
+        &self,
+        func: impl FnOnce(LancePermutationBuilder) -> LancePermutationBuilder,
+    ) -> PyResult<Self> {
+        let mut state = self.state.lock().unwrap();
+        let builder = state
+            .builder
+            .take()
+            .ok_or_else(|| PyRuntimeError::new_err("Builder already consumed"))?;
+        state.builder = Some(func(builder));
+        Ok(Self {
+            state: self.state.clone(),
+        })
+    }
+}
+
+#[pymethods]
+impl PyAsyncPermutationBuilder {
+    #[pyo3(signature = (database, table_name))]
+    pub fn persist(
+        slf: PyRefMut<'_, Self>,
+        database: Bound<'_, PyAny>,
+        table_name: String,
+    ) -> PyResult<Self> {
+        let conn = if database.hasattr("_conn")? {
+            database
+                .getattr("_conn")?
+                .getattr("_inner")?
+                .downcast_into::<Connection>()?
+        } else {
+            database.getattr("_inner")?.downcast_into::<Connection>()?
+        };
+        let database = conn.borrow().database()?;
+        slf.modify(|builder| builder.persist(database, table_name))
+    }
+
+    #[pyo3(signature = (*, ratios=None, counts=None, fixed=None, seed=None, split_names=None))]
+    pub fn split_random(
+        slf: PyRefMut<'_, Self>,
+        ratios: Option<Vec<f64>>,
+        counts: Option<Vec<u64>>,
+        fixed: Option<u64>,
+        seed: Option<u64>,
+        split_names: Option<Vec<String>>,
+    ) -> PyResult<Self> {
+        // Check that exactly one split type is provided
+        let split_args_count = [ratios.is_some(), counts.is_some(), fixed.is_some()]
+            .iter()
+            .filter(|&&x| x)
+            .count();
+
+        if split_args_count != 1 {
+            return Err(pyo3::exceptions::PyValueError::new_err(
+                "Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+            ));
+        }
+
+        let sizes = if let Some(ratios) = ratios {
+            SplitSizes::Percentages(ratios)
+        } else if let Some(counts) = counts {
+            SplitSizes::Counts(counts)
+        } else if let Some(fixed) = fixed {
+            SplitSizes::Fixed(fixed)
+        } else {
+            unreachable!("One of the split arguments must be provided");
+        };
+
+        slf.modify(|builder| {
+            builder.with_split_strategy(SplitStrategy::Random { seed, sizes }, split_names)
+        })
+    }
+
+    #[pyo3(signature = (columns, split_weights, *, discard_weight=0, split_names=None))]
+    pub fn split_hash(
+        slf: PyRefMut<'_, Self>,
+        columns: Vec<String>,
+        split_weights: Vec<u64>,
+        discard_weight: u64,
+        split_names: Option<Vec<String>>,
+    ) -> PyResult<Self> {
+        slf.modify(|builder| {
+            builder.with_split_strategy(
+                SplitStrategy::Hash {
+                    columns,
+                    split_weights,
+                    discard_weight,
+                },
+                split_names,
+            )
+        })
+    }
+
+    #[pyo3(signature = (*, ratios=None, counts=None, fixed=None, split_names=None))]
+    pub fn split_sequential(
+        slf: PyRefMut<'_, Self>,
+        ratios: Option<Vec<f64>>,
+        counts: Option<Vec<u64>>,
+        fixed: Option<u64>,
+        split_names: Option<Vec<String>>,
+    ) -> PyResult<Self> {
+        // Check that exactly one split type is provided
+        let split_args_count = [ratios.is_some(), counts.is_some(), fixed.is_some()]
+            .iter()
+            .filter(|&&x| x)
+            .count();
+
+        if split_args_count != 1 {
+            return Err(pyo3::exceptions::PyValueError::new_err(
+                "Exactly one of 'ratios', 'counts', or 'fixed' must be provided",
+            ));
+        }
+
+        let sizes = if let Some(ratios) = ratios {
+            SplitSizes::Percentages(ratios)
+        } else if let Some(counts) = counts {
+            SplitSizes::Counts(counts)
+        } else if let Some(fixed) = fixed {
+            SplitSizes::Fixed(fixed)
+        } else {
+            unreachable!("One of the split arguments must be provided");
+        };
+
+        slf.modify(|builder| {
+            builder.with_split_strategy(SplitStrategy::Sequential { sizes }, split_names)
+        })
+    }
+
+    pub fn split_calculated(
+        slf: PyRefMut<'_, Self>,
+        calculation: String,
+        split_names: Option<Vec<String>>,
+    ) -> PyResult<Self> {
+        slf.modify(|builder| {
+            builder.with_split_strategy(SplitStrategy::Calculated { calculation }, split_names)
+        })
+    }
+
+    pub fn shuffle(
+        slf: PyRefMut<'_, Self>,
+        seed: Option<u64>,
+        clump_size: Option<u64>,
+    ) -> PyResult<Self> {
+        slf.modify(|builder| {
+            builder.with_shuffle_strategy(ShuffleStrategy::Random { seed, clump_size })
+        })
+    }
+
+    pub fn filter(slf: PyRefMut<'_, Self>, filter: String) -> PyResult<Self> {
+        slf.modify(|builder| builder.with_filter(filter))
+    }
+
+    pub fn execute(slf: PyRefMut<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let mut state = slf.state.lock().unwrap();
+        let builder = state
+            .builder
+            .take()
+            .ok_or_else(|| PyRuntimeError::new_err("Builder already consumed"))?;
+
+        future_into_py(slf.py(), async move {
+            let table = builder.build().await.infer_error()?;
+            Ok(Table::new(table))
+        })
+    }
+}
+
+#[pyclass(name = "PermutationReader")]
+pub struct PyPermutationReader {
+    reader: Arc<PermutationReader>,
+}
+
+impl PyPermutationReader {
+    fn from_reader(reader: PermutationReader) -> Self {
+        Self {
+            reader: Arc::new(reader),
+        }
+    }
+
+    fn parse_selection(selection: Option<Bound<'_, PyAny>>) -> PyResult<Select> {
+        let Some(selection) = selection else {
+            return Ok(Select::All);
+        };
+        let selection = selection.downcast_into::<PyDict>()?;
+        let selection = selection
+            .iter()
+            .map(|(key, value)| {
+                let key = key.extract::<String>()?;
+                let value = value.extract::<String>()?;
+                Ok((key, value))
+            })
+            .collect::<PyResult<Vec<_>>>()?;
+        Ok(Select::dynamic(&selection))
+    }
+}
+
+#[pymethods]
+impl PyPermutationReader {
+    #[classmethod]
+    pub fn from_tables<'py>(
+        cls: &Bound<'py, PyType>,
+        base_table: Bound<'py, PyAny>,
+        permutation_table: Option<Bound<'py, PyAny>>,
+        split: u64,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let base_table = base_table.getattr("_inner")?.downcast_into::<Table>()?;
+        let permutation_table = permutation_table
+            .map(|p| PyResult::Ok(p.getattr("_inner")?.downcast_into::<Table>()?))
+            .transpose()?;
+
+        let base_table = base_table.borrow().inner_ref()?.base_table().clone();
+        let permutation_table = permutation_table
+            .map(|p| PyResult::Ok(p.borrow().inner_ref()?.base_table().clone()))
+            .transpose()?;
+
+        future_into_py(cls.py(), async move {
+            let reader = if let Some(permutation_table) = permutation_table {
+                PermutationReader::try_from_tables(base_table, permutation_table, split)
+                    .await
+                    .infer_error()?
+            } else {
+                PermutationReader::identity(base_table).await
+            };
+            Ok(Self::from_reader(reader))
+        })
+    }
+
+    #[pyo3(signature = (selection=None))]
+    pub fn output_schema<'py>(
+        slf: PyRef<'py, Self>,
+        selection: Option<Bound<'py, PyAny>>,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let selection = Self::parse_selection(selection)?;
+        let reader = slf.reader.clone();
+        future_into_py(slf.py(), async move {
+            let schema = reader.output_schema(selection).await.infer_error()?;
+            Python::with_gil(|py| schema.to_pyarrow(py))
+        })
+    }
+
+    #[pyo3(signature = ())]
+    pub fn count_rows<'py>(slf: PyRef<'py, Self>) -> u64 {
+        slf.reader.count_rows()
+    }
+
+    #[pyo3(signature = (offset))]
+    pub fn with_offset<'py>(slf: PyRef<'py, Self>, offset: u64) -> PyResult<Bound<'py, PyAny>> {
+        let reader = slf.reader.as_ref().clone();
+        future_into_py(slf.py(), async move {
+            let reader = reader.with_offset(offset).await.infer_error()?;
+            Ok(Self::from_reader(reader))
+        })
+    }
+
+    #[pyo3(signature = (limit))]
+    pub fn with_limit<'py>(slf: PyRef<'py, Self>, limit: u64) -> PyResult<Bound<'py, PyAny>> {
+        let reader = slf.reader.as_ref().clone();
+        future_into_py(slf.py(), async move {
+            let reader = reader.with_limit(limit).await.infer_error()?;
+            Ok(Self::from_reader(reader))
+        })
+    }
+
+    #[pyo3(signature = (selection=None, *, batch_size=None))]
+    pub fn read<'py>(
+        slf: PyRef<'py, Self>,
+        selection: Option<Bound<'py, PyAny>>,
+        batch_size: Option<u32>,
+    ) -> PyResult<Bound<'py, PyAny>> {
+        let selection = Self::parse_selection(selection)?;
+        let reader = slf.reader.clone();
+        let batch_size = batch_size.unwrap_or(1024);
+        future_into_py(slf.py(), async move {
+            use lancedb::query::QueryExecutionOptions;
+            let mut execution_options = QueryExecutionOptions::default();
+            execution_options.max_batch_length = batch_size;
+            let stream = reader
+                .read(selection, execution_options)
+                .await
+                .infer_error()?;
+            Ok(RecordBatchStream::new(stream))
+        })
+    }
+}

python/src/query.rs

@@ -9,6 +9,7 @@ use arrow::array::Array;
 use arrow::array::ArrayData;
 use arrow::pyarrow::FromPyArrow;
 use arrow::pyarrow::IntoPyArrow;
+use arrow::pyarrow::ToPyArrow;
 use lancedb::index::scalar::{
     BooleanQuery, BoostQuery, FtsQuery, FullTextSearchQuery, MatchQuery, MultiMatchQuery, Occur,
     Operator, PhraseQuery,
@@ -22,6 +23,7 @@ use lancedb::query::{
 };
 use lancedb::table::AnyQuery;
 use pyo3::prelude::{PyAnyMethods, PyDictMethods};
+use pyo3::pyfunction;
 use pyo3::pymethods;
 use pyo3::types::PyList;
 use pyo3::types::{PyDict, PyString};
@@ -30,6 +32,7 @@ use pyo3::IntoPyObject;
 use pyo3::PyAny;
 use pyo3::PyRef;
 use pyo3::PyResult;
+use pyo3::Python;
 use pyo3::{exceptions::PyRuntimeError, FromPyObject};
 use pyo3::{
     exceptions::{PyNotImplementedError, PyValueError},
@@ -445,6 +448,15 @@ impl Query {
         })
     }
 
+    #[pyo3(signature = ())]
+    pub fn output_schema(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            let schema = inner.output_schema().await.infer_error()?;
+            Python::with_gil(|py| schema.to_pyarrow(py))
+        })
+    }
+
     #[pyo3(signature = (max_batch_length=None, timeout=None))]
     pub fn execute(
         self_: PyRef<'_, Self>,
@@ -515,6 +527,15 @@ impl TakeQuery {
         self.inner = self.inner.clone().with_row_id();
     }
 
+    #[pyo3(signature = ())]
+    pub fn output_schema(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            let schema = inner.output_schema().await.infer_error()?;
+            Python::with_gil(|py| schema.to_pyarrow(py))
+        })
+    }
+
     #[pyo3(signature = (max_batch_length=None, timeout=None))]
     pub fn execute(
         self_: PyRef<'_, Self>,
@@ -601,6 +622,15 @@ impl FTSQuery {
         self.inner = self.inner.clone().postfilter();
     }
 
+    #[pyo3(signature = ())]
+    pub fn output_schema(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            let schema = inner.output_schema().await.infer_error()?;
+            Python::with_gil(|py| schema.to_pyarrow(py))
+        })
+    }
+
     #[pyo3(signature = (max_batch_length=None, timeout=None))]
     pub fn execute(
         self_: PyRef<'_, Self>,
@@ -771,6 +801,15 @@ impl VectorQuery {
         self.inner = self.inner.clone().bypass_vector_index()
     }
 
+    #[pyo3(signature = ())]
+    pub fn output_schema(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
+        let inner = self_.inner.clone();
+        future_into_py(self_.py(), async move {
+            let schema = inner.output_schema().await.infer_error()?;
+            Python::with_gil(|py| schema.to_pyarrow(py))
+        })
+    }
+
     #[pyo3(signature = (max_batch_length=None, timeout=None))]
     pub fn execute(
         self_: PyRef<'_, Self>,
@@ -944,3 +983,15 @@ impl HybridQuery {
         req
     }
 }
+
+/// Convert a Python FTS query to JSON string
+#[pyfunction]
+pub fn fts_query_to_json(query_obj: &Bound<'_, PyAny>) -> PyResult<String> {
+    let wrapped: PyLanceDB<FtsQuery> = query_obj.extract()?;
+    lancedb::table::datafusion::udtf::fts::to_json(&wrapped.0).map_err(|e| {
+        PyErr::new::<pyo3::exceptions::PyValueError, _>(format!(
+            "Failed to serialize FTS query to JSON: {}",
+            e
+        ))
+    })
+}

python/src/table.rs

@@ -3,6 +3,7 @@
 use std::{collections::HashMap, sync::Arc};
 
 use crate::{
+    connection::Connection,
     error::PythonErrorExt,
     index::{extract_index_params, IndexConfig},
     query::{Query, TakeQuery},
@@ -249,7 +250,7 @@ impl Table {
 }
 
 impl Table {
-    fn inner_ref(&self) -> PyResult<&LanceDbTable> {
+    pub(crate) fn inner_ref(&self) -> PyResult<&LanceDbTable> {
         self.inner
             .as_ref()
             .ok_or_else(|| PyRuntimeError::new_err(format!("Table {} is closed", self.name)))
@@ -272,6 +273,13 @@ impl Table {
         self.inner.take();
     }
 
+    pub fn database(&self) -> PyResult<Connection> {
+        let inner = self.inner_ref()?.clone();
+        let inner_connection =
+            lancedb::Connection::new(inner.database().clone(), inner.embedding_registry().clone());
+        Ok(Connection::new(inner_connection))
+    }
+
     pub fn schema(self_: PyRef<'_, Self>) -> PyResult<Bound<'_, PyAny>> {
         let inner = self_.inner_ref()?.clone();
         future_into_py(self_.py(), async move {

rust-toolchain.toml

@@ -1,2 +1,2 @@
 [toolchain]
-channel = "1.86.0"
+channel = "1.90.0"

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.22.2-beta.2"
+version = "0.22.3-beta.5"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
@@ -11,10 +11,12 @@ rust-version.workspace = true
 
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 [dependencies]
+ahash = { workspace = true }
 arrow = { workspace = true }
 arrow-array = { workspace = true }
 arrow-data = { workspace = true }
 arrow-schema = { workspace = true }
+arrow-select = { workspace = true }
 arrow-ord = { workspace = true }
 arrow-cast = { workspace = true }
 arrow-ipc.workspace = true
@@ -24,19 +26,25 @@ datafusion-common.workspace = true
 datafusion-execution.workspace = true
 datafusion-expr.workspace = true
 datafusion-physical-plan.workspace = true
+datafusion.workspace = true
 object_store = { workspace = true }
 snafu = { workspace = true }
 half = { workspace = true }
 lazy_static.workspace = true
 lance = { workspace = true }
+lance-core = { workspace = true }
 lance-datafusion.workspace = true
+lance-datagen = { workspace = true }
+lance-file = { workspace = true }
 lance-io = { workspace = true }
 lance-index = { workspace = true }
 lance-table = { workspace = true }
 lance-linalg = { workspace = true }
 lance-testing = { workspace = true }
 lance-encoding = { workspace = true }
+lance-arrow = { workspace = true }
 lance-namespace = { workspace = true }
+lance-namespace-impls = { workspace = true }
 moka = { workspace = true }
 pin-project = { workspace = true }
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
@@ -46,11 +54,13 @@ bytes = "1"
 futures.workspace = true
 num-traits.workspace = true
 url.workspace = true
+rand.workspace = true
 regex.workspace = true
 serde = { version = "^1" }
 serde_json = { version = "1" }
 async-openai = { version = "0.20.0", optional = true }
 serde_with = { version = "3.8.1" }
+tempfile = "3.5.0"
 aws-sdk-bedrockruntime = { version = "1.27.0", optional = true }
 # For remote feature
 reqwest = { version = "0.12.0", default-features = false, features = [
@@ -61,9 +71,8 @@ reqwest = { version = "0.12.0", default-features = false, features = [
     "macos-system-configuration",
     "stream",
 ], optional = true }
-rand = { version = "0.9", features = ["small_rng"], optional = true }
 http = { version = "1", optional = true } # Matching what is in reqwest
-uuid = { version = "1.7.0", features = ["v4"], optional = true }
+uuid = { version = "1.7.0", features = ["v4"] }
 polars-arrow = { version = ">=0.37,<0.40.0", optional = true }
 polars = { version = ">=0.37,<0.40.0", optional = true }
 hf-hub = { version = "0.4.1", optional = true, default-features = false, features = [
@@ -77,14 +86,9 @@ candle-nn = { version = "0.9.1", optional = true }
 tokenizers = { version = "0.19.1", optional = true }
 semver = { workspace = true }
 
-# For a workaround, see workspace Cargo.toml
-crunchy.workspace = true
-bytemuck_derive.workspace = true
-
 [dev-dependencies]
 anyhow = "1"
 tempfile = "3.5.0"
-rand = { version = "0.9", features = ["small_rng"] }
 random_word = { version = "0.4.3", features = ["en"] }
 uuid = { version = "1.7.0", features = ["v4"] }
 walkdir = "2"
@@ -96,6 +100,7 @@ aws-smithy-runtime = { version = "1.9.1" }
 datafusion.workspace = true
 http-body = "1"                                        # Matching reqwest
 rstest = "0.23.0"
+test-log = "0.2"
 
 
 [features]
@@ -105,7 +110,7 @@ oss = ["lance/oss", "lance-io/oss"]
 gcs = ["lance/gcp", "lance-io/gcp"]
 azure = ["lance/azure", "lance-io/azure"]
 dynamodb = ["lance/dynamodb", "aws"]
-remote = ["dep:reqwest", "dep:http", "dep:rand", "dep:uuid"]
+remote = ["dep:reqwest", "dep:http"]
 fp16kernels = ["lance-linalg/fp16kernels"]
 s3-test = []
 bedrock = ["dep:aws-sdk-bedrockruntime"]

rust/lancedb/src/arrow.rs

@@ -7,6 +7,7 @@ pub use arrow_schema;
 use datafusion_common::DataFusionError;
 use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
 use futures::{Stream, StreamExt, TryStreamExt};
+use lance_datagen::{BatchCount, BatchGeneratorBuilder, RowCount};
 
 #[cfg(feature = "polars")]
 use {crate::polars_arrow_convertors, polars::frame::ArrowChunk, polars::prelude::DataFrame};
@@ -161,6 +162,26 @@ impl IntoArrowStream for datafusion_physical_plan::SendableRecordBatchStream {
     }
 }
 
+pub trait LanceDbDatagenExt {
+    fn into_ldb_stream(
+        self,
+        batch_size: RowCount,
+        num_batches: BatchCount,
+    ) -> SendableRecordBatchStream;
+}
+
+impl LanceDbDatagenExt for BatchGeneratorBuilder {
+    fn into_ldb_stream(
+        self,
+        batch_size: RowCount,
+        num_batches: BatchCount,
+    ) -> SendableRecordBatchStream {
+        let (stream, schema) = self.into_reader_stream(batch_size, num_batches);
+        let stream = stream.map_err(|err| Error::Arrow { source: err });
+        Box::pin(SimpleRecordBatchStream::new(stream, schema))
+    }
+}
+
 #[cfg(feature = "polars")]
 /// An iterator of record batches formed from a Polars DataFrame.
 pub struct PolarsDataFrameRecordBatchReader {

rust/lancedb/src/connection.rs

@@ -19,7 +19,7 @@ use crate::database::listing::{
 use crate::database::{
     CloneTableRequest, CreateNamespaceRequest, CreateTableData, CreateTableMode,
     CreateTableRequest, Database, DatabaseOptions, DropNamespaceRequest, ListNamespacesRequest,
-    OpenTableRequest, TableNamesRequest,
+    OpenTableRequest, ReadConsistency, TableNamesRequest,
 };
 use crate::embeddings::{
     EmbeddingDefinition, EmbeddingFunction, EmbeddingRegistry, MemoryRegistry, WithEmbeddings,
@@ -152,6 +152,7 @@ impl CreateTableBuilder<true> {
         let request = self.into_request()?;
         Ok(Table::new_with_embedding_registry(
             parent.create_table(request).await?,
+            parent,
             embedding_registry,
         ))
     }
@@ -211,9 +212,9 @@ impl CreateTableBuilder<false> {
 
     /// Execute the create table operation
     pub async fn execute(self) -> Result<Table> {
-        Ok(Table::new(
-            self.parent.clone().create_table(self.request).await?,
-        ))
+        let parent = self.parent.clone();
+        let table = parent.create_table(self.request).await?;
+        Ok(Table::new(table, parent))
     }
 }
 
@@ -462,8 +463,10 @@ impl OpenTableBuilder {
 
     /// Open the table
     pub async fn execute(self) -> Result<Table> {
+        let table = self.parent.open_table(self.request).await?;
         Ok(Table::new_with_embedding_registry(
-            self.parent.clone().open_table(self.request).await?,
+            table,
+            self.parent,
             self.embedding_registry,
         ))
     }
@@ -519,16 +522,15 @@ impl CloneTableBuilder {
 
     /// Execute the clone operation
     pub async fn execute(self) -> Result<Table> {
-        Ok(Table::new(
-            self.parent.clone().clone_table(self.request).await?,
-        ))
+        let parent = self.parent.clone();
+        let table = parent.clone_table(self.request).await?;
+        Ok(Table::new(table, parent))
     }
 }
 
 /// A connection to LanceDB
 #[derive(Clone)]
 pub struct Connection {
-    uri: String,
     internal: Arc<dyn Database>,
     embedding_registry: Arc<dyn EmbeddingRegistry>,
 }
@@ -540,9 +542,19 @@ impl std::fmt::Display for Connection {
 }
 
 impl Connection {
+    pub fn new(
+        internal: Arc<dyn Database>,
+        embedding_registry: Arc<dyn EmbeddingRegistry>,
+    ) -> Self {
+        Self {
+            internal,
+            embedding_registry,
+        }
+    }
+
     /// Get the URI of the connection
     pub fn uri(&self) -> &str {
-        self.uri.as_str()
+        self.internal.uri()
     }
 
     /// Get access to the underlying database
@@ -675,6 +687,11 @@ impl Connection {
             .await
     }
 
+    /// Get the read consistency of the connection
+    pub async fn read_consistency(&self) -> Result<ReadConsistency> {
+        self.internal.read_consistency().await
+    }
+
     /// Drop a table in the database.
     ///
     /// # Arguments
@@ -973,7 +990,6 @@ impl ConnectBuilder {
         )?);
         Ok(Connection {
             internal,
-            uri: self.request.uri,
             embedding_registry: self
                 .embedding_registry
                 .unwrap_or_else(|| Arc::new(MemoryRegistry::new())),
@@ -996,7 +1012,6 @@ impl ConnectBuilder {
             let internal = Arc::new(ListingDatabase::connect_with_options(&self.request).await?);
             Ok(Connection {
                 internal,
-                uri: self.request.uri,
                 embedding_registry: self
                     .embedding_registry
                     .unwrap_or_else(|| Arc::new(MemoryRegistry::new())),
@@ -1104,7 +1119,6 @@ impl ConnectNamespaceBuilder {
 
         Ok(Connection {
             internal,
-            uri: format!("namespace://{}", self.ns_impl),
             embedding_registry: self
                 .embedding_registry
                 .unwrap_or_else(|| Arc::new(MemoryRegistry::new())),
@@ -1139,7 +1153,6 @@ mod test_utils {
             let internal = Arc::new(crate::remote::db::RemoteDatabase::new_mock(handler));
             Self {
                 internal,
-                uri: "db://test".to_string(),
                 embedding_registry: Arc::new(MemoryRegistry::new()),
             }
         }
@@ -1156,7 +1169,6 @@ mod test_utils {
             ));
             Self {
                 internal,
-                uri: "db://test".to_string(),
                 embedding_registry: Arc::new(MemoryRegistry::new()),
             }
         }
@@ -1170,13 +1182,13 @@ mod tests {
     use crate::database::listing::{ListingDatabaseOptions, NewTableConfig};
     use crate::query::QueryBase;
     use crate::query::{ExecutableQuery, QueryExecutionOptions};
-    use crate::test_connection::test_utils::new_test_connection;
+    use crate::test_utils::connection::new_test_connection;
     use arrow::compute::concat_batches;
     use arrow_array::RecordBatchReader;
     use arrow_schema::{DataType, Field, Schema};
     use datafusion_physical_plan::stream::RecordBatchStreamAdapter;
     use futures::{stream, TryStreamExt};
-    use lance::error::{ArrowResult, DataFusionResult};
+    use lance_core::error::{ArrowResult, DataFusionResult};
     use lance_testing::datagen::{BatchGenerator, IncrementingInt32};
     use tempfile::tempdir;
 
@@ -1187,7 +1199,7 @@ mod tests {
     #[tokio::test]
     async fn test_connect() {
         let tc = new_test_connection().await.unwrap();
-        assert_eq!(tc.connection.uri, tc.uri);
+        assert_eq!(tc.connection.uri(), tc.uri);
     }
 
     #[cfg(not(windows))]
@@ -1208,7 +1220,7 @@ mod tests {
             .await
             .unwrap();
 
-        assert_eq!(db.uri, relative_uri.to_str().unwrap().to_string());
+        assert_eq!(db.uri(), relative_uri.to_str().unwrap().to_string());
     }
 
     #[tokio::test]

rust/lancedb/src/data/sanitize.rs

@@ -12,7 +12,7 @@ use arrow_array::{
 use arrow_cast::{can_cast_types, cast};
 use arrow_schema::{ArrowError, DataType, Field, Schema};
 use half::f16;
-use lance::arrow::{DataTypeExt, FixedSizeListArrayExt};
+use lance_arrow::{DataTypeExt, FixedSizeListArrayExt};
 use log::warn;
 use num_traits::cast::AsPrimitive;
 
@@ -189,7 +189,7 @@ mod tests {
     };
     use arrow_schema::Field;
     use half::f16;
-    use lance::arrow::FixedSizeListArrayExt;
+    use lance_arrow::FixedSizeListArrayExt;
 
     #[test]
     fn test_coerce_list_to_fixed_size_list() {

rust/lancedb/src/database.rs

@@ -16,6 +16,7 @@
 
 use std::collections::HashMap;
 use std::sync::Arc;
+use std::time::Duration;
 
 use arrow_array::RecordBatchReader;
 use async_trait::async_trait;
@@ -213,6 +214,20 @@ impl CloneTableRequest {
     }
 }
 
+/// How long until a change is reflected from one Table instance to another
+///
+/// Tables are always internally consistent.  If a write method is called on
+/// a table instance it will be immediately visible in that same table instance.
+pub enum ReadConsistency {
+    /// Changes will not be automatically propagated until the checkout_latest
+    /// method is called on the target table
+    Manual,
+    /// Changes will be propagated automatically within the given duration
+    Eventual(Duration),
+    /// Changes are immediately visible in target tables
+    Strong,
+}
+
 /// The `Database` trait defines the interface for database implementations.
 ///
 /// A database is responsible for managing tables and their metadata.
@@ -220,6 +235,10 @@ impl CloneTableRequest {
 pub trait Database:
     Send + Sync + std::any::Any + std::fmt::Debug + std::fmt::Display + 'static
 {
+    /// Get the uri of the database
+    fn uri(&self) -> &str;
+    /// Get the read consistency of the database
+    async fn read_consistency(&self) -> Result<ReadConsistency>;
     /// List immediate child namespace names in the given namespace
     async fn list_namespaces(&self, request: ListNamespacesRequest) -> Result<Vec<String>>;
     /// Create a new namespace

rust/lancedb/src/database/listing.rs

@@ -17,6 +17,7 @@ use object_store::local::LocalFileSystem;
 use snafu::ResultExt;
 
 use crate::connection::ConnectRequest;
+use crate::database::ReadConsistency;
 use crate::error::{CreateDirSnafu, Error, Result};
 use crate::io::object_store::MirroringObjectStoreWrapper;
 use crate::table::NativeTable;
@@ -454,6 +455,7 @@ impl ListingDatabase {
                     // `remove_dir_all` may be used to remove something not be a dataset
                     lance::Error::NotFound { .. } => Error::TableNotFound {
                         name: name.to_owned(),
+                        source: Box::new(err),
                     },
                     _ => Error::from(err),
                 })?;
@@ -598,6 +600,22 @@ impl Database for ListingDatabase {
         Ok(Vec::new())
     }
 
+    fn uri(&self) -> &str {
+        &self.uri
+    }
+
+    async fn read_consistency(&self) -> Result<ReadConsistency> {
+        if let Some(read_consistency_inverval) = self.read_consistency_interval {
+            if read_consistency_inverval.is_zero() {
+                Ok(ReadConsistency::Strong)
+            } else {
+                Ok(ReadConsistency::Eventual(read_consistency_inverval))
+            }
+        } else {
+            Ok(ReadConsistency::Manual)
+        }
+    }
+
     async fn create_namespace(&self, _request: CreateNamespaceRequest) -> Result<()> {
         Err(Error::NotSupported {
             message: "Namespace operations are not supported for listing database".into(),
@@ -1249,7 +1267,8 @@ mod tests {
         )
         .unwrap();
 
-        let source_table_obj = Table::new(source_table.clone());
+        let db = Arc::new(db);
+        let source_table_obj = Table::new(source_table.clone(), db.clone());
         source_table_obj
             .add(Box::new(arrow_array::RecordBatchIterator::new(
                 vec![Ok(batch2)],
@@ -1320,7 +1339,8 @@ mod tests {
             .unwrap();
 
         // Create a tag for the current version
-        let source_table_obj = Table::new(source_table.clone());
+        let db = Arc::new(db);
+        let source_table_obj = Table::new(source_table.clone(), db.clone());
         let mut tags = source_table_obj.tags().await.unwrap();
         tags.create("v1.0", source_table.version().await.unwrap())
             .await
@@ -1336,7 +1356,7 @@ mod tests {
         )
         .unwrap();
 
-        let source_table_obj = Table::new(source_table.clone());
+        let source_table_obj = Table::new(source_table.clone(), db.clone());
         source_table_obj
             .add(Box::new(arrow_array::RecordBatchIterator::new(
                 vec![Ok(batch2)],
@@ -1432,7 +1452,8 @@ mod tests {
         )
         .unwrap();
 
-        let cloned_table_obj = Table::new(cloned_table.clone());
+        let db = Arc::new(db);
+        let cloned_table_obj = Table::new(cloned_table.clone(), db.clone());
         cloned_table_obj
             .add(Box::new(arrow_array::RecordBatchIterator::new(
                 vec![Ok(batch_clone)],
@@ -1452,7 +1473,7 @@ mod tests {
         )
         .unwrap();
 
-        let source_table_obj = Table::new(source_table.clone());
+        let source_table_obj = Table::new(source_table.clone(), db);
         source_table_obj
             .add(Box::new(arrow_array::RecordBatchIterator::new(
                 vec![Ok(batch_source)],
@@ -1495,6 +1516,7 @@ mod tests {
             .unwrap();
 
         // Add more data to create new versions
+        let db = Arc::new(db);
         for i in 0..3 {
             let batch = RecordBatch::try_new(
                 schema.clone(),
@@ -1502,7 +1524,7 @@ mod tests {
             )
             .unwrap();
 
-            let source_table_obj = Table::new(source_table.clone());
+            let source_table_obj = Table::new(source_table.clone(), db.clone());
             source_table_obj
                 .add(Box::new(arrow_array::RecordBatchIterator::new(
                     vec![Ok(batch)],

rust/lancedb/src/database/namespace.rs

@@ -8,17 +8,17 @@ use std::sync::Arc;
 
 use async_trait::async_trait;
 use lance_namespace::{
-    connect as connect_namespace,
     models::{
         CreateEmptyTableRequest, CreateNamespaceRequest, DescribeTableRequest,
         DropNamespaceRequest, DropTableRequest, ListNamespacesRequest, ListTablesRequest,
     },
     LanceNamespace,
 };
+use lance_namespace_impls::ConnectBuilder;
 
-use crate::connection::ConnectRequest;
 use crate::database::listing::ListingDatabase;
 use crate::error::{Error, Result};
+use crate::{connection::ConnectRequest, database::ReadConsistency};
 
 use super::{
     BaseTable, CloneTableRequest, CreateNamespaceRequest as DbCreateNamespaceRequest,
@@ -36,6 +36,8 @@ pub struct LanceNamespaceDatabase {
     read_consistency_interval: Option<std::time::Duration>,
     // Optional session for object stores and caching
     session: Option<Arc<lance::session::Session>>,
+    // database URI
+    uri: String,
 }
 
 impl LanceNamespaceDatabase {
@@ -46,17 +48,23 @@ impl LanceNamespaceDatabase {
         read_consistency_interval: Option<std::time::Duration>,
         session: Option<Arc<lance::session::Session>>,
     ) -> Result<Self> {
-        let namespace = connect_namespace(ns_impl, ns_properties.clone())
-            .await
-            .map_err(|e| Error::InvalidInput {
-                message: format!("Failed to connect to namespace: {:?}", e),
-            })?;
+        let mut builder = ConnectBuilder::new(ns_impl);
+        for (key, value) in ns_properties.clone() {
+            builder = builder.property(key, value);
+        }
+        if let Some(ref sess) = session {
+            builder = builder.session(sess.clone());
+        }
+        let namespace = builder.connect().await.map_err(|e| Error::InvalidInput {
+            message: format!("Failed to connect to namespace: {:?}", e),
+        })?;
 
         Ok(Self {
             namespace,
             storage_options,
             read_consistency_interval,
             session,
+            uri: format!("namespace://{}", ns_impl),
         })
     }
 
@@ -130,6 +138,22 @@ impl std::fmt::Display for LanceNamespaceDatabase {
 
 #[async_trait]
 impl Database for LanceNamespaceDatabase {
+    fn uri(&self) -> &str {
+        &self.uri
+    }
+
+    async fn read_consistency(&self) -> Result<ReadConsistency> {
+        if let Some(read_consistency_inverval) = self.read_consistency_interval {
+            if read_consistency_inverval.is_zero() {
+                Ok(ReadConsistency::Strong)
+            } else {
+                Ok(ReadConsistency::Eventual(read_consistency_inverval))
+            }
+        } else {
+            Ok(ReadConsistency::Manual)
+        }
+    }
+
     async fn list_namespaces(&self, request: DbListNamespacesRequest) -> Result<Vec<String>> {
         let ns_request = ListNamespacesRequest {
             id: if request.namespace.is_empty() {

rust/lancedb/src/dataloader.rs

@@ -0,0 +1,4 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+pub mod permutation;

rust/lancedb/src/dataloader/permutation.rs

@@ -0,0 +1,18 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+//! Contains the [PermutationBuilder] to create a permutation "view" of an existing table.
+//!
+//! A permutation view can apply a filter, divide the data into splits, and shuffle the data.
+//! The permutation table only stores the split ids and row ids.  It is not a materialized copy of
+//! the underlying data and can be very lightweight.
+//!
+//! Building a permutation table should be fairly quick (it is an O(N) operation where N is
+//! the number of rows in the base table) and memory efficient, even for billions or trillions
+//! of rows.
+
+pub mod builder;
+pub mod reader;
+pub mod shuffle;
+pub mod split;
+pub mod util;