Bump version: 0.17.2-beta.0 → 0.17.2-beta.1

Support hybrid search in both rust and node SDKs.

- Adds a new rerankers package to rust LanceDB, with the implementation
of the default RRF reranker
- Adds a new hybrid package to lancedb, with some helper methods related
to hybrid search such as normalizing scores and converting score column
to rank columns
- Adds capability to LanceDB VectorQuery to perform hybrid search if it
has both a nearest vector and full text search parameters.
- Adds wrappers for reranker implementations to nodejs SDK.

Additional rerankers will be added in followup PRs

https://github.com/lancedb/lancedb/issues/1921

---
Notes about how the rust rerankers are wrapped for calling from JS:

I wanted to keep the core reranker logic, and the invocation of the
reranker by the query code, in Rust. This aligns with the philosophy of
the new node SDK where it's just a thin wrapper around Rust. However, I
also wanted to have support for users who want to add custom rerankers
written in Javascript.

When we add a reranker to the query from Javascript, it adds a special
Rust reranker that has a callback to the Javascript code (which could
then turn around and call an underlying Rust reranker implementation if
desired). This adds a bit of complexity, but overall I think it moves us
in the right direction of having the majority of the query logic in the
underlying Rust SDK while keeping the option open to support custom
Javascript Rerankers.

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.14.1-beta.4"
+current_version = "0.14.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/make-release-commit.yml

@@ -97,3 +97,7 @@ jobs:
         if: ${{ !inputs.dry_run && inputs.other }}
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
+      - uses: ./.github/workflows/update_package_lock_nodejs
+        if: ${{ !inputs.dry_run && inputs.other }}
+        with:
+          github_token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/npm-publish.yml

@@ -159,7 +159,7 @@ jobs:
       - name: Install common dependencies
         run: |
           apk add protobuf-dev curl clang mold grep npm bash
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y --default-toolchain 1.80.0
+          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y
           echo "source $HOME/.cargo/env" >> saved_env
           echo "export CC=clang" >> saved_env
           echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=-crt-static,+avx2,+fma,+f16c -Clinker=clang -Clink-arg=-fuse-ld=mold'" >> saved_env
@@ -167,7 +167,7 @@ jobs:
         if: ${{ matrix.config.arch == 'aarch64' }}
         run: |
           source "$HOME/.cargo/env"
-          rustup target add aarch64-unknown-linux-musl --toolchain 1.80.0
+          rustup target add aarch64-unknown-linux-musl
           crt=$(realpath $(dirname $(rustup which rustc))/../lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained)
           sysroot_lib=/usr/aarch64-unknown-linux-musl/usr/lib
           apk_url=https://dl-cdn.alpinelinux.org/alpine/latest-stable/main/aarch64/
@@ -262,7 +262,7 @@ jobs:
       - name: Install common dependencies
         run: |
           apk add protobuf-dev curl clang mold grep npm bash openssl-dev openssl-libs-static
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y --default-toolchain 1.80.0
+          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y
           echo "source $HOME/.cargo/env" >> saved_env
           echo "export CC=clang" >> saved_env
           echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=-crt-static,+avx2,+fma,+f16c -Clinker=clang -Clink-arg=-fuse-ld=mold'" >> saved_env
@@ -272,7 +272,7 @@ jobs:
         if: ${{ matrix.config.arch == 'aarch64' }}
         run: |
           source "$HOME/.cargo/env"
-          rustup target add aarch64-unknown-linux-musl --toolchain 1.80.0
+          rustup target add aarch64-unknown-linux-musl
           crt=$(realpath $(dirname $(rustup which rustc))/../lib/rustlib/aarch64-unknown-linux-musl/lib/self-contained)
           sysroot_lib=/usr/aarch64-unknown-linux-musl/usr/lib
           apk_url=https://dl-cdn.alpinelinux.org/alpine/latest-stable/main/aarch64/
@@ -334,50 +334,51 @@ jobs:
           path: |
             node/dist/lancedb-vectordb-win32*.tgz
 
-  node-windows-arm64:
-    name: vectordb ${{ matrix.config.arch }}-pc-windows-msvc
-    if: startsWith(github.ref, 'refs/tags/v')
-    runs-on: ubuntu-latest
-    container: alpine:edge
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          # - arch: x86_64
-          - arch: aarch64
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install dependencies
-        run: |
-          apk add protobuf-dev curl clang lld llvm19 grep npm bash msitools sed
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y --default-toolchain 1.80.0
-          echo "source $HOME/.cargo/env" >> saved_env
-          echo "export CC=clang" >> saved_env
-          echo "export AR=llvm-ar" >> saved_env
-          source "$HOME/.cargo/env"
-          rustup target add ${{ matrix.config.arch }}-pc-windows-msvc --toolchain 1.80.0
-          (mkdir -p sysroot && cd sysroot && sh ../ci/sysroot-${{ matrix.config.arch }}-pc-windows-msvc.sh)
-          echo "export C_INCLUDE_PATH=/usr/${{ matrix.config.arch }}-pc-windows-msvc/usr/include" >> saved_env
-          echo "export CARGO_BUILD_TARGET=${{ matrix.config.arch }}-pc-windows-msvc" >> saved_env
-      - name: Configure x86_64 build
-        if: ${{ matrix.config.arch == 'x86_64' }}
-        run: |
-          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=+crt-static,+avx2,+fma,+f16c -Clinker=lld -Clink-arg=/LIBPATH:/usr/x86_64-pc-windows-msvc/usr/lib'" >> saved_env
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          echo "export RUSTFLAGS='-Ctarget-feature=+crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=lld -Clink-arg=/LIBPATH:/usr/aarch64-pc-windows-msvc/usr/lib -Clink-arg=arm64rt.lib'" >> saved_env
-      - name: Build Windows Artifacts
-        run: |
-          source ./saved_env
-          bash ci/manylinux_node/build_vectordb.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-pc-windows-msvc
-      - name: Upload Windows Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: node-native-windows-${{ matrix.config.arch }}
-          path: |
-            node/dist/lancedb-vectordb-win32*.tgz
+  # TODO: https://github.com/lancedb/lancedb/issues/1975
+  # node-windows-arm64:
+  #   name: vectordb ${{ matrix.config.arch }}-pc-windows-msvc
+  #   # if: startsWith(github.ref, 'refs/tags/v')
+  #   runs-on: ubuntu-latest
+  #   container: alpine:edge
+  #   strategy:
+  #     fail-fast: false
+  #     matrix:
+  #       config:
+  #         # - arch: x86_64
+  #         - arch: aarch64
+  #   steps:
+  #     - name: Checkout
+  #       uses: actions/checkout@v4
+  #     - name: Install dependencies
+  #       run: |
+  #         apk add protobuf-dev curl clang lld llvm19 grep npm bash msitools sed
+  #         curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y
+  #         echo "source $HOME/.cargo/env" >> saved_env
+  #         echo "export CC=clang" >> saved_env
+  #         echo "export AR=llvm-ar" >> saved_env
+  #         source "$HOME/.cargo/env"
+  #         rustup target add ${{ matrix.config.arch }}-pc-windows-msvc
+  #         (mkdir -p sysroot && cd sysroot && sh ../ci/sysroot-${{ matrix.config.arch }}-pc-windows-msvc.sh)
+  #         echo "export C_INCLUDE_PATH=/usr/${{ matrix.config.arch }}-pc-windows-msvc/usr/include" >> saved_env
+  #         echo "export CARGO_BUILD_TARGET=${{ matrix.config.arch }}-pc-windows-msvc" >> saved_env
+  #     - name: Configure x86_64 build
+  #       if: ${{ matrix.config.arch == 'x86_64' }}
+  #       run: |
+  #         echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=+crt-static,+avx2,+fma,+f16c -Clinker=lld -Clink-arg=/LIBPATH:/usr/x86_64-pc-windows-msvc/usr/lib'" >> saved_env
+  #     - name: Configure aarch64 build
+  #       if: ${{ matrix.config.arch == 'aarch64' }}
+  #       run: |
+  #         echo "export RUSTFLAGS='-Ctarget-feature=+crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=lld -Clink-arg=/LIBPATH:/usr/aarch64-pc-windows-msvc/usr/lib -Clink-arg=arm64rt.lib'" >> saved_env
+  #     - name: Build Windows Artifacts
+  #       run: |
+  #         source ./saved_env
+  #         bash ci/manylinux_node/build_vectordb.sh ${{ matrix.config.arch }} ${{ matrix.config.arch }}-pc-windows-msvc
+  #     - name: Upload Windows Artifacts
+  #       uses: actions/upload-artifact@v4
+  #       with:
+  #         name: node-native-windows-${{ matrix.config.arch }}
+  #         path: |
+  #           node/dist/lancedb-vectordb-win32*.tgz
 
   nodejs-windows:
     name: lancedb ${{ matrix.target }}
@@ -413,57 +414,58 @@ jobs:
           path: |
             nodejs/dist/*.node
 
-  nodejs-windows-arm64:
-    name: lancedb ${{ matrix.config.arch }}-pc-windows-msvc
-    # Only runs on tags that matches the make-release action
-    if: startsWith(github.ref, 'refs/tags/v')
-    runs-on: ubuntu-latest
-    container: alpine:edge
-    strategy:
-      fail-fast: false
-      matrix:
-        config:
-          # - arch: x86_64
-          - arch: aarch64
-    steps:
-      - name: Checkout
-        uses: actions/checkout@v4
-      - name: Install dependencies
-        run: |
-          apk add protobuf-dev curl clang lld llvm19 grep npm bash msitools sed
-          curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y --default-toolchain 1.80.0
-          echo "source $HOME/.cargo/env" >> saved_env
-          echo "export CC=clang" >> saved_env
-          echo "export AR=llvm-ar" >> saved_env
-          source "$HOME/.cargo/env"
-          rustup target add ${{ matrix.config.arch }}-pc-windows-msvc --toolchain 1.80.0
-          (mkdir -p sysroot && cd sysroot && sh ../ci/sysroot-${{ matrix.config.arch }}-pc-windows-msvc.sh)
-          echo "export C_INCLUDE_PATH=/usr/${{ matrix.config.arch }}-pc-windows-msvc/usr/include" >> saved_env
-          echo "export CARGO_BUILD_TARGET=${{ matrix.config.arch }}-pc-windows-msvc" >> saved_env
-          printf '#!/bin/sh\ncargo "$@"' > $HOME/.cargo/bin/cargo-xwin
-          chmod u+x $HOME/.cargo/bin/cargo-xwin
-      - name: Configure x86_64 build
-        if: ${{ matrix.config.arch == 'x86_64' }}
-        run: |
-          echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=+crt-static,+avx2,+fma,+f16c -Clinker=lld -Clink-arg=/LIBPATH:/usr/x86_64-pc-windows-msvc/usr/lib'" >> saved_env
-      - name: Configure aarch64 build
-        if: ${{ matrix.config.arch == 'aarch64' }}
-        run: |
-          echo "export RUSTFLAGS='-Ctarget-feature=+crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=lld -Clink-arg=/LIBPATH:/usr/aarch64-pc-windows-msvc/usr/lib -Clink-arg=arm64rt.lib'" >> saved_env
-      - name: Build Windows Artifacts
-        run: |
-          source ./saved_env
-          bash ci/manylinux_node/build_lancedb.sh ${{ matrix.config.arch }}
-      - name: Upload Windows Artifacts
-        uses: actions/upload-artifact@v4
-        with:
-          name: nodejs-native-windows-${{ matrix.config.arch }}
-          path: |
-            nodejs/dist/*.node
+  # TODO: https://github.com/lancedb/lancedb/issues/1975
+  # nodejs-windows-arm64:
+  #   name: lancedb ${{ matrix.config.arch }}-pc-windows-msvc
+  #   # Only runs on tags that matches the make-release action
+  #   # if: startsWith(github.ref, 'refs/tags/v')
+  #   runs-on: ubuntu-latest
+  #   container: alpine:edge
+  #   strategy:
+  #     fail-fast: false
+  #     matrix:
+  #       config:
+  #         # - arch: x86_64
+  #         - arch: aarch64
+  #   steps:
+  #     - name: Checkout
+  #       uses: actions/checkout@v4
+  #     - name: Install dependencies
+  #       run: |
+  #         apk add protobuf-dev curl clang lld llvm19 grep npm bash msitools sed
+  #         curl --proto '=https' --tlsv1.3 -sSf https://raw.githubusercontent.com/rust-lang/rustup/refs/heads/master/rustup-init.sh | sh -s -- -y
+  #         echo "source $HOME/.cargo/env" >> saved_env
+  #         echo "export CC=clang" >> saved_env
+  #         echo "export AR=llvm-ar" >> saved_env
+  #         source "$HOME/.cargo/env"
+  #         rustup target add ${{ matrix.config.arch }}-pc-windows-msvc
+  #         (mkdir -p sysroot && cd sysroot && sh ../ci/sysroot-${{ matrix.config.arch }}-pc-windows-msvc.sh)
+  #         echo "export C_INCLUDE_PATH=/usr/${{ matrix.config.arch }}-pc-windows-msvc/usr/include" >> saved_env
+  #         echo "export CARGO_BUILD_TARGET=${{ matrix.config.arch }}-pc-windows-msvc" >> saved_env
+  #         printf '#!/bin/sh\ncargo "$@"' > $HOME/.cargo/bin/cargo-xwin
+  #         chmod u+x $HOME/.cargo/bin/cargo-xwin
+  #     - name: Configure x86_64 build
+  #       if: ${{ matrix.config.arch == 'x86_64' }}
+  #       run: |
+  #         echo "export RUSTFLAGS='-Ctarget-cpu=haswell -Ctarget-feature=+crt-static,+avx2,+fma,+f16c -Clinker=lld -Clink-arg=/LIBPATH:/usr/x86_64-pc-windows-msvc/usr/lib'" >> saved_env
+  #     - name: Configure aarch64 build
+  #       if: ${{ matrix.config.arch == 'aarch64' }}
+  #       run: |
+  #         echo "export RUSTFLAGS='-Ctarget-feature=+crt-static,+neon,+fp16,+fhm,+dotprod -Clinker=lld -Clink-arg=/LIBPATH:/usr/aarch64-pc-windows-msvc/usr/lib -Clink-arg=arm64rt.lib'" >> saved_env
+  #     - name: Build Windows Artifacts
+  #       run: |
+  #         source ./saved_env
+  #         bash ci/manylinux_node/build_lancedb.sh ${{ matrix.config.arch }}
+  #     - name: Upload Windows Artifacts
+  #       uses: actions/upload-artifact@v4
+  #       with:
+  #         name: nodejs-native-windows-${{ matrix.config.arch }}
+  #         path: |
+  #           nodejs/dist/*.node
 
   release:
     name: vectordb NPM Publish
-    needs: [node, node-macos, node-linux-gnu, node-linux-musl, node-windows, node-windows-arm64]
+    needs: [node, node-macos, node-linux-gnu, node-linux-musl, node-windows]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -503,7 +505,7 @@ jobs:
 
   release-nodejs:
     name: lancedb NPM Publish
-    needs: [nodejs-macos, nodejs-linux-gnu, nodejs-linux-musl, nodejs-windows, nodejs-windows-arm64]
+    needs: [nodejs-macos, nodejs-linux-gnu, nodejs-linux-musl, nodejs-windows]
     runs-on: ubuntu-latest
     # Only runs on tags that matches the make-release action
     if: startsWith(github.ref, 'refs/tags/v')
@@ -571,7 +573,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: main
-          persist-credentials: false
+          token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
           fetch-depth: 0
           lfs: true
       - uses: ./.github/workflows/update_package_lock
@@ -589,7 +591,7 @@ jobs:
         uses: actions/checkout@v4
         with:
           ref: main
-          persist-credentials: false
+          token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
           fetch-depth: 0
           lfs: true
       - uses: ./.github/workflows/update_package_lock_nodejs

.github/workflows/rust.yml

@@ -185,7 +185,7 @@ jobs:
           Add-Content $env:GITHUB_PATH "C:\BuildTools\VC\Tools\Llvm\x64\bin"
 
           # Add MSVC runtime libraries to LIB
-          $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" + 
+          $env:LIB = "C:\BuildTools\VC\Tools\MSVC\$latestVersion\lib\arm64;" +
                      "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\um\arm64;" +
                      "C:\Program Files (x86)\Windows Kits\10\Lib\10.0.22621.0\ucrt\arm64"
           Add-Content $env:GITHUB_ENV "LIB=$env:LIB"
@@ -238,3 +238,41 @@ jobs:
           $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
           cargo build --target aarch64-pc-windows-msvc
           cargo test --target aarch64-pc-windows-msvc
+
+  msrv:
+    # Check the minimum supported Rust version
+    name: MSRV Check - Rust v${{ matrix.msrv }}
+    runs-on: ubuntu-24.04
+    strategy:
+      matrix:
+        msrv: ["1.78.0"] # This should match up with rust-version in Cargo.toml
+    env:
+      # Need up-to-date compilers for kernels
+      CC: clang-18
+      CXX: clang++-18
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          submodules: true
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+      - name: Install ${{ matrix.msrv }}
+        uses: dtolnay/rust-toolchain@master
+        with:
+          toolchain: ${{ matrix.msrv }}
+      - name: Downgrade  dependencies
+        # These packages have newer requirements for MSRV
+        run: |
+          cargo update -p aws-sdk-bedrockruntime --precise 1.64.0
+          cargo update -p aws-sdk-dynamodb --precise 1.55.0
+          cargo update -p aws-config --precise 1.5.10
+          cargo update -p aws-sdk-kms --precise 1.51.0
+          cargo update -p aws-sdk-s3 --precise 1.65.0
+          cargo update -p aws-sdk-sso --precise 1.50.0
+          cargo update -p aws-sdk-ssooidc --precise 1.51.0
+          cargo update -p aws-sdk-sts --precise 1.51.0
+          cargo update -p home --precise 0.5.9
+      - name: cargo +${{ matrix.msrv }} check
+        run: cargo check --workspace --tests --benches --all-features

Cargo.toml

@@ -18,19 +18,19 @@ repository = "https://github.com/lancedb/lancedb"
 description = "Serverless, low-latency vector database for AI applications"
 keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
-rust-version = "1.80.0"                                                     # TODO: lower this once we upgrade Lance again.
+rust-version = "1.78.0"
 
 [workspace.dependencies]
-lance = { "version" = "=0.21.0", "features" = [
+lance = { "version" = "=0.21.1", "features" = [
     "dynamodb",
-], git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.4" }
-lance-io = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.4" }
-lance-index = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.4" }
-lance-linalg = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.4" }
-lance-table = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.4" }
-lance-testing = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.4" }
-lance-datafusion = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.4" }
-lance-encoding = { version = "=0.21.0", git = "https://github.com/lancedb/lance.git", tag = "v0.21.0-beta.4" }
+], git = "https://github.com/lancedb/lance.git", tag = "v0.21.1-beta.1" }
+lance-io = { version = "=0.21.1", git = "https://github.com/lancedb/lance.git", tag = "v0.21.1-beta.1" }
+lance-index = { version = "=0.21.1", git = "https://github.com/lancedb/lance.git", tag = "v0.21.1-beta.1" }
+lance-linalg = { version = "=0.21.1", git = "https://github.com/lancedb/lance.git", tag = "v0.21.1-beta.1" }
+lance-table = { version = "=0.21.1", git = "https://github.com/lancedb/lance.git", tag = "v0.21.1-beta.1" }
+lance-testing = { version = "=0.21.1", git = "https://github.com/lancedb/lance.git", tag = "v0.21.1-beta.1" }
+lance-datafusion = { version = "=0.21.1", git = "https://github.com/lancedb/lance.git", tag = "v0.21.1-beta.1" }
+lance-encoding = { version = "=0.21.1", git = "https://github.com/lancedb/lance.git", tag = "v0.21.1-beta.1" }
 # Note that this one does not include pyarrow
 arrow = { version = "53.2", optional = false }
 arrow-array = "53.2"

docs/mkdocs.yml

@@ -62,6 +62,7 @@ plugins:
             # for cross references
             - https://arrow.apache.org/docs/objects.inv
             - https://pandas.pydata.org/docs/objects.inv
+            - https://lancedb.github.io/lance/objects.inv
   - mkdocs-jupyter
   - render_swagger:
       allow_arbitrary_locations: true

docs/src/python/python.md

@@ -129,8 +129,16 @@ lists the indices that LanceDb supports.
 
 ::: lancedb.index.LabelList
 
+::: lancedb.index.FTS
+
 ::: lancedb.index.IvfPq
 
+::: lancedb.index.HnswPq
+
+::: lancedb.index.HnswSq
+
+::: lancedb.index.IvfFlat
+
 ## Querying (Asynchronous)
 
 Queries allow you to return data from your database. Basic queries can be

docs/src/python/saas-python.md

@@ -17,4 +17,8 @@ pip install lancedb
 ## Table
 
 ::: lancedb.remote.table.RemoteTable
-
+    options:
+        filters:
+            - "!cleanup_old_versions"
+            - "!compact_files"
+            - "!optimize"

docs/src/search.md

@@ -13,11 +13,15 @@ A vector search finds the approximate or exact nearest neighbors to a given quer
 Distance metrics are a measure of the similarity between a pair of vectors.
 Currently, LanceDB supports the following metrics:
 
-| Metric   | Description                                                                 |
-| -------- | --------------------------------------------------------------------------- |
-| `l2`     | [Euclidean / L2 distance](https://en.wikipedia.org/wiki/Euclidean_distance) |
-| `cosine` | [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity)        |
-| `dot`    | [Dot Production](https://en.wikipedia.org/wiki/Dot_product)                 |
+| Metric    | Description                                                                 |
+| --------- | --------------------------------------------------------------------------- |
+| `l2`      | [Euclidean / L2 distance](https://en.wikipedia.org/wiki/Euclidean_distance) |
+| `cosine`  | [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity)        |
+| `dot`     | [Dot Production](https://en.wikipedia.org/wiki/Dot_product)                 |
+| `hamming` | [Hamming Distance](https://en.wikipedia.org/wiki/Hamming_distance)          |
+
+!!! note
+    The `hamming` metric is only available for binary vectors.
 
 ## Exhaustive search (kNN)
 
@@ -107,6 +111,31 @@ an ANN search means that using an index often involves a trade-off between recal
 See the [IVF_PQ index](./concepts/index_ivfpq.md) for a deeper description of how `IVF_PQ`
 indexes work in LanceDB.
 
+## Binary vector
+
+LanceDB supports binary vectors as a data type, and has the ability to search binary vectors with hamming distance. The binary vectors are stored as uint8 arrays (every 8 bits are stored as a byte):
+
+!!! note
+    The dim of the binary vector must be a multiple of 8. A vector of dim 128 will be stored as a uint8 array of size 16.
+
+=== "Python"
+
+    === "sync API"
+
+        ```python
+        --8<-- "python/python/tests/docs/test_binary_vector.py:imports"
+
+        --8<-- "python/python/tests/docs/test_binary_vector.py:sync_binary_vector"
+        ```
+
+    === "async API"
+
+        ```python
+        --8<-- "python/python/tests/docs/test_binary_vector.py:imports"
+
+        --8<-- "python/python/tests/docs/test_binary_vector.py:async_binary_vector"
+        ```
+
 ## Output search results
 
 LanceDB returns vector search results via different formats commonly used in python.

docs/test/md_testing.py

@@ -16,6 +16,7 @@ excluded_globs = [
     "../src/concepts/*.md",
     "../src/ann_indexes.md",
     "../src/basic.md",
+    "../src/search.md",
     "../src/hybrid_search/hybrid_search.md",
     "../src/reranking/*.md",
     "../src/guides/tuning_retrievers/*.md",

java/core/pom.xml

@@ -8,7 +8,7 @@
     <parent>
         <groupId>com.lancedb</groupId>
         <artifactId>lancedb-parent</artifactId>
-        <version>0.14.1-beta.4</version>
+        <version>0.14.1-final.0</version>
         <relativePath>../pom.xml</relativePath>
     </parent>
 

java/pom.xml

@@ -6,7 +6,7 @@
 
     <groupId>com.lancedb</groupId>
     <artifactId>lancedb-parent</artifactId>
-    <version>0.14.1-beta.4</version>
+    <version>0.14.1-final.0</version>
     <packaging>pom</packaging>
 
     <name>LanceDB Parent</name>

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.14.1-beta.4",
+  "version": "0.14.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.14.1-beta.4",
+      "version": "0.14.1",
       "cpu": [
         "x64",
         "arm64"
@@ -52,14 +52,14 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.14.1-beta.4",
-        "@lancedb/vectordb-darwin-x64": "0.14.1-beta.4",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.14.1-beta.4",
-        "@lancedb/vectordb-linux-arm64-musl": "0.14.1-beta.4",
-        "@lancedb/vectordb-linux-x64-gnu": "0.14.1-beta.4",
-        "@lancedb/vectordb-linux-x64-musl": "0.14.1-beta.4",
-        "@lancedb/vectordb-win32-arm64-msvc": "0.14.1-beta.4",
-        "@lancedb/vectordb-win32-x64-msvc": "0.14.1-beta.4"
+        "@lancedb/vectordb-darwin-arm64": "0.14.1",
+        "@lancedb/vectordb-darwin-x64": "0.14.1",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.14.1",
+        "@lancedb/vectordb-linux-arm64-musl": "0.14.1",
+        "@lancedb/vectordb-linux-x64-gnu": "0.14.1",
+        "@lancedb/vectordb-linux-x64-musl": "0.14.1",
+        "@lancedb/vectordb-win32-arm64-msvc": "0.14.1",
+        "@lancedb/vectordb-win32-x64-msvc": "0.14.1"
       },
       "peerDependencies": {
         "@apache-arrow/ts": "^14.0.2",
@@ -329,6 +329,97 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "node_modules/@lancedb/vectordb-darwin-arm64": {
+      "version": "0.14.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.14.1.tgz",
+      "integrity": "sha512-6t7XHR7dBjDmAS/kz5wbe7LPhKW+WkFA16ZPyh0lmuxfnss4VvN3LE6qQBHjzYzB9U6Nu/4ktQ50xZGEPTnc5A==",
+      "cpu": [
+        "arm64"
+      ],
+      "license": "Apache-2.0",
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-darwin-x64": {
+      "version": "0.14.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.14.1.tgz",
+      "integrity": "sha512-8q6Kd6XnNPKN8wqj75pHVQ4KFl6z9BaI6lWDiEaCNcO3bjPZkcLFNosJq4raxZ9iUi50Yl0qFJ6qR0XFVTwnnw==",
+      "cpu": [
+        "x64"
+      ],
+      "license": "Apache-2.0",
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
+      "version": "0.14.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.14.1.tgz",
+      "integrity": "sha512-4djEMmeNb+p6nW/C4xb8wdMwnIbWfO8fYAwiplOxzxeOpPaUC9rhwUUDCbrJDCpMa8RP5ED4/jC6yT8epaDMDw==",
+      "cpu": [
+        "arm64"
+      ],
+      "license": "Apache-2.0",
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-arm64-musl": {
+      "version": "0.14.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-musl/-/vectordb-linux-arm64-musl-0.14.1.tgz",
+      "integrity": "sha512-c33hSsp16pnC58plzx1OXuifp9Rachx/MshE/L/OReoutt74fFdrRJwUjE4UCAysyY5QdvTrNm9OhDjopQK2Bw==",
+      "cpu": [
+        "arm64"
+      ],
+      "license": "Apache-2.0",
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-x64-gnu": {
+      "version": "0.14.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.14.1.tgz",
+      "integrity": "sha512-psu6cH9iLiSbUEZD1EWbOA4THGYSwJvS2XICO9yN7A6D41AP/ynYMRZNKWo1fpdi2Fjb0xNQwiNhQyqwbi5gzA==",
+      "cpu": [
+        "x64"
+      ],
+      "license": "Apache-2.0",
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-linux-x64-musl": {
+      "version": "0.14.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-musl/-/vectordb-linux-x64-musl-0.14.1.tgz",
+      "integrity": "sha512-Rg4VWW80HaTFmR7EvNSu+nfRQQM8beO/otBn/Nus5mj5zFw/7cacGRmiEYhDnk5iAn8nauV+Jsi9j2U+C2hp5w==",
+      "cpu": [
+        "x64"
+      ],
+      "license": "Apache-2.0",
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
+    "node_modules/@lancedb/vectordb-win32-x64-msvc": {
+      "version": "0.14.1",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.14.1.tgz",
+      "integrity": "sha512-XbifasmMbQIt3V9P0AtQND6M3XFiIAc1ZIgmjzBjOmxwqw4sQUwHMyJGIGOzKFZTK3fPJIGRHId7jAzXuBgfQg==",
+      "cpu": [
+        "x64"
+      ],
+      "license": "Apache-2.0",
+      "optional": true,
+      "os": [
+        "win32"
+      ]
+    },
     "node_modules/@neon-rs/cli": {
       "version": "0.0.160",
       "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.160.tgz",

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.14.1-beta.4",
+  "version": "0.14.1",
   "description": " Serverless, low-latency vector database for AI applications",
   "private": false,
   "main": "dist/index.js",
@@ -92,13 +92,13 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-x64": "0.14.1-beta.4",
-    "@lancedb/vectordb-darwin-arm64": "0.14.1-beta.4",
-    "@lancedb/vectordb-linux-x64-gnu": "0.14.1-beta.4",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.14.1-beta.4",
-    "@lancedb/vectordb-linux-x64-musl": "0.14.1-beta.4",
-    "@lancedb/vectordb-linux-arm64-musl": "0.14.1-beta.4",
-    "@lancedb/vectordb-win32-x64-msvc": "0.14.1-beta.4",
-    "@lancedb/vectordb-win32-arm64-msvc": "0.14.1-beta.4"
+    "@lancedb/vectordb-darwin-x64": "0.14.1",
+    "@lancedb/vectordb-darwin-arm64": "0.14.1",
+    "@lancedb/vectordb-linux-x64-gnu": "0.14.1",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.14.1",
+    "@lancedb/vectordb-linux-x64-musl": "0.14.1",
+    "@lancedb/vectordb-linux-arm64-musl": "0.14.1",
+    "@lancedb/vectordb-win32-x64-msvc": "0.14.1",
+    "@lancedb/vectordb-win32-arm64-msvc": "0.14.1"
   }
 }

nodejs/Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "lancedb-nodejs"
 edition.workspace = true
-version = "0.14.1-beta.4"
+version = "0.14.1"
 license.workspace = true
 description.workspace = true
 repository.workspace = true
@@ -12,7 +12,10 @@ categories.workspace = true
 crate-type = ["cdylib"]
 
 [dependencies]
+async-trait.workspace = true
 arrow-ipc.workspace = true
+arrow-array.workspace = true
+arrow-schema.workspace = true
 env_logger.workspace = true
 futures.workspace = true
 lancedb = { path = "../rust/lancedb", features = ["remote"] }

nodejs/__test__/arrow.test.ts

@@ -20,6 +20,8 @@ import * as arrow18 from "apache-arrow-18";
 
 import {
   convertToTable,
+  fromBufferToRecordBatch,
+  fromRecordBatchToBuffer,
   fromTableToBuffer,
   makeArrowTable,
   makeEmptyTable,
@@ -553,5 +555,28 @@ describe.each([arrow15, arrow16, arrow17, arrow18])(
         });
       });
     });
+
+    describe("converting record batches to buffers", function () {
+      it("can convert to buffered record batch and back again", async function () {
+        const records = [
+          { text: "dog", vector: [0.1, 0.2] },
+          { text: "cat", vector: [0.3, 0.4] },
+        ];
+        const table = await convertToTable(records);
+        const batch = table.batches[0];
+
+        const buffer = await fromRecordBatchToBuffer(batch);
+        const result = await fromBufferToRecordBatch(buffer);
+
+        expect(JSON.stringify(batch.toArray())).toEqual(
+          JSON.stringify(result?.toArray()),
+        );
+      });
+
+      it("converting from buffer returns null if buffer has no record batches", async function () {
+        const result = await fromBufferToRecordBatch(Buffer.from([0x01, 0x02])); // bad data
+        expect(result).toEqual(null);
+      });
+    });
   },
 );

nodejs/__test__/rerankers.test.ts

@@ -0,0 +1,79 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import { RecordBatch } from "apache-arrow";
+import * as tmp from "tmp";
+import { Connection, Index, Table, connect, makeArrowTable } from "../lancedb";
+import { RRFReranker } from "../lancedb/rerankers";
+
+describe("rerankers", function () {
+  let tmpDir: tmp.DirResult;
+  let conn: Connection;
+  let table: Table;
+
+  beforeEach(async () => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    conn = await connect(tmpDir.name);
+    table = await conn.createTable("mytable", [
+      { vector: [0.1, 0.1], text: "dog" },
+      { vector: [0.2, 0.2], text: "cat" },
+    ]);
+    await table.createIndex("text", {
+      config: Index.fts(),
+      replace: true,
+    });
+  });
+
+  it("will query with the custom reranker", async function () {
+    const expectedResult = [
+      {
+        text: "albert",
+        // biome-ignore lint/style/useNamingConvention: this is the lance field name
+        _relevance_score: 0.99,
+      },
+    ];
+    class MyCustomReranker {
+      async rerankHybrid(
+        _query: string,
+        _vecResults: RecordBatch,
+        _ftsResults: RecordBatch,
+      ): Promise<RecordBatch> {
+        // no reranker logic, just return some static data
+        const table = makeArrowTable(expectedResult);
+        return table.batches[0];
+      }
+    }
+
+    let result = await table
+      .query()
+      .nearestTo([0.1, 0.1])
+      .fullTextSearch("dog")
+      .rerank(new MyCustomReranker())
+      .select(["text"])
+      .limit(5)
+      .toArray();
+
+    result = JSON.parse(JSON.stringify(result)); // convert StructRow to Object
+    expect(result).toEqual([
+      {
+        text: "albert",
+        // biome-ignore lint/style/useNamingConvention: this is the lance field name
+        _relevance_score: 0.99,
+      },
+    ]);
+  });
+
+  it("will query with RRFReranker", async function () {
+    // smoke test to see if the Rust wrapping Typescript is wired up correctly
+    const result = await table
+      .query()
+      .nearestTo([0.1, 0.1])
+      .fullTextSearch("dog")
+      .rerank(await RRFReranker.create())
+      .select(["text"])
+      .limit(5)
+      .toArray();
+
+    expect(result).toHaveLength(2);
+  });
+});

nodejs/lancedb/arrow.ts

@@ -27,7 +27,9 @@ import {
   List,
   Null,
   RecordBatch,
+  RecordBatchFileReader,
   RecordBatchFileWriter,
+  RecordBatchReader,
   RecordBatchStreamWriter,
   Schema,
   Struct,
@@ -810,6 +812,30 @@ export async function fromDataToBuffer(
   }
 }
 
+/**
+ * Read a single record batch from a buffer.
+ *
+ * Returns null if the buffer does not contain a record batch
+ */
+export async function fromBufferToRecordBatch(
+  data: Buffer,
+): Promise<RecordBatch | null> {
+  const iter = await RecordBatchFileReader.readAll(Buffer.from(data)).next()
+    .value;
+  const recordBatch = iter?.next().value;
+  return recordBatch || null;
+}
+
+/**
+ * Create a buffer containing a single record batch
+ */
+export async function fromRecordBatchToBuffer(
+  batch: RecordBatch,
+): Promise<Buffer> {
+  const writer = new RecordBatchFileWriter().writeAll([batch]);
+  return Buffer.from(await writer.toUint8Array());
+}
+
 /**
  * Serialize an Arrow Table into a buffer using the Arrow IPC Stream serialization
  *

nodejs/lancedb/index.ts

@@ -62,6 +62,7 @@ export { Index, IndexOptions, IvfPqOptions } from "./indices";
 export { Table, AddDataOptions, UpdateOptions, OptimizeOptions } from "./table";
 
 export * as embedding from "./embedding";
+export * as rerankers from "./rerankers";
 
 /**
  * Connect to a LanceDB instance at the given URI.

nodejs/lancedb/query.ts

@@ -16,6 +16,8 @@ import {
   Table as ArrowTable,
   type IntoVector,
   RecordBatch,
+  fromBufferToRecordBatch,
+  fromRecordBatchToBuffer,
   tableFromIPC,
 } from "./arrow";
 import { type IvfPqOptions } from "./indices";
@@ -25,6 +27,7 @@ import {
   Table as NativeTable,
   VectorQuery as NativeVectorQuery,
 } from "./native";
+import { Reranker } from "./rerankers";
 export class RecordBatchIterator implements AsyncIterator<RecordBatch> {
   private promisedInner?: Promise<NativeBatchIterator>;
   private inner?: NativeBatchIterator;
@@ -542,6 +545,27 @@ export class VectorQuery extends QueryBase<NativeVectorQuery> {
       return this;
     }
   }
+
+  rerank(reranker: Reranker): VectorQuery {
+    super.doCall((inner) =>
+      inner.rerank({
+        rerankHybrid: async (_, args) => {
+          const vecResults = await fromBufferToRecordBatch(args.vecResults);
+          const ftsResults = await fromBufferToRecordBatch(args.ftsResults);
+          const result = await reranker.rerankHybrid(
+            args.query,
+            vecResults as RecordBatch,
+            ftsResults as RecordBatch,
+          );
+
+          const buffer = fromRecordBatchToBuffer(result);
+          return buffer;
+        },
+      }),
+    );
+
+    return this;
+  }
 }
 
 /** A builder for LanceDB queries. */

nodejs/lancedb/rerankers/index.ts

@@ -0,0 +1,17 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import { RecordBatch } from "apache-arrow";
+
+export * from "./rrf";
+
+// Interface for a reranker. A reranker is used to rerank the results from a
+// vector and FTS search. This is useful for combining the results from both
+// search methods.
+export interface Reranker {
+  rerankHybrid(
+    query: string,
+    vecResults: RecordBatch,
+    ftsResults: RecordBatch,
+  ): Promise<RecordBatch>;
+}

nodejs/lancedb/rerankers/rrf.ts

@@ -0,0 +1,40 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+import { RecordBatch } from "apache-arrow";
+import { fromBufferToRecordBatch, fromRecordBatchToBuffer } from "../arrow";
+import { RrfReranker as NativeRRFReranker } from "../native";
+
+/**
+ * Reranks the results using the Reciprocal Rank Fusion (RRF) algorithm.
+ *
+ * Internally this uses the Rust implementation
+ */
+export class RRFReranker {
+  private inner: NativeRRFReranker;
+
+  constructor(inner: NativeRRFReranker) {
+    this.inner = inner;
+  }
+
+  public static async create(k: number = 60) {
+    return new RRFReranker(
+      await NativeRRFReranker.tryNew(new Float32Array([k])),
+    );
+  }
+
+  async rerankHybrid(
+    query: string,
+    vecResults: RecordBatch,
+    ftsResults: RecordBatch,
+  ): Promise<RecordBatch> {
+    const buffer = await this.inner.rerankHybrid(
+      query,
+      await fromRecordBatchToBuffer(vecResults),
+      await fromRecordBatchToBuffer(ftsResults),
+    );
+    const recordBatch = await fromBufferToRecordBatch(buffer);
+
+    return recordBatch as RecordBatch;
+  }
+}

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.14.1-beta.4",
+	"version": "0.14.1",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.14.1-beta.4",
+	"version": "0.14.1",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.14.1-beta.4",
+	"version": "0.14.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-musl",
-	"version": "0.14.1-beta.4",
+	"version": "0.14.1",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-musl.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.14.1-beta.4",
+	"version": "0.14.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-musl",
-	"version": "0.14.1-beta.4",
+	"version": "0.14.1",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-musl.node",

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

@@ -1,6 +1,6 @@
 {
   "name": "@lancedb/lancedb-win32-arm64-msvc",
-  "version": "0.14.1-beta.4",
+  "version": "0.14.1",
   "os": [
     "win32"
   ],

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.14.1-beta.4",
+	"version": "0.14.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.14.0",
+  "version": "0.14.1",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "@lancedb/lancedb",
-      "version": "0.14.0",
+      "version": "0.14.1",
       "cpu": [
         "x64",
         "arm64"

nodejs/package.json

@@ -11,7 +11,7 @@
     "ann"
   ],
   "private": false,
-  "version": "0.14.1-beta.4",
+  "version": "0.14.1",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",

nodejs/src/lib.rs

@@ -24,6 +24,7 @@ mod iterator;
 pub mod merge;
 mod query;
 pub mod remote;
+mod rerankers;
 mod table;
 mod util;
 

nodejs/src/query.rs

@@ -12,6 +12,8 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+use std::sync::Arc;
+
 use lancedb::index::scalar::FullTextSearchQuery;
 use lancedb::query::ExecutableQuery;
 use lancedb::query::Query as LanceDbQuery;
@@ -25,6 +27,8 @@ use napi_derive::napi;
 use crate::error::convert_error;
 use crate::error::NapiErrorExt;
 use crate::iterator::RecordBatchIterator;
+use crate::rerankers::Reranker;
+use crate::rerankers::RerankerCallbacks;
 use crate::util::parse_distance_type;
 
 #[napi]
@@ -218,6 +222,14 @@ impl VectorQuery {
         self.inner = self.inner.clone().with_row_id();
     }
 
+    #[napi]
+    pub fn rerank(&mut self, callbacks: RerankerCallbacks) {
+        self.inner = self
+            .inner
+            .clone()
+            .rerank(Arc::new(Reranker::new(callbacks)));
+    }
+
     #[napi(catch_unwind)]
     pub async fn execute(
         &self,

nodejs/src/rerankers.rs

@@ -0,0 +1,147 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use arrow_array::RecordBatch;
+use async_trait::async_trait;
+use napi::{
+    bindgen_prelude::*,
+    threadsafe_function::{ErrorStrategy, ThreadsafeFunction},
+};
+use napi_derive::napi;
+
+use lancedb::ipc::batches_to_ipc_file;
+use lancedb::rerankers::Reranker as LanceDBReranker;
+use lancedb::{error::Error, ipc::ipc_file_to_batches};
+
+use crate::error::NapiErrorExt;
+
+/// Reranker implementation that "wraps" a NodeJS Reranker implementation.
+/// This contains references to the callbacks that can be used to invoke the
+/// reranking methods on the NodeJS implementation and handles serializing the
+/// record batches to Arrow IPC buffers.
+#[napi]
+pub struct Reranker {
+    /// callback to the Javascript which will call the rerankHybrid method of
+    /// some Reranker implementation
+    rerank_hybrid: ThreadsafeFunction<RerankHybridCallbackArgs, ErrorStrategy::CalleeHandled>,
+}
+
+#[napi]
+impl Reranker {
+    #[napi]
+    pub fn new(callbacks: RerankerCallbacks) -> Self {
+        let rerank_hybrid = callbacks
+            .rerank_hybrid
+            .create_threadsafe_function(0, move |ctx| Ok(vec![ctx.value]))
+            .unwrap();
+
+        Self { rerank_hybrid }
+    }
+}
+
+#[async_trait]
+impl lancedb::rerankers::Reranker for Reranker {
+    async fn rerank_hybrid(
+        &self,
+        query: &str,
+        vector_results: RecordBatch,
+        fts_results: RecordBatch,
+    ) -> lancedb::error::Result<RecordBatch> {
+        let callback_args = RerankHybridCallbackArgs {
+            query: query.to_string(),
+            vec_results: batches_to_ipc_file(&[vector_results])?,
+            fts_results: batches_to_ipc_file(&[fts_results])?,
+        };
+        let promised_buffer: Promise<Buffer> = self
+            .rerank_hybrid
+            .call_async(Ok(callback_args))
+            .await
+            .map_err(|e| Error::Runtime {
+                message: format!("napi error status={}, reason={}", e.status, e.reason),
+            })?;
+        let buffer = promised_buffer.await.map_err(|e| Error::Runtime {
+            message: format!("napi error status={}, reason={}", e.status, e.reason),
+        })?;
+        let mut reader = ipc_file_to_batches(buffer.to_vec())?;
+        let result = reader.next().ok_or(Error::Runtime {
+            message: "reranker result deserialization failed".to_string(),
+        })??;
+
+        return Ok(result);
+    }
+}
+
+impl std::fmt::Debug for Reranker {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        f.write_str("NodeJSRerankerWrapper")
+    }
+}
+
+#[napi(object)]
+pub struct RerankerCallbacks {
+    pub rerank_hybrid: JsFunction,
+}
+
+#[napi(object)]
+pub struct RerankHybridCallbackArgs {
+    pub query: String,
+    pub vec_results: Vec<u8>,
+    pub fts_results: Vec<u8>,
+}
+
+fn buffer_to_record_batch(buffer: Buffer) -> Result<RecordBatch> {
+    let mut reader = ipc_file_to_batches(buffer.to_vec()).default_error()?;
+    reader
+        .next()
+        .ok_or(Error::InvalidInput {
+            message: "expected buffer containing record batch".to_string(),
+        })
+        .default_error()?
+        .map_err(Error::from)
+        .default_error()
+}
+
+/// Wrapper around rust RRFReranker
+#[napi]
+pub struct RRFReranker {
+    inner: lancedb::rerankers::rrf::RRFReranker,
+}
+
+#[napi]
+impl RRFReranker {
+    #[napi]
+    pub async fn try_new(k: &[f32]) -> Result<Self> {
+        let k = k
+            .first()
+            .copied()
+            .ok_or(Error::InvalidInput {
+                message: "must supply RRF Reranker constructor arg 'k'".to_string(),
+            })
+            .default_error()?;
+
+        Ok(Self {
+            inner: lancedb::rerankers::rrf::RRFReranker::new(k),
+        })
+    }
+
+    #[napi]
+    pub async fn rerank_hybrid(
+        &self,
+        query: String,
+        vec_results: Buffer,
+        fts_results: Buffer,
+    ) -> Result<Buffer> {
+        let vec_results = buffer_to_record_batch(vec_results)?;
+        let fts_results = buffer_to_record_batch(fts_results)?;
+
+        let result = self
+            .inner
+            .rerank_hybrid(&query, vec_results, fts_results)
+            .await
+            .unwrap();
+
+        let result_buff = batches_to_ipc_file(&[result]).default_error()?;
+
+        Ok(Buffer::from(result_buff.as_ref()))
+    }
+}

nodejs/src/util.rs

@@ -5,8 +5,9 @@ pub fn parse_distance_type(distance_type: impl AsRef<str>) -> napi::Result<Dista
         "l2" => Ok(DistanceType::L2),
         "cosine" => Ok(DistanceType::Cosine),
         "dot" => Ok(DistanceType::Dot),
+        "hamming" => Ok(DistanceType::Hamming),
         _ => Err(napi::Error::from_reason(format!(
-            "Invalid distance type '{}'.  Must be one of l2, cosine, or dot",
+            "Invalid distance type '{}'.  Must be one of l2, cosine, dot, or hamming",
             distance_type.as_ref()
         ))),
     }

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.17.1-beta.5"
+current_version = "0.17.2-beta.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.17.1-beta.5"
+version = "0.17.2-beta.1"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/pyproject.toml

@@ -1,9 +1,10 @@
 [project]
 name = "lancedb"
 # version in Cargo.toml
+dynamic = ["version"]
 dependencies = [
     "deprecation",
-    "pylance==0.21.0b4",
+    "pylance==0.21.1b1",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
     "packaging",

python/python/lancedb/_lancedb.pyi

@@ -37,7 +37,7 @@ class Table:
     async def count_rows(self, filter: Optional[str]) -> int: ...
     async def create_index(self, column: str, config, replace: Optional[bool]): ...
     async def version(self) -> int: ...
-    async def checkout(self, version): ...
+    async def checkout(self, version: int): ...
     async def checkout_latest(self): ...
     async def restore(self): ...
     async def list_indices(self) -> List[IndexConfig]: ...

python/python/lancedb/db.py

@@ -18,12 +18,12 @@ from pathlib import Path
 from typing import TYPE_CHECKING, Dict, Iterable, List, Literal, Optional, Union
 
 from lancedb.embeddings.registry import EmbeddingFunctionRegistry
-from overrides import EnforceOverrides, override
+from overrides import EnforceOverrides, override  # type: ignore
 
 from lancedb.common import data_to_reader, sanitize_uri, validate_schema
 from lancedb.background_loop import LOOP
 
-from ._lancedb import connect as lancedb_connect
+from ._lancedb import connect as lancedb_connect  # type: ignore
 from .table import (
     AsyncTable,
     LanceTable,
@@ -503,13 +503,7 @@ class LanceDBConnection(DBConnection):
         ignore_missing: bool, default False
             If True, ignore if the table does not exist.
         """
-        try:
-            LOOP.run(self._conn.drop_table(name))
-        except ValueError as e:
-            if not ignore_missing:
-                raise e
-            if f"Table '{name}' was not found" not in str(e):
-                raise e
+        LOOP.run(self._conn.drop_table(name, ignore_missing=ignore_missing))
 
     @override
     def drop_database(self):
@@ -886,15 +880,23 @@ class AsyncConnection(object):
         """
         await self._inner.rename_table(old_name, new_name)
 
-    async def drop_table(self, name: str):
+    async def drop_table(self, name: str, *, ignore_missing: bool = False):
         """Drop a table from the database.
 
         Parameters
         ----------
         name: str
             The name of the table.
+        ignore_missing: bool, default False
+            If True, ignore if the table does not exist.
         """
-        await self._inner.drop_table(name)
+        try:
+            await self._inner.drop_table(name)
+        except ValueError as e:
+            if not ignore_missing:
+                raise e
+            if f"Table '{name}' was not found" not in str(e):
+                raise e
 
     async def drop_database(self):
         """

python/python/lancedb/index.py

@@ -355,6 +355,97 @@ class HnswSq:
     ef_construction: int = 300
 
 
+@dataclass
+class IvfFlat:
+    """Describes an IVF Flat Index
+
+    This index stores raw vectors.
+    These vectors are grouped into partitions of similar vectors.
+    Each partition keeps track of a centroid which is
+    the average value of all vectors in the group.
+
+    Attributes
+    ----------
+    distance_type: str, default "L2"
+        The distance metric used to train the index
+
+        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 to calculate a subvector's code 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. This is a very common distance metric that
+        accounts for both magnitude and direction when determining the distance
+        between vectors. L2 distance has a range of [0, ∞).
+
+        "cosine" - Cosine distance.  Cosine distance is a distance metric
+        calculated from the cosine similarity between two vectors. Cosine
+        similarity is a measure of similarity between two non-zero vectors of an
+        inner product space. It is defined to equal the cosine of the angle
+        between them.  Unlike L2, the cosine distance is not affected by the
+        magnitude of the vectors.  Cosine distance has a range of [0, 2].
+
+        Note: the cosine distance is undefined when one (or both) of the vectors
+        are all zeros (there is no direction).  These vectors are invalid and may
+        never be returned from a vector search.
+
+        "dot" - Dot product. Dot distance is the dot product of two vectors. Dot
+        distance has a range of (-∞, ∞). If the vectors are normalized (i.e. their
+        L2 norm is 1), then dot distance is equivalent to the cosine distance.
+
+        "hamming" - Hamming distance. Hamming distance is a distance metric
+        calculated as the number of positions at which the corresponding bits are
+        different. Hamming distance has a range of [0, vector dimension].
+
+    num_partitions: int, default sqrt(num_rows)
+        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.
+
+    max_iterations: int, default 50
+        Max iteration to train kmeans.
+
+        When training an IVF PQ index we use kmeans to calculate the partitions.
+        This parameter controls how many iterations of kmeans to run.
+
+        Increasing this might improve the quality of the index but in most cases
+        these extra iterations have diminishing returns.
+
+        The default value is 50.
+    sample_rate: int, default 256
+        The rate used to calculate the number of training vectors for kmeans.
+
+        When an IVF PQ 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.
+    """
+
+    distance_type: Literal["l2", "cosine", "dot", "hamming"] = "l2"
+    num_partitions: Optional[int] = None
+    max_iterations: int = 50
+    sample_rate: int = 256
+
+
 @dataclass
 class IvfPq:
     """Describes an IVF PQ Index
@@ -477,4 +568,14 @@ class IvfPq:
     sample_rate: int = 256
 
 
-__all__ = ["BTree", "IvfPq", "HnswPq", "HnswSq", "IndexConfig"]
+__all__ = [
+    "BTree",
+    "IvfPq",
+    "IvfFlat",
+    "HnswPq",
+    "HnswSq",
+    "IndexConfig",
+    "FTS",
+    "Bitmap",
+    "LabelList",
+]

python/python/lancedb/query.py

@@ -126,6 +126,9 @@ class Query(pydantic.BaseModel):
 
     ef: Optional[int] = None
 
+    # Default is true. Set to false to enforce a brute force search.
+    use_index: bool = True
+
 
 class LanceQueryBuilder(ABC):
     """An abstract query builder. Subclasses are defined for vector search,
@@ -253,6 +256,7 @@ class LanceQueryBuilder(ABC):
         self._vector = None
         self._text = None
         self._ef = None
+        self._use_index = True
 
     @deprecation.deprecated(
         deprecated_in="0.3.1",
@@ -511,6 +515,7 @@ class LanceQueryBuilder(ABC):
                 "metric": self._metric,
                 "nprobes": self._nprobes,
                 "refine_factor": self._refine_factor,
+                "use_index": self._use_index,
             },
             prefilter=self._prefilter,
             filter=self._str_query,
@@ -729,6 +734,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             offset=self._offset,
             fast_search=self._fast_search,
             ef=self._ef,
+            use_index=self._use_index,
         )
         result_set = self._table._execute_query(query, batch_size)
         if self._reranker is not None:
@@ -802,6 +808,24 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._str_query = query_string if query_string is not None else self._str_query
         return self
 
+    def bypass_vector_index(self) -> LanceVectorQueryBuilder:
+        """
+        If this is called then any vector index is skipped
+
+        An exhaustive (flat) search will be performed.  The query vector will
+        be compared to every vector in the table.  At high scales this can be
+        expensive.  However, this is often still useful.  For example, skipping
+        the vector index can give you ground truth results which you can use to
+        calculate your recall to select an appropriate value for nprobes.
+
+        Returns
+        -------
+        LanceVectorQueryBuilder
+            The LanceVectorQueryBuilder object.
+        """
+        self._use_index = False
+        return self
+
 
 class LanceFtsQueryBuilder(LanceQueryBuilder):
     """A builder for full text search for LanceDB."""
@@ -1108,6 +1132,8 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
             self._vector_query.refine_factor(self._refine_factor)
         if self._ef:
             self._vector_query.ef(self._ef)
+        if not self._use_index:
+            self._vector_query.bypass_vector_index()
 
         with ThreadPoolExecutor() as executor:
             fts_future = executor.submit(self._fts_query.with_row_id(True).to_arrow)
@@ -1323,6 +1349,24 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         self._text = text
         return self
 
+    def bypass_vector_index(self) -> LanceHybridQueryBuilder:
+        """
+        If this is called then any vector index is skipped
+
+        An exhaustive (flat) search will be performed.  The query vector will
+        be compared to every vector in the table.  At high scales this can be
+        expensive.  However, this is often still useful.  For example, skipping
+        the vector index can give you ground truth results which you can use to
+        calculate your recall to select an appropriate value for nprobes.
+
+        Returns
+        -------
+        LanceHybridQueryBuilder
+            The LanceHybridQueryBuilder object.
+        """
+        self._use_index = False
+        return self
+
 
 class AsyncQueryBase(object):
     def __init__(self, inner: Union[LanceQuery | LanceVectorQuery]):

python/python/lancedb/remote/table.py

@@ -1,24 +1,15 @@
-#  Copyright 2023 LanceDB Developers
-#
-#  Licensed under the Apache License, Version 2.0 (the "License");
-#  you may not use this file except in compliance with the License.
-#  You may obtain a copy of the License at
-#      http://www.apache.org/licenses/LICENSE-2.0
-#
-#  Unless required by applicable law or agreed to in writing, software
-#  distributed under the License is distributed on an "AS IS" BASIS,
-#  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-#  See the License for the specific language governing permissions and
-#  limitations under the License.
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright The LanceDB Authors
 
 from datetime import timedelta
 import logging
 from functools import cached_property
 from typing import Dict, Iterable, List, Optional, Union, Literal
+import warnings
 
 from lancedb._lancedb import IndexConfig
 from lancedb.embeddings.base import EmbeddingFunctionConfig
-from lancedb.index import FTS, BTree, Bitmap, HnswPq, HnswSq, IvfPq, LabelList
+from lancedb.index import FTS, BTree, Bitmap, HnswPq, HnswSq, IvfFlat, IvfPq, LabelList
 from lancedb.remote.db import LOOP
 import pyarrow as pa
 
@@ -90,7 +81,7 @@ class RemoteTable(Table):
         """to_pandas() is not yet supported on LanceDB cloud."""
         return NotImplementedError("to_pandas() is not yet supported on LanceDB cloud.")
 
-    def checkout(self, version):
+    def checkout(self, version: int):
         return LOOP.run(self._table.checkout(version))
 
     def checkout_latest(self):
@@ -234,10 +225,12 @@ class RemoteTable(Table):
             config = HnswPq(distance_type=metric)
         elif index_type == "IVF_HNSW_SQ":
             config = HnswSq(distance_type=metric)
+        elif index_type == "IVF_FLAT":
+            config = IvfFlat(distance_type=metric)
         else:
             raise ValueError(
                 f"Unknown vector index type: {index_type}. Valid options are"
-                " 'IVF_PQ', 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
+                " 'IVF_FLAT', 'IVF_PQ', 'IVF_HNSW_PQ', 'IVF_HNSW_SQ'"
             )
 
         LOOP.run(self._table.create_index(vector_column_name, config=config))
@@ -481,16 +474,28 @@ class RemoteTable(Table):
         )
 
     def cleanup_old_versions(self, *_):
-        """cleanup_old_versions() is not supported on the LanceDB cloud"""
-        raise NotImplementedError(
-            "cleanup_old_versions() is not supported on the LanceDB cloud"
+        """
+        cleanup_old_versions() is a no-op on LanceDB Cloud.
+
+        Tables are automatically cleaned up and optimized.
+        """
+        warnings.warn(
+            "cleanup_old_versions() is a no-op on LanceDB Cloud. "
+            "Tables are automatically cleaned up and optimized."
         )
+        pass
 
     def compact_files(self, *_):
-        """compact_files() is not supported on the LanceDB cloud"""
-        raise NotImplementedError(
-            "compact_files() is not supported on the LanceDB cloud"
+        """
+        compact_files() is a no-op on LanceDB Cloud.
+
+        Tables are automatically compacted and optimized.
+        """
+        warnings.warn(
+            "compact_files() is a no-op on LanceDB Cloud. "
+            "Tables are automatically compacted and optimized."
         )
+        pass
 
     def optimize(
         self,
@@ -498,12 +503,16 @@ class RemoteTable(Table):
         cleanup_older_than: Optional[timedelta] = None,
         delete_unverified: bool = False,
     ):
-        """optimize() is not supported on the LanceDB cloud.
-        Indices are optimized automatically."""
-        raise NotImplementedError(
-            "optimize() is not supported on the LanceDB cloud. "
+        """
+        optimize() is a no-op on LanceDB Cloud.
+
+        Indices are optimized automatically.
+        """
+        warnings.warn(
+            "optimize() is a no-op on LanceDB Cloud. "
             "Indices are optimized automatically."
         )
+        pass
 
     def count_rows(self, filter: Optional[str] = None) -> int:
         return LOOP.run(self._table.count_rows(filter))

python/python/lancedb/table.py

@@ -34,7 +34,7 @@ from lance.dependencies import _check_for_hugging_face
 
 from .common import DATA, VEC, VECTOR_COLUMN_NAME
 from .embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
-from .index import BTree, IvfPq, Bitmap, LabelList, HnswPq, HnswSq, FTS
+from .index import BTree, IvfFlat, IvfPq, Bitmap, LabelList, HnswPq, HnswSq, FTS
 from .merge import LanceMergeInsertBuilder
 from .pydantic import LanceModel, model_to_dict
 from .query import (
@@ -433,7 +433,9 @@ class Table(ABC):
         accelerator: Optional[str] = None,
         index_cache_size: Optional[int] = None,
         *,
-        index_type: Literal["IVF_PQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ"] = "IVF_PQ",
+        index_type: Literal[
+            "IVF_FLAT", "IVF_PQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ"
+        ] = "IVF_PQ",
         num_bits: int = 8,
         max_iterations: int = 50,
         sample_rate: int = 256,
@@ -446,8 +448,9 @@ class Table(ABC):
         ----------
         metric: str, default "L2"
             The distance metric to use when creating the index.
-            Valid values are "L2", "cosine", or "dot".
+            Valid values are "L2", "cosine", "dot", or "hamming".
             L2 is euclidean distance.
+            Hamming is available only for binary vectors.
         num_partitions: int, default 256
             The number of IVF partitions to use when creating the index.
             Default is 256.
@@ -917,9 +920,6 @@ class Table(ABC):
         """
         Clean up old versions of the table, freeing disk space.
 
-        Note: This function is not available in LanceDb Cloud (since LanceDb
-        Cloud manages cleanup for you automatically)
-
         Parameters
         ----------
         older_than: timedelta, default None
@@ -936,21 +936,38 @@ class Table(ABC):
         CleanupStats
             The stats of the cleanup operation, including how many bytes were
             freed.
+
+        See Also
+        --------
+        [Table.optimize][lancedb.table.Table.optimize]: A more comprehensive
+            optimization operation that includes cleanup as well as other operations.
+
+        Notes
+        -----
+        This function is not available in LanceDb Cloud (since LanceDB
+        Cloud manages cleanup for you automatically)
         """
 
     @abstractmethod
     def compact_files(self, *args, **kwargs):
         """
         Run the compaction process on the table.
-
-        Note: This function is not available in LanceDb Cloud (since LanceDb
-        Cloud manages compaction for you automatically)
-
         This can be run after making several small appends to optimize the table
         for faster reads.
 
-        Arguments are passed onto :meth:`lance.dataset.DatasetOptimizer.compact_files`.
+        Arguments are passed onto Lance's
+        [compact_files][lance.dataset.DatasetOptimizer.compact_files].
         For most cases, the default should be fine.
+
+        See Also
+        --------
+        [Table.optimize][lancedb.table.Table.optimize]: A more comprehensive
+            optimization operation that includes cleanup as well as other operations.
+
+        Notes
+        -----
+        This function is not available in LanceDB Cloud (since LanceDB
+        Cloud manages compaction for you automatically)
         """
 
     @abstractmethod
@@ -1075,7 +1092,7 @@ class Table(ABC):
         """
 
     @abstractmethod
-    def checkout(self):
+    def checkout(self, version: int):
         """
         Checks out a specific version of the Table
 
@@ -1394,7 +1411,9 @@ class LanceTable(Table):
         accelerator: Optional[str] = None,
         index_cache_size: Optional[int] = None,
         num_bits: int = 8,
-        index_type: Literal["IVF_PQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ"] = "IVF_PQ",
+        index_type: Literal[
+            "IVF_FLAT", "IVF_PQ", "IVF_HNSW_SQ", "IVF_HNSW_PQ"
+        ] = "IVF_PQ",
         max_iterations: int = 50,
         sample_rate: int = 256,
         m: int = 20,
@@ -1418,6 +1437,13 @@ class LanceTable(Table):
             )
             self.checkout_latest()
             return
+        elif index_type == "IVF_FLAT":
+            config = IvfFlat(
+                distance_type=metric,
+                num_partitions=num_partitions,
+                max_iterations=max_iterations,
+                sample_rate=sample_rate,
+            )
         elif index_type == "IVF_PQ":
             config = IvfPq(
                 distance_type=metric,
@@ -2605,7 +2631,7 @@ class AsyncTable:
         *,
         replace: Optional[bool] = None,
         config: Optional[
-            Union[IvfPq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS]
+            Union[IvfFlat, IvfPq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS]
         ] = None,
     ):
         """Create an index to speed up queries
@@ -2634,7 +2660,7 @@ class AsyncTable:
         """
         if config is not None:
             if not isinstance(
-                config, (IvfPq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS)
+                config, (IvfFlat, IvfPq, HnswPq, HnswSq, BTree, Bitmap, LabelList, FTS)
             ):
                 raise TypeError(
                     "config must be an instance of IvfPq, HnswPq, HnswSq, BTree,"
@@ -2798,6 +2824,8 @@ class AsyncTable:
                 async_query = async_query.column(query.vector_column)
             if query.ef:
                 async_query = async_query.ef(query.ef)
+            if not query.use_index:
+                async_query = async_query.bypass_vector_index()
 
         if not query.prefilter:
             async_query = async_query.postfilter()
@@ -3021,7 +3049,7 @@ class AsyncTable:
 
         return versions
 
-    async def checkout(self, version):
+    async def checkout(self, version: int):
         """
         Checks out a specific version of the Table
 

python/python/tests/docs/test_binary_vector.py

@@ -0,0 +1,44 @@
+import shutil
+
+# --8<-- [start:imports]
+import lancedb
+import numpy as np
+import pytest
+# --8<-- [end:imports]
+
+shutil.rmtree("data/binary_lancedb", ignore_errors=True)
+
+
+def test_binary_vector():
+    # --8<-- [start:sync_binary_vector]
+    db = lancedb.connect("data/binary_lancedb")
+    data = [
+        {
+            "id": i,
+            "vector": np.random.randint(0, 256, size=16),
+        }
+        for i in range(1024)
+    ]
+    tbl = db.create_table("my_binary_vectors", data=data)
+    query = np.random.randint(0, 256, size=16)
+    tbl.search(query).to_arrow()
+    # --8<-- [end:sync_binary_vector]
+    db.drop_table("my_binary_vectors")
+
+
+@pytest.mark.asyncio
+async def test_binary_vector_async():
+    # --8<-- [start:async_binary_vector]
+    db = await lancedb.connect_async("data/binary_lancedb")
+    data = [
+        {
+            "id": i,
+            "vector": np.random.randint(0, 256, size=16),
+        }
+        for i in range(1024)
+    ]
+    tbl = await db.create_table("my_binary_vectors", data=data)
+    query = np.random.randint(0, 256, size=16)
+    await tbl.query().nearest_to(query).to_arrow()
+    # --8<-- [end:async_binary_vector]
+    await db.drop_table("my_binary_vectors")

python/python/tests/test_db.py

@@ -508,6 +508,32 @@ def test_delete_table(tmp_db: lancedb.DBConnection):
     tmp_db.drop_table("does_not_exist", ignore_missing=True)
 
 
+@pytest.mark.asyncio
+async def test_delete_table_async(tmp_db: lancedb.DBConnection):
+    data = pd.DataFrame(
+        {
+            "vector": [[3.1, 4.1], [5.9, 26.5]],
+            "item": ["foo", "bar"],
+            "price": [10.0, 20.0],
+        }
+    )
+
+    tmp_db.create_table("test", data=data)
+
+    with pytest.raises(Exception):
+        tmp_db.create_table("test", data=data)
+
+    assert tmp_db.table_names() == ["test"]
+
+    tmp_db.drop_table("test")
+    assert tmp_db.table_names() == []
+
+    tmp_db.create_table("test", data=data)
+    assert tmp_db.table_names() == ["test"]
+
+    tmp_db.drop_table("does_not_exist", ignore_missing=True)
+
+
 def test_drop_database(tmp_db: lancedb.DBConnection):
     data = pd.DataFrame(
         {
@@ -681,3 +707,25 @@ def test_create_table_with_invalid_names(tmp_db: lancedb.DBConnection):
     with pytest.raises(ValueError):
         tmp_db.create_table("foo$$bar", data)
     tmp_db.create_table("foo.bar", data)
+
+
+def test_bypass_vector_index_sync(tmp_db: lancedb.DBConnection):
+    data = [{"vector": np.random.rand(32)} for _ in range(512)]
+    sample_key = data[100]["vector"]
+    table = tmp_db.create_table(
+        "test",
+        data,
+    )
+
+    table.create_index(
+        num_partitions=2,
+        num_sub_vectors=2,
+    )
+
+    plan_with_index = table.search(sample_key).explain_plan(verbose=True)
+    assert "ANN" in plan_with_index
+
+    plan_without_index = (
+        table.search(sample_key).bypass_vector_index().explain_plan(verbose=True)
+    )
+    assert "KNN" in plan_without_index

python/python/tests/test_index.py

@@ -8,7 +8,7 @@ import pyarrow as pa
 import pytest
 import pytest_asyncio
 from lancedb import AsyncConnection, AsyncTable, connect_async
-from lancedb.index import BTree, IvfPq, Bitmap, LabelList, HnswPq, HnswSq
+from lancedb.index import BTree, IvfFlat, IvfPq, Bitmap, LabelList, HnswPq, HnswSq
 
 
 @pytest_asyncio.fixture
@@ -42,6 +42,27 @@ async def some_table(db_async):
     )
 
 
+@pytest_asyncio.fixture
+async def binary_table(db_async):
+    data = [
+        {
+            "id": i,
+            "vector": [i] * 128,
+        }
+        for i in range(NROWS)
+    ]
+    return await db_async.create_table(
+        "binary_table",
+        data,
+        schema=pa.schema(
+            [
+                pa.field("id", pa.int64()),
+                pa.field("vector", pa.list_(pa.uint8(), 128)),
+            ]
+        ),
+    )
+
+
 @pytest.mark.asyncio
 async def test_create_scalar_index(some_table: AsyncTable):
     # Can create
@@ -143,3 +164,27 @@ async def test_create_hnswsq_index(some_table: AsyncTable):
     await some_table.create_index("vector", config=HnswSq(num_partitions=10))
     indices = await some_table.list_indices()
     assert len(indices) == 1
+
+
+@pytest.mark.asyncio
+async def test_create_index_with_binary_vectors(binary_table: AsyncTable):
+    await binary_table.create_index(
+        "vector", config=IvfFlat(distance_type="hamming", num_partitions=10)
+    )
+    indices = await binary_table.list_indices()
+    assert len(indices) == 1
+    assert indices[0].index_type == "IvfFlat"
+    assert indices[0].columns == ["vector"]
+    assert indices[0].name == "vector_idx"
+
+    stats = await binary_table.index_stats("vector_idx")
+    assert stats.index_type == "IVF_FLAT"
+    assert stats.distance_type == "hamming"
+    assert stats.num_indexed_rows == await binary_table.count_rows()
+    assert stats.num_unindexed_rows == 0
+    assert stats.num_indices == 1
+
+    # the dataset contains vectors with all values from 0 to 255
+    for v in range(256):
+        res = await binary_table.query().nearest_to([v] * 128).to_arrow()
+        assert res["id"][0].as_py() == v

python/src/index.rs

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+use lancedb::index::vector::IvfFlatIndexBuilder;
 use lancedb::index::{
     scalar::{BTreeIndexBuilder, FtsIndexBuilder, TokenizerConfig},
     vector::{IvfHnswPqIndexBuilder, IvfHnswSqIndexBuilder, IvfPqIndexBuilder},
@@ -59,6 +60,18 @@ pub fn extract_index_params(source: &Option<Bound<'_, PyAny>>) -> PyResult<Lance
                 opts.tokenizer_configs = inner_opts;
                 Ok(LanceDbIndex::FTS(opts))
             },
+            "IvfFlat" => {
+                let params = source.extract::<IvfFlatParams>()?;
+                let distance_type = parse_distance_type(params.distance_type)?;
+                let mut ivf_flat_builder = IvfFlatIndexBuilder::default()
+                    .distance_type(distance_type)
+                    .max_iterations(params.max_iterations)
+                    .sample_rate(params.sample_rate);
+                if let Some(num_partitions) = params.num_partitions {
+                    ivf_flat_builder = ivf_flat_builder.num_partitions(num_partitions);
+                }
+                Ok(LanceDbIndex::IvfFlat(ivf_flat_builder))
+            },
             "IvfPq" => {
                 let params = source.extract::<IvfPqParams>()?;
                 let distance_type = parse_distance_type(params.distance_type)?;
@@ -129,6 +142,14 @@ struct FtsParams {
     ascii_folding: bool,
 }
 
+#[derive(FromPyObject)]
+struct IvfFlatParams {
+    distance_type: String,
+    num_partitions: Option<u32>,
+    max_iterations: u32,
+    sample_rate: u32,
+}
+
 #[derive(FromPyObject)]
 struct IvfPqParams {
     distance_type: String,

python/src/util.rs

@@ -43,8 +43,9 @@ pub fn parse_distance_type(distance_type: impl AsRef<str>) -> PyResult<DistanceT
         "l2" => Ok(DistanceType::L2),
         "cosine" => Ok(DistanceType::Cosine),
         "dot" => Ok(DistanceType::Dot),
+        "hamming" => Ok(DistanceType::Hamming),
         _ => Err(PyValueError::new_err(format!(
-            "Invalid distance type '{}'.  Must be one of l2, cosine, or dot",
+            "Invalid distance type '{}'.  Must be one of l2, cosine, dot, or hamming",
             distance_type.as_ref()
         ))),
     }

rust-toolchain.toml

@@ -1,2 +1,2 @@
 [toolchain]
-channel = "1.80.0"
+channel = "1.83.0"

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-node"
-version = "0.14.1-beta.4"
+version = "0.14.1"
 description = "Serverless, low-latency vector database for AI applications"
 license.workspace = true
 edition.workspace = true

rust/lancedb/Cargo.toml

@@ -1,13 +1,13 @@
 [package]
 name = "lancedb"
-version = "0.14.1-beta.4"
+version = "0.14.1"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
 repository.workspace = true
 keywords.workspace = true
 categories.workspace = true
-rust-version = "1.75"
+rust-version.workspace = true
 
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 [dependencies]

rust/lancedb/src/index.rs

@@ -17,6 +17,7 @@ use std::sync::Arc;
 use scalar::FtsIndexBuilder;
 use serde::Deserialize;
 use serde_with::skip_serializing_none;
+use vector::IvfFlatIndexBuilder;
 
 use crate::{table::TableInternal, DistanceType, Error, Result};
 
@@ -56,6 +57,9 @@ pub enum Index {
     /// Full text search index using bm25.
     FTS(FtsIndexBuilder),
 
+    /// IVF index
+    IvfFlat(IvfFlatIndexBuilder),
+
     /// IVF index with Product Quantization
     IvfPq(IvfPqIndexBuilder),
 
@@ -106,6 +110,8 @@ impl IndexBuilder {
 #[derive(Debug, Clone, PartialEq, Deserialize)]
 pub enum IndexType {
     // Vector
+    #[serde(alias = "IVF_FLAT")]
+    IvfFlat,
     #[serde(alias = "IVF_PQ")]
     IvfPq,
     #[serde(alias = "IVF_HNSW_PQ")]
@@ -127,6 +133,7 @@ pub enum IndexType {
 impl std::fmt::Display for IndexType {
     fn fmt(&self, f: &mut std::fmt::Formatter) -> std::fmt::Result {
         match self {
+            Self::IvfFlat => write!(f, "IVF_FLAT"),
             Self::IvfPq => write!(f, "IVF_PQ"),
             Self::IvfHnswPq => write!(f, "IVF_HNSW_PQ"),
             Self::IvfHnswSq => write!(f, "IVF_HNSW_SQ"),
@@ -147,6 +154,7 @@ impl std::str::FromStr for IndexType {
             "BITMAP" => Ok(Self::Bitmap),
             "LABEL_LIST" | "LABELLIST" => Ok(Self::LabelList),
             "FTS" | "INVERTED" => Ok(Self::FTS),
+            "IVF_FLAT" => Ok(Self::IvfFlat),
             "IVF_PQ" => Ok(Self::IvfPq),
             "IVF_HNSW_PQ" => Ok(Self::IvfHnswPq),
             "IVF_HNSW_SQ" => Ok(Self::IvfHnswSq),

rust/lancedb/src/index/vector.rs

@@ -162,6 +162,43 @@ macro_rules! impl_hnsw_params_setter {
     };
 }
 
+/// Builder for an IVF Flat index.
+///
+/// This index stores raw vectors. These vectors are grouped into partitions of similar vectors.
+/// Each partition keeps track of a centroid which is the average value of all vectors in the group.
+///
+/// During a query the centroids are compared with the query vector to find the closest partitions.
+/// The raw vectors in these partitions are then searched to find the closest vectors.
+///
+/// The partitioning process is called IVF and the `num_partitions` parameter controls how many groups to create.
+///
+/// Note that training an IVF Flat index on a large dataset is a slow operation and currently is also a memory intensive operation.
+#[derive(Debug, Clone)]
+pub struct IvfFlatIndexBuilder {
+    pub(crate) distance_type: DistanceType,
+
+    // IVF
+    pub(crate) num_partitions: Option<u32>,
+    pub(crate) sample_rate: u32,
+    pub(crate) max_iterations: u32,
+}
+
+impl Default for IvfFlatIndexBuilder {
+    fn default() -> Self {
+        Self {
+            distance_type: DistanceType::L2,
+            num_partitions: None,
+            sample_rate: 256,
+            max_iterations: 50,
+        }
+    }
+}
+
+impl IvfFlatIndexBuilder {
+    impl_distance_type_setter!();
+    impl_ivf_params_setter!();
+}
+
 /// Builder for an IVF PQ index.
 ///
 /// This index stores a compressed (quantized) copy of every vector.  These vectors

rust/lancedb/src/lib.rs

@@ -214,6 +214,7 @@ mod polars_arrow_convertors;
 pub mod query;
 #[cfg(feature = "remote")]
 pub mod remote;
+pub mod rerankers;
 pub mod table;
 pub mod utils;
 

rust/lancedb/src/query.rs

@@ -15,19 +15,31 @@
 use std::future::Future;
 use std::sync::Arc;
 
+use arrow::compute::concat_batches;
 use arrow_array::{make_array, Array, Float16Array, Float32Array, Float64Array};
 use arrow_schema::DataType;
 use datafusion_physical_plan::ExecutionPlan;
+use futures::{stream, try_join, FutureExt, TryStreamExt};
 use half::f16;
-use lance::dataset::scanner::DatasetRecordBatchStream;
+use lance::{
+    arrow::RecordBatchExt,
+    dataset::{scanner::DatasetRecordBatchStream, ROW_ID},
+};
 use lance_datafusion::exec::execute_plan;
+use lance_index::scalar::inverted::SCORE_COL;
 use lance_index::scalar::FullTextSearchQuery;
+use lance_index::vector::DIST_COL;
+use lance_io::stream::RecordBatchStreamAdapter;
 
 use crate::arrow::SendableRecordBatchStream;
 use crate::error::{Error, Result};
+use crate::rerankers::rrf::RRFReranker;
+use crate::rerankers::{check_reranker_result, NormalizeMethod, Reranker};
 use crate::table::TableInternal;
 use crate::DistanceType;
 
+mod hybrid;
+
 pub(crate) const DEFAULT_TOP_K: usize = 10;
 
 /// Which columns should be retrieved from the database
@@ -339,7 +351,7 @@ pub trait QueryBase {
     fn limit(self, limit: usize) -> Self;
 
     /// Set the offset of the query.
-
+    ///
     /// By default, it fetches starting with the first row.
     /// This method can be used to skip the first `offset` rows.
     fn offset(self, offset: usize) -> Self;
@@ -435,6 +447,16 @@ pub trait QueryBase {
 
     /// Return the `_rowid` meta column from the Table.
     fn with_row_id(self) -> Self;
+
+    /// Rerank the results using the specified reranker.
+    ///
+    /// This is currently only supported for Hybrid Search.
+    fn rerank(self, reranker: Arc<dyn Reranker>) -> Self;
+
+    /// The method to normalize the scores. Can be "rank" or "Score". If "Rank",
+    /// the scores are converted to ranks and then normalized. If "Score", the
+    /// scores are normalized directly.
+    fn norm(self, norm: NormalizeMethod) -> Self;
 }
 
 pub trait HasQuery {
@@ -481,6 +503,16 @@ impl<T: HasQuery> QueryBase for T {
         self.mut_query().with_row_id = true;
         self
     }
+
+    fn rerank(mut self, reranker: Arc<dyn Reranker>) -> Self {
+        self.mut_query().reranker = Some(reranker);
+        self
+    }
+
+    fn norm(mut self, norm: NormalizeMethod) -> Self {
+        self.mut_query().norm = Some(norm);
+        self
+    }
 }
 
 /// Options for controlling the execution of a query
@@ -600,6 +632,13 @@ pub struct Query {
 
     /// If set to false, the filter will be applied after the vector search.
     pub(crate) prefilter: bool,
+
+    /// Implementation of reranker that can be used to reorder or combine query
+    /// results, especially if using hybrid search
+    pub(crate) reranker: Option<Arc<dyn Reranker>>,
+
+    /// Configure how query results are normalized when doing hybrid search
+    pub(crate) norm: Option<NormalizeMethod>,
 }
 
 impl Query {
@@ -614,6 +653,8 @@ impl Query {
             fast_search: false,
             with_row_id: false,
             prefilter: true,
+            reranker: None,
+            norm: None,
         }
     }
 
@@ -862,6 +903,65 @@ impl VectorQuery {
         self.use_index = false;
         self
     }
+
+    pub async fn execute_hybrid(&self) -> Result<SendableRecordBatchStream> {
+        // clone query and specify we want to include row IDs, which can be needed for reranking
+        let fts_query = self.base.clone().with_row_id();
+        let mut vector_query = self.clone().with_row_id();
+
+        vector_query.base.full_text_search = None;
+        let (fts_results, vec_results) = try_join!(fts_query.execute(), vector_query.execute())?;
+
+        let (fts_results, vec_results) = try_join!(
+            fts_results.try_collect::<Vec<_>>(),
+            vec_results.try_collect::<Vec<_>>()
+        )?;
+
+        // try to get the schema to use when combining batches.
+        // if either
+        let (fts_schema, vec_schema) = hybrid::query_schemas(&fts_results, &vec_results);
+
+        // concatenate all the batches together
+        let mut fts_results = concat_batches(&fts_schema, fts_results.iter())?;
+        let mut vec_results = concat_batches(&vec_schema, vec_results.iter())?;
+
+        if matches!(self.base.norm, Some(NormalizeMethod::Rank)) {
+            vec_results = hybrid::rank(vec_results, DIST_COL, None)?;
+            fts_results = hybrid::rank(fts_results, SCORE_COL, None)?;
+        }
+
+        vec_results = hybrid::normalize_scores(vec_results, DIST_COL, None)?;
+        fts_results = hybrid::normalize_scores(fts_results, SCORE_COL, None)?;
+
+        let reranker = self
+            .base
+            .reranker
+            .clone()
+            .unwrap_or(Arc::new(RRFReranker::default()));
+
+        let fts_query = self.base.full_text_search.as_ref().ok_or(Error::Runtime {
+            message: "there should be an FTS search".to_string(),
+        })?;
+
+        let mut results = reranker
+            .rerank_hybrid(&fts_query.query, vec_results, fts_results)
+            .await?;
+
+        check_reranker_result(&results)?;
+
+        let limit = self.base.limit.unwrap_or(DEFAULT_TOP_K);
+        if results.num_rows() > limit {
+            results = results.slice(0, limit);
+        }
+
+        if !self.base.with_row_id {
+            results = results.drop_column(ROW_ID)?;
+        }
+
+        Ok(SendableRecordBatchStream::from(
+            RecordBatchStreamAdapter::new(results.schema(), stream::iter([Ok(results)])),
+        ))
+    }
 }
 
 impl ExecutableQuery for VectorQuery {
@@ -873,6 +973,11 @@ impl ExecutableQuery for VectorQuery {
         &self,
         options: QueryExecutionOptions,
     ) -> Result<SendableRecordBatchStream> {
+        if self.base.full_text_search.is_some() {
+            let hybrid_result = async move { self.execute_hybrid().await }.boxed().await?;
+            return Ok(hybrid_result);
+        }
+
         Ok(SendableRecordBatchStream::from(
             DatasetRecordBatchStream::new(execute_plan(
                 self.create_plan(options).await?,
@@ -894,20 +999,20 @@ impl HasQuery for VectorQuery {
 
 #[cfg(test)]
 mod tests {
-    use std::sync::Arc;
+    use std::{collections::HashSet, sync::Arc};
 
     use super::*;
-    use arrow::{compute::concat_batches, datatypes::Int32Type};
+    use arrow::{array::downcast_array, compute::concat_batches, datatypes::Int32Type};
     use arrow_array::{
-        cast::AsArray, Float32Array, Int32Array, RecordBatch, RecordBatchIterator,
-        RecordBatchReader,
+        cast::AsArray, types::Float32Type, FixedSizeListArray, Float32Array, Int32Array,
+        RecordBatch, RecordBatchIterator, RecordBatchReader, StringArray,
     };
     use arrow_schema::{DataType, Field as ArrowField, Schema as ArrowSchema};
     use futures::{StreamExt, TryStreamExt};
     use lance_testing::datagen::{BatchGenerator, IncrementingInt32, RandomVector};
     use tempfile::tempdir;
 
-    use crate::{connect, Table};
+    use crate::{connect, connection::CreateTableMode, Table};
 
     #[tokio::test]
     async fn test_setters_getters() {
@@ -1274,4 +1379,156 @@ mod tests {
         assert!(query_index.values().contains(&0));
         assert!(query_index.values().contains(&1));
     }
+
+    #[tokio::test]
+    async fn test_hybrid_search() {
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path();
+        let conn = connect(dataset_path.to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+
+        let dims = 2;
+        let schema = Arc::new(ArrowSchema::new(vec![
+            ArrowField::new("text", DataType::Utf8, false),
+            ArrowField::new(
+                "vector",
+                DataType::FixedSizeList(
+                    Arc::new(ArrowField::new("item", DataType::Float32, true)),
+                    dims,
+                ),
+                false,
+            ),
+        ]));
+
+        let text = StringArray::from(vec!["dog", "cat", "a", "b"]);
+        let vectors = vec![
+            Some(vec![Some(0.0), Some(0.0)]),
+            Some(vec![Some(-2.0), Some(-2.0)]),
+            Some(vec![Some(50.0), Some(50.0)]),
+            Some(vec![Some(-30.0), Some(-30.0)]),
+        ];
+        let vector = FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(vectors, dims);
+
+        let record_batch =
+            RecordBatch::try_new(schema.clone(), vec![Arc::new(text), Arc::new(vector)]).unwrap();
+        let record_batch_iter =
+            RecordBatchIterator::new(vec![record_batch].into_iter().map(Ok), schema.clone());
+        let table = conn
+            .create_table("my_table", record_batch_iter)
+            .execute()
+            .await
+            .unwrap();
+
+        table
+            .create_index(&["text"], crate::index::Index::FTS(Default::default()))
+            .replace(true)
+            .execute()
+            .await
+            .unwrap();
+
+        let fts_query = FullTextSearchQuery::new("b".to_string());
+        let results = table
+            .query()
+            .full_text_search(fts_query)
+            .limit(2)
+            .nearest_to(&[-10.0, -10.0])
+            .unwrap()
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+
+        let batch = &results[0];
+
+        let texts: StringArray = downcast_array(batch.column_by_name("text").unwrap());
+        let texts = texts.iter().map(|e| e.unwrap()).collect::<HashSet<_>>();
+        assert!(texts.contains("cat")); // should be close by vector search
+        assert!(texts.contains("b")); // should be close by fts search
+
+        // ensure that this works correctly if there are no matching FTS results
+        let fts_query = FullTextSearchQuery::new("z".to_string());
+        table
+            .query()
+            .full_text_search(fts_query)
+            .limit(2)
+            .nearest_to(&[-10.0, -10.0])
+            .unwrap()
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+    }
+
+    #[tokio::test]
+    async fn test_hybrid_search_empty_table() {
+        let tmp_dir = tempdir().unwrap();
+        let dataset_path = tmp_dir.path();
+        let conn = connect(dataset_path.to_str().unwrap())
+            .execute()
+            .await
+            .unwrap();
+
+        let dims = 2;
+
+        let schema = Arc::new(ArrowSchema::new(vec![
+            ArrowField::new("text", DataType::Utf8, false),
+            ArrowField::new(
+                "vector",
+                DataType::FixedSizeList(
+                    Arc::new(ArrowField::new("item", DataType::Float32, true)),
+                    dims,
+                ),
+                false,
+            ),
+        ]));
+
+        // ensure hybrid search is also supported on a fully empty table
+        let vectors: Vec<Option<Vec<Option<f32>>>> = Vec::new();
+        let record_batch = RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(StringArray::from(Vec::<&str>::new())),
+                Arc::new(
+                    FixedSizeListArray::from_iter_primitive::<Float32Type, _, _>(vectors, dims),
+                ),
+            ],
+        )
+        .unwrap();
+        let record_batch_iter =
+            RecordBatchIterator::new(vec![record_batch].into_iter().map(Ok), schema.clone());
+        let table = conn
+            .create_table("my_table", record_batch_iter)
+            .mode(CreateTableMode::Overwrite)
+            .execute()
+            .await
+            .unwrap();
+        table
+            .create_index(&["text"], crate::index::Index::FTS(Default::default()))
+            .replace(true)
+            .execute()
+            .await
+            .unwrap();
+        let fts_query = FullTextSearchQuery::new("b".to_string());
+        let results = table
+            .query()
+            .full_text_search(fts_query)
+            .limit(2)
+            .nearest_to(&[-10.0, -10.0])
+            .unwrap()
+            .execute()
+            .await
+            .unwrap()
+            .try_collect::<Vec<_>>()
+            .await
+            .unwrap();
+        let batch = &results[0];
+        assert_eq!(0, batch.num_rows());
+        assert_eq!(2, batch.num_columns());
+    }
 }

rust/lancedb/src/query/hybrid.rs

@@ -0,0 +1,346 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use arrow::compute::{
+    kernels::numeric::{div, sub},
+    max, min,
+};
+use arrow_array::{cast::downcast_array, Float32Array, RecordBatch};
+use arrow_schema::{DataType, Field, Schema, SortOptions};
+use lance::dataset::ROW_ID;
+use lance_index::{scalar::inverted::SCORE_COL, vector::DIST_COL};
+use std::sync::Arc;
+
+use crate::error::{Error, Result};
+
+/// Converts results's score column to a rank.
+///
+/// Expects the `column` argument to be type Float32 and will panic if it's not
+pub fn rank(results: RecordBatch, column: &str, ascending: Option<bool>) -> Result<RecordBatch> {
+    let scores = results.column_by_name(column).ok_or(Error::InvalidInput {
+        message: format!(
+            "expected column {} not found in rank. found columns {:?}",
+            column,
+            results
+                .schema()
+                .fields()
+                .iter()
+                .map(|f| f.name())
+                .collect::<Vec<_>>(),
+        ),
+    })?;
+
+    if results.num_rows() == 0 {
+        return Ok(results);
+    }
+
+    let scores: Float32Array = downcast_array(scores);
+    let ranks = Float32Array::from_iter_values(
+        arrow::compute::kernels::rank::rank(
+            &scores,
+            Some(SortOptions {
+                descending: !ascending.unwrap_or(true),
+                ..Default::default()
+            }),
+        )?
+        .iter()
+        .map(|i| *i as f32),
+    );
+
+    let schema = results.schema();
+    let (column_idx, _) = schema.column_with_name(column).unwrap();
+    let mut columns = results.columns().to_vec();
+    columns[column_idx] = Arc::new(ranks);
+
+    let results = RecordBatch::try_new(results.schema(), columns)?;
+
+    Ok(results)
+}
+
+/// Get the query schemas needed when combining the search results.
+///
+/// If either of the record batches are empty, then we create a schema from the
+/// other record batch, and replace the score/distance column. If both record
+/// batches are empty, create empty schemas.
+pub fn query_schemas(
+    fts_results: &[RecordBatch],
+    vec_results: &[RecordBatch],
+) -> (Arc<Schema>, Arc<Schema>) {
+    let (fts_schema, vec_schema) = match (
+        fts_results.first().map(|r| r.schema()),
+        vec_results.first().map(|r| r.schema()),
+    ) {
+        (Some(fts_schema), Some(vec_schema)) => (fts_schema, vec_schema),
+        (None, Some(vec_schema)) => {
+            let fts_schema = with_field_name_replaced(&vec_schema, DIST_COL, SCORE_COL);
+            (Arc::new(fts_schema), vec_schema)
+        }
+        (Some(fts_schema), None) => {
+            let vec_schema = with_field_name_replaced(&fts_schema, DIST_COL, SCORE_COL);
+            (fts_schema, Arc::new(vec_schema))
+        }
+        (None, None) => (Arc::new(empty_fts_schema()), Arc::new(empty_vec_schema())),
+    };
+
+    (fts_schema, vec_schema)
+}
+
+pub fn empty_fts_schema() -> Schema {
+    Schema::new(vec![
+        Arc::new(Field::new(SCORE_COL, DataType::Float32, false)),
+        Arc::new(Field::new(ROW_ID, DataType::UInt64, false)),
+    ])
+}
+
+pub fn empty_vec_schema() -> Schema {
+    Schema::new(vec![
+        Arc::new(Field::new(DIST_COL, DataType::Float32, false)),
+        Arc::new(Field::new(ROW_ID, DataType::UInt64, false)),
+    ])
+}
+
+pub fn with_field_name_replaced(schema: &Schema, target: &str, replacement: &str) -> Schema {
+    let field_idx = schema.fields().iter().enumerate().find_map(|(i, field)| {
+        if field.name() == target {
+            Some(i)
+        } else {
+            None
+        }
+    });
+
+    let mut fields = schema.fields().to_vec();
+    if let Some(idx) = field_idx {
+        let new_field = (*fields[idx]).clone().with_name(replacement);
+        fields[idx] = Arc::new(new_field);
+    }
+
+    Schema::new(fields)
+}
+
+/// Normalize the scores column to have values between 0 and 1.
+///
+/// Expects the `column` argument to be type Float32 and will panic if it's not
+pub fn normalize_scores(
+    results: RecordBatch,
+    column: &str,
+    invert: Option<bool>,
+) -> Result<RecordBatch> {
+    let scores = results.column_by_name(column).ok_or(Error::InvalidInput {
+        message: format!(
+            "expected column {} not found in rank. found columns {:?}",
+            column,
+            results
+                .schema()
+                .fields()
+                .iter()
+                .map(|f| f.name())
+                .collect::<Vec<_>>(),
+        ),
+    })?;
+
+    if results.num_rows() == 0 {
+        return Ok(results);
+    }
+    let mut scores: Float32Array = downcast_array(scores);
+
+    let max = max(&scores).unwrap_or(0.0);
+    let min = min(&scores).unwrap_or(0.0);
+
+    // this is equivalent to np.isclose which is used in python
+    let rng = if max - min < 10e-5 { max } else { max - min };
+
+    // if rng is 0, then min and max are both 0 so we just leave the scores as is
+    if rng != 0.0 {
+        let tmp = div(
+            &sub(&scores, &Float32Array::new_scalar(min))?,
+            &Float32Array::new_scalar(rng),
+        )?;
+        scores = downcast_array(&tmp);
+    }
+
+    if invert.unwrap_or(false) {
+        let tmp = sub(&Float32Array::new_scalar(1.0), &scores)?;
+        scores = downcast_array(&tmp);
+    }
+
+    let schema = results.schema();
+    let (column_idx, _) = schema.column_with_name(column).unwrap();
+    let mut columns = results.columns().to_vec();
+    columns[column_idx] = Arc::new(scores);
+
+    let results = RecordBatch::try_new(results.schema(), columns).unwrap();
+
+    Ok(results)
+}
+
+#[cfg(test)]
+mod test {
+    use super::*;
+    use arrow_array::StringArray;
+    use arrow_schema::{DataType, Field, Schema};
+
+    #[test]
+    fn test_rank() {
+        let schema = Arc::new(Schema::new(vec![
+            Arc::new(Field::new("name", DataType::Utf8, false)),
+            Arc::new(Field::new("score", DataType::Float32, false)),
+        ]));
+
+        let names = StringArray::from(vec!["foo", "bar", "baz", "bean", "dog"]);
+        let scores = Float32Array::from(vec![0.2, 0.4, 0.1, 0.6, 0.45]);
+
+        let batch =
+            RecordBatch::try_new(schema.clone(), vec![Arc::new(names), Arc::new(scores)]).unwrap();
+
+        let result = rank(batch.clone(), "score", Some(false)).unwrap();
+        assert_eq!(2, result.schema().fields().len());
+        assert_eq!("name", result.schema().field(0).name());
+        assert_eq!("score", result.schema().field(1).name());
+
+        let names: StringArray = downcast_array(result.column(0));
+        assert_eq!(
+            names.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec!["foo", "bar", "baz", "bean", "dog"]
+        );
+        let scores: Float32Array = downcast_array(result.column(1));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![4.0, 3.0, 5.0, 1.0, 2.0]
+        );
+
+        // check sort ascending
+        let result = rank(batch.clone(), "score", Some(true)).unwrap();
+        let names: StringArray = downcast_array(result.column(0));
+        assert_eq!(
+            names.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec!["foo", "bar", "baz", "bean", "dog"]
+        );
+        let scores: Float32Array = downcast_array(result.column(1));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![2.0, 3.0, 1.0, 5.0, 4.0]
+        );
+
+        // ensure default sort is ascending
+        let result = rank(batch.clone(), "score", None).unwrap();
+        let names: StringArray = downcast_array(result.column(0));
+        assert_eq!(
+            names.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec!["foo", "bar", "baz", "bean", "dog"]
+        );
+        let scores: Float32Array = downcast_array(result.column(1));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![2.0, 3.0, 1.0, 5.0, 4.0]
+        );
+
+        // check it can handle an empty batch
+        let batch = RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(StringArray::from(Vec::<&str>::new())),
+                Arc::new(Float32Array::from(Vec::<f32>::new())),
+            ],
+        )
+        .unwrap();
+        let result = rank(batch.clone(), "score", None).unwrap();
+        assert_eq!(0, result.num_rows());
+        assert_eq!(2, result.schema().fields().len());
+        assert_eq!("name", result.schema().field(0).name());
+        assert_eq!("score", result.schema().field(1).name());
+
+        // check it returns the expected error when there's no column
+        let result = rank(batch.clone(), "bad_col", None);
+        match result {
+            Err(Error::InvalidInput { message }) => {
+                assert_eq!("expected column bad_col not found in rank. found columns [\"name\", \"score\"]", message);
+            }
+            _ => {
+                panic!("expected invalid input error, received {:?}", result)
+            }
+        }
+    }
+
+    #[test]
+    fn test_normalize_scores() {
+        let schema = Arc::new(Schema::new(vec![
+            Arc::new(Field::new("name", DataType::Utf8, false)),
+            Arc::new(Field::new("score", DataType::Float32, false)),
+        ]));
+
+        let names = Arc::new(StringArray::from(vec!["foo", "bar", "baz", "bean", "dog"]));
+        let scores = Arc::new(Float32Array::from(vec![-4.0, 2.0, 0.0, 3.0, 6.0]));
+
+        let batch =
+            RecordBatch::try_new(schema.clone(), vec![names.clone(), scores.clone()]).unwrap();
+
+        let result = normalize_scores(batch.clone(), "score", Some(false)).unwrap();
+        let names: StringArray = downcast_array(result.column(0));
+        assert_eq!(
+            names.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec!["foo", "bar", "baz", "bean", "dog"]
+        );
+        let scores: Float32Array = downcast_array(result.column(1));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![0.0, 0.6, 0.4, 0.7, 1.0]
+        );
+
+        // check it can invert the normalization
+        let result = normalize_scores(batch.clone(), "score", Some(true)).unwrap();
+        let scores: Float32Array = downcast_array(result.column(1));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![1.0, 1.0 - 0.6, 0.6, 0.3, 0.0]
+        );
+
+        // check that the default is not inverted
+        let result = normalize_scores(batch.clone(), "score", None).unwrap();
+        let scores: Float32Array = downcast_array(result.column(1));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![0.0, 0.6, 0.4, 0.7, 1.0]
+        );
+
+        // check that it will function correctly if all the values are the same
+        let names = Arc::new(StringArray::from(vec!["foo", "bar", "baz", "bean", "dog"]));
+        let scores = Arc::new(Float32Array::from(vec![2.1, 2.1, 2.1, 2.1, 2.1]));
+        let batch =
+            RecordBatch::try_new(schema.clone(), vec![names.clone(), scores.clone()]).unwrap();
+        let result = normalize_scores(batch.clone(), "score", None).unwrap();
+        let scores: Float32Array = downcast_array(result.column(1));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![0.0, 0.0, 0.0, 0.0, 0.0]
+        );
+
+        // check it keeps floating point rounding errors for same score normalized the same
+        // e.g., the behaviour is consistent with python
+        let scores = Arc::new(Float32Array::from(vec![1.0, 1.0, 1.0, 1.0, 0.9999999]));
+        let batch =
+            RecordBatch::try_new(schema.clone(), vec![names.clone(), scores.clone()]).unwrap();
+        let result = normalize_scores(batch.clone(), "score", None).unwrap();
+        let scores: Float32Array = downcast_array(result.column(1));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![
+                1.0 - 0.9999999,
+                1.0 - 0.9999999,
+                1.0 - 0.9999999,
+                1.0 - 0.9999999,
+                0.0
+            ]
+        );
+
+        // check that it can handle if all the scores are 0
+        let scores = Arc::new(Float32Array::from(vec![0.0, 0.0, 0.0, 0.0, 0.0]));
+        let batch =
+            RecordBatch::try_new(schema.clone(), vec![names.clone(), scores.clone()]).unwrap();
+        let result = normalize_scores(batch.clone(), "score", None).unwrap();
+        let scores: Float32Array = downcast_array(result.column(1));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![0.0, 0.0, 0.0, 0.0, 0.0]
+        );
+    }
+}

rust/lancedb/src/remote/table.rs

@@ -563,6 +563,7 @@ impl<S: HttpSend> TableInternal for RemoteTable<S> {
         let (index_type, distance_type) = match index.index {
             // TODO: Should we pass the actual index parameters? SaaS does not
             // yet support them.
+            Index::IvfFlat(index) => ("IVF_FLAT", Some(index.distance_type)),
             Index::IvfPq(index) => ("IVF_PQ", Some(index.distance_type)),
             Index::IvfHnswSq(index) => ("IVF_HNSW_SQ", Some(index.distance_type)),
             Index::BTree(_) => ("BTREE", None),
@@ -873,6 +874,7 @@ mod tests {
     use lance_index::scalar::FullTextSearchQuery;
     use reqwest::Body;
 
+    use crate::index::vector::IvfFlatIndexBuilder;
     use crate::{
         index::{vector::IvfPqIndexBuilder, Index, IndexStatistics, IndexType},
         query::{ExecutableQuery, QueryBase},
@@ -1489,6 +1491,11 @@ mod tests {
     #[tokio::test]
     async fn test_create_index() {
         let cases = [
+            (
+                "IVF_FLAT",
+                Some("hamming"),
+                Index::IvfFlat(IvfFlatIndexBuilder::default().distance_type(DistanceType::Hamming)),
+            ),
             ("IVF_PQ", Some("l2"), Index::IvfPq(Default::default())),
             (
                 "IVF_PQ",

rust/lancedb/src/rerankers.rs

@@ -0,0 +1,87 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::collections::BTreeSet;
+
+use arrow::{
+    array::downcast_array,
+    compute::{concat_batches, filter_record_batch},
+};
+use arrow_array::{BooleanArray, RecordBatch, UInt64Array};
+use async_trait::async_trait;
+use lance::dataset::ROW_ID;
+
+use crate::error::{Error, Result};
+
+pub mod rrf;
+
+/// column name for reranker relevance score
+const RELEVANCE_SCORE: &str = "_relevance_score";
+
+#[derive(Debug, Clone, PartialEq)]
+pub enum NormalizeMethod {
+    Score,
+    Rank,
+}
+
+/// Interface for a reranker. A reranker is used to rerank the results from a
+/// vector and FTS search. This is useful for combining the results from both
+/// search methods.
+#[async_trait]
+pub trait Reranker: std::fmt::Debug + Sync + Send {
+    // TODO support vector reranking and FTS reranking. Currently only hybrid reranking is supported.
+
+    /// Rerank function receives the individual results from the vector and FTS search
+    /// results. You can choose to use any of the results to generate the final results,
+    /// allowing maximum flexibility.
+    async fn rerank_hybrid(
+        &self,
+        query: &str,
+        vector_results: RecordBatch,
+        fts_results: RecordBatch,
+    ) -> Result<RecordBatch>;
+
+    fn merge_results(
+        &self,
+        vector_results: RecordBatch,
+        fts_results: RecordBatch,
+    ) -> Result<RecordBatch> {
+        let combined = concat_batches(&fts_results.schema(), [vector_results, fts_results].iter())?;
+
+        let mut mask = BooleanArray::builder(combined.num_rows());
+        let mut unique_ids = BTreeSet::new();
+        let row_ids = combined.column_by_name(ROW_ID).ok_or(Error::InvalidInput {
+            message: format!(
+                "could not find expected column {} while merging results. found columns {:?}",
+                ROW_ID,
+                combined
+                    .schema()
+                    .fields()
+                    .iter()
+                    .map(|f| f.name())
+                    .collect::<Vec<_>>()
+            ),
+        })?;
+        let row_ids: UInt64Array = downcast_array(row_ids);
+        row_ids.values().iter().for_each(|id| {
+            mask.append_value(unique_ids.insert(id));
+        });
+
+        let combined = filter_record_batch(&combined, &mask.finish())?;
+
+        Ok(combined)
+    }
+}
+
+pub fn check_reranker_result(result: &RecordBatch) -> Result<()> {
+    if result.schema().column_with_name(RELEVANCE_SCORE).is_none() {
+        return Err(Error::Schema {
+            message: format!(
+                "rerank_hybrid must return a RecordBatch with a column named {}",
+                RELEVANCE_SCORE
+            ),
+        });
+    }
+
+    Ok(())
+}

rust/lancedb/src/rerankers/rrf.rs

@@ -0,0 +1,223 @@
+// SPDX-License-Identifier: Apache-2.0
+// SPDX-FileCopyrightText: Copyright The LanceDB Authors
+
+use std::collections::BTreeMap;
+use std::sync::Arc;
+
+use arrow::{
+    array::downcast_array,
+    compute::{sort_to_indices, take},
+};
+use arrow_array::{Float32Array, RecordBatch, UInt64Array};
+use arrow_schema::{DataType, Field, Schema, SortOptions};
+use async_trait::async_trait;
+use lance::dataset::ROW_ID;
+
+use crate::error::{Error, Result};
+use crate::rerankers::{Reranker, RELEVANCE_SCORE};
+
+/// Reranks the results using Reciprocal Rank Fusion(RRF) algorithm based
+/// on the scores of vector and FTS search.
+///
+#[derive(Debug)]
+pub struct RRFReranker {
+    k: f32,
+}
+
+impl RRFReranker {
+    /// Create a new RRFReranker
+    ///
+    /// The parameter k is a constant used in the RRF formula (default is 60).
+    /// Experiments indicate that k = 60 was near-optimal, but that the choice
+    /// is not critical. See paper:
+    /// https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf
+    pub fn new(k: f32) -> Self {
+        Self { k }
+    }
+}
+
+impl Default for RRFReranker {
+    fn default() -> Self {
+        Self { k: 60.0 }
+    }
+}
+
+#[async_trait]
+impl Reranker for RRFReranker {
+    async fn rerank_hybrid(
+        &self,
+        _query: &str,
+        vector_results: RecordBatch,
+        fts_results: RecordBatch,
+    ) -> Result<RecordBatch> {
+        let vector_ids = vector_results
+            .column_by_name(ROW_ID)
+            .ok_or(Error::InvalidInput {
+                message: format!(
+                    "expected column {} not found in vector_results. found columns {:?}",
+                    ROW_ID,
+                    vector_results
+                        .schema()
+                        .fields()
+                        .iter()
+                        .map(|f| f.name())
+                        .collect::<Vec<_>>()
+                ),
+            })?;
+        let fts_ids = fts_results
+            .column_by_name(ROW_ID)
+            .ok_or(Error::InvalidInput {
+                message: format!(
+                    "expected column {} not found in fts_results. found columns {:?}",
+                    ROW_ID,
+                    fts_results
+                        .schema()
+                        .fields()
+                        .iter()
+                        .map(|f| f.name())
+                        .collect::<Vec<_>>()
+                ),
+            })?;
+
+        let vector_ids: UInt64Array = downcast_array(&vector_ids);
+        let fts_ids: UInt64Array = downcast_array(&fts_ids);
+
+        let mut rrf_score_map = BTreeMap::new();
+        let mut update_score_map = |(i, result_id)| {
+            let score = 1.0 / (i as f32 + self.k);
+            rrf_score_map
+                .entry(result_id)
+                .and_modify(|e| *e += score)
+                .or_insert(score);
+        };
+        vector_ids
+            .values()
+            .iter()
+            .enumerate()
+            .for_each(&mut update_score_map);
+        fts_ids
+            .values()
+            .iter()
+            .enumerate()
+            .for_each(&mut update_score_map);
+
+        let combined_results = self.merge_results(vector_results, fts_results)?;
+
+        let combined_row_ids: UInt64Array =
+            downcast_array(combined_results.column_by_name(ROW_ID).unwrap());
+        let relevance_scores = Float32Array::from_iter_values(
+            combined_row_ids
+                .values()
+                .iter()
+                .map(|row_id| rrf_score_map.get(row_id).unwrap())
+                .copied(),
+        );
+
+        // keep track of indices sorted by the relevance column
+        let sort_indices = sort_to_indices(
+            &relevance_scores,
+            Some(SortOptions {
+                descending: true,
+                ..Default::default()
+            }),
+            None,
+        )
+        .unwrap();
+
+        // add relevance scores to columns
+        let mut columns = combined_results.columns().to_vec();
+        columns.push(Arc::new(relevance_scores));
+
+        // sort by the relevance scores
+        let columns = columns
+            .iter()
+            .map(|c| take(c, &sort_indices, None).unwrap())
+            .collect();
+
+        // add relevance score to schema
+        let mut fields = combined_results.schema().fields().to_vec();
+        fields.push(Arc::new(Field::new(
+            RELEVANCE_SCORE,
+            DataType::Float32,
+            false,
+        )));
+        let schema = Schema::new(fields);
+
+        let combined_results = RecordBatch::try_new(Arc::new(schema), columns)?;
+
+        Ok(combined_results)
+    }
+}
+
+#[cfg(test)]
+pub mod test {
+    use super::*;
+    use arrow_array::StringArray;
+
+    #[tokio::test]
+    async fn test_rrf_reranker() {
+        let schema = Arc::new(Schema::new(vec![
+            Field::new("name", DataType::Utf8, false),
+            Field::new(ROW_ID, DataType::UInt64, false),
+        ]));
+
+        let vec_results = RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(StringArray::from(vec!["foo", "bar", "baz", "bean", "dog"])),
+                Arc::new(UInt64Array::from(vec![1, 4, 2, 5, 3])),
+            ],
+        )
+        .unwrap();
+
+        let fts_results = RecordBatch::try_new(
+            schema.clone(),
+            vec![
+                Arc::new(StringArray::from(vec!["bar", "bean", "dog"])),
+                Arc::new(UInt64Array::from(vec![4, 5, 3])),
+            ],
+        )
+        .unwrap();
+
+        // scores should be calculated as:
+        // - foo = 1/1        = 1.0
+        // - bar = 1/2 + 1/1  = 1.5
+        // - baz = 1/3        = 0.333
+        // - bean = 1/4 + 1/2 = 0.75
+        // - dog = 1/5 + 1/3  = 0.533
+        // then we should get the result ranked in descending order
+
+        let reranker = RRFReranker::new(1.0);
+
+        let result = reranker
+            .rerank_hybrid("", vec_results, fts_results)
+            .await
+            .unwrap();
+
+        assert_eq!(3, result.schema().fields().len());
+        assert_eq!("name", result.schema().fields().first().unwrap().name());
+        assert_eq!(ROW_ID, result.schema().fields().get(1).unwrap().name());
+        assert_eq!(
+            RELEVANCE_SCORE,
+            result.schema().fields().get(2).unwrap().name()
+        );
+
+        let names: StringArray = downcast_array(result.column(0));
+        assert_eq!(
+            names.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec!["bar", "foo", "bean", "dog", "baz"]
+        );
+
+        let ids: UInt64Array = downcast_array(result.column(1));
+        assert_eq!(
+            ids.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![4, 1, 5, 3, 2]
+        );
+
+        let scores: Float32Array = downcast_array(result.column(2));
+        assert_eq!(
+            scores.iter().map(|e| e.unwrap()).collect::<Vec<_>>(),
+            vec![1.5, 1.0, 0.75, 1.0 / 5.0 + 1.0 / 3.0, 1.0 / 3.0]
+        );
+    }
+}

rust/lancedb/src/table.rs

@@ -18,9 +18,9 @@ use std::path::Path;
 use std::sync::Arc;
 
 use arrow::array::AsArray;
-use arrow::datatypes::Float32Type;
+use arrow::datatypes::{Float32Type, UInt8Type};
 use arrow_array::{RecordBatchIterator, RecordBatchReader};
-use arrow_schema::{Field, Schema, SchemaRef};
+use arrow_schema::{DataType, Field, Schema, SchemaRef};
 use async_trait::async_trait;
 use datafusion_physical_plan::display::DisplayableExecutionPlan;
 use datafusion_physical_plan::projection::ProjectionExec;
@@ -58,8 +58,8 @@ use crate::embeddings::{EmbeddingDefinition, EmbeddingRegistry, MaybeEmbedded, M
 use crate::error::{Error, Result};
 use crate::index::scalar::FtsIndexBuilder;
 use crate::index::vector::{
-    suggested_num_partitions_for_hnsw, IvfHnswPqIndexBuilder, IvfHnswSqIndexBuilder,
-    IvfPqIndexBuilder, VectorIndex,
+    suggested_num_partitions_for_hnsw, IvfFlatIndexBuilder, IvfHnswPqIndexBuilder,
+    IvfHnswSqIndexBuilder, IvfPqIndexBuilder, VectorIndex,
 };
 use crate::index::IndexStatistics;
 use crate::index::{
@@ -1306,6 +1306,44 @@ impl NativeTable {
             .collect())
     }
 
+    async fn create_ivf_flat_index(
+        &self,
+        index: IvfFlatIndexBuilder,
+        field: &Field,
+        replace: bool,
+    ) -> Result<()> {
+        if !supported_vector_data_type(field.data_type()) {
+            return Err(Error::InvalidInput {
+                message: format!(
+                    "An IVF Flat index cannot be created on the column `{}` which has data type {}",
+                    field.name(),
+                    field.data_type()
+                ),
+            });
+        }
+
+        let num_partitions = if let Some(n) = index.num_partitions {
+            n
+        } else {
+            suggested_num_partitions(self.count_rows(None).await?)
+        };
+        let mut dataset = self.dataset.get_mut().await?;
+        let lance_idx_params = lance::index::vector::VectorIndexParams::ivf_flat(
+            num_partitions as usize,
+            index.distance_type.into(),
+        );
+        dataset
+            .create_index(
+                &[field.name()],
+                IndexType::Vector,
+                None,
+                &lance_idx_params,
+                replace,
+            )
+            .await?;
+        Ok(())
+    }
+
     async fn create_ivf_pq_index(
         &self,
         index: IvfPqIndexBuilder,
@@ -1778,6 +1816,10 @@ impl TableInternal for NativeTable {
             Index::Bitmap(_) => self.create_bitmap_index(field, opts).await,
             Index::LabelList(_) => self.create_label_list_index(field, opts).await,
             Index::FTS(fts_opts) => self.create_fts_index(field, fts_opts, opts.replace).await,
+            Index::IvfFlat(ivf_flat) => {
+                self.create_ivf_flat_index(ivf_flat, field, opts.replace)
+                    .await
+            }
             Index::IvfPq(ivf_pq) => self.create_ivf_pq_index(ivf_pq, field, opts.replace).await,
             Index::IvfHnswPq(ivf_hnsw_pq) => {
                 self.create_ivf_hnsw_pq_index(ivf_hnsw_pq, field, opts.replace)
@@ -1848,14 +1890,21 @@ impl TableInternal for NativeTable {
                 message: format!("Column {} not found in dataset schema", column),
             })?;
 
-            if let arrow_schema::DataType::FixedSizeList(f, dim) = field.data_type() {
-                if !f.data_type().is_floating() {
-                    return Err(Error::InvalidInput {
-                        message: format!(
-                            "The data type of the vector column '{}' is not a floating point type",
-                            column
-                        ),
-                    });
+            let mut is_binary = false;
+            if let arrow_schema::DataType::FixedSizeList(element, dim) = field.data_type() {
+                match element.data_type() {
+                    e_type if e_type.is_floating() => {}
+                    e_type if *e_type == DataType::UInt8 => {
+                        is_binary = true;
+                    }
+                    _ => {
+                        return Err(Error::InvalidInput {
+                            message: format!(
+                                "The data type of the vector column '{}' is not a floating point type",
+                                column
+                            ),
+                        });
+                    }
                 }
                 if dim != query_vector.len() as i32 {
                     return Err(Error::InvalidInput {
@@ -1870,12 +1919,22 @@ impl TableInternal for NativeTable {
                 }
             }
 
-            let query_vector = query_vector.as_primitive::<Float32Type>();
-            scanner.nearest(
-                &column,
-                query_vector,
-                query.base.limit.unwrap_or(DEFAULT_TOP_K),
-            )?;
+            if is_binary {
+                let query_vector = arrow::compute::cast(&query_vector, &DataType::UInt8)?;
+                let query_vector = query_vector.as_primitive::<UInt8Type>();
+                scanner.nearest(
+                    &column,
+                    query_vector,
+                    query.base.limit.unwrap_or(DEFAULT_TOP_K),
+                )?;
+            } else {
+                let query_vector = query_vector.as_primitive::<Float32Type>();
+                scanner.nearest(
+                    &column,
+                    query_vector,
+                    query.base.limit.unwrap_or(DEFAULT_TOP_K),
+                )?;
+            }
         }
         scanner.limit(
             query.base.limit.map(|limit| limit as i64),

rust/lancedb/src/utils.rs

@@ -110,7 +110,7 @@ pub(crate) fn default_vector_column(schema: &Schema, dim: Option<i32>) -> Result
         .iter()
         .filter_map(|field| match field.data_type() {
             arrow_schema::DataType::FixedSizeList(f, d)
-                if f.data_type().is_floating()
+                if (f.data_type().is_floating() || f.data_type() == &DataType::UInt8)
                     && dim.map(|expect| *d == expect).unwrap_or(true) =>
             {
                 Some(field.name())
@@ -171,7 +171,9 @@ pub fn supported_fts_data_type(dtype: &DataType) -> bool {
 
 pub fn supported_vector_data_type(dtype: &DataType) -> bool {
     match dtype {
-        DataType::FixedSizeList(inner, _) => DataType::is_floating(inner.data_type()),
+        DataType::FixedSizeList(inner, _) => {
+            DataType::is_floating(inner.data_type()) || *inner.data_type() == DataType::UInt8
+        }
         _ => false,
     }
 }