Bump version: 0.13.0-beta.0 → 0.13.0-beta.1

Co-authored-by: Akela Drissner-Schmid <32450038+akelad@users.noreply.github.com>

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.7.1"
+current_version = "0.10.0-beta.0"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/java.yml

@@ -3,6 +3,8 @@ on:
   push:
     branches:
       - main
+    paths:
+      - java/**
   pull_request:
     paths:
       - java/**
@@ -21,9 +23,42 @@ env:
   CARGO_INCREMENTAL: "0"
   CARGO_BUILD_JOBS: "1"
 jobs:
-  linux-build:
+  linux-build-java-11:
     runs-on: ubuntu-22.04
-    name: ubuntu-22.04 + Java 11 & 17
+    name: ubuntu-22.04 + Java 11
+    defaults:
+      run:
+        working-directory: ./java
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: java/core/lancedb-jni
+      - name: Run cargo fmt
+        run: cargo fmt --check
+        working-directory: ./java/core/lancedb-jni
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+      - name: Install Java 11
+        uses: actions/setup-java@v4
+        with:
+          distribution: temurin
+          java-version: 11
+          cache: "maven"
+      - name: Java Style Check
+        run: mvn checkstyle:check
+      # Disable because of issues in lancedb rust core code
+      # - name: Rust Clippy
+      #   working-directory: java/core/lancedb-jni
+      #   run: cargo clippy --all-targets -- -D warnings
+      - name: Running tests with Java 11
+        run: mvn clean test
+  linux-build-java-17:
+    runs-on: ubuntu-22.04
+    name: ubuntu-22.04 + Java 17
     defaults:
       run:
         working-directory: ./java
@@ -47,20 +82,12 @@ jobs:
           java-version: 17
           cache: "maven"
       - run: echo "JAVA_17=$JAVA_HOME" >> $GITHUB_ENV
-      - name: Install Java 11
-        uses: actions/setup-java@v4
-        with:
-          distribution: temurin
-          java-version: 11
-          cache: "maven"
       - name: Java Style Check
         run: mvn checkstyle:check
       # Disable because of issues in lancedb rust core code
       # - name: Rust Clippy
       #   working-directory: java/core/lancedb-jni
       #   run: cargo clippy --all-targets -- -D warnings
-      - name: Running tests with Java 11
-        run: mvn clean test
       - name: Running tests with Java 17
         run: |
           export JAVA_TOOL_OPTIONS="$JAVA_TOOL_OPTIONS \
@@ -83,3 +110,4 @@ jobs:
           -Djdk.reflect.useDirectMethodHandle=false \
           -Dio.netty.tryReflectionSetAccessible=true"
           JAVA_HOME=$JAVA_17 mvn clean test
+  

Cargo.toml

@@ -20,29 +20,30 @@ keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
 
 [workspace.dependencies]
-lance = { "version" = "=0.14.1", "features" = ["dynamodb"] }
+lance = { "version" = "=0.16.1", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.14.1" }
+lance-index = { "version" = "=0.16.1" }
-lance-linalg = { "version" = "=0.14.1" }
+lance-linalg = { "version" = "=0.16.1" }
-lance-testing = { "version" = "=0.14.1" }
+lance-testing = { "version" = "=0.16.1" }
-lance-datafusion = { "version" = "=0.14.1" }
+lance-datafusion = { "version" = "=0.16.1" }
+lance-encoding = { "version" = "=0.16.1" }
 # Note that this one does not include pyarrow
-arrow = { version = "51.0", optional = false }
+arrow = { version = "52.2", optional = false }
-arrow-array = "51.0"
+arrow-array = "52.2"
-arrow-data = "51.0"
+arrow-data = "52.2"
-arrow-ipc = "51.0"
+arrow-ipc = "52.2"
-arrow-ord = "51.0"
+arrow-ord = "52.2"
-arrow-schema = "51.0"
+arrow-schema = "52.2"
-arrow-arith = "51.0"
+arrow-arith = "52.2"
-arrow-cast = "51.0"
+arrow-cast = "52.2"
 async-trait = "0"
 chrono = "0.4.35"
-datafusion-physical-plan = "37.1"
+datafusion-physical-plan = "40.0"
 half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",
 ] }
 futures = "0"
 log = "0.4"
-object_store = "0.9.0"
+object_store = "0.10.2"
 pin-project = "1.0.7"
 snafu = "0.7.4"
 url = "2"

README.md

@@ -7,8 +7,8 @@
 
 <a href='https://github.com/lancedb/vectordb-recipes/tree/main' target="_blank"><img alt='LanceDB' src='https://img.shields.io/badge/VectorDB_Recipes-100000?style=for-the-badge&logo=LanceDB&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
 <a href='https://lancedb.github.io/lancedb/' target="_blank"><img alt='lancdb' src='https://img.shields.io/badge/DOCS-100000?style=for-the-badge&logo=lancdb&logoColor=white&labelColor=645cfb&color=645cfb'/></a>
-[![Blog](https://img.shields.io/badge/Blog-12100E?style=for-the-badge&logoColor=white)](https://blog.lancedb.com/) 
+[![Blog](https://img.shields.io/badge/Blog-12100E?style=for-the-badge&logoColor=white)](https://blog.lancedb.com/)
-[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd) 
+[![Discord](https://img.shields.io/badge/Discord-%235865F2.svg?style=for-the-badge&logo=discord&logoColor=white)](https://discord.gg/zMM32dvNtd)
 [![Twitter](https://img.shields.io/badge/Twitter-%231DA1F2.svg?style=for-the-badge&logo=Twitter&logoColor=white)](https://twitter.com/lancedb)
 
 </p>
@@ -44,26 +44,24 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 
 **Javascript**
 ```shell
-npm install vectordb
+npm install @lancedb/lancedb
 ```
 
 ```javascript
-const lancedb = require('vectordb');
+import * as lancedb from "@lancedb/lancedb";
-const db = await lancedb.connect('data/sample-lancedb');
 
-const table = await db.createTable({
+const db = await lancedb.connect("data/sample-lancedb");
-  name: 'vectors',
+const table = await db.createTable("vectors", [
-  data:  [
+	{ id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
-    { id: 1, vector: [0.1, 0.2], item: "foo", price: 10 },
+	{ id: 2, vector: [1.1, 1.2], item: "bar", price: 50 },
-    { id: 2, vector: [1.1, 1.2], item: "bar", price: 50 }
+], {mode: 'overwrite'});
-  ]
-})
 
-const query = table.search([0.1, 0.3]).limit(2);
+
-const results = await query.execute();
+const query = table.vectorSearch([0.1, 0.3]).limit(2);
+const results = await query.toArray();
 
 // You can also search for rows by specific criteria without involving a vector search.
-const rowsByCriteria = await table.search(undefined).where("price >= 10").execute();
+const rowsByCriteria = await table.query().where("price >= 10").toArray();
 ```
 
 **Python**

ci/build_linux_artifacts.sh

@@ -18,4 +18,4 @@ docker run \
     -v $(pwd):/io -w /io \
     --memory-swap=-1 \
     lancedb-node-manylinux \
-    bash ci/manylinux_node/build.sh $ARCH
+    bash ci/manylinux_node/build_vectordb.sh $ARCH

ci/build_linux_artifacts_nodejs.sh

@@ -4,9 +4,9 @@ ARCH=${1:-x86_64}
 
 # We pass down the current user so that when we later mount the local files
 # into the container, the files are accessible by the current user.
-pushd ci/manylinux_nodejs
+pushd ci/manylinux_node
 docker build \
-    -t lancedb-nodejs-manylinux \
+    -t lancedb-node-manylinux-$ARCH \
     --build-arg="ARCH=$ARCH" \
     --build-arg="DOCKER_USER=$(id -u)" \
     --progress=plain \
@@ -17,5 +17,5 @@ popd
 docker run \
     -v $(pwd):/io -w /io \
     --memory-swap=-1 \
-    lancedb-nodejs-manylinux \
+    lancedb-node-manylinux-$ARCH \
-    bash ci/manylinux_nodejs/build.sh $ARCH
+    bash ci/manylinux_node/build_lancedb.sh $ARCH

ci/manylinux_node/Dockerfile

@@ -4,7 +4,7 @@
 # range of linux distributions.
 ARG ARCH=x86_64
 
-FROM quay.io/pypa/manylinux2014_${ARCH}
+FROM quay.io/pypa/manylinux_2_28_${ARCH}
 
 ARG ARCH=x86_64
 ARG DOCKER_USER=default_user

ci/manylinux_node/install_openssl.sh

@@ -6,7 +6,7 @@
 # /usr/bin/ld: failed to set dynamic section sizes: Bad value
 set -e
 
-git clone -b OpenSSL_1_1_1u \
+git clone -b OpenSSL_1_1_1v \
     --single-branch \
     https://github.com/openssl/openssl.git
 

ci/manylinux_node/prepare_manylinux_node.sh

@@ -8,7 +8,7 @@ install_node() {
 
     source "$HOME"/.bashrc
 
-    nvm install --no-progress 16
+    nvm install --no-progress 18
 }
 
 install_rust() {

ci/manylinux_nodejs/Dockerfile

@@ -1,31 +0,0 @@
-# Many linux dockerfile with Rust, Node, and Lance dependencies installed.
-# This container allows building the node modules native libraries in an
-# environment with a very old glibc, so that we are compatible with a wide
-# range of linux distributions.
-ARG ARCH=x86_64
-
-FROM quay.io/pypa/manylinux2014_${ARCH}
-
-ARG ARCH=x86_64
-ARG DOCKER_USER=default_user
-
-# Install static openssl
-COPY install_openssl.sh install_openssl.sh
-RUN ./install_openssl.sh ${ARCH} > /dev/null
-
-# Protobuf is also installed as root.
-COPY install_protobuf.sh install_protobuf.sh
-RUN ./install_protobuf.sh ${ARCH}
-
-ENV DOCKER_USER=${DOCKER_USER}
-# Create a group and user
-RUN echo ${ARCH} && adduser --user-group --create-home --uid ${DOCKER_USER} build_user
-
-# We switch to the user to install Rust and Node, since those like to be
-# installed at the user level.
-USER ${DOCKER_USER}
-
-COPY prepare_manylinux_node.sh prepare_manylinux_node.sh
-RUN cp /prepare_manylinux_node.sh $HOME/ && \
-    cd $HOME && \
-    ./prepare_manylinux_node.sh ${ARCH}

ci/manylinux_nodejs/install_openssl.sh

@@ -1,26 +0,0 @@
-#!/bin/bash
-# Builds openssl from source so we can statically link to it
-
-# this is to avoid the error we get with the system installation:
-# /usr/bin/ld: <library>: version node not found for symbol SSLeay@@OPENSSL_1.0.1
-# /usr/bin/ld: failed to set dynamic section sizes: Bad value
-set -e
-
-git clone -b OpenSSL_1_1_1u \
-    --single-branch \
-    https://github.com/openssl/openssl.git
-
-pushd openssl
-
-if [[ $1 == x86_64* ]]; then
-    ARCH=linux-x86_64
-else
-    # gnu target
-    ARCH=linux-aarch64
-fi
-
-./Configure no-shared $ARCH
-
-make
-
-make install

ci/manylinux_nodejs/install_protobuf.sh

@@ -1,15 +0,0 @@
-#!/bin/bash
-# Installs protobuf compiler. Should be run as root.
-set -e
-
-if [[ $1 == x86_64* ]]; then
-    ARCH=x86_64
-else
-    # gnu target
-    ARCH=aarch_64
-fi
-
-PB_REL=https://github.com/protocolbuffers/protobuf/releases
-PB_VERSION=23.1
-curl -LO $PB_REL/download/v$PB_VERSION/protoc-$PB_VERSION-linux-$ARCH.zip
-unzip protoc-$PB_VERSION-linux-$ARCH.zip -d /usr/local

ci/manylinux_nodejs/prepare_manylinux_node.sh

@@ -1,21 +0,0 @@
-#!/bin/bash
-set -e
-
-install_node() {
-    echo "Installing node..."
-
-    curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.34.0/install.sh | bash
-
-    source "$HOME"/.bashrc
-
-    nvm install --no-progress 16
-}
-
-install_rust() {
-    echo "Installing rust..."
-    curl https://sh.rustup.rs -sSf | bash -s -- -y
-    export PATH="$PATH:/root/.cargo/bin"
-}
-
-install_node
-install_rust

docs/mkdocs.yml

@@ -58,7 +58,7 @@ plugins:
             - https://pandas.pydata.org/docs/objects.inv
   - mkdocs-jupyter
   - render_swagger:
-      allow_arbitrary_locations : true
+      allow_arbitrary_locations: true
 
 markdown_extensions:
   - admonition
@@ -89,9 +89,10 @@ nav:
           - Data management: concepts/data_management.md
       - 🔨 Guides:
           - Working with tables: guides/tables.md
-          - Building an ANN index: ann_indexes.md
+          - Building a vector index: ann_indexes.md
           - Vector Search: search.md
           - Full-text search: fts.md
+          - Building a scalar index: guides/scalar_index.md
           - Hybrid search:
               - Overview: hybrid_search/hybrid_search.md
               - Comparing Rerankers: hybrid_search/eval.md
@@ -100,6 +101,7 @@ nav:
               - Quickstart: reranking/index.md
               - Cohere Reranker: reranking/cohere.md
               - Linear Combination Reranker: reranking/linear_combination.md
+              - Reciprocal Rank Fusion Reranker: reranking/rrf.md
               - Cross Encoder Reranker: reranking/cross_encoder.md
               - ColBERT Reranker: reranking/colbert.md
               - Jina Reranker: reranking/jina.md
@@ -127,25 +129,33 @@ nav:
           - Polars: python/polars_arrow.md
           - DuckDB: python/duckdb.md
           - LangChain:
-            - LangChain 🔗: integrations/langchain.md
+              - LangChain 🔗: integrations/langchain.md
-            - LangChain demo: notebooks/langchain_demo.ipynb
+              - LangChain demo: notebooks/langchain_demo.ipynb
-            - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
+              - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
           - LlamaIndex 🦙:
-            - LlamaIndex docs: integrations/llamaIndex.md
+              - LlamaIndex docs: integrations/llamaIndex.md
-            - LlamaIndex demo: notebooks/llamaIndex_demo.ipynb
+              - LlamaIndex demo: notebooks/llamaIndex_demo.ipynb
           - Pydantic: python/pydantic.md
           - Voxel51: integrations/voxel51.md
           - PromptTools: integrations/prompttools.md
+          - dlt: integrations/dlt.md
       - 🎯 Examples:
           - Overview: examples/index.md
           - 🐍 Python:
               - Overview: examples/examples_python.md
-              - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+              - Build From Scratch: examples/python_examples/build_from_scratch.md
-              - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+              - Multimodal: examples/python_examples/multimodal.md
-              - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+              - Rag: examples/python_examples/rag.md
-              - Example - Calculate CLIP Embeddings with Roboflow Inference: examples/image_embeddings_roboflow.md
+              - Vector Search: examples/python_examples/vector_search.md
-              - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+              - Chatbot: examples/python_examples/chatbot.md
-              - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+              - Evaluation: examples/python_examples/evaluations.md
+              - AI Agent: examples/python_examples/aiagent.md
+              - Miscellaneous: 
+                  - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+                  - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+                  - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+                  - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+                  - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
           - 👾 JavaScript:
               - Overview: examples/examples_js.md
               - Serverless Website Chatbot: examples/serverless_website_chatbot.md
@@ -177,6 +187,7 @@ nav:
       - Building an ANN index: ann_indexes.md
       - Vector Search: search.md
       - Full-text search: fts.md
+      - Building a scalar index: guides/scalar_index.md
       - Hybrid search:
           - Overview: hybrid_search/hybrid_search.md
           - Comparing Rerankers: hybrid_search/eval.md
@@ -185,6 +196,7 @@ nav:
           - Quickstart: reranking/index.md
           - Cohere Reranker: reranking/cohere.md
           - Linear Combination Reranker: reranking/linear_combination.md
+          - Reciprocal Rank Fusion Reranker: reranking/rrf.md
           - Cross Encoder Reranker: reranking/cross_encoder.md
           - ColBERT Reranker: reranking/colbert.md
           - Jina Reranker: reranking/jina.md
@@ -217,16 +229,31 @@ nav:
       - Pydantic: python/pydantic.md
       - Voxel51: integrations/voxel51.md
       - PromptTools: integrations/prompttools.md
+      - dlt: integrations/dlt.md
   - Examples:
       - examples/index.md
-      - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+      - 🐍 Python:
-      - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+          - Overview: examples/examples_python.md
-      - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+          - Build From Scratch: examples/python_examples/build_from_scratch.md
-      - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+          - Multimodal: examples/python_examples/multimodal.md
-      - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+          - Rag: examples/python_examples/rag.md
-      - YouTube Transcript Search (JS): examples/youtube_transcript_bot_with_nodejs.md
+          - Vector Search: examples/python_examples/vector_search.md
-      - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
+          - Chatbot: examples/python_examples/chatbot.md
-      - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
+          - Evaluation: examples/python_examples/evaluations.md
+          - AI Agent: examples/python_examples/aiagent.md
+          - Miscellaneous: 
+              - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+              - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+              - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+              - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+              - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+      - 👾 JavaScript:
+          - Overview: examples/examples_js.md
+          - Serverless Website Chatbot: examples/serverless_website_chatbot.md
+          - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
+          - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
+      - 🦀 Rust:
+          - Overview: examples/examples_rust.md
   - API reference:
       - Overview: api_reference.md
       - Python: python/python.md

docs/requirements.txt

@@ -1,6 +1,7 @@
 mkdocs==1.5.3
 mkdocs-jupyter==0.24.1
 mkdocs-material==9.5.3
-mkdocstrings[python]==0.20.0
+mkdocstrings[python]==0.25.2
+griffe
 mkdocs-render-swagger-plugin
 pydantic

docs/src/assets/colab.svg

@@ -0,0 +1 @@
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+</svg>

docs/src/assets/python.svg

@@ -0,0 +1 @@
+<svg xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" width="97.5" height="28" role="img" aria-label="PYTHON"><title>PYTHON</title><g shape-rendering="crispEdges"><rect width="97.5" height="28" fill="#3670a0"/></g><g fill="#fff" text-anchor="middle" font-family="Verdana,Geneva,DejaVu Sans,sans-serif" text-rendering="geometricPrecision" font-size="100"><image x="9" y="7" width="14" height="14" xlink:href="data:image/svg+xml;base64,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"/><text transform="scale(.1)" x="587.5" y="175" textLength="535" fill="#fff" font-weight="bold">PYTHON</text></g></svg>

docs/src/embeddings/custom_embedding_function.md

@@ -15,198 +15,226 @@ There is another optional layer of abstraction available: `TextEmbeddingFunction
 
 Let's implement `SentenceTransformerEmbeddings` class. All you need to do is implement the `generate_embeddings()` and `ndims` function to handle the input types you expect and register the class in the global `EmbeddingFunctionRegistry`
 
-```python
-from lancedb.embeddings import register
-from lancedb.util import attempt_import_or_raise
 
-@register("sentence-transformers")
+=== "Python"
-class SentenceTransformerEmbeddings(TextEmbeddingFunction):
-    name: str = "all-MiniLM-L6-v2"
-    # set more default instance vars like device, etc.
 
-    def __init__(self, **kwargs):
+    ```python
-        super().__init__(**kwargs)
+    from lancedb.embeddings import register
-        self._ndims = None
+    from lancedb.util import attempt_import_or_raise
-    
-    def generate_embeddings(self, texts):
-        return self._embedding_model().encode(list(texts), ...).tolist()
 
-    def ndims(self):
+    @register("sentence-transformers")
-        if self._ndims is None:
+    class SentenceTransformerEmbeddings(TextEmbeddingFunction):
-            self._ndims = len(self.generate_embeddings("foo")[0])
+        name: str = "all-MiniLM-L6-v2"
-        return self._ndims
+        # set more default instance vars like device, etc.
 
-    @cached(cache={}) 
+        def __init__(self, **kwargs):
-    def _embedding_model(self):
+            super().__init__(**kwargs)
-        return sentence_transformers.SentenceTransformer(name)
+            self._ndims = None
-```
 
-This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and defaul settings.
+        def generate_embeddings(self, texts):
+            return self._embedding_model().encode(list(texts), ...).tolist()
+
+        def ndims(self):
+            if self._ndims is None:
+                self._ndims = len(self.generate_embeddings("foo")[0])
+            return self._ndims
+
+        @cached(cache={})
+        def _embedding_model(self):
+            return sentence_transformers.SentenceTransformer(name)
+    ```
+
+=== "TypeScript"
+
+    ```ts
+    --8<--- "nodejs/examples/custom_embedding_function.ts:imports"
+
+    --8<--- "nodejs/examples/custom_embedding_function.ts:embedding_impl"
+    ```
+
+
+This is a stripped down version of our implementation of `SentenceTransformerEmbeddings` that removes certain optimizations and default settings.
 
 Now you can use this embedding function to create your table schema and that's it! you can then ingest data and run queries without manually vectorizing the inputs.
 
-```python
+=== "Python"
-from lancedb.pydantic import LanceModel, Vector
 
-registry = EmbeddingFunctionRegistry.get_instance()
+    ```python
-stransformer = registry.get("sentence-transformers").create()
+    from lancedb.pydantic import LanceModel, Vector
 
-class TextModelSchema(LanceModel):
+    registry = EmbeddingFunctionRegistry.get_instance()
-    vector: Vector(stransformer.ndims) = stransformer.VectorField()
+    stransformer = registry.get("sentence-transformers").create()
-    text: str = stransformer.SourceField()
 
-tbl = db.create_table("table", schema=TextModelSchema)
+    class TextModelSchema(LanceModel):
+        vector: Vector(stransformer.ndims) = stransformer.VectorField()
+        text: str = stransformer.SourceField()
 
-tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
+    tbl = db.create_table("table", schema=TextModelSchema)
-result = tbl.search("world").limit(5)
-```
 
-NOTE:
+    tbl.add(pd.DataFrame({"text": ["halo", "world"]}))
+    result = tbl.search("world").limit(5)
+    ```
 
-You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case
+=== "TypeScript"
+
+    ```ts
+    --8<--- "nodejs/examples/custom_embedding_function.ts:call_custom_function"
+    ```
+
+!!! note
+
+    You can always implement the `EmbeddingFunction` interface directly if you want or need to, `TextEmbeddingFunction` just makes it much simpler and faster for you to do so, by setting up the boiler plat for text-specific use case
 
 ## Multi-modal embedding function example
-You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support. LanceDB implements `OpenClipEmeddingFunction` class that suppports multi-modal seach. Here's the implementation that you can use as a reference to build your own multi-modal embedding functions.
+You can also use the `EmbeddingFunction` interface to implement more complex workflows such as multi-modal embedding function support.
 
-```python
+=== "Python"
-@register("open-clip")
-class OpenClipEmbeddings(EmbeddingFunction):
-    name: str = "ViT-B-32"
-    pretrained: str = "laion2b_s34b_b79k"
-    device: str = "cpu"
-    batch_size: int = 64
-    normalize: bool = True
-    _model = PrivateAttr()
-    _preprocess = PrivateAttr()
-    _tokenizer = PrivateAttr()
 
-    def __init__(self, *args, **kwargs):
+    LanceDB implements `OpenClipEmeddingFunction` class that suppports multi-modal seach. Here's the implementation that you can use as a reference to build your own multi-modal embedding functions.
-        super().__init__(*args, **kwargs)
-        open_clip = attempt_import_or_raise("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
-        model, _, preprocess = open_clip.create_model_and_transforms(
-            self.name, pretrained=self.pretrained
-        )
-        model.to(self.device)
-        self._model, self._preprocess = model, preprocess
-        self._tokenizer = open_clip.get_tokenizer(self.name)
-        self._ndims = None
 
-    def ndims(self):
+    ```python
-        if self._ndims is None:
+    @register("open-clip")
-            self._ndims = self.generate_text_embeddings("foo").shape[0]
+    class OpenClipEmbeddings(EmbeddingFunction):
-        return self._ndims
+        name: str = "ViT-B-32"
+        pretrained: str = "laion2b_s34b_b79k"
+        device: str = "cpu"
+        batch_size: int = 64
+        normalize: bool = True
+        _model = PrivateAttr()
+        _preprocess = PrivateAttr()
+        _tokenizer = PrivateAttr()
 
-    def compute_query_embeddings(
+        def __init__(self, *args, **kwargs):
-        self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
+            super().__init__(*args, **kwargs)
-    ) -> List[np.ndarray]:
+            open_clip = attempt_import_or_raise("open_clip", "open-clip") # EmbeddingFunction util to import external libs and raise if not found
-        """
+            model, _, preprocess = open_clip.create_model_and_transforms(
-        Compute the embeddings for a given user query
+                self.name, pretrained=self.pretrained
+            )
+            model.to(self.device)
+            self._model, self._preprocess = model, preprocess
+            self._tokenizer = open_clip.get_tokenizer(self.name)
+            self._ndims = None
 
-        Parameters
+        def ndims(self):
-        ----------
+            if self._ndims is None:
-        query : Union[str, PIL.Image.Image]
+                self._ndims = self.generate_text_embeddings("foo").shape[0]
-            The query to embed. A query can be either text or an image.
+            return self._ndims
-        """
+
-        if isinstance(query, str):
+        def compute_query_embeddings(
-            return [self.generate_text_embeddings(query)]
+            self, query: Union[str, "PIL.Image.Image"], *args, **kwargs
-        else:
+        ) -> List[np.ndarray]:
+            """
+            Compute the embeddings for a given user query
+
+            Parameters
+            ----------
+            query : Union[str, PIL.Image.Image]
+                The query to embed. A query can be either text or an image.
+            """
+            if isinstance(query, str):
+                return [self.generate_text_embeddings(query)]
+            else:
+                PIL = attempt_import_or_raise("PIL", "pillow")
+                if isinstance(query, PIL.Image.Image):
+                    return [self.generate_image_embedding(query)]
+                else:
+                    raise TypeError("OpenClip supports str or PIL Image as query")
+
+        def generate_text_embeddings(self, text: str) -> np.ndarray:
+            torch = attempt_import_or_raise("torch")
+            text = self.sanitize_input(text)
+            text = self._tokenizer(text)
+            text.to(self.device)
+            with torch.no_grad():
+                text_features = self._model.encode_text(text.to(self.device))
+                if self.normalize:
+                    text_features /= text_features.norm(dim=-1, keepdim=True)
+                return text_features.cpu().numpy().squeeze()
+
+        def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
+            """
+            Sanitize the input to the embedding function.
+            """
+            if isinstance(images, (str, bytes)):
+                images = [images]
+            elif isinstance(images, pa.Array):
+                images = images.to_pylist()
+            elif isinstance(images, pa.ChunkedArray):
+                images = images.combine_chunks().to_pylist()
+            return images
+
+        def compute_source_embeddings(
+            self, images: IMAGES, *args, **kwargs
+        ) -> List[np.array]:
+            """
+            Get the embeddings for the given images
+            """
+            images = self.sanitize_input(images)
+            embeddings = []
+            for i in range(0, len(images), self.batch_size):
+                j = min(i + self.batch_size, len(images))
+                batch = images[i:j]
+                embeddings.extend(self._parallel_get(batch))
+            return embeddings
+
+        def _parallel_get(self, images: Union[List[str], List[bytes]]) -> List[np.ndarray]:
+            """
+            Issue concurrent requests to retrieve the image data
+            """
+            with concurrent.futures.ThreadPoolExecutor() as executor:
+                futures = [
+                    executor.submit(self.generate_image_embedding, image)
+                    for image in images
+                ]
+                return [future.result() for future in futures]
+
+        def generate_image_embedding(
+            self, image: Union[str, bytes, "PIL.Image.Image"]
+        ) -> np.ndarray:
+            """
+            Generate the embedding for a single image
+
+            Parameters
+            ----------
+            image : Union[str, bytes, PIL.Image.Image]
+                The image to embed. If the image is a str, it is treated as a uri.
+                If the image is bytes, it is treated as the raw image bytes.
+            """
+            torch = attempt_import_or_raise("torch")
+            # TODO handle retry and errors for https
+            image = self._to_pil(image)
+            image = self._preprocess(image).unsqueeze(0)
+            with torch.no_grad():
+                return self._encode_and_normalize_image(image)
+
+        def _to_pil(self, image: Union[str, bytes]):
             PIL = attempt_import_or_raise("PIL", "pillow")
-            if isinstance(query, PIL.Image.Image):
+            if isinstance(image, bytes):
-                return [self.generate_image_embedding(query)]
+                return PIL.Image.open(io.BytesIO(image))
-            else:
+            if isinstance(image, PIL.Image.Image):
-                raise TypeError("OpenClip supports str or PIL Image as query")
+                return image
+            elif isinstance(image, str):
+                parsed = urlparse.urlparse(image)
+                # TODO handle drive letter on windows.
+                if parsed.scheme == "file":
+                    return PIL.Image.open(parsed.path)
+                elif parsed.scheme == "":
+                    return PIL.Image.open(image if os.name == "nt" else parsed.path)
+                elif parsed.scheme.startswith("http"):
+                    return PIL.Image.open(io.BytesIO(url_retrieve(image)))
+                else:
+                    raise NotImplementedError("Only local and http(s) urls are supported")
 
-    def generate_text_embeddings(self, text: str) -> np.ndarray:
+        def _encode_and_normalize_image(self, image_tensor: "torch.Tensor"):
-        torch = attempt_import_or_raise("torch")
+            """
-        text = self.sanitize_input(text)
+            encode a single image tensor and optionally normalize the output
-        text = self._tokenizer(text)
+            """
-        text.to(self.device)
+            image_features = self._model.encode_image(image_tensor)
-        with torch.no_grad():
-            text_features = self._model.encode_text(text.to(self.device))
             if self.normalize:
-                text_features /= text_features.norm(dim=-1, keepdim=True)
+                image_features /= image_features.norm(dim=-1, keepdim=True)
-            return text_features.cpu().numpy().squeeze()
+            return image_features.cpu().numpy().squeeze()
+    ```
 
-    def sanitize_input(self, images: IMAGES) -> Union[List[bytes], np.ndarray]:
+=== "TypeScript"
-        """
-        Sanitize the input to the embedding function.
-        """
-        if isinstance(images, (str, bytes)):
-            images = [images]
-        elif isinstance(images, pa.Array):
-            images = images.to_pylist()
-        elif isinstance(images, pa.ChunkedArray):
-            images = images.combine_chunks().to_pylist()
-        return images
 
-    def compute_source_embeddings(
+    Coming Soon! See this [issue](https://github.com/lancedb/lancedb/issues/1482) to track the status!
-        self, images: IMAGES, *args, **kwargs
-    ) -> List[np.array]:
-        """
-        Get the embeddings for the given images
-        """
-        images = self.sanitize_input(images)
-        embeddings = []
-        for i in range(0, len(images), self.batch_size):
-            j = min(i + self.batch_size, len(images))
-            batch = images[i:j]
-            embeddings.extend(self._parallel_get(batch))
-        return embeddings
-
-    def _parallel_get(self, images: Union[List[str], List[bytes]]) -> List[np.ndarray]:
-        """
-        Issue concurrent requests to retrieve the image data
-        """
-        with concurrent.futures.ThreadPoolExecutor() as executor:
-            futures = [
-                executor.submit(self.generate_image_embedding, image)
-                for image in images
-            ]
-            return [future.result() for future in futures]
-
-    def generate_image_embedding(
-        self, image: Union[str, bytes, "PIL.Image.Image"]
-    ) -> np.ndarray:
-        """
-        Generate the embedding for a single image
-
-        Parameters
-        ----------
-        image : Union[str, bytes, PIL.Image.Image]
-            The image to embed. If the image is a str, it is treated as a uri.
-            If the image is bytes, it is treated as the raw image bytes.
-        """
-        torch = attempt_import_or_raise("torch")
-        # TODO handle retry and errors for https
-        image = self._to_pil(image)
-        image = self._preprocess(image).unsqueeze(0)
-        with torch.no_grad():
-            return self._encode_and_normalize_image(image)
-
-    def _to_pil(self, image: Union[str, bytes]):
-        PIL = attempt_import_or_raise("PIL", "pillow")
-        if isinstance(image, bytes):
-            return PIL.Image.open(io.BytesIO(image))
-        if isinstance(image, PIL.Image.Image):
-            return image
-        elif isinstance(image, str):
-            parsed = urlparse.urlparse(image)
-            # TODO handle drive letter on windows.
-            if parsed.scheme == "file":
-                return PIL.Image.open(parsed.path)
-            elif parsed.scheme == "":
-                return PIL.Image.open(image if os.name == "nt" else parsed.path)
-            elif parsed.scheme.startswith("http"):
-                return PIL.Image.open(io.BytesIO(url_retrieve(image)))
-            else:
-                raise NotImplementedError("Only local and http(s) urls are supported")
-
-    def _encode_and_normalize_image(self, image_tensor: "torch.Tensor"):
-        """
-        encode a single image tensor and optionally normalize the output
-        """
-        image_features = self._model.encode_image(image_tensor)
-        if self.normalize:
-            image_features /= image_features.norm(dim=-1, keepdim=True)
-        return image_features.cpu().numpy().squeeze()
-```

docs/src/embeddings/default_embedding_functions.md

@@ -390,6 +390,7 @@ Supported parameters (to be passed in `create` method) are:
 | `query_input_type` | `str` | `"search_query"` | The type of input data to be used for the query. |
 
 Cohere supports following input types:
+
 | Input Type               | Description                          |
 |-------------------------|---------------------------------------|
 | "`search_document`"     | Used for embeddings stored in a vector|
@@ -517,6 +518,82 @@ tbl.add(df)
 rs = tbl.search("hello").limit(1).to_pandas()
 ```
 
+# IBM watsonx.ai Embeddings
+
+Generate text embeddings using IBM's watsonx.ai platform.
+
+## Supported Models
+
+You can find a list of supported models at [IBM watsonx.ai Documentation](https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx). The currently supported model names are:
+
+- `ibm/slate-125m-english-rtrvr`
+- `ibm/slate-30m-english-rtrvr`
+- `sentence-transformers/all-minilm-l12-v2`
+- `intfloat/multilingual-e5-large`
+
+## Parameters
+
+The following parameters can be passed to the `create` method:
+
+| Parameter  | Type     | Default Value                    | Description                                               |
+|------------|----------|----------------------------------|-----------------------------------------------------------|
+| name       | str      | "ibm/slate-125m-english-rtrvr"   | The model ID of the watsonx.ai model to use               |
+| api_key    | str      | None                             | Optional IBM Cloud API key (or set `WATSONX_API_KEY`)     |
+| project_id | str      | None                             | Optional watsonx project ID (or set `WATSONX_PROJECT_ID`) |
+| url        | str      | None                             | Optional custom URL for the watsonx.ai instance           |
+| params     | dict     | None                             | Optional additional parameters for the embedding model    |
+
+## Usage Example
+
+First, the watsonx.ai library is an optional dependency, so must be installed seperately:
+
+```
+pip install ibm-watsonx-ai
+```
+
+Optionally set environment variables (if not passing credentials to `create` directly):
+
+```sh
+export WATSONX_API_KEY="YOUR_WATSONX_API_KEY"
+export WATSONX_PROJECT_ID="YOUR_WATSONX_PROJECT_ID"
+```
+
+```python
+import os
+import lancedb
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.embeddings import EmbeddingFunctionRegistry
+
+watsonx_embed = EmbeddingFunctionRegistry
+  .get_instance()
+  .get("watsonx")
+  .create(
+    name="ibm/slate-125m-english-rtrvr",
+    # Uncomment and set these if not using environment variables
+    # api_key="your_api_key_here",
+    # project_id="your_project_id_here",
+    # url="your_watsonx_url_here",
+    # params={...},
+  )
+
+class TextModel(LanceModel):
+    text: str = watsonx_embed.SourceField()
+    vector: Vector(watsonx_embed.ndims()) = watsonx_embed.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"},
+]
+
+db = lancedb.connect("~/.lancedb")
+tbl = db.create_table("watsonx_test", schema=TextModel, mode="overwrite")
+
+tbl.add(data)
+
+rs = tbl.search("hello").limit(1).to_pandas()
+print(rs)
+```
+
 ## Multi-modal embedding functions
 Multi-modal embedding functions allow you to query your table using both images and text.
 
@@ -720,4 +797,4 @@ Usage Example:
     table.add(
       pd.DataFrame({"label": labels, "image_uri": uris, "image_bytes": image_bytes})
     )
-```
+```

docs/src/embeddings/embedding_functions.md

@@ -2,8 +2,8 @@ Representing multi-modal data as vector embeddings is becoming a standard practi
 
 For this purpose, LanceDB introduces an **embedding functions API**, that allow you simply set up once, during the configuration stage of your project. After this, the table remembers it, effectively making the embedding functions *disappear in the background* so you don't have to worry about manually passing callables, and instead, simply focus on the rest of your data engineering pipeline.
 
-!!! Note "LanceDB cloud doesn't support embedding functions yet"
+!!! Note "Embedding functions on LanceDB cloud"
-    LanceDB Cloud does not support embedding functions yet. You need to generate embeddings before ingesting into the table or querying.
+    When using embedding functions with LanceDB cloud, the embeddings will be generated on the source device and sent to the cloud. This means that the source device must have the necessary resources to generate the embeddings.
 
 !!! warning
     Using the embedding function registry means that you don't have to explicitly generate the embeddings yourself.

docs/src/embeddings/index.md

@@ -99,28 +99,28 @@ LanceDB registers the Sentence Transformers embeddings function in the registry
 
     Coming Soon!
 
-### Jina Embeddings
+### Embedding function with LanceDB cloud
-
+Embedding functions are now supported on LanceDB cloud. The embeddings will be generated on the source device and sent to the cloud. This means that the source device must have the necessary resources to generate the embeddings. Here's an example using the OpenAI embedding function:
-LanceDB registers the JinaAI embeddings function in the registry as `jina`. You can pass any supported model name to the `create`. By default it uses `"jina-clip-v1"`.
-`jina-clip-v1` can handle both text and images and other models only support `text`.
-
-You need to pass `JINA_API_KEY` in the environment variable or pass it as `api_key` to `create` method.
 
 ```python
 import os
 import lancedb
 from lancedb.pydantic import LanceModel, Vector
 from lancedb.embeddings import get_registry
-os.environ['JINA_API_KEY'] = "jina_*"
+os.environ['OPENAI_API_KEY'] = "..."
 
-db = lancedb.connect("/tmp/db")
+db = lancedb.connect(
-func = get_registry().get("jina").create(name="jina-clip-v1")
+  uri="db://....",
+  api_key="sk_...",
+  region="us-east-1"
+)
+func = get_registry().get("openai").create()
 
 class Words(LanceModel):
     text: str = func.SourceField()
     vector: Vector(func.ndims()) = func.VectorField()
 
-table = db.create_table("words", schema=Words, mode="overwrite")
+table = db.create_table("words", schema=Words)
 table.add(
     [
         {"text": "hello world"},

docs/src/examples/index.md

@@ -10,7 +10,7 @@ LanceDB provides language APIs, allowing you to embed a database in your languag
 
 ## Applications powered by LanceDB
 
-| Project Name                                        | Description                                                                                                          | Screenshot                                |
+| Project Name | Description |
-|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
+| --- | --- |
-| [YOLOExplorer](https://github.com/lancedb/yoloexplorer) | Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds                  | ![YOLOExplorer](https://github.com/lancedb/vectordb-recipes/assets/15766192/ae513a29-8f15-4e0b-99a1-ccd8272b6131) |
+| **Ultralytics Explorer 🚀**<br>[![Ultralytics](https://img.shields.io/badge/Ultralytics-Docs-green?labelColor=0f3bc4&style=flat-square&logo=https://cdn.prod.website-files.com/646dd1f1a3703e451ba81ecc/64994922cf2a6385a4bf4489_UltralyticsYOLO_mark_blue.svg&link=https://docs.ultralytics.com/datasets/explorer/)](https://docs.ultralytics.com/datasets/explorer/)<br>[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/docs/en/datasets/explorer/explorer.ipynb) |  - 🔍 **Explore CV Datasets**: Semantic search, SQL queries, vector similarity, natural language.<br>- 🖥️ **GUI & Python API**: Seamless dataset interaction.<br>- ⚡ **Efficient & Scalable**: Leverages LanceDB for large datasets.<br>- 📊 **Detailed Analysis**: Easily analyze data patterns.<br>- 🌐 **Browser GUI Demo**: Create embeddings, search images, run queries. |
-| [Website Chatbot (Deployable Vercel Template)](https://github.com/lancedb/lancedb-vercel-chatbot) | Create a chatbot from the sitemap of any website/docs of your choice. Built using vectorDB serverless native javascript package. | ![Chatbot](../assets/vercel-template.gif)    |
+| **Website Chatbot🤖**<br>[![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/lancedb-vercel-chatbot)<br>[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Flancedb%2Flancedb-vercel-chatbot&amp;env=OPENAI_API_KEY&amp;envDescription=OpenAI%20API%20Key%20for%20chat%20completion.&amp;project-name=lancedb-vercel-chatbot&amp;repository-name=lancedb-vercel-chatbot&amp;demo-title=LanceDB%20Chatbot%20Demo&amp;demo-description=Demo%20website%20chatbot%20with%20LanceDB.&amp;demo-url=https%3A%2F%2Flancedb.vercel.app&amp;demo-image=https%3A%2F%2Fi.imgur.com%2FazVJtvr.png) | - 🌐 **Chatbot from Sitemap/Docs**: Create a chatbot using site or document context.<br>- 🚀 **Embed LanceDB in Next.js**: Lightweight, on-prem storage.<br>- 🧠 **AI-Powered Context Retrieval**: Efficiently access relevant data.<br>- 🔧 **Serverless & Native JS**: Seamless integration with Next.js.<br>- ⚡ **One-Click Deploy on Vercel**: Quick and easy setup.. |

docs/src/examples/python_examples/aiagent.md

@@ -0,0 +1,27 @@
+# AI Agents: Intelligent Collaboration🤖
+
+Think of a platform💻 where AI Agents🤖 can seamlessly exchange information, coordinate over tasks, and achieve shared targets with great efficiency📈🚀.
+
+## Vector-Based Coordination: The Technical Advantage
+Leveraging LanceDB's vector-based capabilities, our coordination application enables AI agents to communicate and collaborate through dense vector representations 🤖. AI agents can exchange information, coordinate on a task or work towards a common goal, just by giving queries📝.
+
+| **AI Agents** | **Description** | **Links** |
+|:--------------|:----------------|:----------|
+| **AI Agents: Reducing Hallucinationt📊** | 🤖💡 Reduce AI hallucinations using Critique-Based Contexting! Learn by Simplifying and Automating tedious workflows by going through fitness trainer agent example.💪 | [![Github](../../assets/github.svg)][hullucination_github] <br>[![Open In Collab](../../assets/colab.svg)][hullucination_colab] <br>[![Python](../../assets/python.svg)][hullucination_python] <br>[![Ghost](../../assets/ghost.svg)][hullucination_ghost] |                                                                                                                                                                                                                                                
+| **AI Trends Searcher: CrewAI🔍️** | 🔍️ Learn about CrewAI Agents ! Utilize the features of CrewAI - Role-based Agents, Task Management, and Inter-agent Delegation ! Make AI agents work together to do tricky stuff 😺| [![Github](../../assets/github.svg)][trend_github] <br>[![Open In Collab](../../assets/colab.svg)][trend_colab] <br>[![Ghost](../../assets/ghost.svg)][trend_ghost] |                                                                                                                                                                                                                                                
+| **SuperAgent Autogen🤖** | 💻 AI interactions with the Super Agent! Integrating Autogen, LanceDB, LangChain, LiteLLM, and Ollama to create AI agent that excels in understanding and processing complex queries.🤖 | [![Github](../../assets/github.svg)][superagent_github] <br>[![Open In Collab](../../assets/colab.svg)][superagent_colab] |
+
+
+[hullucination_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents
+[hullucination_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents/main.ipynb
+[hullucination_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents/main.py
+[hullucination_ghost]: https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f/
+
+[trend_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/AI-Trends-with-CrewAI
+[trend_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb 
+[trend_ghost]: https://blog.lancedb.com/track-ai-trends-crewai-agents-rag/
+
+[superagent_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/SuperAgent_Autogen
+[superagent_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/SuperAgent_Autogen/main.ipynb
+
+

docs/src/examples/python_examples/build_from_scratch.md

@@ -0,0 +1,13 @@
+# **Build from Scratch with LanceDB 🛠️🚀**
+
+Start building your GenAI applications from the ground up using LanceDB's efficient vector-based document retrieval capabilities! 📑
+
+**Get Started in Minutes ⏱️**
+
+These examples provide a solid foundation for building your own GenAI applications using LanceDB. Jump from idea to proof of concept quickly with applied examples. Get started and see what you can create! 💻
+
+| **Build From Scratch**                     | **Description**                                                                                              | **Links**                                                                                                                                                                                                                                                                                                                                                                                                                |
+|:-------------------------------------------|:-------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+| **Build RAG from Scratch🚀💻**             | 📝 Create a **Retrieval-Augmented Generation** (RAG) model from scratch using LanceDB.                       | [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/RAG-from-Scratch)<br>[![Open In Collab](https://colab.research.google.com/assets/colab-badge.svg)]()                                                                                                                                  |
+| **Local RAG from Scratch with Llama3🔥💡** | 🐫 Build a local RAG model using **Llama3** and **LanceDB** for fast and efficient text generation.          | [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Local-RAG-from-Scratch)<br>[![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Local-RAG-from-Scratch/rag.py)    |
+| **Multi-Head RAG from Scratch📚💻**        | 🤯 Develop a **Multi-Head RAG model** from scratch, enabling generation of text based on multiple documents. | [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Multi-Head-RAG-from-Scratch)<br>[![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/tutorials/Multi-Head-RAG-from-Scratch) |

docs/src/examples/python_examples/chatbot.md

@@ -0,0 +1,41 @@
+**Chatbot Application with LanceDB 🤖**
+====================================================================
+
+ Create an innovative chatbot application that utilizes LanceDB for efficient vector-based response generation! 🌐✨ 
+ 
+**Introduction 👋✨**
+
+ Users can input their queries, allowing the chatbot to retrieve relevant context seamlessly. 🔍📚 This enables the generation of coherent and context-aware replies that enhance user experience. 🌟🤝 Dive into the world of advanced conversational AI and streamline interactions with powerful data management! 🚀💡
+
+
+| **Chatbot**  | **Description**  | **Links**  |
+|:----------------|:-----------------|:-----------|
+| **Databricks DBRX Website Bot ⚡️** | Unlock magical conversations with the Hogwarts chatbot, powered by Open-source RAG, DBRX, LanceDB, LLama-index, and Hugging Face Embeddings, delivering enchanting user experiences and spellbinding interactions ✨ | [![GitHub](../../assets/github.svg)][databricks_github] <br>[![Python](../../assets/python.svg)][databricks_python] |
+| **CLI SDK Manual Chatbot Locally 💻** | CLI chatbot for SDK/hardware documents, powered by Local RAG, LLama3, Ollama, LanceDB, and Openhermes Embeddings, built with Phidata Assistant and Knowledge Base for instant technical support 🤖 | [![GitHub](../../assets/github.svg)][clisdk_github] <br>[![Python](../../assets/python.svg)][clisdk_python] |
+| **Youtube Transcript Search QA Bot 📹** | Unlock the power of YouTube transcripts with a Q&A bot, leveraging natural language search and LanceDB for effortless data management and instant answers 💬 | [![GitHub](../../assets/github.svg)][youtube_github] <br>[![Open In Collab](../../assets/colab.svg)][youtube_colab] <br>[![Python](../../assets/python.svg)][youtube_python] |
+| **Code Documentation Q&A Bot with LangChain 🤖** | Revolutionize code documentation with a Q&A bot, powered by LangChain and LanceDB, allowing effortless querying of documentation using natural language, demonstrated with Numpy 1.26 docs 📚 | [![GitHub](../../assets/github.svg)][docs_github] <br>[![Open In Collab](../../assets/colab.svg)][docs_colab] <br>[![Python](../../assets/python.svg)][docs_python] |
+| **Context-aware Chatbot using Llama 2 & LanceDB 🤖** | Experience the future of conversational AI with a context-aware chatbot, powered by Llama 2, LanceDB, and LangChain, enabling intuitive and meaningful conversations with your data 📚💬 | [![GitHub](../../assets/github.svg)][aware_github] <br>[![Open In Collab](../../assets/colab.svg)][aware_colab] <br>[![Ghost](../../assets/ghost.svg)][aware_ghost] |
+| **Chat with csv using Hybrid Search 📊** | Revolutionize data interaction with a chat application that harnesses LanceDB's hybrid search capabilities to converse with CSV and Excel files, enabling efficient and scalable data exploration and analysis 🚀 | [![GitHub](../../assets/github.svg)][csv_github] <br>[![Open In Collab](../../assets/colab.svg)][csv_colab] <br>[![Ghost](../../assets/ghost.svg)][csv_ghost] |
+
+
+[databricks_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/databricks_DBRX_website_bot
+[databricks_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/databricks_DBRX_website_bot/main.py
+
+[clisdk_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/CLI-SDK-Manual-Chatbot-Locally
+[clisdk_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/CLI-SDK-Manual-Chatbot-Locally/assistant.py
+
+[youtube_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Youtube-Search-QA-Bot
+[youtube_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Youtube-Search-QA-Bot/main.ipynb
+[youtube_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Youtube-Search-QA-Bot/main.py
+
+[docs_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot
+[docs_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.ipynb
+[docs_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.py
+
+[aware_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB
+[aware_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb
+[aware_ghost]: https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755
+
+[csv_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Chat_with_csv_file
+[csv_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Chat_with_csv_file/main.ipynb
+[csv_ghost]: https://blog.lancedb.com/p/d8c71df4-e55f-479a-819e-cde13354a6a3/

docs/src/examples/python_examples/evaluations.md

@@ -0,0 +1,23 @@
+**Evaluation: Assessing Text Performance with Precision 📊💡**
+====================================================================
+
+**Evaluation Fundamentals 📊**
+
+Evaluation is a comprehensive tool designed to measure the performance of text-based inputs, enabling data-driven optimization and improvement 📈. 
+
+**Text Evaluation 101 📚**
+
+By leveraging cutting-edge technologies, this provides a robust framework for evaluating reference and candidate texts across various metrics 📊, ensuring high-quality text outputs that meet specific requirements and standards 📝.
+
+| **Evaluation** | **Description** | **Links** |
+| -------------- | --------------- | --------- |
+| **Evaluating Prompts with Prompttools 🤖** | Compare, visualize & evaluate embedding functions (incl. OpenAI) across metrics like latency & custom evaluation 📈📊 | [![Github](../../assets/github.svg)][prompttools_github] <br>[![Open In Collab](../../assets/colab.svg)][prompttools_colab] |
+| **Evaluating RAG with RAGAs and GPT-4o 📊** | Evaluate RAG pipelines with cutting-edge metrics and tools, integrate with CI/CD for continuous performance checks, and generate responses with GPT-4o 🤖📈 | [![Github](../../assets/github.svg)][RAGAs_github] <br>[![Open In Collab](../../assets/colab.svg)][RAGAs_colab] |
+
+
+
+[prompttools_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts
+[prompttools_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb
+
+[RAGAs_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Evaluating_RAG_with_RAGAs
+[RAGAs_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Evaluating_RAG_with_RAGAs/Evaluating_RAG_with_RAGAs.ipynb

docs/src/examples/python_examples/multimodal.md

@@ -0,0 +1,28 @@
+# **Multimodal Search with LanceDB 🤹‍♂️🔍**
+
+Experience the future of search with LanceDB's multimodal capabilities. Combine text and image queries to find the most relevant results in your corpus ! 🔓💡
+
+**Explore the Future of Search 🚀**
+
+LanceDB supports multimodal search by indexing and querying vector representations of text and image data 🤖. This enables efficient retrieval of relevant documents and images using vector-based similarity search 📊. The platform facilitates cross-modal search, allowing for text-image and image-text retrieval, and supports scalable indexing of high-dimensional vector spaces 💻.
+
+
+
+| **Multimodal**  | **Description**  | **Links**  |
+|:----------------|:-----------------|:-----------|
+| **Multimodal CLIP: DiffusionDB 🌐💥**    | Revolutionize search with Multimodal CLIP and DiffusionDB, combining text and image understanding for a new dimension of discovery! 🔓 | [![GitHub](../../assets/github.svg)][Clip_diffusionDB_github] <br>[![Open In Collab](../../assets/colab.svg)][Clip_diffusionDB_colab] <br>[![Python](../../assets/python.svg)][Clip_diffusionDB_python] <br>[![Ghost](../../assets/ghost.svg)][Clip_diffusionDB_ghost] |
+| **Multimodal CLIP: Youtube Videos 📹👀** | Search Youtube videos using Multimodal CLIP, finding relevant content with ease and accuracy! 🎯                                                                                                                                                                | [![Github](../../assets/github.svg)][Clip_youtube_github]                                                                                                    <br>[![Open In Collab](../../assets/colab.svg)][Clip_youtube_colab]                                                                                           <br> [![Python](../../assets/python.svg)][Clip_youtube_python]         <br>[![Ghost](../../assets/ghost.svg)][Clip_youtube_python] |
+| **Multimodal Image + Text Search 📸🔍**               | Discover relevant documents and images with a single query, using LanceDB's multimodal search capabilities to bridge the gap between text and visuals! 🌉 | [![GitHub](../../assets/github.svg)](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search) <br>[![Open In Collab](../../assets/colab.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.ipynb) <br> [![Python](../../assets/python.svg)](https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.py)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/) |
+| **Cambrian-1: Vision-Centric Image Exploration 🔍👀** | Dive into vision-centric exploration of images with Cambrian-1, powered by LanceDB's multimodal search to uncover new insights! 🔎                        | [![Kaggle](https://img.shields.io/badge/Kaggle-035a7d?style=for-the-badge&logo=kaggle&logoColor=white)](https://www.kaggle.com/code/prasantdixit/cambrian-1-vision-centric-exploration-of-images/)<br> [![Ghost](../../assets/ghost.svg)](https://blog.lancedb.com/cambrian-1-vision-centric-exploration/)                                                                                                                                                                                                                   |
+
+
+[Clip_diffusionDB_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb
+[Clip_diffusionDB_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb/main.ipynb
+[Clip_diffusionDB_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip_diffusiondb/main.py
+[Clip_diffusionDB_ghost]: https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/
+
+
+[Clip_youtube_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search
+[Clip_youtube_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search/main.ipynb
+[Clip_youtube_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search/main.py
+[Clip_youtube_ghost]: https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/

docs/src/examples/python_examples/rag.md

@@ -0,0 +1,84 @@
+
+**RAG: Revolutionize Information Retrieval with LanceDB 🔓🧐**
+====================================================================
+
+Unlock the full potential of Retrieval-Augmented Generation (RAG) with LanceDB, a solution for efficient vector-based information retrieval 📊. 
+
+**Experience the Future of Search 🔄**
+
+RAG integrates large language models (LLMs) with scalable knowledge bases, enabling efficient information retrieval and answer generation 🤖. By applying RAG to industry-specific use cases, developers can optimize query processing 📊, reduce response latency ⏱️, and improve resource utilization 💻. LanceDB provides a robust framework for integrating LLMs with external knowledge sources, facilitating accurate and informative responses 📝.
+
+| **RAG**                                      | **Description**                                                                                                                                                  | **Links**                  |
+|----------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|                                                                                                                                                     
+| **RAG with Matryoshka Embeddings and LlamaIndex** 🪆🔗  | Utilize **Matryoshka embeddings** and **LlamaIndex** to improve the efficiency and accuracy of your RAG models. 📈✨ | [![Github](../../assets/github.svg)][matryoshka_github] <br>[![Open In Collab](../../assets/colab.svg)][matryoshka_colab]  |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Improve RAG with Re-ranking** 📈🔄 | Enhance your RAG applications by implementing **re-ranking strategies** for more relevant document retrieval. 📚🔍 | [![Github](../../assets/github.svg)][rag_reranking_github] <br>[![Open In Collab](../../assets/colab.svg)][rag_reranking_colab] <br>[![Ghost](../../assets/ghost.svg)][rag_reranking_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Instruct-Multitask** 🧠🎯 |  Integrate the **Instruct Embedding Model** with LanceDB to streamline your embedding API, reducing redundant code and overhead. 🌐📊 | [![Github](../../assets/github.svg)][instruct_multitask_github] <br>[![Open In Collab](../../assets/colab.svg)][instruct_multitask_colab] <br>[![Python](../../assets/python.svg)][instruct_multitask_python] <br>[![Ghost](../../assets/ghost.svg)][instruct_multitask_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Improve RAG with HyDE** 🌌🔍 | Use **Hypothetical Document Embeddings** for efficient, accurate, and unsupervised dense retrieval. 📄🔍 | [![Github](../../assets/github.svg)][hyde_github] <br>[![Open In Collab](../../assets/colab.svg)][hyde_colab]<br>[![Ghost](../../assets/ghost.svg)][hyde_ghost]                 |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Improve RAG with LOTR** 🧙‍♂️📜 | Enhance RAG with **Lord of the Retriever (LOTR)** to address 'Lost in the Middle' challenges, especially in medical data. 🌟📜 | [![Github](../../assets/github.svg)][lotr_github] <br>[![Open In Collab](../../assets/colab.svg)][lotr_colab] <br>[![Ghost](../../assets/ghost.svg)][lotr_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Advanced RAG: Parent Document Retriever** 📑🔗 | Use **Parent Document & Bigger Chunk Retriever** to maintain context and relevance when generating related content. 🎵📄 | [![Github](../../assets/github.svg)][parent_doc_retriever_github] <br>[![Open In Collab](../../assets/colab.svg)][parent_doc_retriever_colab] <br>[![Ghost](../../assets/ghost.svg)][parent_doc_retriever_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Corrective RAG with Langgraph** 🔧📊 | Enhance RAG reliability with **Corrective RAG (CRAG)** by self-reflecting and fact-checking for accurate and trustworthy results. ✅🔍 |[![Github](../../assets/github.svg)][corrective_rag_github] <br>[![Open In Collab](../../assets/colab.svg)][corrective_rag_colab] <br>[![Ghost](../../assets/ghost.svg)][corrective_rag_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Contextual Compression with RAG** 🗜️🧠 | Apply **contextual compression techniques** to condense large documents while retaining essential information. 📄🗜️                                               | [![Github](../../assets/github.svg)][compression_rag_github] <br>[![Open In Collab](../../assets/colab.svg)][compression_rag_colab] <br>[![Ghost](../../assets/ghost.svg)][compression_rag_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Improve RAG with FLARE** 🔥|  Enable users to ask questions directly to academic papers, focusing on ArXiv papers, with Forward-Looking Active REtrieval augmented generation.🚀🌟                                                                       | [![Github](../../assets/github.svg)][flare_github] <br>[![Open In Collab](../../assets/colab.svg)][flare_colab] <br>[![Ghost](../../assets/ghost.svg)][flare_ghost] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Query Expansion and Reranker** 🔍🔄 | Enhance RAG with query expansion using Large Language Models and advanced **reranking methods** like Cross Encoders, ColBERT v2, and FlashRank for improved document retrieval precision and recall 🔍📈                                                         | [![Github](../../assets/github.svg)][query_github] <br>[![Open In Collab](../../assets/colab.svg)][query_colab] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **RAG Fusion** ⚡🌐 | Revolutionize search with RAG Fusion, utilizing the **RRF algorithm** to rerank documents based on user queries, and leveraging LanceDB and OPENAI Embeddings for efficient information retrieval ⚡🌐                                                                          | [![Github](../../assets/github.svg)][fusion_github] <br>[![Open In Collab](../../assets/colab.svg)][fusion_colab] |                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        
+| **Agentic RAG** 🤖📚 | Unlock autonomous information retrieval with **Agentic RAG**, a framework of **intelligent agents** that collaborate to synthesize, summarize, and compare data across sources, enabling proactive and informed decision-making 🤖📚                                                              | [![Github](../../assets/github.svg)][agentic_github] <br>[![Open In Collab](../../assets/colab.svg)][agentic_colab] |
+
+
+
+
+
+
+
+
+
+
+
+
+[matryoshka_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex
+[matryoshka_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-with_MatryoshkaEmbed-Llamaindex/RAG_with_MatryoshkaEmbedding_and_Llamaindex.ipynb
+
+[rag_reranking_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/RAG_Reranking
+[rag_reranking_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG_Reranking/main.ipynb
+[rag_reranking_ghost]: https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544
+
+
+[instruct_multitask_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/instruct-multitask
+[instruct_multitask_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/instruct-multitask/main.ipynb
+[instruct_multitask_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/instruct-multitask/main.py
+[instruct_multitask_ghost]: https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543
+
+[hyde_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Advance-RAG-with-HyDE
+[hyde_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance-RAG-with-HyDE/main.ipynb
+[hyde_ghost]: https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb
+
+[lotr_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Advance_RAG_LOTR
+[lotr_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Advance_RAG_LOTR/main.ipynb
+[lotr_ghost]: https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35
+
+[parent_doc_retriever_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/parent_document_retriever
+[parent_doc_retriever_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/parent_document_retriever/main.ipynb
+[parent_doc_retriever_ghost]: https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6
+
+[corrective_rag_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph
+[corrective_rag_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb
+[corrective_rag_ghost]: https://blog.lancedb.com/implementing-corrective-rag-in-the-easiest-way-2/
+
+[compression_rag_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG
+[compression_rag_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb
+[compression_rag_ghost]: https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/
+
+[flare_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR
+[flare_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR/main.ipynb
+[flare_ghost]: https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/
+
+[query_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/QueryExpansion&Reranker
+[query_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/QueryExpansion&Reranker/main.ipynb
+
+
+[fusion_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/RAG_Fusion
+[fusion_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/RAG_Fusion/main.ipynb
+
+[agentic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG
+[agentic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Agentic_RAG/main.ipynb
+
+

docs/src/examples/python_examples/vector_search.md

@@ -0,0 +1,80 @@
+**Vector Search: Unlock Efficient Document Retrieval 🔓👀**
+====================================================================
+
+Unlock the power of vector search with LanceDB, a cutting-edge solution for efficient vector-based document retrieval 📊. 
+
+**Vector Search Capabilities in LanceDB🔝**
+
+LanceDB implements vector search algorithms for efficient document retrieval and analysis 📊. This enables fast and accurate discovery of relevant documents, leveraging dense vector representations 🤖. The platform supports scalable indexing and querying of high-dimensional vector spaces, facilitating precise document matching and retrieval 📈.
+
+| **Vector Search** | **Description** | **Links** |
+|:-----------------|:---------------|:---------|
+| **Inbuilt Hybrid Search 🔄** | Combine the power of traditional search algorithms with LanceDB's vector-based search for a robust and efficient search experience 📊 | [![Github](../../assets/github.svg)][inbuilt_hybrid_search_github] <br>[![Open In Collab](../../assets/colab.svg)][inbuilt_hybrid_search_colab] |                                                                                                                                                                                                                                                                                                    
+| **Hybrid Search with BM25 and LanceDB 💡** | Synergizes BM25's keyword-focused precision (term frequency, document length normalization, bias-free retrieval) with LanceDB's semantic understanding (contextual analysis, query intent alignment) for nuanced search results in complex datasets 📈 | [![Github](../../assets/github.svg)][BM25_github] <br>[![Open In Collab](../../assets/colab.svg)][BM25_colab] <br>[![Ghost](../../assets/ghost.svg)][BM25_ghost] |                                                                                                                                                                                                                                                                                                    
+| **NER-powered Semantic Search 🔎** | Unlock contextual understanding with Named Entity Recognition (NER) methods: Dictionary-Based, Rule-Based, and Deep Learning-Based, to accurately identify and extract entities, enabling precise semantic search results 🗂️ | [![Github](../../assets/github.svg)][NER_github] <br>[![Open In Collab](../../assets/colab.svg)][NER_colab] <br>[![Ghost](../../assets/ghost.svg)][NER_ghost]|                                                                                                                                                                                                                                                                                                    
+| **Audio Similarity Search using Vector Embeddings 🎵** | Create vector embeddings of audio files to find similar audio content, enabling efficient audio similarity search and retrieval in LanceDB's vector store 📻 |[![Github](../../assets/github.svg)][audio_search_github] <br>[![Open In Collab](../../assets/colab.svg)][audio_search_colab] <br>[![Python](../../assets/python.svg)][audio_search_python]|                                                                                                                                                                                                                                                                                                    
+| **LanceDB Embeddings API: Multi-lingual Semantic Search 🌎** | Build a universal semantic search table with LanceDB's Embeddings API, supporting multiple languages (e.g., English, French) using cohere's multi-lingual model, for accurate cross-lingual search results 📄 | [![Github](../../assets/github.svg)][mls_github] <br>[![Open In Collab](../../assets/colab.svg)][mls_colab] <br>[![Python](../../assets/python.svg)][mls_python] |                                                                                                                                                                                                                                                                                                    
+| **Facial Recognition: Face Embeddings 🤖** | Detect, crop, and embed faces using Facenet, then store and query face embeddings in LanceDB for efficient facial recognition and top-K matching results 👥 | [![Github](../../assets/github.svg)][fr_github] <br>[![Open In Collab](../../assets/colab.svg)][fr_colab] |                                                                                                                                                                                                                                                                                                    
+| **Sentiment Analysis: Hotel Reviews 🏨** | Analyze customer sentiments towards the hotel industry using BERT models, storing sentiment labels, scores, and embeddings in LanceDB, enabling queries on customer opinions and potential areas for improvement 💬 | [![Github](../../assets/github.svg)][sentiment_analysis_github] <br>[![Open In Collab](../../assets/colab.svg)][sentiment_analysis_colab] <br>[![Ghost](../../assets/ghost.svg)][sentiment_analysis_ghost] |                                                                                                                                                                                                                                                                                                    
+| **Vector Arithmetic with LanceDB ⚖️** | Unlock powerful semantic search capabilities by performing vector arithmetic on embeddings, enabling complex relationships and nuances in data to be captured, and simplifying the process of retrieving semantically similar results 📊 | [![Github](../../assets/github.svg)][arithmetic_github] <br>[![Open In Collab](../../assets/colab.svg)][arithmetic_colab] <br>[![Ghost](../../assets/ghost.svg)][arithmetic_ghost] |                                                                                                                                                                                                                                                                                                    
+| **Imagebind Demo 🖼️** | Explore the multi-modal capabilities of Imagebind through a Gradio app, leveraging LanceDB API for seamless image search and retrieval experiences 📸 | [![Github](../../assets/github.svg)][imagebind_github] <br> [![Open in Spaces](../../assets/open_hf_space.svg)][imagebind_huggingface] |                                                                                                                                                                                                                                                                                                    
+| **Search Engine using SAM & CLIP 🔍** | Build a search engine within an image using SAM and CLIP models, enabling object-level search and retrieval, with LanceDB indexing and search capabilities to find the closest match between image embeddings and user queries 📸 | [![Github](../../assets/github.svg)][swi_github] <br>[![Open In Collab](../../assets/colab.svg)][swi_colab] <br>[![Ghost](../../assets/ghost.svg)][swi_ghost] |                                                                                                                                                                                                                                                                                                    
+| **Zero Shot Object Localization and Detection with CLIP 🔎** | Perform object detection on images using OpenAI's CLIP, enabling zero-shot localization and detection of objects, with capabilities to split images into patches, parse with CLIP, and plot bounding boxes 📊 | [![Github](../../assets/github.svg)][zsod_github] <br>[![Open In Collab](../../assets/colab.svg)][zsod_colab] |                                                                                                                                                                                                                                                                                                    
+| **Accelerate Vector Search with OpenVINO 🚀** | Boost vector search applications using OpenVINO, achieving significant speedups with CLIP for text-to-image and image-to-image searching, through PyTorch model optimization, FP16 and INT8 format conversion, and quantization with OpenVINO NNCF 📈 | [![Github](../../assets/github.svg)][openvino_github] <br>[![Open In Collab](../../assets/colab.svg)][openvino_colab] <br>[![Ghost](../../assets/ghost.svg)][openvino_ghost] |                                                                                                                                                                                                                                                                                                    
+| **Zero-Shot Image Classification with CLIP and LanceDB 📸** | Achieve zero-shot image classification using CLIP and LanceDB, enabling models to classify images without prior training on specific use cases, unlocking flexible and adaptable image classification capabilities 🔓 | [![Github](../../assets/github.svg)][zsic_github] <br>[![Open In Collab](../../assets/colab.svg)][zsic_colab] <br>[![Ghost](../../assets/ghost.svg)][zsic_ghost] |                                                                                                                                                                                                                                                                                                    
+
+
+
+
+[inbuilt_hybrid_search_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Inbuilt-Hybrid-Search
+[inbuilt_hybrid_search_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Inbuilt-Hybrid-Search/Inbuilt_Hybrid_Search_with_LanceDB.ipynb
+
+[BM25_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Hybrid_search_bm25_lancedb
+[BM25_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Hybrid_search_bm25_lancedb/main.ipynb
+[BM25_ghost]: https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6
+
+[NER_github]: https://github.com/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search
+[NER_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb
+[NER_ghost]: https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493
+
+[audio_search_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/audio_search
+[audio_search_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.ipynb
+[audio_search_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.py
+
+[mls_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa
+[mls_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa/main.ipynb
+[mls_python]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/multi-lingual-wiki-qa/main.py
+
+[fr_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/facial_recognition
+[fr_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/facial_recognition/main.ipynb
+
+[sentiment_analysis_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews
+[sentiment_analysis_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb
+[sentiment_analysis_ghost]: https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6
+
+[arithmetic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Vector-Arithmetic-with-LanceDB
+[arithmetic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Vector-Arithmetic-with-LanceDB/main.ipynb
+[arithmetic_ghost]: https://blog.lancedb.com/vector-arithmetic-with-lancedb-an-intro-to-vector-embeddings/
+
+[imagebind_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/imagebind_demo
+[imagebind_huggingface]: https://huggingface.co/spaces/raghavd99/imagebind2
+
+[swi_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip
+[swi_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb
+[swi_ghost]: https://blog.lancedb.com/search-within-an-image-331b54e4285e
+
+[zsod_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/zero-shot-object-detection-CLIP
+[zsod_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/zero-shot-object-detection-CLIP/zero_shot_object_detection_clip.ipynb
+
+[openvino_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO
+[openvino_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb
+[openvino_ghost]: https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-lancedb/
+
+[zsic_github]: https://github.com/lancedb/vectordb-recipes/blob/main/examples/zero-shot-image-classification
+[zsic_colab]: https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/zero-shot-image-classification/main.ipynb
+[zsic_ghost]: https://blog.lancedb.com/zero-shot-image-classification-with-vector-search/
+
+
+
+
+

docs/src/fts.md

@@ -1,9 +1,14 @@
 # Full-text search
 
-LanceDB provides support for full-text search via [Tantivy](https://github.com/quickwit-oss/tantivy) (currently Python only), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions. Our goal is to push the FTS integration down to the Rust level in the future, so that it's available for Rust and JavaScript users as well.  Follow along at [this Github issue](https://github.com/lancedb/lance/issues/1195)
+LanceDB provides support for full-text search via Lance (before via [Tantivy](https://github.com/quickwit-oss/tantivy) (Python only)), allowing you to incorporate keyword-based search (based on BM25) in your retrieval solutions.
+
+Currently, the Lance full text search is missing some features that are in the Tantivy full text search. This includes phrase queries, re-ranking, and customizing the tokenizer. Thus, in Python, Tantivy is still the default way to do full text search and many of the instructions below apply just to Tantivy-based indices.
 
 
-## Installation
+## Installation (Only for Tantivy-based FTS)
+
+!!! note
+    No need to install the tantivy dependency if using native FTS
 
 To use full-text search, install the dependency [`tantivy-py`](https://github.com/quickwit-oss/tantivy-py):
 
@@ -14,42 +19,83 @@ pip install tantivy==0.20.1
 
 ## Example
 
-Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search.
+Consider that we have a LanceDB table named `my_table`, whose string column `text` we want to index and query via keyword search, the FTS index must be created before you can search via keywords.
 
-```python
+=== "Python"
-import lancedb
 
-uri = "data/sample-lancedb"
+    ```python
-db = lancedb.connect(uri)
+    import lancedb
 
-table = db.create_table(
+    uri = "data/sample-lancedb"
-    "my_table",
+    db = lancedb.connect(uri)
-    data=[
-        {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
-        {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
-    ],
-)
-```
 
-## Create FTS index on single column
+    table = db.create_table(
+        "my_table",
+        data=[
+            {"vector": [3.1, 4.1], "text": "Frodo was a happy puppy"},
+            {"vector": [5.9, 26.5], "text": "There are several kittens playing"},
+        ],
+    )
 
-The FTS index must be created before you can search via keywords.
+    # passing `use_tantivy=False` to use lance FTS index
+    # `use_tantivy=True` by default
+    table.create_fts_index("text")
+    table.search("puppy").limit(10).select(["text"]).to_list()
+    # [{'text': 'Frodo was a happy puppy', '_score': 0.6931471824645996}]
+    # ...
+    ```
 
-```python
+=== "TypeScript"
-table.create_fts_index("text")
-```
 
-To search an FTS index via keywords, LanceDB's `table.search` accepts a string as input:
+    ```typescript
+    import * as lancedb from "@lancedb/lancedb";
+    const uri = "data/sample-lancedb"
+    const db = await lancedb.connect(uri);
 
-```python
+    const data = [
-table.search("puppy").limit(10).select(["text"]).to_list()
+    { vector: [3.1, 4.1], text: "Frodo was a happy puppy" },
-```
+    { vector: [5.9, 26.5], text: "There are several kittens playing" },
+    ];
+    const tbl = await db.createTable("my_table", data, { mode: "overwrite" });
+    await tbl.createIndex("text", {
+        config: lancedb.Index.fts(),
+    });
 
-This returns the result as a list of dictionaries as follows.
+    await tbl
+        .search("puppy")
+        .select(["text"])
+        .limit(10)
+        .toArray();
+    ```
 
-```python
+=== "Rust"
-[{'text': 'Frodo was a happy puppy', 'score': 0.6931471824645996}]
+
-```
+    ```rust
+    let uri = "data/sample-lancedb";
+    let db = connect(uri).execute().await?;
+    let initial_data: Box<dyn RecordBatchReader + Send> = create_some_records()?;
+    let tbl = db
+        .create_table("my_table", initial_data)
+        .execute()
+        .await?;
+    tbl
+        .create_index(&["text"], Index::FTS(FtsIndexBuilder::default()))
+        .execute()
+        .await?;
+
+    tbl
+        .query()
+        .full_text_search(FullTextSearchQuery::new("puppy".to_owned()))
+        .select(lancedb::query::Select::Columns(vec!["text".to_owned()]))
+        .limit(10)
+        .execute()
+        .await?;
+    ```
+
+It would search on all indexed columns by default, so it's useful when there are multiple indexed columns.
+For now, this is supported in tantivy way only.
+
+Passing `fts_columns="text"` if you want to specify the columns to search, but it's not available for Tantivy-based full text search.
 
 !!! note
     LanceDB automatically searches on the existing FTS index if the input to the search is of type `str`. If you provide a vector as input, LanceDB will search the ANN index instead.
@@ -57,20 +103,33 @@ This returns the result as a list of dictionaries as follows.
 ## Tokenization
 By default the text is tokenized by splitting on punctuation and whitespaces and then removing tokens that are longer than 40 chars. For more language specific tokenization then provide the argument tokenizer_name with the 2 letter language code followed by "_stem". So for english it would be "en_stem".
 
-```python
+For now, only the Tantivy-based FTS index supports to specify the tokenizer, so it's only available in Python with `use_tantivy=True`.
-table.create_fts_index("text", tokenizer_name="en_stem")
-```
 
-The following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
+=== "use_tantivy=True"
 
+    ```python
+    table.create_fts_index("text", use_tantivy=True, tokenizer_name="en_stem")
+    ```
+
+=== "use_tantivy=False"
+
+    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
+
+the following [languages](https://docs.rs/tantivy/latest/tantivy/tokenizer/enum.Language.html) are currently supported.
 
 ## Index multiple columns
 
 If you have multiple string columns to index, there's no need to combine them manually -- simply pass them all as a list to `create_fts_index`:
 
-```python
+=== "use_tantivy=True"
-table.create_fts_index(["text1", "text2"])
+
-```
+    ```python
+    table.create_fts_index(["text1", "text2"])
+    ```
+
+=== "use_tantivy=False"
+
+    [**Not supported yet**](https://github.com/lancedb/lance/issues/1195)
 
 Note that the search API call does not change - you can search over all indexed columns at once.
 
@@ -80,19 +139,48 @@ Currently the LanceDB full text search feature supports *post-filtering*, meanin
 applied on top of the full text search results. This can be invoked via the familiar
 `where` syntax:
 
-```python
+=== "Python"
-table.search("puppy").limit(10).where("meta='foo'").to_list()
+
-```
+    ```python
+    table.search("puppy").limit(10).where("meta='foo'").to_list()
+    ```
+
+=== "TypeScript"
+
+    ```typescript
+    await tbl
+    .search("apple")
+    .select(["id", "doc"])
+    .limit(10)
+    .where("meta='foo'")
+    .toArray();
+    ```
+
+=== "Rust"
+
+    ```rust
+    table
+        .query()
+        .full_text_search(FullTextSearchQuery::new(words[0].to_owned()))
+        .select(lancedb::query::Select::Columns(vec!["doc".to_owned()]))
+        .limit(10)
+        .only_if("meta='foo'")
+        .execute()
+        .await?;
+    ```
 
 ## Sorting
 
+!!! warning "Warn"
+    Sorting is available for only Tantivy-based FTS
+
 You can pre-sort the documents by specifying `ordering_field_names` when
 creating the full-text search index. Once pre-sorted, you can then specify
 `ordering_field_name` while searching to return results sorted by the given
-field. For example, 
+field. For example,
 
-```
+```python
-table.create_fts_index(["text_field"], ordering_field_names=["sort_by_field"])
+table.create_fts_index(["text_field"], use_tantivy=True, ordering_field_names=["sort_by_field"])
 
 (table.search("terms", ordering_field_name="sort_by_field")
  .limit(20)
@@ -105,8 +193,8 @@ table.create_fts_index(["text_field"], ordering_field_names=["sort_by_field"])
     error will be raised that looks like `ValueError: The field does not exist: xxx`
 
 !!! note
-    The fields to sort on must be of typed unsigned integer, or else you will see 
+    The fields to sort on must be of typed unsigned integer, or else you will see
-    an error during indexing that looks like 
+    an error during indexing that looks like
     `TypeError: argument 'value': 'float' object cannot be interpreted as an integer`.
 
 !!! note
@@ -116,6 +204,9 @@ table.create_fts_index(["text_field"], ordering_field_names=["sort_by_field"])
 
 ## Phrase queries vs. terms queries
 
+!!! warning "Warn"
+    Phrase queries are available for only Tantivy-based FTS
+
 For full-text search you can specify either a **phrase** query like `"the old man and the sea"`,
 or a **terms** search query like `"(Old AND Man) AND Sea"`. For more details on the terms
 query syntax, see Tantivy's [query parser rules](https://docs.rs/tantivy/latest/tantivy/query/struct.QueryParser.html).
@@ -142,7 +233,7 @@ enforce it in one of two ways:
 
 1. Place the double-quoted query inside single quotes. For example, `table.search('"they could have been dogs OR cats"')` is treated as
 a phrase query.
-2. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
+1. Explicitly declare the `phrase_query()` method. This is useful when you have a phrase query that
 itself contains double quotes. For example, `table.search('the cats OR dogs were not really "pets" at all').phrase_query()`
 is treated as a phrase query.
 
@@ -150,7 +241,7 @@ In general, a query that's declared as a phrase query will be wrapped in double
 double quotes replaced by single quotes.
 
 
-## Configurations
+## Configurations (Only for Tantivy-based FTS)
 
 By default, LanceDB configures a 1GB heap size limit for creating the index. You can
 reduce this if running on a smaller node, or increase this for faster performance while
@@ -164,6 +255,8 @@ table.create_fts_index(["text1", "text2"], writer_heap_size=heap, replace=True)
 
 ## Current limitations
 
+For that Tantivy-based FTS:
+
 1. Currently we do not yet support incremental writes.
    If you add data after FTS index creation, it won't be reflected
    in search results until you do a full reindex.

docs/src/guides/scalar_index.md

@@ -0,0 +1,108 @@
+# Building Scalar Index
+
+Similar to many SQL databases, LanceDB supports several types of Scalar indices to accelerate search
+over scalar columns.
+
+- `BTREE`: The most common type is BTREE. This index is inspired by the btree data structure
+  although only the first few layers of the btree are cached in memory.
+  It will perform well on columns with a large number of unique values and few rows per value.
+- `BITMAP`: this index stores a bitmap for each unique value in the column.
+  This index is useful for columns with a finite number of unique values and many rows per value.
+  For example, columns that represent "categories", "labels", or "tags"
+- `LABEL_LIST`: a special index that is used to index list columns whose values have a finite set of possibilities.
+  For example, a column that contains lists of tags (e.g. `["tag1", "tag2", "tag3"]`) can be indexed with a `LABEL_LIST` index.
+
+| Data Type                                                       | Filter                                    | Index Type   |
+| --------------------------------------------------------------- | ----------------------------------------- | ------------ |
+| Numeric, String, Temporal                                       | `<`, `=`, `>`, `in`, `between`, `is null` | `BTREE`      |
+| Boolean, numbers or strings with fewer than 1,000 unique values | `<`, `=`, `>`, `in`, `between`, `is null` | `BITMAP`     |
+| List of low cardinality of numbers or strings                   | `array_has_any`, `array_has_all`          | `LABEL_LIST` |
+
+=== "Python"
+
+    ```python
+    import lancedb
+    books = [
+      {"book_id": 1, "publisher": "plenty of books", "tags": ["fantasy", "adventure"]},
+      {"book_id": 2, "publisher": "book town", "tags": ["non-fiction"]},
+      {"book_id": 3, "publisher": "oreilly", "tags": ["textbook"]}
+    ]
+
+    db = lancedb.connect("./db")
+    table = db.create_table("books", books)
+    table.create_scalar_index("book_id")  # BTree by default
+    table.create_scalar_index("publisher", index_type="BITMAP")
+    ```
+
+=== "Typescript"
+
+    === "@lancedb/lancedb"
+
+        ```js
+        const db = await lancedb.connect("data");
+        const tbl = await db.openTable("my_vectors");
+
+        await tbl.create_index("book_id");
+        await tlb.create_index("publisher", { config: lancedb.Index.bitmap() })
+        ```
+
+For example, the following scan will be faster if the column `my_col` has a scalar index:
+
+=== "Python"
+
+    ```python
+    import lancedb
+
+    table = db.open_table("books")
+    my_df = table.search().where("book_id = 2").to_pandas()
+    ```
+
+=== "Typescript"
+
+    === "@lancedb/lancedb"
+
+        ```js
+        const db = await lancedb.connect("data");
+        const tbl = await db.openTable("books");
+
+        await tbl
+          .query()
+          .where("book_id = 2")
+          .limit(10)
+          .toArray();
+        ```
+
+Scalar indices can also speed up scans containing a vector search or full text search, and a prefilter:
+
+=== "Python"
+
+    ```python
+    import lancedb
+
+    data = [
+      {"book_id": 1, "vector": [1, 2]},
+      {"book_id": 2, "vector": [3, 4]},
+      {"book_id": 3, "vector": [5, 6]}
+    ]
+    table = db.create_table("book_with_embeddings", data)
+
+    (
+        table.search([1, 2])
+        .where("book_id != 3", prefilter=True)
+        .to_pandas()
+    )
+    ```
+
+=== "Typescript"
+
+    === "@lancedb/lancedb"
+
+        ```js
+        const db = await lancedb.connect("data/lance");
+        const tbl = await db.openTable("book_with_embeddings");
+
+        await tbl.search(Array(1536).fill(1.2))
+          .where("book_id != 3")  // prefilter is default behavior.
+          .limit(10)
+          .toArray();
+        ```

docs/src/integrations/dlt.md

@@ -0,0 +1,142 @@
+# dlt  
+
+[dlt](https://dlthub.com/docs/intro) is an open-source library that you can add to your Python scripts to load data from various and often messy data sources into well-structured, live datasets. dlt's [integration with LanceDB](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb) lets you ingest data from any source (databases, APIs, CSVs, dataframes, JSONs, and more) into LanceDB with a few lines of simple python code. The integration enables automatic normalization of nested data, schema inference, incremental loading and embedding the data. dlt also has integrations with several other tools like dbt, airflow, dagster etc. that can be inserted into your LanceDB workflow.
+
+## How to ingest data into LanceDB  
+
+In this example, we will be fetching movie information from the [Open Movie Database (OMDb) API](https://www.omdbapi.com/) and loading it into a local LanceDB instance. To implement it, you will need an API key for the OMDb API (which can be created freely [here](https://www.omdbapi.com/apikey.aspx)).
+  
+1. **Install `dlt` with LanceDB extras:**  
+    ```sh 
+    pip install dlt[lancedb]
+    ```
+
+2. **Inside an empty directory, initialize a `dlt` project with:**  
+    ```sh
+    dlt init rest_api lancedb
+    ```
+    This will add all the files necessary to create a `dlt` pipeline that can ingest data from any REST API (ex: OMDb API) and load into LanceDB.
+    ```text
+    ├── .dlt
+    │   ├── config.toml
+    │   └── secrets.toml
+    ├── rest_api
+    ├── rest_api_pipeline.py
+    └── requirements.txt
+    ```
+  
+    dlt has a list of pre-built [sources](https://dlthub.com/docs/dlt-ecosystem/verified-sources/) like [SQL databases](https://dlthub.com/docs/dlt-ecosystem/verified-sources/sql_database), [REST APIs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api), [Google Sheets](https://dlthub.com/docs/dlt-ecosystem/verified-sources/google_sheets), [Notion](https://dlthub.com/docs/dlt-ecosystem/verified-sources/notion) etc., that can be used out-of-the-box by running `dlt init <source_name> lancedb`. Since dlt is a python library, it is also very easy to modify these pre-built sources or to write your own custom source from scratch.
+
+
+3. **Specify necessary credentials and/or embedding model details:**  
+    
+    In order to fetch data from the OMDb API, you will need to pass a valid API key into your pipeline. Depending on whether you're using LanceDB OSS or LanceDB cloud, you also may need to provide the necessary credentials to connect to the LanceDB instance. These can be pasted inside `.dlt/sercrets.toml`. 
+
+    dlt's LanceDB integration also allows you to automatically embed the data during ingestion. Depending on the embedding model chosen, you may need to paste the necessary credentials inside `.dlt/sercrets.toml`:
+    ```toml
+    [sources.rest_api]
+    api_key = "api_key" # Enter the API key for the OMDb API
+
+    [destination.lancedb]
+    embedding_model_provider = "sentence-transformers"
+    embedding_model = "all-MiniLM-L6-v2"
+    [destination.lancedb.credentials]
+    uri = ".lancedb"
+    api_key = "api_key" # API key to connect to LanceDB Cloud. Leave out if you are using LanceDB OSS.
+    embedding_model_provider_api_key = "embedding_model_provider_api_key" # Not needed for providers that don't need authentication (ollama, sentence-transformers).
+    ```
+    See [here](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb#configure-the-destination) for more information and for a list of available models and model providers.  
+
+
+4. **Write the pipeline code inside `rest_api_pipeline.py`:**  
+
+    The following code shows how you can configure dlt's REST API source to connect to the [OMDb API](https://www.omdbapi.com/), fetch all movies with the word "godzilla" in the title, and load it into a LanceDB table. The REST API source allows you to pull data from any API with minimal code, to learn more read the [dlt docs](https://dlthub.com/docs/dlt-ecosystem/verified-sources/rest_api).
+
+    ```python   
+
+    # Import necessary modules
+    import dlt
+    from rest_api import rest_api_source
+
+    # Configure the REST API source
+    movies_source = rest_api_source(
+        {
+            "client": {
+                "base_url": "https://www.omdbapi.com/",
+                "auth": { # authentication strategy for the OMDb API
+                    "type": "api_key",
+                    "name": "apikey",
+                    "api_key": dlt.secrets["sources.rest_api.api_token"], # read API credentials directly from secrets.toml
+                    "location": "query"
+                },
+                "paginator": { # pagination strategy for the OMDb API 
+                    "type": "page_number",
+                    "base_page": 1,
+                    "total_path": "totalResults",
+                    "maximum_page": 5
+                }
+            },
+            "resources": [ # list of API endpoints to request
+                {
+                    "name": "movie_search",
+                    "endpoint": {
+                        "path": "/",
+                        "params": {
+                            "s": "godzilla",
+                            "type": "movie"
+                        }
+                    }
+                }
+            ]
+        })
+
+
+    if __name__ == "__main__":
+        # Create a pipeline object
+        pipeline = dlt.pipeline(
+            pipeline_name='movies_pipeline',
+            destination='lancedb', # this tells dlt to load the data into LanceDB
+            dataset_name='movies_data_pipeline',
+        )
+
+        # Run the pipeline
+        load_info = pipeline.run(movies_source)
+
+        # pretty print the information on data that was loaded
+        print(load_info)
+    ```
+
+    The script above will ingest the data into LanceDB as it is, i.e. without creating any embeddings. If we want to embed one of the fields (for example, `"Title"` that contains the movie titles), then we will use dlt's `lancedb_adapter` and modify the script as follows:  
+    
+    - Add the following import statement:
+        ```python
+        from dlt.destinations.adapters import lancedb_adapter
+        ```
+    - Modify the pipeline run like this:
+        ```python
+        load_info = pipeline.run(
+            lancedb_adapter(
+                movies_source,
+                embed="Title",
+            )
+        )
+        ```
+    This will use the embedding model specified inside `.dlt/secrets.toml` to embed the field `"Title"`.
+
+5. **Install necessary dependencies:**  
+    ```sh
+    pip install -r requirements.txt
+    ```  
+
+    Note: You may need to install the dependencies for your embedding models separately.
+    ```sh
+    pip install sentence-transformers
+    ```
+
+6. **Run the pipeline:**
+    Finally, running the following command will ingest the data into your LanceDB instance.
+    ```sh
+    python custom_source.py
+    ```
+
+For more information and advanced usage of dlt's LanceDB integration, read [the dlt documentation](https://dlthub.com/docs/dlt-ecosystem/destinations/lancedb).

docs/src/python/python.md

@@ -113,6 +113,10 @@ lists the indices that LanceDb supports.
 
 ::: lancedb.index.BTree
 
+::: lancedb.index.Bitmap
+
+::: lancedb.index.LabelList
+
 ::: lancedb.index.IvfPq
 
 ## Querying (Asynchronous)

docs/src/reranking/rrf.md

@@ -0,0 +1,53 @@
+# Reciprocal Rank Fusion Reranker
+
+Reciprocal Rank Fusion (RRF) is an algorithm that evaluates the search scores by leveraging the positions/rank of the documents. The implementation follows this [paper](https://plg.uwaterloo.ca/~gvcormac/cormacksigir09-rrf.pdf).
+
+
+!!! note
+    Supported Query Types: Hybrid
+
+
+```python
+import numpy
+import lancedb
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import LanceModel, Vector
+from lancedb.rerankers import RRFReranker
+
+embedder = get_registry().get("sentence-transformers").create()
+db = lancedb.connect("~/.lancedb")
+
+class Schema(LanceModel):
+    text: str = embedder.SourceField()
+    vector: Vector(embedder.ndims()) = embedder.VectorField()
+
+data = [
+    {"text": "hello world"},
+    {"text": "goodbye world"}
+    ]
+tbl = db.create_table("test", schema=Schema, mode="overwrite")
+tbl.add(data)
+reranker = RRFReranker()
+
+# Run hybrid search with a reranker
+tbl.create_fts_index("text", replace=True)
+result = tbl.search("hello", query_type="hybrid").rerank(reranker=reranker).to_list()
+
+```
+
+Accepted Arguments
+----------------
+| Argument | Type | Default | Description |
+| --- | --- | --- | --- |
+| `K` | `int` | `60` | 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 |
+| `return_score` | str | `"relevance"` | Options are "relevance" or "all". The type of score to return. If "relevance", will return only the `_relevance_score`. If "all", will return all scores from the vector and FTS search along with the relevance score. |
+
+
+## Supported Scores for each query type
+You can specify the type of scores you want the reranker to return. The following are the supported scores for each query type:
+
+### Hybrid Search
+|`return_score`| Status | Description |
+| --- | --- | --- |
+| `relevance` | ✅ Supported | Returned rows only have the `_relevance_score` column |
+| `all` | ✅ Supported | Returned rows have vector(`_distance`) and FTS(`score`) along with Hybrid Search score(`_relevance_score`) |

docs/test/md_testing.py

@@ -1,3 +1,5 @@
+#!/usr/bin/env python3
+
 import glob
 from typing import Iterator, List
 from pathlib import Path

docs/test/requirements.txt

@@ -5,4 +5,5 @@ pylance
 duckdb
 --extra-index-url https://download.pytorch.org/whl/cpu
 torch
-polars
+polars>=0.19, <=1.3.0
+

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.7.1",
+  "version": "0.10.0-beta.0",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.7.1",
+      "version": "0.10.0-beta.0",
       "cpu": [
         "x64",
         "arm64"

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.7.1",
+  "version": "0.10.0-beta.0",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",

nodejs/.npmignore

@@ -13,3 +13,12 @@ __test__
 renovate.json
 .idea
 src
+lancedb
+examples
+nodejs-artifacts
+Cargo.toml
+biome.json
+build.rs
+jest.config.js
+tsconfig.json
+typedoc.json

nodejs/Cargo.toml

@@ -20,7 +20,6 @@ napi = { version = "2.16.8", default-features = false, features = [
     "async",
 ] }
 napi-derive = "2.16.4"
-
 # Prevent dynamic linking of lzma, which comes from datafusion
 lzma-sys = { version = "*", features = ["static"] }
 

nodejs/__test__/registry.test.ts

@@ -1,3 +1,4 @@
+import * as apiArrow from "apache-arrow";
 // Copyright 2024 Lance Developers.
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
@@ -69,7 +70,7 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
           return 3;
         }
         embeddingDataType() {
-          return new arrow.Float32();
+          return new arrow.Float32() as apiArrow.Float;
         }
         async computeSourceEmbeddings(data: string[]) {
           return data.map(() => [1, 2, 3]);
@@ -82,7 +83,7 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
 
       const schema = LanceSchema({
         id: new arrow.Int32(),
-        text: func.sourceField(new arrow.Utf8()),
+        text: func.sourceField(new arrow.Utf8() as apiArrow.DataType),
         vector: func.vectorField(),
       });
 
@@ -119,7 +120,7 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
           return 3;
         }
         embeddingDataType() {
-          return new arrow.Float32();
+          return new arrow.Float32() as apiArrow.Float;
         }
         async computeSourceEmbeddings(data: string[]) {
           return data.map(() => [1, 2, 3]);
@@ -144,7 +145,7 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
           return 3;
         }
         embeddingDataType() {
-          return new arrow.Float32();
+          return new arrow.Float32() as apiArrow.Float;
         }
         async computeSourceEmbeddings(data: string[]) {
           return data.map(() => [1, 2, 3]);
@@ -154,7 +155,7 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
 
       const schema = LanceSchema({
         id: new arrow.Int32(),
-        text: func.sourceField(new arrow.Utf8()),
+        text: func.sourceField(new arrow.Utf8() as apiArrow.DataType),
         vector: func.vectorField(),
       });
       const expectedMetadata = new Map<string, string>([

nodejs/__test__/table.test.ts

@@ -31,7 +31,9 @@ import {
   Float64,
   Int32,
   Int64,
+  List,
   Schema,
+  Utf8,
   makeArrowTable,
 } from "../lancedb/arrow";
 import {
@@ -331,6 +333,7 @@ describe("When creating an index", () => {
   const schema = new Schema([
     new Field("id", new Int32(), true),
     new Field("vec", new FixedSizeList(32, new Field("item", new Float32()))),
+    new Field("tags", new List(new Field("item", new Utf8(), true))),
   ]);
   let tbl: Table;
   let queryVec: number[];
@@ -346,6 +349,7 @@ describe("When creating an index", () => {
           vec: Array(32)
             .fill(1)
             .map(() => Math.random()),
+          tags: ["tag1", "tag2", "tag3"],
         })),
       {
         schema,
@@ -428,6 +432,22 @@ describe("When creating an index", () => {
     }
   });
 
+  test("create a bitmap index", async () => {
+    await tbl.createIndex("id", {
+      config: Index.bitmap(),
+    });
+    const indexDir = path.join(tmpDir.name, "test.lance", "_indices");
+    expect(fs.readdirSync(indexDir)).toHaveLength(1);
+  });
+
+  test("create a label list index", async () => {
+    await tbl.createIndex("tags", {
+      config: Index.labelList(),
+    });
+    const indexDir = path.join(tmpDir.name, "test.lance", "_indices");
+    expect(fs.readdirSync(indexDir)).toHaveLength(1);
+  });
+
   test("should be able to get index stats", async () => {
     await tbl.createIndex("id");
 
@@ -706,6 +726,21 @@ describe("when optimizing a dataset", () => {
     expect(stats.prune.bytesRemoved).toBeGreaterThan(0);
     expect(stats.prune.oldVersionsRemoved).toBe(3);
   });
+
+  it("delete unverified", async () => {
+    const version = await table.version();
+    const versionFile = `${tmpDir.name}/${table.name}.lance/_versions/${version - 1}.manifest`;
+    fs.rmSync(versionFile);
+
+    let stats = await table.optimize({ deleteUnverified: false });
+    expect(stats.prune.oldVersionsRemoved).toBe(0);
+
+    stats = await table.optimize({
+      cleanupOlderThan: new Date(),
+      deleteUnverified: true,
+    });
+    expect(stats.prune.oldVersionsRemoved).toBeGreaterThan(1);
+  });
 });
 
 describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
@@ -785,11 +820,26 @@ describe.each([arrow13, arrow14, arrow15, arrow16, arrow17])(
       ];
       const table = await db.createTable("test", data);
 
-      expect(table.search("hello").toArray()).rejects.toThrow(
+      expect(table.search("hello", "vector").toArray()).rejects.toThrow(
         "No embedding functions are defined in the table",
       );
     });
 
+    test("full text search if no embedding function provided", async () => {
+      const db = await connect(tmpDir.name);
+      const data = [
+        { text: "hello world", vector: [0.1, 0.2, 0.3] },
+        { text: "goodbye world", vector: [0.4, 0.5, 0.6] },
+      ];
+      const table = await db.createTable("test", data);
+      await table.createIndex("text", {
+        config: Index.fts(),
+      });
+
+      const results = await table.search("hello").toArray();
+      expect(results[0].text).toBe(data[0].text);
+    });
+
     test.each([
       [0.4, 0.5, 0.599], // number[]
       Float32Array.of(0.4, 0.5, 0.599), // Float32Array

nodejs/examples/custom_embedding_function.ts

@@ -0,0 +1,64 @@
+// --8<-- [start:imports]
+import * as lancedb from "@lancedb/lancedb";
+import {
+  LanceSchema,
+  TextEmbeddingFunction,
+  getRegistry,
+  register,
+} from "@lancedb/lancedb/embedding";
+import { pipeline } from "@xenova/transformers";
+// --8<-- [end:imports]
+
+// --8<-- [start:embedding_impl]
+@register("sentence-transformers")
+class SentenceTransformersEmbeddings extends TextEmbeddingFunction {
+  name = "Xenova/all-miniLM-L6-v2";
+  #ndims!: number;
+  extractor: any;
+
+  async init() {
+    this.extractor = await pipeline("feature-extraction", this.name);
+    this.#ndims = await this.generateEmbeddings(["hello"]).then(
+      (e) => e[0].length,
+    );
+  }
+
+  ndims() {
+    return this.#ndims;
+  }
+
+  toJSON() {
+    return {
+      name: this.name,
+    };
+  }
+  async generateEmbeddings(texts: string[]) {
+    const output = await this.extractor(texts, {
+      pooling: "mean",
+      normalize: true,
+    });
+    return output.tolist();
+  }
+}
+// -8<-- [end:embedding_impl]
+
+// --8<-- [start:call_custom_function]
+const registry = getRegistry();
+
+const sentenceTransformer = await registry
+  .get<SentenceTransformersEmbeddings>("sentence-transformers")!
+  .create();
+
+const schema = LanceSchema({
+  vector: sentenceTransformer.vectorField(),
+  text: sentenceTransformer.sourceField(),
+});
+
+const db = await lancedb.connect("/tmp/db");
+const table = await db.createEmptyTable("table", schema, { mode: "overwrite" });
+
+await table.add([{ text: "hello" }, { text: "world" }]);
+
+const results = await table.search("greeting").limit(1).toArray();
+console.log(results[0].text);
+// -8<-- [end:call_custom_function]

nodejs/examples/full_text_search.ts

@@ -0,0 +1,52 @@
+// Copyright 2024 Lance 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.
+
+import * as lancedb from "@lancedb/lancedb";
+
+const db = await lancedb.connect("data/sample-lancedb");
+
+const words = [
+  "apple",
+  "banana",
+  "cherry",
+  "date",
+  "elderberry",
+  "fig",
+  "grape",
+];
+
+const data = Array.from({ length: 10_000 }, (_, i) => ({
+  vector: Array(1536).fill(i),
+  id: i,
+  item: `item ${i}`,
+  strId: `${i}`,
+  doc: words[i % words.length],
+}));
+
+const tbl = await db.createTable("myVectors", data, { mode: "overwrite" });
+
+await tbl.createIndex("doc", {
+  config: lancedb.Index.fts(),
+});
+
+// --8<-- [start:full_text_search]
+let result = await tbl
+  .search("apple")
+  .select(["id", "doc"])
+  .limit(10)
+  .toArray();
+console.log(result);
+// --8<-- [end:full_text_search]
+
+console.log("SQL search: done");

nodejs/examples/package-lock.json

@@ -9,7 +9,12 @@
       "version": "1.0.0",
       "license": "Apache-2.0",
       "dependencies": {
-        "@lancedb/lancedb": "file:../"
+        "@lancedb/lancedb": "file:../",
+        "@xenova/transformers": "^2.17.2",
+        "tsc": "^2.0.4"
+      },
+      "devDependencies": {
+        "typescript": "^5.5.4"
       },
       "peerDependencies": {
         "typescript": "^5.0.0"
@@ -17,7 +22,7 @@
     },
     "..": {
       "name": "@lancedb/lancedb",
-      "version": "0.6.0",
+      "version": "0.8.0",
       "cpu": [
         "x64",
         "arm64"
@@ -29,44 +34,791 @@
         "win32"
       ],
       "dependencies": {
-        "apache-arrow": "^15.0.0",
         "axios": "^1.7.2",
-        "openai": "^4.29.2",
         "reflect-metadata": "^0.2.2"
       },
       "devDependencies": {
+        "@aws-sdk/client-dynamodb": "^3.33.0",
         "@aws-sdk/client-kms": "^3.33.0",
         "@aws-sdk/client-s3": "^3.33.0",
         "@biomejs/biome": "^1.7.3",
         "@jest/globals": "^29.7.0",
-        "@napi-rs/cli": "^2.18.0",
+        "@napi-rs/cli": "^2.18.3",
         "@types/axios": "^0.14.0",
         "@types/jest": "^29.1.2",
         "@types/tmp": "^0.2.6",
-        "apache-arrow-old": "npm:apache-arrow@13.0.0",
+        "apache-arrow-13": "npm:apache-arrow@13.0.0",
+        "apache-arrow-14": "npm:apache-arrow@14.0.0",
+        "apache-arrow-15": "npm:apache-arrow@15.0.0",
+        "apache-arrow-16": "npm:apache-arrow@16.0.0",
+        "apache-arrow-17": "npm:apache-arrow@17.0.0",
         "eslint": "^8.57.0",
         "jest": "^29.7.0",
         "shx": "^0.3.4",
         "tmp": "^0.2.3",
         "ts-jest": "^29.1.2",
-        "typedoc": "^0.25.7",
+        "typedoc": "^0.26.4",
-        "typedoc-plugin-markdown": "^3.17.1",
+        "typedoc-plugin-markdown": "^4.2.1",
-        "typescript": "^5.3.3",
+        "typescript": "^5.5.4",
         "typescript-eslint": "^7.1.0"
       },
       "engines": {
         "node": ">= 18"
+      },
+      "optionalDependencies": {
+        "@xenova/transformers": ">=2.17 < 3",
+        "openai": "^4.29.2"
+      },
+      "peerDependencies": {
+        "apache-arrow": ">=13.0.0 <=17.0.0"
+      }
+    },
+    "node_modules/@huggingface/jinja": {
+      "version": "0.2.2",
+      "resolved": "https://registry.npmjs.org/@huggingface/jinja/-/jinja-0.2.2.tgz",
+      "integrity": "sha512-/KPde26khDUIPkTGU82jdtTW9UAuvUTumCAbFs/7giR0SxsvZC4hru51PBvpijH6BVkHcROcvZM/lpy5h1jRRA==",
+      "engines": {
+        "node": ">=18"
       }
     },
     "node_modules/@lancedb/lancedb": {
       "resolved": "..",
       "link": true
     },
+    "node_modules/@protobufjs/aspromise": {
+      "version": "1.1.2",
+      "resolved": "https://registry.npmjs.org/@protobufjs/aspromise/-/aspromise-1.1.2.tgz",
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+    },
+    "node_modules/@protobufjs/base64": {
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+    },
+    "node_modules/@protobufjs/codegen": {
+      "version": "2.0.4",
+      "resolved": "https://registry.npmjs.org/@protobufjs/codegen/-/codegen-2.0.4.tgz",
+      "integrity": "sha512-YyFaikqM5sH0ziFZCN3xDC7zeGaB/d0IUb9CATugHWbd1FRFwWwt4ld4OYMPWu5a3Xe01mGAULCdqhMlPl29Jg=="
+    },
+    "node_modules/@protobufjs/eventemitter": {
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+    },
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+      "resolved": "https://registry.npmjs.org/@protobufjs/fetch/-/fetch-1.1.0.tgz",
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+      "dependencies": {
+        "@protobufjs/aspromise": "^1.1.1",
+        "@protobufjs/inquire": "^1.1.0"
+      }
+    },
+    "node_modules/@protobufjs/float": {
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+    },
+    "node_modules/@protobufjs/inquire": {
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+    },
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+    },
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+    },
+    "node_modules/@types/node": {
+      "version": "20.14.11",
+      "resolved": "https://registry.npmjs.org/@types/node/-/node-20.14.11.tgz",
+      "integrity": "sha512-kprQpL8MMeszbz6ojB5/tU8PLN4kesnN8Gjzw349rDlNgsSzg90lAVj3llK99Dh7JON+t9AuscPPFW6mPbTnSA==",
+      "dependencies": {
+        "undici-types": "~5.26.4"
+      }
+    },
+    "node_modules/@xenova/transformers": {
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+      "resolved": "https://registry.npmjs.org/@xenova/transformers/-/transformers-2.17.2.tgz",
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+      "dependencies": {
+        "@huggingface/jinja": "^0.2.2",
+        "onnxruntime-web": "1.14.0",
+        "sharp": "^0.32.0"
+      },
+      "optionalDependencies": {
+        "onnxruntime-node": "1.14.0"
+      }
+    },
+    "node_modules/b4a": {
+      "version": "1.6.6",
+      "resolved": "https://registry.npmjs.org/b4a/-/b4a-1.6.6.tgz",
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+    },
+    "node_modules/bare-events": {
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+      "optional": true
+    },
+    "node_modules/bare-fs": {
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+        "@protobufjs/fetch": "^1.1.0",
+        "@protobufjs/float": "^1.0.2",
+        "@protobufjs/inquire": "^1.1.0",
+        "@protobufjs/path": "^1.1.2",
+        "@protobufjs/pool": "^1.1.0",
+        "@protobufjs/utf8": "^1.1.0",
+        "@types/long": "^4.0.1",
+        "@types/node": ">=13.7.0",
+        "long": "^4.0.0"
+      },
+      "bin": {
+        "pbjs": "bin/pbjs",
+        "pbts": "bin/pbts"
+      }
+    },
+    "node_modules/pump": {
+      "version": "3.0.0",
+      "resolved": "https://registry.npmjs.org/pump/-/pump-3.0.0.tgz",
+      "integrity": "sha512-LwZy+p3SFs1Pytd/jYct4wpv49HiYCqd9Rlc5ZVdk0V+8Yzv6jR5Blk3TRmPL1ft69TxP0IMZGJ+WPFU2BFhww==",
+      "dependencies": {
+        "end-of-stream": "^1.1.0",
+        "once": "^1.3.1"
+      }
+    },
+    "node_modules/queue-tick": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/queue-tick/-/queue-tick-1.0.1.tgz",
+      "integrity": "sha512-kJt5qhMxoszgU/62PLP1CJytzd2NKetjSRnyuj31fDd3Rlcz3fzlFdFLD1SItunPwyqEOkca6GbV612BWfaBag=="
+    },
+    "node_modules/rc": {
+      "version": "1.2.8",
+      "resolved": "https://registry.npmjs.org/rc/-/rc-1.2.8.tgz",
+      "integrity": "sha512-y3bGgqKj3QBdxLbLkomlohkvsA8gdAiUQlSBJnBhfn+BPxg4bc62d8TcBW15wavDfgexCgccckhcZvywyQYPOw==",
+      "dependencies": {
+        "deep-extend": "^0.6.0",
+        "ini": "~1.3.0",
+        "minimist": "^1.2.0",
+        "strip-json-comments": "~2.0.1"
+      },
+      "bin": {
+        "rc": "cli.js"
+      }
+    },
+    "node_modules/readable-stream": {
+      "version": "3.6.2",
+      "resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-3.6.2.tgz",
+      "integrity": "sha512-9u/sniCrY3D5WdsERHzHE4G2YCXqoG5FTHUiCC4SIbr6XcLZBY05ya9EKjYek9O5xOAwjGq+1JdGBAS7Q9ScoA==",
+      "dependencies": {
+        "inherits": "^2.0.3",
+        "string_decoder": "^1.1.1",
+        "util-deprecate": "^1.0.1"
+      },
+      "engines": {
+        "node": ">= 6"
+      }
+    },
+    "node_modules/safe-buffer": {
+      "version": "5.2.1",
+      "resolved": "https://registry.npmjs.org/safe-buffer/-/safe-buffer-5.2.1.tgz",
+      "integrity": "sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==",
+      "funding": [
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/feross"
+        },
+        {
+          "type": "patreon",
+          "url": "https://www.patreon.com/feross"
+        },
+        {
+          "type": "consulting",
+          "url": "https://feross.org/support"
+        }
+      ]
+    },
+    "node_modules/semver": {
+      "version": "7.6.3",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-7.6.3.tgz",
+      "integrity": "sha512-oVekP1cKtI+CTDvHWYFUcMtsK/00wmAEfyqKfNdARm8u1wNVhSgaX7A8d4UuIlUI5e84iEwOhs7ZPYRmzU9U6A==",
+      "bin": {
+        "semver": "bin/semver.js"
+      },
+      "engines": {
+        "node": ">=10"
+      }
+    },
+    "node_modules/sharp": {
+      "version": "0.32.6",
+      "resolved": "https://registry.npmjs.org/sharp/-/sharp-0.32.6.tgz",
+      "integrity": "sha512-KyLTWwgcR9Oe4d9HwCwNM2l7+J0dUQwn/yf7S0EnTtb0eVS4RxO0eUSvxPtzT4F3SY+C4K6fqdv/DO27sJ/v/w==",
+      "hasInstallScript": true,
+      "dependencies": {
+        "color": "^4.2.3",
+        "detect-libc": "^2.0.2",
+        "node-addon-api": "^6.1.0",
+        "prebuild-install": "^7.1.1",
+        "semver": "^7.5.4",
+        "simple-get": "^4.0.1",
+        "tar-fs": "^3.0.4",
+        "tunnel-agent": "^0.6.0"
+      },
+      "engines": {
+        "node": ">=14.15.0"
+      },
+      "funding": {
+        "url": "https://opencollective.com/libvips"
+      }
+    },
+    "node_modules/simple-concat": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/simple-concat/-/simple-concat-1.0.1.tgz",
+      "integrity": "sha512-cSFtAPtRhljv69IK0hTVZQ+OfE9nePi/rtJmw5UjHeVyVroEqJXP1sFztKUy1qU+xvz3u/sfYJLa947b7nAN2Q==",
+      "funding": [
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/feross"
+        },
+        {
+          "type": "patreon",
+          "url": "https://www.patreon.com/feross"
+        },
+        {
+          "type": "consulting",
+          "url": "https://feross.org/support"
+        }
+      ]
+    },
+    "node_modules/simple-get": {
+      "version": "4.0.1",
+      "resolved": "https://registry.npmjs.org/simple-get/-/simple-get-4.0.1.tgz",
+      "integrity": "sha512-brv7p5WgH0jmQJr1ZDDfKDOSeWWg+OVypG99A/5vYGPqJ6pxiaHLy8nxtFjBA7oMa01ebA9gfh1uMCFqOuXxvA==",
+      "funding": [
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/feross"
+        },
+        {
+          "type": "patreon",
+          "url": "https://www.patreon.com/feross"
+        },
+        {
+          "type": "consulting",
+          "url": "https://feross.org/support"
+        }
+      ],
+      "dependencies": {
+        "decompress-response": "^6.0.0",
+        "once": "^1.3.1",
+        "simple-concat": "^1.0.0"
+      }
+    },
+    "node_modules/simple-swizzle": {
+      "version": "0.2.2",
+      "resolved": "https://registry.npmjs.org/simple-swizzle/-/simple-swizzle-0.2.2.tgz",
+      "integrity": "sha512-JA//kQgZtbuY83m+xT+tXJkmJncGMTFT+C+g2h2R9uxkYIrE2yy9sgmcLhCnw57/WSD+Eh3J97FPEDFnbXnDUg==",
+      "dependencies": {
+        "is-arrayish": "^0.3.1"
+      }
+    },
+    "node_modules/streamx": {
+      "version": "2.18.0",
+      "resolved": "https://registry.npmjs.org/streamx/-/streamx-2.18.0.tgz",
+      "integrity": "sha512-LLUC1TWdjVdn1weXGcSxyTR3T4+acB6tVGXT95y0nGbca4t4o/ng1wKAGTljm9VicuCVLvRlqFYXYy5GwgM7sQ==",
+      "dependencies": {
+        "fast-fifo": "^1.3.2",
+        "queue-tick": "^1.0.1",
+        "text-decoder": "^1.1.0"
+      },
+      "optionalDependencies": {
+        "bare-events": "^2.2.0"
+      }
+    },
+    "node_modules/string_decoder": {
+      "version": "1.3.0",
+      "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz",
+      "integrity": "sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA==",
+      "dependencies": {
+        "safe-buffer": "~5.2.0"
+      }
+    },
+    "node_modules/strip-json-comments": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz",
+      "integrity": "sha512-4gB8na07fecVVkOI6Rs4e7T6NOTki5EmL7TUduTs6bu3EdnSycntVJ4re8kgZA+wx9IueI2Y11bfbgwtzuE0KQ==",
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/tar-fs": {
+      "version": "3.0.6",
+      "resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-3.0.6.tgz",
+      "integrity": "sha512-iokBDQQkUyeXhgPYaZxmczGPhnhXZ0CmrqI+MOb/WFGS9DW5wnfrLgtjUJBvz50vQ3qfRwJ62QVoCFu8mPVu5w==",
+      "dependencies": {
+        "pump": "^3.0.0",
+        "tar-stream": "^3.1.5"
+      },
+      "optionalDependencies": {
+        "bare-fs": "^2.1.1",
+        "bare-path": "^2.1.0"
+      }
+    },
+    "node_modules/tar-stream": {
+      "version": "3.1.7",
+      "resolved": "https://registry.npmjs.org/tar-stream/-/tar-stream-3.1.7.tgz",
+      "integrity": "sha512-qJj60CXt7IU1Ffyc3NJMjh6EkuCFej46zUqJ4J7pqYlThyd9bO0XBTmcOIhSzZJVWfsLks0+nle/j538YAW9RQ==",
+      "dependencies": {
+        "b4a": "^1.6.4",
+        "fast-fifo": "^1.2.0",
+        "streamx": "^2.15.0"
+      }
+    },
+    "node_modules/text-decoder": {
+      "version": "1.1.1",
+      "resolved": "https://registry.npmjs.org/text-decoder/-/text-decoder-1.1.1.tgz",
+      "integrity": "sha512-8zll7REEv4GDD3x4/0pW+ppIxSNs7H1J10IKFZsuOMscumCdM2a+toDGLPA3T+1+fLBql4zbt5z83GEQGGV5VA==",
+      "dependencies": {
+        "b4a": "^1.6.4"
+      }
+    },
+    "node_modules/tsc": {
+      "version": "2.0.4",
+      "resolved": "https://registry.npmjs.org/tsc/-/tsc-2.0.4.tgz",
+      "integrity": "sha512-fzoSieZI5KKJVBYGvwbVZs/J5za84f2lSTLPYf6AGiIf43tZ3GNrI1QzTLcjtyDDP4aLxd46RTZq1nQxe7+k5Q==",
+      "license": "MIT",
+      "bin": {
+        "tsc": "bin/tsc"
+      }
+    },
+    "node_modules/tunnel-agent": {
+      "version": "0.6.0",
+      "resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz",
+      "integrity": "sha512-McnNiV1l8RYeY8tBgEpuodCC1mLUdbSN+CYBL7kJsJNInOP8UjDDEwdk6Mw60vdLLrr5NHKZhMAOSrR2NZuQ+w==",
+      "dependencies": {
+        "safe-buffer": "^5.0.1"
+      },
+      "engines": {
+        "node": "*"
+      }
+    },
     "node_modules/typescript": {
-      "version": "5.5.2",
+      "version": "5.5.4",
-      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.5.2.tgz",
+      "resolved": "https://registry.npmjs.org/typescript/-/typescript-5.5.4.tgz",
-      "integrity": "sha512-NcRtPEOsPFFWjobJEtfihkLCZCXZt/os3zf8nTxjVH3RvTSxjrCamJpbExGvYOF+tFHc3pA65qpdwPbzjohhew==",
+      "integrity": "sha512-Mtq29sKDAEYP7aljRgtPOpTvOfbwRWlS6dPRzwjdE+C0R4brX/GUyhHSecbHMFLNBLcJIPt9nl9yG5TZ1weH+Q==",
-      "peer": true,
+      "dev": true,
+      "license": "Apache-2.0",
       "bin": {
         "tsc": "bin/tsc",
         "tsserver": "bin/tsserver"
@@ -74,6 +826,21 @@
       "engines": {
         "node": ">=14.17"
       }
+    },
+    "node_modules/undici-types": {
+      "version": "5.26.5",
+      "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-5.26.5.tgz",
+      "integrity": "sha512-JlCMO+ehdEIKqlFxk6IfVoAUVmgz7cU7zD/h9XZ0qzeosSHmUJVOzSQvvYSYWXkFXC+IfLKSIffhv0sVZup6pA=="
+    },
+    "node_modules/util-deprecate": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
+      "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw=="
+    },
+    "node_modules/wrappy": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/wrappy/-/wrappy-1.0.2.tgz",
+      "integrity": "sha512-l4Sp/DRseor9wL6EvV2+TuQn63dMkPjZ/sp9XkghTEbV9KlPS1xUsZ3u7/IQO4wxtcFB4bgpQPRcR3QCvezPcQ=="
     }
   }
 }

nodejs/examples/package.json

@@ -10,9 +10,19 @@
   "author": "Lance Devs",
   "license": "Apache-2.0",
   "dependencies": {
-    "@lancedb/lancedb": "file:../"
+    "@lancedb/lancedb": "file:../",
+    "@xenova/transformers": "^2.17.2"
   },
-  "peerDependencies": {
+  "devDependencies": {
-    "typescript": "^5.0.0"
+    "typescript": "^5.5.4"
+  },
+  "compilerOptions": {
+    "target": "ESNext",
+    "module": "ESNext",
+    "moduleResolution": "Node",
+    "strict": true,
+    "esModuleInterop": true,
+    "skipLibCheck": true,
+    "forceConsistentCasingInFileNames": true
   }
 }

nodejs/examples/search.ts

@@ -32,6 +32,7 @@ const _results2 = await tbl
   .distanceType("cosine")
   .limit(10)
   .toArray();
+console.log(_results2);
 // --8<-- [end:search2]
 
 console.log("search: done");

nodejs/examples/sentence-transformers.js

@@ -0,0 +1,50 @@
+import * as lancedb from "@lancedb/lancedb";
+
+import { LanceSchema, getRegistry } from "@lancedb/lancedb/embedding";
+import { Utf8 } from "apache-arrow";
+
+const db = await lancedb.connect("/tmp/db");
+const func = await getRegistry().get("huggingface").create();
+
+const facts = [
+  "Albert Einstein was a theoretical physicist.",
+  "The capital of France is Paris.",
+  "The Great Wall of China is one of the Seven Wonders of the World.",
+  "Python is a popular programming language.",
+  "Mount Everest is the highest mountain in the world.",
+  "Leonardo da Vinci painted the Mona Lisa.",
+  "Shakespeare wrote Hamlet.",
+  "The human body has 206 bones.",
+  "The speed of light is approximately 299,792 kilometers per second.",
+  "Water boils at 100 degrees Celsius.",
+  "The Earth orbits the Sun.",
+  "The Pyramids of Giza are located in Egypt.",
+  "Coffee is one of the most popular beverages in the world.",
+  "Tokyo is the capital city of Japan.",
+  "Photosynthesis is the process by which plants make their food.",
+  "The Pacific Ocean is the largest ocean on Earth.",
+  "Mozart was a prolific composer of classical music.",
+  "The Internet is a global network of computers.",
+  "Basketball is a sport played with a ball and a hoop.",
+  "The first computer virus was created in 1983.",
+  "Artificial neural networks are inspired by the human brain.",
+  "Deep learning is a subset of machine learning.",
+  "IBM's Watson won Jeopardy! in 2011.",
+  "The first computer programmer was Ada Lovelace.",
+  "The first chatbot was ELIZA, created in the 1960s.",
+].map((text) => ({ text }));
+
+const factsSchema = LanceSchema({
+  text: func.sourceField(new Utf8()),
+  vector: func.vectorField(),
+});
+
+const tbl = await db.createTable("facts", facts, {
+  mode: "overwrite",
+  schema: factsSchema,
+});
+
+const query = "How many bones are in the human body?";
+const actual = await tbl.search(query).limit(1).toArray();
+
+console.log("Answer: ", actual[0]["text"]);

nodejs/lancedb/arrow.ts

@@ -103,50 +103,11 @@ export type IntoVector =
   | number[]
   | Promise<Float32Array | Float64Array | number[]>;
 
-export type FloatLike =
-  | import("apache-arrow-13").Float
-  | import("apache-arrow-14").Float
-  | import("apache-arrow-15").Float
-  | import("apache-arrow-16").Float
-  | import("apache-arrow-17").Float;
-export type DataTypeLike =
-  | import("apache-arrow-13").DataType
-  | import("apache-arrow-14").DataType
-  | import("apache-arrow-15").DataType
-  | import("apache-arrow-16").DataType
-  | import("apache-arrow-17").DataType;
-
 export function isArrowTable(value: object): value is TableLike {
   if (value instanceof ArrowTable) return true;
   return "schema" in value && "batches" in value;
 }
 
-export function isDataType(value: unknown): value is DataTypeLike {
-  return (
-    value instanceof DataType ||
-    DataType.isNull(value) ||
-    DataType.isInt(value) ||
-    DataType.isFloat(value) ||
-    DataType.isBinary(value) ||
-    DataType.isLargeBinary(value) ||
-    DataType.isUtf8(value) ||
-    DataType.isLargeUtf8(value) ||
-    DataType.isBool(value) ||
-    DataType.isDecimal(value) ||
-    DataType.isDate(value) ||
-    DataType.isTime(value) ||
-    DataType.isTimestamp(value) ||
-    DataType.isInterval(value) ||
-    DataType.isDuration(value) ||
-    DataType.isList(value) ||
-    DataType.isStruct(value) ||
-    DataType.isUnion(value) ||
-    DataType.isFixedSizeBinary(value) ||
-    DataType.isFixedSizeList(value) ||
-    DataType.isMap(value) ||
-    DataType.isDictionary(value)
-  );
-}
 export function isNull(value: unknown): value is Null {
   return value instanceof Null || DataType.isNull(value);
 }
@@ -578,7 +539,7 @@ async function applyEmbeddingsFromMetadata(
   schema: Schema,
 ): Promise<ArrowTable> {
   const registry = getRegistry();
-  const functions = registry.parseFunctions(schema.metadata);
+  const functions = await registry.parseFunctions(schema.metadata);
 
   const columns = Object.fromEntries(
     table.schema.fields.map((field) => [

nodejs/lancedb/connection.ts

@@ -44,10 +44,20 @@ export interface CreateTableOptions {
    * The available options are described at https://lancedb.github.io/lancedb/guides/storage/
    */
   storageOptions?: Record<string, string>;
+  /**
+   * The version of the data storage format to use.
+   *
+   * The default is `legacy`, which is Lance format v1.
+   * `stable` is the new format, which is Lance format v2.
+   */
+  dataStorageVersion?: string;
+
   /**
    * If true then data files will be written with the legacy format
    *
    * The default is true while the new format is in beta
+   *
+   * Deprecated.
    */
   useLegacyFormat?: boolean;
   schema?: SchemaLike;
@@ -240,18 +250,26 @@ export class LocalConnection extends Connection {
   ): Promise<Table> {
     if (typeof nameOrOptions !== "string" && "name" in nameOrOptions) {
       const { name, data, ...options } = nameOrOptions;
+
       return this.createTable(name, data, options);
     }
     if (data === undefined) {
       throw new Error("data is required");
     }
     const { buf, mode } = await Table.parseTableData(data, options);
+    let dataStorageVersion = "legacy";
+    if (options?.dataStorageVersion !== undefined) {
+      dataStorageVersion = options.dataStorageVersion;
+    } else if (options?.useLegacyFormat !== undefined) {
+      dataStorageVersion = options.useLegacyFormat ? "legacy" : "stable";
+    }
+
     const innerTable = await this.inner.createTable(
       nameOrOptions,
       buf,
       mode,
       cleanseStorageOptions(options?.storageOptions),
-      options?.useLegacyFormat,
+      dataStorageVersion,
     );
 
     return new LocalTable(innerTable);
@@ -275,6 +293,13 @@ export class LocalConnection extends Connection {
       metadata = registry.getTableMetadata([embeddingFunction]);
     }
 
+    let dataStorageVersion = "legacy";
+    if (options?.dataStorageVersion !== undefined) {
+      dataStorageVersion = options.dataStorageVersion;
+    } else if (options?.useLegacyFormat !== undefined) {
+      dataStorageVersion = options.useLegacyFormat ? "legacy" : "stable";
+    }
+
     const table = makeEmptyTable(schema, metadata);
     const buf = await fromTableToBuffer(table);
     const innerTable = await this.inner.createEmptyTable(
@@ -282,7 +307,7 @@ export class LocalConnection extends Connection {
       buf,
       mode,
       cleanseStorageOptions(options?.storageOptions),
-      options?.useLegacyFormat,
+      dataStorageVersion,
     );
     return new LocalTable(innerTable);
   }

nodejs/lancedb/embedding/embedding_function.ts

@@ -15,13 +15,12 @@
 import "reflect-metadata";
 import {
   DataType,
-  DataTypeLike,
   Field,
   FixedSizeList,
+  Float,
   Float32,
-  FloatLike,
   type IntoVector,
-  isDataType,
+  Utf8,
   isFixedSizeList,
   isFloat,
   newVectorType,
@@ -41,6 +40,7 @@ export interface EmbeddingFunctionConstructor<
 > {
   new (modelOptions?: T["TOptions"]): T;
 }
+
 /**
  * An embedding function that automatically creates vector representation for a given column.
  */
@@ -82,6 +82,8 @@ export abstract class EmbeddingFunction<
    */
   abstract toJSON(): Partial<M>;
 
+  async init?(): Promise<void>;
+
   /**
    * sourceField is used in combination with `LanceSchema` to provide a declarative data model
    *
@@ -90,11 +92,12 @@ export abstract class EmbeddingFunction<
    * @see {@link lancedb.LanceSchema}
    */
   sourceField(
-    optionsOrDatatype: Partial<FieldOptions> | DataTypeLike,
+    optionsOrDatatype: Partial<FieldOptions> | DataType,
-  ): [DataTypeLike, Map<string, EmbeddingFunction>] {
+  ): [DataType, Map<string, EmbeddingFunction>] {
-    let datatype = isDataType(optionsOrDatatype)
+    let datatype =
-      ? optionsOrDatatype
+      "datatype" in optionsOrDatatype
-      : optionsOrDatatype?.datatype;
+        ? optionsOrDatatype.datatype
+        : optionsOrDatatype;
     if (!datatype) {
       throw new Error("Datatype is required");
     }
@@ -120,15 +123,17 @@ export abstract class EmbeddingFunction<
     let dims: number | undefined = this.ndims();
 
     // `func.vectorField(new Float32())`
-    if (isDataType(optionsOrDatatype)) {
+    if (optionsOrDatatype === undefined) {
-      dtype = optionsOrDatatype;
+      dtype = new Float32();
+    } else if (!("datatype" in optionsOrDatatype)) {
+      dtype = sanitizeType(optionsOrDatatype);
     } else {
       // `func.vectorField({
       //  datatype: new Float32(),
       //  dims: 10
       // })`
       dims = dims ?? optionsOrDatatype?.dims;
-      dtype = optionsOrDatatype?.datatype;
+      dtype = sanitizeType(optionsOrDatatype?.datatype);
     }
 
     if (dtype !== undefined) {
@@ -170,7 +175,7 @@ export abstract class EmbeddingFunction<
   }
 
   /** The datatype of the embeddings */
-  abstract embeddingDataType(): FloatLike;
+  abstract embeddingDataType(): Float;
 
   /**
    * Creates a vector representation for the given values.
@@ -189,6 +194,38 @@ export abstract class EmbeddingFunction<
   }
 }
 
+/**
+ * an abstract class for implementing embedding functions that take text as input
+ */
+export abstract class TextEmbeddingFunction<
+  M extends FunctionOptions = FunctionOptions,
+> extends EmbeddingFunction<string, M> {
+  //** Generate the embeddings for the given texts */
+  abstract generateEmbeddings(
+    texts: string[],
+    // biome-ignore lint/suspicious/noExplicitAny: we don't know what the implementor will do
+    ...args: any[]
+  ): Promise<number[][] | Float32Array[] | Float64Array[]>;
+
+  async computeQueryEmbeddings(data: string): Promise<Awaited<IntoVector>> {
+    return this.generateEmbeddings([data]).then((data) => data[0]);
+  }
+
+  embeddingDataType(): Float {
+    return new Float32();
+  }
+
+  override sourceField(): [DataType, Map<string, EmbeddingFunction>] {
+    return super.sourceField(new Utf8());
+  }
+
+  computeSourceEmbeddings(
+    data: string[],
+  ): Promise<number[][] | Float32Array[] | Float64Array[]> {
+    return this.generateEmbeddings(data);
+  }
+}
+
 export interface FieldOptions<T extends DataType = DataType> {
   datatype: T;
   dims?: number;

nodejs/lancedb/embedding/index.ts

@@ -12,16 +12,16 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { DataType, Field, Schema } from "../arrow";
+import { Field, Schema } from "../arrow";
-import { isDataType } from "../arrow";
 import { sanitizeType } from "../sanitize";
 import { EmbeddingFunction } from "./embedding_function";
 import { EmbeddingFunctionConfig, getRegistry } from "./registry";
 
-export { EmbeddingFunction } from "./embedding_function";
+export { EmbeddingFunction, TextEmbeddingFunction } from "./embedding_function";
 
 // We need to explicitly export '*' so that the `register` decorator actually registers the class.
 export * from "./openai";
+export * from "./transformers";
 export * from "./registry";
 
 /**
@@ -56,15 +56,15 @@ export function LanceSchema(
     Partial<EmbeddingFunctionConfig>
   >();
   Object.entries(fields).forEach(([key, value]) => {
-    if (isDataType(value)) {
+    if (Array.isArray(value)) {
-      arrowFields.push(new Field(key, sanitizeType(value), true));
-    } else {
       const [dtype, metadata] = value as [
         object,
         Map<string, EmbeddingFunction>,
       ];
       arrowFields.push(new Field(key, sanitizeType(dtype), true));
       parseEmbeddingFunctions(embeddingFunctions, key, metadata);
+    } else {
+      arrowFields.push(new Field(key, sanitizeType(value), true));
     }
   });
   const registry = getRegistry();

nodejs/lancedb/embedding/openai.ts

@@ -13,7 +13,7 @@
 // limitations under the License.
 
 import type OpenAI from "openai";
-import { type EmbeddingCreateParams } from "openai/resources";
+import type { EmbeddingCreateParams } from "openai/resources/index";
 import { Float, Float32 } from "../arrow";
 import { EmbeddingFunction } from "./embedding_function";
 import { register } from "./registry";

nodejs/lancedb/embedding/registry.ts

@@ -18,9 +18,14 @@ import {
 } from "./embedding_function";
 import "reflect-metadata";
 import { OpenAIEmbeddingFunction } from "./openai";
+import { TransformersEmbeddingFunction } from "./transformers";
+
+type CreateReturnType<T> = T extends { init: () => Promise<void> }
+  ? Promise<T>
+  : T;
 
 interface EmbeddingFunctionCreate<T extends EmbeddingFunction> {
-  create(options?: T["TOptions"]): T;
+  create(options?: T["TOptions"]): CreateReturnType<T>;
 }
 
 /**
@@ -32,6 +37,13 @@ interface EmbeddingFunctionCreate<T extends EmbeddingFunction> {
 export class EmbeddingFunctionRegistry {
   #functions = new Map<string, EmbeddingFunctionConstructor>();
 
+  /**
+   * Get the number of registered functions
+   */
+  length() {
+    return this.#functions.size;
+  }
+
   /**
    * Register an embedding function
    * @param name The name of the function
@@ -61,38 +73,43 @@ export class EmbeddingFunctionRegistry {
     };
   }
 
+  get(name: "openai"): EmbeddingFunctionCreate<OpenAIEmbeddingFunction>;
+  get(
+    name: "huggingface",
+  ): EmbeddingFunctionCreate<TransformersEmbeddingFunction>;
+  get<T extends EmbeddingFunction<unknown>>(
+    name: string,
+  ): EmbeddingFunctionCreate<T> | undefined;
   /**
    * Fetch an embedding function by name
    * @param name The name of the function
    */
-  get<T extends EmbeddingFunction<unknown>, Name extends string = "">(
+  get(name: string) {
-    name: Name extends "openai" ? "openai" : string,
-    //This makes it so that you can use string constants as "types", or use an explicitly supplied type
-    // ex:
-    // `registry.get("openai") -> EmbeddingFunctionCreate<OpenAIEmbeddingFunction>`
-    // `registry.get<MyCustomEmbeddingFunction>("my_func") -> EmbeddingFunctionCreate<MyCustomEmbeddingFunction> | undefined`
-    //
-    // the reason this is important is that we always know our built in functions are defined so the user isnt forced to do a non null/undefined
-    // ```ts
-    // const openai: OpenAIEmbeddingFunction = registry.get("openai").create()
-    // ```
-  ): Name extends "openai"
-    ? EmbeddingFunctionCreate<OpenAIEmbeddingFunction>
-    : EmbeddingFunctionCreate<T> | undefined {
-    type Output = Name extends "openai"
-      ? EmbeddingFunctionCreate<OpenAIEmbeddingFunction>
-      : EmbeddingFunctionCreate<T> | undefined;
-
     const factory = this.#functions.get(name);
     if (!factory) {
-      return undefined as Output;
+      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+      return undefined as any;
+    }
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+    let create: any;
+    if (factory.prototype.init) {
+      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+      create = async function (options?: any) {
+        const instance = new factory(options);
+        await instance.init!();
+        return instance;
+      };
+    } else {
+      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+      create = function (options?: any) {
+        const instance = new factory(options);
+        return instance;
+      };
     }
 
     return {
-      create: function (options?: T["TOptions"]) {
+      create,
-        return new factory(options);
+    };
-      },
-    } as Output;
   }
 
   /**
@@ -105,10 +122,10 @@ export class EmbeddingFunctionRegistry {
   /**
    * @ignore
    */
-  parseFunctions(
+  async parseFunctions(
     this: EmbeddingFunctionRegistry,
     metadata: Map<string, string>,
-  ): Map<string, EmbeddingFunctionConfig> {
+  ): Promise<Map<string, EmbeddingFunctionConfig>> {
     if (!metadata.has("embedding_functions")) {
       return new Map();
     } else {
@@ -118,25 +135,30 @@ export class EmbeddingFunctionRegistry {
         vectorColumn: string;
         model: EmbeddingFunction["TOptions"];
       };
+
       const functions = <FunctionConfig[]>(
         JSON.parse(metadata.get("embedding_functions")!)
       );
-      return new Map(
+
-        functions.map((f) => {
+      const items: [string, EmbeddingFunctionConfig][] = await Promise.all(
+        functions.map(async (f) => {
           const fn = this.get(f.name);
           if (!fn) {
             throw new Error(`Function "${f.name}" not found in registry`);
           }
+          const func = await this.get(f.name)!.create(f.model);
           return [
             f.name,
             {
               sourceColumn: f.sourceColumn,
               vectorColumn: f.vectorColumn,
-              function: this.get(f.name)!.create(f.model),
+              function: func,
             },
           ];
         }),
       );
+
+      return new Map(items);
     }
   }
   // biome-ignore lint/suspicious/noExplicitAny: <explanation>

nodejs/lancedb/embedding/transformers.ts

@@ -0,0 +1,193 @@
+// Copyright 2023 Lance 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.
+
+import { Float, Float32 } from "../arrow";
+import { EmbeddingFunction } from "./embedding_function";
+import { register } from "./registry";
+
+export type XenovaTransformerOptions = {
+  /** The wasm compatible model to use */
+  model: string;
+  /**
+   * The wasm compatible tokenizer to use
+   * If not provided, it will use the default tokenizer for the model
+   */
+  tokenizer?: string;
+  /**
+   * The number of dimensions of the embeddings
+   *
+   * We will attempt to infer this from the model config if not provided.
+   * Since there isn't a standard way to get this information from the model,
+   * you may need to manually specify this if using a model that doesn't have a 'hidden_size' in the config.
+   * */
+  ndims?: number;
+  /** Options for the tokenizer */
+  tokenizerOptions?: {
+    textPair?: string | string[];
+    padding?: boolean | "max_length";
+    addSpecialTokens?: boolean;
+    truncation?: boolean;
+    maxLength?: number;
+  };
+};
+
+@register("huggingface")
+export class TransformersEmbeddingFunction extends EmbeddingFunction<
+  string,
+  Partial<XenovaTransformerOptions>
+> {
+  #model?: import("@xenova/transformers").PreTrainedModel;
+  #tokenizer?: import("@xenova/transformers").PreTrainedTokenizer;
+  #modelName: XenovaTransformerOptions["model"];
+  #initialized = false;
+  #tokenizerOptions: XenovaTransformerOptions["tokenizerOptions"];
+  #ndims?: number;
+
+  constructor(
+    options: Partial<XenovaTransformerOptions> = {
+      model: "Xenova/all-MiniLM-L6-v2",
+    },
+  ) {
+    super();
+
+    const modelName = options?.model ?? "Xenova/all-MiniLM-L6-v2";
+    this.#tokenizerOptions = {
+      padding: true,
+      ...options.tokenizerOptions,
+    };
+
+    this.#ndims = options.ndims;
+    this.#modelName = modelName;
+  }
+  toJSON() {
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+    const obj: Record<string, any> = {
+      model: this.#modelName,
+    };
+    if (this.#ndims) {
+      obj["ndims"] = this.#ndims;
+    }
+    if (this.#tokenizerOptions) {
+      obj["tokenizerOptions"] = this.#tokenizerOptions;
+    }
+    if (this.#tokenizer) {
+      obj["tokenizer"] = this.#tokenizer.name;
+    }
+    return obj;
+  }
+
+  async init() {
+    let transformers;
+    try {
+      // SAFETY:
+      // since typescript transpiles `import` to `require`, we need to do this in an unsafe way
+      // We can't use `require` because `@xenova/transformers` is an ESM module
+      // and we can't use `import` directly because typescript will transpile it to `require`.
+      // and we want to remain compatible with both ESM and CJS modules
+      // so we use `eval` to bypass typescript for this specific import.
+      transformers = await eval('import("@xenova/transformers")');
+    } catch (e) {
+      throw new Error(`error loading @xenova/transformers\nReason: ${e}`);
+    }
+
+    try {
+      this.#model = await transformers.AutoModel.from_pretrained(
+        this.#modelName,
+      );
+    } catch (e) {
+      throw new Error(
+        `error loading model ${this.#modelName}. Make sure you are using a wasm compatible model.\nReason: ${e}`,
+      );
+    }
+    try {
+      this.#tokenizer = await transformers.AutoTokenizer.from_pretrained(
+        this.#modelName,
+      );
+    } catch (e) {
+      throw new Error(
+        `error loading tokenizer for ${this.#modelName}. Make sure you are using a wasm compatible model:\nReason: ${e}`,
+      );
+    }
+    this.#initialized = true;
+  }
+
+  ndims(): number {
+    if (this.#ndims) {
+      return this.#ndims;
+    } else {
+      const config = this.#model!.config;
+
+      const ndims = config["hidden_size"];
+      if (!ndims) {
+        throw new Error(
+          "hidden_size not found in model config, you may need to manually specify the embedding dimensions. ",
+        );
+      }
+      return ndims;
+    }
+  }
+
+  embeddingDataType(): Float {
+    return new Float32();
+  }
+
+  async computeSourceEmbeddings(data: string[]): Promise<number[][]> {
+    // this should only happen if the user is trying to use the function directly.
+    // Anything going through the registry should already be initialized.
+    if (!this.#initialized) {
+      return Promise.reject(
+        new Error(
+          "something went wrong: embedding function not initialized. Please call init()",
+        ),
+      );
+    }
+    const tokenizer = this.#tokenizer!;
+    const model = this.#model!;
+
+    const inputs = await tokenizer(data, this.#tokenizerOptions);
+    let tokens = await model.forward(inputs);
+    tokens = tokens[Object.keys(tokens)[0]];
+
+    const [nItems, nTokens] = tokens.dims;
+
+    tokens = tensorDiv(tokens.sum(1), nTokens);
+
+    // TODO: support other data types
+    const tokenData = tokens.data;
+    const stride = this.ndims();
+
+    const embeddings = [];
+    for (let i = 0; i < nItems; i++) {
+      const start = i * stride;
+      const end = start + stride;
+      const slice = tokenData.slice(start, end);
+      embeddings.push(Array.from(slice) as number[]); // TODO: Avoid copy here
+    }
+    return embeddings;
+  }
+
+  async computeQueryEmbeddings(data: string): Promise<number[]> {
+    return (await this.computeSourceEmbeddings([data]))[0];
+  }
+}
+
+const tensorDiv = (
+  src: import("@xenova/transformers").Tensor,
+  divBy: number,
+) => {
+  for (let i = 0; i < src.data.length; ++i) {
+    src.data[i] /= divBy;
+  }
+  return src;
+};

nodejs/lancedb/index.ts

@@ -59,7 +59,7 @@ export {
 
 export { Index, IndexOptions, IvfPqOptions } from "./indices";
 
-export { Table, AddDataOptions, UpdateOptions } from "./table";
+export { Table, AddDataOptions, UpdateOptions, OptimizeOptions } from "./table";
 
 export * as embedding from "./embedding";
 

nodejs/lancedb/indices.ts

@@ -175,6 +175,45 @@ export class Index {
   static btree() {
     return new Index(LanceDbIndex.btree());
   }
+
+  /**
+   * Create a bitmap index.
+   *
+   * A `Bitmap` index stores a bitmap for each distinct value in the column for every row.
+   *
+   * This index works best for low-cardinality columns, where the number of unique values
+   * is small (i.e., less than a few hundreds).
+   */
+  static bitmap() {
+    return new Index(LanceDbIndex.bitmap());
+  }
+
+  /**
+   * Create a label list index.
+   *
+   * LabelList index is a scalar index that can be used on `List<T>` columns to
+   * support queries with `array_contains_all` and `array_contains_any`
+   * using an underlying bitmap index.
+   */
+  static labelList() {
+    return new Index(LanceDbIndex.labelList());
+  }
+
+  /**
+   * Create a full text search index
+   *
+   * A full text search index is an index on a string column, so that you can conduct full
+   * text searches on the column.
+   *
+   * The results of a full text search are ordered by relevance measured by BM25.
+   *
+   * You can combine filters with full text search.
+   *
+   * For now, the full text search index only supports English, and doesn't support phrase search.
+   */
+  static fts() {
+    return new Index(LanceDbIndex.fts());
+  }
 }
 
 export interface IndexOptions {

nodejs/lancedb/query.ts

@@ -88,6 +88,19 @@ export interface QueryExecutionOptions {
   maxBatchLength?: number;
 }
 
+/**
+ * Options that control the behavior of a full text search
+ */
+export interface FullTextSearchOptions {
+  /**
+   * The columns to search
+   *
+   * If not specified, all indexed columns will be searched.
+   * For now, only one column can be searched.
+   */
+  columns?: string | string[];
+}
+
 /** Common methods supported by all query types */
 export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
   implements AsyncIterable<RecordBatch>
@@ -134,6 +147,25 @@ export class QueryBase<NativeQueryType extends NativeQuery | NativeVectorQuery>
     return this.where(predicate);
   }
 
+  fullTextSearch(
+    query: string,
+    options?: Partial<FullTextSearchOptions>,
+  ): this {
+    let columns: string[] | null = null;
+    if (options) {
+      if (typeof options.columns === "string") {
+        columns = [options.columns];
+      } else if (Array.isArray(options.columns)) {
+        columns = options.columns;
+      }
+    }
+
+    this.doCall((inner: NativeQueryType) =>
+      inner.fullTextSearch(query, columns),
+    );
+    return this;
+  }
+
   /**
    * Return only the specified columns.
    *

nodejs/lancedb/remote/client.ts

@@ -27,8 +27,7 @@ export class RestfulLanceDBClient {
   #apiKey: string;
   #hostOverride?: string;
   #closed: boolean = false;
-  #connectionTimeout: number = 12 * 1000; // 12 seconds;
+  #timeout: number = 12 * 1000; // 12 seconds;
-  #readTimeout: number = 30 * 1000; // 30 seconds;
   #session?: import("axios").AxiosInstance;
 
   constructor(
@@ -36,15 +35,13 @@ export class RestfulLanceDBClient {
     apiKey: string,
     region: string,
     hostOverride?: string,
-    connectionTimeout?: number,
+    timeout?: number,
-    readTimeout?: number,
   ) {
     this.#dbName = dbName;
     this.#apiKey = apiKey;
     this.#region = region;
     this.#hostOverride = hostOverride ?? this.#hostOverride;
-    this.#connectionTimeout = connectionTimeout ?? this.#connectionTimeout;
+    this.#timeout = timeout ?? this.#timeout;
-    this.#readTimeout = readTimeout ?? this.#readTimeout;
   }
 
   // todo: cache the session.
@@ -59,7 +56,7 @@ export class RestfulLanceDBClient {
           Authorization: `Bearer ${this.#apiKey}`,
         },
         transformResponse: decodeErrorData,
-        timeout: this.#connectionTimeout,
+        timeout: this.#timeout,
       });
     }
   }
@@ -111,7 +108,7 @@ export class RestfulLanceDBClient {
         params,
       });
     } catch (e) {
-      if (e instanceof AxiosError) {
+      if (e instanceof AxiosError && e.response) {
         response = e.response;
       } else {
         throw e;
@@ -165,7 +162,7 @@ export class RestfulLanceDBClient {
         params: new Map(Object.entries(additional.params ?? {})),
       });
     } catch (e) {
-      if (e instanceof AxiosError) {
+      if (e instanceof AxiosError && e.response) {
         response = e.response;
       } else {
         throw e;

nodejs/lancedb/remote/connection.ts

@@ -20,8 +20,7 @@ export interface RemoteConnectionOptions {
   apiKey?: string;
   region?: string;
   hostOverride?: string;
-  connectionTimeout?: number;
+  timeout?: number;
-  readTimeout?: number;
 }
 
 export class RemoteConnection extends Connection {
@@ -33,13 +32,7 @@ export class RemoteConnection extends Connection {
 
   constructor(
     url: string,
-    {
+    { apiKey, region, hostOverride, timeout }: RemoteConnectionOptions,
-      apiKey,
-      region,
-      hostOverride,
-      connectionTimeout,
-      readTimeout,
-    }: RemoteConnectionOptions,
   ) {
     super();
     apiKey = apiKey ?? process.env.LANCEDB_API_KEY;
@@ -68,8 +61,7 @@ export class RemoteConnection extends Connection {
       this.#apiKey,
       this.#region,
       hostOverride,
-      connectionTimeout,
+      timeout,
-      readTimeout,
     );
   }
 

nodejs/lancedb/sanitize.ts

@@ -340,8 +340,14 @@ export function sanitizeType(typeLike: unknown): DataType<any> {
   if (typeof typeLike !== "object" || typeLike === null) {
     throw Error("Expected a Type but object was null/undefined");
   }
-  if (!("typeId" in typeLike) || !(typeof typeLike.typeId !== "function")) {
+  if (
-    throw Error("Expected a Type to have a typeId function");
+    !("typeId" in typeLike) ||
+    !(
+      typeof typeLike.typeId !== "function" ||
+      typeof typeLike.typeId !== "number"
+    )
+  ) {
+    throw Error("Expected a Type to have a typeId property");
   }
   let typeId: Type;
   if (typeof typeLike.typeId === "function") {

nodejs/lancedb/table.ts

@@ -84,6 +84,7 @@ export interface OptimizeOptions {
    * tbl.cleanupOlderVersions(new Date());
    */
   cleanupOlderThan: Date;
+  deleteUnverified: boolean;
 }
 
 /**
@@ -270,19 +271,23 @@ export abstract class Table {
    * @returns {Query} A builder that can be used to parameterize the query
    */
   abstract query(): Query;
+
   /**
    * Create a search query to find the nearest neighbors
-   * of the given query vector
+   * of the given query
-   * @param {string} query - the query. This will be converted to a vector using the table's provided embedding function
+   * @param {string | IntoVector} query - the query, a vector or string
-   * @note  If no embedding functions are defined in the table, this will error when collecting the results.
+   * @param {string} queryType - the type of the query, "vector", "fts", or "auto"
+   * @param {string | string[]} ftsColumns - the columns to search in for full text search
+   *    for now, only one column can be searched at a time.
+   *
+   * when "auto" is used, if the query is a string and an embedding function is defined, it will be treated as a vector query
+   * if the query is a string and no embedding function is defined, it will be treated as a full text search query
    */
-  abstract search(query: string): VectorQuery;
+  abstract search(
-  /**
+    query: string | IntoVector,
-   * Create a search query to find the nearest neighbors
+    queryType?: string,
-   * of the given query vector
+    ftsColumns?: string | string[],
-   * @param {IntoVector} query - the query vector
+  ): VectorQuery | Query;
-   */
-  abstract search(query: IntoVector): VectorQuery;
   /**
    * Search the table with a given query vector.
    *
@@ -490,7 +495,7 @@ export class LocalTable extends Table {
     const mode = options?.mode ?? "append";
     const schema = await this.schema();
     const registry = getRegistry();
-    const functions = registry.parseFunctions(schema.metadata);
+    const functions = await registry.parseFunctions(schema.metadata);
 
     const buffer = await fromDataToBuffer(
       data,
@@ -578,27 +583,50 @@ export class LocalTable extends Table {
   query(): Query {
     return new Query(this.inner);
   }
-  search(query: string | IntoVector): VectorQuery {
-    if (typeof query !== "string") {
-      return this.vectorSearch(query);
-    } else {
-      const queryPromise = this.getEmbeddingFunctions().then(
-        async (functions) => {
-          // TODO: Support multiple embedding functions
-          const embeddingFunc: EmbeddingFunctionConfig | undefined = functions
-            .values()
-            .next().value;
-          if (!embeddingFunc) {
-            return Promise.reject(
-              new Error("No embedding functions are defined in the table"),
-            );
-          }
-          return await embeddingFunc.function.computeQueryEmbeddings(query);
-        },
-      );
 
-      return this.query().nearestTo(queryPromise);
+  search(
+    query: string | IntoVector,
+    queryType: string = "auto",
+    ftsColumns?: string | string[],
+  ): VectorQuery | Query {
+    if (typeof query !== "string") {
+      if (queryType === "fts") {
+        throw new Error("Cannot perform full text search on a vector query");
+      }
+      return this.vectorSearch(query);
     }
+
+    // If the query is a string, we need to determine if it is a vector query or a full text search query
+    if (queryType === "fts") {
+      return this.query().fullTextSearch(query, {
+        columns: ftsColumns,
+      });
+    }
+
+    // The query type is auto or vector
+    // fall back to full text search if no embedding functions are defined and the query is a string
+    if (queryType === "auto" && getRegistry().length() === 0) {
+      return this.query().fullTextSearch(query, {
+        columns: ftsColumns,
+      });
+    }
+
+    const queryPromise = this.getEmbeddingFunctions().then(
+      async (functions) => {
+        // TODO: Support multiple embedding functions
+        const embeddingFunc: EmbeddingFunctionConfig | undefined = functions
+          .values()
+          .next().value;
+        if (!embeddingFunc) {
+          return Promise.reject(
+            new Error("No embedding functions are defined in the table"),
+          );
+        }
+        return await embeddingFunc.function.computeQueryEmbeddings(query);
+      },
+    );
+
+    return this.query().nearestTo(queryPromise);
   }
 
   vectorSearch(vector: IntoVector): VectorQuery {
@@ -644,7 +672,10 @@ export class LocalTable extends Table {
       cleanupOlderThanMs =
         new Date().getTime() - options.cleanupOlderThan.getTime();
     }
-    return await this.inner.optimize(cleanupOlderThanMs);
+    return await this.inner.optimize(
+      cleanupOlderThanMs,
+      options?.deleteUnverified,
+    );
   }
 
   async listIndices(): Promise<IndexConfig[]> {

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.7.1",
+	"version": "0.10.0-beta.0",
 	"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": "0.7.1",
+	"version": "0.10.0-beta.0",
 	"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": "0.7.1",
+	"version": "0.10.0-beta.0",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.7.1",
+	"version": "0.10.0-beta.0",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.7.1",
+	"version": "0.10.0-beta.0",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -10,7 +10,7 @@
     "vector database",
     "ann"
   ],
-  "version": "0.7.1",
+  "version": "0.10.0-beta.0",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",
@@ -32,12 +32,13 @@
   },
   "license": "Apache 2.0",
   "devDependencies": {
+    "@aws-sdk/client-dynamodb": "^3.33.0",
     "@aws-sdk/client-kms": "^3.33.0",
     "@aws-sdk/client-s3": "^3.33.0",
-    "@aws-sdk/client-dynamodb": "^3.33.0",
     "@biomejs/biome": "^1.7.3",
     "@jest/globals": "^29.7.0",
     "@napi-rs/cli": "^2.18.3",
+    "@types/axios": "^0.14.0",
     "@types/jest": "^29.1.2",
     "@types/tmp": "^0.2.6",
     "apache-arrow-13": "npm:apache-arrow@13.0.0",
@@ -52,9 +53,8 @@
     "ts-jest": "^29.1.2",
     "typedoc": "^0.26.4",
     "typedoc-plugin-markdown": "^4.2.1",
-    "typescript": "^5.3.3",
+    "typescript": "^5.5.4",
-    "typescript-eslint": "^7.1.0",
+    "typescript-eslint": "^7.1.0"
-    "@types/axios": "^0.14.0"
   },
   "ava": {
     "timeout": "3m"
@@ -85,6 +85,7 @@
     "reflect-metadata": "^0.2.2"
   },
   "optionalDependencies": {
+    "@xenova/transformers": ">=2.17 < 3",
     "openai": "^4.29.2"
   },
   "peerDependencies": {

nodejs/src/connection.rs

@@ -13,13 +13,16 @@
 // limitations under the License.
 
 use std::collections::HashMap;
+use std::str::FromStr;
 
 use napi::bindgen_prelude::*;
 use napi_derive::*;
 
 use crate::table::Table;
 use crate::ConnectionOptions;
-use lancedb::connection::{ConnectBuilder, Connection as LanceDBConnection, CreateTableMode};
+use lancedb::connection::{
+    ConnectBuilder, Connection as LanceDBConnection, CreateTableMode, LanceFileVersion,
+};
 use lancedb::ipc::{ipc_file_to_batches, ipc_file_to_schema};
 
 #[napi]
@@ -120,7 +123,7 @@ impl Connection {
         buf: Buffer,
         mode: String,
         storage_options: Option<HashMap<String, String>>,
-        use_legacy_format: Option<bool>,
+        data_storage_options: Option<String>,
     ) -> napi::Result<Table> {
         let batches = ipc_file_to_batches(buf.to_vec())
             .map_err(|e| napi::Error::from_reason(format!("Failed to read IPC file: {}", e)))?;
@@ -131,8 +134,11 @@ impl Connection {
                 builder = builder.storage_option(key, value);
             }
         }
-        if let Some(use_legacy_format) = use_legacy_format {
+        if let Some(data_storage_option) = data_storage_options.as_ref() {
-            builder = builder.use_legacy_format(use_legacy_format);
+            builder = builder.data_storage_version(
+                LanceFileVersion::from_str(data_storage_option)
+                    .map_err(|e| napi::Error::from_reason(format!("{}", e)))?,
+            );
         }
         let tbl = builder
             .execute()
@@ -148,7 +154,7 @@ impl Connection {
         schema_buf: Buffer,
         mode: String,
         storage_options: Option<HashMap<String, String>>,
-        use_legacy_format: Option<bool>,
+        data_storage_options: Option<String>,
     ) -> napi::Result<Table> {
         let schema = ipc_file_to_schema(schema_buf.to_vec()).map_err(|e| {
             napi::Error::from_reason(format!("Failed to marshal schema from JS to Rust: {}", e))
@@ -163,8 +169,11 @@ impl Connection {
                 builder = builder.storage_option(key, value);
             }
         }
-        if let Some(use_legacy_format) = use_legacy_format {
+        if let Some(data_storage_option) = data_storage_options.as_ref() {
-            builder = builder.use_legacy_format(use_legacy_format);
+            builder = builder.data_storage_version(
+                LanceFileVersion::from_str(data_storage_option)
+                    .map_err(|e| napi::Error::from_reason(format!("{}", e)))?,
+            );
         }
         let tbl = builder
             .execute()

nodejs/src/index.rs

@@ -14,7 +14,7 @@
 
 use std::sync::Mutex;
 
-use lancedb::index::scalar::BTreeIndexBuilder;
+use lancedb::index::scalar::{BTreeIndexBuilder, FtsIndexBuilder};
 use lancedb::index::vector::IvfPqIndexBuilder;
 use lancedb::index::Index as LanceDbIndex;
 use napi_derive::napi;
@@ -76,4 +76,25 @@ impl Index {
             inner: Mutex::new(Some(LanceDbIndex::BTree(BTreeIndexBuilder::default()))),
         }
     }
+
+    #[napi(factory)]
+    pub fn bitmap() -> Self {
+        Self {
+            inner: Mutex::new(Some(LanceDbIndex::Bitmap(Default::default()))),
+        }
+    }
+
+    #[napi(factory)]
+    pub fn label_list() -> Self {
+        Self {
+            inner: Mutex::new(Some(LanceDbIndex::LabelList(Default::default()))),
+        }
+    }
+
+    #[napi(factory)]
+    pub fn fts() -> Self {
+        Self {
+            inner: Mutex::new(Some(LanceDbIndex::FTS(FtsIndexBuilder::default()))),
+        }
+    }
 }

nodejs/src/query.rs

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+use lancedb::index::scalar::FullTextSearchQuery;
 use lancedb::query::ExecutableQuery;
 use lancedb::query::Query as LanceDbQuery;
 use lancedb::query::QueryBase;
@@ -42,6 +43,12 @@ impl Query {
         self.inner = self.inner.clone().only_if(predicate);
     }
 
+    #[napi]
+    pub fn full_text_search(&mut self, query: String, columns: Option<Vec<String>>) {
+        let query = FullTextSearchQuery::new(query).columns(columns);
+        self.inner = self.inner.clone().full_text_search(query);
+    }
+
     #[napi]
     pub fn select(&mut self, columns: Vec<(String, String)>) {
         self.inner = self.inner.clone().select(Select::dynamic(&columns));
@@ -138,6 +145,12 @@ impl VectorQuery {
         self.inner = self.inner.clone().only_if(predicate);
     }
 
+    #[napi]
+    pub fn full_text_search(&mut self, query: String, columns: Option<Vec<String>>) {
+        let query = FullTextSearchQuery::new(query).columns(columns);
+        self.inner = self.inner.clone().full_text_search(query);
+    }
+
     #[napi]
     pub fn select(&mut self, columns: Vec<(String, String)>) {
         self.inner = self.inner.clone().select(Select::dynamic(&columns));

nodejs/src/table.rs

@@ -265,7 +265,11 @@ impl Table {
     }
 
     #[napi(catch_unwind)]
-    pub async fn optimize(&self, older_than_ms: Option<i64>) -> napi::Result<OptimizeStats> {
+    pub async fn optimize(
+        &self,
+        older_than_ms: Option<i64>,
+        delete_unverified: Option<bool>,
+    ) -> napi::Result<OptimizeStats> {
         let inner = self.inner_ref()?;
 
         let older_than = if let Some(ms) = older_than_ms {
@@ -292,7 +296,8 @@ impl Table {
         let prune_stats = inner
             .optimize(OptimizeAction::Prune {
                 older_than,
-                delete_unverified: None,
+                delete_unverified,
+                error_if_tagged_old_versions: None,
             })
             .await
             .default_error()?

nodejs/tsconfig.json

@@ -9,7 +9,8 @@
     "allowJs": true,
     "resolveJsonModule": true,
     "emitDecoratorMetadata": true,
-    "experimentalDecorators": true
+    "experimentalDecorators": true,
+    "moduleResolution": "Node"
   },
   "exclude": ["./dist/*"],
   "typedocOptions": {

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.10.2"
+current_version = "0.13.0-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 = "0.10.2"
+version = "0.13.0-beta.1"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
@@ -14,11 +14,13 @@ name = "_lancedb"
 crate-type = ["cdylib"]
 
 [dependencies]
-arrow = { version = "51.0.0", features = ["pyarrow"] }
+arrow = { version = "52.1", features = ["pyarrow"] }
 lancedb = { path = "../rust/lancedb" }
 env_logger = "0.10"
-pyo3 = { version = "0.20", features = ["extension-module", "abi3-py38"] }
+pyo3 = { version = "0.21", features = ["extension-module", "abi3-py38", "gil-refs"] }
-pyo3-asyncio = { version = "0.20", features = ["attributes", "tokio-runtime"] }
+# Using this fork for now: https://github.com/awestlake87/pyo3-asyncio/issues/119
+# pyo3-asyncio = { version = "0.20", features = ["attributes", "tokio-runtime"] }
+pyo3-asyncio-0-21 = { version = "0.21.0", features = ["attributes", "tokio-runtime"] }
 
 # Prevent dynamic linking of lzma, which comes from datafusion
 lzma-sys = { version = "*", features = ["static"] }

python/pyproject.toml

@@ -3,7 +3,7 @@ name = "lancedb"
 # version in Cargo.toml
 dependencies = [
     "deprecation",
-    "pylance==0.14.1",
+    "pylance==0.16.1",
     "ratelimiter~=1.0",
     "requests>=2.31.0",
     "retry>=0.9.2",
@@ -18,7 +18,7 @@ description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
 license = { file = "LICENSE" }
 readme = "README.md"
-requires-python = ">=3.8"
+requires-python = ">=3.9"
 keywords = [
     "data-format",
     "data-science",
@@ -56,7 +56,7 @@ tests = [
     "pytest-asyncio",
     "duckdb",
     "pytz",
-    "polars>=0.19",
+    "polars>=0.19, <=1.3.0",
     "tantivy",
 ]
 dev = ["ruff", "pre-commit"]
@@ -76,6 +76,7 @@ embeddings = [
     "awscli>=1.29.57",
     "botocore>=1.31.57",
     "ollama",
+    "ibm-watsonx-ai>=1.1.2",
 ]
 azure = ["adlfs>=2024.2.0"]
 

python/python/lancedb/_lancedb.pyi

@@ -24,7 +24,7 @@ class Connection(object):
         mode: str,
         data: pa.RecordBatchReader,
         storage_options: Optional[Dict[str, str]] = None,
-        use_legacy_format: Optional[bool] = None,
+        data_storage_version: Optional[str] = None,
     ) -> Table: ...
     async def create_empty_table(
         self,
@@ -32,7 +32,7 @@ class Connection(object):
         mode: str,
         schema: pa.Schema,
         storage_options: Optional[Dict[str, str]] = None,
-        use_legacy_format: Optional[bool] = None,
+        data_storage_version: Optional[str] = None,
     ) -> Table: ...
 
 class Table:
@@ -74,6 +74,7 @@ class Query:
     def select(self, columns: Tuple[str, str]): ...
     def limit(self, limit: int): ...
     def nearest_to(self, query_vec: pa.Array) -> VectorQuery: ...
+    def nearest_to_text(self, query: dict) -> Query: ...
     async def execute(self, max_batch_legnth: Optional[int]) -> RecordBatchStream: ...
 
 class VectorQuery:

python/python/lancedb/db.py

@@ -276,6 +276,10 @@ class DBConnection(EnforceOverrides):
         """
         raise NotImplementedError
 
+    @property
+    def uri(self) -> str:
+        return self._uri
+
 
 class LanceDBConnection(DBConnection):
     """
@@ -340,10 +344,6 @@ class LanceDBConnection(DBConnection):
         val += ")"
         return val
 
-    @property
-    def uri(self) -> str:
-        return self._uri
-
     async def _async_get_table_names(self, start_after: Optional[str], limit: int):
         conn = AsyncConnection(await lancedb_connect(self.uri))
         return await conn.table_names(start_after=start_after, limit=limit)
@@ -560,6 +560,7 @@ class AsyncConnection(object):
         fill_value: Optional[float] = None,
         storage_options: Optional[Dict[str, str]] = None,
         *,
+        data_storage_version: Optional[str] = None,
         use_legacy_format: Optional[bool] = None,
     ) -> AsyncTable:
         """Create an [AsyncTable][lancedb.table.AsyncTable] in the database.
@@ -603,9 +604,15 @@ class AsyncConnection(object):
             connection will be inherited by the table, but can be overridden here.
             See available options at
             https://lancedb.github.io/lancedb/guides/storage/
-        use_legacy_format: bool, optional, default True
+        data_storage_version: optional, str, default "legacy"
+            The version of the data storage format to use. Newer versions are more
+            efficient but require newer versions of lance to read.  The default is
+            "legacy" which will use the legacy v1 version.  See the user guide
+            for more details.
+        use_legacy_format: bool, optional, default True. (Deprecated)
             If True, use the legacy format for the table. If False, use the new format.
             The default is True while the new format is in beta.
+            This method is deprecated, use `data_storage_version` instead.
 
 
         Returns
@@ -732,7 +739,7 @@ class AsyncConnection(object):
             fill_value = 0.0
 
         if data is not None:
-            data = _sanitize_data(
+            data, schema = _sanitize_data(
                 data,
                 schema,
                 metadata=metadata,
@@ -765,13 +772,18 @@ class AsyncConnection(object):
         if mode == "create" and exist_ok:
             mode = "exist_ok"
 
+        if not data_storage_version:
+            data_storage_version = (
+                "legacy" if use_legacy_format is None or use_legacy_format else "stable"
+            )
+
         if data is None:
             new_table = await self._inner.create_empty_table(
                 name,
                 mode,
                 schema,
                 storage_options=storage_options,
-                use_legacy_format=use_legacy_format,
+                data_storage_version=data_storage_version,
             )
         else:
             data = data_to_reader(data, schema)
@@ -780,7 +792,7 @@ class AsyncConnection(object):
                 mode,
                 data,
                 storage_options=storage_options,
-                use_legacy_format=use_legacy_format,
+                data_storage_version=data_storage_version,
             )
 
         return AsyncTable(new_table)

python/python/lancedb/embeddings/__init__.py

@@ -26,3 +26,4 @@ from .transformers import TransformersEmbeddingFunction, ColbertEmbeddings
 from .imagebind import ImageBindEmbeddings
 from .utils import with_embeddings
 from .jinaai import JinaEmbeddings
+from .watsonx import WatsonxEmbeddings

python/python/lancedb/embeddings/instructor.py

@@ -127,6 +127,7 @@ class InstructorEmbeddingFunction(TextEmbeddingFunction):
             batch_size=self.batch_size,
             show_progress_bar=self.show_progress_bar,
             normalize_embeddings=self.normalize_embeddings,
+            device=self.device,
         ).tolist()
         return res
 

python/python/lancedb/embeddings/transformers.py

@@ -44,6 +44,7 @@ class TransformersEmbeddingFunction(EmbeddingFunction):
     """
 
     name: str = "colbert-ir/colbertv2.0"
+    device: str = "cpu"
     _tokenizer: Any = PrivateAttr()
     _model: Any = PrivateAttr()
 
@@ -53,6 +54,7 @@ class TransformersEmbeddingFunction(EmbeddingFunction):
         transformers = attempt_import_or_raise("transformers")
         self._tokenizer = transformers.AutoTokenizer.from_pretrained(self.name)
         self._model = transformers.AutoModel.from_pretrained(self.name)
+        self._model.to(self.device)
 
     if PYDANTIC_VERSION.major < 2:  # Pydantic 1.x compat
 
@@ -75,9 +77,9 @@ class TransformersEmbeddingFunction(EmbeddingFunction):
         for text in texts:
             encoding = self._tokenizer(
                 text, return_tensors="pt", padding=True, truncation=True
-            )
+            ).to(self.device)
             emb = self._model(**encoding).last_hidden_state.mean(dim=1).squeeze()
-            embedding.append(emb.detach().numpy())
+            embedding.append(emb.tolist())
 
         return embedding
 

python/python/lancedb/embeddings/watsonx.py

@@ -0,0 +1,111 @@
+#  Copyright (c) 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.
+import os
+from functools import cached_property
+from typing import List, Optional, Dict, Union
+
+from ..util import attempt_import_or_raise
+from .base import TextEmbeddingFunction
+from .registry import register
+
+import numpy as np
+
+DEFAULT_WATSONX_URL = "https://us-south.ml.cloud.ibm.com"
+
+MODELS_DIMS = {
+    "ibm/slate-125m-english-rtrvr": 768,
+    "ibm/slate-30m-english-rtrvr": 384,
+    "sentence-transformers/all-minilm-l12-v2": 384,
+    "intfloat/multilingual-e5-large": 1024,
+}
+
+
+@register("watsonx")
+class WatsonxEmbeddings(TextEmbeddingFunction):
+    """
+    API Docs:
+    ---------
+    https://cloud.ibm.com/apidocs/watsonx-ai#text-embeddings
+
+    Supported embedding models:
+    ---------------------------
+    https://dataplatform.cloud.ibm.com/docs/content/wsj/analyze-data/fm-models-embed.html?context=wx
+    """
+
+    name: str = "ibm/slate-125m-english-rtrvr"
+    api_key: Optional[str] = None
+    project_id: Optional[str] = None
+    url: Optional[str] = None
+    params: Optional[Dict] = None
+
+    @staticmethod
+    def model_names():
+        return [
+            "ibm/slate-125m-english-rtrvr",
+            "ibm/slate-30m-english-rtrvr",
+            "sentence-transformers/all-minilm-l12-v2",
+            "intfloat/multilingual-e5-large",
+        ]
+
+    def ndims(self):
+        return self._ndims
+
+    @cached_property
+    def _ndims(self):
+        if self.name not in MODELS_DIMS:
+            raise ValueError(f"Unknown model name {self.name}")
+        return MODELS_DIMS[self.name]
+
+    def generate_embeddings(
+        self,
+        texts: Union[List[str], np.ndarray],
+        *args,
+        **kwargs,
+    ) -> List[List[float]]:
+        return self._watsonx_client.embed_documents(
+            texts=list(texts),
+            *args,
+            **kwargs,
+        )
+
+    @cached_property
+    def _watsonx_client(self):
+        ibm_watsonx_ai = attempt_import_or_raise("ibm_watsonx_ai")
+        ibm_watsonx_ai_foundation_models = attempt_import_or_raise(
+            "ibm_watsonx_ai.foundation_models"
+        )
+
+        kwargs = {"model_id": self.name}
+        if self.params:
+            kwargs["params"] = self.params
+        if self.project_id:
+            kwargs["project_id"] = self.project_id
+        elif "WATSONX_PROJECT_ID" in os.environ:
+            kwargs["project_id"] = os.environ["WATSONX_PROJECT_ID"]
+        else:
+            raise ValueError("WATSONX_PROJECT_ID must be set or passed")
+
+        creds_kwargs = {}
+        if self.api_key:
+            creds_kwargs["api_key"] = self.api_key
+        elif "WATSONX_API_KEY" in os.environ:
+            creds_kwargs["api_key"] = os.environ["WATSONX_API_KEY"]
+        else:
+            raise ValueError("WATSONX_API_KEY must be set or passed")
+        if self.url:
+            creds_kwargs["url"] = self.url
+        else:
+            creds_kwargs["url"] = DEFAULT_WATSONX_URL
+        kwargs["credentials"] = ibm_watsonx_ai.Credentials(**creds_kwargs)
+
+        return ibm_watsonx_ai_foundation_models.Embeddings(**kwargs)

python/python/lancedb/index.py

@@ -8,7 +8,7 @@ from ._lancedb import (
 )
 
 
-class BTree(object):
+class BTree:
     """Describes a btree index configuration
 
     A btree index is an index on scalar columns.  The index stores a copy of the
@@ -22,7 +22,8 @@ class BTree(object):
     sizeof(Scalar) * 4096 bytes to find the correct row ids.
 
     This index is good for scalar columns with mostly distinct values and does best
-    when the query is highly selective.
+    when the query is highly selective. It works with numeric, temporal, and string
+    columns.
 
     The btree index does not currently have any parameters though parameters such as
     the block size may be added in the future.
@@ -32,7 +33,56 @@ class BTree(object):
         self._inner = LanceDbIndex.btree()
 
 
-class IvfPq(object):
+class Bitmap:
+    """Describe a Bitmap index configuration.
+
+    A `Bitmap` index stores a bitmap for each distinct value in the column for
+    every row.
+
+    This index works best for low-cardinality numeric or string columns,
+    where the number of unique values is small (i.e., less than a few thousands).
+    `Bitmap` index can accelerate the following filters:
+
+    - `<`, `<=`, `=`, `>`, `>=`
+    - `IN (value1, value2, ...)`
+    - `between (value1, value2)`
+    - `is null`
+
+    For example, a bitmap index with a table with 1Bi rows, and 128 distinct values,
+    requires 128 / 8 * 1Bi bytes on disk.
+    """
+
+    def __init__(self):
+        self._inner = LanceDbIndex.bitmap()
+
+
+class LabelList:
+    """Describe a LabelList index configuration.
+
+    `LabelList` is a scalar index that can be used on `List<T>` columns to
+    support queries with `array_contains_all` and `array_contains_any`
+    using an underlying bitmap index.
+
+    For example, it works with `tags`, `categories`, `keywords`, etc.
+    """
+
+    def __init__(self):
+        self._inner = LanceDbIndex.label_list()
+
+
+class FTS:
+    """Describe a FTS index configuration.
+
+    `FTS` is a full-text search index that can be used on `String` columns
+
+    For example, it works with `title`, `description`, `content`, etc.
+    """
+
+    def __init__(self):
+        self._inner = LanceDbIndex.fts()
+
+
+class IvfPq:
     """Describes an IVF PQ Index
 
     This index stores a compressed (quantized) copy of every vector.  These vectors

python/python/lancedb/query.py

@@ -15,7 +15,6 @@ from __future__ import annotations
 
 from abc import ABC, abstractmethod
 from concurrent.futures import ThreadPoolExecutor
-from pathlib import Path
 from typing import (
     TYPE_CHECKING,
     Dict,
@@ -38,7 +37,7 @@ from .arrow import AsyncRecordBatchReader
 from .common import VEC
 from .rerankers.base import Reranker
 from .rerankers.linear_combination import LinearCombinationReranker
-from .util import fs_from_uri, safe_import_pandas
+from .util import safe_import_pandas
 
 if TYPE_CHECKING:
     import PIL
@@ -99,6 +98,9 @@ class Query(pydantic.BaseModel):
     # if True then apply the filter before vector search
     prefilter: bool = False
 
+    # full text search query
+    full_text_query: Optional[Union[str, dict]] = None
+
     # top k results to return
     k: int
 
@@ -131,6 +133,7 @@ class LanceQueryBuilder(ABC):
         query_type: str,
         vector_column_name: str,
         ordering_field_name: str = None,
+        fts_columns: Union[str, List[str]] = None,
     ) -> LanceQueryBuilder:
         """
         Create a query builder based on the given query and query type.
@@ -170,7 +173,9 @@ class LanceQueryBuilder(ABC):
         if isinstance(query, str):
             # fts
             return LanceFtsQueryBuilder(
-                table, query, ordering_field_name=ordering_field_name
+                table,
+                query,
+                ordering_field_name=ordering_field_name,
             )
 
         if isinstance(query, list):
@@ -226,6 +231,7 @@ class LanceQueryBuilder(ABC):
         self._limit = 10
         self._columns = None
         self._where = None
+        self._prefilter = False
         self._with_row_id = False
 
     @deprecation.deprecated(
@@ -428,9 +434,9 @@ class LanceQueryBuilder(ABC):
         >>> query = [100, 100]
         >>> plan = table.search(query).explain_plan(True)
         >>> print(plan) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
-        Projection: fields=[vector, _distance]
+        ProjectionExec: expr=[vector@0 as vector, _distance@2 as _distance]
           FilterExec: _distance@2 IS NOT NULL
-            SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST]
+            SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], preserve_partitioning=[false]
               KNNVectorDistance: metric=l2
                 LanceScan: uri=..., projection=[vector], row_id=true, row_addr=false, ordered=false
 
@@ -451,6 +457,22 @@ class LanceQueryBuilder(ABC):
             },
         ).explain_plan(verbose)
 
+    @abstractmethod
+    def rerank(self, reranker: Reranker) -> LanceQueryBuilder:
+        """Rerank the results using the specified reranker.
+
+        Parameters
+        ----------
+        reranker: Reranker
+            The reranker to use.
+
+        Returns
+        -------
+
+        The LanceQueryBuilder object.
+        """
+        raise NotImplementedError
+
 
 class LanceVectorQueryBuilder(LanceQueryBuilder):
     """
@@ -664,12 +686,21 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
 class LanceFtsQueryBuilder(LanceQueryBuilder):
     """A builder for full text search for LanceDB."""
 
-    def __init__(self, table: "Table", query: str, ordering_field_name: str = None):
+    def __init__(
+        self,
+        table: "Table",
+        query: str,
+        ordering_field_name: str = None,
+        fts_columns: Union[str, List[str]] = None,
+    ):
         super().__init__(table)
         self._query = query
         self._phrase_query = False
         self.ordering_field_name = ordering_field_name
         self._reranker = None
+        if isinstance(fts_columns, str):
+            fts_columns = [fts_columns]
+        self._fts_columns = fts_columns
 
     def phrase_query(self, phrase_query: bool = True) -> LanceFtsQueryBuilder:
         """Set whether to use phrase query.
@@ -689,6 +720,35 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
         return self
 
     def to_arrow(self) -> pa.Table:
+        path, fs, exist = self._table._get_fts_index_path()
+        if exist:
+            return self.tantivy_to_arrow()
+
+        query = self._query
+        if self._phrase_query:
+            raise NotImplementedError(
+                "Phrase query is not yet supported in Lance FTS. "
+                "Use tantivy-based index instead for now."
+            )
+        query = Query(
+            columns=self._columns,
+            filter=self._where,
+            k=self._limit,
+            prefilter=self._prefilter,
+            with_row_id=self._with_row_id,
+            full_text_query={
+                "query": query,
+                "columns": self._fts_columns,
+            },
+            vector=[],
+        )
+        results = self._table._execute_query(query)
+        results = results.read_all()
+        if self._reranker is not None:
+            results = self._reranker.rerank_fts(self._query, results)
+        return results
+
+    def tantivy_to_arrow(self) -> pa.Table:
         try:
             import tantivy
         except ImportError:
@@ -699,24 +759,24 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
         from .fts import search_index
 
         # get the index path
-        index_path = self._table._get_fts_index_path()
+        path, fs, exist = self._table._get_fts_index_path()
-
-        # Check that we are on local filesystem
-        fs, _path = fs_from_uri(index_path)
-        if not isinstance(fs, pa_fs.LocalFileSystem):
-            raise NotImplementedError(
-                "Full-text search is only supported on the local filesystem"
-            )
 
         # check if the index exist
-        if not Path(index_path).exists():
+        if not exist:
             raise FileNotFoundError(
                 "Fts index does not exist. "
                 "Please first call table.create_fts_index(['<field_names>']) to "
                 "create the fts index."
             )
+
+        # Check that we are on local filesystem
+        if not isinstance(fs, pa_fs.LocalFileSystem):
+            raise NotImplementedError(
+                "Tantivy-based full text search "
+                "is only supported on the local filesystem"
+            )
         # open the index
-        index = tantivy.Index.open(index_path)
+        index = tantivy.Index.open(path)
         # get the scores and doc ids
         query = self._query
         if self._phrase_query:
@@ -726,11 +786,11 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
             index, query, self._limit, ordering_field=self.ordering_field_name
         )
         if len(row_ids) == 0:
-            empty_schema = pa.schema([pa.field("score", pa.float32())])
+            empty_schema = pa.schema([pa.field("_score", pa.float32())])
             return pa.Table.from_pylist([], schema=empty_schema)
         scores = pa.array(scores)
         output_tbl = self._table.to_lance().take(row_ids, columns=self._columns)
-        output_tbl = output_tbl.append_column("score", scores)
+        output_tbl = output_tbl.append_column("_score", scores)
         # this needs to match vector search results which are uint64
         row_ids = pa.array(row_ids, type=pa.uint64())
 
@@ -797,6 +857,21 @@ class LanceEmptyQueryBuilder(LanceQueryBuilder):
             limit=self._limit,
         )
 
+    def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
+        """Rerank the results using the specified reranker.
+
+        Parameters
+        ----------
+        reranker: Reranker
+            The reranker to use.
+
+        Returns
+        -------
+        LanceEmptyQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        raise NotImplementedError("Reranking is not yet supported.")
+
 
 class LanceHybridQueryBuilder(LanceQueryBuilder):
     """
@@ -811,7 +886,6 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
 
     def __init__(self, table: "Table", query: str, vector_column: str):
         super().__init__(table)
-        self._validate_fts_index()
         vector_query, fts_query = self._validate_query(query)
         self._fts_query = LanceFtsQueryBuilder(table, fts_query)
         vector_query = self._query_to_vector(table, vector_query, vector_column)
@@ -819,12 +893,6 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         self._norm = "score"
         self._reranker = LinearCombinationReranker(weight=0.7, fill=1.0)
 
-    def _validate_fts_index(self):
-        if self._table._get_fts_index_path() is None:
-            raise ValueError(
-                "Please create a full-text search index " "to perform hybrid search."
-            )
-
     def _validate_query(self, query):
         # Temp hack to support vectorized queries for hybrid search
         if isinstance(query, str):
@@ -856,13 +924,13 @@ class LanceHybridQueryBuilder(LanceQueryBuilder):
         # convert to ranks first if needed
         if self._norm == "rank":
             vector_results = self._rank(vector_results, "_distance")
-            fts_results = self._rank(fts_results, "score")
+            fts_results = self._rank(fts_results, "_score")
         # normalize the scores to be between 0 and 1, 0 being most relevant
         vector_results = self._normalize_scores(vector_results, "_distance")
 
         # In fts higher scores represent relevance. Not inverting them here as
         # rerankers might need to preserve this score to support `return_score="all"`
-        fts_results = self._normalize_scores(fts_results, "score")
+        fts_results = self._normalize_scores(fts_results, "_score")
 
         results = self._reranker.rerank_hybrid(
             self._fts_query._query, vector_results, fts_results
@@ -1177,6 +1245,16 @@ class AsyncQueryBase(object):
             await batch_iter.read_all(), schema=batch_iter.schema
         )
 
+    async def to_list(self) -> List[dict]:
+        """
+        Execute the query and return the results as a list of dictionaries.
+
+        Each list entry is a dictionary with the selected column names as keys,
+        or all table columns if `select` is not called. The vector and the "_distance"
+        fields are returned whether or not they're explicitly selected.
+        """
+        return (await self.to_arrow()).to_pylist()
+
     async def to_pandas(self) -> "pd.DataFrame":
         """
         Execute the query and collect the results into a pandas DataFrame.
@@ -1214,9 +1292,9 @@ class AsyncQueryBase(object):
         ...     plan = await table.query().nearest_to([1, 2]).explain_plan(True)
         ...     print(plan)
         >>> asyncio.run(doctest_example()) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
-        Projection: fields=[vector, _distance]
+        ProjectionExec: expr=[vector@0 as vector, _distance@2 as _distance]
           FilterExec: _distance@2 IS NOT NULL
-            SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST]
+            SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], preserve_partitioning=[false]
               KNNVectorDistance: metric=l2
                 LanceScan: uri=..., projection=[vector], row_id=true, row_addr=false, ordered=false
 
@@ -1304,6 +1382,35 @@ class AsyncQuery(AsyncQueryBase):
             self._inner.nearest_to(AsyncQuery._query_vec_to_array(query_vector))
         )
 
+    def nearest_to_text(
+        self, query: str, columns: Union[str, List[str]] = None
+    ) -> AsyncQuery:
+        """
+        Find the documents that are most relevant to the given text query.
+
+        This method will perform a full text search on the table and return
+        the most relevant documents.  The relevance is determined by BM25.
+
+        The columns to search must be with native FTS index
+        (Tantivy-based can't work with this method).
+
+        By default, all indexed columns are searched,
+        now only one column can be searched at a time.
+
+        Parameters
+        ----------
+        query: str
+            The text query to search for.
+        columns: str or list of str, default None
+            The columns to search in. If None, all indexed columns are searched.
+            For now only one column can be searched at a time.
+        """
+        if isinstance(columns, str):
+            columns = [columns]
+        return AsyncQuery(
+            self._inner.nearest_to_text({"query": query, "columns": columns})
+        )
+
 
 class AsyncVectorQuery(AsyncQueryBase):
     def __init__(self, inner: LanceVectorQuery):

python/python/lancedb/remote/db.py

@@ -49,6 +49,7 @@ class RemoteDBConnection(DBConnection):
         parsed = urlparse(db_url)
         if parsed.scheme != "db":
             raise ValueError(f"Invalid scheme: {parsed.scheme}, only accepts db://")
+        self._uri = str(db_url)
         self.db_name = parsed.netloc
         self.api_key = api_key
         self._client = RestfulLanceDBClient(
@@ -245,7 +246,7 @@ class RemoteDBConnection(DBConnection):
             schema = schema.to_arrow_schema()
 
         if data is not None:
-            data = _sanitize_data(
+            data, schema = _sanitize_data(
                 data,
                 schema,
                 metadata=None,

python/python/lancedb/remote/table.py

@@ -15,15 +15,16 @@ import logging
 import uuid
 from concurrent.futures import Future
 from functools import cached_property
-from typing import Dict, Iterable, Optional, Union
+from typing import Dict, Iterable, Optional, Union, Literal
 
 import pyarrow as pa
 from lance import json_to_schema
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
 from lancedb.merge import LanceMergeInsertBuilder
+from lancedb.embeddings import EmbeddingFunctionRegistry
 
-from ..query import LanceVectorQueryBuilder
+from ..query import LanceVectorQueryBuilder, LanceQueryBuilder
 from ..table import Query, Table, _sanitize_data
 from ..util import inf_vector_column_query, value_to_sql
 from .arrow import to_ipc_binary
@@ -34,10 +35,10 @@ from .db import RemoteDBConnection
 class RemoteTable(Table):
     def __init__(self, conn: RemoteDBConnection, name: str):
         self._conn = conn
-        self._name = name
+        self.name = name
 
     def __repr__(self) -> str:
-        return f"RemoteTable({self._conn.db_name}.{self._name})"
+        return f"RemoteTable({self._conn.db_name}.{self.name})"
 
     def __len__(self) -> int:
         self.count_rows(None)
@@ -48,16 +49,31 @@ class RemoteTable(Table):
         of this Table
 
         """
-        resp = self._conn._client.post(f"/v1/table/{self._name}/describe/")
+        resp = self._conn._client.post(f"/v1/table/{self.name}/describe/")
         schema = json_to_schema(resp["schema"])
         return schema
 
     @property
     def version(self) -> int:
         """Get the current version of the table"""
-        resp = self._conn._client.post(f"/v1/table/{self._name}/describe/")
+        resp = self._conn._client.post(f"/v1/table/{self.name}/describe/")
         return resp["version"]
 
+    @cached_property
+    def embedding_functions(self) -> dict:
+        """
+        Get the embedding functions for the table
+
+        Returns
+        -------
+        funcs: dict
+            A mapping of the vector column to the embedding function
+            or empty dict if not configured.
+        """
+        return EmbeddingFunctionRegistry.get_instance().parse_functions(
+            self.schema.metadata
+        )
+
     def to_arrow(self) -> pa.Table:
         """to_arrow() is not yet supported on LanceDB cloud."""
         raise NotImplementedError("to_arrow() is not yet supported on LanceDB cloud.")
@@ -68,19 +84,20 @@ class RemoteTable(Table):
 
     def list_indices(self):
         """List all the indices on the table"""
-        resp = self._conn._client.post(f"/v1/table/{self._name}/index/list/")
+        resp = self._conn._client.post(f"/v1/table/{self.name}/index/list/")
         return resp
 
     def index_stats(self, index_uuid: str):
         """List all the stats of a specified index"""
         resp = self._conn._client.post(
-            f"/v1/table/{self._name}/index/{index_uuid}/stats/"
+            f"/v1/table/{self.name}/index/{index_uuid}/stats/"
         )
         return resp
 
     def create_scalar_index(
         self,
         column: str,
+        index_type: Literal["BTREE", "BITMAP", "LABEL_LIST", "scalar"] = "scalar",
     ):
         """Creates a scalar index
         Parameters
@@ -88,8 +105,10 @@ class RemoteTable(Table):
         column : str
             The column to be indexed.  Must be a boolean, integer, float,
             or string column.
+        index_type : str
+            The index type of the scalar index. Must be "scalar" (BTREE),
+            "BTREE", "BITMAP", or "LABEL_LIST"
         """
-        index_type = "scalar"
 
         data = {
             "column": column,
@@ -97,11 +116,27 @@ class RemoteTable(Table):
             "replace": True,
         }
         resp = self._conn._client.post(
-            f"/v1/table/{self._name}/create_scalar_index/", data=data
+            f"/v1/table/{self.name}/create_scalar_index/", data=data
         )
 
         return resp
 
+    def create_fts_index(
+        self,
+        column: str,
+        *,
+        replace: bool = False,
+    ):
+        data = {
+            "column": column,
+            "index_type": "FTS",
+            "replace": replace,
+        }
+        resp = self._conn._client.post(
+            f"/v1/table/{self.name}/create_index/", data=data
+        )
+        return resp
+
     def create_index(
         self,
         metric="L2",
@@ -175,7 +210,7 @@ class RemoteTable(Table):
             "index_cache_size": index_cache_size,
         }
         resp = self._conn._client.post(
-            f"/v1/table/{self._name}/create_index/", data=data
+            f"/v1/table/{self.name}/create_index/", data=data
         )
 
         return resp
@@ -210,10 +245,10 @@ class RemoteTable(Table):
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
 
         """
-        data = _sanitize_data(
+        data, _ = _sanitize_data(
             data,
             self.schema,
-            metadata=None,
+            metadata=self.schema.metadata,
             on_bad_vectors=on_bad_vectors,
             fill_value=fill_value,
         )
@@ -222,7 +257,7 @@ class RemoteTable(Table):
         request_id = uuid.uuid4().hex
 
         self._conn._client.post(
-            f"/v1/table/{self._name}/insert/",
+            f"/v1/table/{self.name}/insert/",
             data=payload,
             params={"request_id": request_id, "mode": mode},
             content_type=ARROW_STREAM_CONTENT_TYPE,
@@ -232,6 +267,7 @@ class RemoteTable(Table):
         self,
         query: Union[VEC, str],
         vector_column_name: Optional[str] = None,
+        query_type="auto",
     ) -> LanceVectorQueryBuilder:
         """Create a search query to find the nearest neighbors
         of the given query vector. We currently support [vector search][search]
@@ -291,9 +327,18 @@ class RemoteTable(Table):
             - and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
-        if vector_column_name is None:
+        if vector_column_name is None and query is not None and query_type != "fts":
-            vector_column_name = inf_vector_column_query(self.schema)
+            try:
-        return LanceVectorQueryBuilder(self, query, vector_column_name)
+                vector_column_name = inf_vector_column_query(self.schema)
+            except Exception as e:
+                raise e
+
+        return LanceQueryBuilder.create(
+            self,
+            query,
+            query_type,
+            vector_column_name=vector_column_name,
+        )
 
     def _execute_query(
         self, query: Query, batch_size: Optional[int] = None
@@ -322,12 +367,12 @@ class RemoteTable(Table):
                 v = list(v)
                 q = query.copy()
                 q.vector = v
-                results.append(submit(self._name, q))
+                results.append(submit(self.name, q))
             return pa.concat_tables(
                 [add_index(r.result().to_arrow(), i) for i, r in enumerate(results)]
             ).to_reader()
         else:
-            result = self._conn._client.query(self._name, query)
+            result = self._conn._client.query(self.name, query)
             return result.to_arrow().to_reader()
 
     def merge_insert(self, on: Union[str, Iterable[str]]) -> LanceMergeInsertBuilder:
@@ -336,7 +381,7 @@ class RemoteTable(Table):
 
         See [`Table.merge_insert`][lancedb.table.Table.merge_insert] for more details.
         """
-        super().merge_insert(on)
+        return super().merge_insert(on)
 
     def _do_merge(
         self,
@@ -345,7 +390,7 @@ class RemoteTable(Table):
         on_bad_vectors: str,
         fill_value: float,
     ):
-        data = _sanitize_data(
+        data, _ = _sanitize_data(
             new_data,
             self.schema,
             metadata=None,
@@ -377,7 +422,7 @@ class RemoteTable(Table):
             )
 
         self._conn._client.post(
-            f"/v1/table/{self._name}/merge_insert/",
+            f"/v1/table/{self.name}/merge_insert/",
             data=payload,
             params=params,
             content_type=ARROW_STREAM_CONTENT_TYPE,
@@ -431,7 +476,7 @@ class RemoteTable(Table):
         0  2  [3.0, 4.0]       85.0 # doctest: +SKIP
         """
         payload = {"predicate": predicate}
-        self._conn._client.post(f"/v1/table/{self._name}/delete/", data=payload)
+        self._conn._client.post(f"/v1/table/{self.name}/delete/", data=payload)
 
     def update(
         self,
@@ -492,7 +537,7 @@ class RemoteTable(Table):
             updates = [[k, v] for k, v in values_sql.items()]
 
         payload = {"predicate": where, "updates": updates}
-        self._conn._client.post(f"/v1/table/{self._name}/update/", data=payload)
+        self._conn._client.post(f"/v1/table/{self.name}/update/", data=payload)
 
     def cleanup_old_versions(self, *_):
         """cleanup_old_versions() is not supported on the LanceDB cloud"""
@@ -509,7 +554,7 @@ class RemoteTable(Table):
     def count_rows(self, filter: Optional[str] = None) -> int:
         payload = {"predicate": filter}
         resp = self._conn._client.post(
-            f"/v1/table/{self._name}/count_rows/", data=payload
+            f"/v1/table/{self.name}/count_rows/", data=payload
         )
         return resp
 

python/python/lancedb/rerankers/__init__.py

@@ -5,6 +5,7 @@ from .cross_encoder import CrossEncoderReranker
 from .linear_combination import LinearCombinationReranker
 from .openai import OpenaiReranker
 from .jinaai import JinaReranker
+from .rrf import RRFReranker
 
 __all__ = [
     "Reranker",
@@ -14,4 +15,5 @@ __all__ = [
     "OpenaiReranker",
     "ColbertReranker",
     "JinaReranker",
+    "RRFReranker",
 ]

python/python/lancedb/rerankers/base.py

@@ -1,9 +1,13 @@
 from abc import ABC, abstractmethod
 from packaging.version import Version
+from typing import Union, List, TYPE_CHECKING
 
 import numpy as np
 import pyarrow as pa
 
+if TYPE_CHECKING:
+    from ..table import LanceVectorQueryBuilder
+
 ARROW_VERSION = Version(pa.__version__)
 
 
@@ -130,12 +134,94 @@ class Reranker(ABC):
         combined = pa.concat_tables(
             [vector_results, fts_results], **self._concat_tables_args
         )
-        row_id = combined.column("_rowid")
 
         # deduplicate
-        mask = np.full((combined.shape[0]), False)
+        combined = self._deduplicate(combined)
-        _, mask_indices = np.unique(np.array(row_id), return_index=True)
-        mask[mask_indices] = True
-        combined = combined.filter(mask=mask)
 
         return combined
+
+    def rerank_multivector(
+        self,
+        vector_results: Union[List[pa.Table], List["LanceVectorQueryBuilder"]],
+        query: Union[str, None],  # Some rerankers might not need the query
+        deduplicate: bool = False,
+    ):
+        """
+        This is a rerank function that receives the results from multiple
+        vector searches. For example, this can be used to combine the
+        results of two vector searches with different embeddings.
+
+        Parameters
+        ----------
+        vector_results : List[pa.Table] or List[LanceVectorQueryBuilder]
+            The results from the vector search. Either accepts the query builder
+            if the results haven't been executed yet or the results in arrow format.
+        query : str or None,
+            The input query. Some rerankers might not need the query to rerank.
+            In that case, it can be set to None explicitly. This is inteded to
+            be handled by the reranker implementations.
+        deduplicate : bool, optional
+            Whether to deduplicate the results based on the `_rowid` column,
+            by default False. Requires `_rowid` to be present in the results.
+
+        Returns
+        -------
+        pa.Table
+            The reranked results
+        """
+        vector_results = (
+            [vector_results] if not isinstance(vector_results, list) else vector_results
+        )
+
+        # Make sure all elements are of the same type
+        if not all(isinstance(v, type(vector_results[0])) for v in vector_results):
+            raise ValueError(
+                "All elements in vector_results should be of the same type"
+            )
+
+        # avoids circular import
+        if type(vector_results[0]).__name__ == "LanceVectorQueryBuilder":
+            vector_results = [result.to_arrow() for result in vector_results]
+        elif not isinstance(vector_results[0], pa.Table):
+            raise ValueError(
+                "vector_results should be a list of pa.Table or LanceVectorQueryBuilder"
+            )
+
+        combined = pa.concat_tables(vector_results, **self._concat_tables_args)
+
+        reranked = self.rerank_vector(query, combined)
+
+        # TODO: Allow custom deduplicators here.
+        # currently, this'll just keep the first instance.
+        if deduplicate:
+            if "_rowid" not in combined.column_names:
+                raise ValueError(
+                    "'_rowid' is required for deduplication. \
+                    add _rowid to search results like this: \
+                    `search().with_row_id(True)`"
+                )
+            reranked = self._deduplicate(reranked)
+
+        return reranked
+
+    def _deduplicate(self, table: pa.Table):
+        """
+        Deduplicate the table based on the `_rowid` column.
+        """
+        row_id = table.column("_rowid")
+
+        # deduplicate
+        mask = np.full((table.shape[0]), False)
+        _, mask_indices = np.unique(np.array(row_id), return_index=True)
+        mask[mask_indices] = True
+        deduped_table = table.filter(mask=mask)
+
+        return deduped_table
+
+    def _keep_relevance_score(self, combined_results: pa.Table):
+        if self.score == "relevance":
+            if "_score" in combined_results.column_names:
+                combined_results = combined_results.drop_columns(["_score"])
+            if "_distance" in combined_results.column_names:
+                combined_results = combined_results.drop_columns(["_distance"])
+        return combined_results

python/python/lancedb/rerankers/cohere.py

@@ -88,7 +88,7 @@ class CohereReranker(Reranker):
         combined_results = self.merge_results(vector_results, fts_results)
         combined_results = self._rerank(combined_results, query)
         if self.score == "relevance":
-            combined_results = combined_results.drop_columns(["score", "_distance"])
+            combined_results = self._keep_relevance_score(combined_results)
         elif self.score == "all":
             raise NotImplementedError(
                 "return_score='all' not implemented for cohere reranker"
@@ -113,6 +113,6 @@ class CohereReranker(Reranker):
     ):
         result_set = self._rerank(fts_results, query)
         if self.score == "relevance":
-            result_set = result_set.drop_columns(["score"])
+            result_set = result_set.drop_columns(["_score"])
 
         return result_set

python/python/lancedb/rerankers/colbert.py

@@ -1,5 +1,3 @@
-from functools import cached_property
-
 import pyarrow as pa
 
 from ..util import attempt_import_or_raise
@@ -12,7 +10,7 @@ class ColbertReranker(Reranker):
 
     Parameters
     ----------
-    model_name : str, default "colbert-ir/colbertv2.0"
+    model_name : str, default "colbert" (colbert-ir/colbert-v2.0)
         The name of the cross encoder model to use.
     column : str, default "text"
         The name of the column to use as input to the cross encoder model.
@@ -22,41 +20,26 @@ class ColbertReranker(Reranker):
 
     def __init__(
         self,
-        model_name: str = "colbert-ir/colbertv2.0",
+        model_name: str = "colbert",
         column: str = "text",
         return_score="relevance",
     ):
         super().__init__(return_score)
         self.model_name = model_name
         self.column = column
-        self.torch = attempt_import_or_raise(
+        rerankers = attempt_import_or_raise(
-            "torch"
+            "rerankers"
         )  # import here for faster ops later
+        self.colbert = rerankers.Reranker(self.model_name, model_type="colbert")
 
     def _rerank(self, result_set: pa.Table, query: str):
         docs = result_set[self.column].to_pylist()
+        doc_ids = list(range(len(docs)))
+        result = self.colbert.rank(query, docs, doc_ids=doc_ids)
 
-        tokenizer, model = self._model
+        # get the scores of each document in the same order as the input
+        scores = [result.get_result_by_docid(i).score for i in doc_ids]
 
-        # Encode the query
-        query_encoding = tokenizer(query, return_tensors="pt")
-        query_embedding = model(**query_encoding).last_hidden_state.mean(dim=1)
-        scores = []
-        # Get score for each document
-        for document in docs:
-            document_encoding = tokenizer(
-                document, return_tensors="pt", truncation=True, max_length=512
-            )
-            document_embedding = model(**document_encoding).last_hidden_state
-            # Calculate MaxSim score
-            score = self.maxsim(query_embedding.unsqueeze(0), document_embedding)
-            scores.append(score.item())
-
-        # replace the self.column column with the docs
-        result_set = result_set.drop(self.column)
-        result_set = result_set.append_column(
-            self.column, pa.array(docs, type=pa.string())
-        )
         # add the scores
         result_set = result_set.append_column(
             "_relevance_score", pa.array(scores, type=pa.float32())
@@ -73,7 +56,7 @@ class ColbertReranker(Reranker):
         combined_results = self.merge_results(vector_results, fts_results)
         combined_results = self._rerank(combined_results, query)
         if self.score == "relevance":
-            combined_results = combined_results.drop_columns(["score", "_distance"])
+            combined_results = self._keep_relevance_score(combined_results)
         elif self.score == "all":
             raise NotImplementedError(
                 "OpenAI Reranker does not support score='all' yet"
@@ -105,36 +88,8 @@ class ColbertReranker(Reranker):
     ):
         result_set = self._rerank(fts_results, query)
         if self.score == "relevance":
-            result_set = result_set.drop_columns(["score"])
+            result_set = result_set.drop_columns(["_score"])
 
         result_set = result_set.sort_by([("_relevance_score", "descending")])
 
         return result_set
-
-    @cached_property
-    def _model(self):
-        transformers = attempt_import_or_raise("transformers")
-        tokenizer = transformers.AutoTokenizer.from_pretrained(self.model_name)
-        model = transformers.AutoModel.from_pretrained(self.model_name)
-
-        return tokenizer, model
-
-    def maxsim(self, query_embedding, document_embedding):
-        # Expand dimensions for broadcasting
-        # Query: [batch, length, size] -> [batch, query, 1, size]
-        # Document: [batch, length, size] -> [batch, 1, length, size]
-        expanded_query = query_embedding.unsqueeze(2)
-        expanded_doc = document_embedding.unsqueeze(1)
-
-        # Compute cosine similarity across the embedding dimension
-        sim_matrix = self.torch.nn.functional.cosine_similarity(
-            expanded_query, expanded_doc, dim=-1
-        )
-
-        # Take the maximum similarity for each query token (across all document tokens)
-        # sim_matrix shape: [batch_size, query_length, doc_length]
-        max_sim_scores, _ = self.torch.max(sim_matrix, dim=2)
-
-        # Average these maximum scores across all query tokens
-        avg_max_sim = self.torch.mean(max_sim_scores, dim=1)
-        return avg_max_sim

python/python/lancedb/rerankers/cross_encoder.py

@@ -42,7 +42,8 @@ class CrossEncoderReranker(Reranker):
     @cached_property
     def model(self):
         sbert = attempt_import_or_raise("sentence_transformers")
-        cross_encoder = sbert.CrossEncoder(self.model_name)
+        # Allows overriding the automatically selected device
+        cross_encoder = sbert.CrossEncoder(self.model_name, device=self.device)
 
         return cross_encoder
 
@@ -66,7 +67,7 @@ class CrossEncoderReranker(Reranker):
         combined_results = self._rerank(combined_results, query)
         # sort the results by _score
         if self.score == "relevance":
-            combined_results = combined_results.drop_columns(["score", "_distance"])
+            combined_results = self._keep_relevance_score(combined_results)
         elif self.score == "all":
             raise NotImplementedError(
                 "return_score='all' not implemented for CrossEncoderReranker"
@@ -96,7 +97,7 @@ class CrossEncoderReranker(Reranker):
     ):
         fts_results = self._rerank(fts_results, query)
         if self.score == "relevance":
-            fts_results = fts_results.drop_columns(["score"])
+            fts_results = fts_results.drop_columns(["_score"])
 
         fts_results = fts_results.sort_by([("_relevance_score", "descending")])
         return fts_results

python/python/lancedb/rerankers/jinaai.py

@@ -92,7 +92,7 @@ class JinaReranker(Reranker):
         combined_results = self.merge_results(vector_results, fts_results)
         combined_results = self._rerank(combined_results, query)
         if self.score == "relevance":
-            combined_results = combined_results.drop_columns(["score", "_distance"])
+            combined_results = self._keep_relevance_score(combined_results)
         elif self.score == "all":
             raise NotImplementedError(
                 "return_score='all' not implemented for JinaReranker"
@@ -117,6 +117,6 @@ class JinaReranker(Reranker):
     ):
         result_set = self._rerank(fts_results, query)
         if self.score == "relevance":
-            result_set = result_set.drop_columns(["score"])
+            result_set = result_set.drop_columns(["_score"])
 
         return result_set

python/python/lancedb/rerankers/linear_combination.py

@@ -69,12 +69,12 @@ class LinearCombinationReranker(Reranker):
             vi = vector_list[i]
             fj = fts_list[j]
             # invert the fts score from relevance to distance
-            inverted_fts_score = self._invert_score(fj["score"])
+            inverted_fts_score = self._invert_score(fj["_score"])
             if vi["_rowid"] == fj["_rowid"]:
                 vi["_relevance_score"] = self._combine_score(
                     vi["_distance"], inverted_fts_score
                 )
-                vi["score"] = fj["score"]  # keep the original score
+                vi["_score"] = fj["_score"]  # keep the original score
                 combined_list.append(vi)
                 i += 1
                 j += 1
@@ -103,7 +103,7 @@ class LinearCombinationReranker(Reranker):
             [("_relevance_score", "descending")]
         )
         if self.score == "relevance":
-            tbl = tbl.drop_columns(["score", "_distance"])
+            tbl = self._keep_relevance_score(tbl)
         return tbl
 
     def _combine_score(self, score1, score2):