Bump version: 0.9.0-beta.6 → 0.9.0-beta.7

# Conflicts:
#	python/pyproject.toml

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.5.0"
+current_version = "0.5.2-final.1"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/build_linux_wheel/action.yml

@@ -46,6 +46,7 @@ runs:
       with:
         command: build
         working-directory: python
+        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         target: aarch64-unknown-linux-gnu
         manylinux: ${{ inputs.manylinux }}
         args: ${{ inputs.args }}

.github/workflows/build_mac_wheel/action.yml

@@ -21,5 +21,6 @@ runs:
       with:
         command: build
         args: ${{ inputs.args }}
+        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         working-directory: python
         interpreter: 3.${{ inputs.python-minor-version }}

.github/workflows/build_windows_wheel/action.yml

@@ -26,8 +26,9 @@ runs:
       with:
         command: build
         args: ${{ inputs.args }}
+        docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         working-directory: python
-    - uses: actions/upload-artifact@v3
+    - uses: actions/upload-artifact@v4
       with:
         name: windows-wheels
         path: python\target\wheels

.github/workflows/java.yml

@@ -0,0 +1,85 @@
+name: Build and Run Java JNI Tests
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    paths:
+      - java/**
+      - rust/**
+      - .github/workflows/java.yml
+env:
+  # This env var is used by Swatinem/rust-cache@v2 for the cache
+  # key, so we set it to make sure it is always consistent.
+  CARGO_TERM_COLOR: always
+  # Disable full debug symbol generation to speed up CI build and keep memory down
+  # "1" means line tables only, which is useful for panic tracebacks.
+  RUSTFLAGS: "-C debuginfo=1"
+  RUST_BACKTRACE: "1"
+  # according to: https://matklad.github.io/2021/09/04/fast-rust-builds.html
+  # CI builds are faster with incremental disabled.
+  CARGO_INCREMENTAL: "0"
+  CARGO_BUILD_JOBS: "1"
+jobs:
+  linux-build:
+    runs-on: ubuntu-22.04
+    name: ubuntu-22.04 + Java 11 & 17
+    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 17
+        uses: actions/setup-java@v4
+        with:
+          distribution: temurin
+          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 \
+          -XX:+IgnoreUnrecognizedVMOptions \
+          --add-opens=java.base/java.lang=ALL-UNNAMED \
+          --add-opens=java.base/java.lang.invoke=ALL-UNNAMED \
+          --add-opens=java.base/java.lang.reflect=ALL-UNNAMED \
+          --add-opens=java.base/java.io=ALL-UNNAMED \
+          --add-opens=java.base/java.net=ALL-UNNAMED \
+          --add-opens=java.base/java.nio=ALL-UNNAMED \
+          --add-opens=java.base/java.util=ALL-UNNAMED \
+          --add-opens=java.base/java.util.concurrent=ALL-UNNAMED \
+          --add-opens=java.base/java.util.concurrent.atomic=ALL-UNNAMED \
+          --add-opens=java.base/jdk.internal.ref=ALL-UNNAMED \
+          --add-opens=java.base/sun.nio.ch=ALL-UNNAMED \
+          --add-opens=java.base/sun.nio.cs=ALL-UNNAMED \
+          --add-opens=java.base/sun.security.action=ALL-UNNAMED \
+          --add-opens=java.base/sun.util.calendar=ALL-UNNAMED \
+          --add-opens=java.security.jgss/sun.security.krb5=ALL-UNNAMED \
+          -Djdk.reflect.useDirectMethodHandle=false \
+          -Dio.netty.tryReflectionSetAccessible=true"
+          JAVA_HOME=$JAVA_17 mvn clean test

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

@@ -94,6 +94,6 @@ jobs:
           branch: ${{ github.ref }}
           tags: true
       - uses: ./.github/workflows/update_package_lock
-        if: ${{ inputs.dry_run }} == "false"
+        if: ${{ !inputs.dry_run && inputs.other }}
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/npm-publish.yml

@@ -3,7 +3,7 @@ name: NPM Publish
 on:
   push:
     tags:
-      - 'v*'
+      - "v*"
 
 jobs:
   node:
@@ -111,12 +111,11 @@ jobs:
             runner: ubuntu-latest
           - arch: aarch64
             # For successful fat LTO builds, we need a large runner to avoid OOM errors.
-            runner: buildjet-16vcpu-ubuntu-2204-arm
+            runner: warp-ubuntu-latest-arm64-4x
     steps:
       - name: Checkout
         uses: actions/checkout@v4
-      # Buildjet aarch64 runners have only 1.5 GB RAM per core, vs 3.5 GB per core for
-      # x86_64 runners. To avoid OOM errors on ARM, we create a swap file.
+      # To avoid OOM errors on ARM, we create a swap file.
       - name: Configure aarch64 build
         if: ${{ matrix.config.arch == 'aarch64' }}
         run: |
@@ -323,7 +322,7 @@ jobs:
       - name: Publish to NPM
         env:
           NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
-        # By default, things are published to the latest tag. This is what is 
+        # By default, things are published to the latest tag. This is what is
         # installed by default if the user does not specify a version. This is
         # good for stable releases, but for pre-releases, we want to publish to
         # the "preview" tag so they can install with `npm install lancedb@preview`.
@@ -368,7 +367,7 @@ jobs:
       - uses: ./.github/workflows/update_package_lock_nodejs
         with:
           github_token: ${{ secrets.GITHUB_TOKEN }}
-  
+
   gh-release:
     runs-on: ubuntu-latest
     permissions:

.github/workflows/python.yml

@@ -65,7 +65,7 @@ jobs:
           workspaces: python
       - name: Install
         run: |
-          pip install -e .[tests,dev,embeddings]
+          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests,dev,embeddings]
           pip install tantivy
           pip install mlx
       - name: Doctest
@@ -189,7 +189,7 @@ jobs:
       - name: Install lancedb
         run: |
           pip install "pydantic<2"
-          pip install -e .[tests]
+          pip install --extra-index-url https://pypi.fury.io/lancedb/ -e .[tests]
           pip install tantivy
       - name: Run tests
         run: pytest -m "not slow and not s3_test" -x -v --durations=30 python/tests

.github/workflows/run_tests/action.yml

@@ -15,7 +15,7 @@ runs:
     - name: Install lancedb
       shell: bash
       run: |
-        pip3 install $(ls target/wheels/lancedb-*.whl)[tests,dev]
+        pip3 install --extra-index-url https://pypi.fury.io/lancedb/ $(ls target/wheels/lancedb-*.whl)[tests,dev]
     - name: Setup localstack for integration tests
       if: ${{ inputs.integration == 'true' }}
       shell: bash

.pre-commit-config.yaml

@@ -14,7 +14,7 @@ repos:
   hooks:
     - id: local-biome-check
       name: biome check
-      entry: npx biome check
+      entry: npx @biomejs/biome@1.7.3 check --config-path nodejs/biome.json nodejs/
       language: system
       types: [text]
       files: "nodejs/.*"

Cargo.toml

@@ -1,5 +1,11 @@
 [workspace]
-members = ["rust/ffi/node", "rust/lancedb", "nodejs", "python"]
+members = [
+    "rust/ffi/node",
+    "rust/lancedb",
+    "nodejs",
+    "python",
+    "java/core/lancedb-jni",
+]
 # Python package needs to be built by maturin.
 exclude = ["python"]
 resolver = "2"
@@ -14,10 +20,11 @@ keywords = ["lancedb", "lance", "database", "vector", "search"]
 categories = ["database-implementations"]
 
 [workspace.dependencies]
-lance = { "version" = "=0.11.0", "features" = ["dynamodb"] }
-lance-index = { "version" = "=0.11.0" }
-lance-linalg = { "version" = "=0.11.0" }
-lance-testing = { "version" = "=0.11.0" }
+lance = { "version" = "=0.13.0", "features" = ["dynamodb"] }
+lance-index = { "version" = "=0.13.0" }
+lance-linalg = { "version" = "=0.13.0" }
+lance-testing = { "version" = "=0.13.0" }
+lance-datafusion = { "version" = "=0.13.0" }
 # Note that this one does not include pyarrow
 arrow = { version = "51.0", optional = false }
 arrow-array = "51.0"
@@ -29,6 +36,7 @@ arrow-arith = "51.0"
 arrow-cast = "51.0"
 async-trait = "0"
 chrono = "0.4.35"
+datafusion-physical-plan = "37.1"
 half = { "version" = "=2.4.1", default-features = false, features = [
     "num-traits",
 ] }

README.md

@@ -83,5 +83,5 @@ result = table.search([100, 100]).limit(2).to_pandas()
 ```
 
 ## Blogs, Tutorials & Videos
-* 📈 <a href="https://blog.eto.ai/benchmarking-random-access-in-lance-ed690757a826">2000x better performance with Lance over Parquet</a>
+* 📈 <a href="https://blog.lancedb.com/benchmarking-random-access-in-lance/">2000x better performance with Lance over Parquet</a>
 * 🤖 <a href="https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>

docs/mkdocs.yml

@@ -106,6 +106,9 @@ nav:
           - Versioning & Reproducibility: notebooks/reproducibility.ipynb
           - Configuring Storage: guides/storage.md
           - Sync -> Async Migration Guide: migration.md
+          - Tuning retrieval performance:
+              - Choosing right query type: guides/tuning_retrievers/1_query_types.md
+              - Reranking: guides/tuning_retrievers/2_reranking.md
       - 🧬 Managing embeddings:
           - Overview: embeddings/index.md
           - Embedding functions: embeddings/embedding_functions.md
@@ -121,7 +124,9 @@ nav:
           - LangChain:
             - LangChain 🔗: integrations/langchain.md
             - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
-          - LlamaIndex 🦙: https://docs.llamaindex.ai/en/stable/examples/vector_stores/LanceDBIndexDemo/
+          - LlamaIndex 🦙:
+            - LlamaIndex docs: integrations/llamaIndex.md
+            - LlamaIndex demo:  https://docs.llamaindex.ai/en/stable/examples/vector_stores/LanceDBIndexDemo/
           - Pydantic: python/pydantic.md
           - Voxel51: integrations/voxel51.md
           - PromptTools: integrations/prompttools.md
@@ -152,7 +157,7 @@ nav:
           - Overview: cloud/index.md
           - API reference:
               - 🐍 Python: python/saas-python.md
-              - 👾 JavaScript: javascript/saas-modules.md
+              - 👾 JavaScript: javascript/modules.md
 
   - Quick start: basic.md
   - Concepts:
@@ -181,6 +186,9 @@ nav:
       - Versioning & Reproducibility: notebooks/reproducibility.ipynb
       - Configuring Storage: guides/storage.md
       - Sync -> Async Migration Guide: migration.md
+      - Tuning retrieval performance:
+          - Choosing right query type: guides/tuning_retrievers/1_query_types.md
+          - Reranking: guides/tuning_retrievers/2_reranking.md
   - Managing Embeddings:
       - Overview: embeddings/index.md
       - Embedding functions: embeddings/embedding_functions.md
@@ -219,7 +227,7 @@ nav:
       - Overview: cloud/index.md
       - API reference:
           - 🐍 Python: python/saas-python.md
-          - 👾 JavaScript: javascript/saas-modules.md
+          - 👾 JavaScript: javascript/modules.md
 
 extra_css:
   - styles/global.css

docs/src/basic.md

@@ -180,6 +180,9 @@ table.
 
 !!! info "Under the hood, LanceDB reads in the Apache Arrow data and persists it to disk using the [Lance format](https://www.github.com/lancedb/lance)."
 
+!!! info "Automatic embedding generation with Embedding API"
+    When working with embedding models, it is recommended to use the LanceDB embedding API to automatically create vector representation of the data and queries in the background. See the [quickstart example](#using-the-embedding-api) or the embedding API [guide](./embeddings/)
+
 ### Create an empty table
 
 Sometimes you may not have the data to insert into the table at creation time.
@@ -194,6 +197,9 @@ similar to a `CREATE TABLE` statement in SQL.
       --8<-- "python/python/tests/docs/test_basic.py:create_empty_table_async"
       ```
 
+    !!! note "You can define schema in Pydantic"
+        LanceDB comes with Pydantic support, which allows you to define the schema of your data using Pydantic models. This makes it easy to work with LanceDB tables and data. Learn more about all supported types in [tables guide](./guides/tables.md).
+
 === "Typescript"
 
     ```typescript
@@ -424,6 +430,19 @@ Use the `drop_table()` method on the database to remove a table.
     })
     ```
 
+## Using the Embedding API
+You can use the embedding API when working with embedding models. It automatically vectorizes the data at ingestion and query time and comes with built-in integrations with popular embedding models like Openai, Hugging Face, Sentence Transformers, CLIP and more.
+
+=== "Python"
+
+    ```python
+    --8<-- "python/python/tests/docs/test_embeddings_optional.py:imports"
+    --8<-- "python/python/tests/docs/test_embeddings_optional.py:openai_embeddings"
+    ```
+
+Learn about using the existing integrations and creating custom embedding functions in the [embedding API guide](./embeddings/).
+
+
 ## What's next
 
 This section covered the very basics of using LanceDB. If you're learning about vector databases for the first time, you may want to read the page on [indexing](concepts/index_ivfpq.md) to get familiar with the concepts.

docs/src/embeddings/default_embedding_functions.md

@@ -216,7 +216,7 @@ Generate embeddings via the [ollama](https://github.com/ollama/ollama-python) py
 |------------------------|----------------------------|--------------------------|------------------------------------------------------------------------------------------------------------------------------------------------|
 | `name`                 | `str`                      | `nomic-embed-text`       | The name of the model.                                                                                                                         |
 | `host`                 | `str`                      | `http://localhost:11434` | The Ollama host to connect to.                                                                                                                 |
-| `options`              | `ollama.Options` or `dict` | `None`                   | Additional model parameters listed in the documentation for the [Modelfile](./modelfile.md#valid-parameters-and-values) such as `temperature`. |
+| `options`              | `ollama.Options` or `dict` | `None`                   | Additional model parameters listed in the documentation for the Modelfile such as `temperature`. |
 | `keep_alive`           | `float` or `str`           | `"5m"`                   | Controls how long the model will stay loaded into memory following the request.                                                                |
 | `ollama_client_kwargs` | `dict`                     | `{}`                     | kwargs that can be past to the `ollama.Client`.                                                                                                |
 
@@ -365,6 +365,68 @@ tbl.add(df)
 rs = tbl.search("hello").limit(1).to_pandas()
 ```
 
+### Cohere Embeddings
+Using cohere API requires cohere package, which can be installed using `pip install cohere`. Cohere embeddings are used to generate embeddings for text data. The embeddings can be used for various tasks like semantic search, clustering, and classification.
+You also need to set the `COHERE_API_KEY` environment variable to use the Cohere API.
+
+Supported models are:
+```
+    * embed-english-v3.0
+    * embed-multilingual-v3.0
+    * embed-english-light-v3.0
+    * embed-multilingual-light-v3.0
+    * embed-english-v2.0
+    * embed-english-light-v2.0
+    * embed-multilingual-v2.0
+```
+
+Supported parameters (to be passed in `create` method) are:
+
+| Parameter | Type | Default Value | Description |
+|---|---|---|---|
+| `name` | `str` | `"embed-english-v2.0"` | The model ID of the cohere model to use. Supported base models for Text Embeddings: embed-english-v3.0, embed-multilingual-v3.0, embed-english-light-v3.0, embed-multilingual-light-v3.0, embed-english-v2.0, embed-english-light-v2.0, embed-multilingual-v2.0 |
+| `source_input_type` | `str` | `"search_document"` | The type of input data to be used for the source column. |
+| `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|
+|                         | database for search use-cases.        |
+| "`search_query`"        | Used for embeddings of search queries |
+|                         | run against a vector DB               |
+| "`semantic_similarity`" | Specifies the given text will be used |
+|                         | for Semantic Textual Similarity (STS) |
+| "`classification`"      | Used for embeddings passed through a  |
+|                         | text classifier.                      |
+| "`clustering`"          | Used for the embeddings run through a |
+|                         | clustering algorithm                  |
+
+Usage Example:
+    
+    ```python
+        import lancedb
+        from lancedb.pydantic import LanceModel, Vector
+        from lancedb.embeddings import EmbeddingFunctionRegistry
+
+        cohere = EmbeddingFunctionRegistry
+            .get_instance()
+            .get("cohere")
+            .create(name="embed-multilingual-v2.0")
+
+        class TextModel(LanceModel):
+            text: str = cohere.SourceField()
+            vector: Vector(cohere.ndims()) =  cohere.VectorField()
+
+        data = [ { "text": "hello world" },
+                { "text": "goodbye world" }]
+
+        db = lancedb.connect("~/.lancedb")
+        tbl = db.create_table("test", schema=TextModel, mode="overwrite")
+
+        tbl.add(data)
+    ```
+
 ### AWS Bedrock Text Embedding Functions
 AWS Bedrock supports multiple base models for generating text embeddings. You need to setup the AWS credentials to use this embedding function.
 You can do so by using `awscli` and also add your session_token:

docs/src/embeddings/embedding_functions.md

@@ -2,6 +2,9 @@ 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"
+    LanceDB Cloud does not support embedding functions yet. You need to generate embeddings before ingesting into the table or querying.
+
 !!! warning
     Using the embedding function registry means that you don't have to explicitly generate the embeddings yourself. 
     However, if your embedding function changes, you'll have to re-configure your table with the new embedding function 

docs/src/fts.md

@@ -2,7 +2,6 @@
 
 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)
 
-A hybrid search solution combining vector and full-text search is also on the way.
 
 ## Installation
 
@@ -55,6 +54,16 @@ This returns the result as a list of dictionaries as follows.
 !!! 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.
 
+## 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
+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.
+
+
 ## 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`:
@@ -140,6 +149,7 @@ is treated as a phrase query.
 In general, a query that's declared as a phrase query will be wrapped in double quotes during parsing, with nested
 double quotes replaced by single quotes.
 
+
 ## Configurations
 
 By default, LanceDB configures a 1GB heap size limit for creating the index. You can

docs/src/guides/tables.md

@@ -452,6 +452,27 @@ After a table has been created, you can always add more data to it using the var
     tbl.add(pydantic_model_items)
     ```
 
+    ??? "Ingesting Pydantic models with LanceDB embedding API"
+        When using LanceDB's embedding API, you can add Pydantic models directly to the table. LanceDB will automatically convert the `vector` field to a vector before adding it to the table. You need to specify the default value of `vector` feild as None to allow LanceDB to automatically vectorize the data.
+
+        ```python
+        import lancedb
+        from lancedb.pydantic import LanceModel, Vector
+        from lancedb.embeddings import get_registry
+
+        db = lancedb.connect("~/tmp")
+        embed_fcn = get_registry().get("huggingface").create(name="BAAI/bge-small-en-v1.5")
+
+        class Schema(LanceModel):
+            text: str = embed_fcn.SourceField()
+            vector: Vector(embed_fcn.ndims()) = embed_fcn.VectorField(default=None)
+
+        tbl = db.create_table("my_table", schema=Schema, mode="overwrite")
+        models = [Schema(text="hello"), Schema(text="world")]
+        tbl.add(models)
+        ```
+
+
 
 === "JavaScript"
 
@@ -636,6 +657,31 @@ The `values` parameter is used to provide the new values for the columns as lite
 
     When rows are updated, they are moved out of the index. The row will still show up in ANN queries, but the query will not be as fast as it would be if the row was in the index. If you update a large proportion of rows, consider rebuilding the index afterwards.
 
+## Drop a table
+
+Use the `drop_table()` method on the database to remove a table.
+
+=== "Python"
+
+      ```python
+      --8<-- "python/python/tests/docs/test_basic.py:drop_table"
+      --8<-- "python/python/tests/docs/test_basic.py:drop_table_async"
+      ```
+
+      This permanently removes the table and is not recoverable, unlike deleting rows.
+      By default, if the table does not exist an exception is raised. To suppress this,
+      you can pass in `ignore_missing=True`.
+
+=== "Javascript/Typescript"
+
+      ```typescript
+      --8<-- "docs/src/basic_legacy.ts:drop_table"
+      ```
+
+      This permanently removes the table and is not recoverable, unlike deleting rows.
+      If the table does not exist an exception is raised.
+
+
 ## Consistency
 
 In LanceDB OSS, users can set the `read_consistency_interval` parameter on connections to achieve different levels of read consistency. This parameter determines how frequently the database synchronizes with the underlying storage system to check for updates made by other processes. If another process updates a table, the database will not see the changes until the next synchronization.

docs/src/guides/tuning_retrievers/1_query_types.md

@@ -0,0 +1,128 @@
+## Improving retriever performance
+VectorDBs are used as retreivers in recommender or chatbot-based systems for retrieving relevant data based on user queries. For example, retriever is a critical component of Retrieval Augmented Generation (RAG) acrhitectures. In this section, we will discuss how to improve the performance of retrievers.
+
+There are serveral ways to improve the performance of retrievers. Some of the common techniques are:
+
+* Using different query types
+* Using hybrid search
+* Fine-tuning the embedding models
+* Using different embedding models
+
+Using different embedding models is something that's very specific to the use case and the data. So we will not discuss it here. In this section, we will discuss the first three techniques.
+
+
+!!! note "Note"
+    We'll be using a simple metric called "hit-rate" for evaluating the performance of the retriever across this guide. Hit-rate is the percentage of queries for which the retriever returned the correct answer in the top-k results. For example, if the retriever returned the correct answer in the top-3 results for 70% of the queries, then the hit-rate@3 is 0.7.
+
+
+## The dataset
+We'll be using a QA dataset generated using a LLama2 review paper. The dataset contains 221 query, context and answer triplets. The queries and answers are generated using GPT-4 based on a given query. Full script used to generate the dataset can be found on this [repo](https://github.com/lancedb/ragged). It can be downloaded from [here](https://github.com/AyushExel/assets/blob/main/data_qa.csv)
+
+### Using different query types
+Let's setup the embeddings and the dataset first. We'll use the LanceDB's `huggingface` embeddings integration for this guide. 
+
+```python
+import lancedb
+import pandas as pd
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import Vector, LanceModel
+
+db = lancedb.connect("~/lancedb/query_types")
+df = pd.read_csv("data_qa.csv")
+
+embed_fcn = get_registry().get("huggingface").create(name="BAAI/bge-small-en-v1.")
+
+class Schema(LanceModel):
+    context: str = embed_fcn.SourceField()
+    vector: Vector(embed_fcn.ndims()) = embed_fcn.VectorField()
+
+table = db.create_table("qa", schema=Schema)
+table.add(df[["context"]].to_dict(orient="records"))
+
+queries = df["query"].tolist()
+```
+
+Now that we have the dataset and embeddings table set up, here's how you can run different query types on the dataset.
+
+* <b> Vector Search: </b>
+
+    ```python
+    table.search(quries[0], query_type="vector").limit(5).to_pandas()
+    ```
+    By default, LanceDB uses vector search query type for searching and it automatically converts the input query to a vector before searching when using embedding API. So, the following statement is equivalent to the above statement.
+
+    ```python
+    table.search(quries[0]).limit(5).to_pandas()
+    ```
+
+    Vector or semantic search is useful when you want to find documents that are similar to the query in terms of meaning.
+
+---
+
+* <b> Full-text Search: </b>
+    
+    FTS requires creating an index on the column you want to search on. `replace=True` will replace the existing index if it exists.
+    Once the index is created, you can search using the `fts` query type.
+    ```python
+    table.create_fts_index("context", replace=True)
+    table.search(quries[0], query_type="fts").limit(5).to_pandas()
+    ```
+
+    Full-text search is useful when you want to find documents that contain the query terms.
+
+---
+
+* <b> Hybrid Search: </b>
+
+    Hybrid search is a combination of vector and full-text search. Here's how you can run a hybrid search query on the dataset.
+    ```python
+    table.search(quries[0], query_type="hybrid").limit(5).to_pandas()
+    ```
+    Hybrid search requires a reranker to combine and rank the results from vector and full-text search. We'll cover reranking as a concept in the next section.
+
+    Hybrid search is useful when you want to combine the benefits of both vector and full-text search.
+
+    !!! note "Note"
+        By default, it uses `LinearCombinationReranker` that combines the scores from vector and full-text search using a weighted linear combination. It is the simplest reranker implementation available in LanceDB. You can also use other rerankers like `CrossEncoderReranker` or `CohereReranker` for reranking the results.
+        Learn more about rerankers [here](https://lancedb.github.io/lancedb/reranking/)
+
+    
+
+### Hit rate evaluation results
+
+Now that we have seen how to run different query types on the dataset, let's evaluate the hit-rate of each query type on the dataset.
+For brevity, the entire evaluation script is not shown here. You can find the complete evaluation and benchmarking utility scripts [here](https://github.com/lancedb/ragged).
+
+Here are the hit-rate results for the dataset:
+
+| Query Type | Hit-rate@5 |
+| --- | --- |
+| Vector Search | 0.640 |
+| Full-text Search | 0.595 |
+| Hybrid Search (w/ LinearCombinationReranker) | 0.645 |
+
+**Choosing query type** is very specific to the use case and the data. This synthetic dataset has been generated to be semantically challenging, i.e, the queries don't have a lot of keywords in common with the context. So, vector search performs better than full-text search. However, in real-world scenarios, full-text search might perform better than vector search. Hybrid search is a good choice when you want to combine the benefits of both vector and full-text search.
+
+### Evaluation results on other datasets
+
+The hit-rate results can vary based on the dataset and the query type. Here are the hit-rate results for the other datasets using the same embedding function.
+
+* <b> SQuAD Dataset: </b>
+
+    | Query Type | Hit-rate@5 |
+    | --- | --- |
+    | Vector Search | 0.822 |
+    | Full-text Search | 0.835 |
+    | Hybrid Search (w/ LinearCombinationReranker) | 0.8874 |
+
+* <b> Uber10K sec filing Dataset: </b>
+
+    | Query Type | Hit-rate@5 |
+    | --- | --- |
+    | Vector Search | 0.608 |
+    | Full-text Search | 0.82 |
+    | Hybrid Search (w/ LinearCombinationReranker) | 0.80 |
+
+In these standard datasets, FTS seems to perform much better than vector search because the queries have a lot of keywords in common with the context. So, in general choosing the query type is very specific to the use case and the data.
+
+

docs/src/guides/tuning_retrievers/2_reranking.md

@@ -0,0 +1,78 @@
+Continuing from the previous example, we can now rerank the results using more complex rerankers.
+
+## Reranking search results
+You can rerank any search results using a reranker. The syntax for reranking is as follows:
+
+```python
+from lancedb.rerankers import LinearCombinationReranker
+
+reranker = LinearCombinationReranker()
+table.search(quries[0], query_type="hybrid").rerank(reranker=reranker).limit(5).to_pandas()
+```
+Based on the `query_type`, the `rerank()` function can accept other arguments as well. For example, hybrid search accepts a `normalize` param to determine the score normalization method.
+
+!!! note "Note"
+    LanceDB provides a `Reranker` base class that can be extended to implement custom rerankers. Each reranker must implement the `rerank_hybrid` method. `rerank_vector` and `rerank_fts` methods are optional. For example, the `LinearCombinationReranker` only implements the `rerank_hybrid` method and so it can only be used for reranking hybrid search results.
+
+## Choosing a Reranker
+There are many rerankers available in LanceDB like `CrossEncoderReranker`, `CohereReranker`, and `ColBERT`. The choice of reranker depends on the dataset and the application. You can even implement you own custom reranker by extending the `Reranker` class. For more details about each available reranker and performance comparison, refer to the [rerankers](https://lancedb.github.io/lancedb/reranking/) documentation.
+
+In this example, we'll use the `CohereReranker` to rerank the search results. It requires  `cohere` to be installed and `COHERE_API_KEY` to be set in the environment. To get your API key, sign up on [Cohere](https://cohere.ai/).
+
+```python
+from lancedb.rerankers import CohereReranker
+
+# use Cohere reranker v3
+reranker = CohereReranker(model_name="rerank-english-v3.0") # default model is "rerank-english-v2.0"
+```
+
+### Reranking search results
+Now we can rerank all query type results using the `CohereReranker`:
+
+```python
+
+# rerank hybrid search results
+table.search(quries[0], query_type="hybrid").rerank(reranker=reranker).limit(5).to_pandas()
+
+# rerank vector search results
+table.search(quries[0], query_type="vector").rerank(reranker=reranker).limit(5).to_pandas()
+
+# rerank fts search results
+table.search(quries[0], query_type="fts").rerank(reranker=reranker).limit(5).to_pandas()
+```
+
+Each reranker can accept additional arguments. For example, `CohereReranker` accepts `top_k` and `batch_size` params to control the number of documents to rerank and the batch size for reranking respectively. Similarly, a custom reranker can accept any number of arguments based on the implementation. For example, a reranker can accept a `filter` that implements some custom logic to filter out documents before reranking.
+
+## Results
+
+Let us take a look at the same datasets from the previous sections, using the same embedding table but with Cohere reranker applied to all query types.
+
+!!! note "Note"
+    When reranking fts or vector search results, the search results are over-fetched by a factor of 2 and then reranked. From the reranked set, `top_k` (5 in this case) results are taken. This is done because reranking will have no effect on the hit-rate if we only fetch the `top_k` results.
+
+### Synthetic LLama2 paper dataset
+
+| Query Type | Hit-rate@5 |
+| --- | --- |
+| Vector |  0.640 |
+| FTS   |  0.595  |
+| Reranked vector | 0.677    |
+| Reranked fts  | 0.672    |
+| Hybrid | 0.759 |
+
+### SQuAD Dataset
+
+
+### Uber10K sec filing Dataset
+
+| Query Type | Hit-rate@5 |
+| --- | --- |
+| Vector |  0.608 |
+| FTS   |  0.824  |
+| Reranked vector | 0.671    |
+| Reranked fts  | 0.843    |
+| Hybrid | 0.849 |
+
+
+
+

docs/src/hybrid_search/eval.md

@@ -5,7 +5,9 @@ Hybrid Search is a broad (often misused) term. It can mean anything from combini
 ## The challenge of (re)ranking search results
 Once you have a group of the most relevant search results from multiple search sources, you'd likely standardize the score and rank them accordingly. This process can also be seen as another independent step - reranking.
 There are two approaches for reranking search results from multiple sources.
+
 * <b>Score-based</b>: Calculate final relevance scores based on a weighted linear combination of individual search algorithm scores. Example - Weighted linear combination of semantic search & keyword-based search results.
+
 * <b>Relevance-based</b>: Discards the existing scores and calculates the relevance of each search result - query pair. Example - Cross Encoder models
 
 Even though there are many strategies for reranking search results, none works for all cases. Moreover, evaluating them itself is a challenge. Also, reranking can be dataset, application specific so it's hard to generalize.

docs/src/integrations/llamaIndex.md

@@ -0,0 +1,139 @@
+# Llama-Index
+![Illustration](../assets/llama-index.jpg)
+
+## Quick start
+You would need to install the integration via `pip install llama-index-vector-stores-lancedb` in order to use it. You can run the below script to try it out :
+```python
+import logging
+import sys
+
+# Uncomment to see debug logs
+# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
+# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
+
+from llama_index.core import SimpleDirectoryReader, Document, StorageContext
+from llama_index.core import VectorStoreIndex
+from llama_index.vector_stores.lancedb import LanceDBVectorStore
+import textwrap
+import openai
+
+openai.api_key = "sk-..."
+
+documents = SimpleDirectoryReader("./data/your-data-dir/").load_data()
+print("Document ID:", documents[0].doc_id, "Document Hash:", documents[0].hash)
+
+## For LanceDB cloud :
+# vector_store = LanceDBVectorStore( 
+#     uri="db://db_name", # your remote DB URI
+#     api_key="sk_..", # lancedb cloud api key
+#     region="your-region" # the region you configured
+#     ...
+# )
+
+vector_store = LanceDBVectorStore(
+    uri="./lancedb", mode="overwrite", query_type="vector"
+)
+storage_context = StorageContext.from_defaults(vector_store=vector_store)
+
+index = VectorStoreIndex.from_documents(
+    documents, storage_context=storage_context
+)
+lance_filter = "metadata.file_name = 'paul_graham_essay.txt' "
+retriever = index.as_retriever(vector_store_kwargs={"where": lance_filter})
+response = retriever.retrieve("What did the author do growing up?")
+```
+
+### Filtering
+For metadata filtering, you can use a Lance SQL-like string filter as demonstrated in the example above. Additionally, you can also filter using the `MetadataFilters` class from LlamaIndex:
+```python
+from llama_index.core.vector_stores import (
+    MetadataFilters,
+    FilterOperator,
+    FilterCondition,
+    MetadataFilter,
+)
+
+query_filters = MetadataFilters(
+    filters=[
+        MetadataFilter(
+            key="creation_date", operator=FilterOperator.EQ, value="2024-05-23"
+        ),
+        MetadataFilter(
+            key="file_size", value=75040, operator=FilterOperator.GT
+        ),
+    ],
+    condition=FilterCondition.AND,
+)
+```
+
+### Hybrid Search
+For complete documentation, refer [here](https://lancedb.github.io/lancedb/hybrid_search/hybrid_search/). This example uses the `colbert` reranker. Make sure to install necessary dependencies for the reranker you choose.
+```python
+from lancedb.rerankers import ColbertReranker
+
+reranker = ColbertReranker()
+vector_store._add_reranker(reranker)
+
+query_engine = index.as_query_engine(
+    filters=query_filters,
+    vector_store_kwargs={
+        "query_type": "hybrid",
+    }
+)
+
+response = query_engine.query("How much did Viaweb charge per month?")
+```
+
+In the above snippet, you can change/specify query_type again when creating the engine/retriever.
+
+## API reference
+The exhaustive list of parameters for `LanceDBVectorStore` vector store are :  
+- `connection`: Optional, `lancedb.db.LanceDBConnection` connection object to use. If not provided, a new connection will be created.
+- `uri`: Optional[str], the uri of your database. Defaults to `"/tmp/lancedb"`.
+- `table_name` : Optional[str], Name of your table in the database. Defaults to `"vectors"`.
+- `table`: Optional[Any], `lancedb.db.LanceTable` object to be passed. Defaults to `None`. 
+- `vector_column_name`: Optional[Any], Column name to use for vector's in the table. Defaults to `'vector'`.   
+- `doc_id_key`: Optional[str], Column name to use for document id's in the table. Defaults to `'doc_id'`.  
+- `text_key`: Optional[str], Column name to use for text in the table. Defaults to `'text'`.  
+- `api_key`: Optional[str], API key to use for LanceDB cloud database. Defaults to `None`.  
+- `region`: Optional[str], Region to use for LanceDB cloud database. Only for LanceDB Cloud, defaults to `None`.  
+- `nprobes` : Optional[int], Set the number of probes to use. Only applicable if ANN index is created on the table else its ignored. Defaults to `20`.
+- `refine_factor` : Optional[int], Refine the results by reading extra elements and re-ranking them in memory. Defaults to `None`.
+- `reranker`: Optional[Any], The reranker to use for LanceDB.
+        Defaults to `None`.
+- `overfetch_factor`: Optional[int], The factor by which to fetch more results.
+        Defaults to `1`.
+- `mode`: Optional[str], The mode to use for LanceDB.
+            Defaults to `"overwrite"`.
+- `query_type`:Optional[str], The type of query to use for LanceDB.
+            Defaults to `"vector"`.
+
+
+### Methods 
+
+- __from_table(cls, table: lancedb.db.LanceTable) -> `LanceDBVectorStore`__ : (class method) Creates instance from lancedb table. 
+
+- **_add_reranker(self, reranker: lancedb.rerankers.Reranker) -> `None`** : Add a reranker to an existing vector store. 
+    - Usage :
+        ```python
+        from lancedb.rerankers import ColbertReranker
+        reranker = ColbertReranker()
+        vector_store._add_reranker(reranker)
+        ```               
+- **_table_exists(self, tbl_name: `Optional[str]` = `None`) -> `bool`** : Returns `True` if `tbl_name` exists in database.
+- __create_index(  
+  self, scalar: `Optional[bool]` = False, col_name: `Optional[str]` = None, num_partitions: `Optional[int]` = 256, num_sub_vectors: `Optional[int]` = 96, index_cache_size: `Optional[int]` = None, metric: `Optional[str]` = "L2",  
+) -> `None`__ : Creates a scalar(for non-vector cols) or a vector index on a table.
+        Make sure your vector column has enough data before creating an index on it.
+
+- __add(self, nodes: `List[BaseNode]`, **add_kwargs: `Any`, ) -> `List[str]`__ :
+adds Nodes to the table
+
+- **delete(self, ref_doc_id: `str`) -> `None`**: Delete nodes using with node_ids.
+- **delete_nodes(self, node_ids: `List[str]`) -> `None`** : Delete nodes using with node_ids.
+- __query(
+        self,
+        query: `VectorStoreQuery`,
+        **kwargs: `Any`,
+    ) -> `VectorStoreQueryResult`__:
+        Query index(`VectorStoreIndex`) for top k most similar nodes. Accepts llamaIndex `VectorStoreQuery` object.

docs/test/md_testing.py

@@ -7,8 +7,7 @@ excluded_globs = [
     "../src/fts.md",
     "../src/embedding.md",
     "../src/examples/*.md",
-    "../src/integrations/voxel51.md",
-    "../src/integrations/langchain.md",
+    "../src/integrations/*.md",
     "../src/guides/tables.md",
     "../src/python/duckdb.md",
     "../src/embeddings/*.md",
@@ -17,6 +16,7 @@ excluded_globs = [
     "../src/basic.md",
     "../src/hybrid_search/hybrid_search.md",
     "../src/reranking/*.md",
+    "../src/guides/tuning_retrievers/*.md",
 ]
 
 python_prefix = "py"

java/core/lancedb-jni/Cargo.toml

@@ -0,0 +1,27 @@
+[package]
+name = "lancedb-jni"
+description = "JNI bindings for LanceDB"
+# TODO modify lancedb/Cargo.toml for version and dependencies
+version = "0.4.18"
+edition.workspace = true
+repository.workspace = true
+readme.workspace = true
+license.workspace = true
+keywords.workspace = true
+categories.workspace = true
+publish = false
+
+[lib]
+crate-type = ["cdylib"]
+
+[dependencies]
+lancedb = { path = "../../../rust/lancedb" }
+lance = { workspace = true }
+arrow = { workspace = true, features = ["ffi"] }
+arrow-schema.workspace = true
+tokio = "1.23"
+jni = "0.21.1"
+snafu.workspace = true
+lazy_static.workspace = true
+serde = { version = "^1" }
+serde_json = { version = "1" }

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

@@ -0,0 +1,130 @@
+use crate::ffi::JNIEnvExt;
+use crate::traits::IntoJava;
+use crate::{Error, RT};
+use jni::objects::{JObject, JString, JValue};
+use jni::JNIEnv;
+pub const NATIVE_CONNECTION: &str = "nativeConnectionHandle";
+use crate::Result;
+use lancedb::connection::{connect, Connection};
+
+#[derive(Clone)]
+pub struct BlockingConnection {
+    pub(crate) inner: Connection,
+}
+
+impl BlockingConnection {
+    pub fn create(dataset_uri: &str) -> Result<Self> {
+        let inner = RT.block_on(connect(dataset_uri).execute())?;
+        Ok(Self { inner })
+    }
+
+    pub fn table_names(
+        &self,
+        start_after: Option<String>,
+        limit: Option<i32>,
+    ) -> Result<Vec<String>> {
+        let mut op = self.inner.table_names();
+        if let Some(start_after) = start_after {
+            op = op.start_after(start_after);
+        }
+        if let Some(limit) = limit {
+            op = op.limit(limit as u32);
+        }
+        Ok(RT.block_on(op.execute())?)
+    }
+}
+
+impl IntoJava for BlockingConnection {
+    fn into_java<'a>(self, env: &mut JNIEnv<'a>) -> JObject<'a> {
+        attach_native_connection(env, self)
+    }
+}
+
+fn attach_native_connection<'local>(
+    env: &mut JNIEnv<'local>,
+    connection: BlockingConnection,
+) -> JObject<'local> {
+    let j_connection = create_java_connection_object(env);
+    // This block sets a native Rust object (Connection) as a field in the Java object (j_Connection).
+    // Caution: This creates a potential for memory leaks. The Rust object (Connection) is not
+    // automatically garbage-collected by Java, and its memory will not be freed unless
+    // explicitly handled.
+    //
+    // To prevent memory leaks, ensure the following:
+    // 1. The Java object (`j_Connection`) should implement the `java.io.Closeable` interface.
+    // 2. Users of this Java object should be instructed to always use it within a try-with-resources
+    //    statement (or manually call the `close()` method) to ensure that `self.close()` is invoked.
+    match unsafe { env.set_rust_field(&j_connection, NATIVE_CONNECTION, connection) } {
+        Ok(_) => j_connection,
+        Err(err) => {
+            env.throw_new(
+                "java/lang/RuntimeException",
+                format!("Failed to set native handle for Connection: {}", err),
+            )
+            .expect("Error throwing exception");
+            JObject::null()
+        }
+    }
+}
+
+fn create_java_connection_object<'a>(env: &mut JNIEnv<'a>) -> JObject<'a> {
+    env.new_object("com/lancedb/lancedb/Connection", "()V", &[])
+        .expect("Failed to create Java Lance Connection instance")
+}
+
+#[no_mangle]
+pub extern "system" fn Java_com_lancedb_lancedb_Connection_releaseNativeConnection(
+    mut env: JNIEnv,
+    j_connection: JObject,
+) {
+    let _: BlockingConnection = unsafe {
+        env.take_rust_field(j_connection, NATIVE_CONNECTION)
+            .expect("Failed to take native Connection handle")
+    };
+}
+
+#[no_mangle]
+pub extern "system" fn Java_com_lancedb_lancedb_Connection_connect<'local>(
+    mut env: JNIEnv<'local>,
+    _obj: JObject,
+    dataset_uri_object: JString,
+) -> JObject<'local> {
+    let dataset_uri: String = ok_or_throw!(env, env.get_string(&dataset_uri_object)).into();
+    let blocking_connection = ok_or_throw!(env, BlockingConnection::create(&dataset_uri));
+    blocking_connection.into_java(&mut env)
+}
+
+#[no_mangle]
+pub extern "system" fn Java_com_lancedb_lancedb_Connection_tableNames<'local>(
+    mut env: JNIEnv<'local>,
+    j_connection: JObject,
+    start_after_obj: JObject, // Optional<String>
+    limit_obj: JObject,       // Optional<Integer>
+) -> JObject<'local> {
+    ok_or_throw!(
+        env,
+        inner_table_names(&mut env, j_connection, start_after_obj, limit_obj)
+    )
+}
+
+fn inner_table_names<'local>(
+    env: &mut JNIEnv<'local>,
+    j_connection: JObject,
+    start_after_obj: JObject, // Optional<String>
+    limit_obj: JObject,       // Optional<Integer>
+) -> Result<JObject<'local>> {
+    let start_after = env.get_string_opt(&start_after_obj)?;
+    let limit = env.get_int_opt(&limit_obj)?;
+    let conn =
+        unsafe { env.get_rust_field::<_, _, BlockingConnection>(j_connection, NATIVE_CONNECTION) }?;
+    let table_names = conn.table_names(start_after, limit)?;
+    drop(conn);
+    let j_names = env.new_object("java/util/ArrayList", "()V", &[])?;
+    for item in table_names {
+        let jstr_item = env.new_string(item)?;
+        let item_jobj = JObject::from(jstr_item);
+        let item_gen = JValue::Object(&item_jobj);
+        env.call_method(&j_names, "add", "(Ljava/lang/Object;)Z", &[item_gen])?;
+    }
+    Ok(j_names)
+}

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

@@ -0,0 +1,225 @@
+// 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.
+
+use std::str::Utf8Error;
+
+use arrow_schema::ArrowError;
+use jni::errors::Error as JniError;
+use serde_json::Error as JsonError;
+use snafu::{Location, Snafu};
+
+type BoxedError = Box<dyn std::error::Error + Send + Sync + 'static>;
+
+/// Java Exception types
+pub enum JavaException {
+    IllegalArgumentException,
+    IOException,
+    RuntimeException,
+}
+
+impl JavaException {
+    pub fn as_str(&self) -> &str {
+        match self {
+            Self::IllegalArgumentException => "java/lang/IllegalArgumentException",
+            Self::IOException => "java/io/IOException",
+            Self::RuntimeException => "java/lang/RuntimeException",
+        }
+    }
+}
+/// TODO(lu) change to lancedb-jni
+#[derive(Debug, Snafu)]
+#[snafu(visibility(pub))]
+pub enum Error {
+    #[snafu(display("JNI error: {message}, {location}"))]
+    Jni { message: String, location: Location },
+    #[snafu(display("Invalid argument: {message}, {location}"))]
+    InvalidArgument { message: String, location: Location },
+    #[snafu(display("IO error: {source}, {location}"))]
+    IO {
+        source: BoxedError,
+        location: Location,
+    },
+    #[snafu(display("Arrow error: {message}, {location}"))]
+    Arrow { message: String, location: Location },
+    #[snafu(display("Index error: {message}, {location}"))]
+    Index { message: String, location: Location },
+    #[snafu(display("JSON error: {message}, {location}"))]
+    JSON { message: String, location: Location },
+    #[snafu(display("Dataset at path {path} was not found, {location}"))]
+    DatasetNotFound { path: String, location: Location },
+    #[snafu(display("Dataset already exists: {uri}, {location}"))]
+    DatasetAlreadyExists { uri: String, location: Location },
+    #[snafu(display("Table '{name}' already exists"))]
+    TableAlreadyExists { name: String },
+    #[snafu(display("Table '{name}' was not found"))]
+    TableNotFound { name: String },
+    #[snafu(display("Invalid table name '{name}': {reason}"))]
+    InvalidTableName { name: String, reason: String },
+    #[snafu(display("Embedding function '{name}' was not found: {reason}, {location}"))]
+    EmbeddingFunctionNotFound {
+        name: String,
+        reason: String,
+        location: Location,
+    },
+    #[snafu(display("Other Lance error: {message}, {location}"))]
+    OtherLance { message: String, location: Location },
+    #[snafu(display("Other LanceDB error: {message}, {location}"))]
+    OtherLanceDB { message: String, location: Location },
+}
+
+impl Error {
+    /// Throw as Java Exception
+    pub fn throw(&self, env: &mut jni::JNIEnv) {
+        match self {
+            Self::InvalidArgument { .. }
+            | Self::DatasetNotFound { .. }
+            | Self::DatasetAlreadyExists { .. }
+            | Self::TableAlreadyExists { .. }
+            | Self::TableNotFound { .. }
+            | Self::InvalidTableName { .. }
+            | Self::EmbeddingFunctionNotFound { .. } => {
+                self.throw_as(env, JavaException::IllegalArgumentException)
+            }
+            Self::IO { .. } | Self::Index { .. } => self.throw_as(env, JavaException::IOException),
+            Self::Arrow { .. }
+            | Self::JSON { .. }
+            | Self::OtherLance { .. }
+            | Self::OtherLanceDB { .. }
+            | Self::Jni { .. } => self.throw_as(env, JavaException::RuntimeException),
+        }
+    }
+
+    /// Throw as an concrete Java Exception
+    pub fn throw_as(&self, env: &mut jni::JNIEnv, exception: JavaException) {
+        let message = &format!(
+            "Error when throwing Java exception: {}:{}",
+            exception.as_str(),
+            self
+        );
+        env.throw_new(exception.as_str(), self.to_string())
+            .expect(message);
+    }
+}
+
+pub type Result<T> = std::result::Result<T, Error>;
+
+trait ToSnafuLocation {
+    fn to_snafu_location(&'static self) -> snafu::Location;
+}
+
+impl ToSnafuLocation for std::panic::Location<'static> {
+    fn to_snafu_location(&'static self) -> snafu::Location {
+        snafu::Location::new(self.file(), self.line(), self.column())
+    }
+}
+
+impl From<JniError> for Error {
+    #[track_caller]
+    fn from(source: JniError) -> Self {
+        Self::Jni {
+            message: source.to_string(),
+            location: std::panic::Location::caller().to_snafu_location(),
+        }
+    }
+}
+
+impl From<Utf8Error> for Error {
+    #[track_caller]
+    fn from(source: Utf8Error) -> Self {
+        Self::InvalidArgument {
+            message: source.to_string(),
+            location: std::panic::Location::caller().to_snafu_location(),
+        }
+    }
+}
+
+impl From<ArrowError> for Error {
+    #[track_caller]
+    fn from(source: ArrowError) -> Self {
+        Self::Arrow {
+            message: source.to_string(),
+            location: std::panic::Location::caller().to_snafu_location(),
+        }
+    }
+}
+
+impl From<JsonError> for Error {
+    #[track_caller]
+    fn from(source: JsonError) -> Self {
+        Self::JSON {
+            message: source.to_string(),
+            location: std::panic::Location::caller().to_snafu_location(),
+        }
+    }
+}
+
+impl From<lance::Error> for Error {
+    #[track_caller]
+    fn from(source: lance::Error) -> Self {
+        match source {
+            lance::Error::DatasetNotFound {
+                path,
+                source: _,
+                location,
+            } => Self::DatasetNotFound { path, location },
+            lance::Error::DatasetAlreadyExists { uri, location } => {
+                Self::DatasetAlreadyExists { uri, location }
+            }
+            lance::Error::IO { source, location } => Self::IO { source, location },
+            lance::Error::Arrow { message, location } => Self::Arrow { message, location },
+            lance::Error::Index { message, location } => Self::Index { message, location },
+            lance::Error::InvalidInput { source, location } => Self::InvalidArgument {
+                message: source.to_string(),
+                location,
+            },
+            _ => Self::OtherLance {
+                message: source.to_string(),
+                location: std::panic::Location::caller().to_snafu_location(),
+            },
+        }
+    }
+}
+
+impl From<lancedb::Error> for Error {
+    #[track_caller]
+    fn from(source: lancedb::Error) -> Self {
+        match source {
+            lancedb::Error::InvalidTableName { name, reason } => {
+                Self::InvalidTableName { name, reason }
+            }
+            lancedb::Error::InvalidInput { message } => Self::InvalidArgument {
+                message,
+                location: std::panic::Location::caller().to_snafu_location(),
+            },
+            lancedb::Error::TableNotFound { name } => Self::TableNotFound { name },
+            lancedb::Error::TableAlreadyExists { name } => Self::TableAlreadyExists { name },
+            lancedb::Error::EmbeddingFunctionNotFound { name, reason } => {
+                Self::EmbeddingFunctionNotFound {
+                    name,
+                    reason,
+                    location: std::panic::Location::caller().to_snafu_location(),
+                }
+            }
+            lancedb::Error::Arrow { source } => Self::Arrow {
+                message: source.to_string(),
+                location: std::panic::Location::caller().to_snafu_location(),
+            },
+            lancedb::Error::Lance { source } => Self::from(source),
+            _ => Self::OtherLanceDB {
+                message: source.to_string(),
+                location: std::panic::Location::caller().to_snafu_location(),
+            },
+        }
+    }
+}

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

@@ -0,0 +1,204 @@
+// 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.
+
+use core::slice;
+
+use jni::objects::{JByteBuffer, JObjectArray, JString};
+use jni::sys::jobjectArray;
+use jni::{objects::JObject, JNIEnv};
+
+use crate::error::{Error, Result};
+
+/// TODO(lu) import from lance-jni without duplicate
+/// Extend JNIEnv with helper functions.
+pub trait JNIEnvExt {
+    /// Get integers from Java List<Integer> object.
+    fn get_integers(&mut self, obj: &JObject) -> Result<Vec<i32>>;
+
+    /// Get strings from Java List<String> object.
+    fn get_strings(&mut self, obj: &JObject) -> Result<Vec<String>>;
+
+    /// Get strings from Java String[] object.
+    /// Note that get Option<Vec<String>> from Java Optional<String[]> just doesn't work.
+    #[allow(unused)]
+    fn get_strings_array(&mut self, obj: jobjectArray) -> Result<Vec<String>>;
+
+    /// Get Option<String> from Java Optional<String>.
+    fn get_string_opt(&mut self, obj: &JObject) -> Result<Option<String>>;
+
+    /// Get Option<Vec<String>> from Java Optional<List<String>>.
+    #[allow(unused)]
+    fn get_strings_opt(&mut self, obj: &JObject) -> Result<Option<Vec<String>>>;
+
+    /// Get Option<i32> from Java Optional<Integer>.
+    fn get_int_opt(&mut self, obj: &JObject) -> Result<Option<i32>>;
+
+    /// Get Option<Vec<i32>> from Java Optional<List<Integer>>.
+    fn get_ints_opt(&mut self, obj: &JObject) -> Result<Option<Vec<i32>>>;
+
+    /// Get Option<i64> from Java Optional<Long>.
+    #[allow(unused)]
+    fn get_long_opt(&mut self, obj: &JObject) -> Result<Option<i64>>;
+
+    /// Get Option<u64> from Java Optional<Long>.
+    #[allow(unused)]
+    fn get_u64_opt(&mut self, obj: &JObject) -> Result<Option<u64>>;
+
+    /// Get Option<&[u8]> from Java Optional<ByteBuffer>.
+    #[allow(unused)]
+    fn get_bytes_opt(&mut self, obj: &JObject) -> Result<Option<&[u8]>>;
+
+    fn get_optional<T, F>(&mut self, obj: &JObject, f: F) -> Result<Option<T>>
+    where
+        F: FnOnce(&mut JNIEnv, &JObject) -> Result<T>;
+}
+
+impl JNIEnvExt for JNIEnv<'_> {
+    fn get_integers(&mut self, obj: &JObject) -> Result<Vec<i32>> {
+        let list = self.get_list(obj)?;
+        let mut iter = list.iter(self)?;
+        let mut results = Vec::with_capacity(list.size(self)? as usize);
+        while let Some(elem) = iter.next(self)? {
+            let int_obj = self.call_method(elem, "intValue", "()I", &[])?;
+            let int_value = int_obj.i()?;
+            results.push(int_value);
+        }
+        Ok(results)
+    }
+
+    fn get_strings(&mut self, obj: &JObject) -> Result<Vec<String>> {
+        let list = self.get_list(obj)?;
+        let mut iter = list.iter(self)?;
+        let mut results = Vec::with_capacity(list.size(self)? as usize);
+        while let Some(elem) = iter.next(self)? {
+            let jstr = JString::from(elem);
+            let val = self.get_string(&jstr)?;
+            results.push(val.to_str()?.to_string())
+        }
+        Ok(results)
+    }
+
+    fn get_strings_array(&mut self, obj: jobjectArray) -> Result<Vec<String>> {
+        let jobject_array = unsafe { JObjectArray::from_raw(obj) };
+        let array_len = self.get_array_length(&jobject_array)?;
+        let mut res: Vec<String> = Vec::new();
+        for i in 0..array_len {
+            let item: JString = self.get_object_array_element(&jobject_array, i)?.into();
+            res.push(self.get_string(&item)?.into());
+        }
+        Ok(res)
+    }
+
+    fn get_string_opt(&mut self, obj: &JObject) -> Result<Option<String>> {
+        self.get_optional(obj, |env, inner_obj| {
+            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
+            let java_string_obj = java_obj_gen.l()?;
+            let jstr = JString::from(java_string_obj);
+            let val = env.get_string(&jstr)?;
+            Ok(val.to_str()?.to_string())
+        })
+    }
+
+    fn get_strings_opt(&mut self, obj: &JObject) -> Result<Option<Vec<String>>> {
+        self.get_optional(obj, |env, inner_obj| {
+            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
+            let java_list_obj = java_obj_gen.l()?;
+            env.get_strings(&java_list_obj)
+        })
+    }
+
+    fn get_int_opt(&mut self, obj: &JObject) -> Result<Option<i32>> {
+        self.get_optional(obj, |env, inner_obj| {
+            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
+            let java_int_obj = java_obj_gen.l()?;
+            let int_obj = env.call_method(java_int_obj, "intValue", "()I", &[])?;
+            let int_value = int_obj.i()?;
+            Ok(int_value)
+        })
+    }
+
+    fn get_ints_opt(&mut self, obj: &JObject) -> Result<Option<Vec<i32>>> {
+        self.get_optional(obj, |env, inner_obj| {
+            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
+            let java_list_obj = java_obj_gen.l()?;
+            env.get_integers(&java_list_obj)
+        })
+    }
+
+    fn get_long_opt(&mut self, obj: &JObject) -> Result<Option<i64>> {
+        self.get_optional(obj, |env, inner_obj| {
+            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
+            let java_long_obj = java_obj_gen.l()?;
+            let long_obj = env.call_method(java_long_obj, "longValue", "()J", &[])?;
+            let long_value = long_obj.j()?;
+            Ok(long_value)
+        })
+    }
+
+    fn get_u64_opt(&mut self, obj: &JObject) -> Result<Option<u64>> {
+        self.get_optional(obj, |env, inner_obj| {
+            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
+            let java_long_obj = java_obj_gen.l()?;
+            let long_obj = env.call_method(java_long_obj, "longValue", "()J", &[])?;
+            let long_value = long_obj.j()?;
+            Ok(long_value as u64)
+        })
+    }
+
+    fn get_bytes_opt(&mut self, obj: &JObject) -> Result<Option<&[u8]>> {
+        self.get_optional(obj, |env, inner_obj| {
+            let java_obj_gen = env.call_method(inner_obj, "get", "()Ljava/lang/Object;", &[])?;
+            let java_byte_buffer_obj = java_obj_gen.l()?;
+            let j_byte_buffer = JByteBuffer::from(java_byte_buffer_obj);
+            let raw_data = env.get_direct_buffer_address(&j_byte_buffer)?;
+            let capacity = env.get_direct_buffer_capacity(&j_byte_buffer)?;
+            let data = unsafe { slice::from_raw_parts(raw_data, capacity) };
+            Ok(data)
+        })
+    }
+
+    fn get_optional<T, F>(&mut self, obj: &JObject, f: F) -> Result<Option<T>>
+    where
+        F: FnOnce(&mut JNIEnv, &JObject) -> Result<T>,
+    {
+        if obj.is_null() {
+            return Ok(None);
+        }
+        let is_present = self.call_method(obj, "isPresent", "()Z", &[])?;
+        if !is_present.z()? {
+            // TODO(lu): put get java object into here cuz can only get java Object
+            Ok(None)
+        } else {
+            f(self, obj).map(Some)
+        }
+    }
+}
+
+#[no_mangle]
+pub extern "system" fn Java_com_lancedb_lance_test_JniTestHelper_parseInts(
+    mut env: JNIEnv,
+    _obj: JObject,
+    list_obj: JObject, // List<Integer>
+) {
+    ok_or_throw_without_return!(env, env.get_integers(&list_obj));
+}
+
+#[no_mangle]
+pub extern "system" fn Java_com_lancedb_lance_test_JniTestHelper_parseIntsOpt(
+    mut env: JNIEnv,
+    _obj: JObject,
+    list_obj: JObject, // Optional<List<Integer>>
+) {
+    ok_or_throw_without_return!(env, env.get_ints_opt(&list_obj));
+}

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

@@ -0,0 +1,68 @@
+// 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.
+
+use lazy_static::lazy_static;
+
+// TODO import from lance-jni without duplicate
+#[macro_export]
+macro_rules! ok_or_throw {
+    ($env:expr, $result:expr) => {
+        match $result {
+            Ok(value) => value,
+            Err(err) => {
+                Error::from(err).throw(&mut $env);
+                return JObject::null();
+            }
+        }
+    };
+}
+
+macro_rules! ok_or_throw_without_return {
+    ($env:expr, $result:expr) => {
+        match $result {
+            Ok(value) => value,
+            Err(err) => {
+                Error::from(err).throw(&mut $env);
+                return;
+            }
+        }
+    };
+}
+
+#[macro_export]
+macro_rules! ok_or_throw_with_return {
+    ($env:expr, $result:expr, $ret:expr) => {
+        match $result {
+            Ok(value) => value,
+            Err(err) => {
+                Error::from(err).throw(&mut $env);
+                return $ret;
+            }
+        }
+    };
+}
+
+mod connection;
+pub mod error;
+mod ffi;
+mod traits;
+
+pub use error::{Error, Result};
+
+lazy_static! {
+    static ref RT: tokio::runtime::Runtime = tokio::runtime::Builder::new_multi_thread()
+        .enable_all()
+        .build()
+        .expect("Failed to create tokio runtime");
+}

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

@@ -0,0 +1,122 @@
+// 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.
+
+use jni::objects::{JMap, JObject, JString, JValue};
+use jni::JNIEnv;
+
+use crate::Result;
+
+pub trait FromJObject<T> {
+    fn extract(&self) -> Result<T>;
+}
+
+/// Convert a Rust type into a Java Object.
+pub trait IntoJava {
+    fn into_java<'a>(self, env: &mut JNIEnv<'a>) -> JObject<'a>;
+}
+
+impl FromJObject<i32> for JObject<'_> {
+    fn extract(&self) -> Result<i32> {
+        Ok(JValue::from(self).i()?)
+    }
+}
+
+impl FromJObject<i64> for JObject<'_> {
+    fn extract(&self) -> Result<i64> {
+        Ok(JValue::from(self).j()?)
+    }
+}
+
+impl FromJObject<f32> for JObject<'_> {
+    fn extract(&self) -> Result<f32> {
+        Ok(JValue::from(self).f()?)
+    }
+}
+
+impl FromJObject<f64> for JObject<'_> {
+    fn extract(&self) -> Result<f64> {
+        Ok(JValue::from(self).d()?)
+    }
+}
+
+pub trait FromJString {
+    fn extract(&self, env: &mut JNIEnv) -> Result<String>;
+}
+
+impl FromJString for JString<'_> {
+    fn extract(&self, env: &mut JNIEnv) -> Result<String> {
+        Ok(env.get_string(self)?.into())
+    }
+}
+
+pub trait JMapExt {
+    #[allow(dead_code)]
+    fn get_string(&self, env: &mut JNIEnv, key: &str) -> Result<Option<String>>;
+
+    #[allow(dead_code)]
+    fn get_i32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i32>>;
+
+    #[allow(dead_code)]
+    fn get_i64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i64>>;
+
+    #[allow(dead_code)]
+    fn get_f32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f32>>;
+
+    #[allow(dead_code)]
+    fn get_f64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f64>>;
+}
+
+fn get_map_value<T>(env: &mut JNIEnv, map: &JMap, key: &str) -> Result<Option<T>>
+where
+    for<'a> JObject<'a>: FromJObject<T>,
+{
+    let key_obj: JObject = env.new_string(key)?.into();
+    if let Some(value) = map.get(env, &key_obj)? {
+        if value.is_null() {
+            Ok(None)
+        } else {
+            Ok(Some(value.extract()?))
+        }
+    } else {
+        Ok(None)
+    }
+}
+
+impl JMapExt for JMap<'_, '_, '_> {
+    fn get_string(&self, env: &mut JNIEnv, key: &str) -> Result<Option<String>> {
+        let key_obj: JObject = env.new_string(key)?.into();
+        if let Some(value) = self.get(env, &key_obj)? {
+            let value_str: JString = value.into();
+            Ok(Some(value_str.extract(env)?))
+        } else {
+            Ok(None)
+        }
+    }
+
+    fn get_i32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i32>> {
+        get_map_value(env, self, key)
+    }
+
+    fn get_i64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<i64>> {
+        get_map_value(env, self, key)
+    }
+
+    fn get_f32(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f32>> {
+        get_map_value(env, self, key)
+    }
+
+    fn get_f64(&self, env: &mut JNIEnv, key: &str) -> Result<Option<f64>> {
+        get_map_value(env, self, key)
+    }
+}

java/core/pom.xml

@@ -0,0 +1,94 @@
+<?xml version="1.0" encoding="UTF-8"?>
+
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <parent>
+        <groupId>com.lancedb</groupId>
+        <artifactId>lancedb-parent</artifactId>
+        <version>0.1-SNAPSHOT</version>
+        <relativePath>../pom.xml</relativePath>
+    </parent>
+
+    <artifactId>lancedb-core</artifactId>
+    <name>LanceDB Core</name>
+    <packaging>jar</packaging>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.apache.arrow</groupId>
+            <artifactId>arrow-vector</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.arrow</groupId>
+            <artifactId>arrow-memory-netty</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.arrow</groupId>
+            <artifactId>arrow-c-data</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.apache.arrow</groupId>
+            <artifactId>arrow-dataset</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.json</groupId>
+            <artifactId>json</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.questdb</groupId>
+            <artifactId>jar-jni</artifactId>
+        </dependency>
+        <dependency>
+            <groupId>org.junit.jupiter</groupId>
+            <artifactId>junit-jupiter</artifactId>
+             <scope>test</scope>
+        </dependency>
+    </dependencies>
+
+    <profiles>
+        <profile>
+            <id>build-jni</id>
+            <activation>
+                <activeByDefault>true</activeByDefault>
+            </activation>
+            <build>
+                <plugins>
+                    <plugin>
+                        <groupId>org.questdb</groupId>
+                        <artifactId>rust-maven-plugin</artifactId>
+                        <version>1.1.1</version>
+                        <executions>
+                            <execution>
+                                <id>lancedb-jni</id>
+                                <goals>
+                                    <goal>build</goal>
+                                </goals>
+                                <configuration>
+                                    <path>lancedb-jni</path>
+                                    <!--<release>true</release>-->
+                                    <!-- Copy native libraries to target/classes for runtime access -->
+                                    <copyTo>${project.build.directory}/classes/nativelib</copyTo>
+                                    <copyWithPlatformDir>true</copyWithPlatformDir>
+                                </configuration>
+                            </execution>
+                            <execution>
+                                <id>lancedb-jni-test</id>
+                                <goals>
+                                    <goal>test</goal>
+                                </goals>
+                                <configuration>
+                                    <path>lancedb-jni</path>
+                                    <release>false</release>
+                                    <verbosity>-v</verbosity>
+                                </configuration>
+                            </execution>
+                        </executions>
+                    </plugin>
+                </plugins>
+            </build>
+        </profile>
+    </profiles>
+</project>

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

@@ -0,0 +1,120 @@
+/*
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.lancedb.lancedb;
+
+import io.questdb.jar.jni.JarJniLoader;
+import java.io.Closeable;
+import java.util.List;
+import java.util.Optional;
+
+/**
+ * Represents LanceDB database.
+ */
+public class Connection implements Closeable {
+  static {
+    JarJniLoader.loadLib(Connection.class, "/nativelib", "lancedb_jni");
+  }
+
+  private long nativeConnectionHandle;
+
+  /**
+   * Connect to a LanceDB instance.
+   */
+  public static native Connection connect(String uri);
+
+  /**
+   * Get the names of all tables in the database. The names are sorted in
+   * ascending order.
+   *
+   * @return the table names
+   */
+  public List<String> tableNames() {
+    return tableNames(Optional.empty(), Optional.empty());
+  }
+
+  /**
+   * Get the names of filtered tables in the database. The names are sorted in
+   * ascending order.
+   *
+   * @param limit The number of results to return.
+   * @return the table names
+   */
+  public List<String> tableNames(int limit) {
+    return tableNames(Optional.empty(), Optional.of(limit));
+  }
+
+  /**
+   * Get the names of filtered tables in the database. The names are sorted in
+   * ascending order.
+   *
+   * @param startAfter If present, only return names that come lexicographically after the supplied
+   *                   value. This can be combined with limit to implement pagination
+   *                   by setting this to the last table name from the previous page.
+   * @return the table names
+   */
+  public List<String> tableNames(String startAfter) {
+    return tableNames(Optional.of(startAfter), Optional.empty());
+  }
+
+  /**
+   * Get the names of filtered tables in the database. The names are sorted in
+   * ascending order.
+   *
+   * @param startAfter If present, only return names that come lexicographically after the supplied
+   *                   value. This can be combined with limit to implement pagination
+   *                   by setting this to the last table name from the previous page.
+   * @param limit The number of results to return.
+   * @return the table names
+   */
+  public List<String> tableNames(String startAfter, int limit) {
+    return tableNames(Optional.of(startAfter), Optional.of(limit));
+  }
+
+  /**
+   * Get the names of filtered tables in the database. The names are sorted in
+   * ascending order.
+   *
+   * @param startAfter If present, only return names that come lexicographically after the supplied
+   *                   value. This can be combined with limit to implement pagination
+   *                   by setting this to the last table name from the previous page.
+   * @param limit The number of results to return.
+   * @return the table names
+   */
+  public native List<String> tableNames(
+      Optional<String> startAfter, Optional<Integer> limit);
+
+  /**
+   * Closes this connection and releases any system resources associated with it. If
+   * the connection is
+   * already closed, then invoking this method has no effect.
+   */
+  @Override
+  public void close() {
+    if (nativeConnectionHandle != 0) {
+      releaseNativeConnection(nativeConnectionHandle);
+      nativeConnectionHandle = 0;
+    }
+  }
+
+  /**
+   * Native method to release the Lance connection resources associated with the
+   * given handle.
+   *
+   * @param handle The native handle to the connection resource.
+   */
+  private native void releaseNativeConnection(long handle);
+
+  private Connection() {}
+}

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

@@ -0,0 +1,135 @@
+/*
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+package com.lancedb.lancedb;
+
+import static org.junit.jupiter.api.Assertions.assertEquals;
+import static org.junit.jupiter.api.Assertions.assertTrue;
+
+import java.nio.file.Path;
+import java.util.List;
+import java.net.URL;
+import org.junit.jupiter.api.BeforeAll;
+import org.junit.jupiter.api.Test;
+import org.junit.jupiter.api.io.TempDir;
+
+public class ConnectionTest {
+  private static final String[] TABLE_NAMES = {
+      "dataset_version",
+      "new_empty_dataset",
+      "test",
+      "write_stream"
+  };
+
+  @TempDir
+  static Path tempDir; // Temporary directory for the tests
+  private static URL lanceDbURL;
+
+  @BeforeAll
+  static void setUp() {
+    ClassLoader classLoader = ConnectionTest.class.getClassLoader();
+    lanceDbURL = classLoader.getResource("example_db");
+  }
+
+  @Test
+  void emptyDB() {
+    String databaseUri = tempDir.resolve("emptyDB").toString();
+    try (Connection conn = Connection.connect(databaseUri)) {
+      List<String> tableNames = conn.tableNames();
+      assertTrue(tableNames.isEmpty());
+    }
+  }
+
+  @Test
+  void tableNames() {
+    try (Connection conn = Connection.connect(lanceDbURL.toString())) {
+      List<String> tableNames = conn.tableNames();
+      assertEquals(4, tableNames.size());
+      for (int i = 0; i < TABLE_NAMES.length; i++) {
+        assertEquals(TABLE_NAMES[i], tableNames.get(i));
+      }
+    }
+  }
+
+  @Test
+  void tableNamesStartAfter() {
+    try (Connection conn = Connection.connect(lanceDbURL.toString())) {
+      assertTableNamesStartAfter(conn, TABLE_NAMES[0], 3, TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
+      assertTableNamesStartAfter(conn, TABLE_NAMES[1], 2, TABLE_NAMES[2], TABLE_NAMES[3]);
+      assertTableNamesStartAfter(conn, TABLE_NAMES[2], 1, TABLE_NAMES[3]);
+      assertTableNamesStartAfter(conn, TABLE_NAMES[3], 0);
+      assertTableNamesStartAfter(conn, "a_dataset", 4, TABLE_NAMES[0], TABLE_NAMES[1], TABLE_NAMES[2], TABLE_NAMES[3]);
+      assertTableNamesStartAfter(conn, "o_dataset", 2, TABLE_NAMES[2], TABLE_NAMES[3]);
+      assertTableNamesStartAfter(conn, "v_dataset", 1, TABLE_NAMES[3]);
+      assertTableNamesStartAfter(conn, "z_dataset", 0);
+    }
+  }
+
+  private void assertTableNamesStartAfter(Connection conn, String startAfter, int expectedSize, String... expectedNames) {
+    List<String> tableNames = conn.tableNames(startAfter);
+    assertEquals(expectedSize, tableNames.size());
+    for (int i = 0; i < expectedNames.length; i++) {
+      assertEquals(expectedNames[i], tableNames.get(i));
+    }
+  }
+
+  @Test
+  void tableNamesLimit() {
+      try (Connection conn = Connection.connect(lanceDbURL.toString())) {
+      for (int i = 0; i <= TABLE_NAMES.length; i++) {
+        List<String> tableNames = conn.tableNames(i);
+        assertEquals(i, tableNames.size());
+        for (int j = 0; j < i; j++) {
+          assertEquals(TABLE_NAMES[j], tableNames.get(j));
+        }
+      }
+    }
+  }
+
+  @Test
+  void tableNamesStartAfterLimit() {
+    try (Connection conn = Connection.connect(lanceDbURL.toString())) {
+      List<String> tableNames = conn.tableNames(TABLE_NAMES[0], 2);
+      assertEquals(2, tableNames.size());
+      assertEquals(TABLE_NAMES[1], tableNames.get(0));
+      assertEquals(TABLE_NAMES[2], tableNames.get(1));
+      tableNames = conn.tableNames(TABLE_NAMES[1], 1);
+      assertEquals(1, tableNames.size());
+      assertEquals(TABLE_NAMES[2], tableNames.get(0));
+      tableNames = conn.tableNames(TABLE_NAMES[2], 2);
+      assertEquals(1, tableNames.size());
+      assertEquals(TABLE_NAMES[3], tableNames.get(0));
+      tableNames = conn.tableNames(TABLE_NAMES[3], 2);
+      assertEquals(0, tableNames.size());
+      tableNames = conn.tableNames(TABLE_NAMES[0], 0);
+      assertEquals(0, tableNames.size());
+
+      // Limit larger than the number of remaining tables
+      tableNames = conn.tableNames(TABLE_NAMES[0], 10);
+      assertEquals(3, tableNames.size());
+      assertEquals(TABLE_NAMES[1], tableNames.get(0));
+      assertEquals(TABLE_NAMES[2], tableNames.get(1));
+      assertEquals(TABLE_NAMES[3], tableNames.get(2));
+
+      // Start after a value not in the list
+      tableNames = conn.tableNames("non_existent_table", 2);
+      assertEquals(2, tableNames.size());
+      assertEquals(TABLE_NAMES[2], tableNames.get(0));
+      assertEquals(TABLE_NAMES[3], tableNames.get(1));
+
+      // Start after the last table with a limit
+      tableNames = conn.tableNames(TABLE_NAMES[3], 1);
+      assertEquals(0, tableNames.size());
+    }
+  }
+}

java/core/src/test/resources/example_db/dataset_version.lance/_transactions/0-d51afd07-e3cd-4c76-9b9b-787e13fd55b0.txn

@@ -0,0 +1 @@
+$d51afd07-e3cd-4c76-9b9b-787e13fd55b0<62>=id <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*int320
8
name
 <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*string0
8

java/core/src/test/resources/example_db/new_empty_dataset.lance/_transactions/0-15648e72-076f-4ef1-8b90-10d305b95b3b.txn

@@ -0,0 +1 @@
+$15648e72-076f-4ef1-8b90-10d305b95b3b<33>=id <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*int320
8
name
 <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*string0
8

java/core/src/test/resources/example_db/test.lance/_transactions/0-a3689caf-4f6b-4afc-a3c7-97af75661843.txn

@@ -0,0 +1 @@
+$a3689caf-4f6b-4afc-a3c7-97af75661843<34>oitem <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*string8price
 <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*double8
0vector <20><><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD><EFBFBD>
*fixed_size_list:float:28

java/pom.xml

@@ -0,0 +1,129 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+    xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>com.lancedb</groupId>
+    <artifactId>lancedb-parent</artifactId>
+    <version>0.1-SNAPSHOT</version>
+    <packaging>pom</packaging>
+
+    <name>Lance Parent</name>
+
+    <properties>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+        <maven.compiler.source>11</maven.compiler.source>
+        <maven.compiler.target>11</maven.compiler.target>
+        <arrow.version>15.0.0</arrow.version>
+    </properties>
+
+    <modules>
+        <module>core</module>
+    </modules>
+
+    <dependencyManagement>
+        <dependencies>
+            <dependency>
+                <groupId>org.apache.arrow</groupId>
+                <artifactId>arrow-vector</artifactId>
+                <version>${arrow.version}</version>
+            </dependency>
+            <dependency>
+                <groupId>org.apache.arrow</groupId>
+                <artifactId>arrow-memory-netty</artifactId>
+                <version>${arrow.version}</version>
+            </dependency>
+            <dependency>
+                <groupId>org.apache.arrow</groupId>
+                <artifactId>arrow-c-data</artifactId>
+                <version>${arrow.version}</version>
+            </dependency>
+            <dependency>
+                <groupId>org.apache.arrow</groupId>
+                <artifactId>arrow-dataset</artifactId>
+                <version>${arrow.version}</version>
+            </dependency>
+            <dependency>
+                <groupId>org.questdb</groupId>
+                <artifactId>jar-jni</artifactId>
+                <version>1.1.1</version>
+            </dependency>
+            <dependency>
+                <groupId>org.junit.jupiter</groupId>
+                <artifactId>junit-jupiter</artifactId>
+                <version>5.10.1</version>
+            </dependency>
+            <dependency>
+                <groupId>org.json</groupId>
+                <artifactId>json</artifactId>
+                <version>20210307</version>
+            </dependency>
+        </dependencies>
+    </dependencyManagement>
+
+     <build>
+        <plugins>
+            <plugin>
+                <groupId>org.apache.maven.plugins</groupId>
+                <artifactId>maven-checkstyle-plugin</artifactId>
+                <version>3.3.1</version>
+                <configuration>
+                    <configLocation>google_checks.xml</configLocation>
+                    <consoleOutput>true</consoleOutput>
+                    <failsOnError>true</failsOnError>
+                    <violationSeverity>warning</violationSeverity>
+                    <linkXRef>false</linkXRef>
+                </configuration>
+                <executions>
+                    <execution>
+                        <id>validate</id>
+                        <phase>validate</phase>
+                        <goals>
+                            <goal>check</goal>
+                        </goals>
+                    </execution>
+                </executions>
+            </plugin>
+        </plugins>
+        <pluginManagement>
+            <plugins>
+                <plugin>
+                    <artifactId>maven-clean-plugin</artifactId>
+                    <version>3.1.0</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-resources-plugin</artifactId>
+                    <version>3.0.2</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-compiler-plugin</artifactId>
+                    <version>3.8.1</version>
+                    <configuration>
+                        <compilerArgs>
+                            <arg>-h</arg>
+                            <arg>target/headers</arg>
+                        </compilerArgs>
+                    </configuration>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-surefire-plugin</artifactId>
+                    <version>3.2.5</version>
+                    <configuration>
+                        <argLine>--add-opens=java.base/java.nio=ALL-UNNAMED</argLine>
+                        <forkNode implementation="org.apache.maven.plugin.surefire.extensions.SurefireForkNodeFactory"/>
+                        <useSystemClassLoader>false</useSystemClassLoader>
+                    </configuration>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-jar-plugin</artifactId>
+                    <version>3.0.2</version>
+                </plugin>
+                <plugin>
+                    <artifactId>maven-install-plugin</artifactId>
+                    <version>2.5.2</version>
+                </plugin>
+            </plugins>
+        </pluginManagement>
+    </build>
+</project>

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.5.0",
+  "version": "0.5.2",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.5.0",
+      "version": "0.5.2",
       "cpu": [
         "x64",
         "arm64"

node/package.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.5.0",
+  "version": "0.5.2-final.1",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
   "scripts": {
     "tsc": "tsc -b",
-    "build": "npm run tsc && cargo-cp-artifact --artifact cdylib lancedb-node index.node -- cargo build --message-format=json",
+    "build": "npm run tsc && cargo-cp-artifact --artifact cdylib lancedb_node index.node -- cargo build --message-format=json",
     "build-release": "npm run build -- --release",
     "test": "npm run tsc && mocha -recursive dist/test",
     "integration-test": "npm run tsc && mocha -recursive dist/integration_test",

node/src/index.ts

@@ -695,15 +695,26 @@ export interface MergeInsertArgs {
   whenNotMatchedBySourceDelete?: string | boolean
 }
 
+export enum IndexStatus {
+  Pending = "pending",
+  Indexing = "indexing",
+  Done = "done",
+  Failed = "failed"
+}
+
 export interface VectorIndex {
   columns: string[]
   name: string
   uuid: string
+  status: IndexStatus
 }
 
 export interface IndexStats {
   numIndexedRows: number | null
   numUnindexedRows: number | null
+  indexType: string | null
+  distanceType: string | null
+  completedAt: string | null
 }
 
 /**

node/src/remote/index.ts

@@ -509,7 +509,8 @@ export class RemoteTable<T = number[]> implements Table<T> {
     return (await results.body()).indexes?.map((index: any) => ({
       columns: index.columns,
       name: index.index_name,
-      uuid: index.index_uuid
+      uuid: index.index_uuid,
+      status: index.status
     }))
   }
 
@@ -520,7 +521,10 @@ export class RemoteTable<T = number[]> implements Table<T> {
     const body = await results.body()
     return {
       numIndexedRows: body?.num_indexed_rows,
-      numUnindexedRows: body?.num_unindexed_rows
+      numUnindexedRows: body?.num_unindexed_rows,
+      indexType: body?.index_type,
+      distanceType: body?.distance_type,
+      completedAt: body?.completed_at
     }
   }
 

nodejs/__test__/arrow.test.ts

@@ -31,6 +31,7 @@ import {
   Schema,
   Struct,
   type Table,
+  Type,
   Utf8,
   tableFromIPC,
 } from "apache-arrow";
@@ -51,7 +52,12 @@ import {
   makeArrowTable,
   makeEmptyTable,
 } from "../lancedb/arrow";
-import { type EmbeddingFunction } from "../lancedb/embedding/embedding_function";
+import {
+  EmbeddingFunction,
+  FieldOptions,
+  FunctionOptions,
+} from "../lancedb/embedding/embedding_function";
+import { EmbeddingFunctionConfig } from "../lancedb/embedding/registry";
 
 // biome-ignore lint/suspicious/noExplicitAny: skip
 function sampleRecords(): Array<Record<string, any>> {
@@ -280,23 +286,46 @@ describe("The function makeArrowTable", function () {
   });
 });
 
-class DummyEmbedding implements EmbeddingFunction<string> {
-  public readonly sourceColumn = "string";
-  public readonly embeddingDimension = 2;
-  public readonly embeddingDataType = new Float16();
+class DummyEmbedding extends EmbeddingFunction<string> {
+  toJSON(): Partial<FunctionOptions> {
+    return {};
+  }
 
-  async embed(data: string[]): Promise<number[][]> {
+  async computeSourceEmbeddings(data: string[]): Promise<number[][]> {
     return data.map(() => [0.0, 0.0]);
   }
+
+  ndims(): number {
+    return 2;
+  }
+
+  embeddingDataType() {
+    return new Float16();
+  }
 }
 
-class DummyEmbeddingWithNoDimension implements EmbeddingFunction<string> {
-  public readonly sourceColumn = "string";
+class DummyEmbeddingWithNoDimension extends EmbeddingFunction<string> {
+  toJSON(): Partial<FunctionOptions> {
+    return {};
+  }
 
-  async embed(data: string[]): Promise<number[][]> {
+  embeddingDataType(): Float {
+    return new Float16();
+  }
+
+  async computeSourceEmbeddings(data: string[]): Promise<number[][]> {
     return data.map(() => [0.0, 0.0]);
   }
 }
+const dummyEmbeddingConfig: EmbeddingFunctionConfig = {
+  sourceColumn: "string",
+  function: new DummyEmbedding(),
+};
+
+const dummyEmbeddingConfigWithNoDimension: EmbeddingFunctionConfig = {
+  sourceColumn: "string",
+  function: new DummyEmbeddingWithNoDimension(),
+};
 
 describe("convertToTable", function () {
   it("will infer data types correctly", async function () {
@@ -331,7 +360,7 @@ describe("convertToTable", function () {
 
   it("will apply embeddings", async function () {
     const records = sampleRecords();
-    const table = await convertToTable(records, new DummyEmbedding());
+    const table = await convertToTable(records, dummyEmbeddingConfig);
     expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(true);
     expect(table.getChild("vector")?.type.children[0].type.toString()).toEqual(
       new Float16().toString(),
@@ -340,7 +369,7 @@ describe("convertToTable", function () {
 
   it("will fail if missing the embedding source column", async function () {
     await expect(
-      convertToTable([{ id: 1 }], new DummyEmbedding()),
+      convertToTable([{ id: 1 }], dummyEmbeddingConfig),
     ).rejects.toThrow("'string' was not present");
   });
 
@@ -351,7 +380,7 @@ describe("convertToTable", function () {
     const table = makeEmptyTable(schema);
 
     // If the embedding specifies the dimension we are fine
-    await fromTableToBuffer(table, new DummyEmbedding());
+    await fromTableToBuffer(table, dummyEmbeddingConfig);
 
     // We can also supply a schema and should be ok
     const schemaWithEmbedding = new Schema([
@@ -364,13 +393,13 @@ describe("convertToTable", function () {
     ]);
     await fromTableToBuffer(
       table,
-      new DummyEmbeddingWithNoDimension(),
+      dummyEmbeddingConfigWithNoDimension,
       schemaWithEmbedding,
     );
 
     // Otherwise we will get an error
     await expect(
-      fromTableToBuffer(table, new DummyEmbeddingWithNoDimension()),
+      fromTableToBuffer(table, dummyEmbeddingConfigWithNoDimension),
     ).rejects.toThrow("does not specify `embeddingDimension`");
   });
 
@@ -383,7 +412,7 @@ describe("convertToTable", function () {
         false,
       ),
     ]);
-    const table = await convertToTable([], new DummyEmbedding(), { schema });
+    const table = await convertToTable([], dummyEmbeddingConfig, { schema });
     expect(DataType.isFixedSizeList(table.getChild("vector")?.type)).toBe(true);
     expect(table.getChild("vector")?.type.children[0].type.toString()).toEqual(
       new Float16().toString(),
@@ -393,16 +422,17 @@ describe("convertToTable", function () {
   it("will complain if embeddings present but schema missing embedding column", async function () {
     const schema = new Schema([new Field("string", new Utf8(), false)]);
     await expect(
-      convertToTable([], new DummyEmbedding(), { schema }),
+      convertToTable([], dummyEmbeddingConfig, { schema }),
     ).rejects.toThrow("column vector was missing");
   });
 
   it("will provide a nice error if run twice", async function () {
     const records = sampleRecords();
-    const table = await convertToTable(records, new DummyEmbedding());
+    const table = await convertToTable(records, dummyEmbeddingConfig);
+
     // fromTableToBuffer will try and apply the embeddings again
     await expect(
-      fromTableToBuffer(table, new DummyEmbedding()),
+      fromTableToBuffer(table, dummyEmbeddingConfig),
     ).rejects.toThrow("already existed");
   });
 });

nodejs/__test__/connection.test.ts

@@ -12,9 +12,9 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+import { Field, Float64, Schema } from "apache-arrow";
 import * as tmp from "tmp";
-
-import { Connection, connect } from "../lancedb";
+import { Connection, Table, connect } from "../lancedb";
 
 describe("when connecting", () => {
   let tmpDir: tmp.DirResult;
@@ -57,6 +57,18 @@ describe("given a connection", () => {
     expect(db.isOpen()).toBe(false);
     await expect(db.tableNames()).rejects.toThrow("Connection is closed");
   });
+  it("should be able to create a table from an object arg `createTable(options)`, or args `createTable(name, data, options)`", async () => {
+    let tbl = await db.createTable("test", [{ id: 1 }, { id: 2 }]);
+    await expect(tbl.countRows()).resolves.toBe(2);
+
+    tbl = await db.createTable({
+      name: "test",
+      data: [{ id: 3 }],
+      mode: "overwrite",
+    });
+
+    await expect(tbl.countRows()).resolves.toBe(1);
+  });
 
   it("should fail if creating table twice, unless overwrite is true", async () => {
     let tbl = await db.createTable("test", [{ id: 1 }, { id: 2 }]);
@@ -87,4 +99,39 @@ describe("given a connection", () => {
     tables = await db.tableNames({ startAfter: "a" });
     expect(tables).toEqual(["b", "c"]);
   });
+
+  it("should create tables in v2 mode", async () => {
+    const db = await connect(tmpDir.name);
+    const data = [...Array(10000).keys()].map((i) => ({ id: i }));
+
+    // Create in v1 mode
+    let table = await db.createTable("test", data);
+
+    const isV2 = async (table: Table) => {
+      const data = await table.query().toArrow({ maxBatchLength: 100000 });
+      console.log(data.batches.length);
+      return data.batches.length < 5;
+    };
+
+    await expect(isV2(table)).resolves.toBe(false);
+
+    // Create in v2 mode
+    table = await db.createTable("test_v2", data, { useLegacyFormat: false });
+
+    await expect(isV2(table)).resolves.toBe(true);
+
+    await table.add(data);
+
+    await expect(isV2(table)).resolves.toBe(true);
+
+    // Create empty in v2 mode
+    const schema = new Schema([new Field("id", new Float64(), true)]);
+
+    table = await db.createEmptyTable("test_v2_empty", schema, {
+      useLegacyFormat: false,
+    });
+
+    await table.add(data);
+    await expect(isV2(table)).resolves.toBe(true);
+  });
 });

nodejs/__test__/embedding.test.ts

@@ -0,0 +1,314 @@
+// 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 tmp from "tmp";
+
+import { connect } from "../lancedb";
+import {
+  Field,
+  FixedSizeList,
+  Float,
+  Float16,
+  Float32,
+  Float64,
+  Schema,
+  Utf8,
+} from "../lancedb/arrow";
+import { EmbeddingFunction, LanceSchema } from "../lancedb/embedding";
+import { getRegistry, register } from "../lancedb/embedding/registry";
+
+describe("embedding functions", () => {
+  let tmpDir: tmp.DirResult;
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+  afterEach(() => {
+    tmpDir.removeCallback();
+    getRegistry().reset();
+  });
+
+  it("should be able to create a table with an embedding function", async () => {
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {};
+      }
+      ndims() {
+        return 3;
+      }
+      embeddingDataType(): Float {
+        return new Float32();
+      }
+      async computeQueryEmbeddings(_data: string) {
+        return [1, 2, 3];
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return Array.from({ length: data.length }).fill([
+          1, 2, 3,
+        ]) as number[][];
+      }
+    }
+    const func = new MockEmbeddingFunction();
+    const db = await connect(tmpDir.name);
+    const table = await db.createTable(
+      "test",
+      [
+        { id: 1, text: "hello" },
+        { id: 2, text: "world" },
+      ],
+      {
+        embeddingFunction: {
+          function: func,
+          sourceColumn: "text",
+        },
+      },
+    );
+    // biome-ignore lint/suspicious/noExplicitAny: test
+    const arr = (await table.query().toArray()) as any;
+    expect(arr[0].vector).toBeDefined();
+
+    // we round trip through JSON to make sure the vector properly gets converted to an array
+    // otherwise it'll be a TypedArray or Vector
+    const vector0 = JSON.parse(JSON.stringify(arr[0].vector));
+    expect(vector0).toEqual([1, 2, 3]);
+  });
+
+  it("should be able to create an empty table with an embedding function", async () => {
+    @register()
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {};
+      }
+      ndims() {
+        return 3;
+      }
+      embeddingDataType(): Float {
+        return new Float32();
+      }
+      async computeQueryEmbeddings(_data: string) {
+        return [1, 2, 3];
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return Array.from({ length: data.length }).fill([
+          1, 2, 3,
+        ]) as number[][];
+      }
+    }
+    const schema = new Schema([
+      new Field("text", new Utf8(), true),
+      new Field(
+        "vector",
+        new FixedSizeList(3, new Field("item", new Float32(), true)),
+        true,
+      ),
+    ]);
+
+    const func = new MockEmbeddingFunction();
+    const db = await connect(tmpDir.name);
+    const table = await db.createEmptyTable("test", schema, {
+      embeddingFunction: {
+        function: func,
+        sourceColumn: "text",
+      },
+    });
+    const outSchema = await table.schema();
+    expect(outSchema.metadata.get("embedding_functions")).toBeDefined();
+    await table.add([{ text: "hello world" }]);
+
+    // biome-ignore lint/suspicious/noExplicitAny: test
+    const arr = (await table.query().toArray()) as any;
+    expect(arr[0].vector).toBeDefined();
+
+    // we round trip through JSON to make sure the vector properly gets converted to an array
+    // otherwise it'll be a TypedArray or Vector
+    const vector0 = JSON.parse(JSON.stringify(arr[0].vector));
+    expect(vector0).toEqual([1, 2, 3]);
+  });
+  it("should error when appending to a table with an unregistered embedding function", async () => {
+    @register("mock")
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {};
+      }
+      ndims() {
+        return 3;
+      }
+      embeddingDataType(): Float {
+        return new Float32();
+      }
+      async computeQueryEmbeddings(_data: string) {
+        return [1, 2, 3];
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return Array.from({ length: data.length }).fill([
+          1, 2, 3,
+        ]) as number[][];
+      }
+    }
+    const func = getRegistry().get<MockEmbeddingFunction>("mock")!.create();
+
+    const schema = LanceSchema({
+      id: new Float64(),
+      text: func.sourceField(new Utf8()),
+      vector: func.vectorField(),
+    });
+
+    const db = await connect(tmpDir.name);
+    await db.createTable(
+      "test",
+      [
+        { id: 1, text: "hello" },
+        { id: 2, text: "world" },
+      ],
+      {
+        schema,
+      },
+    );
+
+    getRegistry().reset();
+    const db2 = await connect(tmpDir.name);
+
+    const tbl = await db2.openTable("test");
+
+    expect(tbl.add([{ id: 3, text: "hello" }])).rejects.toThrow(
+      `Function "mock" not found in registry`,
+    );
+  });
+  test.each([new Float16(), new Float32(), new Float64()])(
+    "should be able to provide manual embeddings with multiple float datatype",
+    async (floatType) => {
+      class MockEmbeddingFunction extends EmbeddingFunction<string> {
+        toJSON(): object {
+          return {};
+        }
+        ndims() {
+          return 3;
+        }
+        embeddingDataType(): Float {
+          return floatType;
+        }
+        async computeQueryEmbeddings(_data: string) {
+          return [1, 2, 3];
+        }
+        async computeSourceEmbeddings(data: string[]) {
+          return Array.from({ length: data.length }).fill([
+            1, 2, 3,
+          ]) as number[][];
+        }
+      }
+      const data = [{ text: "hello" }, { text: "hello world" }];
+
+      const schema = new Schema([
+        new Field("vector", new FixedSizeList(3, new Field("item", floatType))),
+        new Field("text", new Utf8()),
+      ]);
+      const func = new MockEmbeddingFunction();
+
+      const name = "test";
+      const db = await connect(tmpDir.name);
+
+      const table = await db.createTable(name, data, {
+        schema,
+        embeddingFunction: {
+          sourceColumn: "text",
+          function: func,
+        },
+      });
+      const res = await table.query().toArray();
+
+      expect([...res[0].vector]).toEqual([1, 2, 3]);
+    },
+  );
+
+  test.each([new Float16(), new Float32(), new Float64()])(
+    "should be able to provide auto embeddings with multiple float datatypes",
+    async (floatType) => {
+      @register("test1")
+      class MockEmbeddingFunctionWithoutNDims extends EmbeddingFunction<string> {
+        toJSON(): object {
+          return {};
+        }
+
+        embeddingDataType(): Float {
+          return floatType;
+        }
+        async computeQueryEmbeddings(_data: string) {
+          return [1, 2, 3];
+        }
+        async computeSourceEmbeddings(data: string[]) {
+          return Array.from({ length: data.length }).fill([
+            1, 2, 3,
+          ]) as number[][];
+        }
+      }
+      @register("test")
+      class MockEmbeddingFunction extends EmbeddingFunction<string> {
+        toJSON(): object {
+          return {};
+        }
+        ndims() {
+          return 3;
+        }
+        embeddingDataType(): Float {
+          return floatType;
+        }
+        async computeQueryEmbeddings(_data: string) {
+          return [1, 2, 3];
+        }
+        async computeSourceEmbeddings(data: string[]) {
+          return Array.from({ length: data.length }).fill([
+            1, 2, 3,
+          ]) as number[][];
+        }
+      }
+      const func = getRegistry().get<MockEmbeddingFunction>("test")!.create();
+      const func2 = getRegistry()
+        .get<MockEmbeddingFunctionWithoutNDims>("test1")!
+        .create();
+
+      const schema = LanceSchema({
+        text: func.sourceField(new Utf8()),
+        vector: func.vectorField(floatType),
+      });
+
+      const schema2 = LanceSchema({
+        text: func2.sourceField(new Utf8()),
+        vector: func2.vectorField({ datatype: floatType, dims: 3 }),
+      });
+      const schema3 = LanceSchema({
+        text: func2.sourceField(new Utf8()),
+        vector: func.vectorField({
+          datatype: new FixedSizeList(3, new Field("item", floatType, true)),
+          dims: 3,
+        }),
+      });
+
+      const expectedSchema = new Schema([
+        new Field("text", new Utf8(), true),
+        new Field(
+          "vector",
+          new FixedSizeList(3, new Field("item", floatType, true)),
+          true,
+        ),
+      ]);
+      const stringSchema = JSON.stringify(schema, null, 2);
+      const stringSchema2 = JSON.stringify(schema2, null, 2);
+      const stringSchema3 = JSON.stringify(schema3, null, 2);
+      const stringExpectedSchema = JSON.stringify(expectedSchema, null, 2);
+
+      expect(stringSchema).toEqual(stringExpectedSchema);
+      expect(stringSchema2).toEqual(stringExpectedSchema);
+      expect(stringSchema3).toEqual(stringExpectedSchema);
+    },
+  );
+});

nodejs/__test__/registry.test.ts

@@ -0,0 +1,169 @@
+// 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 arrow from "apache-arrow";
+import * as arrowOld from "apache-arrow-old";
+
+import * as tmp from "tmp";
+
+import { connect } from "../lancedb";
+import { EmbeddingFunction, LanceSchema } from "../lancedb/embedding";
+import { getRegistry, register } from "../lancedb/embedding/registry";
+
+describe.each([arrow, arrowOld])("LanceSchema", (arrow) => {
+  test("should preserve input order", async () => {
+    const schema = LanceSchema({
+      id: new arrow.Int32(),
+      text: new arrow.Utf8(),
+      vector: new arrow.Float32(),
+    });
+    expect(schema.fields.map((x) => x.name)).toEqual(["id", "text", "vector"]);
+  });
+});
+
+describe("Registry", () => {
+  let tmpDir: tmp.DirResult;
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+
+  afterEach(() => {
+    tmpDir.removeCallback();
+    getRegistry().reset();
+  });
+
+  it("should register a new item to the registry", async () => {
+    @register("mock-embedding")
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {
+          someText: "hello",
+        };
+      }
+      constructor() {
+        super();
+      }
+      ndims() {
+        return 3;
+      }
+      embeddingDataType(): arrow.Float {
+        return new arrow.Float32();
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return data.map(() => [1, 2, 3]);
+      }
+    }
+    const func = getRegistry()
+      .get<MockEmbeddingFunction>("mock-embedding")!
+      .create();
+
+    const schema = LanceSchema({
+      id: new arrow.Int32(),
+      text: func.sourceField(new arrow.Utf8()),
+      vector: func.vectorField(),
+    });
+
+    const db = await connect(tmpDir.name);
+    const table = await db.createTable(
+      "test",
+      [
+        { id: 1, text: "hello" },
+        { id: 2, text: "world" },
+      ],
+      { schema },
+    );
+    const expected = [
+      [1, 2, 3],
+      [1, 2, 3],
+    ];
+    const actual = await table.query().toArrow();
+    const vectors = actual
+      .getChild("vector")
+      ?.toArray()
+      .map((x: unknown) => {
+        if (x instanceof arrow.Vector) {
+          return [...x];
+        } else {
+          return x;
+        }
+      });
+    expect(vectors).toEqual(expected);
+  });
+  test("should error if registering with the same name", async () => {
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {
+          someText: "hello",
+        };
+      }
+      constructor() {
+        super();
+      }
+      ndims() {
+        return 3;
+      }
+      embeddingDataType(): arrow.Float {
+        return new arrow.Float32();
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return data.map(() => [1, 2, 3]);
+      }
+    }
+    register("mock-embedding")(MockEmbeddingFunction);
+    expect(() => register("mock-embedding")(MockEmbeddingFunction)).toThrow(
+      'Embedding function with alias "mock-embedding" already exists',
+    );
+  });
+  test("schema should contain correct metadata", async () => {
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {
+          someText: "hello",
+        };
+      }
+      constructor() {
+        super();
+      }
+      ndims() {
+        return 3;
+      }
+      embeddingDataType(): arrow.Float {
+        return new arrow.Float32();
+      }
+      async computeSourceEmbeddings(data: string[]) {
+        return data.map(() => [1, 2, 3]);
+      }
+    }
+    const func = new MockEmbeddingFunction();
+
+    const schema = LanceSchema({
+      id: new arrow.Int32(),
+      text: func.sourceField(new arrow.Utf8()),
+      vector: func.vectorField(),
+    });
+    const expectedMetadata = new Map<string, string>([
+      [
+        "embedding_functions",
+        JSON.stringify([
+          {
+            sourceColumn: "text",
+            vectorColumn: "vector",
+            name: "MockEmbeddingFunction",
+            model: { someText: "hello" },
+          },
+        ]),
+      ],
+    ]);
+    expect(schema.metadata).toEqual(expectedMetadata);
+  });
+});

nodejs/__test__/table.test.ts

@@ -16,7 +16,12 @@ import * as fs from "fs";
 import * as path from "path";
 import * as tmp from "tmp";
 
+import * as arrow from "apache-arrow";
+import * as arrowOld from "apache-arrow-old";
+
+import { Table, connect } from "../lancedb";
 import {
+  Table as ArrowTable,
   Field,
   FixedSizeList,
   Float32,
@@ -24,15 +29,20 @@ import {
   Int32,
   Int64,
   Schema,
-} from "apache-arrow";
-import { Table, connect } from "../lancedb";
-import { makeArrowTable } from "../lancedb/arrow";
+  makeArrowTable,
+} from "../lancedb/arrow";
+import { EmbeddingFunction, LanceSchema, register } from "../lancedb/embedding";
 import { Index } from "../lancedb/indices";
 
-describe("Given a table", () => {
+// biome-ignore lint/suspicious/noExplicitAny: <explanation>
+describe.each([arrow, arrowOld])("Given a table", (arrow: any) => {
   let tmpDir: tmp.DirResult;
   let table: Table;
-  const schema = new Schema([new Field("id", new Float64(), true)]);
+
+  const schema = new arrow.Schema([
+    new arrow.Field("id", new arrow.Float64(), true),
+  ]);
+
   beforeEach(async () => {
     tmpDir = tmp.dirSync({ unsafeCleanup: true });
     const conn = await connect(tmpDir.name);
@@ -83,6 +93,177 @@ describe("Given a table", () => {
     expect(await table.countRows("id == 7")).toBe(1);
     expect(await table.countRows("id == 10")).toBe(1);
   });
+
+  // https://github.com/lancedb/lancedb/issues/1293
+  test.each([new arrow.Float16(), new arrow.Float32(), new arrow.Float64()])(
+    "can create empty table with non default float type: %s",
+    async (floatType) => {
+      const db = await connect(tmpDir.name);
+
+      const data = [
+        { text: "hello", vector: Array(512).fill(1.0) },
+        { text: "hello world", vector: Array(512).fill(1.0) },
+      ];
+      const f64Schema = new arrow.Schema([
+        new arrow.Field("text", new arrow.Utf8(), true),
+        new arrow.Field(
+          "vector",
+          new arrow.FixedSizeList(512, new arrow.Field("item", floatType)),
+          true,
+        ),
+      ]);
+
+      const f64Table = await db.createEmptyTable("f64", f64Schema, {
+        mode: "overwrite",
+      });
+      try {
+        await f64Table.add(data);
+        const res = await f64Table.query().toArray();
+        expect(res.length).toBe(2);
+      } catch (e) {
+        expect(e).toBeUndefined();
+      }
+    },
+  );
+
+  it("should return the table as an instance of an arrow table", async () => {
+    const arrowTbl = await table.toArrow();
+    expect(arrowTbl).toBeInstanceOf(ArrowTable);
+  });
+});
+
+describe("merge insert", () => {
+  let tmpDir: tmp.DirResult;
+  let table: Table;
+
+  beforeEach(async () => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+    const conn = await connect(tmpDir.name);
+
+    table = await conn.createTable("some_table", [
+      { a: 1, b: "a" },
+      { a: 2, b: "b" },
+      { a: 3, b: "c" },
+    ]);
+  });
+  afterEach(() => tmpDir.removeCallback());
+
+  test("upsert", async () => {
+    const newData = [
+      { a: 2, b: "x" },
+      { a: 3, b: "y" },
+      { a: 4, b: "z" },
+    ];
+    await table
+      .mergeInsert("a")
+      .whenMatchedUpdateAll()
+      .whenNotMatchedInsertAll()
+      .execute(newData);
+    const expected = [
+      { a: 1, b: "a" },
+      { a: 2, b: "x" },
+      { a: 3, b: "y" },
+      { a: 4, b: "z" },
+    ];
+
+    expect(
+      JSON.parse(JSON.stringify((await table.toArrow()).toArray())),
+    ).toEqual(expected);
+  });
+  test("conditional update", async () => {
+    const newData = [
+      { a: 2, b: "x" },
+      { a: 3, b: "y" },
+      { a: 4, b: "z" },
+    ];
+    await table
+      .mergeInsert("a")
+      .whenMatchedUpdateAll({ where: "target.b = 'b'" })
+      .execute(newData);
+
+    const expected = [
+      { a: 1, b: "a" },
+      { a: 2, b: "x" },
+      { a: 3, b: "c" },
+    ];
+    // round trip to arrow and back to json to avoid comparing arrow objects to js object
+    // biome-ignore lint/suspicious/noExplicitAny: test
+    let res: any[] = JSON.parse(
+      JSON.stringify((await table.toArrow()).toArray()),
+    );
+    res = res.sort((a, b) => a.a - b.a);
+
+    expect(res).toEqual(expected);
+  });
+
+  test("insert if not exists", async () => {
+    const newData = [
+      { a: 2, b: "x" },
+      { a: 3, b: "y" },
+      { a: 4, b: "z" },
+    ];
+    await table.mergeInsert("a").whenNotMatchedInsertAll().execute(newData);
+    const expected = [
+      { a: 1, b: "a" },
+      { a: 2, b: "b" },
+      { a: 3, b: "c" },
+      { a: 4, b: "z" },
+    ];
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+    let res: any[] = JSON.parse(
+      JSON.stringify((await table.toArrow()).toArray()),
+    );
+    res = res.sort((a, b) => a.a - b.a);
+    expect(res).toEqual(expected);
+  });
+  test("replace range", async () => {
+    const newData = [
+      { a: 2, b: "x" },
+      { a: 4, b: "z" },
+    ];
+    await table
+      .mergeInsert("a")
+      .whenMatchedUpdateAll()
+      .whenNotMatchedInsertAll()
+      .whenNotMatchedBySourceDelete({ where: "a > 2" })
+      .execute(newData);
+
+    const expected = [
+      { a: 1, b: "a" },
+      { a: 2, b: "x" },
+      { a: 4, b: "z" },
+    ];
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+    let res: any[] = JSON.parse(
+      JSON.stringify((await table.toArrow()).toArray()),
+    );
+    res = res.sort((a, b) => a.a - b.a);
+    expect(res).toEqual(expected);
+  });
+  test("replace range no condition", async () => {
+    const newData = [
+      { a: 2, b: "x" },
+      { a: 4, b: "z" },
+    ];
+    await table
+      .mergeInsert("a")
+      .whenMatchedUpdateAll()
+      .whenNotMatchedInsertAll()
+      .whenNotMatchedBySourceDelete()
+      .execute(newData);
+
+    const expected = [
+      { a: 2, b: "x" },
+      { a: 4, b: "z" },
+    ];
+
+    // biome-ignore lint/suspicious/noExplicitAny: test
+    let res: any[] = JSON.parse(
+      JSON.stringify((await table.toArrow()).toArray()),
+    );
+    res = res.sort((a, b) => a.a - b.a);
+    expect(res).toEqual(expected);
+  });
 });
 
 describe("When creating an index", () => {
@@ -124,6 +305,7 @@ describe("When creating an index", () => {
     const indices = await tbl.listIndices();
     expect(indices.length).toBe(1);
     expect(indices[0]).toEqual({
+      name: "vec_idx",
       indexType: "IvfPq",
       columns: ["vec"],
     });
@@ -180,6 +362,24 @@ describe("When creating an index", () => {
     for await (const r of tbl.query().where("id > 1").select(["id"])) {
       expect(r.numRows).toBe(298);
     }
+    // should also work with 'filter' alias
+    for await (const r of tbl.query().filter("id > 1").select(["id"])) {
+      expect(r.numRows).toBe(298);
+    }
+  });
+
+  test("should be able to get index stats", async () => {
+    await tbl.createIndex("id");
+
+    const stats = await tbl.indexStats("id_idx");
+    expect(stats).toBeDefined();
+    expect(stats?.numIndexedRows).toEqual(300);
+    expect(stats?.numUnindexedRows).toEqual(0);
+  });
+
+  test("when getting stats on non-existent index", async () => {
+    const stats = await tbl.indexStats("some non-existent index");
+    expect(stats).toBeUndefined();
   });
 
   // TODO: Move this test to the query API test (making sure we can reject queries
@@ -447,3 +647,99 @@ describe("when optimizing a dataset", () => {
     expect(stats.prune.oldVersionsRemoved).toBe(3);
   });
 });
+
+describe("table.search", () => {
+  let tmpDir: tmp.DirResult;
+  beforeEach(() => {
+    tmpDir = tmp.dirSync({ unsafeCleanup: true });
+  });
+  afterEach(() => tmpDir.removeCallback());
+
+  test("can search using a string", async () => {
+    @register()
+    class MockEmbeddingFunction extends EmbeddingFunction<string> {
+      toJSON(): object {
+        return {};
+      }
+      ndims() {
+        return 1;
+      }
+      embeddingDataType(): arrow.Float {
+        return new Float32();
+      }
+
+      // Hardcoded embeddings for the sake of testing
+      async computeQueryEmbeddings(_data: string) {
+        switch (_data) {
+          case "greetings":
+            return [0.1];
+          case "farewell":
+            return [0.2];
+          default:
+            return null as never;
+        }
+      }
+
+      // Hardcoded embeddings for the sake of testing
+      async computeSourceEmbeddings(data: string[]) {
+        return data.map((s) => {
+          switch (s) {
+            case "hello world":
+              return [0.1];
+            case "goodbye world":
+              return [0.2];
+            default:
+              return null as never;
+          }
+        });
+      }
+    }
+
+    const func = new MockEmbeddingFunction();
+    const schema = LanceSchema({
+      text: func.sourceField(new arrow.Utf8()),
+      vector: func.vectorField(),
+    });
+    const db = await connect(tmpDir.name);
+    const data = [{ text: "hello world" }, { text: "goodbye world" }];
+    const table = await db.createTable("test", data, { schema });
+
+    const results = await table.search("greetings").then((r) => r.toArray());
+    expect(results[0].text).toBe(data[0].text);
+
+    const results2 = await table.search("farewell").then((r) => r.toArray());
+    expect(results2[0].text).toBe(data[1].text);
+  });
+
+  test("rejects 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);
+
+    expect(table.search("hello")).rejects.toThrow(
+      "No embedding functions are defined in the table",
+    );
+  });
+
+  test.each([
+    [0.4, 0.5, 0.599], // number[]
+    Float32Array.of(0.4, 0.5, 0.599), // Float32Array
+    Float64Array.of(0.4, 0.5, 0.599), // Float64Array
+  ])("can search using vectorlike datatypes", async (vectorlike) => {
+    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);
+
+    // biome-ignore lint/suspicious/noExplicitAny: test
+    const results: any[] = await table.search(vectorlike).toArray();
+
+    expect(results.length).toBe(2);
+    expect(results[0].text).toBe(data[1].text);
+  });
+});

nodejs/biome.json

@@ -48,7 +48,7 @@
         "noUnsafeFinally": "error",
         "noUnsafeOptionalChaining": "error",
         "noUnusedLabels": "error",
-        "noUnusedVariables": "error",
+        "noUnusedVariables": "warn",
         "useIsNan": "error",
         "useValidForDirection": "error",
         "useYield": "error"
@@ -77,7 +77,7 @@
         "noDuplicateObjectKeys": "error",
         "noDuplicateParameters": "error",
         "noEmptyBlockStatements": "error",
-        "noExplicitAny": "error",
+        "noExplicitAny": "warn",
         "noExtraNonNullAssertion": "error",
         "noFallthroughSwitchClause": "error",
         "noFunctionAssign": "error",
@@ -101,7 +101,13 @@
   },
   "overrides": [
     {
-      "include": ["**/*.ts", "**/*.tsx", "**/*.mts", "**/*.cts"],
+      "include": [
+        "**/*.ts",
+        "**/*.tsx",
+        "**/*.mts",
+        "**/*.cts",
+        "__test__/*.test.ts"
+      ],
       "linter": {
         "rules": {
           "correctness": {

nodejs/lancedb/arrow.ts

@@ -17,24 +17,122 @@ import {
   Binary,
   DataType,
   Field,
+  FixedSizeBinary,
   FixedSizeList,
-  type Float,
+  Float,
   Float32,
+  Int,
+  LargeBinary,
   List,
+  Null,
   RecordBatch,
   RecordBatchFileWriter,
   RecordBatchStreamWriter,
   Schema,
   Struct,
   Utf8,
-  type Vector,
+  Vector,
   makeBuilder,
   makeData,
   type makeTable,
   vectorFromArray,
 } from "apache-arrow";
 import { type EmbeddingFunction } from "./embedding/embedding_function";
-import { sanitizeSchema } from "./sanitize";
+import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
+import { sanitizeField, sanitizeSchema, sanitizeType } from "./sanitize";
+export * from "apache-arrow";
+
+export type IntoVector = Float32Array | Float64Array | number[];
+
+export function isArrowTable(value: object): value is ArrowTable {
+  if (value instanceof ArrowTable) return true;
+  return "schema" in value && "batches" in value;
+}
+
+export function isDataType(value: unknown): value is DataType {
+  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);
+}
+export function isInt(value: unknown): value is Int {
+  return value instanceof Int || DataType.isInt(value);
+}
+export function isFloat(value: unknown): value is Float {
+  return value instanceof Float || DataType.isFloat(value);
+}
+export function isBinary(value: unknown): value is Binary {
+  return value instanceof Binary || DataType.isBinary(value);
+}
+export function isLargeBinary(value: unknown): value is LargeBinary {
+  return value instanceof LargeBinary || DataType.isLargeBinary(value);
+}
+export function isUtf8(value: unknown): value is Utf8 {
+  return value instanceof Utf8 || DataType.isUtf8(value);
+}
+export function isLargeUtf8(value: unknown): value is Utf8 {
+  return value instanceof Utf8 || DataType.isLargeUtf8(value);
+}
+export function isBool(value: unknown): value is Utf8 {
+  return value instanceof Utf8 || DataType.isBool(value);
+}
+export function isDecimal(value: unknown): value is Utf8 {
+  return value instanceof Utf8 || DataType.isDecimal(value);
+}
+export function isDate(value: unknown): value is Utf8 {
+  return value instanceof Utf8 || DataType.isDate(value);
+}
+export function isTime(value: unknown): value is Utf8 {
+  return value instanceof Utf8 || DataType.isTime(value);
+}
+export function isTimestamp(value: unknown): value is Utf8 {
+  return value instanceof Utf8 || DataType.isTimestamp(value);
+}
+export function isInterval(value: unknown): value is Utf8 {
+  return value instanceof Utf8 || DataType.isInterval(value);
+}
+export function isDuration(value: unknown): value is Utf8 {
+  return value instanceof Utf8 || DataType.isDuration(value);
+}
+export function isList(value: unknown): value is List {
+  return value instanceof List || DataType.isList(value);
+}
+export function isStruct(value: unknown): value is Struct {
+  return value instanceof Struct || DataType.isStruct(value);
+}
+export function isUnion(value: unknown): value is Struct {
+  return value instanceof Struct || DataType.isUnion(value);
+}
+export function isFixedSizeBinary(value: unknown): value is FixedSizeBinary {
+  return value instanceof FixedSizeBinary || DataType.isFixedSizeBinary(value);
+}
+
+export function isFixedSizeList(value: unknown): value is FixedSizeList {
+  return value instanceof FixedSizeList || DataType.isFixedSizeList(value);
+}
 
 /** Data type accepted by NodeJS SDK */
 export type Data = Record<string, unknown>[] | ArrowTable;
@@ -86,6 +184,7 @@ export class MakeArrowTableOptions {
     vector: new VectorColumnOptions(),
   };
   embeddings?: EmbeddingFunction<unknown>;
+  embeddingFunction?: EmbeddingFunctionConfig;
 
   /**
    * If true then string columns will be encoded with dictionary encoding
@@ -198,6 +297,7 @@ export class MakeArrowTableOptions {
 export function makeArrowTable(
   data: Array<Record<string, unknown>>,
   options?: Partial<MakeArrowTableOptions>,
+  metadata?: Map<string, string>,
 ): ArrowTable {
   if (
     data.length === 0 &&
@@ -209,7 +309,11 @@ export function makeArrowTable(
   const opt = new MakeArrowTableOptions(options !== undefined ? options : {});
   if (opt.schema !== undefined && opt.schema !== null) {
     opt.schema = sanitizeSchema(opt.schema);
-    opt.schema = validateSchemaEmbeddings(opt.schema, data, opt.embeddings);
+    opt.schema = validateSchemaEmbeddings(
+      opt.schema,
+      data,
+      options?.embeddingFunction,
+    );
   }
   const columns: Record<string, Vector> = {};
   // TODO: sample dataset to find missing columns
@@ -290,20 +394,41 @@ export function makeArrowTable(
     // `new ArrowTable(schema, batches)` which does not do any schema inference
     const firstTable = new ArrowTable(columns);
     const batchesFixed = firstTable.batches.map(
-      // eslint-disable-next-line @typescript-eslint/no-non-null-assertion
       (batch) => new RecordBatch(opt.schema!, batch.data),
     );
-    return new ArrowTable(opt.schema, batchesFixed);
-  } else {
-    return new ArrowTable(columns);
+    let schema: Schema;
+    if (metadata !== undefined) {
+      let schemaMetadata = opt.schema.metadata;
+      if (schemaMetadata.size === 0) {
+        schemaMetadata = metadata;
+      } else {
+        for (const [key, entry] of schemaMetadata.entries()) {
+          schemaMetadata.set(key, entry);
+        }
+      }
+
+      schema = new Schema(opt.schema.fields, schemaMetadata);
+    } else {
+      schema = opt.schema;
+    }
+    return new ArrowTable(schema, batchesFixed);
   }
+  const tbl = new ArrowTable(columns);
+  if (metadata !== undefined) {
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+    (<any>tbl.schema).metadata = metadata;
+  }
+  return tbl;
 }
 
 /**
  * Create an empty Arrow table with the provided schema
  */
-export function makeEmptyTable(schema: Schema): ArrowTable {
-  return makeArrowTable([], { schema });
+export function makeEmptyTable(
+  schema: Schema,
+  metadata?: Map<string, string>,
+): ArrowTable {
+  return makeArrowTable([], { schema }, metadata);
 }
 
 /**
@@ -375,13 +500,74 @@ function makeVector(
   }
 }
 
+/** Helper function to apply embeddings from metadata to an input table */
+async function applyEmbeddingsFromMetadata(
+  table: ArrowTable,
+  schema: Schema,
+): Promise<ArrowTable> {
+  const registry = getRegistry();
+  const functions = registry.parseFunctions(schema.metadata);
+
+  const columns = Object.fromEntries(
+    table.schema.fields.map((field) => [
+      field.name,
+      table.getChild(field.name)!,
+    ]),
+  );
+
+  for (const functionEntry of functions.values()) {
+    const sourceColumn = columns[functionEntry.sourceColumn];
+    const destColumn = functionEntry.vectorColumn ?? "vector";
+    if (sourceColumn === undefined) {
+      throw new Error(
+        `Cannot apply embedding function because the source column '${functionEntry.sourceColumn}' was not present in the data`,
+      );
+    }
+    if (columns[destColumn] !== undefined) {
+      throw new Error(
+        `Attempt to apply embeddings to table failed because column ${destColumn} already existed`,
+      );
+    }
+    if (table.batches.length > 1) {
+      throw new Error(
+        "Internal error: `makeArrowTable` unexpectedly created a table with more than one batch",
+      );
+    }
+    const values = sourceColumn.toArray();
+
+    const vectors =
+      await functionEntry.function.computeSourceEmbeddings(values);
+    if (vectors.length !== values.length) {
+      throw new Error(
+        "Embedding function did not return an embedding for each input element",
+      );
+    }
+    let destType: DataType;
+    const dtype = schema.fields.find((f) => f.name === destColumn)!.type;
+    if (isFixedSizeList(dtype)) {
+      destType = sanitizeType(dtype);
+    } else {
+      throw new Error(
+        "Expected FixedSizeList as datatype for vector field, instead got: " +
+          dtype,
+      );
+    }
+    const vector = makeVector(vectors, destType);
+    columns[destColumn] = vector;
+  }
+  const newTable = new ArrowTable(columns);
+  return alignTable(newTable, schema);
+}
+
 /** Helper function to apply embeddings to an input table */
 async function applyEmbeddings<T>(
   table: ArrowTable,
-  embeddings?: EmbeddingFunction<T>,
+  embeddings?: EmbeddingFunctionConfig,
   schema?: Schema,
 ): Promise<ArrowTable> {
-  if (embeddings == null) {
+  if (schema?.metadata.has("embedding_functions")) {
+    return applyEmbeddingsFromMetadata(table, schema!);
+  } else if (embeddings == null || embeddings === undefined) {
     return table;
   }
 
@@ -399,8 +585,9 @@ async function applyEmbeddings<T>(
   const newColumns = Object.fromEntries(colEntries);
 
   const sourceColumn = newColumns[embeddings.sourceColumn];
-  const destColumn = embeddings.destColumn ?? "vector";
-  const innerDestType = embeddings.embeddingDataType ?? new Float32();
+  const destColumn = embeddings.vectorColumn ?? "vector";
+  const innerDestType =
+    embeddings.function.embeddingDataType() ?? new Float32();
   if (sourceColumn === undefined) {
     throw new Error(
       `Cannot apply embedding function because the source column '${embeddings.sourceColumn}' was not present in the data`,
@@ -414,11 +601,9 @@ async function applyEmbeddings<T>(
       // if we call convertToTable with 0 records and a schema that includes the embedding
       return table;
     }
-    if (embeddings.embeddingDimension !== undefined) {
-      const destType = newVectorType(
-        embeddings.embeddingDimension,
-        innerDestType,
-      );
+    const dimensions = embeddings.function.ndims();
+    if (dimensions !== undefined) {
+      const destType = newVectorType(dimensions, innerDestType);
       newColumns[destColumn] = makeVector([], destType);
     } else if (schema != null) {
       const destField = schema.fields.find((f) => f.name === destColumn);
@@ -446,7 +631,9 @@ async function applyEmbeddings<T>(
       );
     }
     const values = sourceColumn.toArray();
-    const vectors = await embeddings.embed(values as T[]);
+    const vectors = await embeddings.function.computeSourceEmbeddings(
+      values as T[],
+    );
     if (vectors.length !== values.length) {
       throw new Error(
         "Embedding function did not return an embedding for each input element",
@@ -486,9 +673,9 @@ async function applyEmbeddings<T>(
  * embedding columns.  If no schema is provded then embedding columns will
  * be placed at the end of the table, after all of the input columns.
  */
-export async function convertToTable<T>(
+export async function convertToTable(
   data: Array<Record<string, unknown>>,
-  embeddings?: EmbeddingFunction<T>,
+  embeddings?: EmbeddingFunctionConfig,
   makeTableOptions?: Partial<MakeArrowTableOptions>,
 ): Promise<ArrowTable> {
   const table = makeArrowTable(data, makeTableOptions);
@@ -496,13 +683,13 @@ export async function convertToTable<T>(
 }
 
 /** Creates the Arrow Type for a Vector column with dimension `dim` */
-function newVectorType<T extends Float>(
+export function newVectorType<T extends Float>(
   dim: number,
   innerType: T,
 ): FixedSizeList<T> {
   // in Lance we always default to have the elements nullable, so we need to set it to true
   // otherwise we often get schema mismatches because the stored data always has schema with nullable elements
-  const children = new Field<T>("item", innerType, true);
+  const children = new Field("item", <T>sanitizeType(innerType), true);
   return new FixedSizeList(dim, children);
 }
 
@@ -513,9 +700,9 @@ function newVectorType<T extends Float>(
  *
  * `schema` is required if data is empty
  */
-export async function fromRecordsToBuffer<T>(
+export async function fromRecordsToBuffer(
   data: Array<Record<string, unknown>>,
-  embeddings?: EmbeddingFunction<T>,
+  embeddings?: EmbeddingFunctionConfig,
   schema?: Schema,
 ): Promise<Buffer> {
   if (schema !== undefined && schema !== null) {
@@ -533,9 +720,9 @@ export async function fromRecordsToBuffer<T>(
  *
  * `schema` is required if data is empty
  */
-export async function fromRecordsToStreamBuffer<T>(
+export async function fromRecordsToStreamBuffer(
   data: Array<Record<string, unknown>>,
-  embeddings?: EmbeddingFunction<T>,
+  embeddings?: EmbeddingFunctionConfig,
   schema?: Schema,
 ): Promise<Buffer> {
   if (schema !== undefined && schema !== null) {
@@ -554,9 +741,9 @@ export async function fromRecordsToStreamBuffer<T>(
  *
  * `schema` is required if the table is empty
  */
-export async function fromTableToBuffer<T>(
+export async function fromTableToBuffer(
   table: ArrowTable,
-  embeddings?: EmbeddingFunction<T>,
+  embeddings?: EmbeddingFunctionConfig,
   schema?: Schema,
 ): Promise<Buffer> {
   if (schema !== undefined && schema !== null) {
@@ -575,19 +762,19 @@ export async function fromTableToBuffer<T>(
  *
  * `schema` is required if the table is empty
  */
-export async function fromDataToBuffer<T>(
+export async function fromDataToBuffer(
   data: Data,
-  embeddings?: EmbeddingFunction<T>,
+  embeddings?: EmbeddingFunctionConfig,
   schema?: Schema,
 ): Promise<Buffer> {
   if (schema !== undefined && schema !== null) {
     schema = sanitizeSchema(schema);
   }
-  if (data instanceof ArrowTable) {
+  if (isArrowTable(data)) {
     return fromTableToBuffer(data, embeddings, schema);
   } else {
-    const table = await convertToTable(data);
-    return fromTableToBuffer(table, embeddings, schema);
+    const table = await convertToTable(data, embeddings, { schema });
+    return fromTableToBuffer(table);
   }
 }
 
@@ -599,9 +786,9 @@ export async function fromDataToBuffer<T>(
  *
  * `schema` is required if the table is empty
  */
-export async function fromTableToStreamBuffer<T>(
+export async function fromTableToStreamBuffer(
   table: ArrowTable,
-  embeddings?: EmbeddingFunction<T>,
+  embeddings?: EmbeddingFunctionConfig,
   schema?: Schema,
 ): Promise<Buffer> {
   const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
@@ -654,7 +841,7 @@ export function createEmptyTable(schema: Schema): ArrowTable {
 function validateSchemaEmbeddings(
   schema: Schema,
   data: Array<Record<string, unknown>>,
-  embeddings: EmbeddingFunction<unknown> | undefined,
+  embeddings: EmbeddingFunctionConfig | undefined,
 ) {
   const fields = [];
   const missingEmbeddingFields = [];
@@ -664,10 +851,25 @@ function validateSchemaEmbeddings(
   // if it does not, we add it to the list of missing embedding fields
   // Finally, we check if those missing embedding fields are `this._embeddings`
   // if they are not, we throw an error
-  for (const field of schema.fields) {
-    if (field.type instanceof FixedSizeList) {
+  for (let field of schema.fields) {
+    if (isFixedSizeList(field.type)) {
+      field = sanitizeField(field);
+
       if (data.length !== 0 && data?.[0]?.[field.name] === undefined) {
-        missingEmbeddingFields.push(field);
+        if (schema.metadata.has("embedding_functions")) {
+          const embeddings = JSON.parse(
+            schema.metadata.get("embedding_functions")!,
+          );
+          if (
+            // biome-ignore lint/suspicious/noExplicitAny: we don't know the type of `f`
+            embeddings.find((f: any) => f["vectorColumn"] === field.name) ===
+            undefined
+          ) {
+            missingEmbeddingFields.push(field);
+          }
+        } else {
+          missingEmbeddingFields.push(field);
+        }
       } else {
         fields.push(field);
       }

nodejs/lancedb/connection.ts

@@ -12,32 +12,11 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { Table as ArrowTable, Schema } from "apache-arrow";
-import { fromTableToBuffer, makeArrowTable, makeEmptyTable } from "./arrow";
-import { ConnectionOptions, Connection as LanceDbConnection } from "./native";
-import { Table } from "./table";
-
-/**
- * Connect to a LanceDB instance at the given URI.
- *
- * Accepted formats:
- *
- * - `/path/to/database` - local database
- * - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
- * - `db://host:port` - remote database (LanceDB cloud)
- * @param {string} uri - The uri of the database. If the database uri starts
- * with `db://` then it connects to a remote database.
- * @see {@link ConnectionOptions} for more details on the URI format.
- */
-export async function connect(
-  uri: string,
-  opts?: Partial<ConnectionOptions>,
-): Promise<Connection> {
-  opts = opts ?? {};
-  opts.storageOptions = cleanseStorageOptions(opts.storageOptions);
-  const nativeConn = await LanceDbConnection.new(uri, opts);
-  return new Connection(nativeConn);
-}
+import { Table as ArrowTable, Data, Schema } from "./arrow";
+import { fromTableToBuffer, makeEmptyTable } from "./arrow";
+import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
+import { Connection as LanceDbConnection } from "./native";
+import { LocalTable, Table } from "./table";
 
 export interface CreateTableOptions {
   /**
@@ -65,6 +44,14 @@ export interface CreateTableOptions {
    * The available options are described at https://lancedb.github.io/lancedb/guides/storage/
    */
   storageOptions?: Record<string, string>;
+  /**
+   * If true then data files will be written with the legacy format
+   *
+   * The default is true while the new format is in beta
+   */
+  useLegacyFormat?: boolean;
+  schema?: Schema;
+  embeddingFunction?: EmbeddingFunctionConfig;
 }
 
 export interface OpenTableOptions {
@@ -103,7 +90,6 @@ export interface TableNamesOptions {
   /** An optional limit to the number of results to return. */
   limit?: number;
 }
-
 /**
  * A LanceDB Connection that allows you to open tables and create new ones.
  *
@@ -122,17 +108,15 @@ export interface TableNamesOptions {
  * Any created tables are independent and will continue to work even if
  * the underlying connection has been closed.
  */
-export class Connection {
-  readonly inner: LanceDbConnection;
-
-  constructor(inner: LanceDbConnection) {
-    this.inner = inner;
+export abstract class Connection {
+  [Symbol.for("nodejs.util.inspect.custom")](): string {
+    return this.display();
   }
 
-  /** Return true if the connection has not been closed */
-  isOpen(): boolean {
-    return this.inner.isOpen();
-  }
+  /**
+   * Return true if the connection has not been closed
+   */
+  abstract isOpen(): boolean;
 
   /**
    * Close the connection, releasing any underlying resources.
@@ -141,14 +125,12 @@ export class Connection {
    *
    * Any attempt to use the connection after it is closed will result in an error.
    */
-  close(): void {
-    this.inner.close();
-  }
+  abstract close(): void;
 
-  /** Return a brief description of the connection */
-  display(): string {
-    return this.inner.display();
-  }
+  /**
+   * Return a brief description of the connection
+   */
+  abstract display(): string;
 
   /**
    * List all the table names in this database.
@@ -156,15 +138,86 @@ export class Connection {
    * Tables will be returned in lexicographical order.
    * @param {Partial<TableNamesOptions>} options - options to control the
    * paging / start point
+   *
    */
-  async tableNames(options?: Partial<TableNamesOptions>): Promise<string[]> {
-    return this.inner.tableNames(options?.startAfter, options?.limit);
-  }
+  abstract tableNames(options?: Partial<TableNamesOptions>): Promise<string[]>;
 
   /**
    * Open a table in the database.
    * @param {string} name - The name of the table
    */
+  abstract openTable(
+    name: string,
+    options?: Partial<OpenTableOptions>,
+  ): Promise<Table>;
+
+  /**
+   * Creates a new Table and initialize it with new data.
+   * @param {object} options - The options object.
+   * @param {string} options.name - The name of the table.
+   * @param {Data} options.data - Non-empty Array of Records to be inserted into the table
+   *
+   */
+  abstract createTable(
+    options: {
+      name: string;
+      data: Data;
+    } & Partial<CreateTableOptions>,
+  ): Promise<Table>;
+  /**
+   * Creates a new Table and initialize it with new data.
+   * @param {string} name - The name of the table.
+   * @param {Record<string, unknown>[] | ArrowTable} data - Non-empty Array of Records
+   * to be inserted into the table
+   */
+  abstract createTable(
+    name: string,
+    data: Record<string, unknown>[] | ArrowTable,
+    options?: Partial<CreateTableOptions>,
+  ): Promise<Table>;
+
+  /**
+   * Creates a new empty Table
+   * @param {string} name - The name of the table.
+   * @param {Schema} schema - The schema of the table
+   */
+  abstract createEmptyTable(
+    name: string,
+    schema: Schema,
+    options?: Partial<CreateTableOptions>,
+  ): Promise<Table>;
+
+  /**
+   * Drop an existing table.
+   * @param {string} name The name of the table to drop.
+   */
+  abstract dropTable(name: string): Promise<void>;
+}
+
+export class LocalConnection extends Connection {
+  readonly inner: LanceDbConnection;
+
+  constructor(inner: LanceDbConnection) {
+    super();
+    this.inner = inner;
+  }
+
+  isOpen(): boolean {
+    return this.inner.isOpen();
+  }
+
+  close(): void {
+    this.inner.close();
+  }
+
+  display(): string {
+    return this.inner.display();
+  }
+
+  async tableNames(options?: Partial<TableNamesOptions>): Promise<string[]> {
+    return this.inner.tableNames(options?.startAfter, options?.limit);
+  }
+
   async openTable(
     name: string,
     options?: Partial<OpenTableOptions>,
@@ -174,48 +227,36 @@ export class Connection {
       cleanseStorageOptions(options?.storageOptions),
       options?.indexCacheSize,
     );
-    return new Table(innerTable);
+
+    return new LocalTable(innerTable);
   }
 
-  /**
-   * Creates a new Table and initialize it with new data.
-   * @param {string} name - The name of the table.
-   * @param {Record<string, unknown>[] | ArrowTable} data - Non-empty Array of Records
-   * to be inserted into the table
-   */
   async createTable(
-    name: string,
-    data: Record<string, unknown>[] | ArrowTable,
+    nameOrOptions:
+      | string
+      | ({ name: string; data: Data } & Partial<CreateTableOptions>),
+    data?: Record<string, unknown>[] | ArrowTable,
     options?: Partial<CreateTableOptions>,
   ): Promise<Table> {
-    let mode: string = options?.mode ?? "create";
-    const existOk = options?.existOk ?? false;
-
-    if (mode === "create" && existOk) {
-      mode = "exist_ok";
+    if (typeof nameOrOptions !== "string" && "name" in nameOrOptions) {
+      const { name, data, ...options } = nameOrOptions;
+      return this.createTable(name, data, options);
     }
-
-    let table: ArrowTable;
-    if (data instanceof ArrowTable) {
-      table = data;
-    } else {
-      table = makeArrowTable(data);
+    if (data === undefined) {
+      throw new Error("data is required");
     }
-    const buf = await fromTableToBuffer(table);
+    const { buf, mode } = await Table.parseTableData(data, options);
     const innerTable = await this.inner.createTable(
-      name,
+      nameOrOptions,
       buf,
       mode,
       cleanseStorageOptions(options?.storageOptions),
+      options?.useLegacyFormat,
     );
-    return new Table(innerTable);
+
+    return new LocalTable(innerTable);
   }
 
-  /**
-   * Creates a new empty Table
-   * @param {string} name - The name of the table.
-   * @param {Schema} schema - The schema of the table
-   */
   async createEmptyTable(
     name: string,
     schema: Schema,
@@ -227,22 +268,25 @@ export class Connection {
     if (mode === "create" && existOk) {
       mode = "exist_ok";
     }
+    let metadata: Map<string, string> | undefined = undefined;
+    if (options?.embeddingFunction !== undefined) {
+      const embeddingFunction = options.embeddingFunction;
+      const registry = getRegistry();
+      metadata = registry.getTableMetadata([embeddingFunction]);
+    }
 
-    const table = makeEmptyTable(schema);
+    const table = makeEmptyTable(schema, metadata);
     const buf = await fromTableToBuffer(table);
     const innerTable = await this.inner.createEmptyTable(
       name,
       buf,
       mode,
       cleanseStorageOptions(options?.storageOptions),
+      options?.useLegacyFormat,
     );
-    return new Table(innerTable);
+    return new LocalTable(innerTable);
   }
 
-  /**
-   * Drop an existing table.
-   * @param {string} name The name of the table to drop.
-   */
   async dropTable(name: string): Promise<void> {
     return this.inner.dropTable(name);
   }
@@ -251,7 +295,7 @@ export class Connection {
 /**
  * Takes storage options and makes all the keys snake case.
  */
-function cleanseStorageOptions(
+export function cleanseStorageOptions(
   options?: Record<string, string>,
 ): Record<string, string> | undefined {
   if (options === undefined) {

nodejs/lancedb/embedding/embedding_function.ts

@@ -1,4 +1,4 @@
-// Copyright 2023 Lance Developers.
+// 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.
@@ -12,67 +12,172 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { type Float } from "apache-arrow";
+import "reflect-metadata";
+import {
+  DataType,
+  Field,
+  FixedSizeList,
+  Float,
+  Float32,
+  type IntoVector,
+  isDataType,
+  isFixedSizeList,
+  isFloat,
+  newVectorType,
+} from "../arrow";
+import { sanitizeType } from "../sanitize";
+
+/**
+ * Options for a given embedding function
+ */
+export interface FunctionOptions {
+  // biome-ignore lint/suspicious/noExplicitAny: options can be anything
+  [key: string]: any;
+}
 
 /**
  * An embedding function that automatically creates vector representation for a given column.
  */
-export interface EmbeddingFunction<T> {
+export abstract class EmbeddingFunction<
+  // biome-ignore lint/suspicious/noExplicitAny: we don't know what the implementor will do
+  T = any,
+  M extends FunctionOptions = FunctionOptions,
+> {
   /**
-   * The name of the column that will be used as input for the Embedding Function.
+   * Convert the embedding function to a JSON object
+   * It is used to serialize the embedding function to the schema
+   * It's important that any object returned by this method contains all the necessary
+   * information to recreate the embedding function
+   *
+   * It should return the same object that was passed to the constructor
+   * If it does not, the embedding function will not be able to be recreated, or could be recreated incorrectly
+   *
+   * @example
+   * ```ts
+   * class MyEmbeddingFunction extends EmbeddingFunction {
+   *   constructor(options: {model: string, timeout: number}) {
+   *     super();
+   *     this.model = options.model;
+   *     this.timeout = options.timeout;
+   *   }
+   *   toJSON() {
+   *     return {
+   *       model: this.model,
+   *       timeout: this.timeout,
+   *     };
+   * }
+   * ```
    */
-  sourceColumn: string;
+  abstract toJSON(): Partial<M>;
 
   /**
-   * The data type of the embedding
+   * sourceField is used in combination with `LanceSchema` to provide a declarative data model
    *
-   * The embedding function should return `number`.  This will be converted into
-   * an Arrow float array.  By default this will be Float32 but this property can
-   * be used to control the conversion.
+   * @param optionsOrDatatype - The options for the field or the datatype
+   *
+   * @see {@link lancedb.LanceSchema}
    */
-  embeddingDataType?: Float;
+  sourceField(
+    optionsOrDatatype: Partial<FieldOptions> | DataType,
+  ): [DataType, Map<string, EmbeddingFunction>] {
+    let datatype = isDataType(optionsOrDatatype)
+      ? optionsOrDatatype
+      : optionsOrDatatype?.datatype;
+    if (!datatype) {
+      throw new Error("Datatype is required");
+    }
+    datatype = sanitizeType(datatype);
+    const metadata = new Map<string, EmbeddingFunction>();
+    metadata.set("source_column_for", this);
+
+    return [datatype, metadata];
+  }
 
   /**
-   * The dimension of the embedding
+   * vectorField is used in combination with `LanceSchema` to provide a declarative data model
    *
-   * This is optional, normally this can be determined by looking at the results of
-   * `embed`.  If this is not specified, and there is an attempt to apply the embedding
-   * to an empty table, then that process will fail.
+   * @param options - The options for the field
+   *
+   * @see {@link lancedb.LanceSchema}
    */
-  embeddingDimension?: number;
+  vectorField(
+    optionsOrDatatype?: Partial<FieldOptions> | DataType,
+  ): [DataType, Map<string, EmbeddingFunction>] {
+    let dtype: DataType | undefined;
+    let vectorType: DataType;
+    let dims: number | undefined = this.ndims();
 
-  /**
-   * The name of the column that will contain the embedding
-   *
-   * By default this is "vector"
-   */
-  destColumn?: string;
+    // `func.vectorField(new Float32())`
+    if (isDataType(optionsOrDatatype)) {
+      dtype = optionsOrDatatype;
+    } else {
+      // `func.vectorField({
+      //  datatype: new Float32(),
+      //  dims: 10
+      // })`
+      dims = dims ?? optionsOrDatatype?.dims;
+      dtype = optionsOrDatatype?.datatype;
+    }
 
-  /**
-   * Should the source column be excluded from the resulting table
-   *
-   * By default the source column is included.  Set this to true and
-   * only the embedding will be stored.
-   */
-  excludeSource?: boolean;
+    if (dtype !== undefined) {
+      // `func.vectorField(new FixedSizeList(dims, new Field("item", new Float32(), true)))`
+      // or `func.vectorField({datatype: new FixedSizeList(dims, new Field("item", new Float32(), true))})`
+      if (isFixedSizeList(dtype)) {
+        vectorType = dtype;
+        // `func.vectorField(new Float32())`
+        // or `func.vectorField({datatype: new Float32()})`
+      } else if (isFloat(dtype)) {
+        // No `ndims` impl and no `{dims: n}` provided;
+        if (dims === undefined) {
+          throw new Error("ndims is required for vector field");
+        }
+        vectorType = newVectorType(dims, dtype);
+      } else {
+        throw new Error(
+          "Expected FixedSizeList or Float as datatype for vector field",
+        );
+      }
+    } else {
+      if (dims === undefined) {
+        throw new Error("ndims is required for vector field");
+      }
+      vectorType = new FixedSizeList(
+        dims,
+        new Field("item", new Float32(), true),
+      );
+    }
+    const metadata = new Map<string, EmbeddingFunction>();
+    metadata.set("vector_column_for", this);
+
+    return [vectorType, metadata];
+  }
+
+  /** The number of dimensions of the embeddings */
+  ndims(): number | undefined {
+    return undefined;
+  }
+
+  /** The datatype of the embeddings */
+  abstract embeddingDataType(): Float;
 
   /**
    * Creates a vector representation for the given values.
    */
-  embed: (data: T[]) => Promise<number[][]>;
+  abstract computeSourceEmbeddings(
+    data: T[],
+  ): Promise<number[][] | Float32Array[] | Float64Array[]>;
+
+  /**
+  Compute the embeddings for a single query
+ */
+  async computeQueryEmbeddings(data: T): Promise<IntoVector> {
+    return this.computeSourceEmbeddings([data]).then(
+      (embeddings) => embeddings[0],
+    );
+  }
 }
 
-/** Test if the input seems to be an embedding function */
-export function isEmbeddingFunction<T>(
-  value: unknown,
-): value is EmbeddingFunction<T> {
-  if (typeof value !== "object" || value === null) {
-    return false;
-  }
-  if (!("sourceColumn" in value) || !("embed" in value)) {
-    return false;
-  }
-  return (
-    typeof value.sourceColumn === "string" && typeof value.embed === "function"
-  );
+export interface FieldOptions<T extends DataType = DataType> {
+  datatype: T;
+  dims?: number;
 }

nodejs/lancedb/embedding/index.ts

@@ -1,2 +1,113 @@
-export { EmbeddingFunction, isEmbeddingFunction } from "./embedding_function";
-export { OpenAIEmbeddingFunction } from "./openai";
+// 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 { DataType, 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";
+
+// We need to explicitly export '*' so that the `register` decorator actually registers the class.
+export * from "./openai";
+export * from "./registry";
+
+/**
+ * Create a schema with embedding functions.
+ *
+ * @param fields
+ * @returns Schema
+ * @example
+ * ```ts
+ * class MyEmbeddingFunction extends EmbeddingFunction {
+ * // ...
+ * }
+ * const func = new MyEmbeddingFunction();
+ * const schema = LanceSchema({
+ *   id: new Int32(),
+ *   text: func.sourceField(new Utf8()),
+ *   vector: func.vectorField(),
+ *   // optional: specify the datatype and/or dimensions
+ *   vector2: func.vectorField({ datatype: new Float32(), dims: 3}),
+ * });
+ *
+ * const table = await db.createTable("my_table", data, { schema });
+ * ```
+ */
+export function LanceSchema(
+  fields: Record<string, [object, Map<string, EmbeddingFunction>] | object>,
+): Schema {
+  const arrowFields: Field[] = [];
+
+  const embeddingFunctions = new Map<
+    EmbeddingFunction,
+    Partial<EmbeddingFunctionConfig>
+  >();
+  Object.entries(fields).forEach(([key, value]) => {
+    if (isDataType(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);
+    }
+  });
+  const registry = getRegistry();
+  const metadata = registry.getTableMetadata(
+    Array.from(embeddingFunctions.values()) as EmbeddingFunctionConfig[],
+  );
+  const schema = new Schema(arrowFields, metadata);
+  return schema;
+}
+
+function parseEmbeddingFunctions(
+  embeddingFunctions: Map<EmbeddingFunction, Partial<EmbeddingFunctionConfig>>,
+  key: string,
+  metadata: Map<string, EmbeddingFunction>,
+): void {
+  if (metadata.has("source_column_for")) {
+    const embedFunction = metadata.get("source_column_for")!;
+    const current = embeddingFunctions.get(embedFunction);
+    if (current !== undefined) {
+      embeddingFunctions.set(embedFunction, {
+        ...current,
+        sourceColumn: key,
+      });
+    } else {
+      embeddingFunctions.set(embedFunction, {
+        sourceColumn: key,
+        function: embedFunction,
+      });
+    }
+  } else if (metadata.has("vector_column_for")) {
+    const embedFunction = metadata.get("vector_column_for")!;
+
+    const current = embeddingFunctions.get(embedFunction);
+    if (current !== undefined) {
+      embeddingFunctions.set(embedFunction, {
+        ...current,
+        vectorColumn: key,
+      });
+    } else {
+      embeddingFunctions.set(embedFunction, {
+        vectorColumn: key,
+        function: embedFunction,
+      });
+    }
+  }
+}

nodejs/lancedb/embedding/openai.ts

@@ -13,17 +13,31 @@
 // limitations under the License.
 
 import type OpenAI from "openai";
-import { type EmbeddingFunction } from "./embedding_function";
+import { Float, Float32 } from "../arrow";
+import { EmbeddingFunction } from "./embedding_function";
+import { register } from "./registry";
 
-export class OpenAIEmbeddingFunction implements EmbeddingFunction<string> {
-  private readonly _openai: OpenAI;
-  private readonly _modelName: string;
+export type OpenAIOptions = {
+  apiKey?: string;
+  model?: string;
+};
+
+@register("openai")
+export class OpenAIEmbeddingFunction extends EmbeddingFunction<
+  string,
+  OpenAIOptions
+> {
+  #openai: OpenAI;
+  #modelName: string;
+
+  constructor(options: OpenAIOptions = { model: "text-embedding-ada-002" }) {
+    super();
+    const openAIKey = options?.apiKey ?? process.env.OPENAI_API_KEY;
+    if (!openAIKey) {
+      throw new Error("OpenAI API key is required");
+    }
+    const modelName = options?.model ?? "text-embedding-ada-002";
 
-  constructor(
-    sourceColumn: string,
-    openAIKey: string,
-    modelName: string = "text-embedding-ada-002",
-  ) {
     /**
      * @type {import("openai").default}
      */
@@ -36,18 +50,40 @@ export class OpenAIEmbeddingFunction implements EmbeddingFunction<string> {
       throw new Error("please install openai@^4.24.1 using npm install openai");
     }
 
-    this.sourceColumn = sourceColumn;
     const configuration = {
       apiKey: openAIKey,
     };
 
-    this._openai = new Openai(configuration);
-    this._modelName = modelName;
+    this.#openai = new Openai(configuration);
+    this.#modelName = modelName;
   }
 
-  async embed(data: string[]): Promise<number[][]> {
-    const response = await this._openai.embeddings.create({
-      model: this._modelName,
+  toJSON() {
+    return {
+      model: this.#modelName,
+    };
+  }
+
+  ndims(): number {
+    switch (this.#modelName) {
+      case "text-embedding-ada-002":
+        return 1536;
+      case "text-embedding-3-large":
+        return 3072;
+      case "text-embedding-3-small":
+        return 1536;
+      default:
+        return null as never;
+    }
+  }
+
+  embeddingDataType(): Float {
+    return new Float32();
+  }
+
+  async computeSourceEmbeddings(data: string[]): Promise<number[][]> {
+    const response = await this.#openai.embeddings.create({
+      model: this.#modelName,
       input: data,
     });
 
@@ -58,5 +94,15 @@ export class OpenAIEmbeddingFunction implements EmbeddingFunction<string> {
     return embeddings;
   }
 
-  sourceColumn: string;
+  async computeQueryEmbeddings(data: string): Promise<number[]> {
+    if (typeof data !== "string") {
+      throw new Error("Data must be a string");
+    }
+    const response = await this.#openai.embeddings.create({
+      model: this.#modelName,
+      input: data,
+    });
+
+    return response.data[0].embedding;
+  }
 }

nodejs/lancedb/embedding/registry.ts

@@ -0,0 +1,176 @@
+// 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 type { EmbeddingFunction } from "./embedding_function";
+import "reflect-metadata";
+
+export interface EmbeddingFunctionOptions {
+  [key: string]: unknown;
+}
+
+export interface EmbeddingFunctionFactory<
+  T extends EmbeddingFunction = EmbeddingFunction,
+> {
+  new (modelOptions?: EmbeddingFunctionOptions): T;
+}
+
+interface EmbeddingFunctionCreate<T extends EmbeddingFunction> {
+  create(options?: EmbeddingFunctionOptions): T;
+}
+
+/**
+ * This is a singleton class used to register embedding functions
+ * and fetch them by name. It also handles serializing and deserializing.
+ * You can implement your own embedding function by subclassing EmbeddingFunction
+ * or TextEmbeddingFunction and registering it with the registry
+ */
+export class EmbeddingFunctionRegistry {
+  #functions: Map<string, EmbeddingFunctionFactory> = new Map();
+
+  /**
+   * Register an embedding function
+   * @param name The name of the function
+   * @param func The function to register
+   * @throws Error if the function is already registered
+   */
+  register<T extends EmbeddingFunctionFactory = EmbeddingFunctionFactory>(
+    this: EmbeddingFunctionRegistry,
+    alias?: string,
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+  ): (ctor: T) => any {
+    const self = this;
+    return function (ctor: T) {
+      if (!alias) {
+        alias = ctor.name;
+      }
+      if (self.#functions.has(alias)) {
+        throw new Error(
+          `Embedding function with alias "${alias}" already exists`,
+        );
+      }
+      self.#functions.set(alias, ctor);
+      Reflect.defineMetadata("lancedb::embedding::name", alias, ctor);
+      return ctor;
+    };
+  }
+
+  /**
+   * Fetch an embedding function by name
+   * @param name The name of the function
+   */
+  get<T extends EmbeddingFunction<unknown> = EmbeddingFunction>(
+    name: string,
+  ): EmbeddingFunctionCreate<T> | undefined {
+    const factory = this.#functions.get(name);
+    if (!factory) {
+      return undefined;
+    }
+    return {
+      create: function (options: EmbeddingFunctionOptions) {
+        return new factory(options) as unknown as T;
+      },
+    };
+  }
+
+  /**
+   * reset the registry to the initial state
+   */
+  reset(this: EmbeddingFunctionRegistry) {
+    this.#functions.clear();
+  }
+
+  /**
+   * @ignore
+   */
+  parseFunctions(
+    this: EmbeddingFunctionRegistry,
+    metadata: Map<string, string>,
+  ): Map<string, EmbeddingFunctionConfig> {
+    if (!metadata.has("embedding_functions")) {
+      return new Map();
+    } else {
+      type FunctionConfig = {
+        name: string;
+        sourceColumn: string;
+        vectorColumn: string;
+        model: EmbeddingFunctionOptions;
+      };
+      const functions = <FunctionConfig[]>(
+        JSON.parse(metadata.get("embedding_functions")!)
+      );
+      return new Map(
+        functions.map((f) => {
+          const fn = this.get(f.name);
+          if (!fn) {
+            throw new Error(`Function "${f.name}" not found in registry`);
+          }
+          return [
+            f.name,
+            {
+              sourceColumn: f.sourceColumn,
+              vectorColumn: f.vectorColumn,
+              function: this.get(f.name)!.create(f.model),
+            },
+          ];
+        }),
+      );
+    }
+  }
+  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+  functionToMetadata(conf: EmbeddingFunctionConfig): Record<string, any> {
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+    const metadata: Record<string, any> = {};
+    const name = Reflect.getMetadata(
+      "lancedb::embedding::name",
+      conf.function.constructor,
+    );
+    metadata["sourceColumn"] = conf.sourceColumn;
+    metadata["vectorColumn"] = conf.vectorColumn ?? "vector";
+    metadata["name"] = name ?? conf.function.constructor.name;
+    metadata["model"] = conf.function.toJSON();
+    return metadata;
+  }
+
+  getTableMetadata(functions: EmbeddingFunctionConfig[]): Map<string, string> {
+    const metadata = new Map<string, string>();
+    const jsonData = functions.map((conf) => this.functionToMetadata(conf));
+    metadata.set("embedding_functions", JSON.stringify(jsonData));
+
+    return metadata;
+  }
+}
+
+const _REGISTRY = new EmbeddingFunctionRegistry();
+
+export function register(name?: string) {
+  return _REGISTRY.register(name);
+}
+
+/**
+ * Utility function to get the global instance of the registry
+ * @returns `EmbeddingFunctionRegistry` The global instance of the registry
+ * @example
+ * ```ts
+ * const registry = getRegistry();
+ * const openai = registry.get("openai").create();
+ */
+export function getRegistry(): EmbeddingFunctionRegistry {
+  return _REGISTRY;
+}
+
+export interface EmbeddingFunctionConfig {
+  sourceColumn: string;
+  vectorColumn?: string;
+  function: EmbeddingFunction;
+}

nodejs/lancedb/index.ts

@@ -12,25 +12,43 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+import {
+  Connection,
+  LocalConnection,
+  cleanseStorageOptions,
+} from "./connection";
+
+import {
+  ConnectionOptions,
+  Connection as LanceDbConnection,
+} from "./native.js";
+
+import { RemoteConnection, RemoteConnectionOptions } from "./remote";
+
 export {
   WriteOptions,
   WriteMode,
   AddColumnsSql,
   ColumnAlteration,
   ConnectionOptions,
+  IndexStatistics,
+  IndexMetadata,
+  IndexConfig,
 } from "./native.js";
+
 export {
   makeArrowTable,
   MakeArrowTableOptions,
   Data,
   VectorColumnOptions,
 } from "./arrow";
+
 export {
-  connect,
   Connection,
   CreateTableOptions,
   TableNamesOptions,
 } from "./connection";
+
 export {
   ExecutableQuery,
   Query,
@@ -38,6 +56,87 @@ export {
   VectorQuery,
   RecordBatchIterator,
 } from "./query";
+
 export { Index, IndexOptions, IvfPqOptions } from "./indices";
-export { Table, AddDataOptions, IndexConfig, UpdateOptions } from "./table";
+
+export { Table, AddDataOptions, UpdateOptions } from "./table";
+
 export * as embedding from "./embedding";
+
+/**
+ * Connect to a LanceDB instance at the given URI.
+ *
+ * Accepted formats:
+ *
+ * - `/path/to/database` - local database
+ * - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
+ * - `db://host:port` - remote database (LanceDB cloud)
+ * @param {string} uri - The uri of the database. If the database uri starts
+ * with `db://` then it connects to a remote database.
+ * @see {@link ConnectionOptions} for more details on the URI format.
+ * @example
+ * ```ts
+ * const conn = await connect("/path/to/database");
+ * ```
+ * @example
+ * ```ts
+ * const conn = await connect(
+ *   "s3://bucket/path/to/database",
+ *   {storageOptions: {timeout: "60s"}
+ * });
+ * ```
+ */
+export async function connect(
+  uri: string,
+  opts?: Partial<ConnectionOptions | RemoteConnectionOptions>,
+): Promise<Connection>;
+/**
+ * Connect to a LanceDB instance at the given URI.
+ *
+ * Accepted formats:
+ *
+ * - `/path/to/database` - local database
+ * - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud storage
+ * - `db://host:port` - remote database (LanceDB cloud)
+ * @param  options - The options to use when connecting to the database
+ * @see {@link ConnectionOptions} for more details on the URI format.
+ * @example
+ * ```ts
+ * const conn = await connect({
+ *   uri: "/path/to/database",
+ *   storageOptions: {timeout: "60s"}
+ * });
+ * ```
+ */
+export async function connect(
+  opts: Partial<RemoteConnectionOptions | ConnectionOptions> & { uri: string },
+): Promise<Connection>;
+export async function connect(
+  uriOrOptions:
+    | string
+    | (Partial<RemoteConnectionOptions | ConnectionOptions> & { uri: string }),
+  opts: Partial<ConnectionOptions | RemoteConnectionOptions> = {},
+): Promise<Connection> {
+  let uri: string | undefined;
+  if (typeof uriOrOptions !== "string") {
+    const { uri: uri_, ...options } = uriOrOptions;
+    uri = uri_;
+    opts = options;
+  } else {
+    uri = uriOrOptions;
+  }
+
+  if (!uri) {
+    throw new Error("uri is required");
+  }
+
+  if (uri?.startsWith("db://")) {
+    return new RemoteConnection(uri, opts as RemoteConnectionOptions);
+  }
+  opts = (opts as ConnectionOptions) ?? {};
+  (<ConnectionOptions>opts).storageOptions = cleanseStorageOptions(
+    (<ConnectionOptions>opts).storageOptions,
+  );
+  const nativeConn = await LanceDbConnection.new(uri, opts);
+  return new LocalConnection(nativeConn);
+}

nodejs/lancedb/merge.ts

@@ -0,0 +1,70 @@
+import { Data, fromDataToBuffer } from "./arrow";
+import { NativeMergeInsertBuilder } from "./native";
+
+/** A builder used to create and run a merge insert operation */
+export class MergeInsertBuilder {
+  #native: NativeMergeInsertBuilder;
+
+  /** Construct a MergeInsertBuilder. __Internal use only.__ */
+  constructor(native: NativeMergeInsertBuilder) {
+    this.#native = native;
+  }
+
+  /**
+   * Rows that exist in both the source table (new data) and
+   * the target table (old data) will be updated, replacing
+   * the old row with the corresponding matching row.
+   *
+   * If there are multiple matches then the behavior is undefined.
+   * Currently this causes multiple copies of the row to be created
+   * but that behavior is subject to change.
+   *
+   * An optional condition may be specified.  If it is, then only
+   * matched rows that satisfy the condtion will be updated.  Any
+   * rows that do not satisfy the condition will be left as they
+   * are.  Failing to satisfy the condition does not cause a
+   * "matched row" to become a "not matched" row.
+   *
+   * The condition should be an SQL string.  Use the prefix
+   * target. to refer to rows in the target table (old data)
+   * and the prefix source. to refer to rows in the source
+   * table (new data).
+   *
+   * For example, "target.last_update < source.last_update"
+   */
+  whenMatchedUpdateAll(options?: { where: string }): MergeInsertBuilder {
+    return new MergeInsertBuilder(
+      this.#native.whenMatchedUpdateAll(options?.where),
+    );
+  }
+  /**
+   * Rows that exist only in the source table (new data) should
+   * be inserted into the target table.
+   */
+  whenNotMatchedInsertAll(): MergeInsertBuilder {
+    return new MergeInsertBuilder(this.#native.whenNotMatchedInsertAll());
+  }
+  /**
+   * Rows that exist only in the target table (old data) will be
+   * deleted.  An optional condition can be provided to limit what
+   * data is deleted.
+   *
+   * @param options.where - An optional condition to limit what data is deleted
+   */
+  whenNotMatchedBySourceDelete(options?: {
+    where: string;
+  }): MergeInsertBuilder {
+    return new MergeInsertBuilder(
+      this.#native.whenNotMatchedBySourceDelete(options?.where),
+    );
+  }
+  /**
+   * Executes the merge insert operation
+   *
+   * Nothing is returned but the `Table` is updated
+   */
+  async execute(data: Data): Promise<void> {
+    const buffer = await fromDataToBuffer(data);
+    await this.#native.execute(buffer);
+  }
+}

nodejs/lancedb/query.ts

@@ -12,7 +12,12 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { Table as ArrowTable, RecordBatch, tableFromIPC } from "apache-arrow";
+import {
+  Table as ArrowTable,
+  type IntoVector,
+  RecordBatch,
+  tableFromIPC,
+} from "./arrow";
 import { type IvfPqOptions } from "./indices";
 import {
   RecordBatchIterator as NativeBatchIterator,
@@ -50,6 +55,39 @@ export class RecordBatchIterator implements AsyncIterator<RecordBatch> {
 }
 /* eslint-enable */
 
+class RecordBatchIterable<
+  NativeQueryType extends NativeQuery | NativeVectorQuery,
+> implements AsyncIterable<RecordBatch>
+{
+  private inner: NativeQueryType;
+  private options?: QueryExecutionOptions;
+
+  constructor(inner: NativeQueryType, options?: QueryExecutionOptions) {
+    this.inner = inner;
+    this.options = options;
+  }
+
+  // biome-ignore lint/suspicious/noExplicitAny: skip
+  [Symbol.asyncIterator](): AsyncIterator<RecordBatch<any>, any, undefined> {
+    return new RecordBatchIterator(
+      this.inner.execute(this.options?.maxBatchLength),
+    );
+  }
+}
+
+/**
+ * Options that control the behavior of a particular query execution
+ */
+export interface QueryExecutionOptions {
+  /**
+   * The maximum number of rows to return in a single batch
+   *
+   * Batches may have fewer rows if the underlying data is stored
+   * in smaller chunks.
+   */
+  maxBatchLength?: number;
+}
+
 /** Common methods supported by all query types */
 export class QueryBase<
   NativeQueryType extends NativeQuery | NativeVectorQuery,
@@ -76,6 +114,14 @@ export class QueryBase<
     this.inner.onlyIf(predicate);
     return this as unknown as QueryType;
   }
+  /**
+   * A filter statement to be applied to this query.
+   * @alias where
+   * @deprecated Use `where` instead
+   */
+  filter(predicate: string): QueryType {
+    return this.where(predicate);
+  }
 
   /**
    * Return only the specified columns.
@@ -108,9 +154,12 @@ export class QueryBase<
    * object insertion order is easy to get wrong and `Map` is more foolproof.
    */
   select(
-    columns: string[] | Map<string, string> | Record<string, string>,
+    columns: string[] | Map<string, string> | Record<string, string> | string,
   ): QueryType {
     let columnTuples: [string, string][];
+    if (typeof columns === "string") {
+      columns = [columns];
+    }
     if (Array.isArray(columns)) {
       columnTuples = columns.map((c) => [c, c]);
     } else if (columns instanceof Map) {
@@ -133,8 +182,10 @@ export class QueryBase<
     return this as unknown as QueryType;
   }
 
-  protected nativeExecute(): Promise<NativeBatchIterator> {
-    return this.inner.execute();
+  protected nativeExecute(
+    options?: Partial<QueryExecutionOptions>,
+  ): Promise<NativeBatchIterator> {
+    return this.inner.execute(options?.maxBatchLength);
   }
 
   /**
@@ -148,8 +199,10 @@ export class QueryBase<
    * single query)
    *
    */
-  protected execute(): RecordBatchIterator {
-    return new RecordBatchIterator(this.nativeExecute());
+  protected execute(
+    options?: Partial<QueryExecutionOptions>,
+  ): RecordBatchIterator {
+    return new RecordBatchIterator(this.nativeExecute(options));
   }
 
   // biome-ignore lint/suspicious/noExplicitAny: skip
@@ -159,18 +212,18 @@ export class QueryBase<
   }
 
   /** Collect the results as an Arrow @see {@link ArrowTable}. */
-  async toArrow(): Promise<ArrowTable> {
+  async toArrow(options?: Partial<QueryExecutionOptions>): Promise<ArrowTable> {
     const batches = [];
-    for await (const batch of this) {
+    for await (const batch of new RecordBatchIterable(this.inner, options)) {
       batches.push(batch);
     }
     return new ArrowTable(batches);
   }
 
   /** Collect the results as an array of objects. */
-  async toArray(): Promise<unknown[]> {
-    const tbl = await this.toArrow();
-    // eslint-disable-next-line @typescript-eslint/no-unsafe-return
+  // biome-ignore lint/suspicious/noExplicitAny: arrow.toArrow() returns any[]
+  async toArray(options?: Partial<QueryExecutionOptions>): Promise<any[]> {
+    const tbl = await this.toArrow(options);
     return tbl.toArray();
   }
 }
@@ -369,9 +422,8 @@ export class Query extends QueryBase<NativeQuery, Query> {
    * Vector searches always have a `limit`.  If `limit` has not been called then
    * a default `limit` of 10 will be used.  @see {@link Query#limit}
    */
-  nearestTo(vector: unknown): VectorQuery {
-    // biome-ignore lint/suspicious/noExplicitAny: skip
-    const vectorQuery = this.inner.nearestTo(Float32Array.from(vector as any));
+  nearestTo(vector: IntoVector): VectorQuery {
+    const vectorQuery = this.inner.nearestTo(Float32Array.from(vector));
     return new VectorQuery(vectorQuery);
   }
 }

nodejs/lancedb/remote/client.ts

@@ -0,0 +1,221 @@
+// Copyright 2023 LanceDB Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import axios, {
+  AxiosError,
+  type AxiosResponse,
+  type ResponseType,
+} from "axios";
+import { Table as ArrowTable } from "../arrow";
+import { tableFromIPC } from "../arrow";
+import { VectorQuery } from "../query";
+
+export class RestfulLanceDBClient {
+  #dbName: string;
+  #region: string;
+  #apiKey: string;
+  #hostOverride?: string;
+  #closed: boolean = false;
+  #connectionTimeout: number = 12 * 1000; // 12 seconds;
+  #readTimeout: number = 30 * 1000; // 30 seconds;
+  #session?: import("axios").AxiosInstance;
+
+  constructor(
+    dbName: string,
+    apiKey: string,
+    region: string,
+    hostOverride?: string,
+    connectionTimeout?: number,
+    readTimeout?: number,
+  ) {
+    this.#dbName = dbName;
+    this.#apiKey = apiKey;
+    this.#region = region;
+    this.#hostOverride = hostOverride ?? this.#hostOverride;
+    this.#connectionTimeout = connectionTimeout ?? this.#connectionTimeout;
+    this.#readTimeout = readTimeout ?? this.#readTimeout;
+  }
+
+  // todo: cache the session.
+  get session(): import("axios").AxiosInstance {
+    if (this.#session !== undefined) {
+      return this.#session;
+    } else {
+      return axios.create({
+        baseURL: this.url,
+        headers: {
+          // biome-ignore lint/style/useNamingConvention: external api
+          Authorization: `Bearer ${this.#apiKey}`,
+        },
+        transformResponse: decodeErrorData,
+        timeout: this.#connectionTimeout,
+      });
+    }
+  }
+
+  get url(): string {
+    return (
+      this.#hostOverride ??
+      `https://${this.#dbName}.${this.#region}.api.lancedb.com`
+    );
+  }
+
+  get headers(): { [key: string]: string } {
+    const headers: { [key: string]: string } = {
+      "x-api-key": this.#apiKey,
+      "x-request-id": "na",
+    };
+    if (this.#region == "local") {
+      headers["Host"] = `${this.#dbName}.${this.#region}.api.lancedb.com`;
+    }
+    if (this.#hostOverride) {
+      headers["x-lancedb-database"] = this.#dbName;
+    }
+    return headers;
+  }
+
+  isOpen(): boolean {
+    return !this.#closed;
+  }
+
+  private checkNotClosed(): void {
+    if (this.#closed) {
+      throw new Error("Connection is closed");
+    }
+  }
+
+  close(): void {
+    this.#session = undefined;
+    this.#closed = true;
+  }
+
+  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+  async get(uri: string, params?: Record<string, any>): Promise<any> {
+    this.checkNotClosed();
+    uri = new URL(uri, this.url).toString();
+    let response;
+    try {
+      response = await this.session.get(uri, {
+        headers: this.headers,
+        params,
+      });
+    } catch (e) {
+      if (e instanceof AxiosError) {
+        response = e.response;
+      } else {
+        throw e;
+      }
+    }
+
+    RestfulLanceDBClient.checkStatus(response!);
+    return response!.data;
+  }
+
+  // biome-ignore lint/suspicious/noExplicitAny: api response
+  async post(uri: string, body?: any): Promise<any>;
+  async post(
+    uri: string,
+    // biome-ignore lint/suspicious/noExplicitAny: api request
+    body: any,
+    additional: {
+      config?: { responseType: "arraybuffer" };
+      headers?: Record<string, string>;
+      params?: Record<string, string>;
+    },
+  ): Promise<Buffer>;
+  async post(
+    uri: string,
+    // biome-ignore lint/suspicious/noExplicitAny: api request
+    body?: any,
+    additional?: {
+      config?: { responseType: ResponseType };
+      headers?: Record<string, string>;
+      params?: Record<string, string>;
+    },
+    // biome-ignore lint/suspicious/noExplicitAny: api response
+  ): Promise<any> {
+    this.checkNotClosed();
+    uri = new URL(uri, this.url).toString();
+    additional = Object.assign(
+      { config: { responseType: "json" } },
+      additional,
+    );
+
+    const headers = { ...this.headers, ...additional.headers };
+
+    if (!headers["Content-Type"]) {
+      headers["Content-Type"] = "application/json";
+    }
+    let response;
+    try {
+      response = await this.session.post(uri, body, {
+        headers,
+        responseType: additional!.config!.responseType,
+        params: new Map(Object.entries(additional.params ?? {})),
+      });
+    } catch (e) {
+      if (e instanceof AxiosError) {
+        response = e.response;
+      } else {
+        throw e;
+      }
+    }
+    RestfulLanceDBClient.checkStatus(response!);
+    if (additional!.config!.responseType === "arraybuffer") {
+      return response!.data;
+    } else {
+      return JSON.parse(response!.data);
+    }
+  }
+
+  async listTables(limit = 10, pageToken = ""): Promise<string[]> {
+    const json = await this.get("/v1/table", { limit, pageToken });
+    return json.tables;
+  }
+
+  async query(tableName: string, query: VectorQuery): Promise<ArrowTable> {
+    const tbl = await this.post(`/v1/table/${tableName}/query`, query, {
+      config: {
+        responseType: "arraybuffer",
+      },
+    });
+    return tableFromIPC(tbl);
+  }
+
+  static checkStatus(response: AxiosResponse): void {
+    if (response.status === 404) {
+      throw new Error(`Not found: ${response.data}`);
+    } else if (response.status >= 400 && response.status < 500) {
+      throw new Error(
+        `Bad Request: ${response.status}, error: ${response.data}`,
+      );
+    } else if (response.status >= 500 && response.status < 600) {
+      throw new Error(
+        `Internal Server Error: ${response.status}, error: ${response.data}`,
+      );
+    } else if (response.status !== 200) {
+      throw new Error(
+        `Unknown Error: ${response.status}, error: ${response.data}`,
+      );
+    }
+  }
+}
+
+function decodeErrorData(data: unknown) {
+  if (Buffer.isBuffer(data)) {
+    const decoded = data.toString("utf-8");
+    return decoded;
+  }
+  return data;
+}

nodejs/lancedb/remote/connection.ts

@@ -0,0 +1,196 @@
+import { Schema } from "apache-arrow";
+import { Data, fromTableToStreamBuffer, makeEmptyTable } from "../arrow";
+import {
+  Connection,
+  CreateTableOptions,
+  OpenTableOptions,
+  TableNamesOptions,
+} from "../connection";
+import { Table } from "../table";
+import { TTLCache } from "../util";
+import { RestfulLanceDBClient } from "./client";
+import { RemoteTable } from "./table";
+
+export interface RemoteConnectionOptions {
+  apiKey?: string;
+  region?: string;
+  hostOverride?: string;
+  connectionTimeout?: number;
+  readTimeout?: number;
+}
+
+export class RemoteConnection extends Connection {
+  #dbName: string;
+  #apiKey: string;
+  #region: string;
+  #client: RestfulLanceDBClient;
+  #tableCache = new TTLCache(300_000);
+
+  constructor(
+    url: string,
+    {
+      apiKey,
+      region,
+      hostOverride,
+      connectionTimeout,
+      readTimeout,
+    }: RemoteConnectionOptions,
+  ) {
+    super();
+    apiKey = apiKey ?? process.env.LANCEDB_API_KEY;
+    region = region ?? process.env.LANCEDB_REGION;
+
+    if (!apiKey) {
+      throw new Error("apiKey is required when connecting to LanceDB Cloud");
+    }
+
+    if (!region) {
+      throw new Error("region is required when connecting to LanceDB Cloud");
+    }
+
+    const parsed = new URL(url);
+    if (parsed.protocol !== "db:") {
+      throw new Error(
+        `invalid protocol: ${parsed.protocol}, only accepts db://`,
+      );
+    }
+
+    this.#dbName = parsed.hostname;
+    this.#apiKey = apiKey;
+    this.#region = region;
+    this.#client = new RestfulLanceDBClient(
+      this.#dbName,
+      this.#apiKey,
+      this.#region,
+      hostOverride,
+      connectionTimeout,
+      readTimeout,
+    );
+  }
+
+  isOpen(): boolean {
+    return this.#client.isOpen();
+  }
+  close(): void {
+    return this.#client.close();
+  }
+
+  display(): string {
+    return `RemoteConnection(${this.#dbName})`;
+  }
+
+  async tableNames(options?: Partial<TableNamesOptions>): Promise<string[]> {
+    const response = await this.#client.get("/v1/table/", {
+      limit: options?.limit ?? 10,
+      // biome-ignore lint/style/useNamingConvention: <explanation>
+      page_token: options?.startAfter ?? "",
+    });
+    const body = await response.body();
+    for (const table of body.tables) {
+      this.#tableCache.set(table, true);
+    }
+    return body.tables;
+  }
+
+  async openTable(
+    name: string,
+    _options?: Partial<OpenTableOptions> | undefined,
+  ): Promise<Table> {
+    if (this.#tableCache.get(name) === undefined) {
+      await this.#client.post(
+        `/v1/table/${encodeURIComponent(name)}/describe/`,
+      );
+      this.#tableCache.set(name, true);
+    }
+    return new RemoteTable(this.#client, name, this.#dbName);
+  }
+
+  async createTable(
+    nameOrOptions:
+      | string
+      | ({ name: string; data: Data } & Partial<CreateTableOptions>),
+    data?: Data,
+    options?: Partial<CreateTableOptions> | undefined,
+  ): Promise<Table> {
+    if (typeof nameOrOptions !== "string" && "name" in nameOrOptions) {
+      const { name, data, ...options } = nameOrOptions;
+      return this.createTable(name, data, options);
+    }
+    if (data === undefined) {
+      throw new Error("data is required");
+    }
+    if (options?.mode) {
+      console.warn(
+        "option 'mode' is not supported in LanceDB Cloud",
+        "LanceDB Cloud only supports the default 'create' mode.",
+        "If the table already exists, an error will be thrown.",
+      );
+    }
+    if (options?.embeddingFunction) {
+      console.warn(
+        "embedding_functions is not yet supported on LanceDB Cloud.",
+        "Please vote https://github.com/lancedb/lancedb/issues/626 ",
+        "for this feature.",
+      );
+    }
+
+    const { buf } = await Table.parseTableData(
+      data,
+      options,
+      true /** streaming */,
+    );
+
+    await this.#client.post(
+      `/v1/table/${encodeURIComponent(nameOrOptions)}/create/`,
+      buf,
+      {
+        config: {
+          responseType: "arraybuffer",
+        },
+        headers: { "Content-Type": "application/vnd.apache.arrow.stream" },
+      },
+    );
+    this.#tableCache.set(nameOrOptions, true);
+    return new RemoteTable(this.#client, nameOrOptions, this.#dbName);
+  }
+
+  async createEmptyTable(
+    name: string,
+    schema: Schema,
+    options?: Partial<CreateTableOptions> | undefined,
+  ): Promise<Table> {
+    if (options?.mode) {
+      console.warn(`mode is not supported on LanceDB Cloud`);
+    }
+
+    if (options?.embeddingFunction) {
+      console.warn(
+        "embeddingFunction is not yet supported on LanceDB Cloud.",
+        "Please vote https://github.com/lancedb/lancedb/issues/626 ",
+        "for this feature.",
+      );
+    }
+    const emptyTable = makeEmptyTable(schema);
+    const buf = await fromTableToStreamBuffer(emptyTable);
+
+    await this.#client.post(
+      `/v1/table/${encodeURIComponent(name)}/create/`,
+      buf,
+      {
+        config: {
+          responseType: "arraybuffer",
+        },
+        headers: { "Content-Type": "application/vnd.apache.arrow.stream" },
+      },
+    );
+
+    this.#tableCache.set(name, true);
+    return new RemoteTable(this.#client, name, this.#dbName);
+  }
+
+  async dropTable(name: string): Promise<void> {
+    await this.#client.post(`/v1/table/${encodeURIComponent(name)}/drop/`);
+
+    this.#tableCache.delete(name);
+  }
+}

nodejs/lancedb/remote/index.ts

@@ -0,0 +1,3 @@
+export { RestfulLanceDBClient } from "./client";
+export { type RemoteConnectionOptions, RemoteConnection } from "./connection";
+export { RemoteTable } from "./table";

nodejs/lancedb/remote/table.ts

@@ -0,0 +1,172 @@
+// Copyright 2023 LanceDB Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { Table as ArrowTable } from "apache-arrow";
+
+import { Data, IntoVector } from "../arrow";
+
+import { IndexStatistics } from "..";
+import { CreateTableOptions } from "../connection";
+import { IndexOptions } from "../indices";
+import { MergeInsertBuilder } from "../merge";
+import { VectorQuery } from "../query";
+import { AddDataOptions, Table, UpdateOptions } from "../table";
+import { RestfulLanceDBClient } from "./client";
+
+export class RemoteTable extends Table {
+  #client: RestfulLanceDBClient;
+  #name: string;
+
+  // Used in the display() method
+  #dbName: string;
+
+  get #tablePrefix() {
+    return `/v1/table/${encodeURIComponent(this.#name)}/`;
+  }
+
+  get name(): string {
+    return this.#name;
+  }
+
+  public constructor(
+    client: RestfulLanceDBClient,
+    tableName: string,
+    dbName: string,
+  ) {
+    super();
+    this.#client = client;
+    this.#name = tableName;
+    this.#dbName = dbName;
+  }
+
+  isOpen(): boolean {
+    return !this.#client.isOpen();
+  }
+
+  close(): void {
+    this.#client.close();
+  }
+
+  display(): string {
+    return `RemoteTable(${this.#dbName}; ${this.#name})`;
+  }
+
+  async schema(): Promise<import("apache-arrow").Schema> {
+    const resp = await this.#client.post(`${this.#tablePrefix}/describe/`);
+    // TODO: parse this into a valid arrow schema
+    return resp.schema;
+  }
+  async add(data: Data, options?: Partial<AddDataOptions>): Promise<void> {
+    const { buf, mode } = await Table.parseTableData(
+      data,
+      options as CreateTableOptions,
+      true,
+    );
+    await this.#client.post(`${this.#tablePrefix}/insert/`, buf, {
+      params: {
+        mode,
+      },
+      headers: {
+        "Content-Type": "application/vnd.apache.arrow.stream",
+      },
+    });
+  }
+
+  async update(
+    updates: Map<string, string> | Record<string, string>,
+    options?: Partial<UpdateOptions>,
+  ): Promise<void> {
+    await this.#client.post(`${this.#tablePrefix}/update/`, {
+      predicate: options?.where ?? null,
+      updates: Object.entries(updates).map(([key, value]) => [key, value]),
+    });
+  }
+  async countRows(filter?: unknown): Promise<number> {
+    const payload = { predicate: filter };
+    return await this.#client.post(`${this.#tablePrefix}/count_rows/`, payload);
+  }
+
+  async delete(predicate: unknown): Promise<void> {
+    const payload = { predicate };
+    await this.#client.post(`${this.#tablePrefix}/delete/`, payload);
+  }
+  async createIndex(
+    column: string,
+    options?: Partial<IndexOptions>,
+  ): Promise<void> {
+    if (options !== undefined) {
+      console.warn("options are not yet supported on the LanceDB cloud");
+    }
+    const indexType = "vector";
+    const metric = "L2";
+    const data = {
+      column,
+      // biome-ignore lint/style/useNamingConvention: external API
+      index_type: indexType,
+      // biome-ignore lint/style/useNamingConvention: external API
+      metric_type: metric,
+    };
+    await this.#client.post(`${this.#tablePrefix}/create_index`, data);
+  }
+  query(): import("..").Query {
+    throw new Error("query() is not yet supported on the LanceDB cloud");
+  }
+  search(query: IntoVector): VectorQuery;
+  search(query: string): Promise<VectorQuery>;
+  search(_query: string | IntoVector): VectorQuery | Promise<VectorQuery> {
+    throw new Error("search() is not yet supported on the LanceDB cloud");
+  }
+  vectorSearch(_vector: unknown): import("..").VectorQuery {
+    throw new Error("vectorSearch() is not yet supported on the LanceDB cloud");
+  }
+  addColumns(_newColumnTransforms: unknown): Promise<void> {
+    throw new Error("addColumns() is not yet supported on the LanceDB cloud");
+  }
+  alterColumns(_columnAlterations: unknown): Promise<void> {
+    throw new Error("alterColumns() is not yet supported on the LanceDB cloud");
+  }
+  dropColumns(_columnNames: unknown): Promise<void> {
+    throw new Error("dropColumns() is not yet supported on the LanceDB cloud");
+  }
+  async version(): Promise<number> {
+    const resp = await this.#client.post(`${this.#tablePrefix}/describe/`);
+    return resp.version;
+  }
+  checkout(_version: unknown): Promise<void> {
+    throw new Error("checkout() is not yet supported on the LanceDB cloud");
+  }
+  checkoutLatest(): Promise<void> {
+    throw new Error(
+      "checkoutLatest() is not yet supported on the LanceDB cloud",
+    );
+  }
+  restore(): Promise<void> {
+    throw new Error("restore() is not yet supported on the LanceDB cloud");
+  }
+  optimize(_options?: unknown): Promise<import("../native").OptimizeStats> {
+    throw new Error("optimize() is not yet supported on the LanceDB cloud");
+  }
+  async listIndices(): Promise<import("../native").IndexConfig[]> {
+    return await this.#client.post(`${this.#tablePrefix}/index/list/`);
+  }
+  toArrow(): Promise<ArrowTable> {
+    throw new Error("toArrow() is not yet supported on the LanceDB cloud");
+  }
+  mergeInsert(_on: string | string[]): MergeInsertBuilder {
+    throw new Error("mergeInsert() is not yet supported on the LanceDB cloud");
+  }
+  async indexStats(_name: string): Promise<IndexStatistics | undefined> {
+    throw new Error("indexStats() is not yet supported on the LanceDB cloud");
+  }
+}

nodejs/lancedb/sanitize.ts

@@ -20,6 +20,7 @@
 // comes from the exact same library instance.  This is not always the case
 // and so we must sanitize the input to ensure that it is compatible.
 
+import type { IntBitWidth, TKeys, TimeBitWidth } from "apache-arrow/type";
 import {
   Binary,
   Bool,
@@ -75,10 +76,9 @@ import {
   Uint64,
   Union,
   Utf8,
-} from "apache-arrow";
-import type { IntBitWidth, TKeys, TimeBitWidth } from "apache-arrow/type";
+} from "./arrow";
 
-function sanitizeMetadata(
+export function sanitizeMetadata(
   metadataLike?: unknown,
 ): Map<string, string> | undefined {
   if (metadataLike === undefined || metadataLike === null) {
@@ -97,7 +97,7 @@ function sanitizeMetadata(
   return metadataLike as Map<string, string>;
 }
 
-function sanitizeInt(typeLike: object) {
+export function sanitizeInt(typeLike: object) {
   if (
     !("bitWidth" in typeLike) ||
     typeof typeLike.bitWidth !== "number" ||
@@ -111,14 +111,14 @@ function sanitizeInt(typeLike: object) {
   return new Int(typeLike.isSigned, typeLike.bitWidth as IntBitWidth);
 }
 
-function sanitizeFloat(typeLike: object) {
+export function sanitizeFloat(typeLike: object) {
   if (!("precision" in typeLike) || typeof typeLike.precision !== "number") {
     throw Error("Expected a Float Type to have a `precision` property");
   }
   return new Float(typeLike.precision as Precision);
 }
 
-function sanitizeDecimal(typeLike: object) {
+export function sanitizeDecimal(typeLike: object) {
   if (
     !("scale" in typeLike) ||
     typeof typeLike.scale !== "number" ||
@@ -134,14 +134,14 @@ function sanitizeDecimal(typeLike: object) {
   return new Decimal(typeLike.scale, typeLike.precision, typeLike.bitWidth);
 }
 
-function sanitizeDate(typeLike: object) {
+export function sanitizeDate(typeLike: object) {
   if (!("unit" in typeLike) || typeof typeLike.unit !== "number") {
     throw Error("Expected a Date type to have a `unit` property");
   }
   return new Date_(typeLike.unit as DateUnit);
 }
 
-function sanitizeTime(typeLike: object) {
+export function sanitizeTime(typeLike: object) {
   if (
     !("unit" in typeLike) ||
     typeof typeLike.unit !== "number" ||
@@ -155,7 +155,7 @@ function sanitizeTime(typeLike: object) {
   return new Time(typeLike.unit, typeLike.bitWidth as TimeBitWidth);
 }
 
-function sanitizeTimestamp(typeLike: object) {
+export function sanitizeTimestamp(typeLike: object) {
   if (!("unit" in typeLike) || typeof typeLike.unit !== "number") {
     throw Error("Expected a Timestamp type to have a `unit` property");
   }
@@ -166,7 +166,7 @@ function sanitizeTimestamp(typeLike: object) {
   return new Timestamp(typeLike.unit, timezone);
 }
 
-function sanitizeTypedTimestamp(
+export function sanitizeTypedTimestamp(
   typeLike: object,
   // eslint-disable-next-line @typescript-eslint/naming-convention
   Datatype:
@@ -182,14 +182,14 @@ function sanitizeTypedTimestamp(
   return new Datatype(timezone);
 }
 
-function sanitizeInterval(typeLike: object) {
+export function sanitizeInterval(typeLike: object) {
   if (!("unit" in typeLike) || typeof typeLike.unit !== "number") {
     throw Error("Expected an Interval type to have a `unit` property");
   }
   return new Interval(typeLike.unit);
 }
 
-function sanitizeList(typeLike: object) {
+export function sanitizeList(typeLike: object) {
   if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
     throw Error(
       "Expected a List type to have an array-like `children` property",
@@ -201,7 +201,7 @@ function sanitizeList(typeLike: object) {
   return new List(sanitizeField(typeLike.children[0]));
 }
 
-function sanitizeStruct(typeLike: object) {
+export function sanitizeStruct(typeLike: object) {
   if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
     throw Error(
       "Expected a Struct type to have an array-like `children` property",
@@ -210,7 +210,7 @@ function sanitizeStruct(typeLike: object) {
   return new Struct(typeLike.children.map((child) => sanitizeField(child)));
 }
 
-function sanitizeUnion(typeLike: object) {
+export function sanitizeUnion(typeLike: object) {
   if (
     !("typeIds" in typeLike) ||
     !("mode" in typeLike) ||
@@ -234,7 +234,7 @@ function sanitizeUnion(typeLike: object) {
   );
 }
 
-function sanitizeTypedUnion(
+export function sanitizeTypedUnion(
   typeLike: object,
   // eslint-disable-next-line @typescript-eslint/naming-convention
   UnionType: typeof DenseUnion | typeof SparseUnion,
@@ -256,7 +256,7 @@ function sanitizeTypedUnion(
   );
 }
 
-function sanitizeFixedSizeBinary(typeLike: object) {
+export function sanitizeFixedSizeBinary(typeLike: object) {
   if (!("byteWidth" in typeLike) || typeof typeLike.byteWidth !== "number") {
     throw Error(
       "Expected a FixedSizeBinary type to have a `byteWidth` property",
@@ -265,7 +265,7 @@ function sanitizeFixedSizeBinary(typeLike: object) {
   return new FixedSizeBinary(typeLike.byteWidth);
 }
 
-function sanitizeFixedSizeList(typeLike: object) {
+export function sanitizeFixedSizeList(typeLike: object) {
   if (!("listSize" in typeLike) || typeof typeLike.listSize !== "number") {
     throw Error("Expected a FixedSizeList type to have a `listSize` property");
   }
@@ -283,7 +283,7 @@ function sanitizeFixedSizeList(typeLike: object) {
   );
 }
 
-function sanitizeMap(typeLike: object) {
+export function sanitizeMap(typeLike: object) {
   if (!("children" in typeLike) || !Array.isArray(typeLike.children)) {
     throw Error(
       "Expected a Map type to have an array-like `children` property",
@@ -300,14 +300,14 @@ function sanitizeMap(typeLike: object) {
   );
 }
 
-function sanitizeDuration(typeLike: object) {
+export function sanitizeDuration(typeLike: object) {
   if (!("unit" in typeLike) || typeof typeLike.unit !== "number") {
     throw Error("Expected a Duration type to have a `unit` property");
   }
   return new Duration(typeLike.unit);
 }
 
-function sanitizeDictionary(typeLike: object) {
+export function sanitizeDictionary(typeLike: object) {
   if (!("id" in typeLike) || typeof typeLike.id !== "number") {
     throw Error("Expected a Dictionary type to have an `id` property");
   }
@@ -329,7 +329,7 @@ function sanitizeDictionary(typeLike: object) {
 }
 
 // biome-ignore lint/suspicious/noExplicitAny: skip
-function sanitizeType(typeLike: unknown): DataType<any> {
+export function sanitizeType(typeLike: unknown): DataType<any> {
   if (typeof typeLike !== "object" || typeLike === null) {
     throw Error("Expected a Type but object was null/undefined");
   }
@@ -449,7 +449,7 @@ function sanitizeType(typeLike: unknown): DataType<any> {
   }
 }
 
-function sanitizeField(fieldLike: unknown): Field {
+export function sanitizeField(fieldLike: unknown): Field {
   if (fieldLike instanceof Field) {
     return fieldLike;
   }

nodejs/lancedb/table.ts

@@ -12,19 +12,33 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { Schema, tableFromIPC } from "apache-arrow";
-import { Data, fromDataToBuffer } from "./arrow";
+import {
+  Table as ArrowTable,
+  Data,
+  IntoVector,
+  Schema,
+  fromDataToBuffer,
+  fromTableToBuffer,
+  fromTableToStreamBuffer,
+  isArrowTable,
+  makeArrowTable,
+  tableFromIPC,
+} from "./arrow";
+import { CreateTableOptions } from "./connection";
+
+import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
 import { IndexOptions } from "./indices";
+import { MergeInsertBuilder } from "./merge";
 import {
   AddColumnsSql,
   ColumnAlteration,
   IndexConfig,
+  IndexStatistics,
   OptimizeStats,
   Table as _NativeTable,
 } from "./native";
 import { Query, VectorQuery } from "./query";
 
-export { IndexConfig } from "./native";
 /**
  * Options for adding data to a table.
  */
@@ -80,19 +94,15 @@ export interface OptimizeOptions {
  * Closing a table is optional.  It not closed, it will be closed when it is garbage
  * collected.
  */
-export class Table {
-  private readonly inner: _NativeTable;
-
-  /** Construct a Table. Internal use only. */
-  constructor(inner: _NativeTable) {
-    this.inner = inner;
+export abstract class Table {
+  [Symbol.for("nodejs.util.inspect.custom")](): string {
+    return this.display();
   }
+  /** Returns the name of the table */
+  abstract get name(): string;
 
   /** Return true if the table has not been closed */
-  isOpen(): boolean {
-    return this.inner.isOpen();
-  }
-
+  abstract isOpen(): boolean;
   /**
    * Close the table, releasing any underlying resources.
    *
@@ -100,33 +110,16 @@ export class Table {
    *
    * Any attempt to use the table after it is closed will result in an error.
    */
-  close(): void {
-    this.inner.close();
-  }
-
+  abstract close(): void;
   /** Return a brief description of the table */
-  display(): string {
-    return this.inner.display();
-  }
-
+  abstract display(): string;
   /** Get the schema of the table. */
-  async schema(): Promise<Schema> {
-    const schemaBuf = await this.inner.schema();
-    const tbl = tableFromIPC(schemaBuf);
-    return tbl.schema;
-  }
-
+  abstract schema(): Promise<Schema>;
   /**
    * Insert records into this Table.
    * @param {Data} data Records to be inserted into the Table
    */
-  async add(data: Data, options?: Partial<AddDataOptions>): Promise<void> {
-    const mode = options?.mode ?? "append";
-
-    const buffer = await fromDataToBuffer(data);
-    await this.inner.add(buffer, mode);
-  }
-
+  abstract add(data: Data, options?: Partial<AddDataOptions>): Promise<void>;
   /**
    * Update existing records in the Table
    *
@@ -152,30 +145,14 @@ export class Table {
    * @param {Partial<UpdateOptions>} options - additional options to control
    * the update behavior
    */
-  async update(
+  abstract update(
     updates: Map<string, string> | Record<string, string>,
     options?: Partial<UpdateOptions>,
-  ) {
-    const onlyIf = options?.where;
-    let columns: [string, string][];
-    if (updates instanceof Map) {
-      columns = Array.from(updates.entries());
-    } else {
-      columns = Object.entries(updates);
-    }
-    await this.inner.update(onlyIf, columns);
-  }
-
+  ): Promise<void>;
   /** Count the total number of rows in the dataset. */
-  async countRows(filter?: string): Promise<number> {
-    return await this.inner.countRows(filter);
-  }
-
+  abstract countRows(filter?: string): Promise<number>;
   /** Delete the rows that satisfy the predicate. */
-  async delete(predicate: string): Promise<void> {
-    await this.inner.delete(predicate);
-  }
-
+  abstract delete(predicate: string): Promise<void>;
   /**
    * Create an index to speed up queries.
    *
@@ -183,6 +160,9 @@ export class Table {
    * Indices on vector columns will speed up vector searches.
    * Indices on scalar columns will speed up filtering (in both
    * vector and non-vector searches)
+   *
+   * @note We currently don't support custom named indexes,
+   * The index name will always be `${column}_idx`
    * @example
    * // If the column has a vector (fixed size list) data type then
    * // an IvfPq vector index will be created.
@@ -202,13 +182,10 @@ export class Table {
    * // Or create a Scalar index
    * await table.createIndex("my_float_col");
    */
-  async createIndex(column: string, options?: Partial<IndexOptions>) {
-    // Bit of a hack to get around the fact that TS has no package-scope.
-    // biome-ignore lint/suspicious/noExplicitAny: skip
-    const nativeIndex = (options?.config as any)?.inner;
-    await this.inner.createIndex(nativeIndex, column, options?.replace);
-  }
-
+  abstract createIndex(
+    column: string,
+    options?: Partial<IndexOptions>,
+  ): Promise<void>;
   /**
    * Create a {@link Query} Builder.
    *
@@ -259,10 +236,20 @@ export class Table {
    * }
    * @returns {Query} A builder that can be used to parameterize the query
    */
-  query(): Query {
-    return new Query(this.inner);
-  }
-
+  abstract query(): Query;
+  /**
+   * Create a search query to find the nearest neighbors
+   * of the given query vector
+   * @param {string} query - the query. This will be converted to a vector using the table's provided embedding function
+   * @rejects {Error} If no embedding functions are defined in the table
+   */
+  abstract search(query: string): Promise<VectorQuery>;
+  /**
+   * Create a search query to find the nearest neighbors
+   * of the given query vector
+   * @param {IntoVector} query - the query vector
+   */
+  abstract search(query: IntoVector): VectorQuery;
   /**
    * Search the table with a given query vector.
    *
@@ -270,11 +257,7 @@ export class Table {
    * is the same thing as calling `nearestTo` on the builder returned
    * by `query`.  @see {@link Query#nearestTo} for more details.
    */
-  vectorSearch(vector: unknown): VectorQuery {
-    return this.query().nearestTo(vector);
-  }
-
-  // TODO: Support BatchUDF
+  abstract vectorSearch(vector: IntoVector): VectorQuery;
   /**
    * Add new columns with defined values.
    * @param {AddColumnsSql[]} newColumnTransforms pairs of column names and
@@ -282,19 +265,14 @@ export class Table {
    * expressions will be evaluated for each row in the table, and can
    * reference existing columns in the table.
    */
-  async addColumns(newColumnTransforms: AddColumnsSql[]): Promise<void> {
-    await this.inner.addColumns(newColumnTransforms);
-  }
+  abstract addColumns(newColumnTransforms: AddColumnsSql[]): Promise<void>;
 
   /**
    * Alter the name or nullability of columns.
    * @param {ColumnAlteration[]} columnAlterations One or more alterations to
    * apply to columns.
    */
-  async alterColumns(columnAlterations: ColumnAlteration[]): Promise<void> {
-    await this.inner.alterColumns(columnAlterations);
-  }
-
+  abstract alterColumns(columnAlterations: ColumnAlteration[]): Promise<void>;
   /**
    * Drop one or more columns from the dataset
    *
@@ -306,15 +284,10 @@ export class Table {
    * be nested column references (e.g. "a.b.c") or top-level column names
    * (e.g. "a").
    */
-  async dropColumns(columnNames: string[]): Promise<void> {
-    await this.inner.dropColumns(columnNames);
-  }
-
+  abstract dropColumns(columnNames: string[]): Promise<void>;
   /** Retrieve the version of the table */
-  async version(): Promise<number> {
-    return await this.inner.version();
-  }
 
+  abstract version(): Promise<number>;
   /**
    * Checks out a specific version of the table _This is an in-place operation._
    *
@@ -340,19 +313,14 @@ export class Table {
    * console.log(await table.version()); // 2
    * ```
    */
-  async checkout(version: number): Promise<void> {
-    await this.inner.checkout(version);
-  }
-
+  abstract checkout(version: number): Promise<void>;
   /**
    * Checkout the latest version of the table. _This is an in-place operation._
    *
    * The table will be set back into standard mode, and will track the latest
    * version of the table.
    */
-  async checkoutLatest(): Promise<void> {
-    await this.inner.checkoutLatest();
-  }
+  abstract checkoutLatest(): Promise<void>;
 
   /**
    * Restore the table to the currently checked out version
@@ -366,10 +334,7 @@ export class Table {
    * Once the operation concludes the table will no longer be in a checked
    * out state and the read_consistency_interval, if any, will apply.
    */
-  async restore(): Promise<void> {
-    await this.inner.restore();
-  }
-
+  abstract restore(): Promise<void>;
   /**
    * Optimize the on-disk data and indices for better performance.
    *
@@ -400,6 +365,200 @@ export class Table {
    *  you have added or modified 100,000 or more records or run more than 20 data
    *  modification operations.
    */
+  abstract optimize(options?: Partial<OptimizeOptions>): Promise<OptimizeStats>;
+  /** List all indices that have been created with {@link Table.createIndex} */
+  abstract listIndices(): Promise<IndexConfig[]>;
+  /** Return the table as an arrow table */
+  abstract toArrow(): Promise<ArrowTable>;
+
+  abstract mergeInsert(on: string | string[]): MergeInsertBuilder;
+
+  /** List all the stats of a specified index
+   *
+   * @param {string} name The name of the index.
+   * @returns {IndexStatistics | undefined} The stats of the index. If the index does not exist, it will return undefined
+   */
+  abstract indexStats(name: string): Promise<IndexStatistics | undefined>;
+
+  static async parseTableData(
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+    data: Record<string, unknown>[] | ArrowTable<any>,
+    options?: Partial<CreateTableOptions>,
+    streaming = false,
+  ) {
+    let mode: string = options?.mode ?? "create";
+    const existOk = options?.existOk ?? false;
+
+    if (mode === "create" && existOk) {
+      mode = "exist_ok";
+    }
+
+    let table: ArrowTable;
+    if (isArrowTable(data)) {
+      table = data;
+    } else {
+      table = makeArrowTable(data, options);
+    }
+    if (streaming) {
+      const buf = await fromTableToStreamBuffer(
+        table,
+        options?.embeddingFunction,
+        options?.schema,
+      );
+      return { buf, mode };
+    } else {
+      const buf = await fromTableToBuffer(
+        table,
+        options?.embeddingFunction,
+        options?.schema,
+      );
+      return { buf, mode };
+    }
+  }
+}
+
+export class LocalTable extends Table {
+  private readonly inner: _NativeTable;
+
+  constructor(inner: _NativeTable) {
+    super();
+    this.inner = inner;
+  }
+  get name(): string {
+    return this.inner.name;
+  }
+  isOpen(): boolean {
+    return this.inner.isOpen();
+  }
+
+  close(): void {
+    this.inner.close();
+  }
+
+  display(): string {
+    return this.inner.display();
+  }
+
+  private async getEmbeddingFunctions(): Promise<
+    Map<string, EmbeddingFunctionConfig>
+  > {
+    const schema = await this.schema();
+    const registry = getRegistry();
+    return registry.parseFunctions(schema.metadata);
+  }
+
+  /** Get the schema of the table. */
+  async schema(): Promise<Schema> {
+    const schemaBuf = await this.inner.schema();
+    const tbl = tableFromIPC(schemaBuf);
+    return tbl.schema;
+  }
+
+  async add(data: Data, options?: Partial<AddDataOptions>): Promise<void> {
+    const mode = options?.mode ?? "append";
+    const schema = await this.schema();
+    const registry = getRegistry();
+    const functions = registry.parseFunctions(schema.metadata);
+
+    const buffer = await fromDataToBuffer(
+      data,
+      functions.values().next().value,
+      schema,
+    );
+    await this.inner.add(buffer, mode);
+  }
+
+  async update(
+    updates: Map<string, string> | Record<string, string>,
+    options?: Partial<UpdateOptions>,
+  ) {
+    const onlyIf = options?.where;
+    let columns: [string, string][];
+    if (updates instanceof Map) {
+      columns = Array.from(updates.entries());
+    } else {
+      columns = Object.entries(updates);
+    }
+    await this.inner.update(onlyIf, columns);
+  }
+
+  async countRows(filter?: string): Promise<number> {
+    return await this.inner.countRows(filter);
+  }
+
+  async delete(predicate: string): Promise<void> {
+    await this.inner.delete(predicate);
+  }
+
+  async createIndex(column: string, options?: Partial<IndexOptions>) {
+    // Bit of a hack to get around the fact that TS has no package-scope.
+    // biome-ignore lint/suspicious/noExplicitAny: skip
+    const nativeIndex = (options?.config as any)?.inner;
+    await this.inner.createIndex(nativeIndex, column, options?.replace);
+  }
+
+  query(): Query {
+    return new Query(this.inner);
+  }
+
+  search(query: string): Promise<VectorQuery>;
+
+  search(query: IntoVector): VectorQuery;
+  search(query: string | IntoVector): Promise<VectorQuery> | VectorQuery {
+    if (typeof query !== "string") {
+      return this.vectorSearch(query);
+    } else {
+      return 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"),
+          );
+        }
+        const embeddings =
+          await embeddingFunc.function.computeQueryEmbeddings(query);
+        return this.query().nearestTo(embeddings);
+      });
+    }
+  }
+
+  vectorSearch(vector: IntoVector): VectorQuery {
+    return this.query().nearestTo(vector);
+  }
+
+  // TODO: Support BatchUDF
+
+  async addColumns(newColumnTransforms: AddColumnsSql[]): Promise<void> {
+    await this.inner.addColumns(newColumnTransforms);
+  }
+
+  async alterColumns(columnAlterations: ColumnAlteration[]): Promise<void> {
+    await this.inner.alterColumns(columnAlterations);
+  }
+
+  async dropColumns(columnNames: string[]): Promise<void> {
+    await this.inner.dropColumns(columnNames);
+  }
+
+  async version(): Promise<number> {
+    return await this.inner.version();
+  }
+
+  async checkout(version: number): Promise<void> {
+    await this.inner.checkout(version);
+  }
+
+  async checkoutLatest(): Promise<void> {
+    await this.inner.checkoutLatest();
+  }
+
+  async restore(): Promise<void> {
+    await this.inner.restore();
+  }
+
   async optimize(options?: Partial<OptimizeOptions>): Promise<OptimizeStats> {
     let cleanupOlderThanMs;
     if (
@@ -412,8 +571,23 @@ export class Table {
     return await this.inner.optimize(cleanupOlderThanMs);
   }
 
-  /** List all indices that have been created with {@link Table.createIndex} */
   async listIndices(): Promise<IndexConfig[]> {
     return await this.inner.listIndices();
   }
+
+  async toArrow(): Promise<ArrowTable> {
+    return await this.query().toArrow();
+  }
+
+  async indexStats(name: string): Promise<IndexStatistics | undefined> {
+    const stats = await this.inner.indexStats(name);
+    if (stats === null) {
+      return undefined;
+    }
+    return stats;
+  }
+  mergeInsert(on: string | string[]): MergeInsertBuilder {
+    on = Array.isArray(on) ? on : [on];
+    return new MergeInsertBuilder(this.inner.mergeInsert(on));
+  }
 }

nodejs/lancedb/util.ts

@@ -0,0 +1,35 @@
+export class TTLCache {
+  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+  private readonly cache: Map<string, { value: any; expires: number }>;
+
+  /**
+   * @param ttl Time to live in milliseconds
+   */
+  constructor(private readonly ttl: number) {
+    this.cache = new Map();
+  }
+
+  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+  get(key: string): any | undefined {
+    const entry = this.cache.get(key);
+    if (entry === undefined) {
+      return undefined;
+    }
+
+    if (entry.expires < Date.now()) {
+      this.cache.delete(key);
+      return undefined;
+    }
+
+    return entry.value;
+  }
+
+  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+  set(key: string, value: any): void {
+    this.cache.set(key, { value, expires: Date.now() + this.ttl });
+  }
+
+  delete(key: string): void {
+    this.cache.delete(key);
+  }
+}

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.5.0",
+	"version": "0.5.2-final.1",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.5.0",
+	"version": "0.5.2-final.1",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.5.0",
+	"version": "0.5.2-final.1",
 	"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": "0.5.0",
+	"version": "0.5.2-final.1",
 	"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": "0.5.0",
+	"version": "0.5.2-final.1",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -1,8 +1,22 @@
 {
   "name": "@lancedb/lancedb",
-  "version": "0.5.0",
-  "main": "./dist/index.js",
-  "types": "./dist/index.d.ts",
+  "description": "LanceDB: A serverless, low-latency vector database for AI applications",
+  "keywords": [
+    "database",
+    "lance",
+    "lancedb",
+    "search",
+    "vector",
+    "vector database",
+    "ann"
+  ],
+  "version": "0.5.2-final.1",
+  "main": "dist/index.js",
+  "exports": {
+    ".": "./dist/index.js",
+    "./embedding": "./dist/embedding/index.js"
+  },
+  "types": "dist/index.d.ts",
   "napi": {
     "name": "lancedb",
     "triples": {
@@ -34,7 +48,8 @@
     "typedoc": "^0.25.7",
     "typedoc-plugin-markdown": "^3.17.1",
     "typescript": "^5.3.3",
-    "typescript-eslint": "^7.1.0"
+    "typescript-eslint": "^7.1.0",
+    "@types/axios": "^0.14.0"
   },
   "ava": {
     "timeout": "3m"
@@ -62,6 +77,8 @@
   },
   "dependencies": {
     "apache-arrow": "^15.0.0",
-    "openai": "^4.29.2"
+    "axios": "^1.7.2",
+    "openai": "^4.29.2",
+    "reflect-metadata": "^0.2.2"
   }
 }

nodejs/src/connection.rs

@@ -56,12 +56,6 @@ impl Connection {
     #[napi(factory)]
     pub async fn new(uri: String, options: ConnectionOptions) -> napi::Result<Self> {
         let mut builder = ConnectBuilder::new(&uri);
-        if let Some(api_key) = options.api_key {
-            builder = builder.api_key(&api_key);
-        }
-        if let Some(host_override) = options.host_override {
-            builder = builder.host_override(&host_override);
-        }
         if let Some(interval) = options.read_consistency_interval {
             builder =
                 builder.read_consistency_interval(std::time::Duration::from_secs_f64(interval));
@@ -126,6 +120,7 @@ impl Connection {
         buf: Buffer,
         mode: String,
         storage_options: Option<HashMap<String, String>>,
+        use_legacy_format: Option<bool>,
     ) -> 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)))?;
@@ -136,6 +131,9 @@ impl Connection {
                 builder = builder.storage_option(key, value);
             }
         }
+        if let Some(use_legacy_format) = use_legacy_format {
+            builder = builder.use_legacy_format(use_legacy_format);
+        }
         let tbl = builder
             .execute()
             .await
@@ -150,6 +148,7 @@ impl Connection {
         schema_buf: Buffer,
         mode: String,
         storage_options: Option<HashMap<String, String>>,
+        use_legacy_format: Option<bool>,
     ) -> 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))
@@ -164,6 +163,9 @@ impl Connection {
                 builder = builder.storage_option(key, value);
             }
         }
+        if let Some(use_legacy_format) = use_legacy_format {
+            builder = builder.use_legacy_format(use_legacy_format);
+        }
         let tbl = builder
             .execute()
             .await

nodejs/src/lib.rs

@@ -20,6 +20,7 @@ mod connection;
 mod error;
 mod index;
 mod iterator;
+pub mod merge;
 mod query;
 mod table;
 mod util;
@@ -27,8 +28,6 @@ mod util;
 #[napi(object)]
 #[derive(Debug)]
 pub struct ConnectionOptions {
-    pub api_key: Option<String>,
-    pub host_override: Option<String>,
     /// (For LanceDB OSS only): The interval, in seconds, at which to check for
     /// updates to the table from other processes. If None, then consistency is not
     /// checked. For performance reasons, this is the default. For strong
@@ -56,6 +55,7 @@ pub enum WriteMode {
 /// Write options when creating a Table.
 #[napi(object)]
 pub struct WriteOptions {
+    /// Write mode for writing to a table.
     pub mode: Option<WriteMode>,
 }
 

nodejs/src/merge.rs

@@ -0,0 +1,53 @@
+use lancedb::{arrow::IntoArrow, ipc::ipc_file_to_batches, table::merge::MergeInsertBuilder};
+use napi::bindgen_prelude::*;
+use napi_derive::napi;
+
+#[napi]
+#[derive(Clone)]
+/// A builder used to create and run a merge insert operation
+pub struct NativeMergeInsertBuilder {
+    pub(crate) inner: MergeInsertBuilder,
+}
+
+#[napi]
+impl NativeMergeInsertBuilder {
+    #[napi]
+    pub fn when_matched_update_all(&self, condition: Option<String>) -> Self {
+        let mut this = self.clone();
+        this.inner.when_matched_update_all(condition);
+        this
+    }
+
+    #[napi]
+    pub fn when_not_matched_insert_all(&self) -> Self {
+        let mut this = self.clone();
+        this.inner.when_not_matched_insert_all();
+        this
+    }
+    #[napi]
+    pub fn when_not_matched_by_source_delete(&self, filter: Option<String>) -> Self {
+        let mut this = self.clone();
+        this.inner.when_not_matched_by_source_delete(filter);
+        this
+    }
+
+    #[napi]
+    pub async fn execute(&self, buf: Buffer) -> napi::Result<()> {
+        let data = ipc_file_to_batches(buf.to_vec())
+            .and_then(IntoArrow::into_arrow)
+            .map_err(|e| napi::Error::from_reason(format!("Failed to read IPC file: {}", e)))?;
+
+        let this = self.clone();
+
+        this.inner
+            .execute(data)
+            .await
+            .map_err(|e| napi::Error::from_reason(format!("Failed to execute merge insert: {}", e)))
+    }
+}
+
+impl From<MergeInsertBuilder> for NativeMergeInsertBuilder {
+    fn from(inner: MergeInsertBuilder) -> Self {
+        Self { inner }
+    }
+}

nodejs/src/query.rs

@@ -15,6 +15,7 @@
 use lancedb::query::ExecutableQuery;
 use lancedb::query::Query as LanceDbQuery;
 use lancedb::query::QueryBase;
+use lancedb::query::QueryExecutionOptions;
 use lancedb::query::Select;
 use lancedb::query::VectorQuery as LanceDbVectorQuery;
 use napi::bindgen_prelude::*;
@@ -62,10 +63,21 @@ impl Query {
     }
 
     #[napi]
-    pub async fn execute(&self) -> napi::Result<RecordBatchIterator> {
-        let inner_stream = self.inner.execute().await.map_err(|e| {
-            napi::Error::from_reason(format!("Failed to execute query stream: {}", e))
-        })?;
+    pub async fn execute(
+        &self,
+        max_batch_length: Option<u32>,
+    ) -> napi::Result<RecordBatchIterator> {
+        let mut execution_opts = QueryExecutionOptions::default();
+        if let Some(max_batch_length) = max_batch_length {
+            execution_opts.max_batch_length = max_batch_length;
+        }
+        let inner_stream = self
+            .inner
+            .execute_with_options(execution_opts)
+            .await
+            .map_err(|e| {
+                napi::Error::from_reason(format!("Failed to execute query stream: {}", e))
+            })?;
         Ok(RecordBatchIterator::new(inner_stream))
     }
 }
@@ -125,10 +137,21 @@ impl VectorQuery {
     }
 
     #[napi]
-    pub async fn execute(&self) -> napi::Result<RecordBatchIterator> {
-        let inner_stream = self.inner.execute().await.map_err(|e| {
-            napi::Error::from_reason(format!("Failed to execute query stream: {}", e))
-        })?;
+    pub async fn execute(
+        &self,
+        max_batch_length: Option<u32>,
+    ) -> napi::Result<RecordBatchIterator> {
+        let mut execution_opts = QueryExecutionOptions::default();
+        if let Some(max_batch_length) = max_batch_length {
+            execution_opts.max_batch_length = max_batch_length;
+        }
+        let inner_stream = self
+            .inner
+            .execute_with_options(execution_opts)
+            .await
+            .map_err(|e| {
+                napi::Error::from_reason(format!("Failed to execute query stream: {}", e))
+            })?;
         Ok(RecordBatchIterator::new(inner_stream))
     }
 }

nodejs/src/table.rs

@@ -23,13 +23,14 @@ use napi_derive::napi;
 
 use crate::error::NapiErrorExt;
 use crate::index::Index;
+use crate::merge::NativeMergeInsertBuilder;
 use crate::query::{Query, VectorQuery};
 
 #[napi]
 pub struct Table {
     // We keep a duplicate of the table name so we can use it for error
     // messages even if the table has been closed
-    name: String,
+    pub name: String,
     pub(crate) inner: Option<LanceDbTable>,
 }
 
@@ -328,16 +329,31 @@ impl Table {
             .map(IndexConfig::from)
             .collect::<Vec<_>>())
     }
+
+    #[napi]
+    pub async fn index_stats(&self, index_name: String) -> napi::Result<Option<IndexStatistics>> {
+        let tbl = self.inner_ref()?.as_native().unwrap();
+        let stats = tbl.index_stats(&index_name).await.default_error()?;
+        Ok(stats.map(IndexStatistics::from))
+    }
+
+    #[napi]
+    pub fn merge_insert(&self, on: Vec<String>) -> napi::Result<NativeMergeInsertBuilder> {
+        let on: Vec<_> = on.iter().map(String::as_str).collect();
+        Ok(self.inner_ref()?.merge_insert(on.as_slice()).into())
+    }
 }
 
 #[napi(object)]
 /// A description of an index currently configured on a column
 pub struct IndexConfig {
+    /// The name of the index
+    pub name: String,
     /// The type of the index
     pub index_type: String,
     /// The columns in the index
     ///
-    /// Currently this is always an array of size 1.  In the future there may
+    /// Currently this is always an array of size 1. In the future there may
     /// be more columns to represent composite indices.
     pub columns: Vec<String>,
 }
@@ -348,6 +364,7 @@ impl From<lancedb::index::IndexConfig> for IndexConfig {
         Self {
             index_type,
             columns: value.columns,
+            name: value.name,
         }
     }
 }
@@ -430,3 +447,40 @@ pub struct AddColumnsSql {
     /// The expression can reference other columns in the table.
     pub value_sql: String,
 }
+
+#[napi(object)]
+pub struct IndexStatistics {
+    /// The number of rows indexed by the index
+    pub num_indexed_rows: f64,
+    /// The number of rows not indexed
+    pub num_unindexed_rows: f64,
+    /// The type of the index
+    pub index_type: Option<String>,
+    /// The metadata for each index
+    pub indices: Vec<IndexMetadata>,
+}
+impl From<lancedb::index::IndexStatistics> for IndexStatistics {
+    fn from(value: lancedb::index::IndexStatistics) -> Self {
+        Self {
+            num_indexed_rows: value.num_indexed_rows as f64,
+            num_unindexed_rows: value.num_unindexed_rows as f64,
+            index_type: value.index_type.map(|t| format!("{:?}", t)),
+            indices: value.indices.into_iter().map(Into::into).collect(),
+        }
+    }
+}
+
+#[napi(object)]
+pub struct IndexMetadata {
+    pub metric_type: Option<String>,
+    pub index_type: Option<String>,
+}
+
+impl From<lancedb::index::IndexMetadata> for IndexMetadata {
+    fn from(value: lancedb::index::IndexMetadata) -> Self {
+        Self {
+            metric_type: value.metric_type,
+            index_type: value.index_type,
+        }
+    }
+}

nodejs/tsconfig.json

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

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.8.0"
+current_version = "0.9.0-beta.7"
 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 = "0.8.0"
+version = "0.9.0-beta.7"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true
@@ -19,6 +19,7 @@ lancedb = { path = "../rust/lancedb" }
 env_logger = "0.10"
 pyo3 = { version = "0.20", features = ["extension-module", "abi3-py38"] }
 pyo3-asyncio = { version = "0.20", features = ["attributes", "tokio-runtime"] }
+base64ct = "=1.6.0" # workaround for https://github.com/RustCrypto/formats/issues/1684
 
 # Prevent dynamic linking of lzma, which comes from datafusion
 lzma-sys = { version = "*", features = ["static"] }

python/pyproject.toml

@@ -3,16 +3,17 @@ name = "lancedb"
 # version in Cargo.toml
 dependencies = [
     "deprecation",
-    "pylance==0.11.0",
+    "pylance==0.13.0",
     "ratelimiter~=1.0",
     "requests>=2.31.0",
     "retry>=0.9.2",
     "tqdm>=4.27.0",
     "pydantic>=1.10",
     "attrs>=21.3.0",
-    "semver",
+    "packaging",
     "cachetools",
     "overrides>=0.7",
+    "urllib3==1.26.19"
 ]
 description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
@@ -57,15 +58,10 @@ tests = [
     "duckdb",
     "pytz",
     "polars>=0.19",
-    "tantivy"
+    "tantivy",
 ]
 dev = ["ruff", "pre-commit"]
-docs = [
-    "mkdocs",
-    "mkdocs-jupyter",
-    "mkdocs-material",
-    "mkdocstrings[python]",
-]
+docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"]
 clip = ["torch", "pillow", "open-clip"]
 embeddings = [
     "openai>=1.6.1",
@@ -100,5 +96,5 @@ addopts = "--strict-markers --ignore-glob=lancedb/embeddings/*.py"
 markers = [
     "slow: marks tests as slow (deselect with '-m \"not slow\"')",
     "asyncio",
-    "s3_test"
+    "s3_test",
 ]

python/python/lancedb/__init__.py

@@ -35,6 +35,7 @@ def connect(
     host_override: Optional[str] = None,
     read_consistency_interval: Optional[timedelta] = None,
     request_thread_pool: Optional[Union[int, ThreadPoolExecutor]] = None,
+    storage_options: Optional[Dict[str, str]] = None,
     **kwargs,
 ) -> DBConnection:
     """Connect to a LanceDB database.
@@ -70,6 +71,9 @@ def connect(
         executor will be used for making requests. This is for LanceDB Cloud
         only and is only used when making batch requests (i.e., passing in
         multiple queries to the search method at once).
+    storage_options: dict, optional
+        Additional options for the storage backend. See available options at
+        https://lancedb.github.io/lancedb/guides/storage/
 
     Examples
     --------
@@ -105,12 +109,16 @@ def connect(
             region,
             host_override,
             request_thread_pool=request_thread_pool,
+            storage_options=storage_options,
             **kwargs,
         )
 
     if kwargs:
         raise ValueError(f"Unknown keyword arguments: {kwargs}")
-    return LanceDBConnection(uri, read_consistency_interval=read_consistency_interval)
+    return LanceDBConnection(
+        uri,
+        read_consistency_interval=read_consistency_interval,
+    )
 
 
 async def connect_async(

python/python/lancedb/_lancedb.pyi

@@ -24,6 +24,7 @@ class Connection(object):
         mode: str,
         data: pa.RecordBatchReader,
         storage_options: Optional[Dict[str, str]] = None,
+        use_legacy_format: Optional[bool] = None,
     ) -> Table: ...
     async def create_empty_table(
         self,
@@ -31,6 +32,7 @@ class Connection(object):
         mode: str,
         schema: pa.Schema,
         storage_options: Optional[Dict[str, str]] = None,
+        use_legacy_format: Optional[bool] = None,
     ) -> Table: ...
 
 class Table:
@@ -72,7 +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: ...
-    async def execute(self) -> RecordBatchStream: ...
+    async def execute(self, max_batch_legnth: Optional[int]) -> RecordBatchStream: ...
 
 class VectorQuery:
     async def execute(self) -> RecordBatchStream: ...

python/python/lancedb/db.py

@@ -509,7 +509,7 @@ class AsyncConnection(object):
         return self._inner.__repr__()
 
     def __enter__(self):
-        self
+        return self
 
     def __exit__(self, *_):
         self.close()
@@ -558,6 +558,8 @@ class AsyncConnection(object):
         on_bad_vectors: Optional[str] = None,
         fill_value: Optional[float] = None,
         storage_options: Optional[Dict[str, str]] = None,
+        *,
+        use_legacy_format: Optional[bool] = None,
     ) -> AsyncTable:
         """Create an [AsyncTable][lancedb.table.AsyncTable] in the database.
 
@@ -600,6 +602,9 @@ 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
+            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.
 
 
         Returns
@@ -761,7 +766,11 @@ class AsyncConnection(object):
 
         if data is None:
             new_table = await self._inner.create_empty_table(
-                name, mode, schema, storage_options=storage_options
+                name,
+                mode,
+                schema,
+                storage_options=storage_options,
+                use_legacy_format=use_legacy_format,
             )
         else:
             data = data_to_reader(data, schema)
@@ -770,6 +779,7 @@ class AsyncConnection(object):
                 mode,
                 data,
                 storage_options=storage_options,
+                use_legacy_format=use_legacy_format,
             )
 
         return AsyncTable(new_table)
@@ -779,7 +789,7 @@ class AsyncConnection(object):
         name: str,
         storage_options: Optional[Dict[str, str]] = None,
         index_cache_size: Optional[int] = None,
-    ) -> Table:
+    ) -> AsyncTable:
         """Open a Lance Table in the database.
 
         Parameters