Bump to 0.1.11

Closes #288 and #287

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.12
+current_version = 0.1.13
 commit = True
 message = Bump version: {current_version} → {new_version}
 tag = True

.github/workflows/docs_test.yml

@@ -81,7 +81,7 @@ jobs:
       run: |
         cd docs/test/node_modules/vectordb 
         npm ci
-        npm run build
+        npm run build-release
         npm run tsc
     - name: Create test files
       run: |

.github/workflows/node.yml

@@ -67,8 +67,12 @@ jobs:
     - name: Build
       run: |
         npm ci
-        npm run build
         npm run tsc
+        npm run build
+        npm run pack-build
+        npm install --no-save ./dist/vectordb-*.tgz
+        # Remove index.node to test with dependency installed
+        rm index.node
     - name: Test
       run: npm run test
   macos:
@@ -94,8 +98,12 @@ jobs:
     - name: Build
       run: |
         npm ci
-        npm run build
         npm run tsc
+        npm run build
+        npm run pack-build
+        npm install --no-save ./dist/vectordb-*.tgz
+        # Remove index.node to test with dependency installed
+        rm index.node
     - name: Test
       run: |
         npm run test

.github/workflows/npm-publish.yml

@@ -0,0 +1,171 @@
+name: NPM Publish
+
+on:
+  release:
+    types: [ published ]
+
+jobs:
+  node:
+    runs-on: ubuntu-latest
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    defaults:
+      run:
+        shell: bash
+        working-directory: node
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - uses: actions/setup-node@v3
+        with:
+          node-version: 20
+          cache: 'npm'
+          cache-dependency-path: node/package-lock.json
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+      - name: Build
+        run: |
+          npm ci
+          npm run tsc
+          npm pack
+      - name: Upload Linux Artifacts
+        uses: actions/upload-artifact@v3
+        with:
+          name: node-package
+          path: |
+            node/vectordb-*.tgz
+
+  node-macos:
+    runs-on: macos-12
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    strategy:
+      fail-fast: false
+      matrix:
+        target: [x86_64-apple-darwin, aarch64-apple-darwin]
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v3
+    - name: Install system dependencies
+      run: brew install protobuf
+    - name: Install npm dependencies
+      run: |
+        cd node
+        npm ci
+    - name: Install rustup target
+      if: ${{ matrix.target == 'aarch64-apple-darwin' }}
+      run: rustup target add aarch64-apple-darwin
+    - name: Build MacOS native node modules
+      run: bash ci/build_macos_artifacts.sh ${{ matrix.target }}
+    - name: Upload Darwin Artifacts
+      uses: actions/upload-artifact@v3
+      with:
+        name: darwin-native
+        path: |
+          node/dist/vectordb-darwin*.tgz
+
+  node-linux:
+    name: node-linux (${{ matrix.arch}}-unknown-linux-${{ matrix.libc }})
+    runs-on: ubuntu-latest
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    strategy:
+      fail-fast: false
+      matrix:
+        libc:
+          - gnu
+          # TODO: re-enable musl once we have refactored to pre-built containers
+          # Right now we have to build node from source which is too expensive.
+          # - musl
+        arch:
+          - x86_64
+          # Building on aarch64 is too slow for now
+          # - aarch64
+    steps:
+    - name: Checkout
+      uses: actions/checkout@v3
+    - name: Change owner to root (for npm)
+      # The docker container is run as root, so we need the files to be owned by root
+      # Otherwise npm is a nightmare: https://github.com/npm/cli/issues/3773
+      run: sudo chown -R root:root .
+    - name: Set up QEMU
+      if: ${{ matrix.arch == 'aarch64' }}
+      uses: docker/setup-qemu-action@v2
+      with:
+        platforms: arm64
+    - name: Build Linux GNU native node modules
+      if: ${{ matrix.libc == 'gnu' }}
+      run: |
+        docker run \
+          -v $(pwd):/io -w /io \
+          rust:1.70-bookworm \
+          bash ci/build_linux_artifacts.sh ${{ matrix.arch }}-unknown-linux-gnu
+    - name: Build musl Linux native node modules
+      if: ${{ matrix.libc == 'musl' }}
+      run: |
+        docker run --platform linux/arm64/v8 \
+          -v $(pwd):/io -w /io \
+          quay.io/pypa/musllinux_1_1_${{ matrix.arch }} \
+          bash ci/build_linux_artifacts.sh ${{ matrix.arch }}-unknown-linux-musl
+    - name: Upload Linux Artifacts
+      uses: actions/upload-artifact@v3
+      with:
+        name: linux-native
+        path: |
+          node/dist/vectordb-linux*.tgz
+
+  node-windows:
+    runs-on: windows-2022
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    strategy:
+      fail-fast: false
+      matrix:
+        target: [x86_64-pc-windows-msvc]
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+      - name: Install Protoc v21.12
+        working-directory: C:\
+        run: |
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          7z x protoc.zip
+          Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
+      - name: Install npm dependencies
+        run: |
+          cd node
+          npm ci
+      - name: Build Windows native node modules
+        run: .\ci\build_windows_artifacts.ps1 ${{ matrix.target }}
+      - name: Upload Windows Artifacts
+        uses: actions/upload-artifact@v3
+        with:
+          name: windows-native
+          path: |
+            node/dist/vectordb-win32*.tgz
+
+  release:
+    needs: [node, node-macos, node-linux]
+    runs-on: ubuntu-latest
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    steps:
+      - uses: actions/download-artifact@v3
+      - name: Display structure of downloaded files
+        run: ls -R
+      - uses: actions/setup-node@v3
+        with:
+          node-version: 20
+      - name: Publish to NPM
+        env:
+          NODE_AUTH_TOKEN: ${{ secrets.LANCEDB_NPM_REGISTRY_TOKEN }}
+        run: |
+          mv */*.tgz .
+          for filename in *.tgz; do
+            npm publish $filename
+          done

.github/workflows/rust.yml

@@ -66,3 +66,24 @@ jobs:
         run: cargo build --all-features
       - name: Run tests
         run: cargo test --all-features
+  windows:
+    runs-on: windows-2022
+    steps:
+      - uses: actions/checkout@v3
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: rust
+      - name: Install Protoc v21.12
+        working-directory: C:\
+        run: |
+          New-Item -Path 'C:\protoc' -ItemType Directory
+          Set-Location C:\protoc
+          Invoke-WebRequest https://github.com/protocolbuffers/protobuf/releases/download/v21.12/protoc-21.12-win64.zip -OutFile C:\protoc\protoc.zip
+          7z x protoc.zip
+          Add-Content $env:GITHUB_PATH "C:\protoc\bin"
+        shell: powershell
+      - name: Run tests
+        run: |
+          $env:VCPKG_ROOT = $env:VCPKG_INSTALLATION_ROOT
+          cargo build
+          cargo test

.gitignore

@@ -5,6 +5,8 @@
 .DS_Store
 venv
 
+.vscode
+
 rust/target
 rust/Cargo.lock
 

Cargo.toml

@@ -6,9 +6,11 @@ members = [
 resolver = "2"
 
 [workspace.dependencies]
-lance = "=0.5.5"
+lance = "=0.5.8"
 arrow-array = "42.0"
 arrow-data = "42.0"
 arrow-schema = "42.0"
 arrow-ipc = "42.0"
+half = { "version" = "2.2.1", default-features = false }
 object_store = "0.6.1"
+

ci/build_linux_artifacts.sh

@@ -0,0 +1,72 @@
+#!/bin/bash
+# Builds the Linux artifacts (node binaries).
+# Usage: ./build_linux_artifacts.sh [target]
+# Targets supported: 
+# - x86_64-unknown-linux-gnu:centos
+# - aarch64-unknown-linux-gnu:centos
+# - aarch64-unknown-linux-musl
+# - x86_64-unknown-linux-musl
+
+# TODO: refactor this into a Docker container we can pull
+
+set -e
+
+setup_dependencies() {
+    echo "Installing system dependencies..."
+    if [[ $1 == *musl ]]; then
+        # musllinux
+        apk add openssl-dev
+    else
+        # rust / debian
+        apt update
+        apt install -y libssl-dev protobuf-compiler
+    fi
+}
+
+install_node() {
+    echo "Installing node..."
+    curl -o- https://raw.githubusercontent.com/nvm-sh/nvm/v0.34.0/install.sh | bash
+    source "$HOME"/.bashrc
+
+    if [[ $1 == *musl ]]; then
+        # This node version is 15, we need 16 or higher:
+        # apk add nodejs-current npm 
+        # So instead we install from source (nvm doesn't provide binaries for musl):
+        nvm install -s --no-progress 17
+    else
+        nvm install --no-progress 17 # latest that supports glibc 2.17
+    fi
+}
+
+build_node_binary() {
+    echo "Building node library for $1..."
+    pushd node
+
+    npm ci
+    
+    if [[ $1 == *musl ]]; then
+        # This is needed for cargo to allow build cdylibs with musl
+        export RUSTFLAGS="-C target-feature=-crt-static"
+    fi
+
+    # Cargo can run out of memory while pulling dependencies, especially when running
+    # in QEMU. This is a workaround for that.
+    export CARGO_NET_GIT_FETCH_WITH_CLI=true
+
+    # We don't pass in target, since the native target here already matches
+    # We need to pass OPENSSL_LIB_DIR and OPENSSL_INCLUDE_DIR for static build to work https://github.com/sfackler/rust-openssl/issues/877
+    OPENSSL_STATIC=1 OPENSSL_LIB_DIR=/usr/lib/x86_64-linux-gnu OPENSSL_INCLUDE_DIR=/usr/include/openssl/ npm run build-release
+    npm run pack-build
+
+    popd
+}
+
+TARGET=${1:-x86_64-unknown-linux-gnu}
+# Others:
+# aarch64-unknown-linux-gnu
+# x86_64-unknown-linux-musl
+# aarch64-unknown-linux-musl
+
+setup_dependencies $TARGET
+install_node $TARGET
+build_node_binary $TARGET

ci/build_macos_artifacts.sh

@@ -0,0 +1,33 @@
+# Builds the macOS artifacts (node binaries).
+# Usage: ./ci/build_macos_artifacts.sh [target]
+# Targets supported: x86_64-apple-darwin aarch64-apple-darwin
+
+prebuild_rust() {
+    # Building here for the sake of easier debugging.
+    pushd rust/ffi/node
+    echo "Building rust library for $1"
+    export RUST_BACKTRACE=1
+    cargo build --release --target $1
+    popd
+}
+
+build_node_binaries() {
+    pushd node
+    echo "Building node library for $1"
+    npm run build-release -- --target $1
+    npm run pack-build -- --target $1
+    popd
+}
+
+if [ -n "$1" ]; then
+    targets=$1
+else
+    targets="x86_64-apple-darwin aarch64-apple-darwin"
+fi
+
+echo "Building artifacts for targets: $targets"
+for target in $targets
+    do
+    prebuild_rust $target
+    build_node_binaries $target
+done

ci/build_windows_artifacts.ps1

@@ -0,0 +1,41 @@
+# Builds the Windows artifacts (node binaries).
+# Usage:  .\ci\build_windows_artifacts.ps1 [target]
+# Targets supported:
+# - x86_64-pc-windows-msvc
+# - i686-pc-windows-msvc
+
+function Prebuild-Rust {
+    param (
+        [string]$target
+    )
+
+    # Building here for the sake of easier debugging.
+    Push-Location -Path "rust/ffi/node"
+    Write-Host "Building rust library for $target"
+    $env:RUST_BACKTRACE=1
+    cargo build --release --target $target
+    Pop-Location
+}
+
+function Build-NodeBinaries {
+    param (
+        [string]$target
+    )
+
+    Push-Location -Path "node"
+    Write-Host "Building node library for $target"
+    npm run build-release -- --target $target
+    npm run pack-build -- --target $target
+    Pop-Location
+}
+
+$targets = $args[0]
+if (-not $targets) {
+    $targets = "x86_64-pc-windows-msvc"
+}
+
+Write-Host "Building artifacts for targets: $targets"
+foreach ($target in $targets) {
+    Prebuild-Rust $target
+    Build-NodeBinaries $target
+}

docs/mkdocs.yml

@@ -50,13 +50,19 @@ markdown_extensions:
 - pymdownx.superfences
 - pymdownx.tabbed:
     alternate_style: true
+- md_in_html
 
 nav:
 - Home: index.md
 - Basics: basic.md
 - Embeddings: embedding.md
 - Python full-text search: fts.md
-- Python integrations: integrations.md
+- Python integrations:
+  - Pandas and PyArrow: python/arrow.md
+  - DuckDB: python/duckdb.md
+  - LangChain 🦜️🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+  - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+  - Pydantic: python/pydantic.md
 - Python examples:
   - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
   - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
@@ -65,6 +71,7 @@ nav:
   - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
 - Javascript examples:
   - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
+  - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
 - References:
   - Vector Search: search.md
   - SQL filters: sql.md

docs/src/ann_indexes.md

@@ -1,7 +1,7 @@
 # ANN (Approximate Nearest Neighbor) Indexes
 
 You can create an index over your vector data to make search faster.
-Vector indexes are faster but less accurate than exhaustive search.
+Vector indexes are faster but less accurate than exhaustive search (KNN or Flat Search).
 LanceDB provides many parameters to fine-tune the index's size, the speed of queries, and the accuracy of results.
 
 Currently, LanceDB does *not* automatically create the ANN index.
@@ -10,7 +10,18 @@ If you can live with <100ms latency, skipping index creation is a simpler workfl
 
 In the future we will look to automatically create and configure the ANN index.
 
-## Creating an ANN Index
+## Types of Index
+
+Lance can support multiple index types, the most widely used one is `IVF_PQ`.
+
+* `IVF_PQ`: use **Inverted File Index (IVF)** to first divide the dataset into `N` partitions,
+   and then use **Product Quantization** to compress vectors in each partition.
+* `DISKANN` (**Experimental**): organize the vector as a on-disk graph, where the vertices approximately
+   represent the nearest neighbors of each vector.
+
+## Creating an IVF_PQ Index
+
+Lance supports `IVF_PQ` index type by default.
 
 === "Python"
      Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
@@ -45,15 +56,18 @@ In the future we will look to automatically create and configure the ANN index.
      await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 256, num_sub_vectors: 96 })
      ```
 
-Since `create_index` has a training step, it can take a few minutes to finish for large tables. You can control the index
-creation by providing the following parameters:
+- **metric** (default: "L2"): The distance metric to use. By default it uses euclidean distance "`L2`".
+We also support "cosine" and "dot" distance as well.
+- **num_partitions** (default: 256): The number of partitions of the index.
+- **num_sub_vectors** (default: 96): The number of sub-vectors (M) that will be created during Product Quantization (PQ).
+For D dimensional vector, it will be divided into `M` of `D/M` sub-vectors, each of which is presented by
+a single PQ code.
+
+<figure markdown>
+  ![IVF PQ](./assets/ivf_pq.png)
+  <figcaption>IVF_PQ index with <code>num_partitions=2, num_sub_vectors=4</code></figcaption>
+</figure>
 
-- **metric** (default: "L2"): The distance metric to use. By default we use euclidean distance. We also support "cosine" distance.
-- **num_partitions** (default: 256): The number of partitions of the index. The number of partitions should be configured so each partition has 3-5K vectors. For example, a table
-with ~1M vectors should use 256 partitions. You can specify arbitrary number of partitions but powers of 2 is most conventional.
-A higher number leads to faster queries, but it makes index generation slower.
-- **num_sub_vectors** (default: 96): The number of subvectors (M) that will be created during Product Quantization (PQ). A larger number makes
-search more accurate, but also makes the index larger and slower to build.
 
 ## Querying an ANN Index
 
@@ -138,3 +152,31 @@ You can select the columns returned by the query using a select clause.
          .select(["id"])
          .execute()
      ```
+
+## FAQ
+
+### When is it necessary to create an ANN vector index.
+
+`LanceDB` has manually tuned SIMD code for computing vector distances.
+In our benchmarks, computing 100K pairs of 1K dimension vectors only take less than 20ms.
+For small dataset (<100K rows) or the applications which can accept 100ms latency, vector indices are usually not necessary.
+
+For large-scale or higher dimension vectors, it is beneficial to create vector index.
+
+### How big is my index, and how many memory will it take.
+
+In LanceDB, all vector indices are disk-based, meaning that when responding to a vector query, only the relevant pages from the index file are loaded from disk and cached in memory. Additionally, each sub-vector is usually encoded into 1 byte PQ code.
+
+For example, with a 1024-dimension dataset, if we choose `num_sub_vectors=64`, each sub-vector has `1024 / 64 = 16` float32 numbers.
+Product quantization can lead to approximately `16 * sizeof(float32) / 1 = 64` times of space reduction.
+
+### How to choose `num_partitions` and `num_sub_vectors` for `IVF_PQ` index.
+
+`num_partitions` is used to decide how many partitions the first level `IVF` index uses.
+Higher number of partitions could lead to more efficient I/O during queries and better accuracy, but it takes much more time to train.
+On `SIFT-1M` dataset, our benchmark shows that keeping each partition 1K-4K rows lead to a good latency / recall.
+
+`num_sub_vectors` decides how many Product Quantization code to generate on each vector. Because
+Product Quantization is a lossy compression of the original vector, the more `num_sub_vectors` usually results to
+less space distortion, and thus yield better accuracy. However, similarly, more `num_sub_vectors` causes heavier I/O and
+more PQ computation, thus, higher latency. `dimension / num_sub_vectors` should be aligned with 8 for better SIMD efficiency.

docs/src/embedding.md

@@ -46,7 +46,7 @@ You can also use an external API like OpenAI to generate embeddings
 
         def embed_func(c):
             rs = openai.Embedding.create(input=c, engine="text-embedding-ada-002")
-        return [record["embedding"] for record in rs["data"]]
+            return [record["embedding"] for record in rs["data"]]
       ```
 
 === "Javascript"
@@ -126,7 +126,7 @@ belong in the same latent space and your results will be nonsensical.
 === "Javascript"
      ```javascript
       const results = await table
-        .search('What's the best pizza topping?')
+        .search("What's the best pizza topping?")
         .limit(10)
         .execute()
      ```

docs/src/examples/transformerjs_embedding_search_nodejs.md

@@ -0,0 +1,121 @@
+# Vector embedding search using TransformersJS
+
+## Embed and query data from LacneDB using TransformersJS
+
+<img id="splash" width="400" alt="transformersjs" src="https://github.com/lancedb/lancedb/assets/43097991/88a31e30-3d6f-4eef-9216-4b7c688f1b4f">
+
+This example shows how to use the [transformers.js](https://github.com/xenova/transformers.js) library to perform vector embedding search using LanceDB's Javascript API.
+
+
+### Setting up
+First, install the dependencies:
+```bash
+npm install vectordb
+npm i @xenova/transformers
+```
+
+We will also be using the [all-MiniLM-L6-v2](https://huggingface.co/Xenova/all-MiniLM-L6-v2) model to make it compatible with Transformers.js
+
+Within our `index.js` file we will import the necessary libraries and define our model and database:
+
+```javascript
+const lancedb = require('vectordb')
+const { pipeline } = await import('@xenova/transformers')
+const pipe = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+```
+
+### Creating the embedding function
+
+Next, we will create a function that will take in a string and return the vector embedding of that string. We will use the `pipe` function we defined earlier to get the vector embedding of the string.
+
+```javascript
+// Define the function. `sourceColumn` is required for LanceDB to know
+// which column to use as input.
+const embed_fun = {}
+embed_fun.sourceColumn = 'text'
+embed_fun.embed = async function (batch) {
+    let result = []
+    // Given a batch of strings, we will use the `pipe` function to get
+    // the vector embedding of each string.
+    for (let text of batch) {
+        // 'mean' pooling and normalizing allows the embeddings to share the
+        // same length.
+        const res = await pipe(text, { pooling: 'mean', normalize: true })
+        result.push(Array.from(res['data']))
+    }
+    return (result)
+}
+```
+
+### Creating the database
+
+Now, we will create the LanceDB database and add the embedding function we defined earlier.
+
+```javascript
+// Link a folder and create a table with data
+const db = await lancedb.connect('data/sample-lancedb')
+
+// You can also import any other data, but make sure that you have a column
+// for the embedding function to use.
+const data = [
+    { id: 1, text: 'Cherry', type: 'fruit' },
+    { id: 2, text: 'Carrot', type: 'vegetable' },
+    { id: 3, text: 'Potato', type: 'vegetable' },
+    { id: 4, text: 'Apple', type: 'fruit' },
+    { id: 5, text: 'Banana', type: 'fruit' }
+]
+
+// Create the table with the embedding function
+const table = await db.createTable('food_table', data, "create", embed_fun)
+```
+
+### Performing the search
+
+Now, we can perform the search using the `search` function. LanceDB automatically uses the embedding function we defined earlier to get the vector embedding of the query string.
+
+```javascript
+// Query the table
+const results = await table
+    .search("a sweet fruit to eat")
+    .metricType("cosine")
+    .limit(2)
+    .execute()
+console.log(results.map(r => r.text))
+```
+```bash
+[ 'Banana', 'Cherry' ]
+```
+
+Output of `results`:
+```bash
+[
+  {
+    vector: Float32Array(384) [
+      -0.057455405592918396,
+      0.03617725893855095,
+      -0.0367760956287384,
+      ... 381 more items
+    ],
+    id: 5,
+    text: 'Banana',
+    type: 'fruit',
+    score: 0.4919965863227844
+  },
+  {
+    vector: Float32Array(384) [
+      0.0009714411571621895,
+      0.008223623037338257,
+      0.009571489877998829,
+      ... 381 more items
+    ],
+    id: 1,
+    text: 'Cherry',
+    type: 'fruit',
+    score: 0.5540297031402588
+  }
+]
+```
+
+### Wrapping it up
+
+In this example, we showed how to use the `transformers.js` library to perform vector embedding search using LanceDB's Javascript API. You can find the full code for this example on [Github](https://github.com/lancedb/lancedb/blob/main/node/examples/js-transformers/index.js)!

docs/src/index.md

@@ -46,7 +46,7 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 
       const uri = "data/sample-lancedb";
       const db = await lancedb.connect(uri);
-      const table = await db.createTable("my_table", 
+      const table = await db.createTable("my_table",
             [{ id: 1, vector: [3.1, 4.1], item: "foo", price: 10.0 },
             { id: 2, vector: [5.9, 26.5], item: "bar", price: 20.0 }])
       const results = await table.search([100, 100]).limit(2).execute();
@@ -67,6 +67,6 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 * [`Embedding Functions`](embedding.md) - functions for working with embeddings.
 * [`Indexing`](ann_indexes.md) - create vector indexes to speed up queries.
 * [`Full text search`](fts.md) - [EXPERIMENTAL] full-text search API
-* [`Ecosystem Integrations`](integrations.md) - integrating LanceDB with python data tooling ecosystem.
+* [`Ecosystem Integrations`](python/integration.md) - integrating LanceDB with python data tooling ecosystem.
 * [`Python API Reference`](python/python.md) - detailed documentation for the LanceDB Python SDK.
 * [`Node API Reference`](javascript/modules.md) - detailed documentation for the LanceDB Python SDK.

docs/src/integrations.md

@@ -1,116 +0,0 @@
-# Integrations
-
-Built on top of Apache Arrow, `LanceDB` is easy to integrate with the Python ecosystem, including Pandas, PyArrow and DuckDB.
-
-## Pandas and PyArrow
-
-First, we need to connect to a `LanceDB` database.
-
-```py
-
-import lancedb
-
-db = lancedb.connect("data/sample-lancedb")
-```
-
-And write a `Pandas DataFrame` to LanceDB directly.
-
-```py
-import pandas as pd
-
-data = pd.DataFrame({
-    "vector": [[3.1, 4.1], [5.9, 26.5]],
-    "item": ["foo", "bar"],
-    "price": [10.0, 20.0]
-})
-table = db.create_table("pd_table", data=data)
-```
-
-You will find detailed instructions of creating dataset and index in [Basic Operations](basic.md) and [Indexing](ann_indexes.md)
-sections.
-
-
-We can now perform similarity searches via `LanceDB`.
-
-```py
-# Open the table previously created.
-table = db.open_table("pd_table")
-
-query_vector = [100, 100]
-# Pandas DataFrame
-df = table.search(query_vector).limit(1).to_df()
-print(df)
-```
-
-```
-    vector     item  price        score
-0  [5.9, 26.5]  bar   20.0  14257.05957
-```
-
-If you have a simple filter, it's faster to provide a where clause to `LanceDB`'s search query.
-If you have more complex criteria, you can always apply the filter to the resulting pandas `DataFrame` from the search query.
-
-```python
-
-# Apply the filter via LanceDB
-results = table.search([100, 100]).where("price < 15").to_df()
-assert len(results) == 1
-assert results["item"].iloc[0] == "foo"
-
-# Apply the filter via Pandas
-df = results = table.search([100, 100]).to_df()
-results = df[df.price < 15]
-assert len(results) == 1
-assert results["item"].iloc[0] == "foo"
-```
-
-## DuckDB
-
-`LanceDB` works with `DuckDB` via [PyArrow integration](https://duckdb.org/docs/guides/python/sql_on_arrow).
-
-Let us start with installing `duckdb` and `lancedb`.
-
-```shell
-pip install duckdb lancedb
-```
-
-We will re-use the dataset created previously
-
-```python
-import lancedb
-
-db = lancedb.connect("data/sample-lancedb")
-table = db.open_table("pd_table")
-arrow_table = table.to_arrow()
-```
-
-`DuckDB` can directly query the `arrow_table`:
-
-```python
-import duckdb 
-
-duckdb.query("SELECT * FROM arrow_table")
-```
-
-```
-┌─────────────┬─────────┬────────┐
-│   vector    │  item   │ price  │
-│   float[]   │ varchar │ double │
-├─────────────┼─────────┼────────┤
-│ [3.1, 4.1]  │ foo     │   10.0 │
-│ [5.9, 26.5] │ bar     │   20.0 │
-└─────────────┴─────────┴────────┘
-```
-```python
-duckdb.query("SELECT mean(price) FROM arrow_table")
-```
-
-```
-Out[16]:
-┌─────────────┐
-│ mean(price) │
-│   double    │
-├─────────────┤
-│        15.0 │
-└─────────────┘
-```

docs/src/python/arrow.md

@@ -0,0 +1,101 @@
+# Pandas and PyArrow
+
+
+Built on top of [Apache Arrow](https://arrow.apache.org/),
+`LanceDB` is easy to integrate with the Python ecosystem, including [Pandas](https://pandas.pydata.org/)
+and PyArrow.
+
+## Create dataset
+
+First, we need to connect to a `LanceDB` database.
+
+```py
+
+import lancedb
+
+db = lancedb.connect("data/sample-lancedb")
+```
+
+Afterwards, we write a `Pandas DataFrame` to LanceDB directly.
+
+```py
+import pandas as pd
+
+data = pd.DataFrame({
+    "vector": [[3.1, 4.1], [5.9, 26.5]],
+    "item": ["foo", "bar"],
+    "price": [10.0, 20.0]
+})
+table = db.create_table("pd_table", data=data)
+```
+
+Similar to [`pyarrow.write_dataset()`](https://arrow.apache.org/docs/python/generated/pyarrow.dataset.write_dataset.html),
+[db.create_table()](../python/#lancedb.db.DBConnection.create_table) accepts a wide-range of forms of data.
+
+For example, if you have a dataset that is larger than memory size, you can create table with `Iterator[pyarrow.RecordBatch]`,
+to lazily generate data:
+
+```py
+
+from typing import Iterable
+import pyarrow as pa
+import lancedb
+
+def make_batches() -> Iterable[pa.RecordBatch]:
+    for i in range(5):
+        yield pa.RecordBatch.from_arrays(
+            [
+                pa.array([[3.1, 4.1], [5.9, 26.5]]),
+                pa.array(["foo", "bar"]),
+                pa.array([10.0, 20.0]),
+            ],
+            ["vector", "item", "price"])
+
+schema=pa.schema([
+    pa.field("vector", pa.list_(pa.float32())),
+    pa.field("item", pa.utf8()),
+    pa.field("price", pa.float32()),
+])
+
+table = db.create_table("iterable_table", data=make_batches(), schema=schema)
+```
+
+You will find detailed instructions of creating dataset in
+[Basic Operations](../basic.md) and [API](../python/#lancedb.db.DBConnection.create_table)
+sections.
+
+## Vector Search
+
+We can now perform similarity search via `LanceDB` Python API.
+
+```py
+# Open the table previously created.
+table = db.open_table("pd_table")
+
+query_vector = [100, 100]
+# Pandas DataFrame
+df = table.search(query_vector).limit(1).to_df()
+print(df)
+```
+
+```
+    vector     item  price        score
+0  [5.9, 26.5]  bar   20.0  14257.05957
+```
+
+If you have a simple filter, it's faster to provide a `where clause` to `LanceDB`'s search query.
+If you have more complex criteria, you can always apply the filter to the resulting Pandas `DataFrame`.
+
+```python
+
+# Apply the filter via LanceDB
+results = table.search([100, 100]).where("price < 15").to_df()
+assert len(results) == 1
+assert results["item"].iloc[0] == "foo"
+
+# Apply the filter via Pandas
+df = results = table.search([100, 100]).to_df()
+results = df[df.price < 15]
+assert len(results) == 1
+assert results["item"].iloc[0] == "foo"
+```

docs/src/python/duckdb.md

@@ -0,0 +1,56 @@
+# DuckDB
+
+`LanceDB` works with `DuckDB` via [PyArrow integration](https://duckdb.org/docs/guides/python/sql_on_arrow).
+
+Let us start with installing `duckdb` and `lancedb`.
+
+```shell
+pip install duckdb lancedb
+```
+
+We will re-use [the dataset created previously](./arrow.md):
+
+```python
+import pandas as pd
+import lancedb
+
+db = lancedb.connect("data/sample-lancedb")
+data = pd.DataFrame({
+    "vector": [[3.1, 4.1], [5.9, 26.5]],
+    "item": ["foo", "bar"],
+    "price": [10.0, 20.0]
+})
+table = db.create_table("pd_table", data=data)
+arrow_table = table.to_arrow()
+```
+
+`DuckDB` can directly query the `arrow_table`:
+
+```python
+import duckdb
+
+duckdb.query("SELECT * FROM arrow_table")
+```
+
+```
+┌─────────────┬─────────┬────────┐
+│   vector    │  item   │ price  │
+│   float[]   │ varchar │ double │
+├─────────────┼─────────┼────────┤
+│ [3.1, 4.1]  │ foo     │   10.0 │
+│ [5.9, 26.5] │ bar     │   20.0 │
+└─────────────┴─────────┴────────┘
+```
+
+```py
+duckdb.query("SELECT mean(price) FROM arrow_table")
+```
+
+```
+┌─────────────┐
+│ mean(price) │
+│   double    │
+├─────────────┤
+│        15.0 │
+└─────────────┘
+```

docs/src/python/integration.md

@@ -0,0 +1,7 @@
+# Integration
+
+Built on top of [Apache Arrow](https://arrow.apache.org/),
+`LanceDB` is very easy to be integrate with Python ecosystems.
+
+* [Pandas and Arrow Integration](./arrow.md)
+* [DuckDB Integration](./duckdb.md)

docs/src/python/pydantic.md

@@ -0,0 +1,35 @@
+# Pydantic
+
+[Pydantic](https://docs.pydantic.dev/latest/) is a data validation library in Python.
+
+## Schema
+
+LanceDB supports to create Apache Arrow Schema from a
+[Pydantic BaseModel](https://docs.pydantic.dev/latest/api/main/#pydantic.main.BaseModel)
+via [pydantic_to_schema()](python.md##lancedb.pydantic.pydantic_to_schema) method.
+
+::: lancedb.pydantic.pydantic_to_schema
+
+## Vector Field
+
+LanceDB provides a [`vector(dim)`](python.md#lancedb.pydantic.vector) method to define a
+vector Field in a Pydantic Model.
+
+::: lancedb.pydantic.vector
+
+## Type Conversion
+
+LanceDB automatically convert Pydantic fields to
+[Apache Arrow DataType](https://arrow.apache.org/docs/python/generated/pyarrow.DataType.html#pyarrow.DataType).
+
+Current supported type conversions:
+
+| Pydantic Field Type | PyArrow Data Type |
+| ------------------- | ----------------- |
+| `int`               | `pyarrow.int64`   |
+| `float`              | `pyarrow.float64`  |
+| `bool`              | `pyarrow.bool`    |
+| `str`               | `pyarrow.utf8()`    |
+| `list`              | `pyarrow.List`    |
+| `BaseModel`         | `pyarrow.Struct`    |
+| `vector(n)`         | `pyarrow.FixedSizeList(float32, n)` |

docs/src/python/python.md

@@ -46,7 +46,14 @@ pip install lancedb
 
 ## Utilities
 
-::: lancedb.schema.schema_to_dict
-::: lancedb.schema.dict_to_schema
 ::: lancedb.vector
 
+## Integrations
+
+### Pydantic
+
+::: lancedb.pydantic.pydantic_to_schema
+
+::: lancedb.pydantic.vector
+
+

docs/src/search.md

@@ -25,9 +25,9 @@ Currently, we support the following metrics:
 
 ### Flat Search
 
+If LanceDB does not create a vector index, LanceDB would need to scan (`Flat Search`) the entire vector column
+and compute the distance for each vector in order to find the closest matches.
 
-If there is no [vector index is created](ann_indexes.md), LanceDB will just brute-force scan
-the vector column and compute the distance.
 
 <!-- Setup Code
 ```python
@@ -106,6 +106,16 @@ as well.
     ```
 
 
-### Search with Vector Index.
+### Approximate Nearest Neighbor (ANN) Search with Vector Index.
+
+To accelerate vector retrievals, it is common to build vector indices.
+A vector index is a data structure specifically designed to efficiently organize and
+search vector data based on their similarity or distance metrics.
+By constructing a vector index, you can reduce the search space and avoid the need
+for brute-force scanning of the entire vector column.
+
+However, fast vector search using indices often entails making a trade-off with accuracy to some extent.
+This is why it is often called **Approximate Nearest Neighbors (ANN)** search, while the Flat Search (KNN)
+always returns 100% recall.
 
 See [ANN Index](ann_indexes.md) for more details.

docs/test/md_testing.js

@@ -7,6 +7,7 @@ const excludedFiles = [
   "../src/embedding.md",
   "../src/examples/serverless_lancedb_with_s3_and_lambda.md",
   "../src/examples/serverless_qa_bot_with_modal_and_langchain.md",
+  "../src/examples/transformerjs_embedding_search_nodejs.md",
   "../src/examples/youtube_transcript_bot_with_nodejs.md",
 ];
 const nodePrefix = "javascript";
@@ -48,4 +49,4 @@ for (const file of files.filter((file) => !excludedFiles.includes(file))) {
     fs.mkdirSync(path.dirname(outPath), { recursive: true });
     fs.writeFileSync(outPath, asyncPrefix + "\n" + lines.join("\n") + asyncSuffix);
   }
-}
+}

node/.npmignore

@@ -0,0 +1,4 @@
+gen_test_data.py
+index.node
+dist/lancedb*.tgz
+vectordb*.tgz

node/README.md

@@ -8,6 +8,10 @@ A JavaScript / Node.js library for [LanceDB](https://github.com/lancedb/lancedb)
 npm install vectordb
 ```
 
+This will download the appropriate native library for your platform. We currently
+support x86_64 Linux, aarch64 Linux, Intel MacOS, and ARM (M1/M2) MacOS. We do not
+yet support Windows or musl-based Linux (such as Alpine Linux).
+
 ## Usage
 
 ### Basic Example
@@ -26,12 +30,34 @@ The [examples](./examples) folder contains complete examples.
 
 ## Development
 
-Run the tests with
+To build everything fresh:
+
+```bash
+npm install
+npm run tsc
+npm run build
+```
+
+Then you should be able to run the tests with:
 
 ```bash
 npm test
 ```
 
+### Rebuilding Rust library
+
+```bash
+npm run build
+```
+
+### Rebuilding Typescript
+
+```bash
+npm run tsc
+```
+
+### Fix lints
+
 To run the linter and have it automatically fix all errors
 
 ```bash

node/examples/js-transformers/index.js

@@ -0,0 +1,66 @@
+// 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.
+
+'use strict'
+
+
+async function example() {
+
+    const lancedb = require('vectordb')
+
+    // Import transformers and the all-MiniLM-L6-v2 model (https://huggingface.co/Xenova/all-MiniLM-L6-v2)
+    const { pipeline } = await import('@xenova/transformers')
+    const pipe = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
+
+
+    // Create embedding function from pipeline which returns a list of vectors from batch
+    // sourceColumn is the name of the column in the data to be embedded
+    //
+    // Output of pipe is a Tensor { data: Float32Array(384) }, so filter for the vector
+    const embed_fun = {}
+    embed_fun.sourceColumn = 'text'
+    embed_fun.embed = async function (batch) {
+        let result = []
+        for (let text of batch) {
+            const res = await pipe(text, { pooling: 'mean', normalize: true })
+            result.push(Array.from(res['data']))
+        }
+        return (result)
+    }
+
+    // Link a folder and create a table with data
+    const db = await lancedb.connect('data/sample-lancedb')
+
+    const data = [
+        { id: 1, text: 'Cherry', type: 'fruit' },
+        { id: 2, text: 'Carrot', type: 'vegetable' },
+        { id: 3, text: 'Potato', type: 'vegetable' },
+        { id: 4, text: 'Apple', type: 'fruit' },
+        { id: 5, text: 'Banana', type: 'fruit' }
+    ]
+
+    const table = await db.createTable('food_table', data, "create", embed_fun)
+
+
+    // Query the table
+    const results = await table
+        .search("a sweet fruit to eat")
+        .metricType("cosine")
+        .limit(2)
+        .execute()
+    console.log(results.map(r => r.text))
+
+}
+
+example().then(_ => { console.log("Done!") })

node/examples/js-transformers/package.json

@@ -0,0 +1,16 @@
+{
+  "name": "vectordb-example-js-transformers",
+  "version": "1.0.0",
+  "description": "Example for using transformers.js with lancedb",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "Lance Devs",
+  "license": "Apache-2.0",
+  "dependencies": {
+    "@xenova/transformers": "^2.4.1",
+    "vectordb": "^0.1.12"
+  }
+
+}

node/native.js

@@ -12,29 +12,26 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
+const { currentTarget } = require('@neon-rs/load');
+
 let nativeLib;
 
-function getPlatformLibrary() {
-    if (process.platform === "darwin" && process.arch == "arm64") {
-        return require('./aarch64-apple-darwin.node');
-    } else if (process.platform === "darwin" && process.arch == "x64") {
-        return require('./x86_64-apple-darwin.node');
-    } else if (process.platform === "linux" && process.arch == "x64") {
-        return require('./x86_64-unknown-linux-gnu.node');
-    } else {
-        throw new Error(`vectordb: unsupported platform ${process.platform}_${process.arch}. Please file a bug report at https://github.com/lancedb/lancedb/issues`)
-    }
-}
-
 try {
-    nativeLib = require('./index.node')
+    nativeLib = require(`vectordb-${currentTarget()}`);
 } catch (e) {
-    if (e.code === "MODULE_NOT_FOUND") {
-        nativeLib = getPlatformLibrary();
-    } else {
-        throw new Error('vectordb: failed to load native library. Please file a bug report at https://github.com/lancedb/lancedb/issues');
-    }
+    try {
+        // Might be developing locally, so try that. But don't expose that error
+        // to the user.
+        nativeLib = require("./index.node");
+    } catch {
+        throw new Error(`vectordb: failed to load native library.
+  You may need to run \`npm install vectordb-${currentTarget()}\`.
+      
+  If that does not work, please file a bug report at https://github.com/lancedb/lancedb/issues
+      
+  Source error: ${e}`);
+    }   
 }
 
-module.exports = nativeLib
-
+// Dynamic require for runtime.
+module.exports = nativeLib;

node/package-lock.json

@@ -1,18 +1,29 @@
 {
   "name": "vectordb",
-  "version": "0.1.10",
+  "version": "0.1.13",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.1.10",
+      "version": "0.1.13",
+      "cpu": [
+        "x64",
+        "arm64"
+      ],
       "license": "Apache-2.0",
+      "os": [
+        "darwin",
+        "linux",
+        "win32"
+      ],
       "dependencies": {
         "@apache-arrow/ts": "^12.0.0",
+        "@neon-rs/load": "^0.0.74",
         "apache-arrow": "^12.0.0"
       },
       "devDependencies": {
+        "@neon-rs/cli": "^0.0.74",
         "@types/chai": "^4.3.4",
         "@types/chai-as-promised": "^7.1.5",
         "@types/mocha": "^10.0.1",
@@ -37,6 +48,13 @@
         "typedoc": "^0.24.7",
         "typedoc-plugin-markdown": "^3.15.3",
         "typescript": "*"
+      },
+      "optionalDependencies": {
+        "vectordb-darwin-arm64": "0.1.13",
+        "vectordb-darwin-x64": "0.1.13",
+        "vectordb-linux-arm64-gnu": "0.1.13",
+        "vectordb-linux-x64-gnu": "0.1.13",
+        "vectordb-win32-x64-msvc": "0.1.13"
       }
     },
     "node_modules/@apache-arrow/ts": {
@@ -204,6 +222,20 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "node_modules/@neon-rs/cli": {
+      "version": "0.0.74",
+      "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.74.tgz",
+      "integrity": "sha512-9lPmNmjej5iKKOTMPryOMubwkgMRyTWRuaq1yokASvI5mPhr2kzPN7UVjdCOjQvpunNPngR9yAHoirpjiWhUHw==",
+      "dev": true,
+      "bin": {
+        "neon": "index.js"
+      }
+    },
+    "node_modules/@neon-rs/load": {
+      "version": "0.0.74",
+      "resolved": "https://registry.npmjs.org/@neon-rs/load/-/load-0.0.74.tgz",
+      "integrity": "sha512-/cPZD907UNz55yrc/ud4wDgQKtU1TvkD9jeqZWG6J4IMmZkp6zgjkQcKA8UvpkZlcpPHvc8J17sGzLFbP/LUYg=="
+    },
     "node_modules/@nodelib/fs.scandir": {
       "version": "2.1.5",
       "resolved": "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz",
@@ -4256,6 +4288,42 @@
       "integrity": "sha512-wa7YjyUGfNZngI/vtK0UHAN+lgDCxBPCylVXGp0zu59Fz5aiGtNXaq3DhIov063MorB+VfufLh3JlF2KdTK3xg==",
       "dev": true
     },
+    "node_modules/vectordb-darwin-arm64": {
+      "version": "0.1.13",
+      "resolved": "https://registry.npmjs.org/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.1.13.tgz",
+      "integrity": "sha512-9lLuX5P8m75EfP85pfC4LxO9J7Tzu4LngX55BVAdFe6qPRHu+iHmLw0QYYSVDqNm3GtDr2qFJlL2ILlsApyYyg==",
+      "cpu": [
+        "arm64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/vectordb-darwin-x64": {
+      "version": "0.1.13",
+      "resolved": "https://registry.npmjs.org/vectordb-darwin-x64/-/vectordb-darwin-x64-0.1.13.tgz",
+      "integrity": "sha512-5mkhBJlcfAqcty7Ww2csgYogq+b0NhtllAbag9IIznvqfcrvITU0H0vm5LGWbRuE/BUUxC25MJhm93YWBzqEVA==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "darwin"
+      ]
+    },
+    "node_modules/vectordb-linux-x64-gnu": {
+      "version": "0.1.13",
+      "resolved": "https://registry.npmjs.org/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.1.13.tgz",
+      "integrity": "sha512-fU+sIHUkXyMdrWjggT93p0blKD+pbgr+x01tn9d2/pbA1ePo2AwuE86rYPA+BjyCUE1QifPgKadzGVVpqWYmnQ==",
+      "cpu": [
+        "x64"
+      ],
+      "optional": true,
+      "os": [
+        "linux"
+      ]
+    },
     "node_modules/vscode-oniguruma": {
       "version": "1.7.0",
       "resolved": "https://registry.npmjs.org/vscode-oniguruma/-/vscode-oniguruma-1.7.0.tgz",
@@ -4601,6 +4669,17 @@
         "@jridgewell/sourcemap-codec": "^1.4.10"
       }
     },
+    "@neon-rs/cli": {
+      "version": "0.0.74",
+      "resolved": "https://registry.npmjs.org/@neon-rs/cli/-/cli-0.0.74.tgz",
+      "integrity": "sha512-9lPmNmjej5iKKOTMPryOMubwkgMRyTWRuaq1yokASvI5mPhr2kzPN7UVjdCOjQvpunNPngR9yAHoirpjiWhUHw==",
+      "dev": true
+    },
+    "@neon-rs/load": {
+      "version": "0.0.74",
+      "resolved": "https://registry.npmjs.org/@neon-rs/load/-/load-0.0.74.tgz",
+      "integrity": "sha512-/cPZD907UNz55yrc/ud4wDgQKtU1TvkD9jeqZWG6J4IMmZkp6zgjkQcKA8UvpkZlcpPHvc8J17sGzLFbP/LUYg=="
+    },
     "@nodelib/fs.scandir": {
       "version": "2.1.5",
       "resolved": "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz",
@@ -7540,6 +7619,24 @@
       "integrity": "sha512-wa7YjyUGfNZngI/vtK0UHAN+lgDCxBPCylVXGp0zu59Fz5aiGtNXaq3DhIov063MorB+VfufLh3JlF2KdTK3xg==",
       "dev": true
     },
+    "vectordb-darwin-arm64": {
+      "version": "0.1.13",
+      "resolved": "https://registry.npmjs.org/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.1.13.tgz",
+      "integrity": "sha512-9lLuX5P8m75EfP85pfC4LxO9J7Tzu4LngX55BVAdFe6qPRHu+iHmLw0QYYSVDqNm3GtDr2qFJlL2ILlsApyYyg==",
+      "optional": true
+    },
+    "vectordb-darwin-x64": {
+      "version": "0.1.13",
+      "resolved": "https://registry.npmjs.org/vectordb-darwin-x64/-/vectordb-darwin-x64-0.1.13.tgz",
+      "integrity": "sha512-5mkhBJlcfAqcty7Ww2csgYogq+b0NhtllAbag9IIznvqfcrvITU0H0vm5LGWbRuE/BUUxC25MJhm93YWBzqEVA==",
+      "optional": true
+    },
+    "vectordb-linux-x64-gnu": {
+      "version": "0.1.13",
+      "resolved": "https://registry.npmjs.org/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.1.13.tgz",
+      "integrity": "sha512-fU+sIHUkXyMdrWjggT93p0blKD+pbgr+x01tn9d2/pbA1ePo2AwuE86rYPA+BjyCUE1QifPgKadzGVVpqWYmnQ==",
+      "optional": true
+    },
     "vscode-oniguruma": {
       "version": "1.7.0",
       "resolved": "https://registry.npmjs.org/vscode-oniguruma/-/vscode-oniguruma-1.7.0.tgz",

node/package.json

@@ -1,16 +1,18 @@
 {
   "name": "vectordb",
-  "version": "0.1.12",
+  "version": "0.1.13",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
   "scripts": {
     "tsc": "tsc -b",
-    "build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json-render-diagnostics",
+    "build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json",
     "build-release": "npm run build -- --release",
-    "test": "npm run tsc; mocha -recursive dist/test",
+    "test": "npm run tsc && mocha -recursive dist/test",
     "lint": "eslint src --ext .js,.ts",
-    "clean": "rm -rf node_modules *.node dist/"
+    "clean": "rm -rf node_modules *.node dist/",
+    "pack-build": "neon pack-build",
+    "check-npm": "printenv && which node && which npm && npm --version"
   },
   "repository": {
     "type": "git",
@@ -25,6 +27,7 @@
   "author": "Lance Devs",
   "license": "Apache-2.0",
   "devDependencies": {
+    "@neon-rs/cli": "^0.0.74",
     "@types/chai": "^4.3.4",
     "@types/chai-as-promised": "^7.1.5",
     "@types/mocha": "^10.0.1",
@@ -52,6 +55,32 @@
   },
   "dependencies": {
     "@apache-arrow/ts": "^12.0.0",
+    "@neon-rs/load": "^0.0.74",
     "apache-arrow": "^12.0.0"
+  },
+  "os": [
+    "darwin",
+    "linux",
+    "win32"
+  ],
+  "cpu": [
+    "x64",
+    "arm64"
+  ],
+  "neon": {
+    "targets": {
+      "x86_64-apple-darwin": "vectordb-darwin-x64",
+      "aarch64-apple-darwin": "vectordb-darwin-arm64",
+      "x86_64-unknown-linux-gnu": "vectordb-linux-x64-gnu",
+      "aarch64-unknown-linux-gnu": "vectordb-linux-arm64-gnu",
+      "x86_64-pc-windows-msvc": "vectordb-win32-x64-msvc"
+    }
+  },
+  "optionalDependencies": {
+    "vectordb-darwin-arm64": "0.1.13",
+    "vectordb-darwin-x64": "0.1.13",
+    "vectordb-linux-arm64-gnu": "0.1.13",
+    "vectordb-linux-x64-gnu": "0.1.13",
+    "vectordb-win32-x64-msvc": "0.1.13"
   }
 }

node/src/index.ts

@@ -27,13 +27,38 @@ const { databaseNew, databaseTableNames, databaseOpenTable, databaseDropTable, t
 export type { EmbeddingFunction }
 export { OpenAIEmbeddingFunction } from './embedding/openai'
 
+export interface AwsCredentials {
+  accessKeyId: string
+
+  secretKey: string
+
+  sessionToken?: string
+}
+
+export interface ConnectionOptions {
+  uri: string
+  awsCredentials?: AwsCredentials
+}
+
 /**
  * Connect to a LanceDB instance at the given URI
  * @param uri The uri of the database.
  */
-export async function connect (uri: string): Promise<Connection> {
-  const db = await databaseNew(uri)
-  return new LocalConnection(db, uri)
+export async function connect (uri: string): Promise<Connection>
+export async function connect (opts: Partial<ConnectionOptions>): Promise<Connection>
+export async function connect (arg: string | Partial<ConnectionOptions>): Promise<Connection> {
+  let opts: ConnectionOptions
+  if (typeof arg === 'string') {
+    opts = { uri: arg }
+  } else {
+    // opts = { uri: arg.uri, awsCredentials = arg.awsCredentials }
+    opts = Object.assign({
+      uri: '',
+      awsCredentials: undefined
+    }, arg)
+  }
+  const db = await databaseNew(opts.uri)
+  return new LocalConnection(db, opts)
 }
 
 /**
@@ -122,28 +147,20 @@ export interface Table<T = number[]> {
   delete: (filter: string) => Promise<void>
 }
 
-export interface AwsCredentials {
-  accessKeyId: string
-
-  secretKey: string
-
-  sessionToken?: string
-}
-
 /**
  * A connection to a LanceDB database.
  */
 export class LocalConnection implements Connection {
-  private readonly _uri: string
+  private readonly _options: ConnectionOptions
   private readonly _db: any
 
-  constructor (db: any, uri: string) {
-    this._uri = uri
+  constructor (db: any, options: ConnectionOptions) {
+    this._options = options
     this._db = db
   }
 
   get uri (): string {
-    return this._uri
+    return this._options.uri
   }
 
   /**
@@ -166,10 +183,14 @@ export class LocalConnection implements Connection {
    * @param embeddings An embedding function to use on this Table
    */
   async openTable<T> (name: string, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
-  async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>, awsCredentials?: AwsCredentials): Promise<Table<T>>
-  async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>, awsCredentials?: AwsCredentials): Promise<Table<T>> {
+  async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
+  async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
     const tbl = await databaseOpenTable.call(this._db, name)
-    return new LocalTable(tbl, name, embeddings, awsCredentials)
+    if (embeddings !== undefined) {
+      return new LocalTable(tbl, name, this._options, embeddings)
+    } else {
+      return new LocalTable(tbl, name, this._options)
+    }
   }
 
   /**
@@ -191,24 +212,28 @@ export class LocalConnection implements Connection {
    * @param embeddings An embedding function to use on this Table
    */
   async createTable<T> (name: string, data: Array<Record<string, unknown>>, mode: WriteMode, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
-  async createTable<T> (name: string, data: Array<Record<string, unknown>>, mode: WriteMode, embeddings?: EmbeddingFunction<T>, awsCredentials?: AwsCredentials): Promise<Table<T>>
-  async createTable<T> (name: string, data: Array<Record<string, unknown>>, mode: WriteMode, embeddings?: EmbeddingFunction<T>, awsCredentials?: AwsCredentials): Promise<Table<T>> {
+  async createTable<T> (name: string, data: Array<Record<string, unknown>>, mode: WriteMode, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
+  async createTable<T> (name: string, data: Array<Record<string, unknown>>, mode: WriteMode, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
     if (mode === undefined) {
       mode = WriteMode.Create
     }
 
     const createArgs = [this._db, name, await fromRecordsToBuffer(data, embeddings), mode.toLowerCase()]
-    if (awsCredentials !== undefined) {
-      createArgs.push(awsCredentials.accessKeyId)
-      createArgs.push(awsCredentials.secretKey)
-      if (awsCredentials.sessionToken !== undefined) {
-        createArgs.push(awsCredentials.sessionToken)
+    if (this._options.awsCredentials !== undefined) {
+      createArgs.push(this._options.awsCredentials.accessKeyId)
+      createArgs.push(this._options.awsCredentials.secretKey)
+      if (this._options.awsCredentials.sessionToken !== undefined) {
+        createArgs.push(this._options.awsCredentials.sessionToken)
       }
     }
 
     const tbl = await tableCreate.call(...createArgs)
 
-    return new LocalTable(tbl, name, embeddings, awsCredentials)
+    if (embeddings !== undefined) {
+      return new LocalTable(tbl, name, this._options, embeddings)
+    } else {
+      return new LocalTable(tbl, name, this._options)
+    }
   }
 
   async createTableArrow (name: string, table: ArrowTable): Promise<Table> {
@@ -230,21 +255,21 @@ export class LocalTable<T = number[]> implements Table<T> {
   private readonly _tbl: any
   private readonly _name: string
   private readonly _embeddings?: EmbeddingFunction<T>
-  private readonly _awsCredentials?: AwsCredentials
+  private readonly _options: ConnectionOptions
 
-  constructor (tbl: any, name: string)
+  constructor (tbl: any, name: string, options: ConnectionOptions)
   /**
    * @param tbl
    * @param name
+   * @param options
    * @param embeddings An embedding function to use when interacting with this table
    */
-  constructor (tbl: any, name: string, embeddings: EmbeddingFunction<T>)
-  constructor (tbl: any, name: string, embeddings?: EmbeddingFunction<T>, awsCredentials?: AwsCredentials)
-  constructor (tbl: any, name: string, embeddings?: EmbeddingFunction<T>, awsCredentials?: AwsCredentials) {
+  constructor (tbl: any, name: string, options: ConnectionOptions, embeddings: EmbeddingFunction<T>)
+  constructor (tbl: any, name: string, options: ConnectionOptions, embeddings?: EmbeddingFunction<T>) {
     this._tbl = tbl
     this._name = name
     this._embeddings = embeddings
-    this._awsCredentials = awsCredentials
+    this._options = options
   }
 
   get name (): string {
@@ -267,11 +292,11 @@ export class LocalTable<T = number[]> implements Table<T> {
    */
   async add (data: Array<Record<string, unknown>>): Promise<number> {
     const callArgs = [this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Append.toString()]
-    if (this._awsCredentials !== undefined) {
-      callArgs.push(this._awsCredentials.accessKeyId)
-      callArgs.push(this._awsCredentials.secretKey)
-      if (this._awsCredentials.sessionToken !== undefined) {
-        callArgs.push(this._awsCredentials.sessionToken)
+    if (this._options.awsCredentials !== undefined) {
+      callArgs.push(this._options.awsCredentials.accessKeyId)
+      callArgs.push(this._options.awsCredentials.secretKey)
+      if (this._options.awsCredentials.sessionToken !== undefined) {
+        callArgs.push(this._options.awsCredentials.sessionToken)
       }
     }
     return tableAdd.call(...callArgs)
@@ -285,11 +310,11 @@ export class LocalTable<T = number[]> implements Table<T> {
    */
   async overwrite (data: Array<Record<string, unknown>>): Promise<number> {
     const callArgs = [this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Overwrite.toString()]
-    if (this._awsCredentials !== undefined) {
-      callArgs.push(this._awsCredentials.accessKeyId)
-      callArgs.push(this._awsCredentials.secretKey)
-      if (this._awsCredentials.sessionToken !== undefined) {
-        callArgs.push(this._awsCredentials.sessionToken)
+    if (this._options.awsCredentials !== undefined) {
+      callArgs.push(this._options.awsCredentials.accessKeyId)
+      callArgs.push(this._options.awsCredentials.secretKey)
+      if (this._options.awsCredentials.sessionToken !== undefined) {
+        callArgs.push(this._options.awsCredentials.sessionToken)
       }
     }
     return tableAdd.call(this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Overwrite.toString())

node/src/test/io.ts

@@ -18,26 +18,48 @@ import { describe } from 'mocha'
 import { assert } from 'chai'
 
 import * as lancedb from '../index'
+import { type ConnectionOptions } from '../index'
 
 describe('LanceDB S3 client', function () {
   if (process.env.TEST_S3_BASE_URL != null) {
     const baseUri = process.env.TEST_S3_BASE_URL
     it('should have a valid url', async function () {
-      const uri = `${baseUri}/valid_url`
-      const table = await createTestDB(uri, 2, 20)
-      const con = await lancedb.connect(uri)
-      assert.equal(con.uri, uri)
+      const opts = { uri: `${baseUri}/valid_url` }
+      const table = await createTestDB(opts, 2, 20)
+      const con = await lancedb.connect(opts)
+      assert.equal(con.uri, opts.uri)
 
       const results = await table.search([0.1, 0.3]).limit(5).execute()
       assert.equal(results.length, 5)
-    })
+    }).timeout(10_000)
+  } else {
+    describe.skip('Skip S3 test', function () {})
+  }
+
+  if (process.env.TEST_S3_BASE_URL != null && process.env.TEST_AWS_ACCESS_KEY_ID != null && process.env.TEST_AWS_SECRET_ACCESS_KEY != null) {
+    const baseUri = process.env.TEST_S3_BASE_URL
+    it('use custom credentials', async function () {
+      const opts: ConnectionOptions = {
+        uri: `${baseUri}/custom_credentials`,
+        awsCredentials: {
+          accessKeyId: process.env.TEST_AWS_ACCESS_KEY_ID as string,
+          secretKey: process.env.TEST_AWS_SECRET_ACCESS_KEY as string
+        }
+      }
+      const table = await createTestDB(opts, 2, 20)
+      const con = await lancedb.connect(opts)
+      assert.equal(con.uri, opts.uri)
+
+      const results = await table.search([0.1, 0.3]).limit(5).execute()
+      assert.equal(results.length, 5)
+    }).timeout(10_000)
   } else {
     describe.skip('Skip S3 test', function () {})
   }
 })
 
-async function createTestDB (uri: string, numDimensions: number = 2, numRows: number = 2): Promise<lancedb.Table> {
-  const con = await lancedb.connect(uri)
+async function createTestDB (opts: ConnectionOptions, numDimensions: number = 2, numRows: number = 2): Promise<lancedb.Table> {
+  const con = await lancedb.connect(opts)
 
   const data = []
   for (let i = 0; i < numRows; i++) {

node/src/test/test.ts

@@ -18,7 +18,7 @@ import * as chai from 'chai'
 import * as chaiAsPromised from 'chai-as-promised'
 
 import * as lancedb from '../index'
-import { type EmbeddingFunction, MetricType, Query, WriteMode } from '../index'
+import { type AwsCredentials, type EmbeddingFunction, MetricType, Query, WriteMode } from '../index'
 
 const expect = chai.expect
 const assert = chai.assert
@@ -32,6 +32,22 @@ describe('LanceDB client', function () {
       assert.equal(con.uri, uri)
     })
 
+    it('should accept an options object', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect({ uri })
+      assert.equal(con.uri, uri)
+    })
+
+    it('should accept custom aws credentials', async function () {
+      const uri = await createTestDB()
+      const awsCredentials: AwsCredentials = {
+        accessKeyId: '',
+        secretKey: ''
+      }
+      const con = await lancedb.connect({ uri, awsCredentials })
+      assert.equal(con.uri, uri)
+    })
+
     it('should return the existing table names', async function () {
       const uri = await createTestDB()
       const con = await lancedb.connect(uri)

python/lancedb/db.py

@@ -13,11 +13,12 @@
 
 from __future__ import annotations
 
-import functools
 import os
 from abc import ABC, abstractmethod
 from pathlib import Path
+from typing import Dict, Iterable, List, Optional, Tuple, Union
 
+import pandas as pd
 import pyarrow as pa
 from pyarrow import fs
 
@@ -38,8 +39,10 @@ class DBConnection(ABC):
     def create_table(
         self,
         name: str,
-        data: DATA = None,
-        schema: pa.Schema = None,
+        data: Optional[
+            Union[List[dict], dict, pd.DataFrame, pa.Table, Iterable[pa.RecordBatch]],
+        ] = None,
+        schema: Optional[pa.Schema] = None,
         mode: str = "create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
@@ -51,7 +54,7 @@ class DBConnection(ABC):
         name: str
             The name of the table.
         data: list, tuple, dict, pd.DataFrame; optional
-            The data to insert into the table.
+            The data to initialize the table. User must provide at least one of `data` or `schema`.
         schema: pyarrow.Schema; optional
             The schema of the table.
         mode: str; default "create"
@@ -64,16 +67,16 @@ class DBConnection(ABC):
         fill_value: float
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
 
-        Note
-        ----
-        The vector index won't be created by default.
-        To create the index, call the `create_index` method on the table.
-
         Returns
         -------
         LanceTable
             A reference to the newly created table.
 
+        !!! note
+
+            The vector index won't be created by default.
+            To create the index, call the `create_index` method on the table.
+
         Examples
         --------
 
@@ -119,7 +122,7 @@ class DBConnection(ABC):
 
         Data is converted to Arrow before being written to disk. For maximum
         control over how data is saved, either provide the PyArrow schema to
-        convert to or else provide a PyArrow table directly.
+        convert to or else provide a [PyArrow Table](pyarrow.Table) directly.
 
         >>> custom_schema = pa.schema([
         ...   pa.field("vector", pa.list_(pa.float32(), 2)),
@@ -138,6 +141,30 @@ class DBConnection(ABC):
         vector: [[[1.1,1.2],[0.2,1.8]]]
         lat: [[45.5,40.1]]
         long: [[-122.7,-74.1]]
+
+
+        It is also possible to create an table from `[Iterable[pa.RecordBatch]]`:
+
+
+        >>> import pyarrow as pa
+        >>> def make_batches():
+        ...     for i in range(5):
+        ...         yield pa.RecordBatch.from_arrays(
+        ...             [
+        ...                 pa.array([[3.1, 4.1], [5.9, 26.5]]),
+        ...                 pa.array(["foo", "bar"]),
+        ...                 pa.array([10.0, 20.0]),
+        ...             ],
+        ...             ["vector", "item", "price"],
+        ...         )
+        >>> schema=pa.schema([
+        ...     pa.field("vector", pa.list_(pa.float32())),
+        ...     pa.field("item", pa.utf8()),
+        ...     pa.field("price", pa.float32()),
+        ... ])
+        >>> db.create_table("table4", make_batches(), schema=schema)
+        LanceTable(table4)
+
         """
         raise NotImplementedError
 
@@ -252,7 +279,7 @@ class LanceDBConnection(DBConnection):
     def create_table(
         self,
         name: str,
-        data: DATA = None,
+        data: Optional[Union[List[dict], dict, pd.DataFrame]] = None,
         schema: pa.Schema = None,
         mode: str = "create",
         on_bad_vectors: str = "error",
@@ -260,114 +287,22 @@ class LanceDBConnection(DBConnection):
     ) -> LanceTable:
         """Create a table in the database.
 
-        Parameters
-        ----------
-        name: str
-            The name of the table.
-        data: list, tuple, dict, pd.DataFrame; optional
-            The data to insert into the table.
-        schema: pyarrow.Schema; optional
-            The schema of the table.
-        mode: str; default "create"
-            The mode to use when creating the table. Can be either "create" or "overwrite".
-            By default, if the table already exists, an exception is raised.
-            If you want to overwrite the table, use mode="overwrite".
-        on_bad_vectors: str, default "error"
-            What to do if any of the vectors are not the same size or contains NaNs.
-            One of "error", "drop", "fill".
-        fill_value: float
-            The value to use when filling vectors. Only used if on_bad_vectors="fill".
-
-        Note
-        ----
-        The vector index won't be created by default.
-        To create the index, call the `create_index` method on the table.
-
-        Returns
-        -------
-        LanceTable
-            A reference to the newly created table.
-
-        Examples
-        --------
-
-        Can create with list of tuples or dictionaries:
-
-        >>> import lancedb
-        >>> db = lancedb.connect("./.lancedb")
-        >>> data = [{"vector": [1.1, 1.2], "lat": 45.5, "long": -122.7},
-        ...         {"vector": [0.2, 1.8], "lat": 40.1, "long":  -74.1}]
-        >>> db.create_table("my_table", data)
-        LanceTable(my_table)
-        >>> db["my_table"].head()
-        pyarrow.Table
-        vector: fixed_size_list<item: float>[2]
-          child 0, item: float
-        lat: double
-        long: double
-        ----
-        vector: [[[1.1,1.2],[0.2,1.8]]]
-        lat: [[45.5,40.1]]
-        long: [[-122.7,-74.1]]
-
-        You can also pass a pandas DataFrame:
-
-        >>> import pandas as pd
-        >>> data = pd.DataFrame({
-        ...    "vector": [[1.1, 1.2], [0.2, 1.8]],
-        ...    "lat": [45.5, 40.1],
-        ...    "long": [-122.7, -74.1]
-        ... })
-        >>> db.create_table("table2", data)
-        LanceTable(table2)
-        >>> db["table2"].head()
-        pyarrow.Table
-        vector: fixed_size_list<item: float>[2]
-          child 0, item: float
-        lat: double
-        long: double
-        ----
-        vector: [[[1.1,1.2],[0.2,1.8]]]
-        lat: [[45.5,40.1]]
-        long: [[-122.7,-74.1]]
-
-        Data is converted to Arrow before being written to disk. For maximum
-        control over how data is saved, either provide the PyArrow schema to
-        convert to or else provide a PyArrow table directly.
-
-        >>> custom_schema = pa.schema([
-        ...   pa.field("vector", pa.list_(pa.float32(), 2)),
-        ...   pa.field("lat", pa.float32()),
-        ...   pa.field("long", pa.float32())
-        ... ])
-        >>> db.create_table("table3", data, schema = custom_schema)
-        LanceTable(table3)
-        >>> db["table3"].head()
-        pyarrow.Table
-        vector: fixed_size_list<item: float>[2]
-          child 0, item: float
-        lat: float
-        long: float
-        ----
-        vector: [[[1.1,1.2],[0.2,1.8]]]
-        lat: [[45.5,40.1]]
-        long: [[-122.7,-74.1]]
+        See
+        ---
+        DBConnection.create_table
         """
         if mode.lower() not in ["create", "overwrite"]:
             raise ValueError("mode must be either 'create' or 'overwrite'")
 
-        if data is not None:
-            tbl = LanceTable.create(
-                self,
-                name,
-                data,
-                schema,
-                mode=mode,
-                on_bad_vectors=on_bad_vectors,
-                fill_value=fill_value,
-            )
-        else:
-            tbl = LanceTable.open(self, name)
+        tbl = LanceTable.create(
+            self,
+            name,
+            data,
+            schema,
+            mode=mode,
+            on_bad_vectors=on_bad_vectors,
+            fill_value=fill_value,
+        )
         return tbl
 
     def open_table(self, name: str) -> LanceTable:

python/lancedb/pydantic.py

@@ -0,0 +1,212 @@
+#  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.
+
+"""Pydantic adapter for LanceDB"""
+
+from __future__ import annotations
+
+import inspect
+import sys
+import types
+from abc import ABC, abstractmethod
+from typing import Any, List, Type, Union, _GenericAlias
+
+import pyarrow as pa
+import pydantic
+from pydantic_core import CoreSchema, core_schema
+
+
+class FixedSizeListMixin(ABC):
+    @staticmethod
+    @abstractmethod
+    def dim() -> int:
+        raise NotImplementedError
+
+    @staticmethod
+    @abstractmethod
+    def value_arrow_type() -> pa.DataType:
+        raise NotImplementedError
+
+
+def vector(
+    dim: int, value_type: pa.DataType = pa.float32()
+) -> Type[FixedSizeListMixin]:
+    """Pydantic Vector Type.
+
+    !!! warning
+        Experimental feature.
+
+    Parameters
+    ----------
+    dim : int
+        The dimension of the vector.
+    value_type : pyarrow.DataType, optional
+        The value type of the vector, by default pa.float32()
+
+    Examples
+    --------
+
+    >>> import pydantic
+    >>> from lancedb.pydantic import vector
+    ...
+    >>> class MyModel(pydantic.BaseModel):
+    ...     id: int
+    ...     url: str
+    ...     embeddings: vector(768)
+    >>> schema = pydantic_to_schema(MyModel)
+    >>> assert schema == pa.schema([
+    ...     pa.field("id", pa.int64(), False),
+    ...     pa.field("url", pa.utf8(), False),
+    ...     pa.field("embeddings", pa.list_(pa.float32(), 768), False)
+    ... ])
+    """
+
+    # TODO: make a public parameterized type.
+    class FixedSizeList(list, FixedSizeListMixin):
+        @staticmethod
+        def dim() -> int:
+            return dim
+
+        @staticmethod
+        def value_arrow_type() -> pa.DataType:
+            return value_type
+
+        @classmethod
+        def __get_pydantic_core_schema__(
+            cls, _source_type: Any, _handler: pydantic.GetCoreSchemaHandler
+        ) -> CoreSchema:
+            return core_schema.no_info_after_validator_function(
+                cls,
+                core_schema.list_schema(
+                    min_length=dim,
+                    max_length=dim,
+                    items_schema=core_schema.float_schema(),
+                ),
+            )
+
+    return FixedSizeList
+
+
+def _py_type_to_arrow_type(py_type: Type[Any]) -> pa.DataType:
+    """Convert Python Type to Arrow DataType.
+
+    Raises
+    ------
+    TypeError
+        If the type is not supported.
+    """
+    if py_type == int:
+        return pa.int64()
+    elif py_type == float:
+        return pa.float64()
+    elif py_type == str:
+        return pa.utf8()
+    elif py_type == bool:
+        return pa.bool_()
+    elif py_type == bytes:
+        return pa.binary()
+    raise TypeError(
+        f"Converting Pydantic type to Arrow Type: unsupported type {py_type}"
+    )
+
+
+def _pydantic_model_to_fields(model: pydantic.BaseModel) -> List[pa.Field]:
+    fields = []
+    for name, field in model.model_fields.items():
+        fields.append(_pydantic_to_field(name, field))
+    return fields
+
+
+def _pydantic_to_arrow_type(field: pydantic.fields.FieldInfo) -> pa.DataType:
+    """Convert a Pydantic FieldInfo to Arrow DataType"""
+    if isinstance(field.annotation, _GenericAlias) or (
+        sys.version_info > (3, 9) and isinstance(field.annotation, types.GenericAlias)
+    ):
+        origin = field.annotation.__origin__
+        args = field.annotation.__args__
+        if origin == list:
+            child = args[0]
+            return pa.list_(_py_type_to_arrow_type(child))
+        elif origin == Union:
+            if len(args) == 2 and args[1] == type(None):
+                return _py_type_to_arrow_type(args[0])
+    elif inspect.isclass(field.annotation):
+        if issubclass(field.annotation, pydantic.BaseModel):
+            # Struct
+            fields = _pydantic_model_to_fields(field.annotation)
+            return pa.struct(fields)
+        elif issubclass(field.annotation, FixedSizeListMixin):
+            return pa.list_(field.annotation.value_arrow_type(), field.annotation.dim())
+    return _py_type_to_arrow_type(field.annotation)
+
+
+def is_nullable(field: pydantic.fields.FieldInfo) -> bool:
+    """Check if a Pydantic FieldInfo is nullable."""
+    if isinstance(field.annotation, _GenericAlias):
+        origin = field.annotation.__origin__
+        args = field.annotation.__args__
+        if origin == Union:
+            if len(args) == 2 and args[1] == type(None):
+                return True
+    return False
+
+
+def _pydantic_to_field(name: str, field: pydantic.fields.FieldInfo) -> pa.Field:
+    """Convert a Pydantic field to a PyArrow Field."""
+    dt = _pydantic_to_arrow_type(field)
+    return pa.field(name, dt, is_nullable(field))
+
+
+def pydantic_to_schema(model: Type[pydantic.BaseModel]) -> pa.Schema:
+    """Convert a Pydantic model to a PyArrow Schema.
+
+    Parameters
+    ----------
+    model : Type[pydantic.BaseModel]
+        The Pydantic BaseModel to convert to Arrow Schema.
+
+    Returns
+    -------
+    pyarrow.Schema
+
+    Examples
+    --------
+
+    >>> from typing import List, Optional
+    >>> import pydantic
+    >>> from lancedb.pydantic import pydantic_to_schema
+    ...
+    >>> class InnerModel(pydantic.BaseModel):
+    ...     a: str
+    ...     b: Optional[float]
+    >>>
+    >>> class FooModel(pydantic.BaseModel):
+    ...     id: int
+    ...     s: Optional[str] = None
+    ...     vec: List[float]
+    ...     li: List[int]
+    ...     inner: InnerModel
+    >>> schema = pydantic_to_schema(FooModel)
+    >>> assert schema == pa.schema([
+    ...     pa.field("id", pa.int64(), False),
+    ...     pa.field("s", pa.utf8(), True),
+    ...     pa.field("vec", pa.list_(pa.float64()), False),
+    ...     pa.field("li", pa.list_(pa.int64()), False),
+    ...     pa.field("inner", pa.struct([
+    ...         pa.field("a", pa.utf8(), False),
+    ...         pa.field("b", pa.float64(), True),
+    ...     ]), False),
+    ... ])
+    """
+    fields = _pydantic_model_to_fields(model)
+    return pa.schema(fields)

python/lancedb/query.py

@@ -226,6 +226,7 @@ class LanceQueryBuilder:
             columns=self._columns,
             nprobes=self._nprobes,
             refine_factor=self._refine_factor,
+            vector_column=self._vector_column,
         )
         return self._table._execute_query(query)
 

python/lancedb/remote/arrow.py

@@ -0,0 +1,22 @@
+#  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 pyarrow as pa
+
+
+def to_ipc_binary(table: pa.Table) -> bytes:
+    """Serialize a PyArrow Table to IPC binary."""
+    sink = pa.BufferOutputStream()
+    with pa.ipc.new_stream(sink, table.schema) as writer:
+        writer.write_table(table)
+    return sink.getvalue().to_pybytes()

python/lancedb/remote/client.py

@@ -13,7 +13,7 @@
 
 
 import functools
-from typing import Any, Callable, Dict, Union
+from typing import Any, Callable, Dict, Optional, Union
 
 import aiohttp
 import attr
@@ -24,6 +24,8 @@ from lancedb.common import Credential
 from lancedb.remote import VectorQuery, VectorQueryResult
 from lancedb.remote.errors import LanceDBClientError
 
+ARROW_STREAM_CONTENT_TYPE = "application/vnd.apache.arrow.stream"
+
 
 def _check_not_closed(f):
     @functools.wraps(f)
@@ -59,9 +61,12 @@ class RestfulLanceDBClient:
 
     @functools.cached_property
     def headers(self) -> Dict[str, str]:
-        return {
+        headers = {
             "x-api-key": self.api_key,
         }
+        if self.region == "local":  # Local test mode
+            headers["Host"] = f"{self.db_name}.{self.region}.api.lancedb.com"
+        return headers
 
     @staticmethod
     async def _check_status(resp: aiohttp.ClientResponse):
@@ -93,7 +98,9 @@ class RestfulLanceDBClient:
     async def post(
         self,
         uri: str,
-        data: Union[Dict[str, Any], BaseModel],
+        data: Union[Dict[str, Any], BaseModel, bytes],
+        params: Optional[Dict[str, Any]] = None,
+        content_type: Optional[str] = None,
         deserialize: Callable = lambda resp: resp.json(),
     ) -> Dict[str, Any]:
         """Send a POST request and returns the deserialized response payload.
@@ -107,10 +114,19 @@ class RestfulLanceDBClient:
         """
         if isinstance(data, BaseModel):
             data: Dict[str, Any] = data.dict(exclude_none=True)
+        if isinstance(data, bytes):
+            req_kwargs = {"data": data}
+        else:
+            req_kwargs = {"json": data}
+
+        headers = self.headers.copy()
+        if content_type is not None:
+            headers["content-type"] = content_type
         async with self.session.post(
             uri,
-            json=data,
-            headers=self.headers,
+            headers=headers,
+            params=params,
+            **req_kwargs,
         ) as resp:
             resp: aiohttp.ClientResponse = resp
             await self._check_status(resp)
@@ -119,11 +135,11 @@ class RestfulLanceDBClient:
     @_check_not_closed
     async def list_tables(self):
         """List all tables in the database."""
-        json = await self.get("/1/table/", {})
+        json = await self.get("/v1/table/", {})
         return json["tables"]
 
     @_check_not_closed
     async def query(self, table_name: str, query: VectorQuery) -> VectorQueryResult:
         """Query a table."""
-        tbl = await self.post(f"/1/table/{table_name}/", query, deserialize=_read_ipc)
+        tbl = await self.post(f"/v1/table/{table_name}/", query, deserialize=_read_ipc)
         return VectorQueryResult(tbl)

python/lancedb/remote/db.py

@@ -12,6 +12,7 @@
 #  limitations under the License.
 
 import asyncio
+import uuid
 from typing import List
 from urllib.parse import urlparse
 
@@ -19,9 +20,11 @@ import pyarrow as pa
 
 from lancedb.common import DATA
 from lancedb.db import DBConnection
-from lancedb.table import Table
+from lancedb.schema import schema_to_json
+from lancedb.table import Table, _sanitize_data
 
-from .client import RestfulLanceDBClient
+from .arrow import to_ipc_binary
+from .client import ARROW_STREAM_CONTENT_TYPE, RestfulLanceDBClient
 
 
 class RemoteDBConnection(DBConnection):
@@ -71,8 +74,31 @@ class RemoteDBConnection(DBConnection):
         name: str,
         data: DATA = None,
         schema: pa.Schema = None,
-        mode: str = "create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
     ) -> Table:
-        raise NotImplementedError
+        if data is None and schema is None:
+            raise ValueError("Either data or schema must be provided.")
+        if data is not None:
+            data = _sanitize_data(
+                data, schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
+            )
+        else:
+            if schema is None:
+                raise ValueError("Either data or schema must be provided")
+            data = pa.Table.from_pylist([], schema=schema)
+
+        from .table import RemoteTable
+
+        data = to_ipc_binary(data)
+        request_id = uuid.uuid4().hex
+
+        self._loop.run_until_complete(
+            self._client.post(
+                f"/v1/table/{name}/create",
+                data=data,
+                params={"request_id": request_id},
+                content_type=ARROW_STREAM_CONTENT_TYPE,
+            )
+        )
+        return RemoteTable(self, name)

python/lancedb/remote/table.py

@@ -11,6 +11,8 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 
+import uuid
+from functools import cached_property
 from typing import Union
 
 import pyarrow as pa
@@ -18,7 +20,10 @@ import pyarrow as pa
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
 
 from ..query import LanceQueryBuilder, Query
-from ..table import Query, Table
+from ..schema import json_to_schema
+from ..table import Query, Table, _sanitize_data
+from .arrow import to_ipc_binary
+from .client import ARROW_STREAM_CONTENT_TYPE
 from .db import RemoteDBConnection
 
 
@@ -30,8 +35,14 @@ class RemoteTable(Table):
     def __repr__(self) -> str:
         return f"RemoteTable({self._conn.db_name}.{self.name})"
 
+    @cached_property
     def schema(self) -> pa.Schema:
-        raise NotImplementedError
+        """Return the schema of the table."""
+        resp = self._conn._loop.run_until_complete(
+            self._conn._client.get(f"/v1/table/{self._name}/describe")
+        )
+        schema = json_to_schema(resp["schema"])
+        return schema
 
     def to_arrow(self) -> pa.Table:
         raise NotImplementedError
@@ -53,7 +64,22 @@ class RemoteTable(Table):
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
     ) -> int:
-        raise NotImplementedError
+        data = _sanitize_data(
+            data, self.schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
+        )
+        payload = to_ipc_binary(data)
+
+        request_id = uuid.uuid4().hex
+
+        self._conn._loop.run_until_complete(
+            self._conn._client.post(
+                f"/v1/table/{self._name}/insert",
+                data=payload,
+                params={"request_id": request_id, "mode": mode},
+                content_type=ARROW_STREAM_CONTENT_TYPE,
+            )
+        )
+        return len(data)
 
     def search(
         self, query: Union[VEC, str], vector_column: str = VECTOR_COLUMN_NAME

python/lancedb/schema.py

@@ -13,10 +13,10 @@
 
 """Schema related utilities."""
 
-import json
 from typing import Any, Dict, Type
 
 import pyarrow as pa
+from lance import json_to_schema, schema_to_json
 
 
 def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataType:
@@ -43,247 +43,3 @@ def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataTyp
     ... ])
     """
     return pa.list_(value_type, dimension)
-
-
-def _type_to_dict(dt: pa.DataType) -> Dict[str, Any]:
-    if pa.types.is_boolean(dt):
-        return {"type": "boolean"}
-    elif pa.types.is_int8(dt):
-        return {"type": "int8"}
-    elif pa.types.is_int16(dt):
-        return {"type": "int16"}
-    elif pa.types.is_int32(dt):
-        return {"type": "int32"}
-    elif pa.types.is_int64(dt):
-        return {"type": "int64"}
-    elif pa.types.is_uint8(dt):
-        return {"type": "uint8"}
-    elif pa.types.is_uint16(dt):
-        return {"type": "uint16"}
-    elif pa.types.is_uint32(dt):
-        return {"type": "uint32"}
-    elif pa.types.is_uint64(dt):
-        return {"type": "uint64"}
-    elif pa.types.is_float16(dt):
-        return {"type": "float16"}
-    elif pa.types.is_float32(dt):
-        return {"type": "float32"}
-    elif pa.types.is_float64(dt):
-        return {"type": "float64"}
-    elif pa.types.is_date32(dt):
-        return {"type": f"date32"}
-    elif pa.types.is_date64(dt):
-        return {"type": f"date64"}
-    elif pa.types.is_time32(dt):
-        return {"type": f"time32:{dt.unit}"}
-    elif pa.types.is_time64(dt):
-        return {"type": f"time64:{dt.unit}"}
-    elif pa.types.is_timestamp(dt):
-        return {"type": f"timestamp:{dt.unit}:{dt.tz if dt.tz is not None else ''}"}
-    elif pa.types.is_string(dt):
-        return {"type": "string"}
-    elif pa.types.is_binary(dt):
-        return {"type": "binary"}
-    elif pa.types.is_large_string(dt):
-        return {"type": "large_string"}
-    elif pa.types.is_large_binary(dt):
-        return {"type": "large_binary"}
-    elif pa.types.is_fixed_size_binary(dt):
-        return {"type": "fixed_size_binary", "width": dt.byte_width}
-    elif pa.types.is_fixed_size_list(dt):
-        return {
-            "type": "fixed_size_list",
-            "width": dt.list_size,
-            "value_type": _type_to_dict(dt.value_type),
-        }
-    elif pa.types.is_list(dt):
-        return {
-            "type": "list",
-            "value_type": _type_to_dict(dt.value_type),
-        }
-    elif pa.types.is_struct(dt):
-        return {
-            "type": "struct",
-            "fields": [_field_to_dict(dt.field(i)) for i in range(dt.num_fields)],
-        }
-    elif pa.types.is_dictionary(dt):
-        return {
-            "type": "dictionary",
-            "index_type": _type_to_dict(dt.index_type),
-            "value_type": _type_to_dict(dt.value_type),
-        }
-    # TODO: support extension types
-
-    raise TypeError(f"Unsupported type: {dt}")
-
-
-def _field_to_dict(field: pa.field) -> Dict[str, Any]:
-    ret = {
-        "name": field.name,
-        "type": _type_to_dict(field.type),
-        "nullable": field.nullable,
-    }
-    if field.metadata is not None:
-        ret["metadata"] = field.metadata
-    return ret
-
-
-def schema_to_dict(schema: pa.Schema) -> Dict[str, Any]:
-    """Convert a PyArrow [Schema](pyarrow.Schema) to a dictionary.
-
-    Parameters
-    ----------
-    schema : pa.Schema
-        The PyArrow Schema to convert
-
-    Returns
-    -------
-    A dict of the data type.
-
-    Examples
-    --------
-
-    >>> import pyarrow as pa
-    >>> import lancedb
-    >>> schema = pa.schema(
-    ...     [
-    ...         pa.field("id", pa.int64()),
-    ...         pa.field("vector", lancedb.vector(512), nullable=False),
-    ...         pa.field(
-    ...             "struct",
-    ...             pa.struct(
-    ...             [
-    ...                 pa.field("a", pa.utf8()),
-    ...                 pa.field("b", pa.float32()),
-    ...             ]
-    ...         ),
-    ...         True,
-    ...     ),
-    ...     ],
-    ...     metadata={"key": "value"},
-    ... )
-    >>> json_schema = schema_to_dict(schema)
-    >>> assert json_schema == {
-    ...     "fields": [
-    ...     {"name": "id", "type": {"type": "int64"}, "nullable": True},
-    ...     {
-    ...         "name": "vector",
-    ...         "type": {
-    ...             "type": "fixed_size_list",
-    ...             "value_type": {"type": "float32"},
-    ...             "width": 512,
-    ...         },
-    ...        "nullable": False,
-    ...    },
-    ...    {
-    ...         "name": "struct",
-    ...         "type": {
-    ...             "type": "struct",
-    ...             "fields": [
-    ...                 {"name": "a", "type": {"type": "string"}, "nullable": True},
-    ...                 {"name": "b", "type": {"type": "float32"}, "nullable": True},
-    ...            ],
-    ...         },
-    ...         "nullable": True,
-    ...     },
-    ...     ],
-    ...     "metadata": {"key": "value"},
-    ... }
-
-    """
-    fields = []
-    for name in schema.names:
-        field = schema.field(name)
-        fields.append(_field_to_dict(field))
-    json_schema = {
-        "fields": fields,
-        "metadata": {
-            k.decode("utf-8"): v.decode("utf-8") for (k, v) in schema.metadata.items()
-        }
-        if schema.metadata is not None
-        else {},
-    }
-    return json_schema
-
-
-def _dict_to_type(dt: Dict[str, Any]) -> pa.DataType:
-    type_name = dt["type"]
-    try:
-        return {
-            "boolean": pa.bool_(),
-            "int8": pa.int8(),
-            "int16": pa.int16(),
-            "int32": pa.int32(),
-            "int64": pa.int64(),
-            "uint8": pa.uint8(),
-            "uint16": pa.uint16(),
-            "uint32": pa.uint32(),
-            "uint64": pa.uint64(),
-            "float16": pa.float16(),
-            "float32": pa.float32(),
-            "float64": pa.float64(),
-            "string": pa.string(),
-            "binary": pa.binary(),
-            "large_string": pa.large_string(),
-            "large_binary": pa.large_binary(),
-            "date32": pa.date32(),
-            "date64": pa.date64(),
-        }[type_name]
-    except KeyError:
-        pass
-
-    if type_name == "fixed_size_binary":
-        return pa.binary(dt["width"])
-    elif type_name == "fixed_size_list":
-        return pa.list_(_dict_to_type(dt["value_type"]), dt["width"])
-    elif type_name == "list":
-        return pa.list_(_dict_to_type(dt["value_type"]))
-    elif type_name == "struct":
-        fields = []
-        for field in dt["fields"]:
-            fields.append(_dict_to_field(field))
-        return pa.struct(fields)
-    elif type_name == "dictionary":
-        return pa.dictionary(
-            _dict_to_type(dt["index_type"]), _dict_to_type(dt["value_type"])
-        )
-    elif type_name.startswith("time32:"):
-        return pa.time32(type_name.split(":")[1])
-    elif type_name.startswith("time64:"):
-        return pa.time64(type_name.split(":")[1])
-    elif type_name.startswith("timestamp:"):
-        fields = type_name.split(":")
-        unit = fields[1]
-        tz = fields[2] if len(fields) > 2 else None
-        return pa.timestamp(unit, tz)
-    raise TypeError(f"Unsupported type: {dt}")
-
-
-def _dict_to_field(field: Dict[str, Any]) -> pa.Field:
-    name = field["name"]
-    nullable = field["nullable"] if "nullable" in field else True
-    dt = _dict_to_type(field["type"])
-    metadata = field.get("metadata", None)
-    return pa.field(name, dt, nullable, metadata)
-
-
-def dict_to_schema(json: Dict[str, Any]) -> pa.Schema:
-    """Reconstruct a PyArrow Schema from a JSON dict.
-
-    Parameters
-    ----------
-    json : Dict[str, Any]
-        The JSON dict to reconstruct Schema from.
-
-    Returns
-    -------
-    A PyArrow Schema.
-    """
-    fields = []
-    for field in json["fields"]:
-        fields.append(_dict_to_field(field))
-    metadata = {
-        k.encode("utf-8"): v.encode("utf-8")
-        for (k, v) in json.get("metadata", {}).items()
-    }
-    return pa.schema(fields, metadata)

python/lancedb/table.py

@@ -16,7 +16,7 @@ from __future__ import annotations
 import os
 from abc import ABC, abstractmethod
 from functools import cached_property
-from typing import List, Union
+from typing import Iterable, List, Union
 
 import lance
 import numpy as np
@@ -44,7 +44,7 @@ def _sanitize_data(data, schema, on_bad_vectors, fill_value):
         data = _sanitize_schema(
             data, schema=schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
         )
-    if not isinstance(data, pa.Table):
+    if not isinstance(data, (pa.Table, Iterable)):
         raise TypeError(f"Unsupported data type: {type(data)}")
     return data
 
@@ -483,7 +483,7 @@ class LanceTable(Table):
             if schema is None:
                 raise ValueError("Either data or schema must be provided")
             data = pa.Table.from_pylist([], schema=schema)
-        lance.write_dataset(data, tbl._dataset_uri, mode=mode)
+        lance.write_dataset(data, tbl._dataset_uri, schema=schema, mode=mode)
         return LanceTable(db, name)
 
     @classmethod

python/pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "lancedb"
-version = "0.1.10"
-dependencies = ["pylance~=0.5.0", "ratelimiter", "retry", "tqdm", "aiohttp", "pydantic", "attr"]
+version = "0.1.11"
+dependencies = ["pylance~=0.5.8", "ratelimiter", "retry", "tqdm", "aiohttp", "pydantic>=2", "attr"]
 description = "lancedb"
 authors = [
     { name = "LanceDB Devs", email = "dev@lancedb.com" },

python/tests/test_db.py

@@ -13,6 +13,7 @@
 
 import numpy as np
 import pandas as pd
+import pyarrow as pa
 import pytest
 
 import lancedb
@@ -75,6 +76,32 @@ def test_ingest_pd(tmp_path):
     assert db.open_table("test").name == db["test"].name
 
 
+def test_ingest_record_batch_iterator(tmp_path):
+    def batch_reader():
+        for i in range(5):
+            yield pa.RecordBatch.from_arrays(
+                [
+                    pa.array([[3.1, 4.1], [5.9, 26.5]]),
+                    pa.array(["foo", "bar"]),
+                    pa.array([10.0, 20.0]),
+                ],
+                ["vector", "item", "price"],
+            )
+
+    db = lancedb.connect(tmp_path)
+    tbl = db.create_table(
+        "test",
+        batch_reader(),
+        schema=pa.schema(
+            [
+                pa.field("vector", pa.list_(pa.float32())),
+                pa.field("item", pa.utf8()),
+                pa.field("price", pa.float32()),
+            ]
+        ),
+    )
+
+
 def test_create_mode(tmp_path):
     db = lancedb.connect(tmp_path)
     data = pd.DataFrame(
@@ -131,6 +158,9 @@ def test_empty_or_nonexistent_table(tmp_path):
     with pytest.raises(Exception):
         db.open_table("does_not_exist")
 
+    schema = pa.schema([pa.field("a", pa.int32())])
+    db.create_table("test", schema=schema)
+
 
 def test_replace_index(tmp_path):
     db = lancedb.connect(uri=tmp_path)

python/tests/test_pydantic.py

@@ -0,0 +1,155 @@
+#  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 json
+import sys
+from typing import List, Optional
+
+import pyarrow as pa
+import pydantic
+import pytest
+
+from lancedb.pydantic import pydantic_to_schema, vector
+
+
+@pytest.mark.skipif(
+    sys.version_info < (3, 9),
+    reason="using native type alias requires python3.9 or higher",
+)
+def test_pydantic_to_arrow():
+    class StructModel(pydantic.BaseModel):
+        a: str
+        b: Optional[float]
+
+    class TestModel(pydantic.BaseModel):
+        id: int
+        s: str
+        vec: list[float]
+        li: List[int]
+        opt: Optional[str] = None
+        st: StructModel
+        # d: dict
+
+    m = TestModel(
+        id=1, s="hello", vec=[1.0, 2.0, 3.0], li=[2, 3, 4], st=StructModel(a="a", b=1.0)
+    )
+
+    schema = pydantic_to_schema(TestModel)
+
+    expect_schema = pa.schema(
+        [
+            pa.field("id", pa.int64(), False),
+            pa.field("s", pa.utf8(), False),
+            pa.field("vec", pa.list_(pa.float64()), False),
+            pa.field("li", pa.list_(pa.int64()), False),
+            pa.field("opt", pa.utf8(), True),
+            pa.field(
+                "st",
+                pa.struct(
+                    [pa.field("a", pa.utf8(), False), pa.field("b", pa.float64(), True)]
+                ),
+                False,
+            ),
+        ]
+    )
+    assert schema == expect_schema
+
+
+def test_pydantic_to_arrow_py38():
+    class StructModel(pydantic.BaseModel):
+        a: str
+        b: Optional[float]
+
+    class TestModel(pydantic.BaseModel):
+        id: int
+        s: str
+        vec: List[float]
+        li: List[int]
+        opt: Optional[str] = None
+        st: StructModel
+        # d: dict
+
+    m = TestModel(
+        id=1, s="hello", vec=[1.0, 2.0, 3.0], li=[2, 3, 4], st=StructModel(a="a", b=1.0)
+    )
+
+    schema = pydantic_to_schema(TestModel)
+
+    expect_schema = pa.schema(
+        [
+            pa.field("id", pa.int64(), False),
+            pa.field("s", pa.utf8(), False),
+            pa.field("vec", pa.list_(pa.float64()), False),
+            pa.field("li", pa.list_(pa.int64()), False),
+            pa.field("opt", pa.utf8(), True),
+            pa.field(
+                "st",
+                pa.struct(
+                    [pa.field("a", pa.utf8(), False), pa.field("b", pa.float64(), True)]
+                ),
+                False,
+            ),
+        ]
+    )
+    assert schema == expect_schema
+
+
+def test_fixed_size_list_field():
+    class TestModel(pydantic.BaseModel):
+        vec: vector(16)
+        li: List[int]
+
+    data = TestModel(vec=list(range(16)), li=[1, 2, 3])
+    assert json.loads(data.model_dump_json()) == {
+        "vec": list(range(16)),
+        "li": [1, 2, 3],
+    }
+
+    schema = pydantic_to_schema(TestModel)
+    assert schema == pa.schema(
+        [
+            pa.field("vec", pa.list_(pa.float32(), 16), False),
+            pa.field("li", pa.list_(pa.int64()), False),
+        ]
+    )
+
+    json_schema = TestModel.model_json_schema()
+    assert json_schema == {
+        "properties": {
+            "vec": {
+                "items": {"type": "number"},
+                "maxItems": 16,
+                "minItems": 16,
+                "title": "Vec",
+                "type": "array",
+            },
+            "li": {"items": {"type": "integer"}, "title": "Li", "type": "array"},
+        },
+        "required": ["vec", "li"],
+        "title": "TestModel",
+        "type": "object",
+    }
+
+
+def test_fixed_size_list_validation():
+    class TestModel(pydantic.BaseModel):
+        vec: vector(8)
+
+    with pytest.raises(pydantic.ValidationError):
+        TestModel(vec=range(9))
+
+    with pytest.raises(pydantic.ValidationError):
+        TestModel(vec=range(7))
+
+    TestModel(vec=range(8))

python/tests/test_query.py

@@ -119,6 +119,7 @@ def test_query_builder_with_different_vector_column():
             columns=["b"],
             nprobes=20,
             refine_factor=None,
+            vector_column="foo_vector",
         )
     )
 

python/tests/test_schema.py

@@ -1,109 +0,0 @@
-#  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 pyarrow as pa
-
-import lancedb
-from lancedb.schema import dict_to_schema, schema_to_dict
-
-
-def test_schema_to_dict():
-    schema = pa.schema(
-        [
-            pa.field("id", pa.int64()),
-            pa.field("vector", lancedb.vector(512), nullable=False),
-            pa.field(
-                "struct",
-                pa.struct(
-                    [
-                        pa.field("a", pa.utf8()),
-                        pa.field("b", pa.float32()),
-                    ]
-                ),
-                True,
-            ),
-            pa.field("d", pa.dictionary(pa.int64(), pa.utf8()), False),
-        ],
-        metadata={"key": "value"},
-    )
-
-    json_schema = schema_to_dict(schema)
-    assert json_schema == {
-        "fields": [
-            {"name": "id", "type": {"type": "int64"}, "nullable": True},
-            {
-                "name": "vector",
-                "type": {
-                    "type": "fixed_size_list",
-                    "value_type": {"type": "float32"},
-                    "width": 512,
-                },
-                "nullable": False,
-            },
-            {
-                "name": "struct",
-                "type": {
-                    "type": "struct",
-                    "fields": [
-                        {"name": "a", "type": {"type": "string"}, "nullable": True},
-                        {"name": "b", "type": {"type": "float32"}, "nullable": True},
-                    ],
-                },
-                "nullable": True,
-            },
-            {
-                "name": "d",
-                "type": {
-                    "type": "dictionary",
-                    "index_type": {"type": "int64"},
-                    "value_type": {"type": "string"},
-                },
-                "nullable": False,
-            },
-        ],
-        "metadata": {"key": "value"},
-    }
-
-    actual_schema = dict_to_schema(json_schema)
-    assert actual_schema == schema
-
-
-def test_temporal_types():
-    schema = pa.schema(
-        [
-            pa.field("t32", pa.time32("s")),
-            pa.field("t32ms", pa.time32("ms")),
-            pa.field("t64", pa.time64("ns")),
-            pa.field("ts", pa.timestamp("s")),
-            pa.field("ts_us_tz", pa.timestamp("us", tz="America/New_York")),
-        ],
-    )
-    json_schema = schema_to_dict(schema)
-
-    assert json_schema == {
-        "fields": [
-            {"name": "t32", "type": {"type": "time32:s"}, "nullable": True},
-            {"name": "t32ms", "type": {"type": "time32:ms"}, "nullable": True},
-            {"name": "t64", "type": {"type": "time64:ns"}, "nullable": True},
-            {"name": "ts", "type": {"type": "timestamp:s:"}, "nullable": True},
-            {
-                "name": "ts_us_tz",
-                "type": {"type": "timestamp:us:America/New_York"},
-                "nullable": True,
-            },
-        ],
-        "metadata": {},
-    }
-
-    actual_schema = dict_to_schema(json_schema)
-    assert actual_schema == schema

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb-node"
-version = "0.1.12"
+version = "0.1.13"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
 edition = "2018"
@@ -15,6 +15,7 @@ arrow-ipc = { workspace = true }
 arrow-schema = { workspace = true }
 once_cell = "1"
 futures = "0.3"
+half = { workspace = true }
 lance = { workspace = true }
 vectordb = { path = "../../vectordb" }
 tokio = { version = "1.23", features = ["rt-multi-thread"] }

rust/vectordb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb"
-version = "0.1.12"
+version = "0.1.13"
 edition = "2021"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
@@ -13,6 +13,7 @@ arrow-data = { workspace = true }
 arrow-schema = { workspace = true }
 object_store = { workspace = true }
 snafu = "0.7.4"
+half = { workspace = true }
 lance = { workspace = true }
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
 

rust/vectordb/src/database.rs

@@ -27,6 +27,7 @@ pub struct Database {
     object_store: ObjectStore,
 
     pub(crate) uri: String,
+    pub(crate) base_path: object_store::path::Path,
 }
 
 const LANCE_EXTENSION: &str = "lance";
@@ -43,12 +44,13 @@ impl Database {
     ///
     /// * A [Database] object.
     pub async fn connect(uri: &str) -> Result<Database> {
-        let (object_store, _) = ObjectStore::from_uri(uri).await?;
+        let (object_store, base_path) = ObjectStore::from_uri(uri).await?;
         if object_store.is_local() {
             Self::try_create_dir(uri).context(CreateDirSnafu { path: uri })?;
         }
         Ok(Database {
             uri: uri.to_string(),
+            base_path,
             object_store,
         })
     }
@@ -70,7 +72,7 @@ impl Database {
     pub async fn table_names(&self) -> Result<Vec<String>> {
         let f = self
             .object_store
-            .read_dir(self.uri.as_str())
+            .read_dir(self.base_path.clone())
             .await?
             .iter()
             .map(|fname| Path::new(fname))
@@ -141,8 +143,9 @@ impl Database {
     /// # Arguments
     /// * `name` - The name of the table.
     pub async fn drop_table(&self, name: &str) -> Result<()> {
-        let dir_name = format!("{}/{}.{}", self.uri, name, LANCE_EXTENSION);
-        self.object_store.remove_dir_all(dir_name).await?;
+        let dir_name = format!("{}.{}", name, LANCE_EXTENSION);
+        let full_path = self.base_path.child(dir_name.clone());
+        self.object_store.remove_dir_all(full_path).await?;
         Ok(())
     }
 }

rust/vectordb/src/table.rs

@@ -16,6 +16,7 @@ use std::path::Path;
 use std::sync::Arc;
 
 use arrow_array::{Float32Array, RecordBatchReader};
+use arrow_schema::SchemaRef;
 use lance::dataset::{Dataset, ReadParams, WriteParams};
 use lance::index::IndexType;
 use snafu::prelude::*;
@@ -144,6 +145,16 @@ impl Table {
         })
     }
 
+    /// Schema of this Table.
+    pub fn schema(&self) -> SchemaRef {
+        Arc::new(self.dataset.schema().into())
+    }
+
+    /// Version of this Table
+    pub fn version(&self) -> u64 {
+        self.dataset.version().version
+    }
+
     /// Create index on the table.
     pub async fn create_index(&mut self, index_builder: &impl VectorIndexBuilder) -> Result<()> {
         use lance::index::DatasetIndexExt;
@@ -274,6 +285,7 @@ mod tests {
     }
 
     #[test]
+    #[cfg(not(windows))]
     fn test_object_store_path() {
         use std::path::Path as StdPath;
         let p = StdPath::new("s3://bucket/path/to/file");
@@ -350,10 +362,7 @@ mod tests {
             ..Default::default()
         };
 
-        table
-            .add(new_batches, Some(param))
-            .await
-            .unwrap();
+        table.add(new_batches, Some(param)).await.unwrap();
         assert_eq!(table.count_rows().await.unwrap(), 10);
         assert_eq!(table.name, "test");
     }