Bump version: 0.1.5 → 0.1.6

Closes #181

.bumpversion.cfg

@@ -0,0 +1,12 @@
+[bumpversion]
+current_version = 0.1.6
+commit = True
+message = Bump version: {current_version} → {new_version}
+tag = True
+tag_name = v{new_version}
+
+[bumpversion:file:node/package.json]
+
+[bumpversion:file:rust/ffi/node/Cargo.toml]
+
+[bumpversion:file:rust/vectordb/Cargo.toml]

.github/workflows/cargo-publish.yml

@@ -0,0 +1,29 @@
+name: Cargo Publish
+
+on:
+  release:
+    types: [ published ]
+
+env:
+  # This env var is used by Swatinem/rust-cache@v2 for the cache
+  # key, so we set it to make sure it is always consistent.
+  CARGO_TERM_COLOR: always
+
+jobs:
+  build:
+    runs-on: ubuntu-22.04
+    timeout-minutes: 30
+    # Only runs on tags that matches the make-release action
+    if: startsWith(github.ref, 'refs/tags/v')
+    steps:
+      - uses: actions/checkout@v3
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: rust
+      - name: Install dependencies
+        run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+      - name: Publish the package
+        run: |
+          cargo publish -p vectordb --all-features --token ${{ secrets.CARGO_REGISTRY_TOKEN }}

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

@@ -0,0 +1,55 @@
+name: Create release commit
+
+on:
+  workflow_dispatch:
+    inputs:
+      dry_run:
+        description: 'Dry run (create the local commit/tags but do not push it)'
+        required: true
+        default: "false"
+        type: choice
+        options:
+          - "true"
+          - "false"
+      part:
+        description: 'What kind of release is this?'
+        required: true
+        default: 'patch'
+        type: choice
+        options:
+          - patch
+          - minor
+          - major
+
+jobs:
+  bump-version:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Check out main
+      uses: actions/checkout@v3
+      with:
+        ref: main
+        persist-credentials: false
+        fetch-depth: 0
+        lfs: true
+    - name: Set git configs for bumpversion
+      shell: bash
+      run: |
+        git config user.name 'Lance Release'
+        git config user.email 'lance-dev@lancedb.com'
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.10"
+    - name: Bump version, create tag and commit
+      run: |
+        pip install bump2version
+        bumpversion --verbose ${{ inputs.part }}
+    - name: Push new version and tag
+      if: ${{ inputs.dry_run }} == "false"
+      uses: ad-m/github-push-action@master
+      with:
+        github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+        branch: main
+        tags: true
+

.github/workflows/pypi-publish.yml

@@ -0,0 +1,31 @@
+name: PyPI Publish
+
+on:
+  release:
+    types: [ published ]
+
+jobs:
+  publish:
+    runs-on: ubuntu-latest
+    # Only runs on tags that matches the python-make-release action
+    if: startsWith(github.ref, 'refs/tags/python-v')
+    defaults:
+      run:
+        shell: bash
+        working-directory: python
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v4
+        with:
+          python-version: "3.8"
+      - name: Build distribution
+        run: |
+          ls -la
+          pip install wheel setuptools --upgrade
+          python setup.py sdist bdist_wheel
+      - name: Publish
+        uses: pypa/gh-action-pypi-publish@v1.8.5
+        with:
+          password: ${{ secrets.LANCEDB_PYPI_API_TOKEN }}
+          packages-dir: python/dist

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

@@ -0,0 +1,56 @@
+name: Python - Create release commit
+
+on:
+  workflow_dispatch:
+    inputs:
+      dry_run:
+        description: 'Dry run (create the local commit/tags but do not push it)'
+        required: true
+        default: "false"
+        type: choice
+        options:
+          - "true"
+          - "false"
+      part:
+        description: 'What kind of release is this?'
+        required: true
+        default: 'patch'
+        type: choice
+        options:
+          - patch
+          - minor
+          - major
+
+jobs:
+  bump-version:
+    runs-on: ubuntu-latest
+    steps:
+    - name: Check out main
+      uses: actions/checkout@v3
+      with:
+        ref: main
+        persist-credentials: false
+        fetch-depth: 0
+        lfs: true
+    - name: Set git configs for bumpversion
+      shell: bash
+      run: |
+        git config user.name 'Lance Release'
+        git config user.email 'lance-dev@lancedb.com'
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v4
+      with:
+        python-version: "3.10"
+    - name: Bump version, create tag and commit
+      working-directory: python
+      run: |
+        pip install bump2version
+        bumpversion --verbose ${{ inputs.part }}
+    - name: Push new version and tag
+      if: ${{ inputs.dry_run }} == "false"
+      uses: ad-m/github-push-action@master
+      with:
+        github_token: ${{ secrets.LANCEDB_RELEASE_TOKEN }}
+        branch: main
+        tags: true
+

.github/workflows/python.yml

@@ -32,9 +32,13 @@ jobs:
       run: |
         pip install -e .
         pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
-        pip install pytest
+        pip install pytest pytest-mock black
+    - name: Black
+      run: black --check --diff --no-color --quiet .
     - name: Run tests
       run: pytest -x -v --durations=30 tests
+    - name: doctest
+      run: pytest --doctest-modules lancedb
   mac:
     timeout-minutes: 30
     runs-on: "macos-12"
@@ -55,6 +59,6 @@ jobs:
       run: |
         pip install -e .
         pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
-        pip install pytest
+        pip install pytest pytest-mock
     - name: Run tests
       run: pytest -x -v --durations=30 tests

.github/workflows/rust.yml

@@ -0,0 +1,67 @@
+name: Rust
+
+on:
+  push:
+    branches:
+      - main
+  pull_request:
+    paths:
+      - rust/**
+      - .github/workflows/rust.yml
+
+env:
+  # This env var is used by Swatinem/rust-cache@v2 for the cache
+  # key, so we set it to make sure it is always consistent.
+  CARGO_TERM_COLOR: always
+  # Disable full debug symbol generation to speed up CI build and keep memory down
+  # "1" means line tables only, which is useful for panic tracebacks.
+  RUSTFLAGS: "-C debuginfo=1"
+  RUST_BACKTRACE: "1"
+
+jobs:
+  linux:
+    timeout-minutes: 30
+    runs-on: ubuntu-22.04
+    defaults:
+      run:
+        shell: bash
+        working-directory: rust
+    steps:
+    - uses: actions/checkout@v3
+      with:
+          fetch-depth: 0
+          lfs: true
+    - uses: Swatinem/rust-cache@v2
+      with:
+          workspaces: rust
+    - name: Install dependencies
+      run: |
+          sudo apt update
+          sudo apt install -y protobuf-compiler libssl-dev
+    - name: Build
+      run: cargo build --all-features
+    - name: Run tests
+      run: cargo test --all-features
+  macos:
+    runs-on: macos-12
+    timeout-minutes: 30
+    defaults:
+      run:
+        shell: bash
+        working-directory: rust
+    steps:
+      - uses: actions/checkout@v3
+        with:
+            fetch-depth: 0
+            lfs: true
+      - name: CPU features
+        run: sysctl -a | grep cpu
+      - uses: Swatinem/rust-cache@v2
+        with:
+          workspaces: rust
+      - name: Install dependencies
+        run: brew install protobuf
+      - name: Build
+        run: cargo build --all-features
+      - name: Run tests
+        run: cargo test --all-features

.gitignore

@@ -15,7 +15,7 @@ site
 python/build
 python/dist
 
-notebooks/.ipynb_checkpoints
+**/.ipynb_checkpoints
 
 **/.hypothesis
 

Cargo.lock

@@ -190,6 +190,7 @@ dependencies = [
  "arrow-data",
  "arrow-schema",
  "flatbuffers",
+ "zstd",
 ]
 
 [[package]]
@@ -654,6 +655,12 @@ version = "3.12.1"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "9b1ce199063694f33ffb7dd4e0ee620741495c32833cde5aa08f02a0bf96f0c8"
 
+[[package]]
+name = "bytemuck"
+version = "1.13.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "17febce684fd15d89027105661fec94afb475cb995fbc59d2865198446ba2eea"
+
 [[package]]
 name = "byteorder"
 version = "1.4.3"
@@ -1646,9 +1653,9 @@ dependencies = [
 
 [[package]]
 name = "lance"
-version = "0.4.17"
+version = "0.4.21"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "86dda8185bd1ffae7b910c1f68035af23be9b717c52e9cc4de176cd30b47f772"
+checksum = "3d6c2e7bcfc71c7167ec70cd06c6d55c644a148f6580218c5a0b66e13ac5b5cc"
 dependencies = [
  "accelerate-src",
  "arrow",
@@ -1657,7 +1664,9 @@ dependencies = [
  "arrow-buffer",
  "arrow-cast",
  "arrow-data",
+ "arrow-ipc",
  "arrow-ord",
+ "arrow-row",
  "arrow-schema",
  "arrow-select",
  "async-recursion",
@@ -1668,6 +1677,7 @@ dependencies = [
  "bytes",
  "cblas",
  "chrono",
+ "dashmap",
  "datafusion",
  "futures",
  "lapack",
@@ -1684,6 +1694,7 @@ dependencies = [
  "prost-types",
  "rand",
  "reqwest",
+ "roaring",
  "shellexpand",
  "snafu",
  "sqlparser-lance",
@@ -2598,6 +2609,12 @@ dependencies = [
  "winreg",
 ]
 
+[[package]]
+name = "retain_mut"
+version = "0.1.7"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "8c31b5c4033f8fdde8700e4657be2c497e7288f01515be52168c631e2e4d4086"
+
 [[package]]
 name = "ring"
 version = "0.16.20"
@@ -2613,6 +2630,17 @@ dependencies = [
  "winapi",
 ]
 
+[[package]]
+name = "roaring"
+version = "0.10.1"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "ef0fb5e826a8bde011ecae6a8539dd333884335c57ff0f003fbe27c25bbe8f71"
+dependencies = [
+ "bytemuck",
+ "byteorder",
+ "retain_mut",
+]
+
 [[package]]
 name = "rustc_version"
 version = "0.4.0"
@@ -3366,6 +3394,7 @@ dependencies = [
  "lance",
  "object_store",
  "rand",
+ "snafu",
  "tempfile",
  "tokio",
 ]

README.md

@@ -10,6 +10,10 @@
 <a href="https://discord.gg/zMM32dvNtd">Discord</a> •
 <a href="https://twitter.com/lancedb">Twitter</a>
 
+</p>
+
+<img max-width="750px" alt="LanceDB Multimodal Search" src="https://github.com/lancedb/lancedb/assets/917119/09c5afc5-7816-4687-bae4-f2ca194426ec">
+
 </p>
 </div>
 
@@ -23,13 +27,15 @@ The key features of LanceDB include:
 
 * Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
 
+* Support for vector similarity search, full-text search and SQL.
+
 * Native Python and Javascript/Typescript support.
 
 * Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
 
 * Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lanecdb.html), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
 
-LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/eto-ai/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
+LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
 
 ## Quick Start
 
@@ -69,4 +75,4 @@ result = table.search([100, 100]).limit(2).to_df()
 
 ## Blogs, Tutorials & Videos
 * 📈 <a href="https://blog.eto.ai/benchmarking-random-access-in-lance-ed690757a826">2000x better performance with Lance over Parquet</a>
-* 🤖 <a href="https://github.com/lancedb/lancedb/blob/main/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>
+* 🤖 <a href="https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb">Build a question and answer bot with LanceDB</a>

docs/mkdocs.yml

@@ -1,29 +1,43 @@
-site_name: LanceDB Documentation
+site_name: LanceDB Docs
+repo_url: https://github.com/lancedb/lancedb
+repo_name: lancedb/lancedb
 docs_dir: src
 
 theme:
   name: "material"
+  logo: assets/logo.png
   features:
     - content.code.copy
+    - content.tabs.link
+  icon:
+    repo: fontawesome/brands/github
 
 plugins:
 - search
+- autorefs
 - mkdocstrings:
     handlers:
       python:
         paths: [../python]
+        selection:
+          docstring_style: numpy
+        rendering:
+          heading_level: 4
+          show_source: false
+          show_symbol_type_in_heading: true
+          show_signature_annotations: true
+          show_root_heading: true
+          members_order: source
+        import:
+          # for cross references
+          - https://arrow.apache.org/docs/objects.inv
+          - https://pandas.pydata.org/docs/objects.inv
 - mkdocs-jupyter
 
-nav:
-- Home: index.md
-- Basics: basic.md
-- Embeddings: embedding.md
-- Indexing: ann_indexes.md
-- Full-text search: fts.md
-- Integrations: integrations.md
-- Python API: python.md
-
 markdown_extensions:
+- admonition
+- pymdownx.superfences
+- pymdownx.details
 - pymdownx.highlight:
     anchor_linenums: true
     line_spans: __span
@@ -31,3 +45,29 @@ markdown_extensions:
 - pymdownx.inlinehilite
 - pymdownx.snippets
 - pymdownx.superfences
+- pymdownx.tabbed:
+    alternate_style: true
+
+nav:
+- Home: index.md
+- Basics: basic.md
+- Embeddings: embedding.md
+- Python full-text search: fts.md
+- Python integrations: integrations.md
+- Python examples:
+  - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
+  - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
+  - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
+  - Serverless QA Bot with S3 and Lambda: examples/serverless_lancedb_with_s3_and_lambda.md
+  - Serverless QA Bot with Modal: examples/serverless_qa_bot_with_modal_and_langchain.md
+- Javascript examples:
+  - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
+- References:
+  - Vector Search: search.md
+  - Indexing: ann_indexes.md
+- API references:
+  - Python API: python/python.md
+  - Javascript API: javascript/modules.md
+
+extra_css:
+  - styles/global.css

docs/src/ann_indexes.md

@@ -12,29 +12,43 @@ In the future we will look to automatically create and configure the ANN index.
 
 ## Creating an ANN Index
 
-Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
+=== "Python"
+     Creating indexes is done via the [create_index](https://lancedb.github.io/lancedb/python/#lancedb.table.LanceTable.create_index) method.
 
-```python
-import lancedb
-import numpy as np
-uri = "~/.lancedb"
-db = lancedb.connect(uri)
+     ```python
+     import lancedb
+     import numpy as np
+     uri = "data/sample-lancedb"
+     db = lancedb.connect(uri)
 
-# Create 10,000 sample vectors
-data = [{"vector": row, "item": f"item {i}"}
+     # Create 10,000 sample vectors
+     data = [{"vector": row, "item": f"item {i}"}
         for i, row in enumerate(np.random.random((10_000, 768)).astype('float32'))]
 
-# Add the vectors to a table
-tbl = db.create_table("my_vectors", data=data)
+     # Add the vectors to a table
+     tbl = db.create_table("my_vectors", data=data)
 
-# Create and train the index - you need to have enough data in the table for an effective training step
-tbl.create_index(num_partitions=256, num_sub_vectors=96)
-```
+     # Create and train the index - you need to have enough data in the table for an effective training step
+     tbl.create_index(num_partitions=256, num_sub_vectors=96)
+     ```
+
+=== "Javascript"
+     ```javascript
+     const vectordb = require('vectordb')
+     const db = await vectordb.connect('data/sample-lancedb')
+
+     let data = []
+     for (let i = 0; i < 10_000; i++) {
+         data.push({vector: Array(1536).fill(i), id: `${i}`, content: "", longId: `${i}`},)
+     }
+     const table = await db.createTable('vectors', data)
+     await table.create_index({ 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 we use euclidean distance. We also support cosine distance.
+- **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.
@@ -53,22 +67,32 @@ There are a couple of parameters that can be used to fine-tune the search:
   e.g., for 1M vectors divided up into 256 partitions, nprobes should be set to ~20-40.<br/>
   Note: nprobes is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
 - **refine_factor** (default: None): Refine the results by reading extra elements and re-ranking them in memory.<br/>
-  A higher number makes search more accurate but also slower. If you find the recall is less than idea, try refine_factor=10 to start.<br/>
+  A higher number makes search more accurate but also slower. If you find the recall is less than ideal, try refine_factor=10 to start.<br/>
   e.g., for 1M vectors divided into 256 partitions, if you're looking for top 20, then refine_factor=200 reranks the whole partition.<br/>
   Note: refine_factor is only applicable if an ANN index is present. If specified on a table without an ANN index, it is ignored.
 
-
-```python
-tbl.search(np.random.random((768))) \
+=== "Python"
+     ```python
+     tbl.search(np.random.random((768))) \
          .limit(2) \
          .nprobes(20) \
          .refine_factor(10) \
          .to_df()
 
                                               vector       item       score
-0  [0.44949695, 0.8444449, 0.06281311, 0.23338133...  item 1141  103.575333
-1  [0.48587373, 0.269207, 0.15095535, 0.65531915,...  item 3953  108.393867
-```
+     0  [0.44949695, 0.8444449, 0.06281311, 0.23338133...  item 1141  103.575333
+     1  [0.48587373, 0.269207, 0.15095535, 0.65531915,...  item 3953  108.393867
+     ```
+
+=== "Javascript"
+     ```javascript
+     const results = await table
+         .search(Array(768).fill(1.2))
+         .limit(2)
+         .nprobes(20)
+         .refineFactor(10)
+         .execute()
+     ```
 
 The search will return the data requested in addition to the score of each item.
 
@@ -78,18 +102,36 @@ The search will return the data requested in addition to the score of each item.
 
 You can further filter the elements returned by a search using a where clause.
 
-```python
-tbl.search(np.random.random((768))).where("item != 'item 1141'").to_df()
-```
+=== "Python"
+     ```python
+     tbl.search(np.random.random((768))).where("item != 'item 1141'").to_df()
+     ```
+
+=== "Javascript"
+     ```javascript
+     const results = await table
+         .search(Array(1536).fill(1.2))
+         .where("item != 'item 1141'")
+         .execute()
+     ```
 
 ### Projections (select clause)
 
 You can select the columns returned by the query using a select clause.
 
-```python
-tbl.search(np.random.random((768))).select(["vector"]).to_df()
+=== "Python"
+     ```python
+     tbl.search(np.random.random((768))).select(["vector"]).to_df()
      vector      score
-0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
-1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
-...
-```
+     0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
+     1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
+     ...
+     ```
+
+=== "Javascript"
+     ```javascript
+     const results = await table
+         .search(Array(1536).fill(1.2))
+         .select(["id"])
+         .execute()
+     ```

docs/src/basic.md

@@ -1,74 +1,142 @@
 # Basic LanceDB Functionality
 
+We'll cover the basics of using LanceDB on your local machine in this section.
+
+??? info "LanceDB runs embedded on your backend application, so there is no need to run a separate server."
+
+      <img src="../assets/lancedb_embedded_explanation.png" width="650px" />
+
+## Installation
+
+=== "Python"
+      ```shell
+      pip install lancedb
+      ```
+
+=== "Javascript"
+      ```shell
+      npm install vectordb
+      ```
+
 ## How to connect to a database
 
-In local mode, LanceDB stores data in a directory on your local machine. To connect to a local database, you can use the following code:
-```python
-import lancedb
-uri = "~/.lancedb"
-db = lancedb.connect(uri)
-```
+=== "Python"
+      ```python
+      import lancedb
+      uri = "~/.lancedb"
+      db = lancedb.connect(uri)
+      ```
 
-LanceDB will create the directory if it doesn't exist (including parent directories).
+      LanceDB will create the directory if it doesn't exist (including parent directories).
 
-If you need a reminder of the uri, use the `db.uri` property.
+      If you need a reminder of the uri, use the `db.uri` property.
+
+=== "Javascript"
+      ```javascript
+      const lancedb = require("vectordb");
+
+      const uri = "~./lancedb";
+      const db = await lancedb.connect(uri);
+      ```
+      
+      LanceDB will create the directory if it doesn't exist (including parent directories).
+
+      If you need a reminder of the uri, you can call `db.uri()`.
 
 ## How to create a table
 
-To create a table, you can use the following code:
-```python
-tbl = db.create_table("my_table",
+=== "Python"
+      ```python
+      tbl = db.create_table("my_table",
                         data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
                               {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
-```
+      ```
 
-Under the hood, LanceDB is converting the input data into an Apache Arrow table
-and persisting it to disk in [Lance format](github.com/eto-ai/lance).
+      If the table already exists, LanceDB will raise an error by default.
+      If you want to overwrite the table, you can pass in `mode="overwrite"`
+      to the `create_table` method.
 
-If the table already exists, LanceDB will raise an error by default.
-If you want to overwrite the table, you can pass in `mode="overwrite"`
-to the `create_table` method.
-
-You can also pass in a pandas DataFrame directly:
-```python
-import pandas as pd
-df = pd.DataFrame([{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+      You can also pass in a pandas DataFrame directly:
+      ```python
+      import pandas as pd
+      df = pd.DataFrame([{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
                         {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
-tbl = db.create_table("table_from_df", data=df)
-```
+      tbl = db.create_table("table_from_df", data=df)
+      ```
+
+=== "Javascript"
+      ```javascript
+      const tb = await db.createTable("my_table",
+                        data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                              {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
+      ```
+      
+!!! warning
+
+      If the table already exists, LanceDB will raise an error by default.
+      If you want to overwrite the table, you can pass in `mode="overwrite"`
+      to the `createTable` function.
+
+??? info "Under the hood, LanceDB is converting the input data into an Apache Arrow table and persisting it to disk in [Lance format](https://www.github.com/lancedb/lance)."
 
 ## How to open an existing table
 
 Once created, you can open a table using the following code:
-```python
-tbl = db.open_table("my_table")
-```
 
-If you forget the name of your table, you can always get a listing of all table names:
+=== "Python"
+      ```python
+      tbl = db.open_table("my_table")
+      ```
 
-```python
-db.table_names()
-```
+      If you forget the name of your table, you can always get a listing of all table names:
+
+      ```python
+      print(db.table_names())
+      ```
+
+=== "Javascript"
+      ```javascript
+      const tbl = await db.openTable("my_table");
+      ```
+
+      If you forget the name of your table, you can always get a listing of all table names:
+
+      ```javascript
+      console.log(db.tableNames());
+      ```
 
 ## How to add data to a table
 
 After a table has been created, you can always add more data to it using
 
-```python
-df = pd.DataFrame([{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
+=== "Python"
+      ```python
+      df = pd.DataFrame([{"vector": [1.3, 1.4], "item": "fizz", "price": 100.0},
                         {"vector": [9.5, 56.2], "item": "buzz", "price": 200.0}])
-tbl.add(df)
-```
+      tbl.add(df)
+      ```
+
+=== "Javascript"
+      ```javascript
+      await tbl.add([vector: [1.3, 1.4], item: "fizz", price: 100.0},
+              {vector: [9.5, 56.2], item: "buzz", price: 200.0}])
+      ```
 
 ## How to search for (approximate) nearest neighbors
 
 Once you've embedded the query, you can find its nearest neighbors using the following code:
 
-```python
-tbl.search([100, 100]).limit(2).to_df()
-```
+=== "Python"
+      ```python
+      tbl.search([100, 100]).limit(2).to_df()
+      ```
 
-This returns a pandas DataFrame with the results.
+      This returns a pandas DataFrame with the results.
+
+=== "Javascript"
+      ```javascript
+      const query = await tbl.search([100, 100]).limit(2).execute();
+      ```
 
 ## What's next
 

docs/src/embedding.md

@@ -25,55 +25,88 @@ def embed_func(batch):
     return [model.encode(sentence) for sentence in batch]
 ```
 
+Please note that currently HuggingFace is only supported in the Python SDK.
+
 ### OpenAI example
 
 You can also use an external API like OpenAI to generate embeddings
 
-```python
-import openai
-import os
+=== "Python"
+      ```python
+        import openai
+        import os
 
-# Configuring the environment variable OPENAI_API_KEY
-if "OPENAI_API_KEY" not in os.environ:
+        # Configuring the environment variable OPENAI_API_KEY
+        if "OPENAI_API_KEY" not in os.environ:
         # OR set the key here as a variable
         openai.api_key = "sk-..."
 
-# verify that the API key is working
-assert len(openai.Model.list()["data"]) > 0
+        # verify that the API key is working
+        assert len(openai.Model.list()["data"]) > 0
 
-def embed_func(c):
+        def embed_func(c):
             rs = openai.Embedding.create(input=c, engine="text-embedding-ada-002")
         return [record["embedding"] for record in rs["data"]]
-```
+      ```
+
+=== "Javascript"
+      ```javascript
+        const lancedb = require("vectordb");
+
+        // You need to provide an OpenAI API key
+        const apiKey = "sk-..."
+        // The embedding function will create embeddings for the 'text' column
+        const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
+      ```
 
 ## Applying an embedding function
 
-Using an embedding function, you can apply it to raw data
-to generate embeddings for each row.
+=== "Python"
+    Using an embedding function, you can apply it to raw data
+    to generate embeddings for each row.
 
-Say if you have a pandas DataFrame with a `text` column that you want to be embedded,
-you can use the [with_embeddings](https://lancedb.github.io/lancedb/python/#lancedb.embeddings.with_embeddings)
-function to generate embeddings and add create a combined pyarrow table:
+    Say if you have a pandas DataFrame with a `text` column that you want to be embedded,
+    you can use the [with_embeddings](https://lancedb.github.io/lancedb/python/#lancedb.embeddings.with_embeddings)
+    function to generate embeddings and add create a combined pyarrow table:
 
-```python
-import pandas as pd
-from lancedb.embeddings import with_embeddings
 
-df = pd.DataFrame([{"text": "pepperoni"},
+     ```python
+     import pandas as pd
+     from lancedb.embeddings import with_embeddings
+
+     df = pd.DataFrame([{"text": "pepperoni"},
                         {"text": "pineapple"}])
-data = with_embeddings(embed_func, df)
+     data = with_embeddings(embed_func, df)
 
-# The output is used to create / append to a table
-# db.create_table("my_table", data=data)
-```
+     # The output is used to create / append to a table
+     # db.create_table("my_table", data=data)
+     ```
 
-If your data is in a different column, you can specify the `column` kwarg to `with_embeddings`.
+     If your data is in a different column, you can specify the `column` kwarg to `with_embeddings`.
 
-By default, LanceDB calls the function with batches of 1000 rows. This can be configured
-using the `batch_size` parameter to `with_embeddings`.
+     By default, LanceDB calls the function with batches of 1000 rows. This can be configured
+     using the `batch_size` parameter to `with_embeddings`.
+
+     LanceDB automatically wraps the function with retry and rate-limit logic to ensure the OpenAI
+     API call is reliable.
+
+=== "Javascript"
+     Using an embedding function, you can apply it to raw data
+     to generate embeddings for each row.
+
+     You can just pass the embedding function created previously and LanceDB will automatically generate
+     embededings for your data.
+
+      ```javascript
+      const db = await lancedb.connect("/tmp/lancedb");
+      const data = [
+        { text: 'pepperoni'  },
+        { text: 'pineapple' }
+      ]
+
+      const table = await db.createTable('vectors', data, embedding)
+      ```
 
-LanceDB automatically wraps the function with retry and rate-limit logic to ensure the OpenAI
-API call is reliable.
 
 ## Searching with an embedding function
 
@@ -81,13 +114,25 @@ At inference time, you also need the same embedding function to embed your query
 It's important that you use the same model / function otherwise the embedding vectors don't
 belong in the same latent space and your results will be nonsensical.
 
-```python
-query = "What's the best pizza topping?"
-query_vector = embed_func([query])[0]
-tbl.search(query_vector).limit(10).to_df()
-```
+=== "Python"
+     ```python
+     query = "What's the best pizza topping?"
+     query_vector = embed_func([query])[0]
+     tbl.search(query_vector).limit(10).to_df()
+     ```
+
+     The above snippet returns a pandas DataFrame with the 10 closest vectors to the query.
+
+=== "Javascript"
+     ```javascript
+      const results = await table
+        .search('What's the best pizza topping?')
+        .limit(10)
+        .execute()
+     ```
+
+     The above snippet returns an array of records with the 10 closest vectors to the query.
 
-The above snippet returns a pandas DataFrame with the 10 closest vectors to the query.
 
 ## Roadmap
 

docs/src/examples/code_documentation_qa_bot_with_langchain.md

@@ -4,4 +4,4 @@
 
 <img id="splash" width="400" alt="langchain" src="https://user-images.githubusercontent.com/917119/236580868-61a246a9-e587-4c2b-8ae5-6fe5f7b7e81e.png">
 
-This example is in a [notebook](https://github.com/lancedb/lancedb/blob/main/notebooks/code_qa_bot.ipynb)
+This example is in a [notebook](https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/code_qa_bot.ipynb)

docs/src/examples/modal_langchain.py

@@ -0,0 +1,119 @@
+import sys
+from modal import Secret, Stub, Image, web_endpoint
+import lancedb
+import re
+import pickle
+import requests
+import zipfile
+from pathlib import Path
+
+from langchain.document_loaders import UnstructuredHTMLLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import LanceDB
+from langchain.llms import OpenAI
+from langchain.chains import RetrievalQA
+
+lancedb_image = Image.debian_slim().pip_install(
+    "lancedb", "langchain", "openai", "pandas", "tiktoken", "unstructured", "tabulate"
+)
+
+stub = Stub(
+    name="example-langchain-lancedb",
+    image=lancedb_image,
+    secrets=[Secret.from_name("my-openai-secret")],
+)
+
+docsearch = None
+docs_path = Path("docs.pkl")
+db_path = Path("lancedb")
+
+
+def get_document_title(document):
+    m = str(document.metadata["source"])
+    title = re.findall("pandas.documentation(.*).html", m)
+    if title[0] is not None:
+        return title[0]
+    return ""
+
+
+def download_docs():
+    pandas_docs = requests.get(
+        "https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip"
+    )
+    with open(Path("pandas.documentation.zip"), "wb") as f:
+        f.write(pandas_docs.content)
+
+    file = zipfile.ZipFile(Path("pandas.documentation.zip"))
+    file.extractall(path=Path("pandas_docs"))
+
+
+def store_docs():
+    docs = []
+
+    if not docs_path.exists():
+        for p in Path("pandas_docs/pandas.documentation").rglob("*.html"):
+            if p.is_dir():
+                continue
+            loader = UnstructuredHTMLLoader(p)
+            raw_document = loader.load()
+
+            m = {}
+            m["title"] = get_document_title(raw_document[0])
+            m["version"] = "2.0rc0"
+            raw_document[0].metadata = raw_document[0].metadata | m
+            raw_document[0].metadata["source"] = str(raw_document[0].metadata["source"])
+            docs = docs + raw_document
+
+        with docs_path.open("wb") as fh:
+            pickle.dump(docs, fh)
+    else:
+        with docs_path.open("rb") as fh:
+            docs = pickle.load(fh)
+
+    return docs
+
+
+def qanda_langchain(query):
+    download_docs()
+    docs = store_docs()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+    )
+    documents = text_splitter.split_documents(docs)
+    embeddings = OpenAIEmbeddings()
+
+    db = lancedb.connect(db_path)
+    table = db.create_table(
+        "pandas_docs",
+        data=[
+            {
+                "vector": embeddings.embed_query("Hello World"),
+                "text": "Hello World",
+                "id": "1",
+            }
+        ],
+        mode="overwrite",
+    )
+    docsearch = LanceDB.from_documents(documents, embeddings, connection=table)
+    qa = RetrievalQA.from_chain_type(
+        llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever()
+    )
+    return qa.run(query)
+
+
+@stub.function()
+@web_endpoint(method="GET")
+def web(query: str):
+    answer = qanda_langchain(query)
+    return {
+        "answer": answer,
+    }
+
+
+@stub.function()
+def cli(query: str):
+    answer = qanda_langchain(query)
+    print(answer)

docs/src/examples/multimodal_search.md

@@ -0,0 +1,7 @@
+# Image multimodal search
+
+## Search through an image dataset using natural language, full text and SQL
+
+<img id="splash" width="400" alt="multimodal search" src="https://github.com/lancedb/lancedb/assets/917119/993a7c9f-be01-449d-942e-1ce1d4ed63af">
+
+This example is in a [notebook](https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/multimodal_search.ipynb)

docs/src/examples/nodejs.md

@@ -1,99 +0,0 @@
-# YouTube transcript QA bot with NodeJS
-
-## use LanceDB's Javascript API and OpenAI to build a QA bot for YouTube transcripts
-
-<img id="splash" width="400" alt="nodejs" src="https://github.com/lancedb/lancedb/assets/917119/3a140e75-bf8e-438a-a1e4-af14a72bcf98">
-
-This Q&A bot will allow you to search through youtube transcripts using natural language! We'll introduce how you can use LanceDB's Javascript API to store and manage your data easily.
-
-For this example we're using a HuggingFace dataset that contains YouTube transcriptions: `jamescalam/youtube-transcriptions`, to make it easier, we've converted it to a LanceDB `db` already, which you can download and put in a working directory:
-
-```wget -c https://eto-public.s3.us-west-2.amazonaws.com/lancedb_demo.tar.gz -O - | tar -xz -C .```
-
-Now, we'll create a simple app that can:
-1. Take a text based query and search for contexts in our corpus, using embeddings generated from the OpenAI Embedding API.
-2. Create a prompt with the contexts, and call the OpenAI Completion API to answer the text based query.
-
-Dependencies and setup of OpenAI API:
-
-```javascript
-const lancedb = require("vectordb");
-const { Configuration, OpenAIApi } = require("openai");
-
-const configuration = new Configuration({
-    apiKey: process.env.OPENAI_API_KEY,
-    });
-const openai = new OpenAIApi(configuration);
-```
-
-First, let's set our question and the context amount. The context amount will be used to query similar documents in our corpus.
-
-```javascript
-const QUESTION = "who was the 12th person on the moon and when did they land?";
-const CONTEXT_AMOUNT = 3;
-```
-
-Now, let's generate an embedding from this question:
-
-```javascript
-const embeddingResponse = await openai.createEmbedding({
-    model: "text-embedding-ada-002",
-    input: QUESTION,
-});
-
-const embedding = embeddingResponse.data["data"][0]["embedding"];
-```
-
-Once we have the embedding, we can connect to LanceDB (using the database we downloaded earlier), and search through the chatbot table.
-We'll extract 3 similar documents found.
-
-```javascript
-const db = await lancedb.connect('./lancedb');
-const tbl = await db.openTable('chatbot');
-const query = tbl.search(embedding);
-query.limit = CONTEXT_AMOUNT;
-const context = await query.execute();
-```
-
-Let's combine the context together so we can pass it into our prompt:
-
-```javascript
-for (let i = 1; i < context.length; i++) {
-    context[0]["text"] += " " + context[i]["text"];
-}
-```
-
-Lastly, let's construct the prompt. You could play around with this to create more accurate/better prompts to yield results.
-
-```javascript
-const prompt = "Answer the question based on the context below.\n\n" +
-    "Context:\n" +
-    `${context[0]["text"]}\n` +
-    `\n\nQuestion: ${QUESTION}\nAnswer:`;
-```
-
-We pass the prompt, along with the context, to the completion API.
-
-```javascript
-const completion = await openai.createCompletion({
-    model: "text-davinci-003",
-    prompt,
-    temperature: 0,
-    max_tokens: 400,
-    top_p: 1,
-    frequency_penalty: 0,
-    presence_penalty: 0,
-});
-```
-
-And that's it!
-
-```javascript
-console.log(completion.data.choices[0].text);
-```
-
-The response is (which is non deterministic):
-
-```
-The 12th person on the moon was Harrison Schmitt and he landed on December 11, 1972.
-```

docs/src/examples/serverless_qa_bot_with_modal_and_langchain.md

@@ -0,0 +1,166 @@
+# Serverless QA Bot with Modal and LangChain
+
+## use LanceDB's LangChain integration with Modal to run a serverless app
+
+<img id="splash" width="400" alt="modal" src="https://github.com/lancedb/lancedb/assets/917119/7d80a40f-60d7-48a6-972f-dab05000eccf">
+
+We're going to build a QA bot for your documentation using LanceDB's LangChain integration and use Modal for deployment.
+
+Modal is an end-to-end compute platform for model inference, batch jobs, task queues, web apps and more. It's a great way to deploy your LanceDB models and apps.
+
+To get started, ensure that you have created an account and logged into [Modal](https://modal.com/). To follow along, the full source code is available on Github [here](https://github.com/lancedb/lancedb/blob/main/docs/src/examples/modal_langchain.py).
+
+### Setting up Modal
+
+We'll start by specifying our dependencies and creating a new Modal `Stub`:
+
+```python
+lancedb_image = Image.debian_slim().pip_install(
+    "lancedb",
+    "langchain",
+    "openai",
+    "pandas",
+    "tiktoken",
+    "unstructured",
+    "tabulate"
+)
+
+stub = Stub(
+    name="example-langchain-lancedb",
+    image=lancedb_image,
+    secrets=[Secret.from_name("my-openai-secret")],
+)
+```
+
+We're using Modal's Secrets injection to secure our OpenAI key. To set your own, you can access the Modal UI and enter your key.
+
+### Setting up caches for LanceDB and LangChain
+
+Next, we can setup some globals to cache our LanceDB database, as well as our LangChain docsource:
+
+```python
+docsearch = None
+docs_path = Path("docs.pkl")
+db_path = Path("lancedb")
+```
+
+### Downloading our dataset
+
+We're going use a pregenerated dataset, which stores HTML files of the Pandas 2.0 documentation. 
+You could switch this out for your own dataset.
+
+```python
+def download_docs():
+    pandas_docs = requests.get("https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip")
+    with open(Path("pandas.documentation.zip"), "wb") as f:
+        f.write(pandas_docs.content)
+
+    file = zipfile.ZipFile(Path("pandas.documentation.zip"))
+    file.extractall(path=Path("pandas_docs"))
+```
+
+### Pre-processing the dataset and generating metadata
+
+Once we've downloaded it, we want to parse and pre-process them using LangChain, and then vectorize them and store it in LanceDB.
+Let's first create a function that uses LangChains `UnstructuredHTMLLoader` to parse them.
+We can then add our own metadata to it and store it alongside the data, we'll later be able to use this for filtering metadata.
+
+```python
+def store_docs():
+    docs = []
+
+    if not docs_path.exists():
+        for p in Path("pandas_docs/pandas.documentation").rglob("*.html"):
+            if p.is_dir():
+                continue
+            loader = UnstructuredHTMLLoader(p)
+            raw_document = loader.load()
+
+            m = {}
+            m["title"] = get_document_title(raw_document[0])
+            m["version"] = "2.0rc0"
+            raw_document[0].metadata = raw_document[0].metadata | m
+            raw_document[0].metadata["source"] = str(raw_document[0].metadata["source"])
+            docs = docs + raw_document
+
+        with docs_path.open("wb") as fh:
+            pickle.dump(docs, fh)
+    else:
+        with docs_path.open("rb") as fh:
+            docs = pickle.load(fh)
+
+    return docs
+```
+
+### Simple LangChain chain for a QA bot
+
+Now we can create a simple LangChain chain for our QA bot. We'll use the `RecursiveCharacterTextSplitter` to split our documents into chunks, and then use the `OpenAIEmbeddings` to vectorize them.
+
+Lastly, we'll create a LanceDB table and store the vectorized documents in it, then create a `RetrievalQA` model from the chain and return it.
+
+```python
+def qanda_langchain(query):
+    download_docs()
+    docs = store_docs()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000,
+        chunk_overlap=200,
+    )
+    documents = text_splitter.split_documents(docs)
+    embeddings = OpenAIEmbeddings()
+
+    db = lancedb.connect(db_path) 
+    table = db.create_table("pandas_docs", data=[
+        {"vector": embeddings.embed_query("Hello World"), "text": "Hello World", "id": "1"}
+    ], mode="overwrite")
+    docsearch = LanceDB.from_documents(documents, embeddings, connection=table)
+    qa = RetrievalQA.from_chain_type(llm=OpenAI(), chain_type="stuff", retriever=docsearch.as_retriever())
+    return qa.run(query)
+```
+
+### Creating our Modal entry points
+
+Now we can create our Modal entry points for our CLI and web endpoint:
+
+```python
+@stub.function()
+@web_endpoint(method="GET")
+def web(query: str):
+    answer = qanda_langchain(query)
+    return {
+        "answer": answer,
+    }
+    
+@stub.function()
+def cli(query: str):
+    answer = qanda_langchain(query)
+    print(answer)
+```
+
+# Testing it out!
+
+Testing the CLI:
+
+```bash
+modal run modal_langchain.py --query "What are the major differences in pandas 2.0?"
+```
+
+Testing the web endpoint:
+
+```bash
+modal serve modal_langchain.py
+```
+
+In the CLI, Modal will provide you a web endpoint. Copy this endpoint URI for the next step.
+Once this is served, then we can hit it with `curl`. 
+
+Note, the first time this runs, it will take a few minutes to download the dataset and vectorize it.
+An actual production example would pre-cache/load the dataset and vectorized documents prior
+
+```bash
+curl --get --data-urlencode "query=What are the major differences in pandas 2.0?" https://your-modal-endpoint-app.modal.run
+
+{"answer":" The major differences in pandas 2.0 include the ability to use any numpy numeric dtype in a Index, installing optional dependencies with pip extras, and enhancements, bug fixes, and performance improvements."}
+```
+

docs/src/examples/youtube_transcript_bot.md

@@ -4,4 +4,4 @@
 
 <img id="splash" width="400" alt="youtube transcript search" src="https://user-images.githubusercontent.com/917119/236965568-def7394d-171c-45f2-939d-8edfeaadd88c.png">
 
-This example is in a [notebook](https://github.com/lancedb/lancedb/blob/main/notebooks/youtube_transcript_search.ipynb)
+This example is in a [notebook](https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb)

docs/src/examples/youtube_transcript_bot_with_nodejs.md

@@ -0,0 +1,139 @@
+# YouTube transcript QA bot with NodeJS
+
+## use LanceDB's Javascript API and OpenAI to build a QA bot for YouTube transcripts
+
+<img id="splash" width="400" alt="nodejs" src="https://github.com/lancedb/lancedb/assets/917119/3a140e75-bf8e-438a-a1e4-af14a72bcf98">
+
+This Q&A bot will allow you to search through youtube transcripts using natural language! We'll introduce how to use LanceDB's Javascript API to store and manage your data easily.
+
+```bash
+npm install vectordb
+```
+
+## Download the data
+
+For this example, we're using a sample of a HuggingFace dataset that contains YouTube transcriptions: `jamescalam/youtube-transcriptions`. Download and extract this file under the `data` folder:
+
+```bash
+wget -c https://eto-public.s3.us-west-2.amazonaws.com/datasets/youtube_transcript/youtube-transcriptions_sample.jsonl
+```
+
+## Prepare Context
+
+Each item in the dataset contains just a short chunk of text. We'll need to merge a bunch of these chunks together on a rolling basis. For this demo, we'll look back 20 records to create a more complete context for each sentence.
+
+First, we need to read and parse the input file.
+
+```javascript
+const lines = (await fs.readFile(INPUT_FILE_NAME, 'utf-8'))
+  .toString()
+  .split('\n')
+  .filter(line => line.length > 0)
+  .map(line => JSON.parse(line))
+
+const data = contextualize(lines, 20, 'video_id')
+```
+
+The contextualize function groups the transcripts by video_id and then creates the expanded context for each item.
+
+```javascript
+function contextualize (rows, contextSize, groupColumn) {
+  const grouped = []
+  rows.forEach(row => {
+    if (!grouped[row[groupColumn]]) {
+      grouped[row[groupColumn]] = []
+    }
+    grouped[row[groupColumn]].push(row)
+  })
+
+  const data = []
+  Object.keys(grouped).forEach(key => {
+    for (let i = 0; i < grouped[key].length; i++) {
+      const start = i - contextSize > 0 ? i - contextSize : 0
+      grouped[key][i].context = grouped[key].slice(start, i + 1).map(r => r.text).join(' ')
+    }
+    data.push(...grouped[key])
+  })
+  return data
+}
+```
+
+## Create the LanceDB Table
+
+To load our data into LanceDB, we need to create embedding (vectors) for each item. For this example, we will use the OpenAI embedding functions, which have a native integration with LanceDB.
+
+```javascript
+// You need to provide an OpenAI API key, here we read it from the OPENAI_API_KEY environment variable
+const apiKey = process.env.OPENAI_API_KEY
+// The embedding function will create embeddings for the 'context' column
+const embedFunction = new lancedb.OpenAIEmbeddingFunction('context', apiKey)
+// Connects to LanceDB
+const db = await lancedb.connect('data/youtube-lancedb')
+const tbl = await db.createTable('vectors', data, embedFunction)
+```
+
+## Create and answer the prompt
+
+We will accept questions in natural language and use our corpus stored in LanceDB to answer them. First, we need to set up the OpenAI client:
+
+```javascript
+const configuration = new Configuration({ apiKey })
+const openai = new OpenAIApi(configuration)
+```
+
+Then we can prompt questions and use LanceDB to retrieve the three most relevant transcripts for this prompt.
+
+```javascript
+const query = await rl.question('Prompt: ')
+const results = await tbl
+  .search(query)
+  .select(['title', 'text', 'context'])
+  .limit(3)
+  .execute()
+```
+
+The query and the transcripts' context are appended together in a single prompt:
+
+```javascript
+function createPrompt (query, context) {
+    let prompt =
+        'Answer the question based on the context below.\n\n' +
+        'Context:\n'
+
+    // need to make sure our prompt is not larger than max size
+    prompt = prompt + context.map(c => c.context).join('\n\n---\n\n').substring(0, 3750)
+    prompt = prompt + `\n\nQuestion: ${query}\nAnswer:`
+    return prompt
+}
+```
+
+We can now use the OpenAI Completion API to process our custom prompt and give us an answer.
+
+```javascript
+const response = await openai.createCompletion({
+  model: 'text-davinci-003',
+  prompt: createPrompt(query, results),
+  max_tokens: 400,
+  temperature: 0,
+  top_p: 1,
+  frequency_penalty: 0,
+  presence_penalty: 0
+})
+console.log(response.data.choices[0].text)
+```
+
+## Let's put it all together now
+
+Now we can provide queries and have them answered based on your local LanceDB data.
+
+```bash
+Prompt: who was the 12th person on the moon and when did they land?
+ The 12th person on the moon was Harrison Schmitt and he landed on December 11, 1972.
+Prompt: Which training method should I use for sentence transformers when I only have pairs of related sentences?
+ NLI with multiple negative ranking loss.
+```
+
+## That's a wrap
+
+In this example, you learned how to use LanceDB to store and query embedding representations of your local data. The complete example code is on [GitHub](https://github.com/lancedb/lancedb/tree/main/node/examples), and you can also download the LanceDB dataset using [this link](https://eto-public.s3.us-west-2.amazonaws.com/datasets/youtube_transcript/youtube-lancedb.zip).
+

docs/src/index.md

@@ -1,6 +1,6 @@
 # Welcome to LanceDB's Documentation
 
-LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrivial, filtering and management of embeddings.
+LanceDB is an open-source database for vector-search built with persistent storage, which greatly simplifies retrevial, filtering and management of embeddings.
 
 The key features of LanceDB include:
 
@@ -8,38 +8,59 @@ The key features of LanceDB include:
 
 * Store, query and filter vectors, metadata and multi-modal data (text, images, videos, point clouds, and more).
 
-* Native Python and Javascript/Typescript support (coming soon).
+* Support for vector similarity search, full-text search and SQL.
+
+* Native Python and Javascript/Typescript support.
 
 * Zero-copy, automatic versioning, manage versions of your data without needing extra infrastructure.
 
-* Ecosystem integrations with [LangChain 🦜️🔗](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lanecdb.html), [LlamaIndex 🦙](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
+* Ecosystem integrations with [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), Apache-Arrow, Pandas, Polars, DuckDB and more on the way.
 
-LanceDB's core is written in Rust 🦀 and is built using Lance, an open-source columnar format designed for performant ML workloads.
+LanceDB's core is written in Rust 🦀 and is built using <a href="https://github.com/lancedb/lance">Lance</a>, an open-source columnar format designed for performant ML workloads.
 
+## Quick Start
 
-## Installation
+=== "Python"
+      ```shell
+      pip install lancedb
+      ```
 
-```shell
-pip install lancedb
-```
+      ```python
+      import lancedb
 
-## Quickstart
-
-```python
-import lancedb
-
-db = lancedb.connect(".")
-table = db.create_table("my_table",
+      uri = "/tmp/lancedb"
+      db = lancedb.connect(uri)
+      table = db.create_table("my_table",
                               data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
                                     {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
-result = table.search([100, 100]).limit(2).to_df()
-```
+      result = table.search([100, 100]).limit(2).to_df()
+      ```
 
-## Complete Demos
+=== "Javascript"
+      ```shell
+      npm install vectordb
+      ```
 
-We will be adding completed demo apps built using LanceDB.
-- [YouTube Transcript Search](../notebooks/youtube_transcript_search.ipynb)
+      ```javascript
+      const lancedb = require("vectordb");
 
+      const uri = "/tmp/lancedb";
+      const db = await lancedb.connect(uri);
+      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();
+      ```
+
+## Complete Demos (Python)
+- [YouTube Transcript Search](notebooks/youtube_transcript_search.ipynb)
+- [Documentation QA Bot using LangChain](notebooks/code_qa_bot.ipynb)
+- [Multimodal search using CLIP](notebooks/multimodal_search.ipynb)
+- [Serverless QA Bot with S3 and Lambda](examples/serverless_lancedb_with_s3_and_lambda.md)
+- [Serverless QA Bot with Modal](examples/serverless_qa_bot_with_modal_and_langchain.md)
+
+## Complete Demos (JavaScript)
+- [YouTube Transcript Search](examples/youtube_transcript_bot_with_nodejs.md)
 
 ## Documentation Quick Links
 * [`Basic Operations`](basic.md) - basic functionality of LanceDB.
@@ -47,4 +68,5 @@ We will be adding completed demo apps built using LanceDB.
 * [`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.
-* [`API Reference`](python.md) - detailed documentation for the LanceDB Python SDK.
+* [`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

@@ -24,9 +24,6 @@ data = pd.DataFrame({
     "price": [10.0, 20.0]
 })
 table = db.create_table("pd_table", data=data)
-
-# Optionally, create a IVF_PQ index
-table.create_index(num_partitions=256, num_sub_vectors=96)
 ```
 
 You will find detailed instructions of creating dataset and index in [Basic Operations](basic.md) and [Indexing](indexing.md)

docs/src/javascript/.nojekyll

@@ -0,0 +1 @@
+TypeDoc added this file to prevent GitHub Pages from using Jekyll. You can turn off this behavior by setting the `githubPages` option to false.

docs/src/javascript/README.md

@@ -0,0 +1,51 @@
+vectordb / [Exports](modules.md)
+
+# LanceDB
+
+A JavaScript / Node.js library for [LanceDB](https://github.com/lancedb/lancedb).
+
+## Installation
+
+```bash
+npm install vectordb
+```
+
+## Usage
+
+### Basic Example
+
+```javascript
+const lancedb = require('vectordb');
+const db = lancedb.connect('<PATH_TO_LANCEDB_DATASET>');
+const table = await db.openTable('my_table');
+const query = await table.search([0.1, 0.3]).setLimit(20).execute();
+console.log(results);
+```
+
+The [examples](./examples) folder contains complete examples.
+
+## Development
+
+The LanceDB javascript is built with npm:
+
+```bash
+npm run tsc
+```
+
+Run the tests with
+
+```bash
+npm test
+```
+
+To run the linter and have it automatically fix all errors
+
+```bash
+npm run lint -- --fix
+```
+
+To build documentation
+
+```bash
+npx typedoc --plugin typedoc-plugin-markdown --out ../docs/src/javascript src/index.ts
+```

docs/src/javascript/classes/Connection.md

@@ -0,0 +1,211 @@
+[vectordb](../README.md) / [Exports](../modules.md) / Connection
+
+# Class: Connection
+
+A connection to a LanceDB database.
+
+## Table of contents
+
+### Constructors
+
+- [constructor](Connection.md#constructor)
+
+### Properties
+
+- [\_db](Connection.md#_db)
+- [\_uri](Connection.md#_uri)
+
+### Accessors
+
+- [uri](Connection.md#uri)
+
+### Methods
+
+- [createTable](Connection.md#createtable)
+- [createTableArrow](Connection.md#createtablearrow)
+- [openTable](Connection.md#opentable)
+- [tableNames](Connection.md#tablenames)
+
+## Constructors
+
+### constructor
+
+• **new Connection**(`db`, `uri`)
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `db` | `any` |
+| `uri` | `string` |
+
+#### Defined in
+
+[index.ts:46](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L46)
+
+## Properties
+
+### \_db
+
+• `Private` `Readonly` **\_db**: `any`
+
+#### Defined in
+
+[index.ts:44](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L44)
+
+___
+
+### \_uri
+
+• `Private` `Readonly` **\_uri**: `string`
+
+#### Defined in
+
+[index.ts:43](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L43)
+
+## Accessors
+
+### uri
+
+• `get` **uri**(): `string`
+
+#### Returns
+
+`string`
+
+#### Defined in
+
+[index.ts:51](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L51)
+
+## Methods
+
+### createTable
+
+▸ **createTable**(`name`, `data`): `Promise`<[`Table`](Table.md)<`number`[]\>\>
+
+Creates a new Table and initialize it with new data.
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `name` | `string` | The name of the table. |
+| `data` | `Record`<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the Table |
+
+#### Returns
+
+`Promise`<[`Table`](Table.md)<`number`[]\>\>
+
+#### Defined in
+
+[index.ts:91](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L91)
+
+▸ **createTable**<`T`\>(`name`, `data`, `embeddings`): `Promise`<[`Table`](Table.md)<`T`\>\>
+
+Creates a new Table and initialize it with new data.
+
+#### Type parameters
+
+| Name |
+| :------ |
+| `T` |
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `name` | `string` | The name of the table. |
+| `data` | `Record`<`string`, `unknown`\>[] | Non-empty Array of Records to be inserted into the Table |
+| `embeddings` | [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`T`\> | An embedding function to use on this Table |
+
+#### Returns
+
+`Promise`<[`Table`](Table.md)<`T`\>\>
+
+#### Defined in
+
+[index.ts:99](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L99)
+
+___
+
+### createTableArrow
+
+▸ **createTableArrow**(`name`, `table`): `Promise`<[`Table`](Table.md)<`number`[]\>\>
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `name` | `string` |
+| `table` | `Table`<`any`\> |
+
+#### Returns
+
+`Promise`<[`Table`](Table.md)<`number`[]\>\>
+
+#### Defined in
+
+[index.ts:109](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L109)
+
+___
+
+### openTable
+
+▸ **openTable**(`name`): `Promise`<[`Table`](Table.md)<`number`[]\>\>
+
+Open a table in the database.
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `name` | `string` | The name of the table. |
+
+#### Returns
+
+`Promise`<[`Table`](Table.md)<`number`[]\>\>
+
+#### Defined in
+
+[index.ts:67](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L67)
+
+▸ **openTable**<`T`\>(`name`, `embeddings`): `Promise`<[`Table`](Table.md)<`T`\>\>
+
+Open a table in the database.
+
+#### Type parameters
+
+| Name |
+| :------ |
+| `T` |
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `name` | `string` | The name of the table. |
+| `embeddings` | [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`T`\> | An embedding function to use on this Table |
+
+#### Returns
+
+`Promise`<[`Table`](Table.md)<`T`\>\>
+
+#### Defined in
+
+[index.ts:74](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L74)
+
+___
+
+### tableNames
+
+▸ **tableNames**(): `Promise`<`string`[]\>
+
+Get the names of all tables in the database.
+
+#### Returns
+
+`Promise`<`string`[]\>
+
+#### Defined in
+
+[index.ts:58](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L58)

docs/src/javascript/classes/OpenAIEmbeddingFunction.md

@@ -0,0 +1,105 @@
+[vectordb](../README.md) / [Exports](../modules.md) / OpenAIEmbeddingFunction
+
+# Class: OpenAIEmbeddingFunction
+
+An embedding function that automatically creates vector representation for a given column.
+
+## Implements
+
+- [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`string`\>
+
+## Table of contents
+
+### Constructors
+
+- [constructor](OpenAIEmbeddingFunction.md#constructor)
+
+### Properties
+
+- [\_modelName](OpenAIEmbeddingFunction.md#_modelname)
+- [\_openai](OpenAIEmbeddingFunction.md#_openai)
+- [sourceColumn](OpenAIEmbeddingFunction.md#sourcecolumn)
+
+### Methods
+
+- [embed](OpenAIEmbeddingFunction.md#embed)
+
+## Constructors
+
+### constructor
+
+• **new OpenAIEmbeddingFunction**(`sourceColumn`, `openAIKey`, `modelName?`)
+
+#### Parameters
+
+| Name | Type | Default value |
+| :------ | :------ | :------ |
+| `sourceColumn` | `string` | `undefined` |
+| `openAIKey` | `string` | `undefined` |
+| `modelName` | `string` | `'text-embedding-ada-002'` |
+
+#### Defined in
+
+[embedding/openai.ts:21](https://github.com/lancedb/lancedb/blob/31dab97/node/src/embedding/openai.ts#L21)
+
+## Properties
+
+### \_modelName
+
+• `Private` `Readonly` **\_modelName**: `string`
+
+#### Defined in
+
+[embedding/openai.ts:19](https://github.com/lancedb/lancedb/blob/31dab97/node/src/embedding/openai.ts#L19)
+
+___
+
+### \_openai
+
+• `Private` `Readonly` **\_openai**: `any`
+
+#### Defined in
+
+[embedding/openai.ts:18](https://github.com/lancedb/lancedb/blob/31dab97/node/src/embedding/openai.ts#L18)
+
+___
+
+### sourceColumn
+
+• **sourceColumn**: `string`
+
+The name of the column that will be used as input for the Embedding Function.
+
+#### Implementation of
+
+[EmbeddingFunction](../interfaces/EmbeddingFunction.md).[sourceColumn](../interfaces/EmbeddingFunction.md#sourcecolumn)
+
+#### Defined in
+
+[embedding/openai.ts:50](https://github.com/lancedb/lancedb/blob/31dab97/node/src/embedding/openai.ts#L50)
+
+## Methods
+
+### embed
+
+▸ **embed**(`data`): `Promise`<`number`[][]\>
+
+Creates a vector representation for the given values.
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `data` | `string`[] |
+
+#### Returns
+
+`Promise`<`number`[][]\>
+
+#### Implementation of
+
+[EmbeddingFunction](../interfaces/EmbeddingFunction.md).[embed](../interfaces/EmbeddingFunction.md#embed)
+
+#### Defined in
+
+[embedding/openai.ts:38](https://github.com/lancedb/lancedb/blob/31dab97/node/src/embedding/openai.ts#L38)

docs/src/javascript/classes/Query.md

@@ -0,0 +1,299 @@
+[vectordb](../README.md) / [Exports](../modules.md) / Query
+
+# Class: Query<T\>
+
+A builder for nearest neighbor queries for LanceDB.
+
+## Type parameters
+
+| Name | Type |
+| :------ | :------ |
+| `T` | `number`[] |
+
+## Table of contents
+
+### Constructors
+
+- [constructor](Query.md#constructor)
+
+### Properties
+
+- [\_columns](Query.md#_columns)
+- [\_embeddings](Query.md#_embeddings)
+- [\_filter](Query.md#_filter)
+- [\_limit](Query.md#_limit)
+- [\_metricType](Query.md#_metrictype)
+- [\_nprobes](Query.md#_nprobes)
+- [\_query](Query.md#_query)
+- [\_queryVector](Query.md#_queryvector)
+- [\_refineFactor](Query.md#_refinefactor)
+- [\_tbl](Query.md#_tbl)
+
+### Methods
+
+- [execute](Query.md#execute)
+- [filter](Query.md#filter)
+- [limit](Query.md#limit)
+- [metricType](Query.md#metrictype)
+- [nprobes](Query.md#nprobes)
+- [refineFactor](Query.md#refinefactor)
+
+## Constructors
+
+### constructor
+
+• **new Query**<`T`\>(`tbl`, `query`, `embeddings?`)
+
+#### Type parameters
+
+| Name | Type |
+| :------ | :------ |
+| `T` | `number`[] |
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `tbl` | `any` |
+| `query` | `T` |
+| `embeddings?` | [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`T`\> |
+
+#### Defined in
+
+[index.ts:241](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L241)
+
+## Properties
+
+### \_columns
+
+• `Private` `Optional` `Readonly` **\_columns**: `string`[]
+
+#### Defined in
+
+[index.ts:236](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L236)
+
+___
+
+### \_embeddings
+
+• `Private` `Optional` `Readonly` **\_embeddings**: [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`T`\>
+
+#### Defined in
+
+[index.ts:239](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L239)
+
+___
+
+### \_filter
+
+• `Private` `Optional` **\_filter**: `string`
+
+#### Defined in
+
+[index.ts:237](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L237)
+
+___
+
+### \_limit
+
+• `Private` **\_limit**: `number`
+
+#### Defined in
+
+[index.ts:233](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L233)
+
+___
+
+### \_metricType
+
+• `Private` `Optional` **\_metricType**: [`MetricType`](../enums/MetricType.md)
+
+#### Defined in
+
+[index.ts:238](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L238)
+
+___
+
+### \_nprobes
+
+• `Private` **\_nprobes**: `number`
+
+#### Defined in
+
+[index.ts:235](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L235)
+
+___
+
+### \_query
+
+• `Private` `Readonly` **\_query**: `T`
+
+#### Defined in
+
+[index.ts:231](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L231)
+
+___
+
+### \_queryVector
+
+• `Private` `Optional` **\_queryVector**: `number`[]
+
+#### Defined in
+
+[index.ts:232](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L232)
+
+___
+
+### \_refineFactor
+
+• `Private` `Optional` **\_refineFactor**: `number`
+
+#### Defined in
+
+[index.ts:234](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L234)
+
+___
+
+### \_tbl
+
+• `Private` `Readonly` **\_tbl**: `any`
+
+#### Defined in
+
+[index.ts:230](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L230)
+
+## Methods
+
+### execute
+
+▸ **execute**<`T`\>(): `Promise`<`T`[]\>
+
+Execute the query and return the results as an Array of Objects
+
+#### Type parameters
+
+| Name | Type |
+| :------ | :------ |
+| `T` | `Record`<`string`, `unknown`\> |
+
+#### Returns
+
+`Promise`<`T`[]\>
+
+#### Defined in
+
+[index.ts:301](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L301)
+
+___
+
+### filter
+
+▸ **filter**(`value`): [`Query`](Query.md)<`T`\>
+
+A filter statement to be applied to this query.
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `value` | `string` | A filter in the same format used by a sql WHERE clause. |
+
+#### Returns
+
+[`Query`](Query.md)<`T`\>
+
+#### Defined in
+
+[index.ts:284](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L284)
+
+___
+
+### limit
+
+▸ **limit**(`value`): [`Query`](Query.md)<`T`\>
+
+Sets the number of results that will be returned
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `value` | `number` | number of results |
+
+#### Returns
+
+[`Query`](Query.md)<`T`\>
+
+#### Defined in
+
+[index.ts:257](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L257)
+
+___
+
+### metricType
+
+▸ **metricType**(`value`): [`Query`](Query.md)<`T`\>
+
+The MetricType used for this Query.
+
+**`See`**
+
+MetricType for the different options
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `value` | [`MetricType`](../enums/MetricType.md) | The metric to the. |
+
+#### Returns
+
+[`Query`](Query.md)<`T`\>
+
+#### Defined in
+
+[index.ts:293](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L293)
+
+___
+
+### nprobes
+
+▸ **nprobes**(`value`): [`Query`](Query.md)<`T`\>
+
+The number of probes used. A higher number makes search more accurate but also slower.
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `value` | `number` | The number of probes used. |
+
+#### Returns
+
+[`Query`](Query.md)<`T`\>
+
+#### Defined in
+
+[index.ts:275](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L275)
+
+___
+
+### refineFactor
+
+▸ **refineFactor**(`value`): [`Query`](Query.md)<`T`\>
+
+Refine the results by reading extra elements and re-ranking them in memory.
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `value` | `number` | refine factor to use in this query. |
+
+#### Returns
+
+[`Query`](Query.md)<`T`\>
+
+#### Defined in
+
+[index.ts:266](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L266)

docs/src/javascript/classes/Table.md

@@ -0,0 +1,215 @@
+[vectordb](../README.md) / [Exports](../modules.md) / Table
+
+# Class: Table<T\>
+
+## Type parameters
+
+| Name | Type |
+| :------ | :------ |
+| `T` | `number`[] |
+
+## Table of contents
+
+### Constructors
+
+- [constructor](Table.md#constructor)
+
+### Properties
+
+- [\_embeddings](Table.md#_embeddings)
+- [\_name](Table.md#_name)
+- [\_tbl](Table.md#_tbl)
+
+### Accessors
+
+- [name](Table.md#name)
+
+### Methods
+
+- [add](Table.md#add)
+- [create\_index](Table.md#create_index)
+- [overwrite](Table.md#overwrite)
+- [search](Table.md#search)
+
+## Constructors
+
+### constructor
+
+• **new Table**<`T`\>(`tbl`, `name`)
+
+#### Type parameters
+
+| Name | Type |
+| :------ | :------ |
+| `T` | `number`[] |
+
+#### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `tbl` | `any` |
+| `name` | `string` |
+
+#### Defined in
+
+[index.ts:121](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L121)
+
+• **new Table**<`T`\>(`tbl`, `name`, `embeddings`)
+
+#### Type parameters
+
+| Name | Type |
+| :------ | :------ |
+| `T` | `number`[] |
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `tbl` | `any` |  |
+| `name` | `string` |  |
+| `embeddings` | [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`T`\> | An embedding function to use when interacting with this table |
+
+#### Defined in
+
+[index.ts:127](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L127)
+
+## Properties
+
+### \_embeddings
+
+• `Private` `Optional` `Readonly` **\_embeddings**: [`EmbeddingFunction`](../interfaces/EmbeddingFunction.md)<`T`\>
+
+#### Defined in
+
+[index.ts:119](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L119)
+
+___
+
+### \_name
+
+• `Private` `Readonly` **\_name**: `string`
+
+#### Defined in
+
+[index.ts:118](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L118)
+
+___
+
+### \_tbl
+
+• `Private` `Readonly` **\_tbl**: `any`
+
+#### Defined in
+
+[index.ts:117](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L117)
+
+## Accessors
+
+### name
+
+• `get` **name**(): `string`
+
+#### Returns
+
+`string`
+
+#### Defined in
+
+[index.ts:134](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L134)
+
+## Methods
+
+### add
+
+▸ **add**(`data`): `Promise`<`number`\>
+
+Insert records into this Table.
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `data` | `Record`<`string`, `unknown`\>[] | Records to be inserted into the Table |
+
+#### Returns
+
+`Promise`<`number`\>
+
+The number of rows added to the table
+
+#### Defined in
+
+[index.ts:152](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L152)
+
+___
+
+### create\_index
+
+▸ **create_index**(`indexParams`): `Promise`<`any`\>
+
+Create an ANN index on this Table vector index.
+
+**`See`**
+
+VectorIndexParams.
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `indexParams` | `IvfPQIndexConfig` | The parameters of this Index, |
+
+#### Returns
+
+`Promise`<`any`\>
+
+#### Defined in
+
+[index.ts:171](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L171)
+
+___
+
+### overwrite
+
+▸ **overwrite**(`data`): `Promise`<`number`\>
+
+Insert records into this Table, replacing its contents.
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `data` | `Record`<`string`, `unknown`\>[] | Records to be inserted into the Table |
+
+#### Returns
+
+`Promise`<`number`\>
+
+The number of rows added to the table
+
+#### Defined in
+
+[index.ts:162](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L162)
+
+___
+
+### search
+
+▸ **search**(`query`): [`Query`](Query.md)<`T`\>
+
+Creates a search query to find the nearest neighbors of the given search term
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `query` | `T` | The query search term |
+
+#### Returns
+
+[`Query`](Query.md)<`T`\>
+
+#### Defined in
+
+[index.ts:142](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L142)

docs/src/javascript/enums/MetricType.md

@@ -0,0 +1,36 @@
+[vectordb](../README.md) / [Exports](../modules.md) / MetricType
+
+# Enumeration: MetricType
+
+Distance metrics type.
+
+## Table of contents
+
+### Enumeration Members
+
+- [Cosine](MetricType.md#cosine)
+- [L2](MetricType.md#l2)
+
+## Enumeration Members
+
+### Cosine
+
+• **Cosine** = ``"cosine"``
+
+Cosine distance
+
+#### Defined in
+
+[index.ts:341](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L341)
+
+___
+
+### L2
+
+• **L2** = ``"l2"``
+
+Euclidean distance
+
+#### Defined in
+
+[index.ts:336](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L336)

docs/src/javascript/enums/WriteMode.md

@@ -0,0 +1,30 @@
+[vectordb](../README.md) / [Exports](../modules.md) / WriteMode
+
+# Enumeration: WriteMode
+
+## Table of contents
+
+### Enumeration Members
+
+- [Append](WriteMode.md#append)
+- [Overwrite](WriteMode.md#overwrite)
+
+## Enumeration Members
+
+### Append
+
+• **Append** = ``"append"``
+
+#### Defined in
+
+[index.ts:326](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L326)
+
+___
+
+### Overwrite
+
+• **Overwrite** = ``"overwrite"``
+
+#### Defined in
+
+[index.ts:325](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L325)

docs/src/javascript/interfaces/EmbeddingFunction.md

@@ -0,0 +1,60 @@
+[vectordb](../README.md) / [Exports](../modules.md) / EmbeddingFunction
+
+# Interface: EmbeddingFunction<T\>
+
+An embedding function that automatically creates vector representation for a given column.
+
+## Type parameters
+
+| Name |
+| :------ |
+| `T` |
+
+## Implemented by
+
+- [`OpenAIEmbeddingFunction`](../classes/OpenAIEmbeddingFunction.md)
+
+## Table of contents
+
+### Properties
+
+- [embed](EmbeddingFunction.md#embed)
+- [sourceColumn](EmbeddingFunction.md#sourcecolumn)
+
+## Properties
+
+### embed
+
+• **embed**: (`data`: `T`[]) => `Promise`<`number`[][]\>
+
+#### Type declaration
+
+▸ (`data`): `Promise`<`number`[][]\>
+
+Creates a vector representation for the given values.
+
+##### Parameters
+
+| Name | Type |
+| :------ | :------ |
+| `data` | `T`[] |
+
+##### Returns
+
+`Promise`<`number`[][]\>
+
+#### Defined in
+
+[embedding/embedding_function.ts:27](https://github.com/lancedb/lancedb/blob/31dab97/node/src/embedding/embedding_function.ts#L27)
+
+___
+
+### sourceColumn
+
+• **sourceColumn**: `string`
+
+The name of the column that will be used as input for the Embedding Function.
+
+#### Defined in
+
+[embedding/embedding_function.ts:22](https://github.com/lancedb/lancedb/blob/31dab97/node/src/embedding/embedding_function.ts#L22)

docs/src/javascript/modules.md

@@ -0,0 +1,61 @@
+[vectordb](README.md) / Exports
+
+# vectordb
+
+## Table of contents
+
+### Enumerations
+
+- [MetricType](enums/MetricType.md)
+- [WriteMode](enums/WriteMode.md)
+
+### Classes
+
+- [Connection](classes/Connection.md)
+- [OpenAIEmbeddingFunction](classes/OpenAIEmbeddingFunction.md)
+- [Query](classes/Query.md)
+- [Table](classes/Table.md)
+
+### Interfaces
+
+- [EmbeddingFunction](interfaces/EmbeddingFunction.md)
+
+### Type Aliases
+
+- [VectorIndexParams](modules.md#vectorindexparams)
+
+### Functions
+
+- [connect](modules.md#connect)
+
+## Type Aliases
+
+### VectorIndexParams
+
+Ƭ **VectorIndexParams**: `IvfPQIndexConfig`
+
+#### Defined in
+
+[index.ts:224](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L224)
+
+## Functions
+
+### connect
+
+▸ **connect**(`uri`): `Promise`<[`Connection`](classes/Connection.md)\>
+
+Connect to a LanceDB instance at the given URI
+
+#### Parameters
+
+| Name | Type | Description |
+| :------ | :------ | :------ |
+| `uri` | `string` | The uri of the database. |
+
+#### Returns
+
+`Promise`<[`Connection`](classes/Connection.md)\>
+
+#### Defined in
+
+[index.ts:34](https://github.com/lancedb/lancedb/blob/31dab97/node/src/index.ts#L34)

docs/src/notebooks/code_qa_bot.ipynb

@@ -72,6 +72,8 @@
     "import lancedb\n",
     "import re\n",
     "import pickle\n",
+    "import requests\n",
+    "import zipfile\n",
     "from pathlib import Path\n",
     "\n",
     "from langchain.document_loaders import UnstructuredHTMLLoader\n",
@@ -85,10 +87,25 @@
   {
    "attachments": {},
    "cell_type": "markdown",
-   "id": "6ccf9b2b",
+   "id": "56cc6d50",
    "metadata": {},
    "source": [
-    "You can download the Pandas documentation from https://pandas.pydata.org/docs/. To make sure we're not littering our repo with docs, we won't include it in the LanceDB repo, so download this and store it locally first."
+    "To make this easier, we've downloaded Pandas documentation and stored the raw HTML files for you to download. We'll download them and then use LangChain's HTML document readers to parse them and store them in LanceDB as a vector store, along with relevant metadata."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7da77e75",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pandas_docs = requests.get(\"https://eto-public.s3.us-west-2.amazonaws.com/datasets/pandas_docs/pandas.documentation.zip\")\n",
+    "with open('/tmp/pandas.documentation.zip', 'wb') as f:\n",
+    "    f.write(pandas_docs.content)\n",
+    "\n",
+    "file = zipfile.ZipFile(\"/tmp/pandas.documentation.zip\")\n",
+    "file.extractall(path=\"/tmp/pandas_docs\")"
    ]
   },
   {
@@ -137,7 +154,8 @@
     "docs = []\n",
     "\n",
     "if not docs_path.exists():\n",
-    "    for p in Path(\"./pandas.documentation\").rglob(\"*.html\"):\n",
+    "    for p in Path(\"/tmp/pandas_docs/pandas.documentation\").rglob(\"*.html\"):\n",
+    "        print(p)\n",
     "        if p.is_dir():\n",
     "            continue\n",
     "        loader = UnstructuredHTMLLoader(p)\n",

docs/src/notebooks/diffusiondb/datagen.py

@@ -0,0 +1,108 @@
+#!/usr/bin/env python
+#
+#  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.
+
+"""Dataset hf://poloclub/diffusiondb
+"""
+
+import io
+from argparse import ArgumentParser
+from multiprocessing import Pool
+
+import lance
+import lancedb
+import pyarrow as pa
+from datasets import load_dataset
+from PIL import Image
+from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast
+
+MODEL_ID = "openai/clip-vit-base-patch32"
+
+device = "cuda"
+
+tokenizer = CLIPTokenizerFast.from_pretrained(MODEL_ID)
+model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32").to(device)
+processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
+
+schema = pa.schema(
+    [
+        pa.field("prompt", pa.string()),
+        pa.field("seed", pa.uint32()),
+        pa.field("step", pa.uint16()),
+        pa.field("cfg", pa.float32()),
+        pa.field("sampler", pa.string()),
+        pa.field("width", pa.uint16()),
+        pa.field("height", pa.uint16()),
+        pa.field("timestamp", pa.timestamp("s")),
+        pa.field("image_nsfw", pa.float32()),
+        pa.field("prompt_nsfw", pa.float32()),
+        pa.field("vector", pa.list_(pa.float32(), 512)),
+        pa.field("image", pa.binary()),
+    ]
+)
+
+
+def pil_to_bytes(img) -> list[bytes]:
+    buf = io.BytesIO()
+    img.save(buf, format="PNG")
+    return buf.getvalue()
+
+
+def generate_clip_embeddings(batch) -> pa.RecordBatch:
+    image = processor(text=None, images=batch["image"], return_tensors="pt")[
+        "pixel_values"
+    ].to(device)
+    img_emb = model.get_image_features(image)
+    batch["vector"] = img_emb.cpu().tolist()
+
+    with Pool() as p:
+        batch["image_bytes"] = p.map(pil_to_bytes, batch["image"])
+    return batch
+
+
+def datagen(args):
+    """Generate DiffusionDB dataset, and use CLIP model to generate image embeddings."""
+    dataset = load_dataset("poloclub/diffusiondb", args.subset)
+    data = []
+    for b in dataset.map(
+        generate_clip_embeddings, batched=True, batch_size=256, remove_columns=["image"]
+    )["train"]:
+        b["image"] = b["image_bytes"]
+        del b["image_bytes"]
+        data.append(b)
+    tbl = pa.Table.from_pylist(data, schema=schema)
+    return tbl
+
+
+def main():
+    parser = ArgumentParser()
+    parser.add_argument(
+        "-o", "--output", metavar="DIR", help="Output lance directory", required=True
+    )
+    parser.add_argument(
+        "-s",
+        "--subset",
+        choices=["2m_all", "2m_first_10k", "2m_first_100k"],
+        default="2m_first_10k",
+        help="subset of the hg dataset",
+    )
+
+    args = parser.parse_args()
+
+    batches = datagen(args)
+    lance.write_dataset(batches, args.output)
+
+
+if __name__ == "__main__":
+    main()

docs/src/notebooks/diffusiondb/requirements.txt

@@ -0,0 +1,9 @@
+datasets
+Pillow
+lancedb
+isort
+black
+transformers
+--index-url https://download.pytorch.org/whl/cu118
+torch
+torchvision

docs/src/notebooks/multimodal_search.ipynb

@@ -0,0 +1,269 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n",
+      "\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.3.1\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\n",
+      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install --quiet -U lancedb\n",
+    "!pip install --quiet gradio transformers torch torchvision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import io\n",
+    "import PIL\n",
+    "import duckdb\n",
+    "import lancedb"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## First run setup: Download data and pre-process"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<lance.dataset.LanceDataset at 0x3045db590>"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remove null prompts\n",
+    "import lance\n",
+    "import pyarrow.compute as pc\n",
+    "\n",
+    "# download s3://eto-public/datasets/diffusiondb/small_10k.lance to this uri\n",
+    "data = lance.dataset(\"~/datasets/rawdata.lance\").to_table()\n",
+    "\n",
+    "# First data processing and full-text-search index\n",
+    "db = lancedb.connect(\"~/datasets/demo\")\n",
+    "tbl = db.create_table(\"diffusiondb\", data.filter(~pc.field(\"prompt\").is_null()))\n",
+    "tbl = tbl.create_fts_index([\"prompt\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create / Open LanceDB Table"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "db = lancedb.connect(\"~/datasets/demo\")\n",
+    "tbl = db.open_table(\"diffusiondb\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create CLIP embedding function for the text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from transformers import CLIPModel, CLIPProcessor, CLIPTokenizerFast\n",
+    "\n",
+    "MODEL_ID = \"openai/clip-vit-base-patch32\"\n",
+    "\n",
+    "tokenizer = CLIPTokenizerFast.from_pretrained(MODEL_ID)\n",
+    "model = CLIPModel.from_pretrained(MODEL_ID)\n",
+    "processor = CLIPProcessor.from_pretrained(MODEL_ID)\n",
+    "\n",
+    "def embed_func(query):\n",
+    "    inputs = tokenizer([query], padding=True, return_tensors=\"pt\")\n",
+    "    text_features = model.get_text_features(**inputs)\n",
+    "    return text_features.detach().numpy()[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Search functions for Gradio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def find_image_vectors(query):\n",
+    "    emb = embed_func(query)\n",
+    "    code = (\n",
+    "        \"import lancedb\\n\"\n",
+    "        \"db = lancedb.connect('~/datasets/demo')\\n\"\n",
+    "        \"tbl = db.open_table('diffusiondb')\\n\\n\"\n",
+    "        f\"embedding = embed_func('{query}')\\n\"\n",
+    "        \"tbl.search(embedding).limit(9).to_df()\"\n",
+    "    )\n",
+    "    return (_extract(tbl.search(emb).limit(9).to_df()), code)\n",
+    "\n",
+    "def find_image_keywords(query):\n",
+    "    code = (\n",
+    "        \"import lancedb\\n\"\n",
+    "        \"db = lancedb.connect('~/datasets/demo')\\n\"\n",
+    "        \"tbl = db.open_table('diffusiondb')\\n\\n\"\n",
+    "        f\"tbl.search('{query}').limit(9).to_df()\"\n",
+    "    )\n",
+    "    return (_extract(tbl.search(query).limit(9).to_df()), code)\n",
+    "\n",
+    "def find_image_sql(query):\n",
+    "    code = (\n",
+    "        \"import lancedb\\n\"\n",
+    "        \"import duckdb\\n\"\n",
+    "        \"db = lancedb.connect('~/datasets/demo')\\n\"\n",
+    "        \"tbl = db.open_table('diffusiondb')\\n\\n\"\n",
+    "        \"diffusiondb = tbl.to_lance()\\n\"\n",
+    "        f\"duckdb.sql('{query}').to_df()\"\n",
+    "    )    \n",
+    "    diffusiondb = tbl.to_lance()\n",
+    "    return (_extract(duckdb.sql(query).to_df()), code)\n",
+    "\n",
+    "def _extract(df):\n",
+    "    image_col = \"image\"\n",
+    "    return [(PIL.Image.open(io.BytesIO(row[image_col])), row[\"prompt\"]) for _, row in df.iterrows()]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setup Gradio interface"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7881\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7881/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import gradio as gr\n",
+    "\n",
+    "\n",
+    "with gr.Blocks() as demo:\n",
+    "    with gr.Row():\n",
+    "        with gr.Tab(\"Embeddings\"):\n",
+    "            vector_query = gr.Textbox(value=\"portraits of a person\", show_label=False)\n",
+    "            b1 = gr.Button(\"Submit\")\n",
+    "        with gr.Tab(\"Keywords\"):\n",
+    "            keyword_query = gr.Textbox(value=\"ninja turtle\", show_label=False)\n",
+    "            b2 = gr.Button(\"Submit\")\n",
+    "        with gr.Tab(\"SQL\"):\n",
+    "            sql_query = gr.Textbox(value=\"SELECT * from diffusiondb WHERE image_nsfw >= 2 LIMIT 9\", show_label=False)\n",
+    "            b3 = gr.Button(\"Submit\")\n",
+    "    with gr.Row():\n",
+    "        code = gr.Code(label=\"Code\", language=\"python\")\n",
+    "    with gr.Row():\n",
+    "        gallery = gr.Gallery(\n",
+    "                label=\"Found images\", show_label=False, elem_id=\"gallery\"\n",
+    "            ).style(columns=[3], rows=[3], object_fit=\"contain\", height=\"auto\")   \n",
+    "        \n",
+    "    b1.click(find_image_vectors, inputs=vector_query, outputs=[gallery, code])\n",
+    "    b2.click(find_image_keywords, inputs=keyword_query, outputs=[gallery, code])\n",
+    "    b3.click(find_image_sql, inputs=sql_query, outputs=[gallery, code])\n",
+    "    \n",
+    "demo.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

docs/src/notebooks/youtube_transcript_search.ipynb

@@ -1,11 +1,12 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "42bf01fb",
    "metadata": {},
    "source": [
-    "# We're going to build question and answer bot\n",
+    "# Youtube Transcript Search QA Bot\n",
     "\n",
     "This Q&A bot will allow you to search through youtube transcripts using natural language! By going through this notebook, we'll introduce how you can use LanceDB to store and manage your data easily."
    ]
@@ -35,6 +36,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "22e570f4",
    "metadata": {},
@@ -87,6 +89,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "5ac2b6a3",
    "metadata": {},
@@ -181,6 +184,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "3044e0b0",
    "metadata": {},
@@ -209,6 +213,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "db586267",
    "metadata": {},
@@ -229,6 +234,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "2106b5bb",
    "metadata": {},
@@ -338,6 +344,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "53e4bff1",
    "metadata": {},
@@ -371,6 +378,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "8ef34fca",
    "metadata": {},
@@ -459,6 +467,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "23afc2f9",
    "metadata": {},
@@ -541,6 +550,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "28705959",
    "metadata": {},
@@ -571,6 +581,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "id": "559a095b",
    "metadata": {},

docs/src/python.md

@@ -1,14 +0,0 @@
-# LanceDB Python API Reference
-
-## Installation
-
-```shell
-pip install lancedb
-```
-
-## ::: lancedb
-## ::: lancedb.db
-## ::: lancedb.table
-## ::: lancedb.query
-## ::: lancedb.embeddings
-## ::: lancedb.context

docs/src/python/python.md

@@ -0,0 +1,43 @@
+# LanceDB Python API Reference
+
+## Installation
+
+```shell
+pip install lancedb
+```
+
+## Connection
+
+::: lancedb.connect
+
+::: lancedb.LanceDBConnection
+
+## Table
+
+::: lancedb.table.LanceTable
+
+## Querying
+
+::: lancedb.query.LanceQueryBuilder
+
+::: lancedb.query.LanceFtsQueryBuilder
+
+## Embeddings
+
+::: lancedb.embeddings.with_embeddings
+
+::: lancedb.embeddings.EmbeddingFunction
+
+## Context
+
+::: lancedb.context.contextualize
+
+::: lancedb.context.Contextualizer
+
+## Full text search
+
+::: lancedb.fts.create_index
+
+::: lancedb.fts.populate_index
+
+::: lancedb.fts.search_index

docs/src/search.md

@@ -0,0 +1,85 @@
+# Vector Search
+
+`Vector Search` finds the nearest vectors from the database.
+In a recommendation system or search engine, you can find similar products from
+the one you searched.
+In LLM and other AI applications,
+each data point can be [presented by the embeddings generated from some models](embedding.md),
+it returns the most relevant features.
+
+A search in high-dimensional vector space, is to find `K-Nearest-Neighbors (KNN)` of the query vector.
+
+## Metric
+
+In LanceDB, a `Metric` is the way to describe the distance between a pair of vectors.
+Currently, we support the following metrics:
+
+| Metric      | Description                          |
+| ----------- | ------------------------------------ |
+| `L2`        | [Euclidean / L2 distance](https://en.wikipedia.org/wiki/Euclidean_distance) |
+| `Cosine`    | [Cosine Similarity](https://en.wikipedia.org/wiki/Cosine_similarity)|
+
+
+## Search
+
+### Flat Search
+
+
+If there is no [vector index is created](ann_indexes.md), LanceDB will just brute-force scan
+the vector column and compute the distance.
+
+=== "Python"
+
+    ```python
+    import lancedb
+    db = lancedb.connect("data/sample-lancedb")
+
+    tbl = db.open_table("my_vectors")
+
+    df = tbl.search(np.random.random((768)))
+        .limit(10)
+        .to_df()
+    ```
+
+=== "JavaScript"
+
+    ```javascript
+    const vectordb = require('vectordb')
+    const db = await vectordb.connect('data/sample-lancedb')
+
+    tbl = db.open_table("my_vectors")
+
+    const results = await tbl.search(Array(768))
+        .limit(20)
+        .execute()
+    ```
+
+By default, `l2` will be used as `Metric` type. You can customize the metric type
+as well.
+
+=== "Python"
+
+    ```python
+    df = tbl.search(np.random.random((768)))
+        .metric("cosine")
+        .limit(10)
+        .to_df()
+    ```
+
+=== "JavaScript"
+
+    ```javascript
+    const vectordb = require('vectordb')
+    const db = await vectordb.connect('data/sample-lancedb')
+
+    tbl = db.open_table("my_vectors")
+
+    const results = await tbl.search(Array(768))
+        .metric("cosine")
+        .limit(20)
+        .execute()
+    ```
+
+### Search with Vector Index.
+
+See [ANN Index](ann_indexes.md) for more details.

docs/src/styles/global.css

@@ -0,0 +1,6 @@
+:root {
+    --md-primary-fg-color:        #625eff;
+    --md-primary-fg-color--dark:  #4338ca;
+    --md-text-font: ui-sans-serif, system-ui, -apple-system, BlinkMacSystemFont, "Segoe UI", Roboto, "Helvetica Neue", Arial, "Noto Sans", sans-serif, "Apple Color Emoji", "Segoe UI Emoji", "Segoe UI Symbol", "Noto Color Emoji";
+    --md-code-font: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, "Liberation Mono", "Courier New", monospace;
+}

node/CHANGELOG.md

@@ -0,0 +1,64 @@
+# Changelog
+
+All notable changes to this project will be documented in this file.
+
+The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
+and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
+
+## [0.1.5] - 2023-06-00
+
+### Added
+
+- Support for macOS X86
+
+## [0.1.4] - 2023-06-03
+
+### Added
+
+- Select / Project query API
+
+### Changed
+
+-  Deprecated created_index in favor of createIndex
+
+## [0.1.3] - 2023-06-01
+
+### Added
+
+- Support S3 and Google Cloud Storage
+- Embedding functions support
+- OpenAI embedding function
+
+## [0.1.2] - 2023-05-27
+
+### Added
+
+- Append records API
+- Extra query params to to nodejs client
+- Create_index API
+ 
+### Fixed
+
+- bugfix: string columns should be converted to Utf8Array (#94)
+
+## [0.1.1] - 2023-05-16
+
+### Added
+
+- create_table API
+- limit parameter for queries
+- Typescript / JavaScript examples
+- Linux support
+
+## [0.1.0] - 2023-05-16
+
+### Added
+
+- Initial  JavaScript / Node.js library for LanceDB
+- Read-only api to query LanceDB datasets
+- Supports macOS arm only
+
+## [pre-0.1.0]
+
+- Various prototypes / test builds
+

node/README.md

@@ -41,3 +41,9 @@ To run the linter and have it automatically fix all errors
 ```bash
 npm run lint -- --fix
 ```
+
+To build documentation
+
+```bash
+npx typedoc --plugin typedoc-plugin-markdown --out ../docs/src/javascript src/index.ts
+```

node/examples/js-openai/index.js

@@ -0,0 +1,41 @@
+// 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')
+  // You need to provide an OpenAI API key, here we read it from the OPENAI_API_KEY environment variable
+  const apiKey = process.env.OPENAI_API_KEY
+  // The embedding function will create embeddings for the 'text' column(text in this case)
+  const embedding = new lancedb.OpenAIEmbeddingFunction('text', apiKey)
+
+  const db = await lancedb.connect('data/sample-lancedb')
+
+  const data = [
+    { id: 1, text: 'Black T-Shirt', price: 10 },
+    { id: 2, text: 'Leather Jacket', price: 50 }
+  ]
+
+  const table = await db.createTable('vectors', data, embedding)
+  console.log(await db.tableNames())
+
+  const results = await table
+    .search('keeps me warm')
+    .limit(1)
+    .execute()
+  console.log(results[0].text)
+}
+
+example().then(_ => { console.log('All done!') })

node/examples/js-openai/package.json

@@ -0,0 +1,15 @@
+{
+  "name": "vectordb-example-js-openai",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "Lance Devs",
+  "license": "Apache-2.0",
+  "dependencies": {
+    "vectordb": "file:../..",
+    "openai": "^3.2.1"
+  }
+}

node/examples/js-youtube-transcripts/index.js

@@ -0,0 +1,122 @@
+// 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'
+
+const lancedb = require('vectordb')
+const fs = require('fs/promises')
+const readline = require('readline/promises')
+const { stdin: input, stdout: output } = require('process')
+const { Configuration, OpenAIApi } = require('openai')
+
+// Download file from XYZ
+const INPUT_FILE_NAME = 'data/youtube-transcriptions_sample.jsonl';
+
+(async () => {
+  // You need to provide an OpenAI API key, here we read it from the OPENAI_API_KEY environment variable
+  const apiKey = process.env.OPENAI_API_KEY
+  // The embedding function will create embeddings for the 'context' column
+  const embedFunction = new lancedb.OpenAIEmbeddingFunction('context', apiKey)
+
+  // Connects to LanceDB
+  const db = await lancedb.connect('data/youtube-lancedb')
+
+  // Open the vectors table or create one if it does not exist
+  let tbl
+  if ((await db.tableNames()).includes('vectors')) {
+    tbl = await db.openTable('vectors', embedFunction)
+  } else {
+    tbl = await createEmbeddingsTable(db, embedFunction)
+  }
+
+  // Use OpenAI Completion API to generate and answer based on the context that LanceDB provides
+  const configuration = new Configuration({ apiKey })
+  const openai = new OpenAIApi(configuration)
+  const rl = readline.createInterface({ input, output })
+  try {
+    while (true) {
+      const query = await rl.question('Prompt: ')
+      const results = await tbl
+        .search(query)
+        .select(['title', 'text', 'context'])
+        .limit(3)
+        .execute()
+
+      // console.table(results)
+
+      const response = await openai.createCompletion({
+        model: 'text-davinci-003',
+        prompt: createPrompt(query, results),
+        max_tokens: 400,
+        temperature: 0,
+        top_p: 1,
+        frequency_penalty: 0,
+        presence_penalty: 0
+      })
+      console.log(response.data.choices[0].text)
+    }
+  } catch (err) {
+    console.log('Error: ', err)
+  } finally {
+    rl.close()
+  }
+  process.exit(1)
+})()
+
+async function createEmbeddingsTable (db, embedFunction) {
+  console.log(`Creating embeddings from ${INPUT_FILE_NAME}`)
+  // read the input file into a JSON array, skipping empty lines
+  const lines = (await fs.readFile(INPUT_FILE_NAME, 'utf-8'))
+    .toString()
+    .split('\n')
+    .filter(line => line.length > 0)
+    .map(line => JSON.parse(line))
+
+  const data = contextualize(lines, 20, 'video_id')
+  return await db.createTable('vectors', data, embedFunction)
+}
+
+// Each transcript has a small text column, we include previous transcripts in order to
+// have more context information when creating embeddings
+function contextualize (rows, contextSize, groupColumn) {
+  const grouped = []
+  rows.forEach(row => {
+    if (!grouped[row[groupColumn]]) {
+      grouped[row[groupColumn]] = []
+    }
+    grouped[row[groupColumn]].push(row)
+  })
+
+  const data = []
+  Object.keys(grouped).forEach(key => {
+    for (let i = 0; i < grouped[key].length; i++) {
+      const start = i - contextSize > 0 ? i - contextSize : 0
+      grouped[key][i].context = grouped[key].slice(start, i + 1).map(r => r.text).join(' ')
+    }
+    data.push(...grouped[key])
+  })
+  return data
+}
+
+// Creates a prompt by aggregating all relevant contexts
+function createPrompt (query, context) {
+  let prompt =
+      'Answer the question based on the context below.\n\n' +
+      'Context:\n'
+
+  // need to make sure our prompt is not larger than max size
+  prompt = prompt + context.map(c => c.context).join('\n\n---\n\n').substring(0, 3750)
+  prompt = prompt + `\n\nQuestion: ${query}\nAnswer:`
+  return prompt
+}

node/examples/js-youtube-transcripts/package.json

@@ -0,0 +1,15 @@
+{
+  "name": "vectordb-example-js-openai",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.js",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "Lance Devs",
+  "license": "Apache-2.0",
+  "dependencies": {
+    "vectordb": "file:../..",
+    "openai": "^3.2.1"
+  }
+}

node/examples/js/package.json

@@ -9,6 +9,6 @@
   "author": "Lance Devs",
   "license": "Apache-2.0",
   "dependencies": {
-    "vectordb": "^0.1.0"
+    "vectordb": "file:../.."
   }
 }

node/examples/ts/package.json

@@ -17,6 +17,6 @@
     "typescript": "*"
   },
   "dependencies": {
-    "vectordb": "^0.1.0"
+    "vectordb": "file:../.."
   }
 }

node/gen_test_data.py

@@ -1,8 +0,0 @@
-import lancedb
-
-uri = "sample-lancedb"
-db = lancedb.connect(uri)
-table = db.create_table("my_table",
-                         data=[{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
-                               {"vector": [5.9, 26.5], "item": "bar", "price": 20.0}])
-

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.1.1",
+  "version": "0.1.6",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
@@ -9,7 +9,8 @@
     "build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json-render-diagnostics",
     "build-release": "npm run build -- --release",
     "test": "mocha -recursive dist/test",
-    "lint": "eslint src --ext .js,.ts"
+    "lint": "eslint src --ext .js,.ts",
+    "clean": "rm -rf node_modules *.node dist/"
   },
   "repository": {
     "type": "git",
@@ -27,6 +28,7 @@
     "@types/chai": "^4.3.4",
     "@types/mocha": "^10.0.1",
     "@types/node": "^18.16.2",
+    "@types/sinon": "^10.0.15",
     "@types/temp": "^0.9.1",
     "@typescript-eslint/eslint-plugin": "^5.59.1",
     "cargo-cp-artifact": "^0.1",
@@ -37,9 +39,13 @@
     "eslint-plugin-n": "^15.7.0",
     "eslint-plugin-promise": "^6.1.1",
     "mocha": "^10.2.0",
+    "openai": "^3.2.1",
+    "sinon": "^15.1.0",
     "temp": "^0.9.4",
     "ts-node": "^10.9.1",
     "ts-node-dev": "^2.0.0",
+    "typedoc": "^0.24.7",
+    "typedoc-plugin-markdown": "^3.15.3",
     "typescript": "*"
   },
   "dependencies": {

node/src/arrow.ts

@@ -24,7 +24,7 @@ import {
 } from 'apache-arrow'
 import { type EmbeddingFunction } from './index'
 
-export function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Table {
+export async function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Table> {
   if (data.length === 0) {
     throw new Error('At least one record needs to be provided')
   }
@@ -51,7 +51,7 @@ export function convertToTable<T> (data: Array<Record<string, unknown>>, embeddi
       }
 
       if (columnsKey === embeddings?.sourceColumn) {
-        const vectors = embeddings.embed(values as T[])
+        const vectors = await embeddings.embed(values as T[])
         const listBuilder = newVectorListBuilder()
         vectors.map(v => listBuilder.append(v))
         records.vector = listBuilder.finish().toVector()
@@ -79,7 +79,7 @@ function newVectorListBuilder (): ListBuilder<Float32, any> {
 }
 
 export async function fromRecordsToBuffer<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Buffer> {
-  const table = convertToTable(data, embeddings)
+  const table = await convertToTable(data, embeddings)
   const writer = RecordBatchFileWriter.writeAll(table)
   return Buffer.from(await writer.toUint8Array())
 }

node/src/embedding/embedding_function.ts

@@ -0,0 +1,28 @@
+// 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.
+
+/**
+ * An embedding function that automatically creates vector representation for a given column.
+ */
+export interface EmbeddingFunction<T> {
+  /**
+     * The name of the column that will be used as input for the Embedding Function.
+     */
+  sourceColumn: string
+
+  /**
+     * Creates a vector representation for the given values.
+     */
+  embed: (data: T[]) => Promise<number[][]>
+}

node/src/embedding/openai.ts

@@ -0,0 +1,51 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { type EmbeddingFunction } from '../index'
+
+export class OpenAIEmbeddingFunction implements EmbeddingFunction<string> {
+  private readonly _openai: any
+  private readonly _modelName: string
+
+  constructor (sourceColumn: string, openAIKey: string, modelName: string = 'text-embedding-ada-002') {
+    let openai
+    try {
+      // eslint-disable-next-line @typescript-eslint/no-var-requires
+      openai = require('openai')
+    } catch {
+      throw new Error('please install openai using npm install openai')
+    }
+
+    this.sourceColumn = sourceColumn
+    const configuration = new openai.Configuration({
+      apiKey: openAIKey
+    })
+    this._openai = new openai.OpenAIApi(configuration)
+    this._modelName = modelName
+  }
+
+  async embed (data: string[]): Promise<number[][]> {
+    const response = await this._openai.createEmbedding({
+      model: this._modelName,
+      input: data
+    })
+    const embeddings: number[][] = []
+    for (let i = 0; i < response.data.data.length; i++) {
+      embeddings.push(response.data.data[i].embedding as number[])
+    }
+    return embeddings
+  }
+
+  sourceColumn: string
+}

node/src/index.ts

@@ -19,10 +19,14 @@ import {
   Vector
 } from 'apache-arrow'
 import { fromRecordsToBuffer } from './arrow'
+import type { EmbeddingFunction } from './embedding/embedding_function'
 
 // eslint-disable-next-line @typescript-eslint/no-var-requires
 const { databaseNew, databaseTableNames, databaseOpenTable, tableCreate, tableSearch, tableAdd, tableCreateVectorIndex } = require('../native.js')
 
+export type { EmbeddingFunction }
+export { OpenAIEmbeddingFunction } from './embedding/openai'
+
 /**
  * Connect to a LanceDB instance at the given URI
  * @param uri The uri of the database.
@@ -135,14 +139,8 @@ export class Table<T = number[]> {
    * Creates a search query to find the nearest neighbors of the given search term
    * @param query The query search term
    */
-  search (query: T): Query {
-    let queryVector: number[]
-    if (this._embeddings !== undefined) {
-      queryVector = this._embeddings.embed([query])[0]
-    } else {
-      queryVector = query as number[]
-    }
-    return new Query(this._tbl, queryVector)
+  search (query: T): Query<T> {
+    return new Query(this._tbl, query, this._embeddings)
   }
 
   /**
@@ -170,9 +168,16 @@ export class Table<T = number[]> {
    *
    * @param indexParams The parameters of this Index, @see VectorIndexParams.
    */
-  async create_index (indexParams: VectorIndexParams): Promise<any> {
+  async createIndex (indexParams: VectorIndexParams): Promise<any> {
     return tableCreateVectorIndex.call(this._tbl, indexParams)
   }
+
+  /**
+   * @deprecated Use [Table.createIndex]
+   */
+  async create_index (indexParams: VectorIndexParams): Promise<any> {
+    return await this.createIndex(indexParams)
+  }
 }
 
 interface IvfPQIndexConfig {
@@ -228,32 +233,35 @@ export type VectorIndexParams = IvfPQIndexConfig
 /**
  * A builder for nearest neighbor queries for LanceDB.
  */
-export class Query {
+export class Query<T = number[]> {
   private readonly _tbl: any
-  private readonly _queryVector: number[]
+  private readonly _query: T
+  private _queryVector?: number[]
   private _limit: number
   private _refineFactor?: number
   private _nprobes: number
-  private readonly _columns?: string[]
+  private _select?: string[]
   private _filter?: string
   private _metricType?: MetricType
+  private readonly _embeddings?: EmbeddingFunction<T>
 
-  constructor (tbl: any, queryVector: number[]) {
+  constructor (tbl: any, query: T, embeddings?: EmbeddingFunction<T>) {
     this._tbl = tbl
-    this._queryVector = queryVector
+    this._query = query
     this._limit = 10
     this._nprobes = 20
     this._refineFactor = undefined
-    this._columns = undefined
+    this._select = undefined
     this._filter = undefined
     this._metricType = undefined
+    this._embeddings = embeddings
   }
 
   /***
    * Sets the number of results that will be returned
    * @param value number of results
    */
-  limit (value: number): Query {
+  limit (value: number): Query<T> {
     this._limit = value
     return this
   }
@@ -262,7 +270,7 @@ export class Query {
    * Refine the results by reading extra elements and re-ranking them in memory.
    * @param value refine factor to use in this query.
    */
-  refineFactor (value: number): Query {
+  refineFactor (value: number): Query<T> {
     this._refineFactor = value
     return this
   }
@@ -271,7 +279,7 @@ export class Query {
    * The number of probes used. A higher number makes search more accurate but also slower.
    * @param value The number of probes used.
    */
-  nprobes (value: number): Query {
+  nprobes (value: number): Query<T> {
     this._nprobes = value
     return this
   }
@@ -280,16 +288,27 @@ export class Query {
    * A filter statement to be applied to this query.
    * @param value A filter in the same format used by a sql WHERE clause.
    */
-  filter (value: string): Query {
+  filter (value: string): Query<T> {
     this._filter = value
     return this
   }
 
+  where = this.filter
+
+  /** Return only the specified columns.
+   *
+   * @param value Only select the specified columns. If not specified, all columns will be returned.
+   */
+  select (value: string[]): Query<T> {
+    this._select = value
+    return this
+  }
+
   /**
    * The MetricType used for this Query.
    * @param value The metric to the. @see MetricType for the different options
    */
-  metricType (value: MetricType): Query {
+  metricType (value: MetricType): Query<T> {
     this._metricType = value
     return this
   }
@@ -298,6 +317,12 @@ export class Query {
    * Execute the query and return the results as an Array of Objects
    */
   async execute<T = Record<string, unknown>> (): Promise<T[]> {
+    if (this._embeddings !== undefined) {
+      this._queryVector = (await this._embeddings.embed([this._query]))[0]
+    } else {
+      this._queryVector = this._query as number[]
+    }
+
     const buffer = await tableSearch.call(this._tbl, this)
     const data = tableFromIPC(buffer)
     return data.toArray().map((entry: Record<string, unknown>) => {
@@ -319,21 +344,6 @@ export enum WriteMode {
   Append = 'append'
 }
 
-/**
- * An embedding function that automatically creates vector representation for a given column.
- */
-export interface EmbeddingFunction<T> {
-  /**
-   * The name of the column that will be used as input for the Embedding Function.
-   */
-  sourceColumn: string
-
-  /**
-   * Creates a vector representation for the given values.
-   */
-  embed: (data: T[]) => number[][]
-}
-
 /**
  * Distance metrics type.
  */

node/src/test/embedding/openai.ts

@@ -0,0 +1,50 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { describe } from 'mocha'
+import { assert } from 'chai'
+
+import { OpenAIEmbeddingFunction } from '../../embedding/openai'
+
+// eslint-disable-next-line @typescript-eslint/no-var-requires
+const { OpenAIApi } = require('openai')
+// eslint-disable-next-line @typescript-eslint/no-var-requires
+const { stub } = require('sinon')
+
+describe('OpenAPIEmbeddings', function () {
+  const stubValue = {
+    data: {
+      data: [
+        {
+          embedding: Array(1536).fill(1.0)
+        },
+        {
+          embedding: Array(1536).fill(2.0)
+        }
+      ]
+    }
+  }
+
+  describe('#embed', function () {
+    it('should create vector embeddings', async function () {
+      const openAIStub = stub(OpenAIApi.prototype, 'createEmbedding').returns(stubValue)
+      const f = new OpenAIEmbeddingFunction('text', 'sk-key')
+      const vectors = await f.embed(['abc', 'def'])
+      assert.isTrue(openAIStub.calledOnce)
+      assert.equal(vectors.length, 2)
+      assert.deepEqual(vectors[0], stubValue.data.data[0].embedding)
+      assert.deepEqual(vectors[1], stubValue.data.data[1].embedding)
+    })
+  })
+})

node/src/test/test.ts

@@ -64,13 +64,36 @@ describe('LanceDB client', function () {
       assert.equal(results[0].id, 1)
     })
 
-    it('uses a filter', async function () {
+    it('uses a filter / where clause', async function () {
+      // eslint-disable-next-line @typescript-eslint/explicit-function-return-type
+      const assertResults = (results: Array<Record<string, unknown>>) => {
+        assert.equal(results.length, 1)
+        assert.equal(results[0].id, 2)
+      }
+
       const uri = await createTestDB()
       const con = await lancedb.connect(uri)
       const table = await con.openTable('vectors')
-      const results = await table.search([0.1, 0.1]).filter('id == 2').execute()
-      assert.equal(results.length, 1)
-      assert.equal(results[0].id, 2)
+      let results = await table.search([0.1, 0.1]).filter('id == 2').execute()
+      assertResults(results)
+      results = await table.search([0.1, 0.1]).where('id == 2').execute()
+      assertResults(results)
+    })
+
+    it('select only a subset of columns', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      const results = await table.search([0.1, 0.1]).select(['is_active']).execute()
+      assert.equal(results.length, 2)
+      // vector and score are always returned
+      assert.isDefined(results[0].vector)
+      assert.isDefined(results[0].score)
+      assert.isDefined(results[0].is_active)
+
+      assert.isUndefined(results[0].id)
+      assert.isUndefined(results[0].name)
+      assert.isUndefined(results[0].price)
     })
   })
 
@@ -137,7 +160,7 @@ describe('LanceDB client', function () {
       const uri = await createTestDB(32, 300)
       const con = await lancedb.connect(uri)
       const table = await con.openTable('vectors')
-      await table.create_index({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2 })
+      await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2 })
     }).timeout(10_000) // Timeout is high partially because GH macos runner is pretty slow
   })
 
@@ -154,7 +177,7 @@ describe('LanceDB client', function () {
         ['bar', [3.1, 3.2]]
       ])
 
-      embed (data: string[]): number[][] {
+      async embed (data: string[]): Promise<number[][]> {
         return data.map(datum => this._embedding_map.get(datum) ?? [0.0, 0.0])
       }
     }
@@ -181,11 +204,13 @@ describe('Query object', function () {
       .limit(1)
       .metricType(MetricType.Cosine)
       .refineFactor(100)
+      .select(['a', 'b'])
       .nprobes(20) as Record<string, any>
     assert.equal(query._limit, 1)
     assert.equal(query._metricType, MetricType.Cosine)
     assert.equal(query._refineFactor, 100)
     assert.equal(query._nprobes, 20)
+    assert.deepEqual(query._select, ['a', 'b'])
   })
 })
 

python/.bumpversion.cfg

@@ -0,0 +1,8 @@
+[bumpversion]
+current_version = 0.1.8
+commit = True
+message = [python] Bump version: {current_version} → {new_version}
+tag = True
+tag_name = python-v{new_version}
+
+[bumpversion:file:pyproject.toml]

python/lancedb/__init__.py

@@ -22,8 +22,21 @@ def connect(uri: URI) -> LanceDBConnection:
     uri: str or Path
         The uri of the database.
 
+    Examples
+    --------
+
+    For a local directory, provide a path for the database:
+
+    >>> import lancedb
+    >>> db = lancedb.connect("~/.lancedb")
+
+    For object storage, use a URI prefix:
+
+    >>> db = lancedb.connect("s3://my-bucket/lancedb")
+
     Returns
     -------
+    conn : LanceDBConnection
         A connection to a LanceDB database.
     """
     return LanceDBConnection(uri)

python/lancedb/conftest.py

@@ -0,0 +1,18 @@
+import builtins
+import os
+
+import pytest
+
+# import lancedb so we don't have to in every example
+import lancedb
+
+
+@pytest.fixture(autouse=True)
+def doctest_setup(monkeypatch, tmpdir):
+    # disable color for doctests so we don't have to include
+    # escape codes in docstrings
+    monkeypatch.setitem(os.environ, "NO_COLOR", "1")
+    # Explicitly set the column width
+    monkeypatch.setitem(os.environ, "COLUMNS", "80")
+    # Work in a temporary directory
+    monkeypatch.chdir(tmpdir)

python/lancedb/context.py

@@ -13,21 +13,109 @@
 from __future__ import annotations
 
 import pandas as pd
+from .exceptions import MissingValueError, MissingColumnError
 
 
 def contextualize(raw_df: pd.DataFrame) -> Contextualizer:
     """Create a Contextualizer object for the given DataFrame.
-    Used to create context windows.
+
+    Used to create context windows. Context windows are rolling subsets of text
+    data.
+
+    The input text column should already be separated into rows that will be the
+    unit of the window. So to create a context window over tokens, start with
+    a DataFrame with one token per row. To create a context window over sentences,
+    start with a DataFrame with one sentence per row.
+
+    Examples
+    --------
+    >>> from lancedb.context import contextualize
+    >>> import pandas as pd
+    >>> data = pd.DataFrame({
+    ...    'token': ['The', 'quick', 'brown', 'fox', 'jumped', 'over',
+    ...              'the', 'lazy', 'dog', 'I', 'love', 'sandwiches'],
+    ...    'document_id': [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2]
+    ... })
+
+    ``window`` determines how many rows to include in each window. In our case
+    this how many tokens, but depending on the input data, it could be sentences,
+    paragraphs, messages, etc.
+
+    >>> contextualize(data).window(3).stride(1).text_col('token').to_df()
+                    token  document_id
+    0     The quick brown            1
+    1     quick brown fox            1
+    2    brown fox jumped            1
+    3     fox jumped over            1
+    4     jumped over the            1
+    5       over the lazy            1
+    6        the lazy dog            1
+    7          lazy dog I            1
+    8          dog I love            1
+    9   I love sandwiches            2
+    10    love sandwiches            2
+    >>> contextualize(data).window(7).stride(1).min_window_size(7).text_col('token').to_df()
+                                      token  document_id
+    0   The quick brown fox jumped over the            1
+    1  quick brown fox jumped over the lazy            1
+    2    brown fox jumped over the lazy dog            1
+    3        fox jumped over the lazy dog I            1
+    4       jumped over the lazy dog I love            1
+    5   over the lazy dog I love sandwiches            1
+
+    ``stride`` determines how many rows to skip between each window start. This can
+    be used to reduce the total number of windows generated.
+
+    >>> contextualize(data).window(4).stride(2).text_col('token').to_df()
+                        token  document_id
+    0     The quick brown fox            1
+    2   brown fox jumped over            1
+    4    jumped over the lazy            1
+    6          the lazy dog I            1
+    8   dog I love sandwiches            1
+    10        love sandwiches            2
+
+    ``groupby`` determines how to group the rows. For example, we would like to have
+    context windows that don't cross document boundaries. In this case, we can
+    pass ``document_id`` as the group by.
+
+    >>> contextualize(data).window(4).stride(2).text_col('token').groupby('document_id').to_df()
+                       token  document_id
+    0    The quick brown fox            1
+    2  brown fox jumped over            1
+    4   jumped over the lazy            1
+    6           the lazy dog            1
+    9      I love sandwiches            2
+
+    ``min_window_size`` determines the minimum size of the  context windows that are generated
+    This can be used to trim the last few context windows which have size less than
+    ``min_window_size``. By default context windows of size 1 are skipped.
+
+    >>> contextualize(data).window(6).stride(3).text_col('token').groupby('document_id').to_df()
+                                 token  document_id
+    0  The quick brown fox jumped over            1
+    3     fox jumped over the lazy dog            1
+    6                     the lazy dog            1
+    9                I love sandwiches            2
+
+    >>> contextualize(data).window(6).stride(3).min_window_size(4).text_col('token').groupby('document_id').to_df()
+                                 token  document_id
+    0  The quick brown fox jumped over            1
+    3     fox jumped over the lazy dog            1
+
     """
     return Contextualizer(raw_df)
 
 
 class Contextualizer:
+    """Create context windows from a DataFrame. See [lancedb.context.contextualize][]."""
+
     def __init__(self, raw_df):
         self._text_col = None
         self._groupby = None
         self._stride = None
         self._window = None
+        self._min_window_size = 2
         self._raw_df = raw_df
 
     def window(self, window: int) -> Contextualizer:
@@ -75,17 +163,50 @@ class Contextualizer:
         self._text_col = text_col
         return self
 
+    def min_window_size(self, min_window_size: int) -> Contextualizer:
+        """Set the (optional) min_window_size size for the context window.
+
+        Parameters
+        ----------
+        min_window_size: int
+            The min_window_size.
+        """
+        self._min_window_size = min_window_size
+        return self
+
     def to_df(self) -> pd.DataFrame:
         """Create the context windows and return a DataFrame."""
 
+        if self._text_col not in self._raw_df.columns.tolist():
+            raise MissingColumnError(self._text_col)
+
+        if self._window is None or self._window < 1:
+            raise MissingValueError(
+                "The value of window is None or less than 1. Specify the "
+                "window size (number of rows to include in each window)"
+            )
+
+        if self._stride is None or self._stride < 1:
+            raise MissingValueError(
+                "The value of stride is None or less than 1. Specify the "
+                "stride (number of rows to skip between each window)"
+            )
+
         def process_group(grp):
             # For each group, create the text rolling window
+            # with values of size >= min_window_size
             text = grp[self._text_col].values
-            contexts = grp.iloc[: -self._window : self._stride, :].copy()
-            contexts[self._text_col] = [
-                " ".join(text[start_i : start_i + self._window])
-                for start_i in range(0, len(grp) - self._window, self._stride)
+            contexts = grp.iloc[:: self._stride, :].copy()
+            windows = [
+                " ".join(text[start_i : min(start_i + self._window, len(grp))])
+                for start_i in range(0, len(grp), self._stride)
+                if start_i + self._window <= len(grp)
+                or len(grp) - start_i >= self._min_window_size
             ]
+            # if last few rows dropped
+            if len(windows) < len(contexts):
+                contexts = contexts.iloc[: len(windows)]
+            contexts[self._text_col] = windows
             return contexts
 
         if self._groupby is None:

python/lancedb/db.py

@@ -13,18 +13,46 @@
 
 from __future__ import annotations
 
+import os
 from pathlib import Path
+import os
 
 import pyarrow as pa
+from pyarrow import fs
 
 from .common import DATA, URI
 from .table import LanceTable
-from .util import get_uri_scheme
+from .util import get_uri_scheme, get_uri_location
 
 
 class LanceDBConnection:
     """
     A connection to a LanceDB database.
+
+    Parameters
+    ----------
+    uri: str or Path
+        The root uri of the database.
+
+    Examples
+    --------
+    >>> import lancedb
+    >>> db = lancedb.connect("./.lancedb")
+    >>> db.create_table("my_table", data=[{"vector": [1.1, 1.2], "b": 2},
+    ...                                   {"vector": [0.5, 1.3], "b": 4}])
+    LanceTable(my_table)
+    >>> db.create_table("another_table", data=[{"vector": [0.4, 0.4], "b": 6}])
+    LanceTable(another_table)
+    >>> db.table_names()
+    ['another_table', 'my_table']
+    >>> len(db)
+    2
+    >>> db["my_table"]
+    LanceTable(my_table)
+    >>> "my_table" in db
+    True
+    >>> db.drop_table("my_table")
+    >>> db.drop_table("another_table")
     """
 
     def __init__(self, uri: URI):
@@ -45,13 +73,27 @@ class LanceDBConnection:
 
         Returns
         -------
+        list of str
             A list of table names.
         """
-        if get_uri_scheme(self.uri) == "file":
-            return [p.stem for p in Path(self.uri).glob("*.lance")]
-        raise NotImplementedError(
-            "List table_names is only supported for local filesystem for now"
+        try:
+            filesystem, path = fs.FileSystem.from_uri(self.uri)
+        except pa.ArrowInvalid:
+            raise NotImplementedError("Unsupported scheme: " + self.uri)
+
+        try:
+            paths = filesystem.get_file_info(
+                fs.FileSelector(get_uri_location(self.uri))
             )
+        except FileNotFoundError:
+            # It is ok if the file does not exist since it will be created
+            paths = []
+        tables = [
+            os.path.splitext(file_info.base_name)[0]
+            for file_info in paths
+            if file_info.extension == "lance"
+        ]
+        return tables
 
     def __len__(self) -> int:
         return len(self.table_names())
@@ -91,7 +133,73 @@ class LanceDBConnection:
 
         Returns
         -------
-        A LanceTable object representing the table.
+        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]]
         """
         if data is not None:
             tbl = LanceTable.create(self, name, data, schema, mode=mode)
@@ -112,3 +220,15 @@ class LanceDBConnection:
         A LanceTable object representing the table.
         """
         return LanceTable(self, name)
+
+    def drop_table(self, name: str):
+        """Drop a table from the database.
+
+        Parameters
+        ----------
+        name: str
+            The name of the table.
+        """
+        filesystem, path = pa.fs.FileSystem.from_uri(self.uri)
+        table_path = os.path.join(path, name + ".lance")
+        filesystem.delete_dir(table_path)

python/lancedb/embeddings.py

@@ -29,7 +29,31 @@ def with_embeddings(
     wrap_api: bool = True,
     show_progress: bool = False,
     batch_size: int = 1000,
-):
+) -> pa.Table:
+    """Add a vector column to a table using the given embedding function.
+
+    The new columns will be called "vector".
+
+    Parameters
+    ----------
+    func : Callable
+        A function that takes a list of strings and returns a list of vectors.
+    data : pa.Table or pd.DataFrame
+        The data to add an embedding column to.
+    column : str, default "text"
+        The name of the column to use as input to the embedding function.
+    wrap_api : bool, default True
+        Whether to wrap the embedding function in a retry and rate limiter.
+    show_progress : bool, default False
+        Whether to show a progress bar.
+    batch_size : int, default 1000
+        The number of row values to pass to each call of the embedding function.
+
+    Returns
+    -------
+    pa.Table
+        The input table with a new column called "vector" containing the embeddings.
+    """
     func = EmbeddingFunction(func)
     if wrap_api:
         func = func.retry().rate_limit()

python/lancedb/exceptions.py

@@ -0,0 +1,22 @@
+"""Custom exception handling"""
+
+
+class MissingValueError(ValueError):
+    """Exception raised when a required value is missing."""
+
+    pass
+
+
+class MissingColumnError(KeyError):
+    """
+    Exception raised when a column name specified is not in
+    the  DataFrame object
+    """
+
+    def __init__(self, column_name):
+        self.column_name = column_name
+
+    def __str__(self):
+        return (
+            f"Error: Column '{self.column_name}' does not exist in the DataFrame object"
+        )

python/lancedb/fts.py

@@ -68,6 +68,11 @@ def populate_index(index: tantivy.Index, table: LanceTable, fields: List[str]) -
         The table to index
     fields : List[str]
         List of fields to index
+
+    Returns
+    -------
+    int
+        The number of rows indexed
     """
     # first check the fields exist and are string or large string type
     for name in fields:
@@ -118,6 +123,8 @@ def search_index(
     query = index.parse_query(query)
     # get top results
     results = searcher.search(query, limit)
+    if results.count == 0:
+        return tuple(), tuple()
     return tuple(
         zip(
             *[

python/lancedb/query.py

@@ -11,6 +11,7 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 from __future__ import annotations
+from typing import Literal
 
 import numpy as np
 import pandas as pd
@@ -22,6 +23,24 @@ from .common import VECTOR_COLUMN_NAME
 class LanceQueryBuilder:
     """
     A builder for nearest neighbor queries for LanceDB.
+
+    Examples
+    --------
+    >>> import lancedb
+    >>> data = [{"vector": [1.1, 1.2], "b": 2},
+    ...         {"vector": [0.5, 1.3], "b": 4},
+    ...         {"vector": [0.4, 0.4], "b": 6},
+    ...         {"vector": [0.4, 0.4], "b": 10}]
+    >>> db = lancedb.connect("./.lancedb")
+    >>> table = db.create_table("my_table", data=data)
+    >>> (table.search([0.4, 0.4])
+    ...       .metric("cosine")
+    ...       .where("b < 10")
+    ...       .select(["b"])
+    ...       .limit(2)
+    ...       .to_df())
+       b      vector  score
+    0  6  [0.4, 0.4]    0.0
     """
 
     def __init__(self, table: "lancedb.table.LanceTable", query: np.ndarray):
@@ -44,6 +63,7 @@ class LanceQueryBuilder:
 
         Returns
         -------
+        LanceQueryBuilder
             The LanceQueryBuilder object.
         """
         self._limit = limit
@@ -59,6 +79,7 @@ class LanceQueryBuilder:
 
         Returns
         -------
+        LanceQueryBuilder
             The LanceQueryBuilder object.
         """
         self._columns = columns
@@ -74,21 +95,23 @@ class LanceQueryBuilder:
 
         Returns
         -------
+        LanceQueryBuilder
             The LanceQueryBuilder object.
         """
         self._where = where
         return self
 
-    def metric(self, metric: str) -> LanceQueryBuilder:
+    def metric(self, metric: Literal["L2", "cosine"]) -> LanceQueryBuilder:
         """Set the distance metric to use.
 
         Parameters
         ----------
-        metric: str
-            The distance metric to use. By default "l2" is used.
+        metric: "L2" or "cosine"
+            The distance metric to use. By default "L2" is used.
 
         Returns
         -------
+        LanceQueryBuilder
             The LanceQueryBuilder object.
         """
         self._metric = metric
@@ -97,6 +120,12 @@ class LanceQueryBuilder:
     def nprobes(self, nprobes: int) -> LanceQueryBuilder:
         """Set the number of probes to use.
 
+        Higher values will yield better recall (more likely to find vectors if
+        they exist) at the expense of latency.
+
+        See discussion in [Querying an ANN Index][../querying-an-ann-index] for
+        tuning advice.
+
         Parameters
         ----------
         nprobes: int
@@ -104,13 +133,20 @@ class LanceQueryBuilder:
 
         Returns
         -------
+        LanceQueryBuilder
             The LanceQueryBuilder object.
         """
         self._nprobes = nprobes
         return self
 
     def refine_factor(self, refine_factor: int) -> LanceQueryBuilder:
-        """Set the refine factor to use.
+        """Set the refine factor to use, increasing the number of vectors sampled.
+
+        As an example, a refine factor of 2 will sample 2x as many vectors as
+        requested, re-ranks them, and returns the top half most relevant results.
+
+        See discussion in [Querying an ANN Index][querying-an-ann-index] for
+        tuning advice.
 
         Parameters
         ----------
@@ -119,6 +155,7 @@ class LanceQueryBuilder:
 
         Returns
         -------
+        LanceQueryBuilder
             The LanceQueryBuilder object.
         """
         self._refine_factor = refine_factor
@@ -164,6 +201,8 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
         index = tantivy.Index.open(index_path)
         # get the scores and doc ids
         row_ids, scores = search_index(index, self._query, self._limit)
+        if len(row_ids) == 0:
+            return pd.DataFrame()
         scores = pa.array(scores)
         output_tbl = self._table.to_lance().take(row_ids, columns=self._columns)
         output_tbl = output_tbl.append_column("score", scores)

python/lancedb/table.py

@@ -47,6 +47,40 @@ def _sanitize_data(data, schema):
 class LanceTable:
     """
     A table in a LanceDB database.
+
+    Examples
+    --------
+
+    Create using [LanceDBConnection.create_table][lancedb.LanceDBConnection.create_table]
+    (more examples in that method's documentation).
+
+    >>> import lancedb
+    >>> db = lancedb.connect("./.lancedb")
+    >>> table = db.create_table("my_table", data=[{"vector": [1.1, 1.2], "b": 2}])
+    >>> table.head()
+    pyarrow.Table
+    vector: fixed_size_list<item: float>[2]
+      child 0, item: float
+    b: int64
+    ----
+    vector: [[[1.1,1.2]]]
+    b: [[2]]
+
+    Can append new data with [LanceTable.add][lancedb.table.LanceTable.add].
+
+    >>> table.add([{"vector": [0.5, 1.3], "b": 4}])
+    2
+
+    Can query the table with [LanceTable.search][lancedb.table.LanceTable.search].
+
+    >>> table.search([0.4, 0.4]).select(["b"]).to_df()
+       b      vector  score
+    0  4  [0.5, 1.3]   0.82
+    1  2  [1.1, 1.2]   1.13
+
+    Search queries are much faster when an index is created. See
+    [LanceTable.create_index][lancedb.table.LanceTable.create_index].
+
     """
 
     def __init__(
@@ -64,7 +98,12 @@ class LanceTable:
 
     @property
     def schema(self) -> pa.Schema:
-        """Return the schema of the table."""
+        """Return the schema of the table.
+
+        Returns
+        -------
+        pa.Schema
+            A PyArrow schema object."""
         return self._dataset.schema
 
     def list_versions(self):
@@ -72,12 +111,39 @@ class LanceTable:
         return self._dataset.versions()
 
     @property
-    def version(self):
+    def version(self) -> int:
         """Get the current version of the table"""
         return self._dataset.version
 
     def checkout(self, version: int):
-        """Checkout a version of the table"""
+        """Checkout a version of the table. This is an in-place operation.
+
+        This allows viewing previous versions of the table.
+
+        Parameters
+        ----------
+        version : int
+            The version to checkout.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> db = lancedb.connect("./.lancedb")
+        >>> table = db.create_table("my_table", [{"vector": [1.1, 0.9], "type": "vector"}])
+        >>> table.version
+        1
+        >>> table.to_pandas()
+               vector    type
+        0  [1.1, 0.9]  vector
+        >>> table.add([{"vector": [0.5, 0.2], "type": "vector"}])
+        2
+        >>> table.version
+        2
+        >>> table.checkout(1)
+        >>> table.to_pandas()
+               vector    type
+        0  [1.1, 0.9]  vector
+        """
         max_ver = max([v["version"] for v in self._dataset.versions()])
         if version < 1 or version > max_ver:
             raise ValueError(f"Invalid version {version}")
@@ -98,11 +164,20 @@ class LanceTable:
         return self._dataset.head(n)
 
     def to_pandas(self) -> pd.DataFrame:
-        """Return the table as a pandas DataFrame."""
+        """Return the table as a pandas DataFrame.
+
+        Returns
+        -------
+        pd.DataFrame
+        """
         return self.to_arrow().to_pandas()
 
     def to_arrow(self) -> pa.Table:
-        """Return the table as a pyarrow Table."""
+        """Return the table as a pyarrow Table.
+
+        Returns
+        -------
+        pa.Table"""
         return self._dataset.to_table()
 
     @property
@@ -175,7 +250,8 @@ class LanceTable:
 
         Returns
         -------
-        The number of vectors added to the table.
+        int
+            The number of vectors in the table.
         """
         data = _sanitize_data(data, self.schema)
         lance.write_dataset(data, self._dataset_uri, mode=mode)
@@ -193,7 +269,8 @@ class LanceTable:
 
         Returns
         -------
-        A LanceQueryBuilder object representing the query.
+        LanceQueryBuilder
+            A query builder object representing the query.
             Once executed, the query returns selected columns, the vector,
             and also the "score" column which is the distance between the query
             vector and the returned vector.
@@ -253,8 +330,7 @@ def _sanitize_vector_column(data: pa.Table, vector_column_name: str) -> pa.Table
     vector_column_name: str
         The name of the vector column.
     """
-    i = data.column_names.index(vector_column_name)
-    if i < 0:
+    if vector_column_name not in data.column_names:
         raise ValueError(f"Missing vector column: {vector_column_name}")
     vec_arr = data[vector_column_name].combine_chunks()
     if pa.types.is_fixed_size_list(vec_arr.type):
@@ -266,4 +342,6 @@ def _sanitize_vector_column(data: pa.Table, vector_column_name: str) -> pa.Table
         values = values.cast(pa.float32())
     list_size = len(values) / len(data)
     vec_arr = pa.FixedSizeListArray.from_arrays(values, list_size)
-    return data.set_column(i, vector_column_name, vec_arr)
+    return data.set_column(
+        data.column_names.index(vector_column_name), vector_column_name, vec_arr
+    )

python/lancedb/util.py

@@ -41,3 +41,23 @@ def get_uri_scheme(uri: str) -> str:
         # So we add special handling here for schemes that are a single character
         scheme = "file"
     return scheme
+
+
+def get_uri_location(uri: str) -> str:
+    """
+    Get the location of a URI. If the parameter is not a url, assumes it is just a path
+
+    Parameters
+    ----------
+    uri : str
+        The URI to parse.
+
+    Returns
+    -------
+    str: Location part of the URL, without scheme
+    """
+    parsed = urlparse(uri)
+    if not parsed.netloc:
+        return parsed.path
+    else:
+        return parsed.netloc + parsed.path

python/pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "lancedb"
-version = "0.1.4"
-dependencies = ["pylance>=0.4.17", "ratelimiter", "retry", "tqdm"]
+version = "0.1.8"
+dependencies = ["pylance>=0.4.20", "ratelimiter", "retry", "tqdm"]
 description = "lancedb"
 authors = [
     { name = "LanceDB Devs", email = "dev@lancedb.com" },
@@ -33,11 +33,11 @@ classifiers = [
 ]
 
 [project.urls]
-repository = "https://github.com/eto-ai/lancedb"
+repository = "https://github.com/lancedb/lancedb"
 
 [project.optional-dependencies]
 tests = [
-    "pytest"
+    "pytest", "pytest-mock", "doctest"
 ]
 dev = [
     "ruff", "pre-commit", "black"

python/tests/test_context.py

@@ -0,0 +1,77 @@
+#  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 pandas as pd
+import pytest
+
+from lancedb.context import contextualize
+
+
+@pytest.fixture
+def raw_df() -> pd.DataFrame:
+    return pd.DataFrame(
+        {
+            "token": [
+                "The",
+                "quick",
+                "brown",
+                "fox",
+                "jumped",
+                "over",
+                "the",
+                "lazy",
+                "dog",
+                "I",
+                "love",
+                "sandwiches",
+            ],
+            "document_id": [1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2],
+        }
+    )
+
+
+def test_contextualizer(raw_df: pd.DataFrame):
+    result = (
+        contextualize(raw_df)
+        .window(6)
+        .stride(3)
+        .text_col("token")
+        .groupby("document_id")
+        .to_df()["token"]
+        .to_list()
+    )
+
+    assert result == [
+        "The quick brown fox jumped over",
+        "fox jumped over the lazy dog",
+        "the lazy dog",
+        "I love sandwiches",
+    ]
+
+
+def test_contextualizer_with_threshold(raw_df: pd.DataFrame):
+    result = (
+        contextualize(raw_df)
+        .window(6)
+        .stride(3)
+        .text_col("token")
+        .groupby("document_id")
+        .min_window_size(4)
+        .to_df()["token"]
+        .to_list()
+    )
+
+    assert result == [
+        "The quick brown fox jumped over",
+        "fox jumped over the lazy dog",
+    ]

python/tests/test_db.py

@@ -97,3 +97,26 @@ def test_create_mode(tmp_path):
     )
     tbl = db.create_table("test", data=new_data, mode="overwrite")
     assert tbl.to_pandas().item.tolist() == ["fizz", "buzz"]
+
+
+def test_delete_table(tmp_path):
+    db = lancedb.connect(tmp_path)
+    data = pd.DataFrame(
+        {
+            "vector": [[3.1, 4.1], [5.9, 26.5]],
+            "item": ["foo", "bar"],
+            "price": [10.0, 20.0],
+        }
+    )
+    db.create_table("test", data=data)
+
+    with pytest.raises(Exception):
+        db.create_table("test", data=data)
+
+    assert db.table_names() == ["test"]
+
+    db.drop_table("test")
+    assert db.table_names() == []
+
+    db.create_table("test", data=data)
+    assert db.table_names() == ["test"]

python/tests/test_fts.py

@@ -82,3 +82,10 @@ def test_create_index_multiple_columns(tmp_path, table):
     assert len(df) == 10
     assert "text" in df.columns
     assert "text2" in df.columns
+
+
+def test_empty_rs(tmp_path, table, mocker):
+    table.create_fts_index(["text", "text2"])
+    mocker.patch("lancedb.fts.search_index", return_value=([], []))
+    df = table.search("puppy").limit(10).to_df()
+    assert len(df) == 0

python/tests/test_io.py

@@ -0,0 +1,50 @@
+#  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 os
+import pytest
+
+import lancedb
+
+# You need to setup AWS credentials an a base path to run this test. Example
+#    AWS_PROFILE=default TEST_S3_BASE_URL=s3://my_bucket/dataset pytest tests/test_io.py
+
+
+@pytest.mark.skipif(
+    (os.environ.get("TEST_S3_BASE_URL") is None),
+    reason="please setup s3 base url",
+)
+def test_s3_io():
+    db = lancedb.connect(os.environ.get("TEST_S3_BASE_URL"))
+    assert db.table_names() == []
+
+    table = db.create_table(
+        "test",
+        data=[
+            {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+            {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+        ],
+    )
+    rs = table.search([100, 100]).limit(1).to_df()
+    assert len(rs) == 1
+    assert rs["item"].iloc[0] == "bar"
+
+    rs = table.search([100, 100]).where("price < 15").limit(2).to_df()
+    assert len(rs) == 1
+    assert rs["item"].iloc[0] == "foo"
+
+    assert db.table_names() == ["test"]
+    assert "test" in db
+    assert len(db) == 1
+
+    assert db.open_table("test").name == db["test"].name

python/tests/test_query.py

@@ -30,23 +30,17 @@ class MockTable:
 
 @pytest.fixture
 def table(tmp_path) -> MockTable:
-    df = pd.DataFrame(
+    df = pa.table(
         {
-            "vector": [[1, 2], [3, 4]],
-            "id": [1, 2],
-            "str_field": ["a", "b"],
-            "float_field": [1.0, 2.0],
+            "vector": pa.array(
+                [[1, 2], [3, 4]], type=pa.list_(pa.float32(), list_size=2)
+            ),
+            "id": pa.array([1, 2]),
+            "str_field": pa.array(["a", "b"]),
+            "float_field": pa.array([1.0, 2.0]),
         }
     )
-    schema = pa.schema(
-        [
-            pa.field("vector", pa.list_(pa.float32(), list_size=2)),
-            pa.field("id", pa.int32()),
-            pa.field("str_field", pa.string()),
-            pa.field("float_field", pa.float64()),
-        ]
-    )
-    lance.write_dataset(df, tmp_path, schema)
+    lance.write_dataset(df, tmp_path)
     return MockTable(tmp_path)
 
 
@@ -65,7 +59,7 @@ def test_query_builder_with_filter(table):
 def test_query_builder_with_metric(table):
     query = [4, 8]
     df_default = LanceQueryBuilder(table, query).to_df()
-    df_l2 = LanceQueryBuilder(table, query).metric("l2").to_df()
+    df_l2 = LanceQueryBuilder(table, query).metric("L2").to_df()
     tm.assert_frame_equal(df_default, df_l2)
 
     df_cosine = LanceQueryBuilder(table, query).metric("cosine").limit(1).to_df()

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb-node"
-version = "0.1.0"
+version = "0.1.6"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
 edition = "2018"

rust/ffi/node/src/index/vector.rs

@@ -97,6 +97,7 @@ fn get_index_params_builder(
                 let ivf_params = IvfBuildParams {
                     num_partitions: np,
                     max_iters,
+                    centroids: None,
                 };
                 index_builder.ivf_params(ivf_params)
             });

rust/ffi/node/src/lib.rs

@@ -129,6 +129,17 @@ fn table_search(mut cx: FunctionContext) -> JsResult<JsPromise> {
     let limit = query_obj
         .get::<JsNumber, _, _>(&mut cx, "_limit")?
         .value(&mut cx);
+    let select = query_obj
+        .get_opt::<JsArray, _, _>(&mut cx, "_select")?
+        .map(|arr| {
+            let js_array = arr.deref();
+            let mut projection_vec: Vec<String> = Vec::new();
+            for i in 0..js_array.len(&mut cx) {
+                let entry: Handle<JsString> = js_array.get(&mut cx, i).unwrap();
+                projection_vec.push(entry.value(&mut cx));
+            }
+            projection_vec
+        });
     let filter = query_obj
         .get_opt::<JsString, _, _>(&mut cx, "_filter")?
         .map(|s| s.value(&mut cx));
@@ -161,7 +172,8 @@ fn table_search(mut cx: FunctionContext) -> JsResult<JsPromise> {
             .refine_factor(refine_factor)
             .nprobes(nprobes)
             .filter(filter)
-            .metric_type(metric_type);
+            .metric_type(metric_type)
+            .select(select);
         let record_batch_stream = builder.execute();
         let results = record_batch_stream
             .and_then(|stream| stream.try_collect::<Vec<_>>().map_err(Error::from))

rust/vectordb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb"
-version = "0.0.1"
+version = "0.1.6"
 edition = "2021"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
@@ -13,8 +13,8 @@ arrow-array = "37.0"
 arrow-data = "37.0"
 arrow-schema = "37.0"
 object_store = "0.5.6"
-
-lance = "0.4.17"
+snafu = "0.7.4"
+lance = "0.4.21"
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
 
 [dev-dependencies]

rust/vectordb/src/database.rs

@@ -17,8 +17,9 @@ use std::path::Path;
 
 use arrow_array::RecordBatchReader;
 use lance::io::object_store::ObjectStore;
+use snafu::prelude::*;
 
-use crate::error::Result;
+use crate::error::{CreateDirSnafu, Result};
 use crate::table::Table;
 
 pub struct Database {
@@ -41,12 +42,9 @@ impl Database {
     ///
     /// * A [Database] object.
     pub async fn connect(uri: &str) -> Result<Database> {
-        let object_store = ObjectStore::new(uri).await?;
+        let (object_store, _) = ObjectStore::from_uri(uri).await?;
         if object_store.is_local() {
-            let path = Path::new(uri);
-            if !path.try_exists()? {
-                create_dir_all(&path)?;
-            }
+            Self::try_create_dir(uri).context(CreateDirSnafu { path: uri })?;
         }
         Ok(Database {
             uri: uri.to_string(),
@@ -54,6 +52,15 @@ impl Database {
         })
     }
 
+    /// Try to create a local directory to store the lancedb dataset
+    fn try_create_dir(path: &str) -> core::result::Result<(), std::io::Error> {
+        let path = Path::new(path);
+        if !path.try_exists()? {
+            create_dir_all(&path)?;
+        }
+        Ok(())
+    }
+
     /// Get the names of all tables in the database.
     ///
     /// # Returns
@@ -62,7 +69,7 @@ impl Database {
     pub async fn table_names(&self) -> Result<Vec<String>> {
         let f = self
             .object_store
-            .read_dir("/")
+            .read_dir(self.uri.as_str())
             .await?
             .iter()
             .map(|fname| Path::new(fname))

rust/vectordb/src/error.rs

@@ -12,44 +12,50 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#[derive(Debug)]
-pub enum Error {
-    IO(String),
-    Lance(String),
-}
+use snafu::Snafu;
 
-impl std::fmt::Display for Error {
-    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
-        let (catalog, message) = match self {
-            Self::IO(s) => ("I/O", s.as_str()),
-            Self::Lance(s) => ("Lance", s.as_str()),
-        };
-        write!(f, "LanceDBError({catalog}): {message}")
-    }
+#[derive(Debug, Snafu)]
+#[snafu(visibility(pub(crate)))]
+pub enum Error {
+    #[snafu(display("LanceDBError: Invalid table name: {name}"))]
+    InvalidTableName { name: String },
+    #[snafu(display("LanceDBError: Table '{name}' was not found"))]
+    TableNotFound { name: String },
+    #[snafu(display("LanceDBError: Table '{name}' already exists"))]
+    TableAlreadyExists { name: String },
+    #[snafu(display("LanceDBError: Unable to created lance dataset at {path}: {source}"))]
+    CreateDir {
+        path: String,
+        source: std::io::Error,
+    },
+    #[snafu(display("LanceDBError: {message}"))]
+    Store { message: String },
+    #[snafu(display("LanceDBError: {message}"))]
+    Lance { message: String },
 }
 
 pub type Result<T> = std::result::Result<T, Error>;
 
-impl From<std::io::Error> for Error {
-    fn from(e: std::io::Error) -> Self {
-        Self::IO(e.to_string())
-    }
-}
-
 impl From<lance::Error> for Error {
     fn from(e: lance::Error) -> Self {
-        Self::Lance(e.to_string())
+        Self::Lance {
+            message: e.to_string(),
+        }
     }
 }
 
 impl From<object_store::Error> for Error {
     fn from(e: object_store::Error) -> Self {
-        Self::IO(e.to_string())
+        Self::Store {
+            message: e.to_string(),
+        }
     }
 }
 
 impl From<object_store::path::Error> for Error {
     fn from(e: object_store::path::Error) -> Self {
-        Self::IO(e.to_string())
+        Self::Store {
+            message: e.to_string(),
+        }
     }
 }

rust/vectordb/src/index/vector.rs

@@ -20,6 +20,8 @@ pub trait VectorIndexBuilder {
     fn get_column(&self) -> Option<String>;
     fn get_index_name(&self) -> Option<String>;
     fn build(&self) -> VectorIndexParams;
+
+    fn get_replace(&self) -> bool;
 }
 
 pub struct IvfPQIndexBuilder {
@@ -28,6 +30,7 @@ pub struct IvfPQIndexBuilder {
     metric_type: Option<MetricType>,
     ivf_params: Option<IvfBuildParams>,
     pq_params: Option<PQBuildParams>,
+    replace: bool,
 }
 
 impl IvfPQIndexBuilder {
@@ -38,6 +41,7 @@ impl IvfPQIndexBuilder {
             metric_type: None,
             ivf_params: None,
             pq_params: None,
+            replace: true,
         }
     }
 }
@@ -67,6 +71,11 @@ impl IvfPQIndexBuilder {
         self.pq_params = Some(pq_params);
         self
     }
+
+    pub fn replace(&mut self, replace: bool) -> &mut IvfPQIndexBuilder {
+        self.replace = replace;
+        self
+    }
 }
 
 impl VectorIndexBuilder for IvfPQIndexBuilder {
@@ -84,6 +93,10 @@ impl VectorIndexBuilder for IvfPQIndexBuilder {
 
         VectorIndexParams::with_ivf_pq_params(pq_params.metric_type, ivf_params, pq_params)
     }
+
+    fn get_replace(&self) -> bool {
+        self.replace
+    }
 }
 
 #[cfg(test)]

rust/vectordb/src/query.rs

@@ -27,6 +27,7 @@ pub struct Query {
     pub query_vector: Float32Array,
     pub limit: usize,
     pub filter: Option<String>,
+    pub select: Option<Vec<String>>,
     pub nprobes: usize,
     pub refine_factor: Option<u32>,
     pub metric_type: Option<MetricType>,
@@ -54,6 +55,7 @@ impl Query {
             metric_type: None,
             use_index: false,
             filter: None,
+            select: None,
         }
     }
 
@@ -72,6 +74,9 @@ impl Query {
         )?;
         scanner.nprobs(self.nprobes);
         scanner.use_index(self.use_index);
+        self.select
+            .as_ref()
+            .map(|p| scanner.project(p.as_slice()));
         self.filter.as_ref().map(|f| scanner.filter(f));
         self.refine_factor.map(|rf| scanner.refine(rf));
         self.metric_type.map(|mt| scanner.distance_metric(mt));
@@ -138,10 +143,23 @@ impl Query {
         self
     }
 
+    ///  A filter statement to be applied to this query.
+    ///
+    /// # Arguments
+    ///
+    /// * `filter` -  value A filter in the same format used by a sql WHERE clause.
     pub fn filter(mut self, filter: Option<String>) -> Query {
         self.filter = filter;
         self
     }
+
+    /// Return only the specified columns.
+    ///
+    /// Only select the specified columns. If not specified, all columns will be returned.
+    pub fn select(mut self, columns: Option<Vec<String>>) -> Query {
+        self.select = columns;
+        self
+    }
 }
 
 #[cfg(test)]
@@ -159,7 +177,7 @@ mod tests {
     #[tokio::test]
     async fn test_setters_getters() {
         let mut batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
-        let ds = Dataset::write(&mut batches, ":memory:", None)
+        let ds = Dataset::write(&mut batches, "memory://foo", None)
             .await
             .unwrap();
 
@@ -188,7 +206,7 @@ mod tests {
     #[tokio::test]
     async fn test_execute() {
         let mut batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
-        let ds = Dataset::write(&mut batches, ":memory:", None)
+        let ds = Dataset::write(&mut batches, "memory://foo", None)
             .await
             .unwrap();
 

rust/vectordb/src/table.rs

@@ -18,8 +18,9 @@ use std::sync::Arc;
 use arrow_array::{Float32Array, RecordBatchReader};
 use lance::dataset::{Dataset, WriteMode, WriteParams};
 use lance::index::IndexType;
+use snafu::prelude::*;
 
-use crate::error::{Error, Result};
+use crate::error::{Error, InvalidTableNameSnafu, Result};
 use crate::index::vector::VectorIndexBuilder;
 use crate::query::Query;
 
@@ -27,6 +28,7 @@ pub const VECTOR_COLUMN_NAME: &str = "vector";
 pub const LANCE_FILE_EXTENSION: &str = "lance";
 
 /// A table in a LanceDB database.
+#[derive(Debug)]
 pub struct Table {
     name: String,
     uri: String,
@@ -57,9 +59,16 @@ impl Table {
         let uri = table_uri
             .as_path()
             .to_str()
-            .ok_or(Error::IO(format!("Invalid table name: {}", name)))?;
+            .context(InvalidTableNameSnafu { name })?;
 
-        let dataset = Dataset::open(&uri).await?;
+        let dataset = Dataset::open(&uri).await.map_err(|e| match e {
+            lance::Error::DatasetNotFound { .. } => Error::TableNotFound {
+                name: name.to_string(),
+            },
+            e => Error::Lance {
+                message: e.to_string(),
+            },
+        })?;
         Ok(Table {
             name: name.to_string(),
             uri: uri.to_string(),
@@ -88,14 +97,22 @@ impl Table {
         let uri = table_uri
             .as_path()
             .to_str()
-            .ok_or(Error::IO(format!("Invalid table name: {}", name)))?
+            .context(InvalidTableNameSnafu { name })?
             .to_string();
-        let dataset =
-            Arc::new(Dataset::write(&mut batches, &uri, Some(WriteParams::default())).await?);
+        let dataset = Dataset::write(&mut batches, &uri, Some(WriteParams::default()))
+            .await
+            .map_err(|e| match e {
+                lance::Error::DatasetAlreadyExists { .. } => Error::TableAlreadyExists {
+                    name: name.to_string(),
+                },
+                e => Error::Lance {
+                    message: e.to_string(),
+                },
+            })?;
         Ok(Table {
             name: name.to_string(),
             uri,
-            dataset,
+            dataset: Arc::new(dataset),
         })
     }
 
@@ -113,6 +130,7 @@ impl Table {
                 IndexType::Vector,
                 index_builder.get_index_name(),
                 &index_builder.build(),
+                index_builder.get_replace(),
             )
             .await?;
         self.dataset = Arc::new(dataset);
@@ -178,15 +196,6 @@ mod tests {
     use super::*;
     use crate::index::vector::IvfPQIndexBuilder;
 
-    #[tokio::test]
-    async fn test_new_table_not_exists() {
-        let tmp_dir = tempdir().unwrap();
-        let uri = tmp_dir.path().to_str().unwrap();
-
-        let table = Table::open(&uri, "test").await;
-        assert!(table.is_err());
-    }
-
     #[tokio::test]
     async fn test_open() {
         let tmp_dir = tempdir().unwrap();
@@ -203,6 +212,14 @@ mod tests {
         assert_eq!(table.name, "test")
     }
 
+    #[tokio::test]
+    async fn test_open_not_found() {
+        let tmp_dir = tempdir().unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();
+        let table = Table::open(uri, "test").await;
+        assert!(matches!(table.unwrap_err(), Error::TableNotFound { .. }));
+    }
+
     #[test]
     fn test_object_store_path() {
         use std::path::Path as StdPath;
@@ -211,6 +228,23 @@ mod tests {
         assert_eq!(c.to_str().unwrap(), "s3://bucket/path/to/file/subfile");
     }
 
+    #[tokio::test]
+    async fn test_create_already_exists() {
+        let tmp_dir = tempdir().unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();
+
+        let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
+        let _ = batches.schema().clone();
+        Table::create(&uri, "test", batches).await.unwrap();
+
+        let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
+        let result = Table::create(&uri, "test", batches).await;
+        assert!(matches!(
+            result.unwrap_err(),
+            Error::TableAlreadyExists { .. }
+        ));
+    }
+
     #[tokio::test]
     async fn test_add() {
         let tmp_dir = tempdir().unwrap();