[python] Bump version: 0.2.1 → 0.2.2

This reverts commit ab97e5d632.

Co-authored-by: Chang She <chang@lancedb.com>

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.19
+current_version = 0.2.4
 commit = True
 message = Bump version: {current_version} → {new_version}
 tag = True

.github/workflows/trigger-vectordb-recipes.yml

@@ -0,0 +1,26 @@
+name: Trigger vectordb-recipers workflow
+on:
+  push:
+    branches: [ main ]
+  pull_request:
+    paths:
+      - .github/workflows/trigger-vectordb-recipes.yml
+  workflow_dispatch:
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Trigger vectordb-recipes workflow
+        uses: actions/github-script@v6
+        with:
+          github-token: ${{ secrets.VECTORDB_RECIPES_ACTION_TOKEN }}
+          script: |
+            const result = await github.rest.actions.createWorkflowDispatch({
+                owner: 'lancedb',
+                repo: 'vectordb-recipes',
+                workflow_id: 'examples-test.yml',
+                ref: 'main'
+            });
+            console.log(result);

Cargo.toml

@@ -6,12 +6,11 @@ members = [
 resolver = "2"
 
 [workspace.dependencies]
-lance = "=0.5.9"
-arrow-array = "42.0"
-arrow-data = "42.0"
-arrow-schema = "42.0"
-arrow-ipc = "42.0"
+lance = "=0.6.5"
+arrow-array = "43.0"
+arrow-data = "43.0"
+arrow-schema = "43.0"
+arrow-ipc = "43.0"
 half = { "version" = "=2.2.1", default-features = false }
 object_store = "0.6.1"
 snafu = "0.7.4"
-

docs/mkdocs.yml

@@ -1,5 +1,6 @@
 site_name: LanceDB Docs
 repo_url: https://github.com/lancedb/lancedb
+edit_uri: https://github.com/lancedb/lancedb/tree/main/docs/src
 repo_name: lancedb/lancedb
 docs_dir: src
 
@@ -10,6 +11,16 @@ theme:
   features:
     - content.code.copy
     - content.tabs.link
+    - content.action.edit
+    - toc.follow
+    - toc.integrate
+    - navigation.top
+    - navigation.tabs
+    - navigation.tabs.sticky
+    - navigation.footer
+    - navigation.tracking
+    - navigation.instant
+    - navigation.indexes
   icon:
     repo: fontawesome/brands/github
   custom_dir: overrides
@@ -53,11 +64,43 @@ markdown_extensions:
 - md_in_html
 
 nav:
-- Home: index.md
+- Home:
+  - 🏢 Home: index.md
+  - 💡 Basics: basic.md
+  - 🧬 Embeddings: embedding.md
+  - 🔍 Python full-text search: fts.md
+  - 🔌 Integrations:
+    - integrations/index.md
+    - Pandas and PyArrow: python/arrow.md
+    - DuckDB: python/duckdb.md
+    - LangChain 🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
+    - LangChain JS/TS 🔗: https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb
+    - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
+    - Pydantic: python/pydantic.md
+    - Voxel51: integrations/voxel51.md
+    - PromptTools: integrations/prompttools.md
+  - 🐍 Python examples:
+    - examples/index.md
+    - 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:
+    - Examples: examples/index_js.md
+    - Serverless Website Chatbot: examples/serverless_website_chatbot.md
+    - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
+    - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
+  - 📚 Guides:
+    - Tables: guides/tables.md
+    - Vector Search: search.md
+    - SQL filters: sql.md
+    - Indexing: ann_indexes.md
 - Basics: basic.md
 - Embeddings: embedding.md
 - Python full-text search: fts.md
 - Integrations:
+  - integrations/index.md
   - Pandas and PyArrow: python/arrow.md
   - DuckDB: python/duckdb.md
   - LangChain 🦜️🔗: https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html
@@ -65,23 +108,34 @@ nav:
   - LlamaIndex 🦙: https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html
   - Pydantic: python/pydantic.md
   - Voxel51: integrations/voxel51.md
+  - PromptTools: integrations/prompttools.md
 - Python examples:
+  - examples/index.md
   - 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:
+  - examples/index_js.md
   - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.md
+  - Serverless Chatbot from any website: examples/serverless_website_chatbot.md
   - TransformersJS Embedding Search: examples/transformerjs_embedding_search_nodejs.md
 
-- References:
+- Guides:
+  - Tables: guides/tables.md
   - Vector Search: search.md
   - SQL filters: sql.md
   - Indexing: ann_indexes.md
 - API references:
   - Python API: python/python.md
   - Javascript API: javascript/modules.md
+- LanceDB Cloud↗: https://noteforms.com/forms/lancedb-mailing-list-cloud-kty1o5?notionforms=1&utm_source=notionforms
 
 extra_css:
   - styles/global.css
+
+extra:
+  analytics:
+    provider: google
+    property: G-B7NFM40W74

docs/src/ann_indexes.md

@@ -94,7 +94,7 @@ There are a couple of parameters that can be used to fine-tune the search:
          .to_df()
      ```
      ```
-                                              vector       item       score
+                                              vector       item       _distance
      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
      ```
@@ -109,9 +109,8 @@ There are a couple of parameters that can be used to fine-tune the search:
          .execute()
      ```
 
-The search will return the data requested in addition to the score of each item.
+The search will return the data requested in addition to the distance of each item.
 
-**Note:** The score is the distance between the query vector and the element. A lower number means that the result is more relevant.
 
 ### Filtering (where clause)
 
@@ -139,7 +138,7 @@ You can select the columns returned by the query using a select clause.
      tbl.search(np.random.random((1536))).select(["vector"]).to_df()
      ```
      ```
-     vector      score
+        vector                                             _distance
      0  [0.30928212, 0.022668175, 0.1756372, 0.4911822...  93.971092
      1  [0.2525465, 0.01723831, 0.261568, 0.002007689,...  95.173485
      ...

docs/src/basic.md

@@ -79,6 +79,18 @@ We'll cover the basics of using LanceDB on your local machine in this section.
 
 ??? 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)."
 
+### Creating an empty table
+
+Sometimes you may not have the data to insert into the table at creation time.
+In this case, you can create an empty table and specify the schema.
+
+=== "Python"
+      ```python
+      import pyarrow as pa
+      schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
+      tbl = db.create_table("empty_table", schema=schema)
+      ```
+
 ## How to open an existing table
 
 Once created, you can open a table using the following code:
@@ -122,6 +134,22 @@ After a table has been created, you can always add more data to it using
               {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"
+      ```python
+      tbl.search([100, 100]).limit(2).to_df()
+      ```
+
+      This returns a pandas DataFrame with the results.
+
+=== "Javascript"
+      ```javascript
+      const query = await tbl.search([100, 100]).limit(2).execute();
+      ```
+
 ## How to delete rows from a table
 
 Use the `delete()` method on tables to delete rows from a table. To choose
@@ -151,24 +179,34 @@ To see what expressions are supported, see the [SQL filters](sql.md) section.
 
       Read more: [vectordb.Table.delete](javascript/interfaces/Table.md#delete)
 
-## How to search for (approximate) nearest neighbors
+## How to remove a table
 
-Once you've embedded the query, you can find its nearest neighbors using the following code:
+Use the `drop_table()` method on the database to remove a table.
 
 === "Python"
       ```python
-      tbl.search([100, 100]).limit(2).to_df()
+      db.drop_table("my_table")
       ```
 
-      This returns a pandas DataFrame with the results.
+This permanently removes the table and is not recoverable, unlike deleting rows.
+By default, if the table does not exist an exception is raised. To suppress this,
+you can pass in `ignore_missing=True`.
 
-=== "Javascript"
-      ```javascript
-      const query = await tbl.search([100, 100]).limit(2).execute();
-      ```
 
 ## What's next
 
 This section covered the very basics of the LanceDB API.
 LanceDB supports many additional features when creating indices to speed up search and options for search.
 These are contained in the next section of the documentation.
+
+## Note: Bundling vectorDB apps with webpack
+Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying on Vercel.
+```javascript
+/** @type {import('next').NextConfig} */
+module.exports = ({
+  webpack(config) {
+    config.externals.push({ vectordb: 'vectordb' })
+    return config;
+  }
+})
+```

docs/src/embedding.md

@@ -66,7 +66,7 @@ You can also use an external API like OpenAI to generate embeddings
     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)
+    you can use the [with_embeddings](https://lancedb.github.io/lancedb/python/python/#lancedb.embeddings.with_embeddings)
     function to generate embeddings and add create a combined pyarrow table:
 
 

docs/src/examples/index.md

@@ -0,0 +1,23 @@
+# Examples
+
+Here are some of the examples, projects and applications using LanceDB python library. Some examples are covered in detail in the next sections. You can find more on [VectorDB Recipes](https://github.com/lancedb/vectordb-recipes)
+
+| Example | Interactive Envs | Scripts  |
+|-------- | ---------------- | ------   |
+| | | |
+| [Youtube transcript search bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/youtube_bot/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/main.py)|
+| [Langchain: Code Docs QA bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Code-Documentation-QA-Bot/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/main.py) |
+| [AI Agents: Reducing Hallucination](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/reducing_hallucinations_ai_agents/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/main.py)|
+| [Multimodal CLIP: DiffusionDB](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_clip/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_clip/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_clip/main.py)  |
+| [Multimodal CLIP: Youtube videos](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_video_search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_video_search/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>| [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_video_search/main.py)  |
+| [Movie Recommender](https://github.com/lancedb/vectordb-recipes/tree/main/examples/movie-recommender/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/movie-recommender/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/movie-recommender/main.py)  |
+| [Audio Search](https://github.com/lancedb/vectordb-recipes/tree/main/examples/audio_search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/audio_search/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/audio_search/main.py)  |
+| [Multimodal Image + Text Search](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_search/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/multimodal_search/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> | [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/multimodal_search/main.py)  |
+| [Evaluating Prompts with Prompttools](https://github.com/lancedb/vectordb-recipes/tree/main/examples/prompttools-eval-prompts/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> |  |
+
+## Projects & Applications powered by LanceDB
+
+| Project Name                                        | Description                                                                                                          | Screenshot                                |
+|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
+| [YOLOExplorer](https://github.com/lancedb/yoloexplorer) | Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds                  | ![YOLOExplorer](https://github.com/lancedb/vectordb-recipes/assets/15766192/ae513a29-8f15-4e0b-99a1-ccd8272b6131) |
+| [Website Chatbot (Deployable Vercel Template)](https://github.com/lancedb/lancedb-vercel-chatbot) | Create a chatbot from the sitemap of any website/docs of your choice. Built using vectorDB serverless native javascript package. | ![Chatbot](../assets/vercel-template.gif)    |

docs/src/examples/index_js.md

@@ -0,0 +1,19 @@
+# Examples
+
+Here are some of the examples, projects and applications using vectordb native javascript library.
+Some examples are covered in detail in the next sections. You can find more on [VectorDB Recipes](https://github.com/lancedb/vectordb-recipes)
+
+| Example | Scripts  |
+|-------- | ------   |
+| | |
+| [Youtube transcript search bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/) | [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/youtube_bot/index.js)|
+| [Langchain: Code Docs QA bot](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/) | [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/Code-Documentation-QA-Bot/index.js)|
+| [AI Agents: Reducing Hallucination](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/) | [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/reducing_hallucinations_ai_agents/index.js)|
+| [TransformersJS Embedding example](https://github.com/lancedb/vectordb-recipes/tree/main/examples/js-transformers/) | [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/tree/main/examples/js-transformers/index.js)  |
+
+## Projects & Applications
+
+| Project Name                                        | Description                                                                                                          | Screenshot                                |
+|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
+| [YOLOExplorer](https://github.com/lancedb/yoloexplorer) | Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds                  | ![YOLOExplorer](https://github.com/lancedb/vectordb-recipes/assets/15766192/ae513a29-8f15-4e0b-99a1-ccd8272b6131) |
+| [Website Chatbot (Deployable Vercel Template)](https://github.com/lancedb/lancedb-vercel-chatbot) | Create a chatbot from the sitemap of any website/docs of your choice. Built using vectorDB serverless native javascript package. | ![Chatbot](../assets/vercel-template.gif)    |

docs/src/examples/serverless_website_chatbot.md

@@ -0,0 +1,61 @@
+# LanceDB Chatbot - Vercel Next.js Template
+Use an AI chatbot with website context retrieved from a vector store like LanceDB. LanceDB is lightweight and can be embedded directly into Next.js, with data stored on-prem.
+
+## One click deploy on Vercel
+[![Deploy with Vercel](https://vercel.com/button)](https://vercel.com/new/clone?repository-url=https%3A%2F%2Fgithub.com%2Flancedb%2Flancedb-vercel-chatbot&env=OPENAI_API_KEY&envDescription=OpenAI%20API%20Key%20for%20chat%20completion.&project-name=lancedb-vercel-chatbot&repository-name=lancedb-vercel-chatbot&demo-title=LanceDB%20Chatbot%20Demo&demo-description=Demo%20website%20chatbot%20with%20LanceDB.&demo-url=https%3A%2F%2Flancedb.vercel.app&demo-image=https%3A%2F%2Fi.imgur.com%2FazVJtvr.png)
+
+![Demo website landing page](../assets/vercel-template.gif)
+
+## Development
+
+First, rename `.env.example` to `.env.local`, and fill out `OPENAI_API_KEY` with your OpenAI API key. You can get one [here](https://openai.com/blog/openai-api).
+
+Run the development server:
+
+```bash
+npm run dev
+# or
+yarn dev
+# or
+pnpm dev
+```
+
+Open [http://localhost:3000](http://localhost:3000) with your browser to see the result.
+
+This project uses [`next/font`](https://nextjs.org/docs/basic-features/font-optimization) to automatically optimize and load Inter, a custom Google Font.
+
+## Learn More
+
+To learn more about LanceDB or Next.js, take a look at the following resources:
+
+- [LanceDB Documentation](https://lancedb.github.io/lancedb/) - learn about LanceDB, the developer-friendly serverless vector database.
+- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.
+- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.
+
+## LanceDB on Next.js and Vercel
+
+FYI: these configurations have been pre-implemented in this template.
+
+Since LanceDB contains a prebuilt Node binary, you must configure `next.config.js` to exclude it from webpack. This is required for both using Next.js and deploying on Vercel.
+```js
+/** @type {import('next').NextConfig} */
+module.exports = ({
+  webpack(config) {
+    config.externals.push({ vectordb: 'vectordb' })
+    return config;
+  }
+})
+```
+
+To deploy on Vercel, we need to make sure that the NodeJS runtime static file analysis for Vercel can find the binary, since LanceDB uses dynamic imports by default. We can do this by modifying `package.json` in the `scripts` section.
+```json
+{
+  ...
+  "scripts": {
+    ...
+    "vercel-build": "sed -i 's/nativeLib = require(`@lancedb\\/vectordb-\\${currentTarget()}`);/nativeLib = require(`@lancedb\\/vectordb-linux-x64-gnu`);/' node_modules/vectordb/native.js && next build",
+    ...
+  },
+  ...
+}
+```

docs/src/examples/transformerjs_embedding_search_nodejs.md

@@ -99,7 +99,7 @@ Output of `results`:
     id: 5,
     text: 'Banana',
     type: 'fruit',
-    score: 0.4919965863227844
+    _distance: 0.4919965863227844
   },
   {
     vector: Float32Array(384) [
@@ -111,7 +111,7 @@ Output of `results`:
     id: 1,
     text: 'Cherry',
     type: 'fruit',
-    score: 0.5540297031402588
+    _distance: 0.5540297031402588
   }
 ]
 ```

docs/src/examples/youtube_transcript_bot.md

@@ -7,7 +7,7 @@
 
 <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/youtube_bot/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab">
 
-Scripts - [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/youtube_bot/main.py)  [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/youtube_bot/index.js)
+Scripts - [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](https://github.com/lancedb/vectordb-recipesexamples/youtube_bot/main.py)  [![JavaScript](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](https://github.com/lancedb/vectordb-recipes/examples/youtube_bot/index.js)
 
 
 This example is in a [notebook](https://github.com/lancedb/lancedb/blob/main/docs/src/notebooks/youtube_transcript_search.ipynb)

docs/src/guides/tables.md

@@ -0,0 +1,359 @@
+ <a href="https://colab.research.google.com/github/lancedb/lancedb/blob/main/docs/src/notebooks/tables_guide.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a><br/>
+A Table is a collection of Records in a LanceDB Database. You can follow along on colab!
+
+## Creating a LanceDB Table
+
+=== "Python"
+    ### LanceDB Connection
+
+    ```python
+    import lancedb
+    db = lancedb.connect("./.lancedb")
+    ```
+
+    LanceDB allows ingesting data from various sources - `dict`, `list[dict]`, `pd.DataFrame`, `pa.Table` or a `Iterator[pa.RecordBatch]`. Let's take a look at some of the these.
+
+    ### From list of tuples or dictionaries
+
+    ```python
+    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)
+
+    db["my_table"].head()
+    ```
+
+    !!! info "Note"
+        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.
+
+        ```python
+        db.create_table("name", data, mode="overwrite")
+        ```
+
+
+    ### From pandas DataFrame
+
+    ```python
+    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)
+
+    db["table2"].head() 
+    ```
+    !!! info "Note"
+        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.
+  
+    ```python
+    custom_schema = pa.schema([
+    pa.field("vector", pa.list_(pa.float32(), 2)),
+    pa.field("lat", pa.float32()),
+    pa.field("long", pa.float32())
+    ])
+
+    table = db.create_table("table3", data, schema=custom_schema)
+    ```
+
+    ### From PyArrow Tables
+    You can also create LanceDB tables directly from pyarrow tables
+    
+    ```python
+    table = pa.Table.from_arrays(
+            [
+                pa.array([[3.1, 4.1], [5.9, 26.5]],
+                        pa.list_(pa.float32(), 2)),
+                pa.array(["foo", "bar"]),
+                pa.array([10.0, 20.0]),
+            ],
+            ["vector", "item", "price"],
+        )
+
+    db = lancedb.connect("db")
+
+    tbl = db.create_table("test1", table)
+    ```
+
+    ### From Pydantic Models
+    LanceDB supports to create Apache Arrow Schema from a Pydantic BaseModel via pydantic_to_schema() method.
+
+    ```python
+    from lancedb.pydantic import vector, LanceModel
+
+    class Content(LanceModel):
+        movie_id: int
+        vector: vector(128)
+        genres: str
+        title: str
+        imdb_id: int
+            
+        @property
+        def imdb_url(self) -> str:
+            return f"https://www.imdb.com/title/tt{self.imdb_id}"
+
+    import pyarrow as pa
+    db = lancedb.connect("~/.lancedb")
+    table_name = "movielens_small"
+    table = db.create_table(table_name, schema=Content.to_arrow_schema())
+    ```
+
+    ### Using Iterators / Writing Large Datasets
+
+    It is recommended to use itertators to add large datasets in batches when creating your table in one go. This does not create multiple versions of your dataset unlike manually adding batches using `table.add()`
+
+    LanceDB additionally supports pyarrow's `RecordBatch` Iterators or other generators producing supported data types.
+
+    Here's an example using using `RecordBatch` iterator for creating tables.
+    
+    ```python
+    import pyarrow as pa
+
+    def make_batches():
+        for i in range(5):
+            yield pa.RecordBatch.from_arrays(
+                [
+                    pa.array([[3.1, 4.1], [5.9, 26.5]],
+                            pa.list_(pa.float32(), 2)),
+                    pa.array(["foo", "bar"]),
+                    pa.array([10.0, 20.0]),
+                ],
+                ["vector", "item", "price"],
+            )
+
+    schema = pa.schema([
+        pa.field("vector", pa.list_(pa.float32(), 2)),
+        pa.field("item", pa.utf8()),
+        pa.field("price", pa.float32()),
+    ])
+
+    db.create_table("table4", make_batches(), schema=schema)
+    ```
+
+    You can also use iterators of other types like Pandas dataframe or Pylists directly in the above example.
+
+    ## Creating Empty Table
+    You can also create empty tables in python. Initialize it with schema and later ingest data into it.
+    
+    ```python
+    import lancedb
+    import pyarrow as pa
+  
+    schema = pa.schema(
+      [
+          pa.field("vector", pa.list_(pa.float32(), 2)),
+          pa.field("item", pa.string()),
+          pa.field("price", pa.float32()),
+      ])
+    tbl = db.create_table("table5", schema=schema)
+    data = [
+        {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+        {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+    ]
+    tbl.add(data=data)
+    ```
+
+    You can also use Pydantic to specify the schema
+
+    ```python
+    import lancedb
+    from lancedb.pydantic import LanceModel, vector
+
+    class Model(LanceModel):
+          vector: vector(2)
+    
+    tbl = db.create_table("table5", schema=Model.to_arrow_schema())
+    ```
+
+=== "Javascript/Typescript"
+
+    ### VectorDB Connection
+
+    ```javascript
+    const lancedb = require("vectordb");
+
+    const uri = "data/sample-lancedb";
+    const db = await lancedb.connect(uri);
+    ```
+
+    ### Creating a Table
+
+    You can create a LanceDB table in javascript using an array of records.
+
+    ```javascript
+    data
+    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}])
+    ```
+
+    !!! info "Note"
+    If the table already exists, LanceDB will raise an error by default. If you want to overwrite the table, you need to specify the `WriteMode` in the createTable function.
+
+    ```javascript
+    const table = await con.createTable(tableName, data, { writeMode: WriteMode.Overwrite })
+    ```
+
+## Open existing tables
+
+If you forget the name of your table, you can always get a listing of all table names:
+
+
+=== "Python"
+    ### Get a list of existing Tables
+
+    ```python
+    print(db.table_names())
+    ```
+=== "Javascript/Typescript"
+
+    ```javascript
+    console.log(await db.tableNames());
+    ```
+
+Then, you can open any existing tables
+
+=== "Python"
+
+    ```python
+    tbl = db.open_table("my_table")
+    ```
+=== "Javascript/Typescript"
+
+    ```javascript
+    const tbl = await db.openTable("my_table");
+    ```
+
+## Adding to a Table
+After a table has been created, you can always add more data to it using
+
+=== "Python"
+    You can add any of the valid data structures accepted by LanceDB table, i.e, `dict`, `list[dict]`, `pd.DataFrame`, or a `Iterator[pa.RecordBatch]`. Here are some examples.
+
+    ### Adding Pandas DataFrame
+
+    ```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)
+    ```
+
+    You can also add a large dataset batch in one go using Iterator of any supported data types.
+
+    ### Adding to table using Iterator
+    
+    ```python
+    import pandas as pd
+
+    def make_batches():
+        for i in range(5):
+            yield pd.DataFrame(
+                {
+                    "vector": [[3.1, 4.1], [1, 1]],
+                    "item": ["foo", "bar"],
+                    "price": [10.0, 20.0],
+                })
+    
+    tbl.add(make_batches())
+    ```
+  
+    The other arguments accepted:
+
+    | Name | Type | Description | Default |
+    |---|---|---|---|
+    | data | DATA | The data to insert into the table. | required |
+    | mode | str | The mode to use when writing the data. Valid values are "append" and "overwrite". | append |
+    | on_bad_vectors | str | What to do if any of the vectors are not the same size or contains NaNs. One of "error", "drop", "fill". | drop |
+    | fill value | float | The value to use when filling vectors: Only used if on_bad_vectors="fill". | 0.0 |
+
+  
+=== "Javascript/Typescript"
+
+    ```javascript
+    await tbl.add([{vector: [1.3, 1.4], item: "fizz", price: 100.0},
+        {vector: [9.5, 56.2], item: "buzz", price: 200.0}])
+    ```
+
+## Deleting from a Table
+
+Use the `delete()` method on tables to delete rows from a table. To choose which rows to delete, provide a filter that matches on the metadata columns. This can delete any number of rows that match the filter.
+
+=== "Python"
+
+    ```python
+    tbl.delete('item = "fizz"')
+    ```
+
+    ### Deleting row with specific column value
+
+    ```python
+    import lancedb
+    import pandas as pd
+
+    data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
+    db = lancedb.connect("./.lancedb")
+    table = db.create_table("my_table", data)
+    table.to_pandas()
+    #   x      vector
+    # 0  1  [1.0, 2.0]
+    # 1  2  [3.0, 4.0]
+    # 2  3  [5.0, 6.0]
+
+    table.delete("x = 2")
+    table.to_pandas()
+    #   x      vector
+    # 0  1  [1.0, 2.0]
+    # 1  3  [5.0, 6.0]
+    ```  
+
+    ### Delete from a list of values
+
+    ```python
+    to_remove = [1, 5]
+    to_remove = ", ".join(str(v) for v in to_remove)
+
+    table.delete(f"x IN ({to_remove})")
+    table.to_pandas()
+    #   x      vector
+    # 0  3  [5.0, 6.0]
+    ```
+  
+=== "Javascript/Typescript"
+
+    ```javascript
+    await tbl.delete('item = "fizz"')
+    ```
+
+    ### Deleting row with specific column value
+
+    ```javascript
+    const con = await lancedb.connect("./.lancedb")
+    const data = [
+      {id: 1, vector: [1, 2]},
+      {id: 2, vector: [3, 4]},
+      {id: 3, vector: [5, 6]},
+    ];
+    const tbl = await con.createTable("my_table", data)
+    await tbl.delete("id = 2")
+    await tbl.countRows() // Returns 2
+    ```
+
+    ### Delete from a list of values
+
+    ```javascript
+    const to_remove = [1, 5];
+    await tbl.delete(`id IN (${to_remove.join(",")})`)
+    await tbl.countRows() // Returns 1
+    ```
+
+## What's Next?
+
+Learn how to Query your tables and create indices

docs/src/index.md

@@ -1,20 +1,23 @@
-# Welcome to LanceDB's Documentation
+# LanceDB
 
 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:
+![Illustration](/lancedb/assets/ecosystem-illustration.png)
 
-* Production-scale vector search with no servers to manage.
+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.
+* Support for production-scale vector similarity search, full-text search and SQL, with no servers to manage.
 
 * 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/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.
+* Persisted on HDD, allowing scalability without breaking the bank.
+
+* Ingest your favorite data formats directly, like pandas DataFrames, Pydantic objects and more.
+
 
 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.
 
@@ -69,4 +72,4 @@ LanceDB's core is written in Rust 🦀 and is built using <a href="https://githu
 * [`Full text search`](fts.md) - [EXPERIMENTAL] full-text search API
 * [`Ecosystem Integrations`](python/integration.md) - integrating LanceDB with python data tooling ecosystem.
 * [`Python API Reference`](python/python.md) - detailed documentation for the LanceDB Python SDK.
-* [`Node API Reference`](javascript/modules.md) - detailed documentation for the LanceDB Python SDK.
+* [`Node API Reference`](javascript/modules.md) - detailed documentation for the LanceDB Node SDK.

docs/src/integrations/index.md

@@ -0,0 +1,21 @@
+# Integrations
+
+## Data Formats
+
+LanceDB supports ingesting from your favorite data tools.
+
+![Illustration](/lancedb/assets/ecosystem-illustration.png)
+
+
+## Tools
+
+LanceDB is integrated with most of the popular AI tools, with more coming soon.
+Get started using these examples and quick links.
+
+| Integrations | |
+|---|---:|
+| <h3> LlamaIndex </h3>LlamaIndex is a simple, flexible data framework for connecting custom data sources to large language models. Llama index integrates with LanceDB as the serverless VectorDB. <h3>[Lean More](https://gpt-index.readthedocs.io/en/latest/examples/vector_stores/LanceDBIndexDemo.html) </h3> |<img src="../assets/llama-index.jpg" alt="image" width="150" height="auto">|
+| <h3>Langchain</h3>Langchain allows building applications with LLMs through composability <h3>[Lean More](https://python.langchain.com/en/latest/modules/indexes/vectorstores/examples/lancedb.html) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
+| <h3>Langchain TS</h3> Javascript bindings for Langchain. It integrates with LanceDB's serverless vectordb allowing you to build powerful AI applications through composibility using only serverless functions. <h3>[Learn More]( https://js.langchain.com/docs/modules/data_connection/vectorstores/integrations/lancedb) | <img src="../assets/langchain.png" alt="image" width="150" height="auto">|
+| <h3>Voxel51</h3>  It is an open source toolkit that enables you to build better computer vision workflows by improving the quality of your datasets and delivering insights about your models.<h3>[Learn More](./voxel51.md) | <img src="../assets/voxel.gif" alt="image" width="150" height="auto">|
+| <h3>PromptTools</h3>  Offers a set of free, open-source tools for testing and experimenting with models, prompts, and configurations. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. You can use it to experiment with different configurations of LanceDB, and test how LanceDB integrates with the LLM of your choice.<h3>[Learn More](./prompttools.md) | <img src="../assets/prompttools.jpeg" alt="image" width="150" height="auto">|

docs/src/integrations/prompttools.md

@@ -0,0 +1,7 @@
+
+[PromptTools](https://github.com/hegelai/prompttools) offers a set of free, open-source tools for testing and experimenting with models, prompts, and configurations. The core idea is to enable developers to evaluate prompts using familiar interfaces like code and notebooks. You can use it to experiment with different configurations of LanceDB, and test how LanceDB integrates with the LLM of your choice.
+
+[Evaluating Prompts with PromptTools](./examples/prompttools-eval-prompts/) | <a href="https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/prompttools-eval-prompts/main.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
+
+![Alt text](https://prompttools.readthedocs.io/en/latest/_images/demo.gif "a title")
+

docs/src/notebooks/tables_guide.ipynb

@@ -0,0 +1,831 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d24eb4c6-e246-44ca-ba7c-6eae7923bd4c",
+   "metadata": {},
+   "source": [
+    "## LanceDB Tables\n",
+    "A Table is a collection of Records in a LanceDB Database.\n",
+    "\n",
+    "![illustration](../assets/ecosystem-illustration.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "c1b4e34b-a49c-471d-a343-a5940bb5138a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install lancedb -qq"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4e5a8d07-d9a1-48c1-913a-8e0629289579",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lancedb\n",
+    "db = lancedb.connect(\"./.lancedb\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "66fb93d5-3551-406b-99b2-488442d61d06",
+   "metadata": {},
+   "source": [
+    "LanceDB allows ingesting data from various sources - `dict`, `list[dict]`, `pd.DataFrame`, `pa.Table` or a `Iterator[pa.RecordBatch]`. Let's take a look at some of the these.\n",
+    "\n",
+    " ### From list of tuples or dictionaries"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5df12f66-8d99-43ad-8d0b-22189ec0a6b9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyarrow.Table\n",
+       "vector: fixed_size_list<item: float>[2]\n",
+       "  child 0, item: float\n",
+       "lat: double\n",
+       "long: double\n",
+       "----\n",
+       "vector: [[[1.1,1.2],[0.2,1.8]]]\n",
+       "lat: [[45.5,40.1]]\n",
+       "long: [[-122.7,-74.1]]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import lancedb\n",
+    "\n",
+    "db = lancedb.connect(\"./.lancedb\")\n",
+    "\n",
+    "data = [{\"vector\": [1.1, 1.2], \"lat\": 45.5, \"long\": -122.7},\n",
+    "        {\"vector\": [0.2, 1.8], \"lat\": 40.1, \"long\": -74.1}]\n",
+    "\n",
+    "db.create_table(\"my_table\", data)\n",
+    "\n",
+    "db[\"my_table\"].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "10ce802f-1a10-49ee-8ee3-a9bfb302d86c",
+   "metadata": {},
+   "source": [
+    "## From pandas DataFrame\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f4d87ae9-0ccb-48eb-b31d-bb8f2370e47e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "pyarrow.Table\n",
+       "vector: fixed_size_list<item: float>[2]\n",
+       "  child 0, item: float\n",
+       "lat: double\n",
+       "long: double\n",
+       "----\n",
+       "vector: [[[1.1,1.2],[0.2,1.8]]]\n",
+       "lat: [[45.5,40.1]]\n",
+       "long: [[-122.7,-74.1]]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "data = pd.DataFrame({\n",
+    "    \"vector\": [[1.1, 1.2], [0.2, 1.8]],\n",
+    "    \"lat\": [45.5, 40.1],\n",
+    "    \"long\": [-122.7, -74.1]\n",
+    "})\n",
+    "\n",
+    "db.create_table(\"table2\", data)\n",
+    "\n",
+    "db[\"table2\"].head() "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4be81469-5b57-4f78-9c72-3938c0378d9d",
+   "metadata": {},
+   "source": [
+    "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.\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "25f34bcf-fca0-4431-8601-eac95d1bd347",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vector: fixed_size_list<item: float>[2]\n",
+       "  child 0, item: float\n",
+       "lat: float\n",
+       "long: float"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pyarrow as pa\n",
+    "\n",
+    "custom_schema = pa.schema([\n",
+    "pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n",
+    "pa.field(\"lat\", pa.float32()),\n",
+    "pa.field(\"long\", pa.float32())\n",
+    "])\n",
+    "\n",
+    "table = db.create_table(\"table3\", data, schema=custom_schema, mode=\"overwrite\")\n",
+    "table.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4df51925-7ca2-4005-9c72-38b3d26240c6",
+   "metadata": {},
+   "source": [
+    "### From PyArrow Tables\n",
+    "\n",
+    "You can also create LanceDB tables directly from pyarrow tables"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "90a880f6-be43-4c9d-ba65-0b05197c0f6f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vector: fixed_size_list<item: float>[2]\n",
+       "  child 0, item: float\n",
+       "item: string\n",
+       "price: double"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "table = pa.Table.from_arrays(\n",
+    "        [\n",
+    "            pa.array([[3.1, 4.1], [5.9, 26.5]],\n",
+    "                    pa.list_(pa.float32(), 2)),\n",
+    "            pa.array([\"foo\", \"bar\"]),\n",
+    "            pa.array([10.0, 20.0]),\n",
+    "        ],\n",
+    "        [\"vector\", \"item\", \"price\"],\n",
+    "    )\n",
+    "\n",
+    "db = lancedb.connect(\"db\")\n",
+    "\n",
+    "tbl = db.create_table(\"test1\", table, mode=\"overwrite\")\n",
+    "tbl.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0f36c51c-d902-449d-8292-700e53990c32",
+   "metadata": {},
+   "source": [
+    "### From Pydantic Models\n",
+    "\n",
+    "LanceDB supports to create Apache Arrow Schema from a Pydantic BaseModel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "d81121d7-e4b7-447c-a48c-974b6ebb464a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "movie_id: int64 not null\n",
+       "vector: fixed_size_list<item: float>[128] not null\n",
+       "  child 0, item: float\n",
+       "genres: string not null\n",
+       "title: string not null\n",
+       "imdb_id: int64 not null"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from lancedb.pydantic import vector, LanceModel\n",
+    "\n",
+    "class Content(LanceModel):\n",
+    "    movie_id: int\n",
+    "    vector: vector(128)\n",
+    "    genres: str\n",
+    "    title: str\n",
+    "    imdb_id: int\n",
+    "        \n",
+    "    @property\n",
+    "    def imdb_url(self) -> str:\n",
+    "        return f\"https://www.imdb.com/title/tt{self.imdb_id}\"\n",
+    "\n",
+    "import pyarrow as pa\n",
+    "db = lancedb.connect(\"~/.lancedb\")\n",
+    "table_name = \"movielens_small\"\n",
+    "table = db.create_table(table_name, schema=Content)\n",
+    "table.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "860e1f77-e860-46a9-98b7-b2979092ccd6",
+   "metadata": {},
+   "source": [
+    "### Using Iterators / Writing Large Datasets\n",
+    "\n",
+    "It is recommended to use itertators to add large datasets in batches when creating your table in one go. This does not create multiple versions of your dataset unlike manually adding batches using `table.add()`\n",
+    "\n",
+    "LanceDB additionally supports pyarrow's `RecordBatch` Iterators or other generators producing supported data types.\n",
+    "\n",
+    "## Here's an example using using `RecordBatch` iterator for creating tables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "bc247142-4e3c-41a2-b94c-8e00d2c2a508",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LanceTable(table4)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pyarrow as pa\n",
+    "\n",
+    "def make_batches():\n",
+    "    for i in range(5):\n",
+    "        yield pa.RecordBatch.from_arrays(\n",
+    "            [\n",
+    "                pa.array([[3.1, 4.1], [5.9, 26.5]],\n",
+    "                        pa.list_(pa.float32(), 2)),\n",
+    "                pa.array([\"foo\", \"bar\"]),\n",
+    "                pa.array([10.0, 20.0]),\n",
+    "            ],\n",
+    "            [\"vector\", \"item\", \"price\"],\n",
+    "        )\n",
+    "\n",
+    "schema = pa.schema([\n",
+    "    pa.field(\"vector\", pa.list_(pa.float32(), 2)),\n",
+    "    pa.field(\"item\", pa.utf8()),\n",
+    "    pa.field(\"price\", pa.float32()),\n",
+    "])\n",
+    "\n",
+    "db.create_table(\"table4\", make_batches(), schema=schema)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "94f7dd2b-bae4-4bdf-8534-201437c31027",
+   "metadata": {},
+   "source": [
+    "### Using pandas `DataFrame` Iterator and Pydantic Schema\n",
+    "\n",
+    "You can set the schema via pyarrow schema object or using Pydantic object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "25ad3523-e0c9-4c28-b3df-38189c4e0e5f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "vector: fixed_size_list<item: float>[2] not null\n",
+       "  child 0, item: float\n",
+       "item: string not null\n",
+       "price: double not null"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pyarrow as pa\n",
+    "import pandas as pd\n",
+    "\n",
+    "class PydanticSchema(LanceModel):\n",
+    "    vector: vector(2)\n",
+    "    item: str\n",
+    "    price: float\n",
+    "\n",
+    "def make_batches():\n",
+    "    for i in range(5):\n",
+    "        yield pd.DataFrame(\n",
+    "                    {\n",
+    "                        \"vector\": [[3.1, 4.1], [1, 1]],\n",
+    "                        \"item\": [\"foo\", \"bar\"],\n",
+    "                        \"price\": [10.0, 20.0],\n",
+    "                    })\n",
+    "\n",
+    "tbl = db.create_table(\"table5\", make_batches(), schema=PydanticSchema)\n",
+    "tbl.schema"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4aa955e9-fcd0-4c99-b644-f218f3bb3f1a",
+   "metadata": {},
+   "source": [
+    "## Creating Empty Table\n",
+    "\n",
+    "You can create an empty table by just passing the schema and later add to it using `table.add()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "2814173a-eacc-4dd8-a64d-6312b44582cc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import lancedb\n",
+    "from lancedb.pydantic import LanceModel, vector\n",
+    "\n",
+    "class Model(LanceModel):\n",
+    "      vector: vector(2)\n",
+    "\n",
+    "tbl = db.create_table(\"table6\", schema=Model.to_arrow_schema())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d1b0f5c-a1d9-459f-8614-8376b6f577e1",
+   "metadata": {},
+   "source": [
+    "## Open Existing Tables\n",
+    "\n",
+    "If you forget the name of your table, you can always get a listing of all table names:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "df9e13c0-41f6-437f-9dfa-2fd71d3d9c45",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['table6', 'table4', 'table5', 'movielens_small']"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "db.table_names()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "9343f5ad-6024-42ee-ac2f-6c1471df8679",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>vector</th>\n",
+       "      <th>item</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>[3.1, 4.1]</td>\n",
+       "      <td>foo</td>\n",
+       "      <td>10.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>[5.9, 26.5]</td>\n",
+       "      <td>bar</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        vector item  price\n",
+       "0   [3.1, 4.1]  foo   10.0\n",
+       "1  [5.9, 26.5]  bar   20.0\n",
+       "2   [3.1, 4.1]  foo   10.0\n",
+       "3  [5.9, 26.5]  bar   20.0\n",
+       "4   [3.1, 4.1]  foo   10.0\n",
+       "5  [5.9, 26.5]  bar   20.0\n",
+       "6   [3.1, 4.1]  foo   10.0\n",
+       "7  [5.9, 26.5]  bar   20.0\n",
+       "8   [3.1, 4.1]  foo   10.0\n",
+       "9  [5.9, 26.5]  bar   20.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tbl = db.open_table(\"table4\")\n",
+    "tbl.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5019246f-12e3-4f78-88a8-9f4939802c76",
+   "metadata": {},
+   "source": [
+    "## Adding to table\n",
+    "After a table has been created, you can always add more data to it using\n",
+    "\n",
+    "You can add any of the valid data structures accepted by LanceDB table, i.e, `dict`, `list[dict]`, `pd.DataFrame`, or a `Iterator[pa.RecordBatch]`. Here are some examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "8a56250f-73a1-4c26-a6ad-5c7a0ce3a9ab",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame([{\"vector\": [1.3, 1.4], \"item\": \"fizz\", \"price\": 100.0},\n",
+    "              {\"vector\": [9.5, 56.2], \"item\": \"buzz\", \"price\": 200.0}])\n",
+    "tbl.add(df)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9985f6ee-67e1-45a9-b233-94e3d121ecbf",
+   "metadata": {},
+   "source": [
+    "You can also add a large dataset batch in one go using Iterator of supported data types\n",
+    "\n",
+    "### Adding via Iterator\n",
+    "\n",
+    "here, we'll use pandas DataFrame Iterator"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "030c7057-b98e-4e2f-be14-b8c1f927f83c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "def make_batches():\n",
+    "    for i in range(5):\n",
+    "        yield pd.DataFrame(\n",
+    "                {\n",
+    "                    \"vector\": [[3.1, 4.1], [1, 1]],\n",
+    "                    \"item\": [\"foo\", \"bar\"],\n",
+    "                    \"price\": [10.0, 20.0],\n",
+    "                })\n",
+    "tbl.add(make_batches())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8316d5d-0a23-4675-b0ee-178711db873a",
+   "metadata": {},
+   "source": [
+    "## Deleting from a Table\n",
+    "\n",
+    "Use the `delete()` method on tables to delete rows from a table. To choose which rows to delete, provide a filter that matches on the metadata columns. This can delete any number of rows that match the filter, like:\n",
+    "\n",
+    "\n",
+    "```python\n",
+    "tbl.delete('item = \"fizz\"')\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "e7a17de2-08d2-41b7-bd05-f63d1045ab1f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "32\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "print(len(tbl))\n",
+    "      \n",
+    "tbl.delete(\"price = 20.0\")\n",
+    "      \n",
+    "len(tbl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "74ac180b-5432-4c14-b1a8-22c35ac83af8",
+   "metadata": {},
+   "source": [
+    "### Delete from a list of values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "fe3310bd-08f4-4a22-a63b-b3127d22f9f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         vector  item  price\n",
+      "0    [3.1, 4.1]   foo   10.0\n",
+      "1    [3.1, 4.1]   foo   10.0\n",
+      "2    [3.1, 4.1]   foo   10.0\n",
+      "3    [3.1, 4.1]   foo   10.0\n",
+      "4    [3.1, 4.1]   foo   10.0\n",
+      "5    [1.3, 1.4]  fizz  100.0\n",
+      "6   [9.5, 56.2]  buzz  200.0\n",
+      "7    [3.1, 4.1]   foo   10.0\n",
+      "8    [3.1, 4.1]   foo   10.0\n",
+      "9    [3.1, 4.1]   foo   10.0\n",
+      "10   [3.1, 4.1]   foo   10.0\n",
+      "11   [3.1, 4.1]   foo   10.0\n",
+      "12   [3.1, 4.1]   foo   10.0\n",
+      "13   [3.1, 4.1]   foo   10.0\n",
+      "14   [3.1, 4.1]   foo   10.0\n",
+      "15   [3.1, 4.1]   foo   10.0\n",
+      "16   [3.1, 4.1]   foo   10.0\n"
+     ]
+    },
+    {
+     "ename": "OSError",
+     "evalue": "LanceError(IO): Error during planning: column foo does not exist",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[30], line 4\u001b[0m\n\u001b[1;32m      2\u001b[0m to_remove \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mstr\u001b[39m(v) \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m to_remove)\n\u001b[1;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(tbl\u001b[38;5;241m.\u001b[39mto_pandas())\n\u001b[0;32m----> 4\u001b[0m \u001b[43mtbl\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mitem IN (\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mto_remove\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m)\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      5\u001b[0m tbl\u001b[38;5;241m.\u001b[39mto_pandas()\n",
+      "File \u001b[0;32m~/Documents/lancedb/lancedb/python/lancedb/table.py:610\u001b[0m, in \u001b[0;36mLanceTable.delete\u001b[0;34m(self, where)\u001b[0m\n\u001b[1;32m    609\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdelete\u001b[39m(\u001b[38;5;28mself\u001b[39m, where: \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m--> 610\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_dataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mwhere\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Documents/lancedb/lancedb/env/lib/python3.11/site-packages/lance/dataset.py:489\u001b[0m, in \u001b[0;36mLanceDataset.delete\u001b[0;34m(self, predicate)\u001b[0m\n\u001b[1;32m    487\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(predicate, pa\u001b[38;5;241m.\u001b[39mcompute\u001b[38;5;241m.\u001b[39mExpression):\n\u001b[1;32m    488\u001b[0m     predicate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mstr\u001b[39m(predicate)\n\u001b[0;32m--> 489\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ds\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdelete\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpredicate\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mOSError\u001b[0m: LanceError(IO): Error during planning: column foo does not exist"
+     ]
+    }
+   ],
+   "source": [
+    "to_remove = [\"foo\", \"buzz\"]\n",
+    "to_remove = \", \".join(str(v) for v in to_remove)\n",
+    "print(tbl.to_pandas())\n",
+    "tbl.delete(f\"item IN ({to_remove})\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "87d5bc21-847f-4c81-b56e-f6dbe5d05aac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.DataFrame(\n",
+    "                    {\n",
+    "                        \"vector\": [[3.1, 4.1], [1, 1]],\n",
+    "                        \"item\": [\"foo\", \"bar\"],\n",
+    "                        \"price\": [10.0, 20.0],\n",
+    "                    })\n",
+    "\n",
+    "tbl = db.create_table(\"table7\", data=df, mode=\"overwrite\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "9cba4519-eb3a-4941-ab7e-873d762e750f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "to_remove = [10.0, 20.0]\n",
+    "to_remove = \", \".join(str(v) for v in to_remove)\n",
+    "\n",
+    "tbl.delete(f\"price IN ({to_remove})\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "5bdc9801-d5ed-4871-92d0-88b27108e788",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>vector</th>\n",
+       "      <th>item</th>\n",
+       "      <th>price</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "Empty DataFrame\n",
+       "Columns: [vector, item, price]\n",
+       "Index: []"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tbl.to_pandas()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "752d33d4-ce1c-48e5-90d2-c85f0982182d",
+   "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": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

docs/src/python/arrow.md

@@ -79,7 +79,7 @@ print(df)
 ```
 
 ```
-    vector     item  price        score
+    vector     item  price    _distance
 0  [5.9, 26.5]  bar   20.0  14257.05957
 ```
 

docs/src/python/pydantic.md

@@ -3,7 +3,6 @@
 [Pydantic](https://docs.pydantic.dev/latest/) is a data validation library in Python.
 LanceDB integrates with Pydantic for schema inference, data ingestion, and query result casting.
 
-
 ## Schema
 
 LanceDB supports to create Apache Arrow Schema from a

docs/src/python/python.md

@@ -56,4 +56,4 @@ pip install lancedb
 
 ::: lancedb.pydantic.vector
 
-
+::: lancedb.pydantic.LanceModel

docs/src/styles/global.css

@@ -4,3 +4,12 @@
     --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;
 }
+
+.md-nav__item, .md-tabs__item {
+    font-size: large;
+}
+
+/* Maximum space for text block */
+.md-grid {
+    max-width: 90%;
+  }

docs/test/md_testing.js

@@ -2,18 +2,17 @@ const glob = require("glob");
 const fs = require("fs");
 const path = require("path");
 
-const excludedFiles = [
+const globString = "../src/**/*.md";
+const excludedGlobs = [
   "../src/fts.md",
   "../src/embedding.md",
-  "../src/examples/serverless_lancedb_with_s3_and_lambda.md",
-  "../src/examples/serverless_qa_bot_with_modal_and_langchain.md",
-  "../src/examples/transformerjs_embedding_search_nodejs.md",
-  "../src/examples/youtube_transcript_bot_with_nodejs.md",
+  "../src/examples/*.md",
+  "../src/guides/tables.md",
 ];
+
 const nodePrefix = "javascript";
 const nodeFile = ".js";
 const nodeFolder = "node";
-const globString = "../src/**/*.md";
 const asyncPrefix = "(async () => {\n";
 const asyncSuffix = "})();";
 
@@ -32,6 +31,7 @@ function* yieldLines(lines, prefix, suffix) {
 }
 
 const files = glob.sync(globString, { recursive: true });
+const excludedFiles = glob.sync(excludedGlobs, { recursive: true });
 
 for (const file of files.filter((file) => !excludedFiles.includes(file))) {
   const lines = [];

docs/test/md_testing.py

@@ -2,19 +2,21 @@ import glob
 from typing import Iterator
 from pathlib import Path
 
-excluded_files = [
+glob_string = "../src/**/*.md"
+excluded_globs = [
     "../src/fts.md",
     "../src/embedding.md",
-    "../src/examples/serverless_lancedb_with_s3_and_lambda.md",
-    "../src/examples/serverless_qa_bot_with_modal_and_langchain.md",
-    "../src/examples/youtube_transcript_bot_with_nodejs.md",
+    "../src/examples/*.md",
     "../src/integrations/voxel51.md",
+    "../src/guides/tables.md"
 ]
 
 python_prefix = "py"
 python_file = ".py"
 python_folder = "python"
-glob_string = "../src/**/*.md"
+
+files = glob.glob(glob_string, recursive=True)
+excluded_files = [f for excluded_glob in excluded_globs for f in glob.glob(excluded_glob, recursive=True)]
 
 def yield_lines(lines: Iterator[str], prefix: str, suffix: str):
     in_code_block = False
@@ -30,7 +32,7 @@ def yield_lines(lines: Iterator[str], prefix: str, suffix: str):
         elif in_code_block:
             yield line[strip_length:]
 
-for file in filter(lambda file: file not in excluded_files, glob.glob(glob_string, recursive=True)):
+for file in filter(lambda file: file not in excluded_files, files):
     with open(file, "r") as f:
         lines = list(yield_lines(iter(f), "```", "```"))
 

node/examples/js-transformers/index.js

@@ -50,7 +50,7 @@ async function example() {
         { id: 5, text: 'Banana', type: 'fruit' }
     ]
 
-    const table = await db.createTable('food_table', data, "create", embed_fun)
+    const table = await db.createTable('food_table', data, embed_fun)
 
 
     // Query the table

node/examples/js-transformers/package.json

@@ -10,7 +10,7 @@
   "license": "Apache-2.0",
   "dependencies": {
     "@xenova/transformers": "^2.4.1",
-    "vectordb": "^0.1.12"
+    "vectordb": "file:../.."
   }
 
 }

node/native.js

@@ -12,26 +12,25 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-const { currentTarget } = require('@neon-rs/load');
+const { currentTarget } = require('@neon-rs/load')
 
-let nativeLib;
+let nativeLib
 
 try {
-    nativeLib = require(`@lancedb/vectordb-${currentTarget()}`);
-} catch (e) {
-    try {
-        // Might be developing locally, so try that. But don't expose that error
-        // to the user.
-        nativeLib = require("./index.node");
+  // When developing locally, give preference to the local built library
+  nativeLib = require('./index.node')
 } catch {
+  try {
+    nativeLib = require(`@lancedb/vectordb-${currentTarget()}`)
+  } catch (e) {
     throw new Error(`vectordb: failed to load native library.
   You may need to run \`npm install @lancedb/vectordb-${currentTarget()}\`.
 
   If that does not work, please file a bug report at https://github.com/lancedb/lancedb/issues
       
-  Source error: ${e}`);
+  Source error: ${e}`)
   }
 }
 
 // Dynamic require for runtime.
-module.exports = nativeLib;
+module.exports = nativeLib

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.1.19",
+  "version": "0.2.4",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.1.19",
+      "version": "0.2.4",
       "cpu": [
         "x64",
         "arm64"
@@ -51,11 +51,11 @@
         "typescript": "*"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.1.19",
-        "@lancedb/vectordb-darwin-x64": "0.1.19",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.1.19",
-        "@lancedb/vectordb-linux-x64-gnu": "0.1.19",
-        "@lancedb/vectordb-win32-x64-msvc": "0.1.19"
+        "@lancedb/vectordb-darwin-arm64": "0.2.4",
+        "@lancedb/vectordb-darwin-x64": "0.2.4",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.2.4",
+        "@lancedb/vectordb-linux-x64-gnu": "0.2.4",
+        "@lancedb/vectordb-win32-x64-msvc": "0.2.4"
       }
     },
     "node_modules/@apache-arrow/ts": {
@@ -315,9 +315,9 @@
       }
     },
     "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.1.19.tgz",
-      "integrity": "sha512-efQhJkBKvMNhjFq3Sw3/qHo9D9gb9UqiIr98n3STsbNxBQjMnWemXn91Ckl40siRG1O8qXcINW7Qs/EGmus+kg==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.2.4.tgz",
+      "integrity": "sha512-MqiZXamHYEOfguPsHWLBQ56IabIN6Az8u2Hx8LCyXcxW9gcyJZMSAfJc+CcA4KYHKotv0KsVBhgxZ3kaZQQyiw==",
       "cpu": [
         "arm64"
       ],
@@ -327,9 +327,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.1.19.tgz",
-      "integrity": "sha512-r6OZNVyemAssABz2w7CRhe7dyREwBEfTytn+ux1zzTnzsgMgDovCQ0rQ3WZcxWvcy7SFCxiemA9IP1b/lsb4tQ==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.2.4.tgz",
+      "integrity": "sha512-DzL+mw5WhKDwXdEFlPh8M9zSDhGnfks7NvEh6ZqKbU6znH206YB7g3OA4WfFyV579IIEQ8jd4v/XDthNzQKuSA==",
       "cpu": [
         "x64"
       ],
@@ -339,9 +339,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.1.19.tgz",
-      "integrity": "sha512-mL/hRmZp6Kw7hmGJBdOZfp/tTYiCdlOcs8DA/+nr2eiXERv0gIhyiKvr2P5DwbBmut3qXEkDalMHTo95BSdL2A==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.2.4.tgz",
+      "integrity": "sha512-LP1nNfIpFxCgcCMlIQdseDX9dZU27TNhCL41xar8euqcetY5uKvi0YqhiVlpNO85Ss1FRQBgQ/GtnOM6Bo7oBQ==",
       "cpu": [
         "arm64"
       ],
@@ -351,9 +351,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.1.19.tgz",
-      "integrity": "sha512-AG0FHksbbr+cHVKPi4B8cmBtqb6T9E0uaK4kyZkXrX52/xtv9RYVZcykaB/tSSm0XNFPWWRnx9R8UqNZV/hxMA==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.2.4.tgz",
+      "integrity": "sha512-m4RhOI5JJWPU9Ip2LlRIzXu4mwIv9M//OyAuTLiLKRm8726jQHhYi5VFUEtNzqY0o0p6pS0b3XbifYQ+cyJn3Q==",
       "cpu": [
         "x64"
       ],
@@ -363,9 +363,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.1.19.tgz",
-      "integrity": "sha512-PDWZ2hvLVXH4Z4WIO1rsWY8ev3NpNm7aXlaey32P+l1Iz9Hia9+F2GBpp2UiEQKfvbk82ucAvBLRmpSsHY8Tlw==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.2.4.tgz",
+      "integrity": "sha512-lMF/2e3YkKWnTYv0R7cUCfjMkAqepNaHSc/dvJzCNsFVEhfDsFdScQFLToARs5GGxnq4fOf+MKpaHg/W6QTxiA==",
       "cpu": [
         "x64"
       ],
@@ -4852,33 +4852,33 @@
       }
     },
     "@lancedb/vectordb-darwin-arm64": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.1.19.tgz",
-      "integrity": "sha512-efQhJkBKvMNhjFq3Sw3/qHo9D9gb9UqiIr98n3STsbNxBQjMnWemXn91Ckl40siRG1O8qXcINW7Qs/EGmus+kg==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.2.4.tgz",
+      "integrity": "sha512-MqiZXamHYEOfguPsHWLBQ56IabIN6Az8u2Hx8LCyXcxW9gcyJZMSAfJc+CcA4KYHKotv0KsVBhgxZ3kaZQQyiw==",
       "optional": true
     },
     "@lancedb/vectordb-darwin-x64": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.1.19.tgz",
-      "integrity": "sha512-r6OZNVyemAssABz2w7CRhe7dyREwBEfTytn+ux1zzTnzsgMgDovCQ0rQ3WZcxWvcy7SFCxiemA9IP1b/lsb4tQ==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.2.4.tgz",
+      "integrity": "sha512-DzL+mw5WhKDwXdEFlPh8M9zSDhGnfks7NvEh6ZqKbU6znH206YB7g3OA4WfFyV579IIEQ8jd4v/XDthNzQKuSA==",
       "optional": true
     },
     "@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.1.19.tgz",
-      "integrity": "sha512-mL/hRmZp6Kw7hmGJBdOZfp/tTYiCdlOcs8DA/+nr2eiXERv0gIhyiKvr2P5DwbBmut3qXEkDalMHTo95BSdL2A==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.2.4.tgz",
+      "integrity": "sha512-LP1nNfIpFxCgcCMlIQdseDX9dZU27TNhCL41xar8euqcetY5uKvi0YqhiVlpNO85Ss1FRQBgQ/GtnOM6Bo7oBQ==",
       "optional": true
     },
     "@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.1.19.tgz",
-      "integrity": "sha512-AG0FHksbbr+cHVKPi4B8cmBtqb6T9E0uaK4kyZkXrX52/xtv9RYVZcykaB/tSSm0XNFPWWRnx9R8UqNZV/hxMA==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.2.4.tgz",
+      "integrity": "sha512-m4RhOI5JJWPU9Ip2LlRIzXu4mwIv9M//OyAuTLiLKRm8726jQHhYi5VFUEtNzqY0o0p6pS0b3XbifYQ+cyJn3Q==",
       "optional": true
     },
     "@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.1.19",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.1.19.tgz",
-      "integrity": "sha512-PDWZ2hvLVXH4Z4WIO1rsWY8ev3NpNm7aXlaey32P+l1Iz9Hia9+F2GBpp2UiEQKfvbk82ucAvBLRmpSsHY8Tlw==",
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.2.4.tgz",
+      "integrity": "sha512-lMF/2e3YkKWnTYv0R7cUCfjMkAqepNaHSc/dvJzCNsFVEhfDsFdScQFLToARs5GGxnq4fOf+MKpaHg/W6QTxiA==",
       "optional": true
     },
     "@neon-rs/cli": {

node/package.json

@@ -1,6 +1,6 @@
 {
   "name": "vectordb",
-  "version": "0.1.19",
+  "version": "0.2.4",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
@@ -9,7 +9,7 @@
     "build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json",
     "build-release": "npm run build -- --release",
     "test": "npm run tsc && mocha -recursive dist/test",
-    "lint": "eslint src --ext .js,.ts",
+    "lint": "eslint native.js src --ext .js,.ts",
     "clean": "rm -rf node_modules *.node dist/",
     "pack-build": "neon pack-build",
     "check-npm": "printenv && which node && which npm && npm --version"
@@ -78,10 +78,10 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-arm64": "0.1.19",
-    "@lancedb/vectordb-darwin-x64": "0.1.19",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.1.19",
-    "@lancedb/vectordb-linux-x64-gnu": "0.1.19",
-    "@lancedb/vectordb-win32-x64-msvc": "0.1.19"
+    "@lancedb/vectordb-darwin-arm64": "0.2.4",
+    "@lancedb/vectordb-darwin-x64": "0.2.4",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.2.4",
+    "@lancedb/vectordb-linux-x64-gnu": "0.2.4",
+    "@lancedb/vectordb-win32-x64-msvc": "0.2.4"
   }
 }

node/src/arrow.ts

@@ -13,18 +13,19 @@
 // limitations under the License.
 
 import {
-  Field,
+  Field, type FixedSizeListBuilder,
   Float32,
-  List, type ListBuilder,
   makeBuilder,
   RecordBatchFileWriter,
-  Table, Utf8,
+  Utf8,
   type Vector,
-  vectorFromArray
+  FixedSizeList,
+  vectorFromArray, type Schema, Table as ArrowTable
 } from 'apache-arrow'
 import { type EmbeddingFunction } from './index'
 
-export async function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Table> {
+// Converts an Array of records into an Arrow Table, optionally applying an embeddings function to it.
+export async function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<ArrowTable> {
   if (data.length === 0) {
     throw new Error('At least one record needs to be provided')
   }
@@ -34,8 +35,8 @@ export async function convertToTable<T> (data: Array<Record<string, unknown>>, e
 
   for (const columnsKey of columns) {
     if (columnsKey === 'vector') {
-      const listBuilder = newVectorListBuilder()
       const vectorSize = (data[0].vector as any[]).length
+      const listBuilder = newVectorBuilder(vectorSize)
       for (const datum of data) {
         if ((datum[columnsKey] as any[]).length !== vectorSize) {
           throw new Error(`Invalid vector size, expected ${vectorSize}`)
@@ -52,9 +53,7 @@ export async function convertToTable<T> (data: Array<Record<string, unknown>>, e
 
       if (columnsKey === embeddings?.sourceColumn) {
         const vectors = await embeddings.embed(values as T[])
-        const listBuilder = newVectorListBuilder()
-        vectors.map(v => listBuilder.append(v))
-        records.vector = listBuilder.finish().toVector()
+        records.vector = vectorFromArray(vectors, newVectorType(vectors[0].length))
       }
 
       if (typeof values[0] === 'string') {
@@ -66,20 +65,47 @@ export async function convertToTable<T> (data: Array<Record<string, unknown>>, e
     }
   }
 
-  return new Table(records)
+  return new ArrowTable(records)
 }
 
 // Creates a new Arrow ListBuilder that stores a Vector column
-function newVectorListBuilder (): ListBuilder<Float32, any> {
-  const children = new Field<Float32>('item', new Float32())
-  const list = new List(children)
+function newVectorBuilder (dim: number): FixedSizeListBuilder<Float32> {
   return makeBuilder({
-    type: list
+    type: newVectorType(dim)
   })
 }
 
+// Creates the Arrow Type for a Vector column with dimension `dim`
+function newVectorType (dim: number): FixedSizeList<Float32> {
+  const children = new Field<Float32>('item', new Float32())
+  return new FixedSizeList(dim, children)
+}
+
+// Converts an Array of records into Arrow IPC format
 export async function fromRecordsToBuffer<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Buffer> {
   const table = await convertToTable(data, embeddings)
   const writer = RecordBatchFileWriter.writeAll(table)
   return Buffer.from(await writer.toUint8Array())
 }
+
+// Converts an Arrow Table into Arrow IPC format
+export async function fromTableToBuffer<T> (table: ArrowTable, embeddings?: EmbeddingFunction<T>): Promise<Buffer> {
+  if (embeddings !== undefined) {
+    const source = table.getChild(embeddings.sourceColumn)
+
+    if (source === null) {
+      throw new Error(`The embedding source column ${embeddings.sourceColumn} was not found in the Arrow Table`)
+    }
+
+    const vectors = await embeddings.embed(source.toArray() as T[])
+    const column = vectorFromArray(vectors, newVectorType(vectors[0].length))
+    table = table.assign(new ArrowTable({ vector: column }))
+  }
+  const writer = RecordBatchFileWriter.writeAll(table)
+  return Buffer.from(await writer.toUint8Array())
+}
+
+// Creates an empty Arrow Table
+export function createEmptyTable (schema: Schema): ArrowTable {
+  return new ArrowTable(schema)
+}

node/src/index.ts

@@ -13,10 +13,10 @@
 // limitations under the License.
 
 import {
-  RecordBatchFileWriter,
-  type Table as ArrowTable
+  type Schema,
+  Table as ArrowTable
 } from 'apache-arrow'
-import { fromRecordsToBuffer } from './arrow'
+import { createEmptyTable, fromRecordsToBuffer, fromTableToBuffer } from './arrow'
 import type { EmbeddingFunction } from './embedding/embedding_function'
 import { RemoteConnection } from './remote'
 import { Query } from './query'
@@ -42,6 +42,8 @@ export interface ConnectionOptions {
 
   awsCredentials?: AwsCredentials
 
+  awsRegion?: string
+
   // API key for the remote connections
   apiKey?: string
   // Region to connect
@@ -51,6 +53,40 @@ export interface ConnectionOptions {
   hostOverride?: string
 }
 
+function getAwsArgs (opts: ConnectionOptions): any[] {
+  const callArgs = []
+  const awsCredentials = opts.awsCredentials
+  if (awsCredentials !== undefined) {
+    callArgs.push(awsCredentials.accessKeyId)
+    callArgs.push(awsCredentials.secretKey)
+    callArgs.push(awsCredentials.sessionToken)
+  } else {
+    callArgs.push(undefined)
+    callArgs.push(undefined)
+    callArgs.push(undefined)
+  }
+
+  callArgs.push(opts.awsRegion)
+  return callArgs
+}
+
+export interface CreateTableOptions<T> {
+  // Name of Table
+  name: string
+
+  // Data to insert into the Table
+  data?: Array<Record<string, unknown>> | ArrowTable | undefined
+
+  // Optional Arrow Schema for this table
+  schema?: Schema | undefined
+
+  // Optional embedding function used to create embeddings
+  embeddingFunction?: EmbeddingFunction<T> | undefined
+
+  // WriteOptions for this operation
+  writeOptions?: WriteOptions | undefined
+}
+
 /**
  * Connect to a LanceDB instance at the given URI
  * @param uri The uri of the database.
@@ -97,6 +133,17 @@ export interface Connection {
    */
   openTable<T>(name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
 
+  /**
+   * Creates a new Table, optionally initializing it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Array of Records to be inserted into the table
+   * @param schema - An Arrow Schema that describe this table columns
+   * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
+   * @param {WriteOptions} writeOptions - The write options to use when creating the table.
+   */
+  createTable<T> ({ name, data, schema, embeddingFunction, writeOptions }: CreateTableOptions<T>): Promise<Table<T>>
+
   /**
    * Creates a new Table and initialize it with new data.
    *
@@ -132,8 +179,6 @@ export interface Connection {
    */
   createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>, options: WriteOptions): Promise<Table<T>>
 
-  createTableArrow(name: string, table: ArrowTable): Promise<Table>
-
   /**
    * Drop an existing table.
    * @param name The name of the table to drop.
@@ -221,16 +266,16 @@ export interface Table<T = number[]> {
  * A connection to a LanceDB database.
  */
 export class LocalConnection implements Connection {
-  private readonly _options: ConnectionOptions
+  private readonly _options: () => ConnectionOptions
   private readonly _db: any
 
   constructor (db: any, options: ConnectionOptions) {
-    this._options = options
+    this._options = () => options
     this._db = db
   }
 
   get uri (): string {
-    return this._options.uri
+    return this._options().uri
   }
 
   /**
@@ -256,15 +301,16 @@ export class LocalConnection implements Connection {
   async openTable<T> (name: string, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
   async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
   async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
-    const tbl = await databaseOpenTable.call(this._db, name)
+    const tbl = await databaseOpenTable.call(this._db, name, ...getAwsArgs(this._options()))
     if (embeddings !== undefined) {
-      return new LocalTable(tbl, name, this._options, embeddings)
+      return new LocalTable(tbl, name, this._options(), embeddings)
     } else {
-      return new LocalTable(tbl, name, this._options)
+      return new LocalTable(tbl, name, this._options())
     }
   }
 
-  async createTable<T> (name: string, data: Array<Record<string, unknown>>, optsOrEmbedding?: WriteOptions | EmbeddingFunction<T>, opt?: WriteOptions): Promise<Table<T>> {
+  async createTable<T> (name: string | CreateTableOptions<T>, data?: Array<Record<string, unknown>>, optsOrEmbedding?: WriteOptions | EmbeddingFunction<T>, opt?: WriteOptions): Promise<Table<T>> {
+    if (typeof name === 'string') {
       let writeOptions: WriteOptions = new DefaultWriteOptions()
       if (opt !== undefined && isWriteOptions(opt)) {
         writeOptions = opt
@@ -276,28 +322,45 @@ export class LocalConnection implements Connection {
       if (optsOrEmbedding !== undefined && isEmbeddingFunction(optsOrEmbedding)) {
         embeddings = optsOrEmbedding
       }
-    const createArgs = [this._db, name, await fromRecordsToBuffer(data, embeddings), writeOptions.writeMode?.toString()]
-    if (this._options.awsCredentials !== undefined) {
-      createArgs.push(this._options.awsCredentials.accessKeyId)
-      createArgs.push(this._options.awsCredentials.secretKey)
-      if (this._options.awsCredentials.sessionToken !== undefined) {
-        createArgs.push(this._options.awsCredentials.sessionToken)
+      return await this.createTableImpl({ name, data, embeddingFunction: embeddings, writeOptions })
     }
+    return await this.createTableImpl(name)
   }
 
-    const tbl = await tableCreate.call(...createArgs)
+  private async createTableImpl<T> ({ name, data, schema, embeddingFunction, writeOptions = new DefaultWriteOptions() }: {
+    name: string
+    data?: Array<Record<string, unknown>> | ArrowTable | undefined
+    schema?: Schema | undefined
+    embeddingFunction?: EmbeddingFunction<T> | undefined
+    writeOptions?: WriteOptions | undefined
+  }): Promise<Table<T>> {
+    let buffer: Buffer
 
-    if (embeddings !== undefined) {
-      return new LocalTable(tbl, name, this._options, embeddings)
+    function isEmpty (data: Array<Record<string, unknown>> | ArrowTable<any>): boolean {
+      if (data instanceof ArrowTable) {
+        return data.data.length === 0
+      }
+      return data.length === 0
+    }
+
+    if ((data === undefined) || isEmpty(data)) {
+      if (schema === undefined) {
+        throw new Error('Either data or schema needs to defined')
+      }
+      buffer = await fromTableToBuffer(createEmptyTable(schema))
+    } else if (data instanceof ArrowTable) {
+      buffer = await fromTableToBuffer(data, embeddingFunction)
     } else {
-      return new LocalTable(tbl, name, this._options)
-    }
+      // data is Array<Record<...>>
+      buffer = await fromRecordsToBuffer(data, embeddingFunction)
     }
 
-  async createTableArrow (name: string, table: ArrowTable): Promise<Table> {
-    const writer = RecordBatchFileWriter.writeAll(table)
-    await tableCreate.call(this._db, name, Buffer.from(await writer.toUint8Array()))
-    return await this.openTable(name)
+    const tbl = await tableCreate.call(this._db, name, buffer, writeOptions?.writeMode?.toString(), ...getAwsArgs(this._options()))
+    if (embeddingFunction !== undefined) {
+      return new LocalTable(tbl, name, this._options(), embeddingFunction)
+    } else {
+      return new LocalTable(tbl, name, this._options())
+    }
   }
 
   /**
@@ -310,10 +373,10 @@ export class LocalConnection implements Connection {
 }
 
 export class LocalTable<T = number[]> implements Table<T> {
-  private readonly _tbl: any
+  private _tbl: any
   private readonly _name: string
   private readonly _embeddings?: EmbeddingFunction<T>
-  private readonly _options: ConnectionOptions
+  private readonly _options: () => ConnectionOptions
 
   constructor (tbl: any, name: string, options: ConnectionOptions)
   /**
@@ -327,7 +390,7 @@ export class LocalTable<T = number[]> implements Table<T> {
     this._tbl = tbl
     this._name = name
     this._embeddings = embeddings
-    this._options = options
+    this._options = () => options
   }
 
   get name (): string {
@@ -349,15 +412,12 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @return The number of rows added to the table
    */
   async add (data: Array<Record<string, unknown>>): Promise<number> {
-    const callArgs = [this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Append.toString()]
-    if (this._options.awsCredentials !== undefined) {
-      callArgs.push(this._options.awsCredentials.accessKeyId)
-      callArgs.push(this._options.awsCredentials.secretKey)
-      if (this._options.awsCredentials.sessionToken !== undefined) {
-        callArgs.push(this._options.awsCredentials.sessionToken)
-      }
-    }
-    return tableAdd.call(...callArgs)
+    return tableAdd.call(
+      this._tbl,
+      await fromRecordsToBuffer(data, this._embeddings),
+      WriteMode.Append.toString(),
+      ...getAwsArgs(this._options())
+    ).then((newTable: any) => { this._tbl = newTable })
   }
 
   /**
@@ -367,15 +427,12 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @return The number of rows added to the table
    */
   async overwrite (data: Array<Record<string, unknown>>): Promise<number> {
-    const callArgs = [this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Overwrite.toString()]
-    if (this._options.awsCredentials !== undefined) {
-      callArgs.push(this._options.awsCredentials.accessKeyId)
-      callArgs.push(this._options.awsCredentials.secretKey)
-      if (this._options.awsCredentials.sessionToken !== undefined) {
-        callArgs.push(this._options.awsCredentials.sessionToken)
-      }
-    }
-    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Overwrite.toString())
+    return tableAdd.call(
+      this._tbl,
+      await fromRecordsToBuffer(data, this._embeddings),
+      WriteMode.Overwrite.toString(),
+      ...getAwsArgs(this._options())
+    ).then((newTable: any) => { this._tbl = newTable })
   }
 
   /**
@@ -384,7 +441,7 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @param indexParams The parameters of this Index, @see VectorIndexParams.
    */
   async createIndex (indexParams: VectorIndexParams): Promise<any> {
-    return tableCreateVectorIndex.call(this._tbl, indexParams)
+    return tableCreateVectorIndex.call(this._tbl, indexParams).then((newTable: any) => { this._tbl = newTable })
   }
 
   /**
@@ -400,7 +457,7 @@ export class LocalTable<T = number[]> implements Table<T> {
    * @param filter A filter in the same format used by a sql WHERE clause.
    */
   async delete (filter: string): Promise<void> {
-    return tableDelete.call(this._tbl, filter)
+    return tableDelete.call(this._tbl, filter).then((newTable: any) => { this._tbl = newTable })
   }
 }
 

node/src/query.ts

@@ -112,7 +112,8 @@ export class Query<T = number[]> {
       this._queryVector = this._query as number[]
     }
 
-    const buffer = await tableSearch.call(this._tbl, this)
+    const isElectron = this.isElectron()
+    const buffer = await tableSearch.call(this._tbl, this, isElectron)
     const data = tableFromIPC(buffer)
 
     return data.toArray().map((entry: Record<string, unknown>) => {
@@ -127,4 +128,14 @@ export class Query<T = number[]> {
       return newObject as unknown as T
     })
   }
+
+  // See https://github.com/electron/electron/issues/2288
+  private isElectron (): boolean {
+    try {
+      // eslint-disable-next-line no-prototype-builtins
+      return (process?.versions?.hasOwnProperty('electron') || navigator?.userAgent?.toLowerCase()?.includes(' electron'))
+    } catch (e) {
+      return false
+    }
+  }
 }

node/src/remote/client.ts

@@ -104,4 +104,34 @@ export class HttpLancedbClient {
     }
     return response
   }
+
+  /**
+   * Sent POST request.
+   */
+  public async post (path: string, data?: any, params?: Record<string, string | number>): Promise<AxiosResponse> {
+    const response = await axios.post(
+        `${this._url}${path}`,
+        data,
+        {
+          headers: {
+            'Content-Type': 'application/json',
+            'x-api-key': this._apiKey(),
+            ...(this._dbName !== undefined ? { 'x-lancedb-database': this._dbName } : {})
+          },
+          params,
+          timeout: 30000
+        }
+    ).catch((err) => {
+      console.error('error: ', err)
+      return err.response
+    })
+    if (response.status !== 200) {
+      const errorData = new TextDecoder().decode(response.data)
+      throw new Error(
+          `Server Error, status: ${response.status as number}, ` +
+          `message: ${response.statusText as string}: ${errorData}`
+      )
+    }
+    return response
+  }
 }

node/src/remote/index.ts

@@ -14,11 +14,11 @@
 
 import {
   type EmbeddingFunction, type Table, type VectorIndexParams, type Connection,
-  type ConnectionOptions
+  type ConnectionOptions, type CreateTableOptions, type WriteOptions
 } from '../index'
 import { Query } from '../query'
 
-import { type Table as ArrowTable, Vector } from 'apache-arrow'
+import { Vector } from 'apache-arrow'
 import { HttpLancedbClient } from './client'
 
 /**
@@ -66,18 +66,12 @@ export class RemoteConnection implements Connection {
     }
   }
 
-  async createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table>
-  async createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
-  async createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
-    throw new Error('Not implemented')
-  }
-
-  async createTableArrow (name: string, table: ArrowTable): Promise<Table> {
+  async createTable<T> (name: string | CreateTableOptions<T>, data?: Array<Record<string, unknown>>, optsOrEmbedding?: WriteOptions | EmbeddingFunction<T>, opt?: WriteOptions): Promise<Table<T>> {
     throw new Error('Not implemented')
   }
 
   async dropTable (name: string): Promise<void> {
-    throw new Error('Not implemented')
+    await this._client.post(`/v1/table/${name}/drop/`)
   }
 }
 

node/src/test/io.ts

@@ -47,7 +47,9 @@ describe('LanceDB S3 client', function () {
         }
       }
       const table = await createTestDB(opts, 2, 20)
+      console.log(table)
       const con = await lancedb.connect(opts)
+      console.log(con)
       assert.equal(con.uri, opts.uri)
 
       const results = await table.search([0.1, 0.3]).limit(5).execute()
@@ -70,5 +72,5 @@ async function createTestDB (opts: ConnectionOptions, numDimensions: number = 2,
     data.push({ id: i + 1, name: `name_${i}`, price: i + 10, is_active: (i % 2 === 0), vector })
   }
 
-  return await con.createTable('vectors', data)
+  return await con.createTable('vectors_2', data)
 }

node/src/test/test.ts

@@ -19,6 +19,7 @@ import * as chaiAsPromised from 'chai-as-promised'
 
 import * as lancedb from '../index'
 import { type AwsCredentials, type EmbeddingFunction, MetricType, Query, WriteMode, DefaultWriteOptions, isWriteOptions } from '../index'
+import { Field, Int32, makeVector, Schema, Utf8, Table as ArrowTable, vectorFromArray } from 'apache-arrow'
 
 const expect = chai.expect
 const assert = chai.assert
@@ -107,9 +108,9 @@ describe('LanceDB client', function () {
       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
+      // vector and _distance are always returned
       assert.isDefined(results[0].vector)
-      assert.isDefined(results[0].score)
+      assert.isDefined(results[0]._distance)
       assert.isDefined(results[0].is_active)
 
       assert.isUndefined(results[0].id)
@@ -119,6 +120,45 @@ describe('LanceDB client', function () {
   })
 
   describe('when creating a new dataset', function () {
+    it('create an empty table', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('id', new Int32()), new Field('name', new Utf8())]
+      )
+      const table = await con.createTable({ name: 'vectors', schema })
+      assert.equal(table.name, 'vectors')
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+
+    it('create a table with a empty data array', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('id', new Int32()), new Field('name', new Utf8())]
+      )
+      const table = await con.createTable({ name: 'vectors', schema, data: [] })
+      assert.equal(table.name, 'vectors')
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+
+    it('create a table from an Arrow Table', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const i32s = new Int32Array(new Array<number>(10))
+      const i32 = makeVector(i32s)
+
+      const data = new ArrowTable({ vector: i32 })
+
+      const table = await con.createTable({ name: 'vectors', data })
+      assert.equal(table.name, 'vectors')
+      assert.equal(await table.countRows(), 10)
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+
     it('creates a new table from javascript objects', async function () {
       const dir = await track().mkdir('lancejs')
       const con = await lancedb.connect(dir)
@@ -291,6 +331,20 @@ describe('LanceDB client', function () {
       const results = await table.search('foo').execute()
       assert.equal(results.length, 2)
     })
+
+    it('should create embeddings for Arrow Table', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+      const embeddingFunction = new TextEmbedding('name')
+
+      const names = vectorFromArray(['foo', 'bar'], new Utf8())
+      const data = new ArrowTable({ name: names })
+
+      const table = await con.createTable({ name: 'vectors', data, embeddingFunction })
+      assert.equal(table.name, 'vectors')
+      const results = await table.search('foo').execute()
+      assert.equal(results.length, 2)
+    })
   })
 })
 

python/.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.16
+current_version = 0.2.2
 commit = True
 message = [python] Bump version: {current_version} → {new_version}
 tag = True

python/lancedb/db.py

@@ -22,6 +22,7 @@ import pyarrow as pa
 from pyarrow import fs
 
 from .common import DATA, URI
+from .pydantic import LanceModel
 from .table import LanceTable, Table
 from .util import fs_from_uri, get_uri_location, get_uri_scheme
 
@@ -39,7 +40,7 @@ class DBConnection(ABC):
         self,
         name: str,
         data: Optional[DATA] = None,
-        schema: Optional[pa.Schema] = None,
+        schema: Optional[pa.Schema, LanceModel] = None,
         mode: str = "create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
@@ -52,7 +53,7 @@ class DBConnection(ABC):
             The name of the table.
         data: list, tuple, dict, pd.DataFrame; optional
             The data to initialize the table. User must provide at least one of `data` or `schema`.
-        schema: pyarrow.Schema; optional
+        schema: pyarrow.Schema or LanceModel; optional
             The schema of the table.
         mode: str; default "create"
             The mode to use when creating the table. Can be either "create" or "overwrite".
@@ -148,14 +149,14 @@ class DBConnection(ABC):
         ...     for i in range(5):
         ...         yield pa.RecordBatch.from_arrays(
         ...             [
-        ...                 pa.array([[3.1, 4.1], [5.9, 26.5]]),
+        ...                 pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)),
         ...                 pa.array(["foo", "bar"]),
         ...                 pa.array([10.0, 20.0]),
         ...             ],
         ...             ["vector", "item", "price"],
         ...         )
         >>> schema=pa.schema([
-        ...     pa.field("vector", pa.list_(pa.float32())),
+        ...     pa.field("vector", pa.list_(pa.float32(), 2)),
         ...     pa.field("item", pa.utf8()),
         ...     pa.field("price", pa.float32()),
         ... ])
@@ -192,6 +193,13 @@ class DBConnection(ABC):
         """
         raise NotImplementedError
 
+    def drop_database(self):
+        """
+        Drop database
+        This is the same thing as dropping all the tables
+        """
+        raise NotImplementedError
+
 
 class LanceDBConnection(DBConnection):
     """
@@ -277,7 +285,7 @@ class LanceDBConnection(DBConnection):
         self,
         name: str,
         data: Optional[DATA] = None,
-        schema: pa.Schema = None,
+        schema: Optional[pa.Schema, LanceModel] = None,
         mode: str = "create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
@@ -333,3 +341,7 @@ class LanceDBConnection(DBConnection):
         except FileNotFoundError:
             if not ignore_missing:
                 raise
+
+    def drop_database(self):
+        filesystem, path = fs_from_uri(self.uri)
+        filesystem.delete_dir(path)

python/lancedb/pydantic.py

@@ -275,10 +275,16 @@ class LanceModel(pydantic.BaseModel):
 
     @classmethod
     def to_arrow_schema(cls):
+        """
+        Get the Arrow Schema for this model.
+        """
         return pydantic_to_schema(cls)
 
     @classmethod
     def field_names(cls) -> List[str]:
+        """
+        Get the field names of this model.
+        """
         if PYDANTIC_VERSION.major < 2:
             return list(cls.__fields__.keys())
         return list(cls.model_fields.keys())

python/lancedb/query.py

@@ -73,7 +73,7 @@ class LanceQueryBuilder:
     ...       .select(["b"])
     ...       .limit(2)
     ...       .to_df())
-       b      vector  score
+       b      vector  _distance
     0  6  [0.4, 0.4]        0.0
     """
 
@@ -205,7 +205,7 @@ class LanceQueryBuilder:
         """
         Execute the query and return the results as a pandas DataFrame.
         In addition to the selected columns, LanceDB also returns a vector
-        and also the "score" column which is the distance between the query
+        and also the "_distance" column which is the distance between the query
         vector and the returned vector.
         """
 
@@ -217,7 +217,7 @@ class LanceQueryBuilder:
         [Apache Arrow Table](https://arrow.apache.org/docs/python/generated/pyarrow.Table.html#pyarrow.Table).
 
         In addition to the selected columns, LanceDB also returns a vector
-        and also the "score" column which is the distance between the query
+        and also the "_distance" column which is the distance between the query
         vector and the returned vectors.
         """
         vector = self._query if isinstance(self._query, list) else self._query.tolist()

python/lancedb/remote/client.py

@@ -97,7 +97,12 @@ class RestfulLanceDBClient:
         """Send a GET request and returns the deserialized response payload."""
         if isinstance(params, BaseModel):
             params: Dict[str, Any] = params.dict(exclude_none=True)
-        async with self.session.get(uri, params=params, headers=self.headers) as resp:
+        async with self.session.get(
+            uri,
+            params=params,
+            headers=self.headers,
+            timeout=aiohttp.ClientTimeout(total=30),
+        ) as resp:
             await self._check_status(resp)
             return await resp.json()
 
@@ -109,6 +114,7 @@ class RestfulLanceDBClient:
         params: Optional[Dict[str, Any]] = None,
         content_type: Optional[str] = None,
         deserialize: Callable = lambda resp: resp.json(),
+        request_id: Optional[str] = None,
     ) -> Dict[str, Any]:
         """Send a POST request and returns the deserialized response payload.
 
@@ -117,6 +123,8 @@ class RestfulLanceDBClient:
         uri : str
             The uri to send the POST request to.
         data: Union[Dict[str, Any], BaseModel]
+        request_id: Optional[str]
+            Optional client side request id to be sent in the request headers.
 
         """
         if isinstance(data, BaseModel):
@@ -129,10 +137,13 @@ class RestfulLanceDBClient:
         headers = self.headers.copy()
         if content_type is not None:
             headers["content-type"] = content_type
+        if request_id is not None:
+            headers["x-request-id"] = request_id
         async with self.session.post(
             uri,
             headers=headers,
             params=params,
+            timeout=aiohttp.ClientTimeout(total=30),
             **req_kwargs,
         ) as resp:
             resp: aiohttp.ClientResponse = resp

python/lancedb/remote/db.py

@@ -104,8 +104,22 @@ class RemoteDBConnection(DBConnection):
             self._client.post(
                 f"/v1/table/{name}/create/",
                 data=data,
-                params={"request_id": request_id},
+                request_id=request_id,
                 content_type=ARROW_STREAM_CONTENT_TYPE,
             )
         )
         return RemoteTable(self, name)
+
+    def drop_table(self, name: str):
+        """Drop a table from the database.
+
+        Parameters
+        ----------
+        name: str
+            The name of the table.
+        """
+        self._loop.run_until_complete(
+            self._client.post(
+                f"/v1/table/{name}/drop/",
+            )
+        )

python/lancedb/remote/table.py

@@ -45,7 +45,15 @@ class RemoteTable(Table):
         return schema
 
     def to_arrow(self) -> pa.Table:
-        raise NotImplementedError
+        """Return the table as an Arrow table."""
+        raise NotImplementedError("to_arrow() is not supported on the LanceDB cloud")
+
+    def to_pandas(self):
+        """Return the table as a Pandas DataFrame.
+
+        Intercept `to_arrow()` for better error message.
+        """
+        return NotImplementedError("to_pandas() is not supported on the LanceDB cloud")
 
     def create_index(
         self,

python/lancedb/table.py

@@ -13,16 +13,18 @@
 
 from __future__ import annotations
 
+import inspect
 import os
 from abc import ABC, abstractmethod
 from functools import cached_property
-from typing import Iterable, List, Union
+from typing import Iterable, List, Optional, Union
 
 import lance
 import numpy as np
 import pyarrow as pa
 import pyarrow.compute as pc
 from lance import LanceDataset
+from lance.dataset import ReaderLike
 from lance.vector import vec_to_table
 
 from .common import DATA, VEC, VECTOR_COLUMN_NAME
@@ -46,15 +48,31 @@ def _sanitize_data(data, schema, on_bad_vectors, fill_value):
     if isinstance(data, dict):
         data = vec_to_table(data)
     if pd is not None and isinstance(data, pd.DataFrame):
-        data = pa.Table.from_pandas(data)
+        data = pa.Table.from_pandas(data, preserve_index=False)
         data = _sanitize_schema(
             data, schema=schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
         )
+        # Do not serialize Pandas metadata
+        metadata = data.schema.metadata if data.schema.metadata is not None else {}
+        metadata = {k: v for k, v in metadata.items() if k != b"pandas"}
+        schema = data.schema.with_metadata(metadata)
+        data = pa.Table.from_arrays(data.columns, schema=schema)
+    if isinstance(data, Iterable):
+        data = _to_record_batch_generator(data, schema, on_bad_vectors, fill_value)
     if not isinstance(data, (pa.Table, Iterable)):
         raise TypeError(f"Unsupported data type: {type(data)}")
     return data
 
 
+def _to_record_batch_generator(data: Iterable, schema, on_bad_vectors, fill_value):
+    for batch in data:
+        if not isinstance(batch, pa.RecordBatch):
+            table = _sanitize_data(batch, schema, on_bad_vectors, fill_value)
+            for batch in table.to_batches():
+                yield batch
+        yield batch
+
+
 class Table(ABC):
     """
     A [Table](Table) is a collection of Records in a LanceDB [Database](Database).
@@ -84,7 +102,7 @@ class Table(ABC):
     Can query the table with [Table.search][lancedb.table.Table.search].
 
     >>> table.search([0.4, 0.4]).select(["b"]).to_df()
-       b      vector  score
+       b      vector  _distance
     0  4  [0.5, 1.3]       0.82
     1  2  [1.1, 1.2]       1.13
 
@@ -92,9 +110,10 @@ class Table(ABC):
     [Table.create_index][lancedb.table.Table.create_index].
     """
 
+    @property
     @abstractmethod
     def schema(self) -> pa.Schema:
-        """Return the [Arrow Schema](https://arrow.apache.org/docs/python/api/datatypes.html#) of
+        """The [Arrow Schema](https://arrow.apache.org/docs/python/api/datatypes.html#) of
         this [Table](Table)
 
         """
@@ -194,7 +213,7 @@ class Table(ABC):
         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
+            and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
         raise NotImplementedError
@@ -261,10 +280,11 @@ class LanceTable(Table):
         self.name = name
         self._version = version
 
-    def _reset_dataset(self):
+    def _reset_dataset(self, version=None):
         try:
             if "_dataset" in self.__dict__:
                 del self.__dict__["_dataset"]
+            self._version = version
         except AttributeError:
             pass
 
@@ -290,7 +310,9 @@ class LanceTable(Table):
     def checkout(self, version: int):
         """Checkout a version of the table. This is an in-place operation.
 
-        This allows viewing previous versions of the table.
+        This allows viewing previous versions of the table. If you wish to
+        keep writing to the dataset starting from an old version, then use
+        the `restore` function.
 
         Parameters
         ----------
@@ -318,7 +340,54 @@ class LanceTable(Table):
         max_ver = max([v["version"] for v in self._dataset.versions()])
         if version < 1 or version > max_ver:
             raise ValueError(f"Invalid version {version}")
-        self._version = version
+        self._reset_dataset(version=version)
+
+    def restore(self, version: int = None):
+        """Restore a version of the table. This is an in-place operation.
+
+        This creates a new version where the data is equivalent to the
+        specified previous version. Data is not copied (as of python-v0.2.1).
+
+        Parameters
+        ----------
+        version : int, default None
+            The version to restore. If unspecified then restores the currently
+            checked out version. If the currently checked out version is the
+            latest version then this is a no-op.
+
+        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"}])
+        >>> table.version
+        2
+        >>> table.restore(1)
+        >>> table.to_pandas()
+               vector    type
+        0  [1.1, 0.9]  vector
+        >>> len(table.list_versions())
+        3
+        """
+        max_ver = max([v["version"] for v in self._dataset.versions()])
+        if version is None:
+            version = self.version
+        elif version < 1 or version > max_ver:
+            raise ValueError(f"Invalid version {version}")
+        else:
+            self.checkout(version)
+
+        if version == max_ver:
+            # no-op if restoring the latest version
+            return
+
+        self._dataset.restore()
         self._reset_dataset()
 
     def __len__(self):
@@ -434,7 +503,70 @@ class LanceTable(Table):
         data = _sanitize_data(
             data, self.schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
         )
-        lance.write_dataset(data, self._dataset_uri, mode=mode)
+        lance.write_dataset(data, self._dataset_uri, schema=self.schema, mode=mode)
+        self._reset_dataset()
+
+    def merge(
+        self,
+        other_table: Union[LanceTable, ReaderLike],
+        left_on: str,
+        right_on: Optional[str] = None,
+        schema: Optional[pa.Schema, LanceModel] = None,
+    ):
+        """Merge another table into this table.
+
+        Performs a left join, where the dataset is the left side and other_table
+        is the right side. Rows existing in the dataset but not on the left will
+        be filled with null values, unless Lance doesn't support null values for
+        some types, in which case an error will be raised. The only overlapping
+        column allowed is the join column. If other overlapping columns exist,
+        an error will be raised.
+
+        Parameters
+        ----------
+        other_table: LanceTable or Reader-like
+            The data to be merged. Acceptable types are:
+            - Pandas DataFrame, Pyarrow Table, Dataset, Scanner,
+            Iterator[RecordBatch], or RecordBatchReader
+            - LanceTable
+        left_on: str
+            The name of the column in the dataset to join on.
+        right_on: str or None
+            The name of the column in other_table to join on. If None, defaults to
+            left_on.
+        schema: pa.Schema or LanceModel, optional
+            The schema of the other_table.
+            If not provided, the schema is inferred from the data.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> import pyarrow as pa
+        >>> df = pa.table({'x': [1, 2, 3], 'y': ['a', 'b', 'c']})
+        >>> db = lancedb.connect("./.lancedb")
+        >>> table = db.create_table("dataset", df)
+        >>> table.to_pandas()
+           x  y
+        0  1  a
+        1  2  b
+        2  3  c
+        >>> new_df = pa.table({'x': [1, 2, 3], 'z': ['d', 'e', 'f']})
+        >>> table.merge(new_df, 'x')
+        >>> table.to_pandas()
+           x  y  z
+        0  1  a  d
+        1  2  b  e
+        2  3  c  f
+        """
+        if isinstance(schema, LanceModel):
+            schema = schema.to_arrow_schema()
+        if isinstance(other_table, LanceTable):
+            other_table = other_table.to_lance()
+        if isinstance(other_table, LanceDataset):
+            other_table = other_table.to_table()
+        self._dataset.merge(
+            other_table, left_on=left_on, right_on=right_on, schema=schema
+        )
         self._reset_dataset()
 
     def search(
@@ -455,7 +587,7 @@ class LanceTable(Table):
         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
+            and also the "_distance" column which is the distance between the query
             vector and the returned vector.
         """
         if isinstance(query, str):
@@ -506,7 +638,7 @@ class LanceTable(Table):
         data: list-of-dict, dict, pd.DataFrame, default None
             The data to insert into the table.
             At least one of `data` or `schema` must be provided.
-        schema: dict, optional
+        schema: pa.Schema or LanceModel, optional
             The schema of the table. If not provided, the schema is inferred from the data.
             At least one of `data` or `schema` must be provided.
         mode: str, default "create"
@@ -519,6 +651,8 @@ class LanceTable(Table):
             The value to use when filling vectors. Only used if on_bad_vectors="fill".
         """
         tbl = LanceTable(db, name)
+        if inspect.isclass(schema) and issubclass(schema, LanceModel):
+            schema = schema.to_arrow_schema()
         if data is not None:
             data = _sanitize_data(
                 data, schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value

python/pyproject.toml

@@ -1,11 +1,18 @@
 [project]
 name = "lancedb"
-version = "0.1.16"
-dependencies = ["pylance==0.5.10", "ratelimiter", "retry", "tqdm", "aiohttp", "pydantic", "attr", "semver"]
-description = "lancedb"
-authors = [
-    { name = "LanceDB Devs", email = "dev@lancedb.com" },
+version = "0.2.2"
+dependencies = [
+    "pylance==0.6.5",
+    "ratelimiter",
+    "retry",
+    "tqdm",
+    "aiohttp",
+    "pydantic",
+    "attr",
+    "semver>=3.0"
 ]
+description = "lancedb"
+authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]
 license = { file = "LICENSE" }
 readme = "README.md"
 requires-python = ">=3.8"
@@ -36,21 +43,12 @@ classifiers = [
 repository = "https://github.com/lancedb/lancedb"
 
 [project.optional-dependencies]
-tests = [
-    "pandas>=1.4", "pytest", "pytest-mock", "pytest-asyncio"
-]
-dev = [
-    "ruff", "pre-commit", "black"
-]
-docs = [
-    "mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"
-]
+tests = ["pandas>=1.4", "pytest", "pytest-mock", "pytest-asyncio"]
+dev = ["ruff", "pre-commit", "black"]
+docs = ["mkdocs", "mkdocs-jupyter", "mkdocs-material", "mkdocstrings[python]"]
 
 [build-system]
-requires = [
-  "setuptools",
-  "wheel",
-]
+requires = ["setuptools", "wheel"]
 build-backend = "setuptools.build_meta"
 
 [tool.isort]

python/tests/test_db.py

@@ -17,6 +17,7 @@ import pyarrow as pa
 import pytest
 
 import lancedb
+from lancedb.pydantic import LanceModel, vector
 
 
 def test_basic(tmp_path):
@@ -76,30 +77,78 @@ def test_ingest_pd(tmp_path):
     assert db.open_table("test").name == db["test"].name
 
 
-def test_ingest_record_batch_iterator(tmp_path):
-    def batch_reader():
-        for i in range(5):
-            yield pa.RecordBatch.from_arrays(
+def test_ingest_iterator(tmp_path):
+    class PydanticSchema(LanceModel):
+        vector: vector(2)
+        item: str
+        price: float
+
+    arrow_schema = pa.schema(
         [
-                    pa.array([[3.1, 4.1], [5.9, 26.5]]),
+            pa.field("vector", pa.list_(pa.float32(), 2)),
+            pa.field("item", pa.utf8()),
+            pa.field("price", pa.float32()),
+        ]
+    )
+
+    def make_batches():
+        for _ in range(5):
+            yield from [
+                # pandas
+                pd.DataFrame(
+                    {
+                        "vector": [[3.1, 4.1], [1, 1]],
+                        "item": ["foo", "bar"],
+                        "price": [10.0, 20.0],
+                    }
+                ),
+                # pylist
+                [
+                    {"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
+                    {"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
+                ],
+                # recordbatch
+                pa.RecordBatch.from_arrays(
+                    [
+                        pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)),
                         pa.array(["foo", "bar"]),
                         pa.array([10.0, 20.0]),
                     ],
                     ["vector", "item", "price"],
-            )
-
-    db = lancedb.connect(tmp_path)
-    tbl = db.create_table(
-        "test",
-        batch_reader(),
-        schema=pa.schema(
-            [
-                pa.field("vector", pa.list_(pa.float32())),
-                pa.field("item", pa.utf8()),
-                pa.field("price", pa.float32()),
-            ]
                 ),
-    )
+                # pa Table
+                pa.Table.from_arrays(
+                    [
+                        pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)),
+                        pa.array(["foo", "bar"]),
+                        pa.array([10.0, 20.0]),
+                    ],
+                    ["vector", "item", "price"],
+                ),
+                # pydantic list
+                [
+                    PydanticSchema(vector=[3.1, 4.1], item="foo", price=10.0),
+                    PydanticSchema(vector=[5.9, 26.5], item="bar", price=20.0),
+                ]
+                # TODO: test pydict separately. it is unique column number and names contraint
+            ]
+
+    def run_tests(schema):
+        db = lancedb.connect(tmp_path)
+        tbl = db.create_table("table2", make_batches(), schema=schema, mode="overwrite")
+
+        tbl.to_pandas()
+        assert tbl.search([3.1, 4.1]).limit(1).to_df()["_distance"][0] == 0.0
+        assert tbl.search([5.9, 26.5]).limit(1).to_df()["_distance"][0] == 0.0
+
+        tbl_len = len(tbl)
+        tbl.add(make_batches())
+        assert len(tbl) == tbl_len * 2
+        assert len(tbl.list_versions()) == 2
+        db.drop_database()
+
+    run_tests(arrow_schema)
+    run_tests(PydanticSchema)
 
 
 def test_create_mode(tmp_path):
@@ -154,6 +203,47 @@ def test_delete_table(tmp_path):
     db.drop_table("does_not_exist", ignore_missing=True)
 
 
+def test_drop_database(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],
+        }
+    )
+    new_data = pd.DataFrame(
+        {
+            "vector": [[5.1, 4.1], [5.9, 10.5]],
+            "item": ["kiwi", "avocado"],
+            "price": [12.0, 17.0],
+        }
+    )
+    db.create_table("test", data=data)
+    with pytest.raises(Exception):
+        db.create_table("test", data=data)
+
+    assert db.table_names() == ["test"]
+
+    db.create_table("new_test", data=new_data)
+    db.drop_database()
+    assert db.table_names() == []
+
+    # it should pass when no tables are present
+    db.create_table("test", data=new_data)
+    db.drop_table("test")
+    assert db.table_names() == []
+    db.drop_database()
+    assert db.table_names() == []
+
+    # creating an empty database with schema
+    schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))])
+    db.create_table("empty_table", schema=schema)
+    # dropping a empty database should pass
+    db.drop_database()
+    assert db.table_names() == []
+
+
 def test_empty_or_nonexistent_table(tmp_path):
     db = lancedb.connect(tmp_path)
     with pytest.raises(Exception):
@@ -162,8 +252,14 @@ def test_empty_or_nonexistent_table(tmp_path):
     with pytest.raises(Exception):
         db.open_table("does_not_exist")
 
-    schema = pa.schema([pa.field("a", pa.int32())])
-    db.create_table("test", schema=schema)
+    schema = pa.schema([pa.field("a", pa.int64(), nullable=False)])
+    test = db.create_table("test", schema=schema)
+
+    class TestModel(LanceModel):
+        a: int
+
+    test2 = db.create_table("test2", schema=TestModel)
+    assert test.schema == test2.schema
 
 
 def test_replace_index(tmp_path):

python/tests/test_fts.py

@@ -66,7 +66,7 @@ def test_search_index(tmp_path, table):
     results = ldb.fts.search_index(index, query="puppy", limit=10)
     assert len(results) == 2
     assert len(results[0]) == 10  # row_ids
-    assert len(results[1]) == 10  # scores
+    assert len(results[1]) == 10  # _distance
 
 
 def test_create_index_from_table(tmp_path, table):

python/tests/test_query.py

@@ -108,11 +108,11 @@ def test_query_builder_with_metric(table):
         .limit(1)
         .to_df()
     )
-    assert df_cosine.score[0] == pytest.approx(
+    assert df_cosine._distance[0] == pytest.approx(
         cosine_distance(query, df_cosine.vector[0]),
         abs=1e-6,
     )
-    assert 0 <= df_cosine.score[0] <= 1
+    assert 0 <= df_cosine._distance[0] <= 1
 
 
 def test_query_builder_with_different_vector_column():

python/tests/test_table.py

@@ -16,6 +16,7 @@ from pathlib import Path
 from typing import List
 from unittest.mock import PropertyMock, patch
 
+import lance
 import numpy as np
 import pandas as pd
 import pyarrow as pa
@@ -268,3 +269,50 @@ def test_add_with_nans(db):
     arrow_tbl = table.to_lance().to_table(filter="item == 'bar'")
     v = arrow_tbl["vector"].to_pylist()[0]
     assert np.allclose(v, np.array([0.0, 0.0]))
+
+
+def test_restore(db):
+    table = LanceTable.create(
+        db,
+        "my_table",
+        data=[{"vector": [1.1, 0.9], "type": "vector"}],
+    )
+    table.add([{"vector": [0.5, 0.2], "type": "vector"}])
+    table.restore(1)
+    assert len(table.list_versions()) == 3
+    assert len(table) == 1
+
+    expected = table.to_arrow()
+    table.checkout(1)
+    table.restore()
+    assert len(table.list_versions()) == 4
+    assert table.to_arrow() == expected
+
+    table.restore(4)  # latest version should be no-op
+    assert len(table.list_versions()) == 4
+
+    with pytest.raises(ValueError):
+        table.restore(5)
+
+    with pytest.raises(ValueError):
+        table.restore(0)
+
+
+def test_merge(db, tmp_path):
+    table = LanceTable.create(
+        db,
+        "my_table",
+        data=[{"vector": [1.1, 0.9], "id": 0}, {"vector": [1.2, 1.9], "id": 1}],
+    )
+    other_table = pa.table({"document": ["foo", "bar"], "id": [0, 1]})
+    table.merge(other_table, left_on="id")
+    assert len(table.list_versions()) == 2
+    expected = pa.table(
+        {"vector": [[1.1, 0.9], [1.2, 1.9]], "id": [0, 1], "document": ["foo", "bar"]},
+        schema=table.schema,
+    )
+    assert table.to_arrow() == expected
+
+    other_dataset = lance.write_dataset(other_table, tmp_path / "other_table.lance")
+    table.restore(1)
+    table.merge(other_dataset, left_on="id")

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb-node"
-version = "0.1.19"
+version = "0.2.4"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
 edition = "2018"

rust/ffi/node/src/arrow.rs

@@ -14,60 +14,33 @@
 
 use std::io::Cursor;
 use std::ops::Deref;
-use std::sync::Arc;
 
-use arrow_array::cast::as_list_array;
-use arrow_array::{Array, ArrayRef, FixedSizeListArray, RecordBatch};
+use arrow_array::RecordBatch;
 use arrow_ipc::reader::FileReader;
 use arrow_ipc::writer::FileWriter;
-use arrow_schema::{DataType, Field, Schema};
-use lance::arrow::{FixedSizeListArrayExt, RecordBatchExt};
+use arrow_schema::SchemaRef;
 use vectordb::table::VECTOR_COLUMN_NAME;
 
 use crate::error::{MissingColumnSnafu, Result};
 use snafu::prelude::*;
 
-pub(crate) fn convert_record_batch(record_batch: RecordBatch) -> Result<RecordBatch> {
-    let column = get_column(VECTOR_COLUMN_NAME, &record_batch)?;
-
-    // TODO: we should just consume the underlying js buffer in the future instead of this arrow around a bunch of times
-    let arr = as_list_array(column.as_ref());
-    let list_size = arr.values().len() / record_batch.num_rows();
-    let r = FixedSizeListArray::try_new_from_values(arr.values().to_owned(), list_size as i32)?;
-
-    let schema = Arc::new(Schema::new(vec![Field::new(
-        VECTOR_COLUMN_NAME,
-        DataType::FixedSizeList(
-            Arc::new(Field::new("item", DataType::Float32, true)),
-            list_size as i32,
-        ),
-        true,
-    )]));
-
-    let mut new_batch = RecordBatch::try_new(schema.clone(), vec![Arc::new(r)])?;
-
-    if record_batch.num_columns() > 1 {
-        let rb = record_batch.drop_column(VECTOR_COLUMN_NAME)?;
-        new_batch = new_batch.merge(&rb)?;
-    }
-    Ok(new_batch)
-}
-
-fn get_column(column_name: &str, record_batch: &RecordBatch) -> Result<ArrayRef> {
+fn validate_vector_column(record_batch: &RecordBatch) -> Result<()> {
     record_batch
-        .column_by_name(column_name)
-        .cloned()
-        .context(MissingColumnSnafu { name: column_name })
+        .column_by_name(VECTOR_COLUMN_NAME)
+        .map(|_| ())
+        .context(MissingColumnSnafu { name: VECTOR_COLUMN_NAME })
 }
 
-pub(crate) fn arrow_buffer_to_record_batch(slice: &[u8]) -> Result<Vec<RecordBatch>> {
+pub(crate) fn arrow_buffer_to_record_batch(slice: &[u8]) -> Result<(Vec<RecordBatch>, SchemaRef)> {
     let mut batches: Vec<RecordBatch> = Vec::new();
     let file_reader = FileReader::try_new(Cursor::new(slice), None)?;
+    let schema = file_reader.schema().clone();
     for b in file_reader {
-        let record_batch = convert_record_batch(b?)?;
+        let record_batch = b?;
+        validate_vector_column(&record_batch)?;
         batches.push(record_batch);
     }
-    Ok(batches)
+    Ok((batches, schema))
 }
 
 pub(crate) fn record_batch_to_buffer(batches: Vec<RecordBatch>) -> Result<Vec<u8>> {

rust/ffi/node/src/error.rs

@@ -67,6 +67,14 @@ impl From<neon::result::Throw> for Error {
     }
 }
 
+impl<T> From<std::sync::mpsc::SendError<T>> for Error {
+    fn from(value: std::sync::mpsc::SendError<T>) -> Self {
+        Self::Neon {
+            message: value.to_string(),
+        }
+    }
+}
+
 /// ResultExt is used to transform a [`Result`] into a [`NeonResult`],
 /// so it can be returned as a JavaScript error
 /// Copied from [Neon](https://github.com/neon-bindings/neon/blob/4c2e455a9e6814f1ba0178616d63caec7f4df317/crates/neon/src/result/mod.rs#L88)

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

@@ -12,20 +12,20 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use std::convert::TryFrom;
-
 use lance::index::vector::ivf::IvfBuildParams;
 use lance::index::vector::pq::PQBuildParams;
 use lance::index::vector::MetricType;
 use neon::context::FunctionContext;
 use neon::prelude::*;
+use std::convert::TryFrom;
 
 use vectordb::index::vector::{IvfPQIndexBuilder, VectorIndexBuilder};
 
 use crate::error::Error::InvalidIndexType;
 use crate::error::ResultExt;
 use crate::neon_ext::js_object_ext::JsObjectExt;
-use crate::{runtime, JsTable};
+use crate::runtime;
+use crate::table::JsTable;
 
 pub(crate) fn table_create_vector_index(mut cx: FunctionContext) -> JsResult<JsPromise> {
     let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
@@ -33,22 +33,17 @@ pub(crate) fn table_create_vector_index(mut cx: FunctionContext) -> JsResult<JsP
     let index_params_builder = get_index_params_builder(&mut cx, index_params).or_throw(&mut cx)?;
 
     let rt = runtime(&mut cx)?;
-    let channel = cx.channel();
 
     let (deferred, promise) = cx.promise();
-    let table = js_table.table.clone();
+    let channel = cx.channel();
+    let mut table = js_table.table.clone();
 
-    rt.block_on(async move {
-        let add_result = table
-            .lock()
-            .unwrap()
-            .create_index(&index_params_builder)
-            .await;
+    rt.spawn(async move {
+        let idx_result = table.create_index(&index_params_builder).await;
 
         deferred.settle_with(&channel, move |mut cx| {
-            add_result
-                .map(|_| cx.undefined())
-                .or_else(|err| cx.throw_error(err.to_string()))
+            idx_result.or_throw(&mut cx)?;
+            Ok(cx.boxed(JsTable::from(table)))
         });
     });
     Ok(promise)

rust/ffi/node/src/lib.rs

@@ -12,36 +12,30 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use std::collections::HashMap;
-use std::convert::TryFrom;
-use std::ops::Deref;
-use std::sync::{Arc, Mutex};
+use std::sync::Arc;
 
-use arrow_array::{Float32Array, RecordBatchIterator};
 use async_trait::async_trait;
-use futures::{TryFutureExt, TryStreamExt};
-use lance::dataset::{WriteMode, WriteParams};
-use lance::index::vector::MetricType;
 use lance::io::object_store::ObjectStoreParams;
 use neon::prelude::*;
-use neon::types::buffer::TypedArray;
 use object_store::aws::{AwsCredential, AwsCredentialProvider};
 use object_store::CredentialProvider;
 use once_cell::sync::OnceCell;
 use tokio::runtime::Runtime;
 
 use vectordb::database::Database;
-use vectordb::table::{ReadParams, Table};
+use vectordb::table::ReadParams;
 
-use crate::arrow::{arrow_buffer_to_record_batch, record_batch_to_buffer};
 use crate::error::ResultExt;
-use crate::neon_ext::js_object_ext::JsObjectExt;
+use crate::query::JsQuery;
+use crate::table::JsTable;
 
 mod arrow;
 mod convert;
 mod error;
 mod index;
 mod neon_ext;
+mod query;
+mod table;
 
 struct JsDatabase {
     database: Arc<Database>,
@@ -49,12 +43,6 @@ struct JsDatabase {
 
 impl Finalize for JsDatabase {}
 
-struct JsTable {
-    table: Arc<Mutex<Table>>,
-}
-
-impl Finalize for JsTable {}
-
 // TODO: object_store didn't export this type so I copied it.
 // Make a request to object_store to export this type
 #[derive(Debug)]
@@ -133,26 +121,28 @@ fn database_table_names(mut cx: FunctionContext) -> JsResult<JsPromise> {
     Ok(promise)
 }
 
-fn get_aws_creds<T>(
+/// Get AWS creds arguments from the context
+/// Consumes 3 arguments
+fn get_aws_creds(
     cx: &mut FunctionContext,
     arg_starting_location: i32,
-) -> Result<Option<AwsCredentialProvider>, NeonResult<T>> {
+) -> NeonResult<Option<AwsCredentialProvider>> {
     let secret_key_id = cx
         .argument_opt(arg_starting_location)
-        .map(|arg| arg.downcast_or_throw::<JsString, FunctionContext>(cx).ok())
-        .flatten()
+        .filter(|arg| arg.is_a::<JsString, _>(cx))
+        .and_then(|arg| arg.downcast_or_throw::<JsString, FunctionContext>(cx).ok())
         .map(|v| v.value(cx));
 
     let secret_key = cx
         .argument_opt(arg_starting_location + 1)
-        .map(|arg| arg.downcast_or_throw::<JsString, FunctionContext>(cx).ok())
-        .flatten()
+        .filter(|arg| arg.is_a::<JsString, _>(cx))
+        .and_then(|arg| arg.downcast_or_throw::<JsString, FunctionContext>(cx).ok())
         .map(|v| v.value(cx));
 
     let temp_token = cx
         .argument_opt(arg_starting_location + 2)
-        .map(|arg| arg.downcast_or_throw::<JsString, FunctionContext>(cx).ok())
-        .flatten()
+        .filter(|arg| arg.is_a::<JsString, _>(cx))
+        .and_then(|arg| arg.downcast_or_throw::<JsString, FunctionContext>(cx).ok())
         .map(|v| v.value(cx));
 
     match (secret_key_id, secret_key, temp_token) {
@@ -164,7 +154,21 @@ fn get_aws_creds<T>(
             }),
         ))),
         (None, None, None) => Ok(None),
-        _ => Err(cx.throw_error("Invalid credentials configuration")),
+        _ => cx.throw_error("Invalid credentials configuration"),
+    }
+}
+
+/// Get AWS region arguments from the context
+fn get_aws_region(cx: &mut FunctionContext, arg_location: i32) -> NeonResult<Option<String>> {
+    let region = cx
+        .argument_opt(arg_location)
+        .filter(|arg| arg.is_a::<JsString, _>(cx))
+        .map(|arg| arg.downcast_or_throw::<JsString, FunctionContext>(cx));
+
+    match region {
+        Some(Ok(region)) => Ok(Some(region.value(cx))),
+        None => Ok(None),
+        Some(Err(e)) => Err(e),
     }
 }
 
@@ -174,14 +178,14 @@ fn database_open_table(mut cx: FunctionContext) -> JsResult<JsPromise> {
         .downcast_or_throw::<JsBox<JsDatabase>, _>(&mut cx)?;
     let table_name = cx.argument::<JsString>(0)?.value(&mut cx);
 
-    let aws_creds = match get_aws_creds(&mut cx, 1) {
-        Ok(creds) => creds,
-        Err(err) => return err,
-    };
+    let aws_creds = get_aws_creds(&mut cx, 1)?;
+
+    let aws_region = get_aws_region(&mut cx, 4)?;
 
     let params = ReadParams {
         store_options: Some(ObjectStoreParams {
             aws_credentials: aws_creds,
+            aws_region,
             ..ObjectStoreParams::default()
         }),
         ..ReadParams::default()
@@ -196,8 +200,8 @@ fn database_open_table(mut cx: FunctionContext) -> JsResult<JsPromise> {
         let table_rst = database.open_table_with_params(&table_name, &params).await;
 
         deferred.settle_with(&channel, move |mut cx| {
-            let table = Arc::new(Mutex::new(table_rst.or_throw(&mut cx)?));
-            Ok(cx.boxed(JsTable { table }))
+            let js_table = JsTable::from(table_rst.or_throw(&mut cx)?);
+            Ok(cx.boxed(js_table))
         });
     });
     Ok(promise)
@@ -224,221 +228,17 @@ fn database_drop_table(mut cx: FunctionContext) -> JsResult<JsPromise> {
     Ok(promise)
 }
 
-fn table_search(mut cx: FunctionContext) -> JsResult<JsPromise> {
-    let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
-    let query_obj = cx.argument::<JsObject>(0)?;
-
-    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));
-    let refine_factor = query_obj
-        .get_opt_u32(&mut cx, "_refineFactor")
-        .or_throw(&mut cx)?;
-    let nprobes = query_obj.get_usize(&mut cx, "_nprobes").or_throw(&mut cx)?;
-    let metric_type = query_obj
-        .get_opt::<JsString, _, _>(&mut cx, "_metricType")?
-        .map(|s| s.value(&mut cx))
-        .map(|s| MetricType::try_from(s.as_str()).unwrap());
-
-    let rt = runtime(&mut cx)?;
-    let channel = cx.channel();
-
-    let (deferred, promise) = cx.promise();
-    let table = js_table.table.clone();
-    let query_vector = query_obj.get::<JsArray, _, _>(&mut cx, "_queryVector")?;
-    let query = convert::js_array_to_vec(query_vector.deref(), &mut cx);
-
-    rt.spawn(async move {
-        let builder = table
-            .lock()
-            .unwrap()
-            .search(Float32Array::from(query))
-            .limit(limit as usize)
-            .refine_factor(refine_factor)
-            .nprobes(nprobes)
-            .filter(filter)
-            .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(vectordb::error::Error::from)
-            })
-            .await;
-
-        deferred.settle_with(&channel, move |mut cx| {
-            let results = results.or_throw(&mut cx)?;
-            let buffer = record_batch_to_buffer(results).or_throw(&mut cx)?;
-            Ok(JsBuffer::external(&mut cx, buffer))
-        });
-    });
-    Ok(promise)
-}
-
-fn table_create(mut cx: FunctionContext) -> JsResult<JsPromise> {
-    let db = cx
-        .this()
-        .downcast_or_throw::<JsBox<JsDatabase>, _>(&mut cx)?;
-    let table_name = cx.argument::<JsString>(0)?.value(&mut cx);
-    let buffer = cx.argument::<JsBuffer>(1)?;
-    let batches = arrow_buffer_to_record_batch(buffer.as_slice(&mut cx)).or_throw(&mut cx)?;
-    let schema = batches[0].schema();
-
-    // Write mode
-    let mode = match cx.argument::<JsString>(2)?.value(&mut cx).as_str() {
-        "overwrite" => WriteMode::Overwrite,
-        "append" => WriteMode::Append,
-        "create" => WriteMode::Create,
-        _ => return cx.throw_error("Table::create only supports 'overwrite' and 'create' modes"),
-    };
-
-    let rt = runtime(&mut cx)?;
-    let channel = cx.channel();
-
-    let (deferred, promise) = cx.promise();
-    let database = db.database.clone();
-
-    let aws_creds = match get_aws_creds(&mut cx, 3) {
-        Ok(creds) => creds,
-        Err(err) => return err,
-    };
-
-    let params = WriteParams {
-        store_params: Some(ObjectStoreParams {
-            aws_credentials: aws_creds,
-            ..ObjectStoreParams::default()
-        }),
-        mode: mode,
-        ..WriteParams::default()
-    };
-
-    rt.block_on(async move {
-        let batch_reader = RecordBatchIterator::new(batches.into_iter().map(Ok), schema);
-        let table_rst = database
-            .create_table(&table_name, batch_reader, Some(params))
-            .await;
-
-        deferred.settle_with(&channel, move |mut cx| {
-            let table = Arc::new(Mutex::new(table_rst.or_throw(&mut cx)?));
-            Ok(cx.boxed(JsTable { table }))
-        });
-    });
-    Ok(promise)
-}
-
-fn table_add(mut cx: FunctionContext) -> JsResult<JsPromise> {
-    let write_mode_map: HashMap<&str, WriteMode> = HashMap::from([
-        ("create", WriteMode::Create),
-        ("append", WriteMode::Append),
-        ("overwrite", WriteMode::Overwrite),
-    ]);
-
-    let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
-    let buffer = cx.argument::<JsBuffer>(0)?;
-    let write_mode = cx.argument::<JsString>(1)?.value(&mut cx);
-
-    let batches = arrow_buffer_to_record_batch(buffer.as_slice(&mut cx)).or_throw(&mut cx)?;
-    let schema = batches[0].schema();
-
-    let rt = runtime(&mut cx)?;
-    let channel = cx.channel();
-
-    let (deferred, promise) = cx.promise();
-    let table = js_table.table.clone();
-    let write_mode = write_mode_map.get(write_mode.as_str()).cloned();
-
-    let aws_creds = match get_aws_creds(&mut cx, 2) {
-        Ok(creds) => creds,
-        Err(err) => return err,
-    };
-
-    let params = WriteParams {
-        store_params: Some(ObjectStoreParams {
-            aws_credentials: aws_creds,
-            ..ObjectStoreParams::default()
-        }),
-        mode: write_mode.unwrap_or(WriteMode::Append),
-        ..WriteParams::default()
-    };
-
-    rt.block_on(async move {
-        let batch_reader = RecordBatchIterator::new(batches.into_iter().map(Ok), schema);
-        let add_result = table.lock().unwrap().add(batch_reader, Some(params)).await;
-
-        deferred.settle_with(&channel, move |mut cx| {
-            let _added = add_result.or_throw(&mut cx)?;
-            Ok(cx.boolean(true))
-        });
-    });
-    Ok(promise)
-}
-
-fn table_count_rows(mut cx: FunctionContext) -> JsResult<JsPromise> {
-    let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
-    let rt = runtime(&mut cx)?;
-    let channel = cx.channel();
-
-    let (deferred, promise) = cx.promise();
-    let table = js_table.table.clone();
-
-    rt.block_on(async move {
-        let num_rows_result = table.lock().unwrap().count_rows().await;
-
-        deferred.settle_with(&channel, move |mut cx| {
-            let num_rows = num_rows_result.or_throw(&mut cx)?;
-            Ok(cx.number(num_rows as f64))
-        });
-    });
-    Ok(promise)
-}
-
-fn table_delete(mut cx: FunctionContext) -> JsResult<JsPromise> {
-    let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
-    let rt = runtime(&mut cx)?;
-    let channel = cx.channel();
-
-    let (deferred, promise) = cx.promise();
-    let table = js_table.table.clone();
-
-    let predicate = cx.argument::<JsString>(0)?.value(&mut cx);
-
-    let delete_result = rt.block_on(async move { table.lock().unwrap().delete(&predicate).await });
-
-    deferred.settle_with(&channel, move |mut cx| {
-        delete_result.or_throw(&mut cx)?;
-        Ok(cx.undefined())
-    });
-
-    Ok(promise)
-}
-
 #[neon::main]
 fn main(mut cx: ModuleContext) -> NeonResult<()> {
     cx.export_function("databaseNew", database_new)?;
     cx.export_function("databaseTableNames", database_table_names)?;
     cx.export_function("databaseOpenTable", database_open_table)?;
     cx.export_function("databaseDropTable", database_drop_table)?;
-    cx.export_function("tableSearch", table_search)?;
-    cx.export_function("tableCreate", table_create)?;
-    cx.export_function("tableAdd", table_add)?;
-    cx.export_function("tableCountRows", table_count_rows)?;
-    cx.export_function("tableDelete", table_delete)?;
+    cx.export_function("tableSearch", JsQuery::js_search)?;
+    cx.export_function("tableCreate", JsTable::js_create)?;
+    cx.export_function("tableAdd", JsTable::js_add)?;
+    cx.export_function("tableCountRows", JsTable::js_count_rows)?;
+    cx.export_function("tableDelete", JsTable::js_delete)?;
     cx.export_function(
         "tableCreateVectorIndex",
         index::vector::table_create_vector_index,

rust/ffi/node/src/query.rs

@@ -0,0 +1,107 @@
+use std::convert::TryFrom;
+use std::ops::Deref;
+
+use arrow_array::Float32Array;
+use futures::{TryFutureExt, TryStreamExt};
+use lance::index::vector::MetricType;
+use neon::context::FunctionContext;
+use neon::handle::Handle;
+use neon::prelude::*;
+use neon::types::buffer::TypedArray;
+
+use crate::arrow::record_batch_to_buffer;
+use crate::error::ResultExt;
+use crate::neon_ext::js_object_ext::JsObjectExt;
+use crate::table::JsTable;
+use crate::{convert, runtime};
+
+pub(crate) struct JsQuery {}
+
+impl JsQuery {
+    pub(crate) fn js_search(mut cx: FunctionContext) -> JsResult<JsPromise> {
+        let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
+        let query_obj = cx.argument::<JsObject>(0)?;
+
+        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));
+        let refine_factor = query_obj
+            .get_opt_u32(&mut cx, "_refineFactor")
+            .or_throw(&mut cx)?;
+        let nprobes = query_obj.get_usize(&mut cx, "_nprobes").or_throw(&mut cx)?;
+        let metric_type = query_obj
+            .get_opt::<JsString, _, _>(&mut cx, "_metricType")?
+            .map(|s| s.value(&mut cx))
+            .map(|s| MetricType::try_from(s.as_str()).unwrap());
+
+        let is_electron = cx
+            .argument::<JsBoolean>(1)
+            .or_throw(&mut cx)?
+            .value(&mut cx);
+
+        let rt = runtime(&mut cx)?;
+
+        let (deferred, promise) = cx.promise();
+        let channel = cx.channel();
+        let query_vector = query_obj.get::<JsArray, _, _>(&mut cx, "_queryVector")?;
+        let query = convert::js_array_to_vec(query_vector.deref(), &mut cx);
+        let table = js_table.table.clone();
+
+        rt.spawn(async move {
+            let builder = table
+                .search(Float32Array::from(query))
+                .limit(limit as usize)
+                .refine_factor(refine_factor)
+                .nprobes(nprobes)
+                .filter(filter)
+                .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(vectordb::error::Error::from)
+                })
+                .await;
+
+            deferred.settle_with(&channel, move |mut cx| {
+                let results = results.or_throw(&mut cx)?;
+                let buffer = record_batch_to_buffer(results).or_throw(&mut cx)?;
+                Self::new_js_buffer(buffer, &mut cx, is_electron)
+            });
+        });
+        Ok(promise)
+    }
+
+    // Creates a new JsBuffer from a rust buffer with a special logic for electron
+    fn new_js_buffer<'a>(
+        buffer: Vec<u8>,
+        cx: &mut TaskContext<'a>,
+        is_electron: bool,
+    ) -> NeonResult<Handle<'a, JsBuffer>> {
+        if is_electron {
+            // Electron does not support `external`: https://github.com/neon-bindings/neon/pull/937
+            let mut js_buffer = JsBuffer::new(cx, buffer.len()).or_throw(cx)?;
+            let buffer_data = js_buffer.as_mut_slice(cx);
+            buffer_data.copy_from_slice(buffer.as_slice());
+            Ok(js_buffer)
+        } else {
+            Ok(JsBuffer::external(cx, buffer))
+        }
+    }
+}

rust/ffi/node/src/table.rs

@@ -0,0 +1,168 @@
+// 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 arrow_array::RecordBatchIterator;
+use lance::dataset::{WriteMode, WriteParams};
+use lance::io::object_store::ObjectStoreParams;
+
+use crate::arrow::arrow_buffer_to_record_batch;
+use neon::prelude::*;
+use neon::types::buffer::TypedArray;
+use vectordb::Table;
+
+use crate::error::ResultExt;
+use crate::{get_aws_creds, get_aws_region, runtime, JsDatabase};
+
+pub(crate) struct JsTable {
+    pub table: Table,
+}
+
+impl Finalize for JsTable {}
+
+impl From<Table> for JsTable {
+    fn from(table: Table) -> Self {
+        JsTable { table }
+    }
+}
+
+impl JsTable {
+    pub(crate) fn js_create(mut cx: FunctionContext) -> JsResult<JsPromise> {
+        let db = cx
+            .this()
+            .downcast_or_throw::<JsBox<JsDatabase>, _>(&mut cx)?;
+        let table_name = cx.argument::<JsString>(0)?.value(&mut cx);
+        let buffer = cx.argument::<JsBuffer>(1)?;
+        let (batches, schema) = arrow_buffer_to_record_batch(buffer.as_slice(&mut cx)).or_throw(&mut cx)?;
+
+        // Write mode
+        let mode = match cx.argument::<JsString>(2)?.value(&mut cx).as_str() {
+            "overwrite" => WriteMode::Overwrite,
+            "append" => WriteMode::Append,
+            "create" => WriteMode::Create,
+            _ => {
+                return cx.throw_error("Table::create only supports 'overwrite' and 'create' modes")
+            }
+        };
+
+        let rt = runtime(&mut cx)?;
+        let channel = cx.channel();
+
+        let (deferred, promise) = cx.promise();
+        let database = db.database.clone();
+
+        let aws_creds = get_aws_creds(&mut cx, 3)?;
+        let aws_region = get_aws_region(&mut cx, 6)?;
+
+        let params = WriteParams {
+            store_params: Some(ObjectStoreParams {
+                aws_credentials: aws_creds,
+                aws_region,
+                ..ObjectStoreParams::default()
+            }),
+            mode: mode,
+            ..WriteParams::default()
+        };
+
+        rt.spawn(async move {
+            let batch_reader = RecordBatchIterator::new(batches.into_iter().map(Ok), schema);
+            let table_rst = database
+                .create_table(&table_name, batch_reader, Some(params))
+                .await;
+
+            deferred.settle_with(&channel, move |mut cx| {
+                let table = table_rst.or_throw(&mut cx)?;
+                Ok(cx.boxed(JsTable::from(table)))
+            });
+        });
+        Ok(promise)
+    }
+
+    pub(crate) fn js_add(mut cx: FunctionContext) -> JsResult<JsPromise> {
+        let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
+        let buffer = cx.argument::<JsBuffer>(0)?;
+        let write_mode = cx.argument::<JsString>(1)?.value(&mut cx);
+        let (batches, schema) = arrow_buffer_to_record_batch(buffer.as_slice(&mut cx)).or_throw(&mut cx)?;
+        let rt = runtime(&mut cx)?;
+        let channel = cx.channel();
+        let mut table = js_table.table.clone();
+
+        let (deferred, promise) = cx.promise();
+        let write_mode = match write_mode.as_str() {
+            "create" => WriteMode::Create,
+            "append" => WriteMode::Append,
+            "overwrite" => WriteMode::Overwrite,
+            s => return cx.throw_error(format!("invalid write mode {}", s)),
+        };
+        let aws_creds = get_aws_creds(&mut cx, 2)?;
+        let aws_region = get_aws_region(&mut cx, 5)?;
+
+        let params = WriteParams {
+            store_params: Some(ObjectStoreParams {
+                aws_credentials: aws_creds,
+                aws_region,
+                ..ObjectStoreParams::default()
+            }),
+            mode: write_mode,
+            ..WriteParams::default()
+        };
+
+        rt.spawn(async move {
+            let batch_reader = RecordBatchIterator::new(batches.into_iter().map(Ok), schema);
+            let add_result = table.add(batch_reader, Some(params)).await;
+
+            deferred.settle_with(&channel, move |mut cx| {
+                let _added = add_result.or_throw(&mut cx)?;
+                Ok(cx.boxed(JsTable::from(table)))
+            });
+        });
+        Ok(promise)
+    }
+
+    pub(crate) fn js_count_rows(mut cx: FunctionContext) -> JsResult<JsPromise> {
+        let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
+        let rt = runtime(&mut cx)?;
+        let (deferred, promise) = cx.promise();
+        let channel = cx.channel();
+        let table = js_table.table.clone();
+
+        rt.spawn(async move {
+            let num_rows_result = table.count_rows().await;
+
+            deferred.settle_with(&channel, move |mut cx| {
+                let num_rows = num_rows_result.or_throw(&mut cx)?;
+                Ok(cx.number(num_rows as f64))
+            });
+        });
+        Ok(promise)
+    }
+
+    pub(crate) fn js_delete(mut cx: FunctionContext) -> JsResult<JsPromise> {
+        let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
+        let rt = runtime(&mut cx)?;
+        let (deferred, promise) = cx.promise();
+        let predicate = cx.argument::<JsString>(0)?.value(&mut cx);
+        let channel = cx.channel();
+        let mut table = js_table.table.clone();
+
+        rt.spawn(async move {
+            let delete_result = table.delete(&predicate).await;
+
+            deferred.settle_with(&channel, move |mut cx| {
+                delete_result.or_throw(&mut cx)?;
+                Ok(cx.boxed(JsTable::from(table)))
+            })
+        });
+        Ok(promise)
+    }
+}

rust/vectordb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb"
-version = "0.1.19"
+version = "0.2.4"
 edition = "2021"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"

rust/vectordb/src/query.rs

@@ -53,7 +53,7 @@ impl Query {
             nprobes: 20,
             refine_factor: None,
             metric_type: None,
-            use_index: false,
+            use_index: true,
             filter: None,
             select: None,
         }