[python] Bump version: 0.1.16 → 0.2.0

BREAKING CHANGE: The `score` column has been renamed to `_distance` to
more accurately describe the semantics (smaller means closer / better).

---------

Co-authored-by: Lei Xu <lei@lancedb.com>

.github/workflows/python.yml

@@ -30,7 +30,7 @@ jobs:
         python-version: 3.${{ matrix.python-minor-version }}
     - name: Install lancedb
       run: |
-        pip install -e .
+        pip install -e .[tests]
         pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
         pip install pytest pytest-mock black isort
     - name: Black
@@ -59,7 +59,7 @@ jobs:
         python-version: "3.11"
     - name: Install lancedb
       run: |
-        pip install -e .
+        pip install -e .[tests]
         pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
         pip install pytest pytest-mock black
     - name: Black

.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

@@ -6,11 +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 = "=0.6.1"
+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 +11,7 @@ theme:
   features:
     - content.code.copy
     - content.tabs.link
+    - content.action.edit
   icon:
     repo: fontawesome/brands/github
   custom_dir: overrides
@@ -75,7 +77,8 @@ nav:
   - YouTube Transcript Search: examples/youtube_transcript_bot_with_nodejs.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
@@ -85,3 +88,8 @@ nav:
 
 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
@@ -151,21 +179,19 @@ 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
 

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/guides/tables.md

@@ -0,0 +1,352 @@
+A Table is a collection of Records in a LanceDB Database.
+
+## 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 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 RecordBatch Iterator / Writing Large Datasets
+
+    It is recommended to use RecordBatch itertator 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()`
+
+    ```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.array(["foo", "bar"]),
+                    pa.array([10.0, 20.0]),
+                ],
+                ["vector", "item", "price"],
+            )
+
+    schema = pa.schema([
+        pa.field("vector", pa.list_(pa.float32())),
+        pa.field("item", pa.utf8()),
+        pa.field("price", pa.float32()),
+    ])
+
+    db.create_table("table4", make_batches(), schema=schema)
+    ```
+
+    You can also use Pandas dataframe directly in the above example by converting it to `RecordBatch` object
+
+    ```python
+    import pandas as pd
+    import pyarrow as pa
+
+    df = pd.DataFrame({'vector': [[0,1], [2,3], [4,5],[6,7]],
+                        'month': [3, 5, 7, 9],
+                        'day': [1, 5, 9, 13],
+                        'n_legs': [2, 4, 5, 100],
+                        'animals': ["Flamingo", "Horse", "Brittle stars", "Centipede"]})
+
+    batch = pa.RecordBatch.from_pandas(df)
+    ```
+
+    ## 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 pyArrow RecordBatch Iterator.
+
+    ### Adding RecordBatch Iterator
+    
+    ```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.array(["foo", "bar"]),
+                    pa.array([10.0, 20.0]),
+                ],
+                ["vector", "item", "price"],
+            )
+    
+    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"')
+    ```
+
+    ## Examples
+
+    ### 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

@@ -69,4 +69,4 @@ 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/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

@@ -1,6 +1,7 @@
 # Pydantic
 
 [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
 

docs/src/python/python.md

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

docs/test/md_testing.js

@@ -5,10 +5,12 @@ const path = require("path");
 const excludedFiles = [
   "../src/fts.md",
   "../src/embedding.md",
+  "../src/ann_indexes.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/guides/tables.md",
 ];
 const nodePrefix = "javascript";
 const nodeFile = ".js";

docs/test/md_testing.py

@@ -9,6 +9,7 @@ excluded_files = [
     "../src/examples/serverless_qa_bot_with_modal_and_langchain.md",
     "../src/examples/youtube_transcript_bot_with_nodejs.md",
     "../src/integrations/voxel51.md",
+    "../src/guides/tables.md"
 ]
 
 python_prefix = "py"

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-${currentTarget()}`);
-} catch (e) {
-    try {
-        // Might be developing locally, so try that. But don't expose that error
-        // to the user.
-        nativeLib = require("./index.node");
-    } catch {
-        throw new Error(`vectordb: failed to load native library.
+  // When developing locally, give preference to the local built library
+  nativeLib = require('./index.node')
+} catch {
+  try {
+    nativeLib = require(`@lancedb/vectordb-${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": "0.1.18",
+  "version": "0.1.19",
   "lockfileVersion": 2,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.1.18",
+      "version": "0.1.19",
       "cpu": [
         "x64",
         "arm64"
@@ -51,11 +51,11 @@
         "typescript": "*"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.1.18",
-        "@lancedb/vectordb-darwin-x64": "0.1.18",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.1.18",
-        "@lancedb/vectordb-linux-x64-gnu": "0.1.18",
-        "@lancedb/vectordb-win32-x64-msvc": "0.1.18"
+        "@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"
       }
     },
     "node_modules/@apache-arrow/ts": {
@@ -315,9 +315,9 @@
       }
     },
     "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.1.18.tgz",
-      "integrity": "sha512-vu8MCFgaAAGmTJF+4RaoApROMpRVVgrCk+V9my4adAfWkkXbSmtxiDgiIwwL1VqdGb8UwzGn3kVbNW7idE1ojA==",
+      "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==",
       "cpu": [
         "arm64"
       ],
@@ -327,9 +327,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.1.18.tgz",
-      "integrity": "sha512-ZU30bd6frRyKJ515ow972PlqO2wIiNT4Ohor9+KbUwl/VKDyAwKOKG8cWhRJXTxk0k1oqpiJ6+Q28TcYJ0sSAw==",
+      "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==",
       "cpu": [
         "x64"
       ],
@@ -339,9 +339,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.1.18.tgz",
-      "integrity": "sha512-2UroC026bUYwyciSRonYlXei0SoYbKgfWpozxYOu7GgBAV2CQQtaAPgWJTEl6ZiCNeBmBTx+j0h3+ydUfZA73Q==",
+      "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==",
       "cpu": [
         "arm64"
       ],
@@ -351,9 +351,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.1.18.tgz",
-      "integrity": "sha512-DoQBskl22JAJFZh219ZOJ6o+f1niTZp0qRYngHa/kTIpLKzHWQ0OTtMCz32VBAjAsKjSLNxHE8rrT/S6tvS7KQ==",
+      "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==",
       "cpu": [
         "x64"
       ],
@@ -363,9 +363,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.1.18.tgz",
-      "integrity": "sha512-a/kUM3V6rWuXS80pPECYxKfCUAnq56Of/GPCvnAkpk9C9ldyX10iff4aA6DiPHjEk9V2ytqDfJKl9N3QcMLKLA==",
+      "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==",
       "cpu": [
         "x64"
       ],
@@ -4852,33 +4852,33 @@
       }
     },
     "@lancedb/vectordb-darwin-arm64": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.1.18.tgz",
-      "integrity": "sha512-vu8MCFgaAAGmTJF+4RaoApROMpRVVgrCk+V9my4adAfWkkXbSmtxiDgiIwwL1VqdGb8UwzGn3kVbNW7idE1ojA==",
+      "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==",
       "optional": true
     },
     "@lancedb/vectordb-darwin-x64": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.1.18.tgz",
-      "integrity": "sha512-ZU30bd6frRyKJ515ow972PlqO2wIiNT4Ohor9+KbUwl/VKDyAwKOKG8cWhRJXTxk0k1oqpiJ6+Q28TcYJ0sSAw==",
+      "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==",
       "optional": true
     },
     "@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.1.18.tgz",
-      "integrity": "sha512-2UroC026bUYwyciSRonYlXei0SoYbKgfWpozxYOu7GgBAV2CQQtaAPgWJTEl6ZiCNeBmBTx+j0h3+ydUfZA73Q==",
+      "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==",
       "optional": true
     },
     "@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.1.18.tgz",
-      "integrity": "sha512-DoQBskl22JAJFZh219ZOJ6o+f1niTZp0qRYngHa/kTIpLKzHWQ0OTtMCz32VBAjAsKjSLNxHE8rrT/S6tvS7KQ==",
+      "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==",
       "optional": true
     },
     "@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.1.18",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.1.18.tgz",
-      "integrity": "sha512-a/kUM3V6rWuXS80pPECYxKfCUAnq56Of/GPCvnAkpk9C9ldyX10iff4aA6DiPHjEk9V2ytqDfJKl9N3QcMLKLA==",
+      "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==",
       "optional": true
     },
     "@neon-rs/cli": {

node/package.json

@@ -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"

node/src/index.ts

@@ -310,7 +310,7 @@ 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
@@ -357,7 +357,7 @@ export class LocalTable<T = number[]> implements Table<T> {
         callArgs.push(this._options.awsCredentials.sessionToken)
       }
     }
-    return tableAdd.call(...callArgs)
+    return tableAdd.call(...callArgs).then((newTable: any) => { this._tbl = newTable })
   }
 
   /**
@@ -375,7 +375,7 @@ export class LocalTable<T = number[]> implements Table<T> {
         callArgs.push(this._options.awsCredentials.sessionToken)
       }
     }
-    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Overwrite.toString())
+    return tableAdd.call(...callArgs).then((newTable: any) => { this._tbl = newTable })
   }
 
   /**
@@ -384,7 +384,7 @@ 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 +400,7 @@ 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/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

@@ -77,7 +77,7 @@ export class RemoteConnection implements Connection {
   }
 
   async dropTable (name: string): Promise<void> {
-    throw new Error('Not implemented')
+    await this._client.post(`/v1/table/${name}/drop/`)
   }
 }
 

node/src/test/test.ts

@@ -107,9 +107,9 @@ 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)
@@ -250,6 +250,14 @@ describe('LanceDB client', function () {
       const createIndex = table.createIndex({ type: 'ivf_pq', column: 'name', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
       await expect(createIndex).to.be.rejectedWith(/VectorIndex requires the column data type to be fixed size list of float32s/)
     })
+
+    it('it should fail when the column is not a vector', async function () {
+      const uri = await createTestDB(32, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      const createIndex = table.createIndex({ type: 'ivf_pq', column: 'name', num_partitions: -1, max_iters: 2, num_sub_vectors: 2 })
+      await expect(createIndex).to.be.rejectedWith('num_partitions: must be > 0')
+    })
   })
 
   describe('when using a custom embedding function', function () {

python/.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.15
+current_version = 0.2.0
 commit = True
 message = [python] Bump version: {current_version} → {new_version}
 tag = True

python/lancedb/common.py

@@ -11,17 +11,18 @@
 #  See the License for the specific language governing permissions and
 #  limitations under the License.
 from pathlib import Path
-from typing import List, Union
+from typing import Iterable, List, Union
 
 import numpy as np
-import pandas as pd
 import pyarrow as pa
 
+from .util import safe_import_pandas
+
+pd = safe_import_pandas()
+
+DATA = Union[List[dict], dict, "pd.DataFrame", pa.Table, Iterable[pa.RecordBatch]]
 VEC = Union[list, np.ndarray, pa.Array, pa.ChunkedArray]
 URI = Union[str, Path]
-
-# TODO support generator
-DATA = Union[List[dict], dict, pd.DataFrame]
 VECTOR_COLUMN_NAME = "vector"
 
 

python/lancedb/context.py

@@ -12,12 +12,13 @@
 #  limitations under the License.
 from __future__ import annotations
 
-import pandas as pd
-
 from .exceptions import MissingColumnError, MissingValueError
+from .util import safe_import_pandas
+
+pd = safe_import_pandas()
 
 
-def contextualize(raw_df: pd.DataFrame) -> Contextualizer:
+def contextualize(raw_df: "pd.DataFrame") -> Contextualizer:
     """Create a Contextualizer object for the given DataFrame.
 
     Used to create context windows. Context windows are rolling subsets of text
@@ -175,8 +176,12 @@ class Contextualizer:
         self._min_window_size = min_window_size
         return self
 
-    def to_df(self) -> pd.DataFrame:
+    def to_df(self) -> "pd.DataFrame":
         """Create the context windows and return a DataFrame."""
+        if pd is None:
+            raise ImportError(
+                "pandas is required to create context windows using lancedb"
+            )
 
         if self._text_col not in self._raw_df.columns.tolist():
             raise MissingColumnError(self._text_col)

python/lancedb/db.py

@@ -16,13 +16,13 @@ from __future__ import annotations
 import os
 from abc import ABC, abstractmethod
 from pathlib import Path
-from typing import Dict, Iterable, List, Optional, Tuple, Union
+from typing import Optional
 
-import pandas as pd
 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
 
@@ -39,10 +39,8 @@ class DBConnection(ABC):
     def create_table(
         self,
         name: str,
-        data: Optional[
-            Union[List[dict], dict, pd.DataFrame, pa.Table, Iterable[pa.RecordBatch]],
-        ] = None,
-        schema: Optional[pa.Schema] = None,
+        data: Optional[DATA] = None,
+        schema: Optional[pa.Schema, LanceModel] = None,
         mode: str = "create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
@@ -55,7 +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".
@@ -195,6 +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):
     """
@@ -279,8 +284,8 @@ class LanceDBConnection(DBConnection):
     def create_table(
         self,
         name: str,
-        data: Optional[Union[List[dict], dict, pd.DataFrame]] = None,
-        schema: pa.Schema = None,
+        data: Optional[DATA] = None,
+        schema: Optional[pa.Schema, LanceModel] = None,
         mode: str = "create",
         on_bad_vectors: str = "error",
         fill_value: float = 0.0,
@@ -319,14 +324,24 @@ class LanceDBConnection(DBConnection):
         """
         return LanceTable.open(self, name)
 
-    def drop_table(self, name: str):
+    def drop_table(self, name: str, ignore_missing: bool = False):
         """Drop a table from the database.
 
         Parameters
         ----------
         name: str
             The name of the table.
+        ignore_missing: bool, default False
+            If True, ignore if the table does not exist.
         """
+        try:
+            filesystem, path = fs_from_uri(self.uri)
+            table_path = os.path.join(path, name + ".lance")
+            filesystem.delete_dir(table_path)
+        except FileNotFoundError:
+            if not ignore_missing:
+                raise
+
+    def drop_database(self):
         filesystem, path = fs_from_uri(self.uri)
-        table_path = os.path.join(path, name + ".lance")
-        filesystem.delete_dir(table_path)
+        filesystem.delete_dir(path)

python/lancedb/embeddings.py

@@ -16,15 +16,19 @@ import sys
 from typing import Callable, Union
 
 import numpy as np
-import pandas as pd
 import pyarrow as pa
 from lance.vector import vec_to_table
 from retry import retry
 
+from .util import safe_import_pandas
+
+pd = safe_import_pandas()
+DATA = Union[pa.Table, "pd.DataFrame"]
+
 
 def with_embeddings(
     func: Callable,
-    data: Union[pa.Table, pd.DataFrame],
+    data: DATA,
     column: str = "text",
     wrap_api: bool = True,
     show_progress: bool = False,
@@ -60,7 +64,7 @@ def with_embeddings(
     func = func.batch_size(batch_size)
     if show_progress:
         func = func.show_progress()
-    if isinstance(data, pd.DataFrame):
+    if pd is not None and isinstance(data, pd.DataFrame):
         data = pa.Table.from_pandas(data, preserve_index=False)
     embeddings = func(data[column].to_numpy())
     table = vec_to_table(np.array(embeddings))

python/lancedb/pydantic.py

@@ -249,3 +249,42 @@ def pydantic_to_schema(model: Type[pydantic.BaseModel]) -> pa.Schema:
     """
     fields = _pydantic_model_to_fields(model)
     return pa.schema(fields)
+
+
+class LanceModel(pydantic.BaseModel):
+    """
+    A Pydantic Model base class that can be converted to a LanceDB Table.
+
+    Examples
+    --------
+    >>> import lancedb
+    >>> from lancedb.pydantic import LanceModel, vector
+    >>>
+    >>> class TestModel(LanceModel):
+    ...     name: str
+    ...     vector: vector(2)
+    ...
+    >>> db = lancedb.connect("/tmp")
+    >>> table = db.create_table("test", schema=TestModel.to_arrow_schema())
+    >>> table.add([
+    ...     TestModel(name="test", vector=[1.0, 2.0])
+    ... ])
+    >>> table.search([0., 0.]).limit(1).to_pydantic(TestModel)
+    [TestModel(name='test', vector=FixedSizeList(dim=2))]
+    """
+
+    @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

@@ -13,17 +13,20 @@
 
 from __future__ import annotations
 
-from typing import List, Literal, Optional, Union
+from typing import List, Literal, Optional, Type, Union
 
 import numpy as np
-import pandas as pd
 import pyarrow as pa
-from pydantic import BaseModel
+import pydantic
 
 from .common import VECTOR_COLUMN_NAME
+from .pydantic import LanceModel
+from .util import safe_import_pandas
+
+pd = safe_import_pandas()
 
 
-class Query(BaseModel):
+class Query(pydantic.BaseModel):
     """A Query"""
 
     vector_column: str = VECTOR_COLUMN_NAME
@@ -70,8 +73,8 @@ class LanceQueryBuilder:
     ...       .select(["b"])
     ...       .limit(2)
     ...       .to_df())
-       b      vector  score
-    0  6  [0.4, 0.4]    0.0
+       b      vector  _distance
+    0  6  [0.4, 0.4]        0.0
     """
 
     def __init__(
@@ -198,11 +201,11 @@ class LanceQueryBuilder:
         self._refine_factor = refine_factor
         return self
 
-    def to_df(self) -> pd.DataFrame:
+    def to_df(self) -> "pd.DataFrame":
         """
         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.
         """
 
@@ -214,7 +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()
@@ -230,9 +233,26 @@ class LanceQueryBuilder:
         )
         return self._table._execute_query(query)
 
+    def to_pydantic(self, model: Type[LanceModel]) -> List[LanceModel]:
+        """Return the table as a list of pydantic models.
+
+        Parameters
+        ----------
+        model: Type[LanceModel]
+            The pydantic model to use.
+
+        Returns
+        -------
+        List[LanceModel]
+        """
+        return [
+            model(**{k: v for k, v in row.items() if k in model.field_names()})
+            for row in self.to_arrow().to_pylist()
+        ]
+
 
 class LanceFtsQueryBuilder(LanceQueryBuilder):
-    def to_arrow(self) -> pd.Table:
+    def to_arrow(self) -> pa.Table:
         try:
             import tantivy
         except ImportError:

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

@@ -20,7 +20,6 @@ import pyarrow as pa
 
 from lancedb.common import DATA
 from lancedb.db import DBConnection
-from lancedb.schema import schema_to_json
 from lancedb.table import Table, _sanitize_data
 
 from .arrow import to_ipc_binary
@@ -105,8 +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

@@ -16,11 +16,11 @@ from functools import cached_property
 from typing import Union
 
 import pyarrow as pa
+from lance import json_to_schema
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
 
-from ..query import LanceQueryBuilder, Query
-from ..schema import json_to_schema
+from ..query import LanceQueryBuilder
 from ..table import Query, Table, _sanitize_data
 from .arrow import to_ipc_binary
 from .client import ARROW_STREAM_CONTENT_TYPE
@@ -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/schema.py

@@ -12,11 +12,7 @@
 #  limitations under the License.
 
 """Schema related utilities."""
-
-from typing import Any, Dict, Type
-
 import pyarrow as pa
-from lance import json_to_schema, schema_to_json
 
 
 def vector(dimension: int, value_type: pa.DataType = pa.float32()) -> pa.DataType:

python/lancedb/table.py

@@ -13,6 +13,7 @@
 
 from __future__ import annotations
 
+import inspect
 import os
 from abc import ABC, abstractmethod
 from functools import cached_property
@@ -20,30 +21,41 @@ from typing import Iterable, List, Union
 
 import lance
 import numpy as np
-import pandas as pd
 import pyarrow as pa
 import pyarrow.compute as pc
 from lance import LanceDataset
 from lance.vector import vec_to_table
 
 from .common import DATA, VEC, VECTOR_COLUMN_NAME
+from .pydantic import LanceModel
 from .query import LanceFtsQueryBuilder, LanceQueryBuilder, Query
-from .util import fs_from_uri
+from .util import fs_from_uri, safe_import_pandas
+
+pd = safe_import_pandas()
 
 
 def _sanitize_data(data, schema, on_bad_vectors, fill_value):
     if isinstance(data, list):
+        # convert to list of dict if data is a bunch of LanceModels
+        if isinstance(data[0], LanceModel):
+            schema = data[0].__class__.to_arrow_schema()
+            data = [dict(d) for d in data]
         data = pa.Table.from_pylist(data)
         data = _sanitize_schema(
             data, schema=schema, on_bad_vectors=on_bad_vectors, fill_value=fill_value
         )
     if isinstance(data, dict):
         data = vec_to_table(data)
-    if isinstance(data, pd.DataFrame):
-        data = pa.Table.from_pandas(data)
+    if pd is not None and isinstance(data, pd.DataFrame):
+        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 not isinstance(data, (pa.Table, Iterable)):
         raise TypeError(f"Unsupported data type: {type(data)}")
     return data
@@ -78,23 +90,24 @@ 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
-    0  4  [0.5, 1.3]   0.82
-    1  2  [1.1, 1.2]   1.13
+       b      vector  _distance
+    0  4  [0.5, 1.3]       0.82
+    1  2  [1.1, 1.2]       1.13
 
     Search queries are much faster when an index is created. See
     [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)
 
         """
         raise NotImplementedError
 
-    def to_pandas(self) -> pd.DataFrame:
+    def to_pandas(self):
         """Return the table as a pandas DataFrame.
 
         Returns
@@ -188,7 +201,7 @@ 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
@@ -328,7 +341,7 @@ class LanceTable(Table):
         """Return the first n rows of the table."""
         return self._dataset.head(n)
 
-    def to_pandas(self) -> pd.DataFrame:
+    def to_pandas(self) -> "pd.DataFrame":
         """Return the table as a pandas DataFrame.
 
         Returns
@@ -428,7 +441,7 @@ 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 search(
@@ -449,7 +462,7 @@ 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):
@@ -500,7 +513,7 @@ 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"
@@ -513,6 +526,8 @@ 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/lancedb/util.py

@@ -15,7 +15,6 @@ import os
 from typing import Tuple
 from urllib.parse import urlparse
 
-import pyarrow as pa
 import pyarrow.fs as pa_fs
 
 
@@ -76,3 +75,12 @@ def fs_from_uri(uri: str) -> Tuple[pa_fs.FileSystem, str]:
         return fs, path
 
     return pa_fs.FileSystem.from_uri(uri)
+
+
+def safe_import_pandas():
+    try:
+        import pandas as pd
+
+        return pd
+    except ImportError:
+        return None

python/pyproject.toml

@@ -1,11 +1,18 @@
 [project]
 name = "lancedb"
-version = "0.1.15"
-dependencies = ["pylance~=0.5.8", "ratelimiter", "retry", "tqdm", "aiohttp", "pydantic", "attr", "semver"]
-description = "lancedb"
-authors = [
-    { name = "LanceDB Devs", email = "dev@lancedb.com" },
+version = "0.2.0"
+dependencies = [
+    "pylance==0.6.1",
+    "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 = [
-    "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
 
 
 def test_basic(tmp_path):
@@ -101,6 +102,11 @@ def test_ingest_record_batch_iterator(tmp_path):
         ),
     )
 
+    tbl_len = len(tbl)
+    tbl.add(batch_reader())
+    assert len(tbl) == tbl_len * 2
+    assert len(tbl.list_versions()) == 2
+
 
 def test_create_mode(tmp_path):
     db = lancedb.connect(tmp_path)
@@ -149,6 +155,51 @@ def test_delete_table(tmp_path):
     db.create_table("test", data=data)
     assert db.table_names() == ["test"]
 
+    # dropping a table that does not exist should pass
+    # if ignore_missing=True
+    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)
@@ -158,8 +209,14 @@ 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_pydantic.py

@@ -20,7 +20,7 @@ import pyarrow as pa
 import pydantic
 import pytest
 
-from lancedb.pydantic import PYDANTIC_VERSION, pydantic_to_schema, vector
+from lancedb.pydantic import PYDANTIC_VERSION, LanceModel, pydantic_to_schema, vector
 
 
 @pytest.mark.skipif(
@@ -163,3 +163,13 @@ def test_fixed_size_list_validation():
         TestModel(vec=range(7))
 
     TestModel(vec=range(8))
+
+
+def test_lance_model():
+    class TestModel(LanceModel):
+        vec: vector(16)
+        li: List[int]
+
+    schema = pydantic_to_schema(TestModel)
+    assert schema == TestModel.to_arrow_schema()
+    assert TestModel.field_names() == ["vec", "li"]

python/tests/test_query.py

@@ -20,6 +20,7 @@ import pyarrow as pa
 import pytest
 
 from lancedb.db import LanceDBConnection
+from lancedb.pydantic import LanceModel, vector
 from lancedb.query import LanceQueryBuilder, Query
 from lancedb.table import LanceTable
 
@@ -64,6 +65,24 @@ def table(tmp_path) -> MockTable:
     return MockTable(tmp_path)
 
 
+def test_cast(table):
+    class TestModel(LanceModel):
+        vector: vector(2)
+        id: int
+        str_field: str
+        float_field: float
+
+    q = LanceQueryBuilder(table, [0, 0], "vector").limit(1)
+    results = q.to_pydantic(TestModel)
+    assert len(results) == 1
+    r0 = results[0]
+    assert isinstance(r0, TestModel)
+    assert r0.id == 1
+    assert r0.vector == [1, 2]
+    assert r0.str_field == "a"
+    assert r0.float_field == 1.0
+
+
 def test_query_builder(table):
     df = LanceQueryBuilder(table, [0, 0], "vector").limit(1).select(["id"]).to_df()
     assert df["id"].values[0] == 1
@@ -89,11 +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

@@ -13,15 +13,16 @@
 
 import functools
 from pathlib import Path
+from typing import List
 from unittest.mock import PropertyMock, patch
 
 import numpy as np
 import pandas as pd
 import pyarrow as pa
 import pytest
-from lance.vector import vec_to_table
 
 from lancedb.db import LanceDBConnection
+from lancedb.pydantic import LanceModel, vector
 from lancedb.table import LanceTable
 
 
@@ -135,6 +136,17 @@ def test_add(db):
     _add(table, schema)
 
 
+def test_add_pydantic_model(db):
+    class TestModel(LanceModel):
+        vector: vector(16)
+        li: List[int]
+
+    data = TestModel(vector=list(range(16)), li=[1, 2, 3])
+    table = LanceTable.create(db, "test", data=[data])
+    assert len(table) == 1
+    assert table.schema == TestModel.to_arrow_schema()
+
+
 def _add(table, schema):
     # table = LanceTable(db, "test")
     assert len(table) == 2

rust/ffi/node/Cargo.toml

@@ -13,6 +13,7 @@ crate-type = ["cdylib"]
 arrow-array = { workspace = true }
 arrow-ipc = { workspace = true }
 arrow-schema = { workspace = true }
+conv = "0.3.3"
 once_cell = "1"
 futures = "0.3"
 half = { workspace = true }

rust/ffi/node/src/error.rs

@@ -22,8 +22,15 @@ use snafu::Snafu;
 pub enum Error {
     #[snafu(display("column '{name}' is missing"))]
     MissingColumn { name: String },
+    #[snafu(display("{name}: {message}"))]
+    RangeError { name: String, message: String },
+    #[snafu(display("{index_type} is not a valid index type"))]
+    InvalidIndexType { index_type: String },
+
     #[snafu(display("{message}"))]
     LanceDB { message: String },
+    #[snafu(display("{message}"))]
+    Neon { message: String },
 }
 
 pub type Result<T> = std::result::Result<T, Error>;
@@ -52,6 +59,22 @@ impl From<ArrowError> for Error {
     }
 }
 
+impl From<neon::result::Throw> for Error {
+    fn from(value: neon::result::Throw) -> Self {
+        Self::Neon {
+            message: value.to_string(),
+        }
+    }
+}
+
+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

@@ -12,40 +12,38 @@
 // 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::{runtime, JsTable};
+use crate::error::Error::InvalidIndexType;
+use crate::error::ResultExt;
+use crate::neon_ext::js_object_ext::JsObjectExt;
+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)?;
     let index_params = cx.argument::<JsObject>(0)?;
-    let index_params_builder = get_index_params_builder(&mut cx, index_params).unwrap();
+    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)
@@ -54,27 +52,21 @@ pub(crate) fn table_create_vector_index(mut cx: FunctionContext) -> JsResult<JsP
 fn get_index_params_builder(
     cx: &mut FunctionContext,
     obj: Handle<JsObject>,
-) -> Result<impl VectorIndexBuilder, String> {
-    let idx_type = obj
-        .get::<JsString, _, _>(cx, "type")
-        .map_err(|t| t.to_string())?
-        .value(cx);
+) -> crate::error::Result<impl VectorIndexBuilder> {
+    let idx_type = obj.get::<JsString, _, _>(cx, "type")?.value(cx);
 
     match idx_type.as_str() {
         "ivf_pq" => {
             let mut index_builder: IvfPQIndexBuilder = IvfPQIndexBuilder::new();
             let mut pq_params = PQBuildParams::default();
 
-            obj.get_opt::<JsString, _, _>(cx, "column")
-                .map_err(|t| t.to_string())?
+            obj.get_opt::<JsString, _, _>(cx, "column")?
                 .map(|s| index_builder.column(s.value(cx)));
 
-            obj.get_opt::<JsString, _, _>(cx, "index_name")
-                .map_err(|t| t.to_string())?
+            obj.get_opt::<JsString, _, _>(cx, "index_name")?
                 .map(|s| index_builder.index_name(s.value(cx)));
 
-            obj.get_opt::<JsString, _, _>(cx, "metric_type")
-                .map_err(|t| t.to_string())?
+            obj.get_opt::<JsString, _, _>(cx, "metric_type")?
                 .map(|s| MetricType::try_from(s.value(cx).as_str()))
                 .map(|mt| {
                     let metric_type = mt.unwrap();
@@ -82,15 +74,8 @@ fn get_index_params_builder(
                     pq_params.metric_type = metric_type;
                 });
 
-            let num_partitions = obj
-                .get_opt::<JsNumber, _, _>(cx, "num_partitions")
-                .map_err(|t| t.to_string())?
-                .map(|s| s.value(cx) as usize);
-
-            let max_iters = obj
-                .get_opt::<JsNumber, _, _>(cx, "max_iters")
-                .map_err(|t| t.to_string())?
-                .map(|s| s.value(cx) as usize);
+            let num_partitions = obj.get_opt_usize(cx, "num_partitions")?;
+            let max_iters = obj.get_opt_usize(cx, "max_iters")?;
 
             num_partitions.map(|np| {
                 let max_iters = max_iters.unwrap_or(50);
@@ -102,32 +87,28 @@ fn get_index_params_builder(
                 index_builder.ivf_params(ivf_params)
             });
 
-            obj.get_opt::<JsBoolean, _, _>(cx, "use_opq")
-                .map_err(|t| t.to_string())?
+            obj.get_opt::<JsBoolean, _, _>(cx, "use_opq")?
                 .map(|s| pq_params.use_opq = s.value(cx));
 
-            obj.get_opt::<JsNumber, _, _>(cx, "num_sub_vectors")
-                .map_err(|t| t.to_string())?
-                .map(|s| pq_params.num_sub_vectors = s.value(cx) as usize);
+            obj.get_opt_usize(cx, "num_sub_vectors")?
+                .map(|s| pq_params.num_sub_vectors = s);
 
-            obj.get_opt::<JsNumber, _, _>(cx, "num_bits")
-                .map_err(|t| t.to_string())?
-                .map(|s| pq_params.num_bits = s.value(cx) as usize);
+            obj.get_opt_usize(cx, "num_bits")?
+                .map(|s| pq_params.num_bits = s);
 
-            obj.get_opt::<JsNumber, _, _>(cx, "max_iters")
-                .map_err(|t| t.to_string())?
-                .map(|s| pq_params.max_iters = s.value(cx) as usize);
+            obj.get_opt_usize(cx, "max_iters")?
+                .map(|s| pq_params.max_iters = s);
 
-            obj.get_opt::<JsNumber, _, _>(cx, "max_opq_iters")
-                .map_err(|t| t.to_string())?
-                .map(|s| pq_params.max_opq_iters = s.value(cx) as usize);
+            obj.get_opt_usize(cx, "max_opq_iters")?
+                .map(|s| pq_params.max_opq_iters = s);
 
-            obj.get_opt::<JsBoolean, _, _>(cx, "replace")
-                .map_err(|t| t.to_string())?
+            obj.get_opt::<JsBoolean, _, _>(cx, "replace")?
                 .map(|s| index_builder.replace(s.value(cx)));
 
             Ok(index_builder)
         }
-        t => Err(format!("{} is not a valid index type", t).to_string()),
+        index_type => Err(InvalidIndexType {
+            index_type: index_type.into(),
+        }),
     }
 }

rust/ffi/node/src/lib.rs

@@ -12,35 +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::error::Error;
-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::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>,
@@ -48,12 +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)]
@@ -195,8 +184,8 @@ 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)
@@ -223,220 +212,17 @@ 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::<JsNumber, _, _>(&mut cx, "_refineFactor")?
-        .map(|s| s.value(&mut cx))
-        .map(|i| i as u32);
-    let nprobes = query_obj
-        .get::<JsNumber, _, _>(&mut cx, "_nprobes")?
-        .value(&mut cx) as usize;
-    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(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/neon_ext.rs

@@ -0,0 +1,15 @@
+// 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.
+
+pub mod js_object_ext;

rust/ffi/node/src/neon_ext/js_object_ext.rs

@@ -0,0 +1,82 @@
+// 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 crate::error::{Error, Result};
+use neon::prelude::*;
+
+// extends neon's [JsObject] with helper functions to extract properties
+pub trait JsObjectExt {
+    fn get_opt_u32(&self, cx: &mut FunctionContext, key: &str) -> Result<Option<u32>>;
+    fn get_usize(&self, cx: &mut FunctionContext, key: &str) -> Result<usize>;
+    fn get_opt_usize(&self, cx: &mut FunctionContext, key: &str) -> Result<Option<usize>>;
+}
+
+impl JsObjectExt for JsObject {
+    fn get_opt_u32(&self, cx: &mut FunctionContext, key: &str) -> Result<Option<u32>> {
+        let val_opt = self
+            .get_opt::<JsNumber, _, _>(cx, key)?
+            .map(|s| f64_to_u32_safe(s.value(cx), key));
+        val_opt.transpose()
+    }
+
+    fn get_usize(&self, cx: &mut FunctionContext, key: &str) -> Result<usize> {
+        let val = self.get::<JsNumber, _, _>(cx, key)?.value(cx);
+        f64_to_usize_safe(val, key)
+    }
+
+    fn get_opt_usize(&self, cx: &mut FunctionContext, key: &str) -> Result<Option<usize>> {
+        let val_opt = self
+            .get_opt::<JsNumber, _, _>(cx, key)?
+            .map(|s| f64_to_usize_safe(s.value(cx), key));
+        val_opt.transpose()
+    }
+}
+
+fn f64_to_u32_safe(n: f64, key: &str) -> Result<u32> {
+    use conv::*;
+
+    n.approx_as::<u32>().map_err(|e| match e {
+        FloatError::NegOverflow(_) => Error::RangeError {
+            name: key.into(),
+            message: "must be > 0".to_string(),
+        },
+        FloatError::PosOverflow(_) => Error::RangeError {
+            name: key.into(),
+            message: format!("must be < {}", u32::MAX),
+        },
+        FloatError::NotANumber(_) => Error::RangeError {
+            name: key.into(),
+            message: "not a valid number".to_string(),
+        },
+    })
+}
+
+fn f64_to_usize_safe(n: f64, key: &str) -> Result<usize> {
+    use conv::*;
+
+    n.approx_as::<usize>().map_err(|e| match e {
+        FloatError::NegOverflow(_) => Error::RangeError {
+            name: key.into(),
+            message: "must be > 0".to_string(),
+        },
+        FloatError::PosOverflow(_) => Error::RangeError {
+            name: key.into(),
+            message: format!("must be < {}", usize::MAX),
+        },
+        FloatError::NotANumber(_) => Error::RangeError {
+            name: key.into(),
+            message: "not a valid number".to_string(),
+        },
+    })
+}

rust/ffi/node/src/query.rs

@@ -0,0 +1,84 @@
+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 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 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)?;
+                Ok(JsBuffer::external(&mut cx, buffer))
+            });
+        });
+        Ok(promise)
+    }
+}

rust/ffi/node/src/table.rs

@@ -0,0 +1,174 @@
+// 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, 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 = 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.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 = 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 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 = 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,
+            ..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/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,
         }