Bump version: 0.9.0-beta.5 → 0.9.0-beta.6

# Conflicts:
#	python/pyproject.toml

.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.6.0"
+current_version = "0.5.2"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

.github/workflows/build_windows_wheel/action.yml

@@ -28,7 +28,7 @@ runs:
         args: ${{ inputs.args }}
         docker-options: "-e PIP_EXTRA_INDEX_URL=https://pypi.fury.io/lancedb/"
         working-directory: python
-    - uses: actions/upload-artifact@v3
+    - uses: actions/upload-artifact@v4
       with:
         name: windows-wheels
         path: python\target\wheels

.github/workflows/docs_test.yml

@@ -24,7 +24,7 @@ env:
 jobs:
   test-python:
     name: Test doc python code
-    runs-on: "warp-ubuntu-latest-x64-4x"
+    runs-on: "buildjet-8vcpu-ubuntu-2204"
     steps:
     - name: Checkout
       uses: actions/checkout@v4
@@ -56,7 +56,7 @@ jobs:
         for d in *; do cd "$d"; echo "$d".py; python "$d".py; cd ..; done
   test-node:
     name: Test doc nodejs code
-    runs-on: "warp-ubuntu-latest-x64-4x"
+    runs-on: "buildjet-8vcpu-ubuntu-2204"
     timeout-minutes: 60
     strategy:
       fail-fast: false

.github/workflows/pypi-publish.yml

@@ -15,15 +15,21 @@ jobs:
           - platform: x86_64
             manylinux: "2_17"
             extra_args: ""
+            runner: ubuntu-22.04
           - platform: x86_64
             manylinux: "2_28"
             extra_args: "--features fp16kernels"
+            runner: ubuntu-22.04
           - platform: aarch64
-            manylinux: "2_24"
+            manylinux: "2_17"
             extra_args: ""
-          # We don't build fp16 kernels for aarch64, because it uses 
-          # cross compilation image, which doesn't have a new enough compiler.
-    runs-on: "ubuntu-22.04"
+            # For successful fat LTO builds, we need a large runner to avoid OOM errors.
+            runner: ubuntu-2404-8x-arm64
+          - platform: aarch64
+            manylinux: "2_28"
+            extra_args: "--features fp16kernels"
+            runner: ubuntu-2404-8x-arm64
+    runs-on: ${{ matrix.config.runner }}
     steps:
       - uses: actions/checkout@v4
         with:
@@ -83,7 +89,7 @@ jobs:
       - name: Set up Python
         uses: actions/setup-python@v4
         with:
-          python-version: 3.8
+          python-version: 3.12
       - uses: ./.github/workflows/build_windows_wheel
         with:
           python-minor-version: 8
@@ -157,4 +163,4 @@ jobs:
           token: ${{ secrets.GITHUB_TOKEN }}
           generate_release_notes: false
           name: Python LanceDB v${{ steps.extract_version.outputs.version }}
-          body: ${{ steps.python_release_notes.outputs.changelog }}
+          body: ${{ steps.python_release_notes.outputs.changelog }}

.pre-commit-config.yaml

@@ -14,7 +14,7 @@ repos:
   hooks:
     - id: local-biome-check
       name: biome check
-      entry: npx @biomejs/biome@1.8.3 check --config-path nodejs/biome.json nodejs/
+      entry: npx @biomejs/biome@1.7.3 check --config-path nodejs/biome.json nodejs/
       language: system
       types: [text]
       files: "nodejs/.*"

docs/mkdocs.yml

@@ -57,8 +57,6 @@ plugins:
             - https://arrow.apache.org/docs/objects.inv
             - https://pandas.pydata.org/docs/objects.inv
   - mkdocs-jupyter
-  - render_swagger:
-      allow_arbitrary_locations : true
 
 markdown_extensions:
   - admonition
@@ -160,7 +158,6 @@ nav:
           - API reference:
               - 🐍 Python: python/saas-python.md
               - 👾 JavaScript: javascript/modules.md
-              - REST API: cloud/rest.md
 
   - Quick start: basic.md
   - Concepts:
@@ -231,7 +228,6 @@ nav:
       - API reference:
           - 🐍 Python: python/saas-python.md
           - 👾 JavaScript: javascript/modules.md
-          - REST API: cloud/rest.md
 
 extra_css:
   - styles/global.css

docs/openapi.yml

@@ -1,479 +0,0 @@
-openapi: 3.1.0
-info:
-  version: 1.0.0
-  title: LanceDB Cloud API
-  description: |
-    LanceDB Cloud API is a RESTful API that allows users to access and modify data stored in LanceDB Cloud.
-    Table actions are considered temporary resource creations and all use POST method.
-  contact:
-    name: LanceDB support
-    url: https://lancedb.com
-    email: contact@lancedb.com
-
-servers:
-  - url: https://{db}.{region}.api.lancedb.com
-    description: LanceDB Cloud REST endpoint.
-    variables:
-      db:
-        default: ""
-        description: the name of DB
-      region:
-        default: "us-east-1"
-        description: the service region of the DB
-
-security:
-  - key_auth: []
-
-components:
-  securitySchemes:
-    key_auth:
-      name: x-api-key
-      type: apiKey
-      in: header
-  parameters:
-    table_name:
-      name: name
-      in: path
-      description: name of the table
-      required: true
-      schema:
-        type: string
-  responses:
-    invalid_request:
-      description: Invalid request
-      content:
-        text/plain:
-          schema:
-            type: string
-    not_found:
-      description: Not found
-      content:
-        text/plain:
-          schema:
-            type: string
-    unauthorized:
-      description: Unauthorized
-      content:
-        text/plain:
-          schema:
-            type: string
-  requestBodies:
-    arrow_stream_buffer:
-      description: Arrow IPC stream buffer
-      required: true
-      content:
-        application/vnd.apache.arrow.stream:
-          schema:
-            type: string
-            format: binary
-
-paths:
-  /v1/table/:
-    get:
-      description: List tables, optionally, with pagination.
-      tags:
-        - Tables
-      summary: List Tables
-      operationId: listTables
-      parameters:
-        - name: limit
-          in: query
-          description: Limits the number of items to return.
-          schema:
-            type: integer
-        - name: page_token
-          in: query
-          description: Specifies the starting position of the next query
-          schema:
-            type: string
-      responses:
-        "200":
-          description: Successfully returned a list of tables in the DB
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  tables:
-                    type: array
-                    items:
-                      type: string
-                  page_token:
-                    type: string
-
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/create/:
-    post:
-      description: Create a new table
-      summary: Create a new table
-      operationId: createTable
-      tags:
-        - Tables
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Table successfully created
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/query/:
-    post:
-      description: Vector Query
-      url: https://{db-uri}.{aws-region}.api.lancedb.com/v1/table/{name}/query/
-      tags:
-        - Data
-      summary: Vector Query
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                vector:
-                  type: FixedSizeList
-                  description: |
-                    The targetted vector to search for. Required.
-                vector_column:
-                  type: string
-                  description: |
-                    The column to query, it can be inferred from the schema if there is only one vector column.
-                prefilter:
-                  type: boolean
-                  description: |
-                    Whether to prefilter the data. Optional.
-                k:
-                  type: integer
-                  description: |
-                    The number of search results to return. Default is 10.
-                distance_type:
-                  type: string
-                  description: |
-                    The distance metric to use for search. L2, Cosine, Dot and Hamming are supported. Default is L2.
-                bypass_vector_index:
-                  type: boolean
-                  description: |
-                    Whether to bypass vector index. Optional.
-                filter:
-                  type: string
-                  description: |
-                    A filter expression that specifies the rows to query. Optional.
-                columns:
-                  type: array
-                  items:
-                    type: string
-                  description: |
-                    The columns to return. Optional.
-                nprobe:
-                  type: integer
-                  description: |
-                    The number of probes to use for search. Optional.
-                refine_factor:
-                  type: integer
-                  description: |
-                    The refine factor to use for search. Optional.
-
-      responses:
-        "200":
-          description: top k results if query is successfully executed
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  results:
-                    type: array
-                    items:
-                      type: object
-                      properties:
-                        id:
-                          type: integer
-                        selected_col_1_to_return:
-                          type: col_1_type
-                        selected_col_n_to_return:
-                          type: col_n_type
-                        _distance:
-                          type: float
-
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/insert/:
-    post:
-      description: Insert new data to the Table.
-      tags:
-        - Data
-      operationId: insertData
-      summary: Insert new data.
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Insert successful
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/merge_insert/:
-    post:
-      description: Create a "merge insert" operation
-        This operation can add rows, update rows, and remove rows all in a single
-        transaction. See python method `lancedb.table.Table.merge_insert` for examples.
-      tags:
-        - Data
-      summary: Merge Insert
-      operationId: mergeInsert
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-        - name: on
-          in: query
-          description: |
-            The column to use as the primary key for the merge operation.
-          required: true
-          schema:
-            type: string
-        - name: when_matched_update_all
-          in: query
-          description: |
-            Rows that exist in both the source table (new data) and
-            the target table (old data) will be updated, replacing
-            the old row with the corresponding matching row.
-          required: false
-          schema:
-            type: boolean
-        - name: when_matched_update_all_filt
-          in: query
-          description: |
-            If present then only rows that satisfy the filter expression will
-            be updated
-          required: false
-          schema:
-            type: string
-        - name: when_not_matched_insert_all
-          in: query
-          description: |
-            Rows that exist only in the source table (new data) will be
-            inserted into the target table (old data).
-          required: false
-          schema:
-            type: boolean
-        - name: when_not_matched_by_source_delete
-          in: query
-          description: |
-            Rows that exist only in the target table (old data) will be
-            deleted. An optional condition (`when_not_matched_by_source_delete_filt`)
-            can be provided to limit what data is deleted.
-          required: false
-          schema:
-            type: boolean
-        - name: when_not_matched_by_source_delete_filt
-          in: query
-          description: |
-            The filter expression that specifies the rows to delete.
-          required: false
-          schema:
-            type: string
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Merge Insert successful
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/delete/:
-    post:
-      description: Delete rows from a table.
-      tags:
-        - Data
-      summary: Delete rows from a table
-      operationId: deleteData
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                predicate:
-                  type: string
-                  description: |
-                    A filter expression that specifies the rows to delete.
-      responses:
-        "200":
-          description: Delete successful
-        "401":
-          $ref: "#/components/responses/unauthorized"
-  /v1/table/{name}/drop/:
-    post:
-      description: Drop a table
-      tags:
-        - Tables
-      summary: Drop a table
-      operationId: dropTable
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        $ref: "#/components/requestBodies/arrow_stream_buffer"
-      responses:
-        "200":
-          description: Drop successful
-        "401":
-          $ref: "#/components/responses/unauthorized"
-
-  /v1/table/{name}/describe/:
-    post:
-      description: Describe a table and return Table Information.
-      tags:
-        - Tables
-      summary: Describe a table
-      operationId: describeTable
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      responses:
-        "200":
-          description: Table information
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  table:
-                    type: string
-                  version:
-                    type: integer
-                  schema:
-                    type: string
-                  stats:
-                    type: object
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-
-  /v1/table/{name}/index/list/:
-    post:
-      description: List indexes of a table
-      tags:
-        - Tables
-      summary: List indexes of a table
-      operationId: listIndexes
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      responses:
-        "200":
-          description: Available list of indexes on the table.
-          content:
-            application/json:
-              schema:
-                type: object
-                properties:
-                  indexes:
-                    type: array
-                    items:
-                      type: object
-                      properties:
-                        columns:
-                          type: array
-                          items:
-                            type: string
-                        index_name:
-                          type: string
-                        index_uuid:
-                          type: string
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/create_index/:
-    post:
-      description: Create vector index on a Table
-      tags:
-        - Tables
-      summary: Create vector index on a Table
-      operationId: createIndex
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                column:
-                  type: string
-                metric_type:
-                  type: string
-                  nullable: false
-                  description: |
-                    The metric type to use for the index. L2, Cosine, Dot are supported.
-                index_type:
-                  type: string
-      responses:
-        "200":
-          description: Index successfully created
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"
-  /v1/table/{name}/create_scalar_index/:
-    post:
-      description: Create a scalar index on a table
-      tags:
-        - Tables
-      summary: Create a scalar index on a table
-      operationId: createScalarIndex
-      parameters:
-        - $ref: "#/components/parameters/table_name"
-      requestBody:
-        required: true
-        content:
-          application/json:
-            schema:
-              type: object
-              properties:
-                column:
-                  type: string
-                index_type:
-                  type: string
-                  required: false
-      responses:
-        "200":
-          description: Scalar Index successfully created
-        "400":
-          $ref: "#/components/responses/invalid_request"
-        "401":
-          $ref: "#/components/responses/unauthorized"
-        "404":
-          $ref: "#/components/responses/not_found"

docs/requirements.txt

@@ -2,5 +2,4 @@ mkdocs==1.5.3
 mkdocs-jupyter==0.24.1
 mkdocs-material==9.5.3
 mkdocstrings[python]==0.20.0
-mkdocs-render-swagger-plugin
-pydantic
+pydantic

docs/src/cloud/rest.md

@@ -1 +0,0 @@
-!!swagger ../../openapi.yml!!

docs/src/embeddings/default_embedding_functions.md

@@ -193,13 +193,13 @@ from lancedb.pydantic import LanceModel, Vector
 
 model = get_registry().get("huggingface").create(name='facebook/bart-base')
 
-class Words(LanceModel):
+class TextModel(LanceModel):
     text: str = model.SourceField()
     vector: Vector(model.ndims()) = model.VectorField()
 
 df = pd.DataFrame({"text": ["hi hello sayonara", "goodbye world"]})
 table = db.create_table("greets", schema=Words)
-table.add(df)
+table.add()
 query = "old greeting"
 actual = table.search(query).limit(1).to_pydantic(Words)[0]
 print(actual.text)

docs/src/guides/storage.md

@@ -265,108 +265,6 @@ For **read-only access**, LanceDB will need a policy such as:
 }
 ```
 
-#### DynamoDB Commit Store for concurrent writes
-
-By default, S3 does not support concurrent writes. Having two or more processes
-writing to the same table at the same time can lead to data corruption. This is
-because S3, unlike other object stores, does not have any atomic put or copy
-operation.
-
-To enable concurrent writes, you can configure LanceDB to use a DynamoDB table
-as a commit store. This table will be used to coordinate writes between
-different processes. To enable this feature, you must modify your connection
-URI to use the `s3+ddb` scheme and add a query parameter `ddbTableName` with the
-name of the table to use.
-
-=== "Python"
-
-    ```python
-    import lancedb
-    db = await lancedb.connect_async(
-        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-    )
-    ```
-
-=== "JavaScript"
-
-    ```javascript
-    const lancedb = require("lancedb");
-
-    const db = await lancedb.connect(
-        "s3+ddb://bucket/path?ddbTableName=my-dynamodb-table",
-    );
-    ```
-
-The DynamoDB table must be created with the following schema:
-
-- Hash key: `base_uri` (string)
-- Range key: `version` (number)
-
-You can create this programmatically with:
-
-=== "Python"
-
-    <!-- skip-test -->
-    ```python
-    import boto3
-
-    dynamodb = boto3.client("dynamodb")
-    table = dynamodb.create_table(
-        TableName=table_name,
-        KeySchema=[
-            {"AttributeName": "base_uri", "KeyType": "HASH"},
-            {"AttributeName": "version", "KeyType": "RANGE"},
-        ],
-        AttributeDefinitions=[
-            {"AttributeName": "base_uri", "AttributeType": "S"},
-            {"AttributeName": "version", "AttributeType": "N"},
-        ],
-        ProvisionedThroughput={"ReadCapacityUnits": 1, "WriteCapacityUnits": 1},
-    )
-    ```
-
-=== "JavaScript"
-
-    <!-- skip-test -->
-    ```javascript
-    import {
-      CreateTableCommand,
-      DynamoDBClient,
-    } from "@aws-sdk/client-dynamodb";
-
-    const dynamodb = new DynamoDBClient({
-      region: CONFIG.awsRegion,
-      credentials: {
-        accessKeyId: CONFIG.awsAccessKeyId,
-        secretAccessKey: CONFIG.awsSecretAccessKey,
-      },
-      endpoint: CONFIG.awsEndpoint,
-    });
-    const command = new CreateTableCommand({
-      TableName: table_name,
-      AttributeDefinitions: [
-        {
-          AttributeName: "base_uri",
-          AttributeType: "S",
-        },
-        {
-          AttributeName: "version",
-          AttributeType: "N",
-        },
-      ],
-      KeySchema: [
-        { AttributeName: "base_uri", KeyType: "HASH" },
-        { AttributeName: "version", KeyType: "RANGE" },
-      ],
-      ProvisionedThroughput: {
-        ReadCapacityUnits: 1,
-        WriteCapacityUnits: 1,
-      },
-    });
-    await client.send(command);
-    ```
-
-
 #### S3-compatible stores
 
 LanceDB can also connect to S3-compatible stores, such as MinIO. To do so, you must specify both region and endpoint:

docs/src/guides/tables.md

@@ -116,21 +116,21 @@ This guide will show how to create tables, insert data into them, and update the
 
 ### From a Polars DataFrame
 
-LanceDB supports [Polars](https://pola.rs/), a modern, fast DataFrame library
-written in Rust. Just like in Pandas, the Polars integration is enabled by PyArrow
-under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
-is on the way.
+    LanceDB supports [Polars](https://pola.rs/), a modern, fast DataFrame library
+    written in Rust. Just like in Pandas, the Polars integration is enabled by PyArrow
+    under the hood. A deeper integration between LanceDB Tables and Polars DataFrames
+    is on the way.
 
-```python
-import polars as pl
+    ```python
+    import polars as pl
 
-data = pl.DataFrame({
-    "vector": [[3.1, 4.1], [5.9, 26.5]],
-    "item": ["foo", "bar"],
-    "price": [10.0, 20.0]
-})
-table = db.create_table("pl_table", data=data)
-```
+    data = pl.DataFrame({
+        "vector": [[3.1, 4.1], [5.9, 26.5]],
+        "item": ["foo", "bar"],
+        "price": [10.0, 20.0]
+    })
+    table = db.create_table("pl_table", data=data)
+    ```
 
 ### From an Arrow Table
 === "Python"

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.6.0",
+  "version": "0.5.2",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.6.0",
+      "version": "0.5.2",
       "cpu": [
         "x64",
         "arm64"

node/package.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.6.0",
+  "version": "0.5.2",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
   "scripts": {
     "tsc": "tsc -b",
-    "build": "npm run tsc && cargo-cp-artifact --artifact cdylib lancedb_node index.node -- cargo build -p lancedb-node --message-format=json",
+    "build": "npm run tsc && cargo-cp-artifact --artifact cdylib lancedb_node index.node -- cargo build --message-format=json",
     "build-release": "npm run build -- --release",
     "test": "npm run tsc && mocha -recursive dist/test",
     "integration-test": "npm run tsc && mocha -recursive dist/integration_test",

nodejs/Cargo.toml

@@ -15,11 +15,11 @@ crate-type = ["cdylib"]
 arrow-ipc.workspace = true
 futures.workspace = true
 lancedb = { path = "../rust/lancedb" }
-napi = { version = "2.16.8", default-features = false, features = [
-    "napi9",
+napi = { version = "2.15", default-features = false, features = [
+    "napi7",
     "async",
 ] }
-napi-derive = "2.16.4"
+napi-derive = "2"
 
 # Prevent dynamic linking of lzma, which comes from datafusion
 lzma-sys = { version = "*", features = ["static"] }

nodejs/__test__/registry.test.ts

@@ -63,7 +63,6 @@ describe("Registry", () => {
         return data.map(() => [1, 2, 3]);
       }
     }
-
     const func = getRegistry()
       .get<MockEmbeddingFunction>("mock-embedding")!
       .create();

nodejs/__test__/s3_integration.test.ts

@@ -14,11 +14,6 @@
 
 /* eslint-disable @typescript-eslint/naming-convention */
 
-import {
-  CreateTableCommand,
-  DeleteTableCommand,
-  DynamoDBClient,
-} from "@aws-sdk/client-dynamodb";
 import {
   CreateKeyCommand,
   KMSClient,
@@ -43,7 +38,6 @@ const CONFIG = {
   awsAccessKeyId: "ACCESSKEY",
   awsSecretAccessKey: "SECRETKEY",
   awsEndpoint: "http://127.0.0.1:4566",
-  dynamodbEndpoint: "http://127.0.0.1:4566",
   awsRegion: "us-east-1",
 };
 
@@ -72,6 +66,7 @@ class S3Bucket {
     } catch {
       // It's fine if the bucket doesn't exist
     }
+    // biome-ignore lint/style/useNamingConvention: we dont control s3's api
     await client.send(new CreateBucketCommand({ Bucket: name }));
     return new S3Bucket(name);
   }
@@ -84,27 +79,32 @@ class S3Bucket {
   static async deleteBucket(client: S3Client, name: string) {
     // Must delete all objects before we can delete the bucket
     const objects = await client.send(
+      // biome-ignore lint/style/useNamingConvention: we dont control s3's api
       new ListObjectsV2Command({ Bucket: name }),
     );
     if (objects.Contents) {
       for (const object of objects.Contents) {
         await client.send(
+          // biome-ignore lint/style/useNamingConvention: we dont control s3's api
           new DeleteObjectCommand({ Bucket: name, Key: object.Key }),
         );
       }
     }
 
+    // biome-ignore lint/style/useNamingConvention: we dont control s3's api
     await client.send(new DeleteBucketCommand({ Bucket: name }));
   }
 
   public async assertAllEncrypted(path: string, keyId: string) {
     const client = S3Bucket.s3Client();
     const objects = await client.send(
+      // biome-ignore lint/style/useNamingConvention: we dont control s3's api
       new ListObjectsV2Command({ Bucket: this.name, Prefix: path }),
     );
     if (objects.Contents) {
       for (const object of objects.Contents) {
         const metadata = await client.send(
+          // biome-ignore lint/style/useNamingConvention: we dont control s3's api
           new HeadObjectCommand({ Bucket: this.name, Key: object.Key }),
         );
         expect(metadata.ServerSideEncryption).toBe("aws:kms");
@@ -143,6 +143,7 @@ class KmsKey {
 
   public async delete() {
     const client = KmsKey.kmsClient();
+    // biome-ignore lint/style/useNamingConvention: we dont control s3's api
     await client.send(new ScheduleKeyDeletionCommand({ KeyId: this.keyId }));
   }
 }
@@ -223,91 +224,3 @@ maybeDescribe("storage_options", () => {
     await bucket.assertAllEncrypted("test/table2.lance", kmsKey.keyId);
   });
 });
-
-class DynamoDBCommitTable {
-  name: string;
-  constructor(name: string) {
-    this.name = name;
-  }
-
-  static dynamoClient() {
-    return new DynamoDBClient({
-      region: CONFIG.awsRegion,
-      credentials: {
-        accessKeyId: CONFIG.awsAccessKeyId,
-        secretAccessKey: CONFIG.awsSecretAccessKey,
-      },
-      endpoint: CONFIG.awsEndpoint,
-    });
-  }
-
-  public static async create(name: string): Promise<DynamoDBCommitTable> {
-    const client = DynamoDBCommitTable.dynamoClient();
-    const command = new CreateTableCommand({
-      TableName: name,
-      AttributeDefinitions: [
-        {
-          AttributeName: "base_uri",
-          AttributeType: "S",
-        },
-        {
-          AttributeName: "version",
-          AttributeType: "N",
-        },
-      ],
-      KeySchema: [
-        { AttributeName: "base_uri", KeyType: "HASH" },
-        { AttributeName: "version", KeyType: "RANGE" },
-      ],
-      ProvisionedThroughput: {
-        ReadCapacityUnits: 1,
-        WriteCapacityUnits: 1,
-      },
-    });
-    await client.send(command);
-    return new DynamoDBCommitTable(name);
-  }
-
-  public async delete() {
-    const client = DynamoDBCommitTable.dynamoClient();
-    await client.send(new DeleteTableCommand({ TableName: this.name }));
-  }
-}
-
-maybeDescribe("DynamoDB Lock", () => {
-  let bucket: S3Bucket;
-  let commitTable: DynamoDBCommitTable;
-
-  beforeAll(async () => {
-    bucket = await S3Bucket.create("lancedb2");
-    commitTable = await DynamoDBCommitTable.create("commitTable");
-  });
-
-  afterAll(async () => {
-    await commitTable.delete();
-    await bucket.delete();
-  });
-
-  it("can be used to configure a DynamoDB table for commit log", async () => {
-    const uri = `s3+ddb://${bucket.name}/test?ddbTableName=${commitTable.name}`;
-    const db = await connect(uri, {
-      storageOptions: CONFIG,
-      readConsistencyInterval: 0,
-    });
-
-    const table = await db.createTable("test", [{ a: 1, b: 2 }]);
-
-    // 5 concurrent appends
-    const futs = Array.from({ length: 5 }, async () => {
-      // Open a table so each append has a separate table reference. Otherwise
-      // they will share the same table reference and the internal ReadWriteLock
-      // will prevent any real concurrency.
-      const table = await db.openTable("test");
-      await table.add([{ a: 2, b: 3 }]);
-    });
-    await Promise.all(futs);
-
-    const rowCount = await table.countRows();
-    expect(rowCount).toBe(6);
-  });
-});

nodejs/__test__/table.test.ts

@@ -39,9 +39,7 @@ describe.each([arrow, arrowOld])("Given a table", (arrow: any) => {
   let tmpDir: tmp.DirResult;
   let table: Table;
 
-  const schema:
-    | import("apache-arrow").Schema
-    | import("apache-arrow-old").Schema = new arrow.Schema([
+  const schema = new arrow.Schema([
     new arrow.Field("id", new arrow.Float64(), true),
   ]);
 
@@ -317,7 +315,7 @@ describe("When creating an index", () => {
       .query()
       .limit(2)
       .nearestTo(queryVec)
-      .distanceType("dot")
+      .distanceType("DoT")
       .toArrow();
     expect(rst.numRows).toBe(2);
 

nodejs/biome.json

@@ -1,5 +1,5 @@
 {
-  "$schema": "https://biomejs.dev/schemas/1.8.3/schema.json",
+  "$schema": "https://biomejs.dev/schemas/1.7.3/schema.json",
   "organizeImports": {
     "enabled": true
   },
@@ -100,16 +100,6 @@
     "globals": []
   },
   "overrides": [
-    {
-      "include": ["__test__/s3_integration.test.ts"],
-      "linter": {
-        "rules": {
-          "style": {
-            "useNamingConvention": "off"
-          }
-        }
-      }
-    },
     {
       "include": [
         "**/*.ts",

nodejs/lancedb/arrow.ts

@@ -15,7 +15,6 @@
 import {
   Table as ArrowTable,
   Binary,
-  BufferType,
   DataType,
   Field,
   FixedSizeBinary,
@@ -38,68 +37,14 @@ import {
   type makeTable,
   vectorFromArray,
 } from "apache-arrow";
-import { Buffers } from "apache-arrow/data";
 import { type EmbeddingFunction } from "./embedding/embedding_function";
 import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
-import {
-  sanitizeField,
-  sanitizeSchema,
-  sanitizeTable,
-  sanitizeType,
-} from "./sanitize";
+import { sanitizeField, sanitizeSchema, sanitizeType } from "./sanitize";
 export * from "apache-arrow";
-export type SchemaLike =
-  | Schema
-  | {
-      fields: FieldLike[];
-      metadata: Map<string, string>;
-      get names(): unknown[];
-    };
-export type FieldLike =
-  | Field
-  | {
-      type: string;
-      name: string;
-      nullable?: boolean;
-      metadata?: Map<string, string>;
-    };
-
-export type DataLike =
-  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-  | import("apache-arrow").Data<Struct<any>>
-  | {
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      type: any;
-      length: number;
-      offset: number;
-      stride: number;
-      nullable: boolean;
-      children: DataLike[];
-      get nullCount(): number;
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      values: Buffers<any>[BufferType.DATA];
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      typeIds: Buffers<any>[BufferType.TYPE];
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      nullBitmap: Buffers<any>[BufferType.VALIDITY];
-      // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-      valueOffsets: Buffers<any>[BufferType.OFFSET];
-    };
-
-export type RecordBatchLike =
-  | RecordBatch
-  | {
-      schema: SchemaLike;
-      data: DataLike;
-    };
-
-export type TableLike =
-  | ArrowTable
-  | { schema: SchemaLike; batches: RecordBatchLike[] };
 
 export type IntoVector = Float32Array | Float64Array | number[];
 
-export function isArrowTable(value: object): value is TableLike {
+export function isArrowTable(value: object): value is ArrowTable {
   if (value instanceof ArrowTable) return true;
   return "schema" in value && "batches" in value;
 }
@@ -190,7 +135,7 @@ export function isFixedSizeList(value: unknown): value is FixedSizeList {
 }
 
 /** Data type accepted by NodeJS SDK */
-export type Data = Record<string, unknown>[] | TableLike;
+export type Data = Record<string, unknown>[] | ArrowTable;
 
 /*
  * Options to control how a column should be converted to a vector array
@@ -217,7 +162,7 @@ export class MakeArrowTableOptions {
    * The schema must be specified if there are no records (e.g. to make
    * an empty table)
    */
-  schema?: SchemaLike;
+  schema?: Schema;
 
   /*
    * Mapping from vector column name to expected type
@@ -365,7 +310,7 @@ export function makeArrowTable(
   if (opt.schema !== undefined && opt.schema !== null) {
     opt.schema = sanitizeSchema(opt.schema);
     opt.schema = validateSchemaEmbeddings(
-      opt.schema as Schema,
+      opt.schema,
       data,
       options?.embeddingFunction,
     );
@@ -449,7 +394,7 @@ export function makeArrowTable(
     // `new ArrowTable(schema, batches)` which does not do any schema inference
     const firstTable = new ArrowTable(columns);
     const batchesFixed = firstTable.batches.map(
-      (batch) => new RecordBatch(opt.schema as Schema, batch.data),
+      (batch) => new RecordBatch(opt.schema!, batch.data),
     );
     let schema: Schema;
     if (metadata !== undefined) {
@@ -462,9 +407,9 @@ export function makeArrowTable(
         }
       }
 
-      schema = new Schema(opt.schema.fields as Field[], schemaMetadata);
+      schema = new Schema(opt.schema.fields, schemaMetadata);
     } else {
-      schema = opt.schema as Schema;
+      schema = opt.schema;
     }
     return new ArrowTable(schema, batchesFixed);
   }
@@ -480,7 +425,7 @@ export function makeArrowTable(
  * Create an empty Arrow table with the provided schema
  */
 export function makeEmptyTable(
-  schema: SchemaLike,
+  schema: Schema,
   metadata?: Map<string, string>,
 ): ArrowTable {
   return makeArrowTable([], { schema }, metadata);
@@ -618,17 +563,18 @@ async function applyEmbeddingsFromMetadata(
 async function applyEmbeddings<T>(
   table: ArrowTable,
   embeddings?: EmbeddingFunctionConfig,
-  schema?: SchemaLike,
+  schema?: Schema,
 ): Promise<ArrowTable> {
-  if (schema !== undefined && schema !== null) {
-    schema = sanitizeSchema(schema);
-  }
   if (schema?.metadata.has("embedding_functions")) {
-    return applyEmbeddingsFromMetadata(table, schema! as Schema);
+    return applyEmbeddingsFromMetadata(table, schema!);
   } else if (embeddings == null || embeddings === undefined) {
     return table;
   }
 
+  if (schema !== undefined && schema !== null) {
+    schema = sanitizeSchema(schema);
+  }
+
   // Convert from ArrowTable to Record<String, Vector>
   const colEntries = [...Array(table.numCols).keys()].map((_, idx) => {
     const name = table.schema.fields[idx].name;
@@ -704,7 +650,7 @@ async function applyEmbeddings<T>(
         `When using embedding functions and specifying a schema the schema should include the embedding column but the column ${destColumn} was missing`,
       );
     }
-    return alignTable(newTable, schema as Schema);
+    return alignTable(newTable, schema);
   }
   return newTable;
 }
@@ -798,7 +744,7 @@ export async function fromRecordsToStreamBuffer(
 export async function fromTableToBuffer(
   table: ArrowTable,
   embeddings?: EmbeddingFunctionConfig,
-  schema?: SchemaLike,
+  schema?: Schema,
 ): Promise<Buffer> {
   if (schema !== undefined && schema !== null) {
     schema = sanitizeSchema(schema);
@@ -825,7 +771,7 @@ export async function fromDataToBuffer(
     schema = sanitizeSchema(schema);
   }
   if (isArrowTable(data)) {
-    return fromTableToBuffer(sanitizeTable(data), embeddings, schema);
+    return fromTableToBuffer(data, embeddings, schema);
   } else {
     const table = await convertToTable(data, embeddings, { schema });
     return fromTableToBuffer(table);
@@ -843,7 +789,7 @@ export async function fromDataToBuffer(
 export async function fromTableToStreamBuffer(
   table: ArrowTable,
   embeddings?: EmbeddingFunctionConfig,
-  schema?: SchemaLike,
+  schema?: Schema,
 ): Promise<Buffer> {
   const tableWithEmbeddings = await applyEmbeddings(table, embeddings, schema);
   const writer = RecordBatchStreamWriter.writeAll(tableWithEmbeddings);
@@ -908,6 +854,7 @@ function validateSchemaEmbeddings(
   for (let field of schema.fields) {
     if (isFixedSizeList(field.type)) {
       field = sanitizeField(field);
+
       if (data.length !== 0 && data?.[0]?.[field.name] === undefined) {
         if (schema.metadata.has("embedding_functions")) {
           const embeddings = JSON.parse(

nodejs/lancedb/connection.ts

@@ -12,7 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { Data, Schema, SchemaLike, TableLike } from "./arrow";
+import { Table as ArrowTable, Data, Schema } from "./arrow";
 import { fromTableToBuffer, makeEmptyTable } from "./arrow";
 import { EmbeddingFunctionConfig, getRegistry } from "./embedding/registry";
 import { Connection as LanceDbConnection } from "./native";
@@ -50,7 +50,7 @@ export interface CreateTableOptions {
    * The default is true while the new format is in beta
    */
   useLegacyFormat?: boolean;
-  schema?: SchemaLike;
+  schema?: Schema;
   embeddingFunction?: EmbeddingFunctionConfig;
 }
 
@@ -167,12 +167,12 @@ export abstract class Connection {
   /**
    * Creates a new Table and initialize it with new data.
    * @param {string} name - The name of the table.
-   * @param {Record<string, unknown>[] | TableLike} data - Non-empty Array of Records
+   * @param {Record<string, unknown>[] | ArrowTable} data - Non-empty Array of Records
    * to be inserted into the table
    */
   abstract createTable(
     name: string,
-    data: Record<string, unknown>[] | TableLike,
+    data: Record<string, unknown>[] | ArrowTable,
     options?: Partial<CreateTableOptions>,
   ): Promise<Table>;
 
@@ -183,7 +183,7 @@ export abstract class Connection {
    */
   abstract createEmptyTable(
     name: string,
-    schema: import("./arrow").SchemaLike,
+    schema: Schema,
     options?: Partial<CreateTableOptions>,
   ): Promise<Table>;
 
@@ -235,7 +235,7 @@ export class LocalConnection extends Connection {
     nameOrOptions:
       | string
       | ({ name: string; data: Data } & Partial<CreateTableOptions>),
-    data?: Record<string, unknown>[] | TableLike,
+    data?: Record<string, unknown>[] | ArrowTable,
     options?: Partial<CreateTableOptions>,
   ): Promise<Table> {
     if (typeof nameOrOptions !== "string" && "name" in nameOrOptions) {
@@ -259,7 +259,7 @@ export class LocalConnection extends Connection {
 
   async createEmptyTable(
     name: string,
-    schema: import("./arrow").SchemaLike,
+    schema: Schema,
     options?: Partial<CreateTableOptions>,
   ): Promise<Table> {
     let mode: string = options?.mode ?? "create";

nodejs/lancedb/embedding/embedding_function.ts

@@ -35,11 +35,6 @@ export interface FunctionOptions {
   [key: string]: any;
 }
 
-export interface EmbeddingFunctionConstructor<
-  T extends EmbeddingFunction = EmbeddingFunction,
-> {
-  new (modelOptions?: T["TOptions"]): T;
-}
 /**
  * An embedding function that automatically creates vector representation for a given column.
  */
@@ -48,12 +43,6 @@ export abstract class EmbeddingFunction<
   T = any,
   M extends FunctionOptions = FunctionOptions,
 > {
-  /**
-   * @ignore
-   *  This is only used for associating the options type with the class for type checking
-   */
-  // biome-ignore lint/style/useNamingConvention: we want to keep the name as it is
-  readonly TOptions!: M;
   /**
    * Convert the embedding function to a JSON object
    * It is used to serialize the embedding function to the schema

nodejs/lancedb/embedding/openai.ts

@@ -13,29 +13,24 @@
 // limitations under the License.
 
 import type OpenAI from "openai";
-import { type EmbeddingCreateParams } from "openai/resources";
 import { Float, Float32 } from "../arrow";
 import { EmbeddingFunction } from "./embedding_function";
 import { register } from "./registry";
 
 export type OpenAIOptions = {
-  apiKey: string;
-  model: EmbeddingCreateParams["model"];
+  apiKey?: string;
+  model?: string;
 };
 
 @register("openai")
 export class OpenAIEmbeddingFunction extends EmbeddingFunction<
   string,
-  Partial<OpenAIOptions>
+  OpenAIOptions
 > {
   #openai: OpenAI;
-  #modelName: OpenAIOptions["model"];
+  #modelName: string;
 
-  constructor(
-    options: Partial<OpenAIOptions> = {
-      model: "text-embedding-ada-002",
-    },
-  ) {
+  constructor(options: OpenAIOptions = { model: "text-embedding-ada-002" }) {
     super();
     const openAIKey = options?.apiKey ?? process.env.OPENAI_API_KEY;
     if (!openAIKey) {
@@ -78,7 +73,7 @@ export class OpenAIEmbeddingFunction extends EmbeddingFunction<
       case "text-embedding-3-small":
         return 1536;
       default:
-        throw new Error(`Unknown model: ${this.#modelName}`);
+        return null as never;
     }
   }
 

nodejs/lancedb/embedding/registry.ts

@@ -12,15 +12,21 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import {
-  type EmbeddingFunction,
-  type EmbeddingFunctionConstructor,
-} from "./embedding_function";
+import type { EmbeddingFunction } from "./embedding_function";
 import "reflect-metadata";
-import { OpenAIEmbeddingFunction } from "./openai";
+
+export interface EmbeddingFunctionOptions {
+  [key: string]: unknown;
+}
+
+export interface EmbeddingFunctionFactory<
+  T extends EmbeddingFunction = EmbeddingFunction,
+> {
+  new (modelOptions?: EmbeddingFunctionOptions): T;
+}
 
 interface EmbeddingFunctionCreate<T extends EmbeddingFunction> {
-  create(options?: T["TOptions"]): T;
+  create(options?: EmbeddingFunctionOptions): T;
 }
 
 /**
@@ -30,7 +36,7 @@ interface EmbeddingFunctionCreate<T extends EmbeddingFunction> {
  * or TextEmbeddingFunction and registering it with the registry
  */
 export class EmbeddingFunctionRegistry {
-  #functions = new Map<string, EmbeddingFunctionConstructor>();
+  #functions: Map<string, EmbeddingFunctionFactory> = new Map();
 
   /**
    * Register an embedding function
@@ -38,9 +44,7 @@ export class EmbeddingFunctionRegistry {
    * @param func The function to register
    * @throws Error if the function is already registered
    */
-  register<
-    T extends EmbeddingFunctionConstructor = EmbeddingFunctionConstructor,
-  >(
+  register<T extends EmbeddingFunctionFactory = EmbeddingFunctionFactory>(
     this: EmbeddingFunctionRegistry,
     alias?: string,
     // biome-ignore lint/suspicious/noExplicitAny: <explanation>
@@ -65,34 +69,18 @@ export class EmbeddingFunctionRegistry {
    * Fetch an embedding function by name
    * @param name The name of the function
    */
-  get<T extends EmbeddingFunction<unknown>, Name extends string = "">(
-    name: Name extends "openai" ? "openai" : string,
-    //This makes it so that you can use string constants as "types", or use an explicitly supplied type
-    // ex:
-    // `registry.get("openai") -> EmbeddingFunctionCreate<OpenAIEmbeddingFunction>`
-    // `registry.get<MyCustomEmbeddingFunction>("my_func") -> EmbeddingFunctionCreate<MyCustomEmbeddingFunction> | undefined`
-    //
-    // the reason this is important is that we always know our built in functions are defined so the user isnt forced to do a non null/undefined
-    // ```ts
-    // const openai: OpenAIEmbeddingFunction = registry.get("openai").create()
-    // ```
-  ): Name extends "openai"
-    ? EmbeddingFunctionCreate<OpenAIEmbeddingFunction>
-    : EmbeddingFunctionCreate<T> | undefined {
-    type Output = Name extends "openai"
-      ? EmbeddingFunctionCreate<OpenAIEmbeddingFunction>
-      : EmbeddingFunctionCreate<T> | undefined;
-
+  get<T extends EmbeddingFunction<unknown> = EmbeddingFunction>(
+    name: string,
+  ): EmbeddingFunctionCreate<T> | undefined {
     const factory = this.#functions.get(name);
     if (!factory) {
-      return undefined as Output;
+      return undefined;
     }
-
     return {
-      create: function (options?: T["TOptions"]) {
-        return new factory(options);
+      create: function (options: EmbeddingFunctionOptions) {
+        return new factory(options) as unknown as T;
       },
-    } as Output;
+    };
   }
 
   /**
@@ -116,7 +104,7 @@ export class EmbeddingFunctionRegistry {
         name: string;
         sourceColumn: string;
         vectorColumn: string;
-        model: EmbeddingFunction["TOptions"];
+        model: EmbeddingFunctionOptions;
       };
       const functions = <FunctionConfig[]>(
         JSON.parse(metadata.get("embedding_functions")!)

nodejs/lancedb/query.ts

@@ -300,9 +300,7 @@ export class VectorQuery extends QueryBase<NativeVectorQuery, VectorQuery> {
    *
    * By default "l2" is used.
    */
-  distanceType(
-    distanceType: Required<IvfPqOptions>["distanceType"],
-  ): VectorQuery {
+  distanceType(distanceType: string): VectorQuery {
     this.inner.distanceType(distanceType);
     return this;
   }

nodejs/lancedb/remote/client.ts

@@ -55,7 +55,7 @@ export class RestfulLanceDBClient {
       return axios.create({
         baseURL: this.url,
         headers: {
-          // biome-ignore lint: external API
+          // biome-ignore lint/style/useNamingConvention: external api
           Authorization: `Bearer ${this.#apiKey}`,
         },
         transformResponse: decodeErrorData,

nodejs/lancedb/remote/connection.ts

@@ -1,10 +1,5 @@
 import { Schema } from "apache-arrow";
-import {
-  Data,
-  SchemaLike,
-  fromTableToStreamBuffer,
-  makeEmptyTable,
-} from "../arrow";
+import { Data, fromTableToStreamBuffer, makeEmptyTable } from "../arrow";
 import {
   Connection,
   CreateTableOptions,
@@ -161,7 +156,7 @@ export class RemoteConnection extends Connection {
 
   async createEmptyTable(
     name: string,
-    schema: SchemaLike,
+    schema: Schema,
     options?: Partial<CreateTableOptions> | undefined,
   ): Promise<Table> {
     if (options?.mode) {

nodejs/lancedb/sanitize.ts

@@ -20,12 +20,10 @@
 // comes from the exact same library instance.  This is not always the case
 // and so we must sanitize the input to ensure that it is compatible.
 
-import { BufferType, Data } from "apache-arrow";
 import type { IntBitWidth, TKeys, TimeBitWidth } from "apache-arrow/type";
 import {
   Binary,
   Bool,
-  DataLike,
   DataType,
   DateDay,
   DateMillisecond,
@@ -58,14 +56,9 @@ import {
   Map_,
   Null,
   type Precision,
-  RecordBatch,
-  RecordBatchLike,
   Schema,
-  SchemaLike,
   SparseUnion,
   Struct,
-  Table,
-  TableLike,
   Time,
   TimeMicrosecond,
   TimeMillisecond,
@@ -495,7 +488,7 @@ export function sanitizeField(fieldLike: unknown): Field {
  * instance because they might be using a different instance of apache-arrow
  * than lancedb is using.
  */
-export function sanitizeSchema(schemaLike: SchemaLike): Schema {
+export function sanitizeSchema(schemaLike: unknown): Schema {
   if (schemaLike instanceof Schema) {
     return schemaLike;
   }
@@ -521,68 +514,3 @@ export function sanitizeSchema(schemaLike: SchemaLike): Schema {
   );
   return new Schema(sanitizedFields, metadata);
 }
-
-export function sanitizeTable(tableLike: TableLike): Table {
-  if (tableLike instanceof Table) {
-    return tableLike;
-  }
-  if (typeof tableLike !== "object" || tableLike === null) {
-    throw Error("Expected a Table but object was null/undefined");
-  }
-  if (!("schema" in tableLike)) {
-    throw Error(
-      "The table passed in does not appear to be a table (no 'schema' property)",
-    );
-  }
-  if (!("batches" in tableLike)) {
-    throw Error(
-      "The table passed in does not appear to be a table (no 'columns' property)",
-    );
-  }
-  const schema = sanitizeSchema(tableLike.schema);
-
-  const batches = tableLike.batches.map(sanitizeRecordBatch);
-  return new Table(schema, batches);
-}
-
-function sanitizeRecordBatch(batchLike: RecordBatchLike): RecordBatch {
-  if (batchLike instanceof RecordBatch) {
-    return batchLike;
-  }
-  if (typeof batchLike !== "object" || batchLike === null) {
-    throw Error("Expected a RecordBatch but object was null/undefined");
-  }
-  if (!("schema" in batchLike)) {
-    throw Error(
-      "The record batch passed in does not appear to be a record batch (no 'schema' property)",
-    );
-  }
-  if (!("data" in batchLike)) {
-    throw Error(
-      "The record batch passed in does not appear to be a record batch (no 'data' property)",
-    );
-  }
-  const schema = sanitizeSchema(batchLike.schema);
-  const data = sanitizeData(batchLike.data);
-  return new RecordBatch(schema, data);
-}
-function sanitizeData(
-  dataLike: DataLike,
-  // biome-ignore lint/suspicious/noExplicitAny: <explanation>
-): import("apache-arrow").Data<Struct<any>> {
-  if (dataLike instanceof Data) {
-    return dataLike;
-  }
-  return new Data(
-    dataLike.type,
-    dataLike.offset,
-    dataLike.length,
-    dataLike.nullCount,
-    {
-      [BufferType.OFFSET]: dataLike.valueOffsets,
-      [BufferType.DATA]: dataLike.values,
-      [BufferType.VALIDITY]: dataLike.nullBitmap,
-      [BufferType.TYPE]: dataLike.typeIds,
-    },
-  );
-}

nodejs/lancedb/table.ts

@@ -17,7 +17,6 @@ import {
   Data,
   IntoVector,
   Schema,
-  TableLike,
   fromDataToBuffer,
   fromTableToBuffer,
   fromTableToStreamBuffer,
@@ -39,8 +38,6 @@ import {
   Table as _NativeTable,
 } from "./native";
 import { Query, VectorQuery } from "./query";
-import { sanitizeTable } from "./sanitize";
-export { IndexConfig } from "./native";
 
 /**
  * Options for adding data to a table.
@@ -384,7 +381,8 @@ export abstract class Table {
   abstract indexStats(name: string): Promise<IndexStatistics | undefined>;
 
   static async parseTableData(
-    data: Record<string, unknown>[] | TableLike,
+    // biome-ignore lint/suspicious/noExplicitAny: <explanation>
+    data: Record<string, unknown>[] | ArrowTable<any>,
     options?: Partial<CreateTableOptions>,
     streaming = false,
   ) {
@@ -397,9 +395,9 @@ export abstract class Table {
 
     let table: ArrowTable;
     if (isArrowTable(data)) {
-      table = sanitizeTable(data);
+      table = data;
     } else {
-      table = makeArrowTable(data as Record<string, unknown>[], options);
+      table = makeArrowTable(data, options);
     }
     if (streaming) {
       const buf = await fromTableToStreamBuffer(

nodejs/npm/darwin-arm64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-arm64",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["darwin"],
 	"cpu": ["arm64"],
 	"main": "lancedb.darwin-arm64.node",

nodejs/npm/darwin-x64/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-darwin-x64",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["darwin"],
 	"cpu": ["x64"],
 	"main": "lancedb.darwin-x64.node",

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

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-arm64-gnu",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["linux"],
 	"cpu": ["arm64"],
 	"main": "lancedb.linux-arm64-gnu.node",

nodejs/npm/linux-x64-gnu/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-linux-x64-gnu",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["linux"],
 	"cpu": ["x64"],
 	"main": "lancedb.linux-x64-gnu.node",

nodejs/npm/win32-x64-msvc/package.json

@@ -1,6 +1,6 @@
 {
 	"name": "@lancedb/lancedb-win32-x64-msvc",
-	"version": "0.6.0",
+	"version": "0.5.2",
 	"os": ["win32"],
 	"cpu": ["x64"],
 	"main": "lancedb.win32-x64-msvc.node",

nodejs/package.json

@@ -10,7 +10,7 @@
     "vector database",
     "ann"
   ],
-  "version": "0.6.0",
+  "version": "0.5.2",
   "main": "dist/index.js",
   "exports": {
     ".": "./dist/index.js",
@@ -34,10 +34,9 @@
   "devDependencies": {
     "@aws-sdk/client-kms": "^3.33.0",
     "@aws-sdk/client-s3": "^3.33.0",
-    "@aws-sdk/client-dynamodb": "^3.33.0",
     "@biomejs/biome": "^1.7.3",
     "@jest/globals": "^29.7.0",
-    "@napi-rs/cli": "^2.18.3",
+    "@napi-rs/cli": "^2.18.0",
     "@types/jest": "^29.1.2",
     "@types/tmp": "^0.2.6",
     "apache-arrow-old": "npm:apache-arrow@13.0.0",
@@ -69,7 +68,7 @@
     "lint-ci": "biome ci .",
     "docs": "typedoc --plugin typedoc-plugin-markdown --out ../docs/src/js lancedb/index.ts",
     "lint": "biome check . && biome format .",
-    "lint-fix": "biome check --write . && biome format --write .",
+    "lint-fix": "biome check --apply-unsafe  . && biome format --write .",
     "prepublishOnly": "napi prepublish -t npm",
     "test": "jest --verbose",
     "integration": "S3_TEST=1 npm run test",
@@ -77,13 +76,9 @@
     "version": "napi version"
   },
   "dependencies": {
+    "apache-arrow": "^15.0.0",
     "axios": "^1.7.2",
+    "openai": "^4.29.2",
     "reflect-metadata": "^0.2.2"
-  },
-  "optionalDependencies": {
-    "openai": "^4.29.2"
-  },
-  "peerDependencies": {
-    "apache-arrow": "^15.0.0"
   }
 }

nodejs/src/connection.rs

@@ -89,7 +89,7 @@ impl Connection {
     }
 
     /// List all tables in the dataset.
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn table_names(
         &self,
         start_after: Option<String>,
@@ -113,7 +113,7 @@ impl Connection {
     /// - name: The name of the table.
     /// - buf: The buffer containing the IPC file.
     ///
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn create_table(
         &self,
         name: String,
@@ -141,7 +141,7 @@ impl Connection {
         Ok(Table::new(tbl))
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn create_empty_table(
         &self,
         name: String,
@@ -173,7 +173,7 @@ impl Connection {
         Ok(Table::new(tbl))
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn open_table(
         &self,
         name: String,
@@ -197,7 +197,7 @@ impl Connection {
     }
 
     /// Drop table with the name. Or raise an error if the table does not exist.
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn drop_table(&self, name: String) -> napi::Result<()> {
         self.get_inner()?
             .drop_table(&name)

nodejs/src/iterator.rs

@@ -30,7 +30,7 @@ impl RecordBatchIterator {
         Self { inner }
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async unsafe fn next(&mut self) -> napi::Result<Option<Buffer>> {
         if let Some(rst) = self.inner.next().await {
             let batch = rst.map_err(|e| {

nodejs/src/merge.rs

@@ -31,7 +31,7 @@ impl NativeMergeInsertBuilder {
         this
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn execute(&self, buf: Buffer) -> napi::Result<()> {
         let data = ipc_file_to_batches(buf.to_vec())
             .and_then(IntoArrow::into_arrow)

nodejs/src/query.rs

@@ -62,7 +62,7 @@ impl Query {
         Ok(VectorQuery { inner })
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn execute(
         &self,
         max_batch_length: Option<u32>,
@@ -136,7 +136,7 @@ impl VectorQuery {
         self.inner = self.inner.clone().limit(limit as usize);
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn execute(
         &self,
         max_batch_length: Option<u32>,

nodejs/src/table.rs

@@ -70,7 +70,7 @@ impl Table {
     }
 
     /// Return Schema as empty Arrow IPC file.
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn schema(&self) -> napi::Result<Buffer> {
         let schema =
             self.inner_ref()?.schema().await.map_err(|e| {
@@ -86,7 +86,7 @@ impl Table {
         })?))
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn add(&self, buf: Buffer, mode: String) -> napi::Result<()> {
         let batches = ipc_file_to_batches(buf.to_vec())
             .map_err(|e| napi::Error::from_reason(format!("Failed to read IPC file: {}", e)))?;
@@ -108,7 +108,7 @@ impl Table {
         })
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn count_rows(&self, filter: Option<String>) -> napi::Result<i64> {
         self.inner_ref()?
             .count_rows(filter)
@@ -122,7 +122,7 @@ impl Table {
             })
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn delete(&self, predicate: String) -> napi::Result<()> {
         self.inner_ref()?.delete(&predicate).await.map_err(|e| {
             napi::Error::from_reason(format!(
@@ -132,7 +132,7 @@ impl Table {
         })
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn create_index(
         &self,
         index: Option<&Index>,
@@ -151,7 +151,7 @@ impl Table {
         builder.execute().await.default_error()
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn update(
         &self,
         only_if: Option<String>,
@@ -167,17 +167,17 @@ impl Table {
         op.execute().await.default_error()
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub fn query(&self) -> napi::Result<Query> {
         Ok(Query::new(self.inner_ref()?.query()))
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub fn vector_search(&self, vector: Float32Array) -> napi::Result<VectorQuery> {
         self.query()?.nearest_to(vector)
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn add_columns(&self, transforms: Vec<AddColumnsSql>) -> napi::Result<()> {
         let transforms = transforms
             .into_iter()
@@ -196,7 +196,7 @@ impl Table {
         Ok(())
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn alter_columns(&self, alterations: Vec<ColumnAlteration>) -> napi::Result<()> {
         for alteration in &alterations {
             if alteration.rename.is_none() && alteration.nullable.is_none() {
@@ -222,7 +222,7 @@ impl Table {
         Ok(())
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn drop_columns(&self, columns: Vec<String>) -> napi::Result<()> {
         let col_refs = columns.iter().map(String::as_str).collect::<Vec<_>>();
         self.inner_ref()?
@@ -237,7 +237,7 @@ impl Table {
         Ok(())
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn version(&self) -> napi::Result<i64> {
         self.inner_ref()?
             .version()
@@ -246,7 +246,7 @@ impl Table {
             .default_error()
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn checkout(&self, version: i64) -> napi::Result<()> {
         self.inner_ref()?
             .checkout(version as u64)
@@ -254,17 +254,17 @@ impl Table {
             .default_error()
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn checkout_latest(&self) -> napi::Result<()> {
         self.inner_ref()?.checkout_latest().await.default_error()
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn restore(&self) -> napi::Result<()> {
         self.inner_ref()?.restore().await.default_error()
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn optimize(&self, older_than_ms: Option<i64>) -> napi::Result<OptimizeStats> {
         let inner = self.inner_ref()?;
 
@@ -318,7 +318,7 @@ impl Table {
         })
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn list_indices(&self) -> napi::Result<Vec<IndexConfig>> {
         Ok(self
             .inner_ref()?
@@ -330,14 +330,14 @@ impl Table {
             .collect::<Vec<_>>())
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub async fn index_stats(&self, index_name: String) -> napi::Result<Option<IndexStatistics>> {
         let tbl = self.inner_ref()?.as_native().unwrap();
         let stats = tbl.index_stats(&index_name).await.default_error()?;
         Ok(stats.map(IndexStatistics::from))
     }
 
-    #[napi(catch_unwind)]
+    #[napi]
     pub fn merge_insert(&self, on: Vec<String>) -> napi::Result<NativeMergeInsertBuilder> {
         let on: Vec<_> = on.iter().map(String::as_str).collect();
         Ok(self.inner_ref()?.merge_insert(on.as_slice()).into())

python/.bumpversion.toml

@@ -1,5 +1,5 @@
 [tool.bumpversion]
-current_version = "0.9.0"
+current_version = "0.9.0-beta.6"
 parse = """(?x)
     (?P<major>0|[1-9]\\d*)\\.
     (?P<minor>0|[1-9]\\d*)\\.

python/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-python"
-version = "0.9.0"
+version = "0.9.0-beta.6"
 edition.workspace = true
 description = "Python bindings for LanceDB"
 license.workspace = true

python/pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "packaging",
     "cachetools",
     "overrides>=0.7",
+    "urllib3==1.26.19"
 ]
 description = "lancedb"
 authors = [{ name = "LanceDB Devs", email = "dev@lancedb.com" }]

python/python/lancedb/__init__.py

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

python/python/lancedb/db.py

@@ -28,11 +28,12 @@ from lancedb.common import data_to_reader, validate_schema
 
 from ._lancedb import connect as lancedb_connect
 from .pydantic import LanceModel
-from .table import AsyncTable, LanceTable, Table, _sanitize_data, _table_path
+from .table import AsyncTable, LanceTable, Table, _sanitize_data
 from .util import (
     fs_from_uri,
     get_uri_location,
     get_uri_scheme,
+    join_uri,
     validate_table_name,
 )
 
@@ -456,18 +457,16 @@ class LanceDBConnection(DBConnection):
             If True, ignore if the table does not exist.
         """
         try:
-            table_uri = _table_path(self.uri, name)
-            filesystem, path = fs_from_uri(table_uri)
-            filesystem.delete_dir(path)
+            filesystem, path = fs_from_uri(self.uri)
+            table_path = join_uri(path, name + ".lance")
+            filesystem.delete_dir(table_path)
         except FileNotFoundError:
             if not ignore_missing:
                 raise
 
     @override
     def drop_database(self):
-        dummy_table_uri = _table_path(self.uri, "dummy")
-        uri = dummy_table_uri.removesuffix("dummy.lance")
-        filesystem, path = fs_from_uri(uri)
+        filesystem, path = fs_from_uri(self.uri)
         filesystem.delete_dir(path)
 
 

python/python/lancedb/query.py

@@ -117,6 +117,8 @@ class Query(pydantic.BaseModel):
 
     with_row_id: bool = False
 
+    fast_search: bool = False
+
 
 class LanceQueryBuilder(ABC):
     """An abstract query builder. Subclasses are defined for vector search,
@@ -125,12 +127,14 @@ class LanceQueryBuilder(ABC):
 
     @classmethod
     def create(
-        cls,
-        table: "Table",
-        query: Optional[Union[np.ndarray, str, "PIL.Image.Image", Tuple]],
-        query_type: str,
-        vector_column_name: str,
-        ordering_field_name: str = None,
+            cls,
+            table: "Table",
+            query: Optional[Union[np.ndarray, str, "PIL.Image.Image", Tuple]],
+            query_type: str,
+            vector_column_name: str,
+            ordering_field_name: Optional[str] = None,
+            fts_columns: Union[str, List[str]] = [],
+            fast_search: bool = False,
     ) -> LanceQueryBuilder:
         """
         Create a query builder based on the given query and query type.
@@ -147,13 +151,18 @@ class LanceQueryBuilder(ABC):
             If "auto", the query type is inferred based on the query.
         vector_column_name: str
             The name of the vector column to use for vector search.
+        fast_search: bool
+            Skip flat search of unindexed data.
         """
-        if query is None:
-            return LanceEmptyQueryBuilder(table)
-
+        # Check hybrid search first as it supports empty query pattern
         if query_type == "hybrid":
             # hybrid fts and vector query
-            return LanceHybridQueryBuilder(table, query, vector_column_name)
+            return LanceHybridQueryBuilder(
+                table, query, vector_column_name, fts_columns=fts_columns
+            )
+
+        if query is None:
+            return LanceEmptyQueryBuilder(table)
 
         # remember the string query for reranking purpose
         str_query = query if isinstance(query, str) else None
@@ -165,12 +174,17 @@ class LanceQueryBuilder(ABC):
         )
 
         if query_type == "hybrid":
-            return LanceHybridQueryBuilder(table, query, vector_column_name)
+            return LanceHybridQueryBuilder(
+                table, query, vector_column_name, fts_columns=fts_columns
+            )
 
         if isinstance(query, str):
             # fts
             return LanceFtsQueryBuilder(
-                table, query, ordering_field_name=ordering_field_name
+                table,
+                query,
+                ordering_field_name=ordering_field_name,
+                fts_columns=fts_columns,
             )
 
         if isinstance(query, list):
@@ -180,7 +194,9 @@ class LanceQueryBuilder(ABC):
         else:
             raise TypeError(f"Unsupported query type: {type(query)}")
 
-        return LanceVectorQueryBuilder(table, query, vector_column_name, str_query)
+        return LanceVectorQueryBuilder(
+            table, query, vector_column_name, str_query, fast_search
+        )
 
     @classmethod
     def _resolve_query(cls, table, query, query_type, vector_column_name):
@@ -196,8 +212,6 @@ class LanceQueryBuilder(ABC):
         elif query_type == "auto":
             if isinstance(query, (list, np.ndarray)):
                 return query, "vector"
-            if isinstance(query, tuple):
-                return query, "hybrid"
             else:
                 conf = table.embedding_functions.get(vector_column_name)
                 if conf is not None:
@@ -224,9 +238,14 @@ class LanceQueryBuilder(ABC):
     def __init__(self, table: "Table"):
         self._table = table
         self._limit = 10
+        self._offset = 0
         self._columns = None
         self._where = None
+        self._prefilter = False
         self._with_row_id = False
+        self._vector = None
+        self._text = None
+        self._ef = None
 
     @deprecation.deprecated(
         deprecated_in="0.3.1",
@@ -337,11 +356,13 @@ class LanceQueryBuilder(ABC):
         ----------
         limit: int
             The maximum number of results to return.
-            By default the query is limited to the first 10.
-            Call this method and pass 0, a negative value,
-            or None to remove the limit.
-            *WARNING* if you have a large dataset, removing
-            the limit can potentially result in reading a
+            The default query limit is 10 results.
+            For ANN/KNN queries, you must specify a limit.
+            Entering 0, a negative number, or None will reset
+            the limit to the default value of 10.
+            *WARNING* if you have a large dataset, setting
+            the limit to a large number, e.g. the table size,
+            can potentially result in reading a
             large amount of data into memory and cause
             out of memory issues.
 
@@ -351,11 +372,33 @@ class LanceQueryBuilder(ABC):
             The LanceQueryBuilder object.
         """
         if limit is None or limit <= 0:
-            self._limit = None
+            if isinstance(self, LanceVectorQueryBuilder):
+                raise ValueError("Limit is required for ANN/KNN queries")
+            else:
+                self._limit = None
         else:
             self._limit = limit
         return self
 
+    def offset(self, offset: int) -> LanceQueryBuilder:
+        """Set the offset for the results.
+
+        Parameters
+        ----------
+        offset: int
+            The offset to start fetching results from.
+
+        Returns
+        -------
+        LanceQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        if offset is None or offset <= 0:
+            self._offset = 0
+        else:
+            self._offset = offset
+        return self
+
     def select(self, columns: Union[list[str], dict[str, str]]) -> LanceQueryBuilder:
         """Set the columns to return.
 
@@ -417,6 +460,80 @@ class LanceQueryBuilder(ABC):
         self._with_row_id = with_row_id
         return self
 
+    def explain_plan(self, verbose: Optional[bool] = False) -> str:
+        """Return the execution plan for this query.
+
+        Examples
+        --------
+        >>> import lancedb
+        >>> db = lancedb.connect("./.lancedb")
+        >>> table = db.create_table("my_table", [{"vector": [99, 99]}])
+        >>> query = [100, 100]
+        >>> plan = table.search(query).explain_plan(True)
+        >>> print(plan) # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
+        ProjectionExec: expr=[vector@0 as vector, _distance@2 as _distance]
+        GlobalLimitExec: skip=0, fetch=10
+          FilterExec: _distance@2 IS NOT NULL
+            SortExec: TopK(fetch=10), expr=[_distance@2 ASC NULLS LAST], preserve_partitioning=[false]
+              KNNVectorDistance: metric=l2
+                LanceScan: uri=..., projection=[vector], row_id=true, row_addr=false, ordered=false
+
+        Parameters
+        ----------
+        verbose : bool, default False
+            Use a verbose output format.
+
+        Returns
+        -------
+        plan : str
+        """  # noqa: E501
+        ds = self._table.to_lance()
+        return ds.scanner(
+            nearest={
+                "column": self._vector_column,
+                "q": self._query,
+                "k": self._limit,
+                "metric": self._metric,
+                "nprobes": self._nprobes,
+                "refine_factor": self._refine_factor,
+            },
+            prefilter=self._prefilter,
+            filter=self._str_query,
+            limit=self._limit,
+            with_row_id=self._with_row_id,
+            offset=self._offset,
+        ).explain_plan(verbose)
+
+    def vector(self, vector: Union[np.ndarray, list]) -> LanceQueryBuilder:
+        """Set the vector to search for.
+
+        Parameters
+        ----------
+        vector: np.ndarray or list
+            The vector to search for.
+
+        Returns
+        -------
+        LanceQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        raise NotImplementedError
+
+    def text(self, text: str) -> LanceQueryBuilder:
+        """Set the text to search for.
+
+        Parameters
+        ----------
+        text: str
+            The text to search for.
+
+        Returns
+        -------
+        LanceQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        raise NotImplementedError
+
 
 class LanceVectorQueryBuilder(LanceQueryBuilder):
     """
@@ -440,11 +557,12 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
     """
 
     def __init__(
-        self,
-        table: "Table",
-        query: Union[np.ndarray, list, "PIL.Image.Image"],
-        vector_column: str,
-        str_query: Optional[str] = None,
+            self,
+            table: "Table",
+            query: Union[np.ndarray, list, "PIL.Image.Image"],
+            vector_column: str,
+            str_query: Optional[str] = None,
+            fast_search: bool = False,
     ):
         super().__init__(table)
         self._query = query
@@ -455,13 +573,14 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._prefilter = False
         self._reranker = None
         self._str_query = str_query
+        self._fast_search = fast_search
 
-    def metric(self, metric: Literal["L2", "cosine"]) -> LanceVectorQueryBuilder:
+    def metric(self, metric: Literal["L2", "cosine", "dot"]) -> LanceVectorQueryBuilder:
         """Set the distance metric to use.
 
         Parameters
         ----------
-        metric: "L2" or "cosine"
+        metric: "L2" or "cosine" or "dot"
             The distance metric to use. By default "L2" is used.
 
         Returns
@@ -469,7 +588,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         LanceVectorQueryBuilder
             The LanceQueryBuilder object.
         """
-        self._metric = metric
+        self._metric = metric.lower()
         return self
 
     def nprobes(self, nprobes: int) -> LanceVectorQueryBuilder:
@@ -494,6 +613,28 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         self._nprobes = nprobes
         return self
 
+    def ef(self, ef: int) -> LanceVectorQueryBuilder:
+        """Set the number of candidates to consider during search.
+
+        Higher values will yield better recall (more likely to find vectors if
+        they exist) at the expense of latency.
+
+        This only applies to the HNSW-related index.
+        The default value is 1.5 * limit.
+
+        Parameters
+        ----------
+        ef: int
+            The number of candidates to consider during search.
+
+        Returns
+        -------
+        LanceVectorQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        self._ef = ef
+        return self
+
     def refine_factor(self, refine_factor: int) -> LanceVectorQueryBuilder:
         """Set the refine factor to use, increasing the number of vectors sampled.
 
@@ -554,15 +695,11 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
             refine_factor=self._refine_factor,
             vector_column=self._vector_column,
             with_row_id=self._with_row_id,
+            offset=self._offset,
+            fast_search=self._fast_search,
+            ef=self._ef,
         )
         result_set = self._table._execute_query(query, batch_size)
-        if self._reranker is not None:
-            rs_table = result_set.read_all()
-            result_set = self._reranker.rerank_vector(self._str_query, rs_table)
-            # convert result_set back to RecordBatchReader
-            result_set = pa.RecordBatchReader.from_batches(
-                result_set.schema, result_set.to_batches()
-            )
 
         return result_set
 
@@ -591,7 +728,7 @@ class LanceVectorQueryBuilder(LanceQueryBuilder):
         return self
 
     def rerank(
-        self, reranker: Reranker, query_string: Optional[str] = None
+            self, reranker: Reranker, query_string: Optional[str] = None
     ) -> LanceVectorQueryBuilder:
         """Rerank the results using the specified reranker.
 
@@ -756,12 +893,34 @@ class LanceFtsQueryBuilder(LanceQueryBuilder):
 
 class LanceEmptyQueryBuilder(LanceQueryBuilder):
     def to_arrow(self) -> pa.Table:
-        ds = self._table.to_lance()
-        return ds.to_table(
+        return self.to_batches().read_all()
+
+    def to_batches(self, /, batch_size: Optional[int] = None) -> pa.RecordBatchReader:
+        query = Query(
             columns=self._columns,
             filter=self._where,
-            limit=self._limit,
+            k=self._limit or 10,
+            with_row_id=self._with_row_id,
+            vector=[],
+            # not actually respected in remote query
+            offset=self._offset or 0,
         )
+        return self._table._execute_query(query)
+
+    def rerank(self, reranker: Reranker) -> LanceEmptyQueryBuilder:
+        """Rerank the results using the specified reranker.
+
+        Parameters
+        ----------
+        reranker: Reranker
+            The reranker to use.
+
+        Returns
+        -------
+        LanceEmptyQueryBuilder
+            The LanceQueryBuilder object.
+        """
+        raise NotImplementedError("Reranking is not yet supported.")
 
 
 class LanceHybridQueryBuilder(LanceQueryBuilder):

python/python/lancedb/remote/client.py

@@ -55,11 +55,13 @@ class RestfulLanceDBClient:
     region: str
     api_key: Credential
     host_override: Optional[str] = attrs.field(default=None)
+    db_prefix: Optional[str] = attrs.field(default=None)
 
     closed: bool = attrs.field(default=False, init=False)
 
     connection_timeout: float = attrs.field(default=120.0, kw_only=True)
     read_timeout: float = attrs.field(default=300.0, kw_only=True)
+    storage_options: Optional[Dict[str, str]] = attrs.field(default=None, kw_only=True)
 
     @functools.cached_property
     def session(self) -> requests.Session:
@@ -92,6 +94,18 @@ class RestfulLanceDBClient:
             headers["Host"] = f"{self.db_name}.{self.region}.api.lancedb.com"
         if self.host_override:
             headers["x-lancedb-database"] = self.db_name
+        if self.storage_options:
+            if self.storage_options.get("account_name") is not None:
+                headers["x-azure-storage-account-name"] = self.storage_options[
+                    "account_name"
+                ]
+            if self.storage_options.get("azure_storage_account_name") is not None:
+                headers["x-azure-storage-account-name"] = self.storage_options[
+                    "azure_storage_account_name"
+                ]
+        if self.db_prefix:
+            headers["x-lancedb-database-prefix"] = self.db_prefix
+
         return headers
 
     @staticmethod
@@ -158,6 +172,7 @@ class RestfulLanceDBClient:
             headers["content-type"] = content_type
         if request_id is not None:
             headers["x-request-id"] = request_id
+
         with self.session.post(
             urljoin(self.url, uri),
             headers=headers,
@@ -245,7 +260,6 @@ def retry_adapter(options: Dict[str, Any]) -> HTTPAdapter:
             connect=connect_retries,
             read=read_retries,
             backoff_factor=backoff_factor,
-            backoff_jitter=backoff_jitter,
             status_forcelist=statuses,
             allowed_methods=methods,
         )

python/python/lancedb/remote/db.py

@@ -15,7 +15,7 @@ import inspect
 import logging
 import uuid
 from concurrent.futures import ThreadPoolExecutor
-from typing import Iterable, List, Optional, Union
+from typing import Dict, Iterable, List, Optional, Union
 from urllib.parse import urlparse
 
 from cachetools import TTLCache
@@ -44,20 +44,25 @@ class RemoteDBConnection(DBConnection):
         request_thread_pool: Optional[ThreadPoolExecutor] = None,
         connection_timeout: float = 120.0,
         read_timeout: float = 300.0,
+        storage_options: Optional[Dict[str, str]] = None,
     ):
         """Connect to a remote LanceDB database."""
         parsed = urlparse(db_url)
         if parsed.scheme != "db":
             raise ValueError(f"Invalid scheme: {parsed.scheme}, only accepts db://")
         self.db_name = parsed.netloc
+        prefix = parsed.path.lstrip("/")
+        self.db_prefix = None if not prefix else prefix
         self.api_key = api_key
         self._client = RestfulLanceDBClient(
             self.db_name,
             region,
             api_key,
             host_override,
+            self.db_prefix,
             connection_timeout=connection_timeout,
             read_timeout=read_timeout,
+            storage_options=storage_options,
         )
         self._request_thread_pool = request_thread_pool
         self._table_cache = TTLCache(maxsize=10000, ttl=300)

python/python/lancedb/remote/table.py

@@ -15,13 +15,14 @@ import logging
 import uuid
 from concurrent.futures import Future
 from functools import cached_property
-from typing import Dict, Iterable, Optional, Union
+from typing import Dict, Iterable, Optional, Union, Literal
 
 import pyarrow as pa
 from lance import json_to_schema
 
 from lancedb.common import DATA, VEC, VECTOR_COLUMN_NAME
 from lancedb.merge import LanceMergeInsertBuilder
+from lancedb.query import LanceQueryBuilder
 
 from ..query import LanceVectorQueryBuilder
 from ..table import Query, Table, _sanitize_data
@@ -81,6 +82,7 @@ class RemoteTable(Table):
     def create_scalar_index(
         self,
         column: str,
+        index_type: Literal["BTREE", "BITMAP", "LABEL_LIST", "scalar"] = "scalar",
     ):
         """Creates a scalar index
         Parameters
@@ -89,8 +91,6 @@ class RemoteTable(Table):
             The column to be indexed.  Must be a boolean, integer, float,
             or string column.
         """
-        index_type = "scalar"
-
         data = {
             "column": column,
             "index_type": index_type,
@@ -228,10 +228,21 @@ class RemoteTable(Table):
             content_type=ARROW_STREAM_CONTENT_TYPE,
         )
 
+    def query(
+        self,
+        query: Union[VEC, str] = None,
+        query_type: str = "vector",
+        vector_column_name: Optional[str] = None,
+        fast_search: bool = False,
+    ) -> LanceVectorQueryBuilder:
+        return self.search(query, query_type, vector_column_name, fast_search)
+
     def search(
         self,
-        query: Union[VEC, str],
+        query: Union[VEC, str] = None,
+        query_type: str = "vector",
         vector_column_name: Optional[str] = None,
+        fast_search: bool = False,
     ) -> LanceVectorQueryBuilder:
         """Create a search query to find the nearest neighbors
         of the given query vector. We currently support [vector search][search]
@@ -278,6 +289,11 @@ class RemoteTable(Table):
             - If the table has multiple vector columns then the *vector_column_name*
             needs to be specified. Otherwise, an error is raised.
 
+        fast_search: bool, optional
+            Skip a flat search of unindexed data. This may improve
+            search performance but search results will not include unindexed data.
+
+            - *default False*.
         Returns
         -------
         LanceQueryBuilder
@@ -293,7 +309,14 @@ class RemoteTable(Table):
         """
         if vector_column_name is None:
             vector_column_name = inf_vector_column_query(self.schema)
-        return LanceVectorQueryBuilder(self, query, vector_column_name)
+
+        return LanceQueryBuilder.create(
+            self,
+            query,
+            query_type,
+            vector_column_name=vector_column_name,
+            fast_search=fast_search,
+        )
 
     def _execute_query(
         self, query: Query, batch_size: Optional[int] = None

python/python/lancedb/rerankers/__init__.py

@@ -4,7 +4,6 @@ from .colbert import ColbertReranker
 from .cross_encoder import CrossEncoderReranker
 from .linear_combination import LinearCombinationReranker
 from .openai import OpenaiReranker
-from .jina import JinaReranker
 
 __all__ = [
     "Reranker",
@@ -13,5 +12,4 @@ __all__ = [
     "LinearCombinationReranker",
     "OpenaiReranker",
     "ColbertReranker",
-    "JinaReranker",
 ]

python/python/lancedb/rerankers/jina.py

@@ -1,103 +0,0 @@
-from functools import cached_property
-from typing import Union
-
-import pyarrow as pa
-
-from ..util import attempt_import_or_raise
-from .base import Reranker
-
-
-class JinaReranker(Reranker):
-    """
-    Reranks the results using Jina reranker model.
-
-    Parameters
-    ----------
-    model_name : str, default "jinaai/jina-reranker-v1-turbo-en"
-        The name of the reranker to use. For all models, see
-          https://huggingface.co/jinaai/jina-reranker-v1-turbo-en
-    column : str, default "text"
-        The name of the column to use as input to the cross encoder model.
-    device : str, default None
-        The device to use for the cross encoder model. If None, will use "cuda"
-        if available, otherwise "cpu".
-    """
-
-    def __init__(
-        self,
-        model_name: str = "jinaai/jina-reranker-v1-turbo-en",
-        column: str = "text",
-        device: Union[str, None] = None,
-        return_score="relevance",
-    ):
-        super().__init__(return_score)
-        torch = attempt_import_or_raise("torch")
-        self.model_name = model_name
-        self.column = column
-        self.device = device
-        if self.device is None:
-            self.device = "cuda" if torch.cuda.is_available() else "cpu"
-
-    @cached_property
-    def model(self):
-        transformers = attempt_import_or_raise("transformers")
-        model = transformers.AutoModelForSequenceClassification.from_pretrained(
-            self.model_name, num_labels=1, trust_remote_code=True
-        )
-
-        return model
-
-    def _rerank(self, result_set: pa.Table, query: str):
-        passages = result_set[self.column].to_pylist()
-        cross_inp = [[query, passage] for passage in passages]
-        cross_scores = self.model.compute_score(cross_inp)
-        result_set = result_set.append_column(
-            "_relevance_score", pa.array(cross_scores, type=pa.float32())
-        )
-
-        return result_set
-
-    def rerank_hybrid(
-        self,
-        query: str,
-        vector_results: pa.Table,
-        fts_results: pa.Table,
-    ):
-        combined_results = self.merge_results(vector_results, fts_results)
-        combined_results = self._rerank(combined_results, query)
-        # sort the results by _score
-        if self.score == "relevance":
-            combined_results = combined_results.drop_columns(["score", "_distance"])
-        elif self.score == "all":
-            raise NotImplementedError(
-                "return_score='all' not implemented for CrossEncoderReranker"
-            )
-        combined_results = combined_results.sort_by(
-            [("_relevance_score", "descending")]
-        )
-
-        return combined_results
-
-    def rerank_vector(
-        self,
-        query: str,
-        vector_results: pa.Table,
-    ):
-        vector_results = self._rerank(vector_results, query)
-        if self.score == "relevance":
-            vector_results = vector_results.drop_columns(["_distance"])
-
-        vector_results = vector_results.sort_by([("_relevance_score", "descending")])
-        return vector_results
-
-    def rerank_fts(
-        self,
-        query: str,
-        fts_results: pa.Table,
-    ):
-        fts_results = self._rerank(fts_results, query)
-        if self.score == "relevance":
-            fts_results = fts_results.drop_columns(["score"])
-
-        fts_results = fts_results.sort_by([("_relevance_score", "descending")])
-        return fts_results

python/python/lancedb/table.py

@@ -30,7 +30,6 @@ from typing import (
     Tuple,
     Union,
 )
-from urllib.parse import urlparse
 
 import lance
 import numpy as np
@@ -48,7 +47,6 @@ from .pydantic import LanceModel, model_to_dict
 from .query import AsyncQuery, AsyncVectorQuery, LanceQueryBuilder, Query
 from .util import (
     fs_from_uri,
-    get_uri_scheme,
     inf_vector_column_query,
     join_uri,
     safe_import_pandas,
@@ -210,26 +208,6 @@ def _to_record_batch_generator(
             yield b
 
 
-def _table_path(base: str, table_name: str) -> str:
-    """
-    Get a table path that can be used in PyArrow FS.
-
-    Removes any weird schemes (such as "s3+ddb") and drops any query params.
-    """
-    uri = _table_uri(base, table_name)
-    # Parse as URL
-    parsed = urlparse(uri)
-    # If scheme is s3+ddb, convert to s3
-    if parsed.scheme == "s3+ddb":
-        parsed = parsed._replace(scheme="s3")
-    # Remove query parameters
-    return parsed._replace(query=None).geturl()
-
-
-def _table_uri(base: str, table_name: str) -> str:
-    return join_uri(base, f"{table_name}.lance")
-
-
 class Table(ABC):
     """
     A Table is a collection of Records in a LanceDB Database.
@@ -930,7 +908,7 @@ class LanceTable(Table):
     @classmethod
     def open(cls, db, name, **kwargs):
         tbl = cls(db, name, **kwargs)
-        fs, path = fs_from_uri(tbl._dataset_path)
+        fs, path = fs_from_uri(tbl._dataset_uri)
         file_info = fs.get_file_info(path)
         if file_info.type != pa.fs.FileType.Directory:
             raise FileNotFoundError(
@@ -940,14 +918,9 @@ class LanceTable(Table):
 
         return tbl
 
-    @cached_property
-    def _dataset_path(self) -> str:
-        # Cacheable since it's deterministic
-        return _table_path(self._conn.uri, self.name)
-
-    @cached_property
+    @property
     def _dataset_uri(self) -> str:
-        return _table_uri(self._conn.uri, self.name)
+        return join_uri(self._conn.uri, f"{self.name}.lance")
 
     @property
     def _dataset(self) -> LanceDataset:
@@ -1257,10 +1230,6 @@ class LanceTable(Table):
         )
 
     def _get_fts_index_path(self):
-        if get_uri_scheme(self._dataset_uri) != "file":
-            raise NotImplementedError(
-                "Full-text search is not supported on object stores."
-            )
         return join_uri(self._dataset_uri, "_indices", "tantivy")
 
     def add(

python/python/lancedb/util.py

@@ -139,11 +139,8 @@ def join_uri(base: Union[str, pathlib.Path], *parts: str) -> str:
         # using pathlib for local paths make this windows compatible
         # `get_uri_scheme` returns `file` for windows drive names (e.g. `c:\path`)
         return str(pathlib.Path(base, *parts))
-    else:
-        # there might be query parameters in the base URI
-        url = urlparse(base)
-        new_path = "/".join([p.rstrip("/") for p in [url.path, *parts]])
-        return url._replace(path=new_path).geturl()
+    # for remote paths, just use os.path.join
+    return "/".join([p.rstrip("/") for p in [base, *parts]])
 
 
 def attempt_import_or_raise(module: str, mitigation=None):

python/python/tests/test_remote_db.py

@@ -21,6 +21,7 @@ class FakeLanceDBClient:
         pass
 
     def query(self, table_name: str, query: VectorQuery) -> VectorQueryResult:
+        print(f"{query=}")
         assert table_name == "test"
         t = pa.schema([]).empty_table()
         return VectorQueryResult(t)
@@ -39,3 +40,21 @@ def test_remote_db():
     table = conn["test"]
     table.schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2))])
     table.search([1.0, 2.0]).to_pandas()
+
+
+def test_empty_query_with_filter():
+    conn = lancedb.connect("db://client-will-be-injected", api_key="fake")
+    setattr(conn, "_client", FakeLanceDBClient())
+
+    table = conn["test"]
+    table.schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2))])
+    print(table.query().select(["vector"]).where("foo == bar").to_arrow())
+
+
+def test_fast_search_query_with_filter():
+    conn = lancedb.connect("db://client-will-be-injected", api_key="fake")
+    setattr(conn, "_client", FakeLanceDBClient())
+
+    table = conn["test"]
+    table.schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), 2))])
+    print(table.query([0, 0], fast_search=True).select(["vector"]).where("foo == bar").to_arrow())

python/python/tests/test_rerankers.py

@@ -1,3 +1,5 @@
+import os
+
 import lancedb
 import numpy as np
 import pytest
@@ -9,7 +11,6 @@ from lancedb.rerankers import (
     ColbertReranker,
     CrossEncoderReranker,
     OpenaiReranker,
-    JinaReranker,
 )
 from lancedb.table import LanceTable
 
@@ -118,18 +119,136 @@ def test_linear_combination(tmp_path):
     )
 
 
-@pytest.mark.slow
-@pytest.mark.parametrize(
-    "reranker",
-    [
-        ColbertReranker(),
-        OpenaiReranker(),
-        CohereReranker(),
-        CrossEncoderReranker(),
-        JinaReranker(),
-    ],
+@pytest.mark.skipif(
+    os.environ.get("COHERE_API_KEY") is None, reason="COHERE_API_KEY not set"
 )
-def test_colbert_reranker(tmp_path, reranker):
+def test_cohere_reranker(tmp_path):
+    pytest.importorskip("cohere")
+    reranker = CohereReranker()
+    table, schema = get_test_table(tmp_path)
+    # Hybrid search setting
+    result1 = (
+        table.search("Our father who art in heaven", query_type="hybrid")
+        .rerank(normalize="score", reranker=CohereReranker())
+        .to_pydantic(schema)
+    )
+    result2 = (
+        table.search("Our father who art in heaven", query_type="hybrid")
+        .rerank(reranker=reranker)
+        .to_pydantic(schema)
+    )
+    assert result1 == result2
+
+    query = "Our father who art in heaven"
+    query_vector = table.to_pandas()["vector"][0]
+    result = (
+        table.search((query_vector, query))
+        .limit(30)
+        .rerank(reranker=reranker)
+        .to_arrow()
+    )
+
+    assert len(result) == 30
+    err = (
+        "The _relevance_score column of the results returned by the reranker "
+        "represents the relevance of the result to the query & should "
+        "be descending."
+    )
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+    # Vector search setting
+    query = "Our father who art in heaven"
+    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
+    assert len(result) == 30
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+    result_explicit = (
+        table.search(query_vector)
+        .rerank(reranker=reranker, query_string=query)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result_explicit) == 30
+    with pytest.raises(
+        ValueError
+    ):  # This raises an error because vector query is provided without reanking query
+        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
+
+    # FTS search setting
+    result = (
+        table.search(query, query_type="fts")
+        .rerank(reranker=reranker)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result) > 0
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+
+def test_cross_encoder_reranker(tmp_path):
+    pytest.importorskip("sentence_transformers")
+    reranker = CrossEncoderReranker()
+    table, schema = get_test_table(tmp_path)
+    result1 = (
+        table.search("Our father who art in heaven", query_type="hybrid")
+        .rerank(normalize="score", reranker=reranker)
+        .to_pydantic(schema)
+    )
+    result2 = (
+        table.search("Our father who art in heaven", query_type="hybrid")
+        .rerank(reranker=reranker)
+        .to_pydantic(schema)
+    )
+    assert result1 == result2
+
+    query = "Our father who art in heaven"
+    query_vector = table.to_pandas()["vector"][0]
+    result = (
+        table.search((query_vector, query), query_type="hybrid")
+        .limit(30)
+        .rerank(reranker=reranker)
+        .to_arrow()
+    )
+
+    assert len(result) == 30
+
+    err = (
+        "The _relevance_score column of the results returned by the reranker "
+        "represents the relevance of the result to the query & should "
+        "be descending."
+    )
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+    # Vector search setting
+    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
+    assert len(result) == 30
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+    result_explicit = (
+        table.search(query_vector)
+        .rerank(reranker=reranker, query_string=query)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result_explicit) == 30
+    with pytest.raises(
+        ValueError
+    ):  # This raises an error because vector query is provided without reanking query
+        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
+
+    # FTS search setting
+    result = (
+        table.search(query, query_type="fts")
+        .rerank(reranker=reranker)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result) > 0
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+
+def test_colbert_reranker(tmp_path):
+    pytest.importorskip("transformers")
+    reranker = ColbertReranker()
     table, schema = get_test_table(tmp_path)
     result1 = (
         table.search("Our father who art in heaven", query_type="hybrid")
@@ -186,3 +305,67 @@ def test_colbert_reranker(tmp_path, reranker):
     )
     assert len(result) > 0
     assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+
+@pytest.mark.skipif(
+    os.environ.get("OPENAI_API_KEY") is None, reason="OPENAI_API_KEY not set"
+)
+def test_openai_reranker(tmp_path):
+    pytest.importorskip("openai")
+    table, schema = get_test_table(tmp_path)
+    reranker = OpenaiReranker()
+    result1 = (
+        table.search("Our father who art in heaven", query_type="hybrid")
+        .rerank(normalize="score", reranker=reranker)
+        .to_pydantic(schema)
+    )
+    result2 = (
+        table.search("Our father who art in heaven", query_type="hybrid")
+        .rerank(reranker=OpenaiReranker())
+        .to_pydantic(schema)
+    )
+    assert result1 == result2
+
+    # test explicit hybrid query
+    query = "Our father who art in heaven"
+    query_vector = table.to_pandas()["vector"][0]
+    result = (
+        table.search((query_vector, query))
+        .limit(30)
+        .rerank(reranker=reranker)
+        .to_arrow()
+    )
+
+    assert len(result) == 30
+
+    err = (
+        "The _relevance_score column of the results returned by the reranker "
+        "represents the relevance of the result to the query & should "
+        "be descending."
+    )
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+
+    # Vector search setting
+    result = table.search(query).rerank(reranker=reranker).limit(30).to_arrow()
+    assert len(result) == 30
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err
+    result_explicit = (
+        table.search(query_vector)
+        .rerank(reranker=reranker, query_string=query)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result_explicit) == 30
+    with pytest.raises(
+        ValueError
+    ):  # This raises an error because vector query is provided without reanking query
+        table.search(query_vector).rerank(reranker=reranker).limit(30).to_arrow()
+    # FTS search setting
+    result = (
+        table.search(query, query_type="fts")
+        .rerank(reranker=reranker)
+        .limit(30)
+        .to_arrow()
+    )
+    assert len(result) > 0
+    assert np.all(np.diff(result.column("_relevance_score").to_numpy()) <= 0), err

python/python/tests/test_s3.py

@@ -13,8 +13,6 @@
 
 import asyncio
 import copy
-from datetime import timedelta
-import threading
 
 import pytest
 import pyarrow as pa
@@ -27,7 +25,6 @@ CONFIG = {
     "aws_access_key_id": "ACCESSKEY",
     "aws_secret_access_key": "SECRETKEY",
     "aws_endpoint": "http://localhost:4566",
-    "dynamodb_endpoint": "http://localhost:4566",
     "aws_region": "us-east-1",
 }
 
@@ -159,104 +156,3 @@ def test_s3_sse(s3_bucket: str, kms_key: str):
         validate_objects_encrypted(s3_bucket, path, kms_key)
 
     asyncio.run(test())
-
-
-@pytest.fixture(scope="module")
-def commit_table():
-    ddb = get_boto3_client("dynamodb", endpoint_url=CONFIG["dynamodb_endpoint"])
-    table_name = "lance-integtest"
-    try:
-        ddb.delete_table(TableName=table_name)
-    except ddb.exceptions.ResourceNotFoundException:
-        pass
-    ddb.create_table(
-        TableName=table_name,
-        KeySchema=[
-            {"AttributeName": "base_uri", "KeyType": "HASH"},
-            {"AttributeName": "version", "KeyType": "RANGE"},
-        ],
-        AttributeDefinitions=[
-            {"AttributeName": "base_uri", "AttributeType": "S"},
-            {"AttributeName": "version", "AttributeType": "N"},
-        ],
-        ProvisionedThroughput={"ReadCapacityUnits": 1, "WriteCapacityUnits": 1},
-    )
-    yield table_name
-    ddb.delete_table(TableName=table_name)
-
-
-@pytest.mark.s3_test
-def test_s3_dynamodb(s3_bucket: str, commit_table: str):
-    storage_options = copy.copy(CONFIG)
-
-    uri = f"s3+ddb://{s3_bucket}/test?ddbTableName={commit_table}"
-    data = pa.table({"x": [1, 2, 3]})
-
-    async def test():
-        db = await lancedb.connect_async(
-            uri,
-            storage_options=storage_options,
-            read_consistency_interval=timedelta(0),
-        )
-
-        table = await db.create_table("test", data)
-
-        # Five concurrent writers
-        async def insert():
-            # independent table refs for true concurrent writes.
-            table = await db.open_table("test")
-            await table.add(data, mode="append")
-
-        tasks = [insert() for _ in range(5)]
-        await asyncio.gather(*tasks)
-
-        row_count = await table.count_rows()
-        assert row_count == 3 * 6
-
-    asyncio.run(test())
-
-
-@pytest.mark.s3_test
-def test_s3_dynamodb_sync(s3_bucket: str, commit_table: str, monkeypatch):
-    # Sync API doesn't support storage_options, so we have to provide as env vars
-    for key, value in CONFIG.items():
-        monkeypatch.setenv(key.upper(), value)
-
-    uri = f"s3+ddb://{s3_bucket}/test2?ddbTableName={commit_table}"
-    data = pa.table({"x": ["a", "b", "c"]})
-
-    db = lancedb.connect(
-        uri,
-        read_consistency_interval=timedelta(0),
-    )
-
-    table = db.create_table("test_ddb_sync", data)
-
-    # Five concurrent writers
-    def insert():
-        table = db.open_table("test_ddb_sync")
-        table.add(data, mode="append")
-
-    threads = []
-    for _ in range(5):
-        thread = threading.Thread(target=insert)
-        threads.append(thread)
-        thread.start()
-
-    for thread in threads:
-        thread.join()
-
-    row_count = table.count_rows()
-    assert row_count == 3 * 6
-
-    # FTS indices should error since they are not supported yet.
-    with pytest.raises(
-        NotImplementedError, match="Full-text search is not supported on object stores."
-    ):
-        table.create_fts_index("x")
-
-    # make sure list tables still works
-    assert db.table_names() == ["test_ddb_sync"]
-    db.drop_table("test_ddb_sync")
-    assert db.table_names() == []
-    db.drop_database()

python/python/tests/test_table.py

@@ -735,7 +735,7 @@ def test_create_scalar_index(db):
     indices = table.to_lance().list_indices()
     assert len(indices) == 1
     scalar_index = indices[0]
-    assert scalar_index["type"] == "Scalar"
+    assert scalar_index["type"] == "BTree"
 
     # Confirm that prefiltering still works with the scalar index column
     results = table.search().where("x = 'c'").to_arrow()

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb-node"
-version = "0.6.0"
+version = "0.5.2"
 description = "Serverless, low-latency vector database for AI applications"
 license.workspace = true
 edition.workspace = true

rust/lancedb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "lancedb"
-version = "0.6.0"
+version = "0.5.2"
 edition.workspace = true
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license.workspace = true
@@ -55,11 +55,10 @@ walkdir = "2"
 # For s3 integration tests (dev deps aren't allowed to be optional atm)
 # We pin these because the content-length check breaks with localstack
 # https://github.com/smithy-lang/smithy-rs/releases/tag/release-2024-05-21
-aws-sdk-dynamodb = { version = "=1.23.0" }
 aws-sdk-s3 = { version = "=1.23.0" }
 aws-sdk-kms = { version = "=1.21.0" }
 aws-config = { version = "1.0" }
-aws-smithy-runtime = { version = "=1.3.1" }
+aws-smithy-runtime = { version = "=1.3.0" }
 
 [features]
 default = []

rust/lancedb/README.md

@@ -6,12 +6,3 @@
 LanceDB Rust SDK, a serverless vector database.
 
 Read more at: https://lancedb.com/
-
-> [!TIP]
-> A transitive dependency of `lancedb` is `lzma-sys`, which uses dynamic linking
-> by default. If you want to statically link `lzma-sys`, you should activate it's
-> `static` feature by adding the following to your dependencies:
->
-> ```toml
-> lzma-sys = { version = "*", features = ["static"] }
-> ```

rust/lancedb/src/table.rs

@@ -1889,7 +1889,6 @@ impl TableInternal for NativeTable {
                 }
                 columns.push(field.name.clone());
             }
-
             let index_type = if is_vector {
                 crate::index::IndexType::IvfPq
             } else {

rust/lancedb/tests/object_store_test.rs

@@ -25,9 +25,7 @@ const CONFIG: &[(&str, &str)] = &[
     ("access_key_id", "ACCESS_KEY"),
     ("secret_access_key", "SECRET_KEY"),
     ("endpoint", "http://127.0.0.1:4566"),
-    ("dynamodb_endpoint", "http://127.0.0.1:4566"),
     ("allow_http", "true"),
-    ("region", "us-east-1"),
 ];
 
 async fn aws_config() -> SdkConfig {
@@ -290,126 +288,3 @@ async fn test_encryption() -> Result<()> {
 
     Ok(())
 }
-
-struct DynamoDBCommitTable(String);
-
-impl DynamoDBCommitTable {
-    async fn new(name: &str) -> Self {
-        let config = aws_config().await;
-        let client = aws_sdk_dynamodb::Client::new(&config);
-
-        // In case it wasn't deleted earlier
-        Self::delete_table(client.clone(), name).await;
-        tokio::time::sleep(std::time::Duration::from_millis(200)).await;
-
-        use aws_sdk_dynamodb::types::*;
-
-        client
-            .create_table()
-            .table_name(name)
-            .attribute_definitions(
-                AttributeDefinition::builder()
-                    .attribute_name("base_uri")
-                    .attribute_type(ScalarAttributeType::S)
-                    .build()
-                    .unwrap(),
-            )
-            .attribute_definitions(
-                AttributeDefinition::builder()
-                    .attribute_name("version")
-                    .attribute_type(ScalarAttributeType::N)
-                    .build()
-                    .unwrap(),
-            )
-            .key_schema(
-                KeySchemaElement::builder()
-                    .attribute_name("base_uri")
-                    .key_type(KeyType::Hash)
-                    .build()
-                    .unwrap(),
-            )
-            .key_schema(
-                KeySchemaElement::builder()
-                    .attribute_name("version")
-                    .key_type(KeyType::Range)
-                    .build()
-                    .unwrap(),
-            )
-            .provisioned_throughput(
-                ProvisionedThroughput::builder()
-                    .read_capacity_units(1)
-                    .write_capacity_units(1)
-                    .build()
-                    .unwrap(),
-            )
-            .send()
-            .await
-            .unwrap();
-
-        Self(name.to_string())
-    }
-
-    async fn delete_table(client: aws_sdk_dynamodb::Client, name: &str) {
-        match client
-            .delete_table()
-            .table_name(name)
-            .send()
-            .await
-            .map_err(|err| err.into_service_error())
-        {
-            Ok(_) => {}
-            Err(e) if e.is_resource_not_found_exception() => {}
-            Err(e) => panic!("Failed to delete table: {}", e),
-        };
-    }
-}
-
-impl Drop for DynamoDBCommitTable {
-    fn drop(&mut self) {
-        let table_name = self.0.clone();
-        tokio::task::spawn(async move {
-            let config = aws_config().await;
-            let client = aws_sdk_dynamodb::Client::new(&config);
-            Self::delete_table(client, &table_name).await;
-        });
-    }
-}
-
-#[tokio::test]
-async fn test_concurrent_dynamodb_commit() {
-    // test concurrent commit on dynamodb
-    let bucket = S3Bucket::new("test-dynamodb").await;
-    let table = DynamoDBCommitTable::new("test_table").await;
-
-    let uri = format!("s3+ddb://{}?ddbTableName={}", bucket.0, table.0);
-    let db = lancedb::connect(&uri)
-        .storage_options(CONFIG.iter().cloned())
-        .execute()
-        .await
-        .unwrap();
-
-    let data = test_data();
-    let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
-
-    let table = db.create_table("test_table", data).execute().await.unwrap();
-
-    let data = test_data();
-
-    let mut tasks = vec![];
-    for _ in 0..5 {
-        let table = db.open_table("test_table").execute().await.unwrap();
-        let data = data.clone();
-        tasks.push(tokio::spawn(async move {
-            let data = RecordBatchIterator::new(vec![Ok(data.clone())], data.schema());
-            table.add(data).execute().await.unwrap();
-        }));
-    }
-
-    for task in tasks {
-        task.await.unwrap();
-    }
-
-    table.checkout_latest().await.unwrap();
-    let row_count = table.count_rows(None).await.unwrap();
-    assert_eq!(row_count, 18);
-}