Bump version: 0.4.5 → 0.4.6

* Bring the feature parity of Rust connect methods.
* A global connect method that can connect to local and remote / cloud
table, as the same as in js/python today.

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.4.4
+current_version = 0.4.6
 commit = True
 message = Bump version: {current_version} → {new_version}
 tag = True

node/package-lock.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.4.3",
+  "version": "0.4.5",
   "lockfileVersion": 3,
   "requires": true,
   "packages": {
     "": {
       "name": "vectordb",
-      "version": "0.4.3",
+      "version": "0.4.5",
       "cpu": [
         "x64",
         "arm64"
@@ -53,11 +53,11 @@
         "uuid": "^9.0.0"
       },
       "optionalDependencies": {
-        "@lancedb/vectordb-darwin-arm64": "0.4.3",
+        "@lancedb/vectordb-darwin-arm64": "0.4.5",
-        "@lancedb/vectordb-darwin-x64": "0.4.3",
+        "@lancedb/vectordb-darwin-x64": "0.4.5",
-        "@lancedb/vectordb-linux-arm64-gnu": "0.4.3",
+        "@lancedb/vectordb-linux-arm64-gnu": "0.4.5",
-        "@lancedb/vectordb-linux-x64-gnu": "0.4.3",
+        "@lancedb/vectordb-linux-x64-gnu": "0.4.5",
-        "@lancedb/vectordb-win32-x64-msvc": "0.4.3"
+        "@lancedb/vectordb-win32-x64-msvc": "0.4.5"
       }
     },
     "node_modules/@75lb/deep-merge": {
@@ -329,9 +329,9 @@
       }
     },
     "node_modules/@lancedb/vectordb-darwin-arm64": {
-      "version": "0.4.3",
+      "version": "0.4.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.4.3.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-arm64/-/vectordb-darwin-arm64-0.4.5.tgz",
-      "integrity": "sha512-47CvvSaV1EdUsFEpXUJApTk+hMzAhCxVizipCFUlXCgcmzpCDL86wNgJij/X9a+j6zADhIX//Lsu0qd/an/Bpw==",
+      "integrity": "sha512-sR+Q9dRBzMm+NGqM7EiK07c7pQz/V4J//23p05CeO/YATjKYyU3jE/dmVenLjJGW2UUrRYiyUQ9X6Up+OOgdhA==",
       "cpu": [
         "arm64"
       ],
@@ -341,9 +341,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-darwin-x64": {
-      "version": "0.4.3",
+      "version": "0.4.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.4.3.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-darwin-x64/-/vectordb-darwin-x64-0.4.5.tgz",
-      "integrity": "sha512-UlZZv8CmJIuRJNJG+Y1VmFsGyPR8W/72Q5EwgMMsSES6zpMQ9pNdBDWhL3UGX6nMRgnbprkwYiWJ3xHhJvtqtw==",
+      "integrity": "sha512-/BIyUeVkLaUlOEQN4HUQ9J9ZdNWkDpZPUUS9kfz5iYIjotgwpSfznF8Q1GY5BVuXa2ke7GC3tnkwwd5ZMOuDsA==",
       "cpu": [
         "x64"
       ],
@@ -353,9 +353,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-arm64-gnu": {
-      "version": "0.4.3",
+      "version": "0.4.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.4.3.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-arm64-gnu/-/vectordb-linux-arm64-gnu-0.4.5.tgz",
-      "integrity": "sha512-L6NVJr/lKEd8+904FzZNpT8BGQMs2cHNYbGJMIaVvGnMiIJgKAFKtOyGtdDjoe1xRZoEw21yjRGksGbnRO5wHQ==",
+      "integrity": "sha512-bq8vX7znIf2Dap41YIbB5uA/YahwaLvFPNH0WmwqeBWxF64/AJ74DsZk51ftwczQMsyLK74M8f1PzniapMAR+Q==",
       "cpu": [
         "arm64"
       ],
@@ -365,9 +365,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-linux-x64-gnu": {
-      "version": "0.4.3",
+      "version": "0.4.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.4.3.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-linux-x64-gnu/-/vectordb-linux-x64-gnu-0.4.5.tgz",
-      "integrity": "sha512-OBx3WF3pK0xNfFJeErmuD9R2QWLa3XdeZspyTsIrQmBDeKj3HKh8y7Scpx4NH5Y09+9JNqRRKRZN7OqWTYhITg==",
+      "integrity": "sha512-5qCWFyxihyMDYIGRAdQ7zv3enBEDxPR08dCmXr2Bu9yYI3SUqfuSvFX1NwflVeB+RzRMMbeG4xiaEbo7H7/H3Q==",
       "cpu": [
         "x64"
       ],
@@ -377,9 +377,9 @@
       ]
     },
     "node_modules/@lancedb/vectordb-win32-x64-msvc": {
-      "version": "0.4.3",
+      "version": "0.4.5",
-      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.4.3.tgz",
+      "resolved": "https://registry.npmjs.org/@lancedb/vectordb-win32-x64-msvc/-/vectordb-win32-x64-msvc-0.4.5.tgz",
-      "integrity": "sha512-n9IvR81NXZKnSN91mrgeXbEyCiGM+YLJpOgbdHoEtMP04VDnS+iSU4jGOtQBKErvWeCJQaGFQ9qzdcVchpRGyw==",
+      "integrity": "sha512-z3dZ6TDzm2EU5gNuejshArs3o84v1rdXnds22TTuc9fVhwg5JG87FyHFZKU1MGuyLuZW22Me0YDuS9VR+eAp0Q==",
       "cpu": [
         "x64"
       ],

node/package.json

@@ -1,12 +1,12 @@
 {
   "name": "vectordb",
-  "version": "0.4.4",
+  "version": "0.4.6",
   "description": " Serverless, low-latency vector database for AI applications",
   "main": "dist/index.js",
   "types": "dist/index.d.ts",
   "scripts": {
     "tsc": "tsc -b",
-    "build": "cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json && tsc -b",
+    "build": "npm run tsc && cargo-cp-artifact --artifact cdylib vectordb-node index.node -- cargo build --message-format=json",
     "build-release": "npm run build -- --release",
     "test": "npm run tsc && mocha -recursive dist/test",
     "integration-test": "npm run tsc && mocha -recursive dist/integration_test",
@@ -81,10 +81,10 @@
     }
   },
   "optionalDependencies": {
-    "@lancedb/vectordb-darwin-arm64": "0.4.4",
+    "@lancedb/vectordb-darwin-arm64": "0.4.6",
-    "@lancedb/vectordb-darwin-x64": "0.4.4",
+    "@lancedb/vectordb-darwin-x64": "0.4.6",
-    "@lancedb/vectordb-linux-arm64-gnu": "0.4.4",
+    "@lancedb/vectordb-linux-arm64-gnu": "0.4.6",
-    "@lancedb/vectordb-linux-x64-gnu": "0.4.4",
+    "@lancedb/vectordb-linux-x64-gnu": "0.4.6",
-    "@lancedb/vectordb-win32-x64-msvc": "0.4.4"
+    "@lancedb/vectordb-win32-x64-msvc": "0.4.6"
   }
 }

node/src/test/test.ts

@@ -391,24 +391,6 @@ describe('LanceDB client', function () {
       })
     }).timeout(120000)
 
-    it('fails to create a new table when the vector column is missing', async function () {
-      const dir = await track().mkdir('lancejs')
-      const con = await lancedb.connect(dir)
-
-      const data = [
-        {
-          id: 1,
-          price: 10
-        }
-      ]
-
-      const create = con.createTable('missing_vector', data)
-      await expect(create).to.be.rejectedWith(
-        Error,
-        "column 'vector' is missing"
-      )
-    })
-
     it('use overwrite flag to overwrite existing table', async function () {
       const dir = await track().mkdir('lancejs')
       const con = await lancedb.connect(dir)

nodejs/Cargo.toml

@@ -10,14 +10,15 @@ crate-type = ["cdylib"]
 
 [dependencies]
 arrow-ipc.workspace = true
+futures.workspace = true
+lance-linalg.workspace = true
+lance.workspace = true
+vectordb = { path = "../rust/vectordb" }
 napi = { version = "2.14", default-features = false, features = [
     "napi7",
     "async"
 ] }
 napi-derive = "2.14"
-vectordb = { path = "../rust/vectordb" }
-lance.workspace = true
-lance-linalg.workspace = true
 
 [build-dependencies]
 napi-build = "2.1"

nodejs/__test__/table.test.ts

@@ -53,6 +53,16 @@ describe("Test creating index", () => {
     const indexDir = path.join(tmpDir, "test.lance", "_indices");
     expect(fs.readdirSync(indexDir)).toHaveLength(1);
     // TODO: check index type.
+
+    // Search without specifying the column
+    let query_vector = data.toArray()[5].vec.toJSON();
+    let rst = await tbl.query().nearestTo(query_vector).limit(2).toArrow();
+    expect(rst.numRows).toBe(2);
+
+    // Search with specifying the column
+    let rst2 = await tbl.search(query_vector, "vec").limit(2).toArrow();
+    expect(rst2.numRows).toBe(2);
+    expect(rst.toString()).toEqual(rst2.toString());
   });
 
   test("no vector column available", async () => {
@@ -71,6 +81,80 @@ describe("Test creating index", () => {
     await tbl.createIndex("val").build();
     const indexDir = path.join(tmpDir, "no_vec.lance", "_indices");
     expect(fs.readdirSync(indexDir)).toHaveLength(1);
+
+    for await (const r of tbl.query().filter("id > 1").select(["id"])) {
+      expect(r.numRows).toBe(1);
+    }
+  });
+
+  test("two columns with different dimensions", async () => {
+    const db = await connect(tmpDir);
+    const schema = new Schema([
+      new Field("id", new Int32(), true),
+      new Field("vec", new FixedSizeList(32, new Field("item", new Float32()))),
+      new Field(
+        "vec2",
+        new FixedSizeList(64, new Field("item", new Float32()))
+      ),
+    ]);
+    const tbl = await db.createTable(
+      "two_vectors",
+      makeArrowTable(
+        Array(300)
+          .fill(1)
+          .map((_, i) => ({
+            id: i,
+            vec: Array(32)
+              .fill(1)
+              .map(() => Math.random()),
+            vec2: Array(64) // different dimension
+              .fill(1)
+              .map(() => Math.random()),
+          })),
+        { schema }
+      )
+    );
+
+    // Only build index over v1
+    await expect(tbl.createIndex().build()).rejects.toThrow(
+      /.*More than one vector columns found.*/
+    );
+    tbl
+      .createIndex("vec")
+      .ivf_pq({ num_partitions: 2, num_sub_vectors: 2 })
+      .build();
+
+    const rst = await tbl
+      .query()
+      .nearestTo(
+        Array(32)
+          .fill(1)
+          .map(() => Math.random())
+      )
+      .limit(2)
+      .toArrow();
+    expect(rst.numRows).toBe(2);
+
+    // Search with specifying the column
+    await expect(
+      tbl
+        .search(
+          Array(64)
+            .fill(1)
+            .map(() => Math.random()),
+          "vec"
+        )
+        .limit(2)
+        .toArrow()
+    ).rejects.toThrow(/.*does not match the dimension.*/);
+
+    const query64 = Array(64)
+      .fill(1)
+      .map(() => Math.random());
+    const rst64_1 = await tbl.query().nearestTo(query64).limit(2).toArrow();
+    const rst64_2 = await tbl.search(query64, "vec2").limit(2).toArrow();
+    expect(rst64_1.toString()).toEqual(rst64_2.toString());
+    expect(rst64_1.numRows).toBe(2);
   });
 
   test("create scalar index", async () => {

nodejs/src/index.rs

@@ -91,7 +91,6 @@ impl IndexBuilder {
 
     #[napi]
     pub async fn build(&self) -> napi::Result<()> {
-        println!("nodejs::index.rs : build");
         self.inner
             .build()
             .await

nodejs/src/iterator.rs

@@ -0,0 +1,47 @@
+// Copyright 2024 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+use futures::StreamExt;
+use lance::io::RecordBatchStream;
+use napi::bindgen_prelude::*;
+use napi_derive::napi;
+use vectordb::ipc::batches_to_ipc_file;
+
+/** Typescript-style Async Iterator over RecordBatches */
+#[napi]
+pub struct RecordBatchIterator {
+    inner: Box<dyn RecordBatchStream + Unpin>,
+}
+
+#[napi]
+impl RecordBatchIterator {
+    pub(crate) fn new(inner: Box<dyn RecordBatchStream + Unpin>) -> Self {
+        Self { inner }
+    }
+
+    #[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| {
+                napi::Error::from_reason(format!("Failed to get next batch from stream: {}", e))
+            })?;
+            batches_to_ipc_file(&[batch])
+                .map_err(|e| napi::Error::from_reason(format!("Failed to write IPC file: {}", e)))
+                .map(|buf| Some(Buffer::from(buf)))
+        } else {
+            // We are done with the stream.
+            Ok(None)
+        }
+    }
+}

nodejs/src/lib.rs

@@ -17,6 +17,7 @@ use napi_derive::*;
 
 mod connection;
 mod index;
+mod iterator;
 mod query;
 mod table;
 

nodejs/src/query.rs

@@ -16,7 +16,7 @@ use napi::bindgen_prelude::*;
 use napi_derive::napi;
 use vectordb::query::Query as LanceDBQuery;
 
-use crate::table::Table;
+use crate::{iterator::RecordBatchIterator, table::Table};
 
 #[napi]
 pub struct Query {
@@ -32,17 +32,50 @@ impl Query {
     }
 
     #[napi]
-    pub fn vector(&mut self, vector: Float32Array) {
+    pub fn column(&mut self, column: String) {
-        let inn = self.inner.clone().nearest_to(&vector);
+        self.inner = self.inner.clone().column(&column);
-        self.inner = inn;
     }
 
     #[napi]
-    pub fn to_arrow(&self) -> napi::Result<()> {
+    pub fn filter(&mut self, filter: String) {
-        // let buf = self.inner.to_arrow().map_err(|e| {
+        self.inner = self.inner.clone().filter(filter);
-        //     napi::Error::from_reason(format!("Failed to convert query to arrow: {}", e))
+    }
-        // })?;
+
-        // Ok(buf)
+    #[napi]
-        todo!()
+    pub fn select(&mut self, columns: Vec<String>) {
+        self.inner = self.inner.clone().select(&columns);
+    }
+
+    #[napi]
+    pub fn limit(&mut self, limit: u32) {
+        self.inner = self.inner.clone().limit(limit as usize);
+    }
+
+    #[napi]
+    pub fn prefilter(&mut self, prefilter: bool) {
+        self.inner = self.inner.clone().prefilter(prefilter);
+    }
+
+    #[napi]
+    pub fn nearest_to(&mut self, vector: Float32Array) {
+        self.inner = self.inner.clone().nearest_to(&vector);
+    }
+
+    #[napi]
+    pub fn refine_factor(&mut self, refine_factor: u32) {
+        self.inner = self.inner.clone().refine_factor(refine_factor);
+    }
+
+    #[napi]
+    pub fn nprobes(&mut self, nprobe: u32) {
+        self.inner = self.inner.clone().nprobes(nprobe as usize);
+    }
+
+    #[napi]
+    pub async fn execute_stream(&self) -> napi::Result<RecordBatchIterator> {
+        let inner_stream = self.inner.execute_stream().await.map_err(|e| {
+            napi::Error::from_reason(format!("Failed to execute query stream: {}", e))
+        })?;
+        Ok(RecordBatchIterator::new(Box::new(inner_stream)))
     }
 }

nodejs/vectordb/native.d.ts

@@ -54,9 +54,20 @@ export class IndexBuilder {
   scalar(): void
   build(): Promise<void>
 }
+/** Typescript-style Async Iterator over RecordBatches  */
+export class RecordBatchIterator {
+  next(): Promise<Buffer | null>
+}
 export class Query {
-  vector(vector: Float32Array): void
+  column(column: string): void
-  toArrow(): void
+  filter(filter: string): void
+  select(columns: Array<string>): void
+  limit(limit: number): void
+  prefilter(prefilter: boolean): void
+  nearestTo(vector: Float32Array): void
+  refineFactor(refineFactor: number): void
+  nprobes(nprobe: number): void
+  executeStream(): Promise<RecordBatchIterator>
 }
 export class Table {
   /** Return Schema as empty Arrow IPC file. */

nodejs/vectordb/native.js

@@ -295,12 +295,13 @@ if (!nativeBinding) {
   throw new Error(`Failed to load native binding`)
 }
 
-const { Connection, IndexType, MetricType, IndexBuilder, Query, Table, WriteMode, connect } = nativeBinding
+const { Connection, IndexType, MetricType, IndexBuilder, RecordBatchIterator, Query, Table, WriteMode, connect } = nativeBinding
 
 module.exports.Connection = Connection
 module.exports.IndexType = IndexType
 module.exports.MetricType = MetricType
 module.exports.IndexBuilder = IndexBuilder
+module.exports.RecordBatchIterator = RecordBatchIterator
 module.exports.Query = Query
 module.exports.Table = Table
 module.exports.WriteMode = WriteMode

nodejs/vectordb/query.ts

@@ -12,46 +12,73 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-import { RecordBatch } from "apache-arrow";
+import { RecordBatch, tableFromIPC, Table as ArrowTable } from "apache-arrow";
-import { Table } from "./table";
+import {
+  RecordBatchIterator as NativeBatchIterator,
+  Query as NativeQuery,
+  Table as NativeTable,
+} from "./native";
 
-// TODO: re-eanble eslint once we have a real implementation
-/* eslint-disable */
 class RecordBatchIterator implements AsyncIterator<RecordBatch> {
-  next(
+  private promised_inner?: Promise<NativeBatchIterator>;
-    ...args: [] | [undefined]
+  private inner?: NativeBatchIterator;
-  ): Promise<IteratorResult<RecordBatch<any>, any>> {
+
-    throw new Error("Method not implemented.");
+  constructor(
+    inner?: NativeBatchIterator,
+    promise?: Promise<NativeBatchIterator>
+  ) {
+    // TODO: check promise reliably so we dont need to pass two arguments.
+    this.inner = inner;
+    this.promised_inner = promise;
   }
-  return?(value?: any): Promise<IteratorResult<RecordBatch<any>, any>> {
+
-    throw new Error("Method not implemented.");
+  async next(): Promise<IteratorResult<RecordBatch<any>, any>> {
-  }
+    if (this.inner === undefined) {
-  throw?(e?: any): Promise<IteratorResult<RecordBatch<any>, any>> {
+      this.inner = await this.promised_inner;
-    throw new Error("Method not implemented.");
+    }
+    if (this.inner === undefined) {
+      throw new Error("Invalid iterator state state");
+    }
+    const n = await this.inner.next();
+    if (n == null) {
+      return Promise.resolve({ done: true, value: null });
+    }
+    const tbl = tableFromIPC(n);
+    if (tbl.batches.length != 1) {
+      throw new Error("Expected only one batch");
+    }
+    return Promise.resolve({ done: false, value: tbl.batches[0] });
   }
 }
 /* eslint-enable */
 
 /** Query executor */
 export class Query implements AsyncIterable<RecordBatch> {
-  private readonly tbl: Table;
+  private readonly inner: NativeQuery;
-  private _filter?: string;
-  private _limit?: number;
 
-  // Vector search
+  constructor(tbl: NativeTable) {
-  private _vector?: Float32Array;
+    this.inner = tbl.query();
-  private _nprobes?: number;
+  }
-  private _refine_factor?: number = 1;
 
-  constructor(tbl: Table) {
+  /** Set the column to run query. */
-    this.tbl = tbl;
+  column(column: string): Query {
+    this.inner.column(column);
+    return this;
   }
 
   /** Set the filter predicate, only returns the results that satisfy the filter.
    *
    */
   filter(predicate: string): Query {
-    this._filter = predicate;
+    this.inner.filter(predicate);
+    return this;
+  }
+
+  /**
+   * Select the columns to return. If not set, all columns are returned.
+   */
+  select(columns: string[]): Query {
+    this.inner.select(columns);
     return this;
   }
 
@@ -59,35 +86,67 @@ export class Query implements AsyncIterable<RecordBatch> {
    * Set the limit of rows to return.
    */
   limit(limit: number): Query {
-    this._limit = limit;
+    this.inner.limit(limit);
+    return this;
+  }
+
+  prefilter(prefilter: boolean): Query {
+    this.inner.prefilter(prefilter);
     return this;
   }
 
   /**
    * Set the query vector.
    */
-  vector(vector: number[]): Query {
+  nearestTo(vector: number[]): Query {
-    this._vector = Float32Array.from(vector);
+    this.inner.nearestTo(Float32Array.from(vector));
     return this;
   }
 
   /**
-   * Set the number of probes to use for the query.
+   * Set the number of IVF partitions to use for the query.
    */
   nprobes(nprobes: number): Query {
-    this._nprobes = nprobes;
+    this.inner.nprobes(nprobes);
     return this;
   }
 
   /**
    * Set the refine factor for the query.
    */
-  refine_factor(refine_factor: number): Query {
+  refineFactor(refine_factor: number): Query {
-    this._refine_factor = refine_factor;
+    this.inner.refineFactor(refine_factor);
     return this;
   }
 
-  [Symbol.asyncIterator](): AsyncIterator<RecordBatch<any>, any, undefined> {
+  /**
-    throw new RecordBatchIterator();
+   * Execute the query and return the results as an AsyncIterator.
+   */
+  async executeStream(): Promise<RecordBatchIterator> {
+    const inner = await this.inner.executeStream();
+    return new RecordBatchIterator(inner);
+  }
+
+  /** Collect the results as an Arrow Table. */
+  async toArrow(): Promise<ArrowTable> {
+    const batches = [];
+    for await (const batch of this) {
+      batches.push(batch);
+    }
+    return new ArrowTable(batches);
+  }
+
+  /** Returns a JSON Array of All results.
+   *
+   */
+  async toArray(): Promise<any[]> {
+    const tbl = await this.toArrow();
+    // eslint-disable-next-line @typescript-eslint/no-unsafe-return
+    return tbl.toArray();
+  }
+
+  [Symbol.asyncIterator](): AsyncIterator<RecordBatch<any>> {
+    const promise = this.inner.executeStream();
+    return new RecordBatchIterator(undefined, promise);
   }
 }

nodejs/vectordb/table.ts

@@ -95,10 +95,58 @@ export class Table {
     return builder;
   }
 
-  search(vector?: number[]): Query {
+  /**
-    const q = new Query(this);
+   * Create a generic {@link Query} Builder.
-    if (vector !== undefined) {
+   *
-      q.vector(vector);
+   * When appropriate, various indices and statistics based pruning will be used to
+   * accelerate the query.
+   *
+   * @example
+   *
+   * ### Run a SQL-style query
+   * ```typescript
+   * for await (const batch of table.query()
+   *                          .filter("id > 1").select(["id"]).limit(20)) {
+   *  console.log(batch);
+   * }
+   * ```
+   *
+   * ### Run Top-10 vector similarity search
+   * ```typescript
+   * for await (const batch of table.query()
+   *                    .nearestTo([1, 2, 3])
+   *                    .refineFactor(5).nprobe(10)
+   *                    .limit(10)) {
+   *  console.log(batch);
+   * }
+   *```
+   *
+   * ### Scan the full dataset
+   * ```typescript
+   * for await (const batch of table.query()) {
+   *   console.log(batch);
+   * }
+   *
+   * ### Return the full dataset as Arrow Table
+   * ```typescript
+   * let arrowTbl = await table.query().nearestTo([1.0, 2.0, 0.5, 6.7]).toArrow();
+   * ```
+   *
+   * @returns {@link Query}
+   */
+  query(): Query {
+    return new Query(this.inner);
+  }
+
+  /** Search the table with a given query vector.
+   *
+   * This is a convenience method for preparing an ANN {@link Query}.
+   */
+  search(vector: number[], column?: string): Query {
+    const q = this.query();
+    q.nearestTo(vector);
+    if (column !== undefined) {
+      q.column(column);
     }
     return q;
   }

rust/ffi/node/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb-node"
-version = "0.4.4"
+version = "0.4.6"
 description = "Serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"
 edition = "2018"

rust/ffi/node/src/arrow.rs

@@ -1,4 +1,4 @@
-// Copyright 2023 Lance Developers.
+// Copyright 2024 Lance Developers.
 //
 // Licensed under the Apache License, Version 2.0 (the "License");
 // you may not use this file except in compliance with the License.
@@ -19,19 +19,8 @@ use arrow_array::RecordBatch;
 use arrow_ipc::reader::FileReader;
 use arrow_ipc::writer::FileWriter;
 use arrow_schema::SchemaRef;
-use vectordb::table::VECTOR_COLUMN_NAME;
 
-use crate::error::{MissingColumnSnafu, Result};
+use crate::error::Result;
-use snafu::prelude::*;
-
-fn validate_vector_column(record_batch: &RecordBatch) -> Result<()> {
-    record_batch
-        .column_by_name(VECTOR_COLUMN_NAME)
-        .map(|_| ())
-        .context(MissingColumnSnafu {
-            name: VECTOR_COLUMN_NAME,
-        })
-}
 
 pub(crate) fn arrow_buffer_to_record_batch(slice: &[u8]) -> Result<(Vec<RecordBatch>, SchemaRef)> {
     let mut batches: Vec<RecordBatch> = Vec::new();
@@ -39,7 +28,6 @@ pub(crate) fn arrow_buffer_to_record_batch(slice: &[u8]) -> Result<(Vec<RecordBa
     let schema = file_reader.schema();
     for b in file_reader {
         let record_batch = b?;
-        validate_vector_column(&record_batch)?;
         batches.push(record_batch);
     }
     Ok((batches, schema))

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

@@ -19,6 +19,7 @@ use neon::{
 };
 
 use crate::{error::ResultExt, runtime, table::JsTable};
+use vectordb::Table;
 
 pub(crate) fn table_create_scalar_index(mut cx: FunctionContext) -> JsResult<JsPromise> {
     let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
@@ -35,7 +36,9 @@ pub(crate) fn table_create_scalar_index(mut cx: FunctionContext) -> JsResult<JsP
         let idx_result = table
             .as_native()
             .unwrap()
-            .create_scalar_index(&column, replace)
+            .create_index(&[&column])
+            .replace(replace)
+            .build()
             .await;
 
         deferred.settle_with(&channel, move |mut cx| {

rust/ffi/node/src/table.rs

@@ -16,6 +16,7 @@ use arrow_array::{RecordBatch, RecordBatchIterator};
 use lance::dataset::optimize::CompactionOptions;
 use lance::dataset::{WriteMode, WriteParams};
 use lance::io::ObjectStoreParams;
+use vectordb::table::OptimizeAction;
 
 use crate::arrow::{arrow_buffer_to_record_batch, record_batch_to_buffer};
 use neon::prelude::*;
@@ -245,27 +246,30 @@ impl JsTable {
             .map(|val| val.value(&mut cx) as i64)
             .unwrap_or_else(|| 2 * 7 * 24 * 60); // 2 weeks
         let older_than = chrono::Duration::minutes(older_than);
-        let delete_unverified: bool = cx
+        let delete_unverified: Option<bool> = Some(
-            .argument_opt(1)
+            cx.argument_opt(1)
-            .and_then(|val| val.downcast::<JsBoolean, _>(&mut cx).ok())
+                .and_then(|val| val.downcast::<JsBoolean, _>(&mut cx).ok())
-            .map(|val| val.value(&mut cx))
+                .map(|val| val.value(&mut cx))
-            .unwrap_or_default();
+                .unwrap_or_default(),
+        );
 
         rt.spawn(async move {
             let stats = table
-                .as_native()
+                .optimize(OptimizeAction::Prune {
-                .unwrap()
+                    older_than,
-                .cleanup_old_versions(older_than, Some(delete_unverified))
+                    delete_unverified,
+                })
                 .await;
 
             deferred.settle_with(&channel, move |mut cx| {
                 let stats = stats.or_throw(&mut cx)?;
 
+                let prune_stats = stats.prune.as_ref().expect("Prune stats missing");
                 let output_metrics = JsObject::new(&mut cx);
-                let bytes_removed = cx.number(stats.bytes_removed as f64);
+                let bytes_removed = cx.number(prune_stats.bytes_removed as f64);
                 output_metrics.set(&mut cx, "bytesRemoved", bytes_removed)?;
 
-                let old_versions = cx.number(stats.old_versions as f64);
+                let old_versions = cx.number(prune_stats.old_versions as f64);
                 output_metrics.set(&mut cx, "oldVersions", old_versions)?;
 
                 let output_table = cx.boxed(JsTable::from(table));
@@ -317,13 +321,15 @@ impl JsTable {
 
         rt.spawn(async move {
             let stats = table
-                .as_native()
+                .optimize(OptimizeAction::Compact {
-                .unwrap()
+                    options,
-                .compact_files(options, None)
+                    remap_options: None,
+                })
                 .await;
 
             deferred.settle_with(&channel, move |mut cx| {
                 let stats = stats.or_throw(&mut cx)?;
+                let stats = stats.compaction.as_ref().expect("Compact stats missing");
 
                 let output_metrics = JsObject::new(&mut cx);
                 let fragments_removed = cx.number(stats.fragments_removed as f64);

rust/vectordb/Cargo.toml

@@ -1,6 +1,6 @@
 [package]
 name = "vectordb"
-version = "0.4.4"
+version = "0.4.6"
 edition = "2021"
 description = "LanceDB: A serverless, low-latency vector database for AI applications"
 license = "Apache-2.0"

rust/vectordb/src/connection.rs

@@ -68,6 +68,87 @@ pub trait Connection: Send + Sync {
     async fn drop_table(&self, name: &str) -> Result<()>;
 }
 
+#[derive(Debug)]
+pub struct ConnectOptions {
+    /// Database URI
+    ///
+    /// # Accpeted URI formats
+    ///
+    /// - `/path/to/database` - local database on file system.
+    /// - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud object store
+    /// - `db://dbname` - Lance Cloud
+    pub uri: String,
+
+    /// Lance Cloud API key
+    pub api_key: Option<String>,
+    /// Lance Cloud region
+    pub region: Option<String>,
+    /// Lance Cloud host override
+    pub host_override: Option<String>,
+
+    /// The maximum number of indices to cache in memory. Defaults to 256.
+    pub index_cache_size: u32,
+}
+
+impl ConnectOptions {
+    /// Create a new [`ConnectOptions`] with the given database URI.
+    pub fn new(uri: &str) -> Self {
+        Self {
+            uri: uri.to_string(),
+            api_key: None,
+            region: None,
+            host_override: None,
+            index_cache_size: 256,
+        }
+    }
+
+    pub fn api_key(mut self, api_key: &str) -> Self {
+        self.api_key = Some(api_key.to_string());
+        self
+    }
+
+    pub fn region(mut self, region: &str) -> Self {
+        self.region = Some(region.to_string());
+        self
+    }
+
+    pub fn host_override(mut self, host_override: &str) -> Self {
+        self.host_override = Some(host_override.to_string());
+        self
+    }
+
+    pub fn index_cache_size(mut self, index_cache_size: u32) -> Self {
+        self.index_cache_size = index_cache_size;
+        self
+    }
+}
+
+/// Connect to a LanceDB database.
+///
+/// # Arguments
+///
+/// - `uri` - URI where the database is located, can be a local file or a supported remote cloud storage
+///
+/// ## Accepted URI formats
+///
+///  - `/path/to/database` - local database on file system.
+///  - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud object store
+/// - `db://dbname` - Lance Cloud
+///
+pub async fn connect(uri: &str) -> Result<Arc<dyn Connection>> {
+    let options = ConnectOptions::new(uri);
+    connect_with_options(&options).await
+}
+
+/// Connect with [`ConnectOptions`].
+///
+/// # Arguments
+/// - `options` - [`ConnectOptions`] to connect to the database.
+pub async fn connect_with_options(options: &ConnectOptions) -> Result<Arc<dyn Connection>> {
+    let db = Database::connect(&options.uri).await?;
+    Ok(Arc::new(db))
+}
+
 pub struct Database {
     object_store: ObjectStore,
     query_string: Option<String>,

rust/vectordb/src/index.rs

@@ -14,6 +14,7 @@
 
 use std::{cmp::max, sync::Arc};
 
+use lance::index::scalar::ScalarIndexParams;
 use lance_index::{DatasetIndexExt, IndexType};
 pub use lance_linalg::distance::MetricType;
 
@@ -232,10 +233,14 @@ impl IndexBuilder {
         let mut dataset = tbl.clone_inner_dataset();
         match params {
             IndexParams::Scalar { replace } => {
-                self.table
+                dataset
-                    .as_native()
+                    .create_index(
-                    .unwrap()
+                        &[&column],
-                    .create_scalar_index(column, replace)
+                        IndexType::Scalar,
+                        None,
+                        &ScalarIndexParams::default(),
+                        replace,
+                    )
                     .await?
             }
             IndexParams::IvfPq {

rust/vectordb/src/ipc.rs

@@ -16,10 +16,10 @@
 
 use std::io::Cursor;
 
-use arrow_array::RecordBatchReader;
+use arrow_array::{RecordBatch, RecordBatchReader};
-use arrow_ipc::reader::StreamReader;
+use arrow_ipc::{reader::StreamReader, writer::FileWriter};
 
-use crate::Result;
+use crate::{Error, Result};
 
 /// Convert a Arrow IPC file to a batch reader
 pub fn ipc_file_to_batches(buf: Vec<u8>) -> Result<impl RecordBatchReader> {
@@ -28,6 +28,22 @@ pub fn ipc_file_to_batches(buf: Vec<u8>) -> Result<impl RecordBatchReader> {
     Ok(reader)
 }
 
+/// Convert record batches to Arrow IPC file
+pub fn batches_to_ipc_file(batches: &[RecordBatch]) -> Result<Vec<u8>> {
+    if batches.is_empty() {
+        return Err(Error::Store {
+            message: "No batches to write".to_string(),
+        });
+    }
+    let schema = batches[0].schema();
+    let mut writer = FileWriter::try_new(vec![], &schema)?;
+    for batch in batches {
+        writer.write(batch)?;
+    }
+    writer.finish()?;
+    Ok(writer.into_inner()?)
+}
+
 #[cfg(test)]
 mod tests {
 

rust/vectordb/src/lib.rs

@@ -41,15 +41,32 @@
 //!
 //! ### Quick Start
 //!
-//! <div class="warning">Rust API is not stable yet.</div>
+//! <div class="warning">Rust API is not stable yet, please expect breaking changes.</div>
 //!
 //! #### Connect to a database.
 //!
 //! ```rust
-//! use vectordb::connection::Database;
+//! use vectordb::connect;
 //! # use arrow_schema::{Field, Schema};
 //! # tokio::runtime::Runtime::new().unwrap().block_on(async {
-//! let db = Database::connect("data/sample-lancedb").await.unwrap();
+//! let db = connect("data/sample-lancedb").await.unwrap();
+//! # });
+//! ```
+//!
+//! LanceDB accepts the different form of database path:
+//!
+//! - `/path/to/database` - local database on file system.
+//! - `s3://bucket/path/to/database` or `gs://bucket/path/to/database` - database on cloud object store
+//! - `db://dbname` - Lance Cloud
+//!
+//! You can also use [`ConnectOptions`] to configure the connectoin to the database.
+//!
+//! ```rust
+//! use vectordb::{connect_with_options, ConnectOptions};
+//! # tokio::runtime::Runtime::new().unwrap().block_on(async {
+//! let options = ConnectOptions::new("data/sample-lancedb")
+//!     .index_cache_size(1024);
+//! let db = connect_with_options(&options).await.unwrap();
 //! # });
 //! ```
 //!
@@ -57,6 +74,8 @@
 //! It treats [`FixedSizeList<Float16/Float32>`](https://docs.rs/arrow/latest/arrow/array/struct.FixedSizeListArray.html)
 //! columns as vector columns.
 //!
+//! For more details, please refer to [LanceDB documentation](https://lancedb.github.io/lancedb/).
+//!
 //! #### Create a table
 //!
 //! To create a Table, you need to provide a [`arrow_schema::Schema`] and a [`arrow_array::RecordBatch`] stream.
@@ -67,10 +86,11 @@
 //! use arrow_array::{RecordBatch, RecordBatchIterator};
 //! # use arrow_array::{FixedSizeListArray, Float32Array, Int32Array, types::Float32Type};
 //! # use vectordb::connection::{Database, Connection};
+//! # use vectordb::connect;
 //!
 //! # tokio::runtime::Runtime::new().unwrap().block_on(async {
 //! # let tmpdir = tempfile::tempdir().unwrap();
-//! # let db = Database::connect(tmpdir.path().to_str().unwrap()).await.unwrap();
+//! # let db = connect(tmpdir.path().to_str().unwrap()).await.unwrap();
 //! let schema = Arc::new(Schema::new(vec![
 //!   Field::new("id", DataType::Int32, false),
 //!   Field::new("vector", DataType::FixedSizeList(
@@ -94,13 +114,13 @@
 //!
 //! ```no_run
 //! # use std::sync::Arc;
-//! # use vectordb::connection::{Database, Connection};
+//! # use vectordb::connect;
 //! # use arrow_array::{FixedSizeListArray, types::Float32Type, RecordBatch,
 //! #   RecordBatchIterator, Int32Array};
 //! # use arrow_schema::{Schema, Field, DataType};
 //! # tokio::runtime::Runtime::new().unwrap().block_on(async {
 //! # let tmpdir = tempfile::tempdir().unwrap();
-//! # let db = Database::connect(tmpdir.path().to_str().unwrap()).await.unwrap();
+//! # let db = connect(tmpdir.path().to_str().unwrap()).await.unwrap();
 //! # let tbl = db.open_table("idx_test").await.unwrap();
 //! tbl.create_index(&["vector"])
 //!     .ivf_pq()
@@ -166,4 +186,6 @@ pub use connection::{Connection, Database};
 pub use error::{Error, Result};
 pub use table::{Table, TableRef};
 
+/// Connect to a database
+pub use connection::{connect, connect_with_options, ConnectOptions};
 pub use lance::dataset::WriteMode;

rust/vectordb/src/query.rs

@@ -22,6 +22,7 @@ use lance_linalg::distance::MetricType;
 
 use crate::error::Result;
 use crate::utils::default_vector_column;
+use crate::Error;
 
 const DEFAULT_TOP_K: usize = 10;
 
@@ -93,6 +94,19 @@ impl Query {
                 let arrow_schema = Schema::from(self.dataset.schema());
                 default_vector_column(&arrow_schema, Some(query.len() as i32))?
             };
+            let field = self.dataset.schema().field(&column).ok_or(Error::Store {
+                message: format!("Column {} not found in dataset schema", column),
+            })?;
+            if !matches!(field.data_type(), arrow_schema::DataType::FixedSizeList(f, dim) if f.data_type().is_floating() && dim == query.len() as i32)
+            {
+                return Err(Error::Store {
+                    message: format!(
+                        "Vector column '{}' does not match the dimension of the query vector: dim={}",
+                        column,
+                        query.len(),
+                    ),
+                });
+            }
             scanner.nearest(&column, query, self.limit.unwrap_or(DEFAULT_TOP_K))?;
         } else {
             // If there is no vector query, it's ok to not have a limit

rust/vectordb/src/table.rs

@@ -27,9 +27,9 @@ use lance::dataset::optimize::{
 };
 pub use lance::dataset::ReadParams;
 use lance::dataset::{Dataset, UpdateBuilder, WriteParams};
-use lance::index::scalar::ScalarIndexParams;
 use lance::io::WrappingObjectStore;
-use lance_index::{optimize::OptimizeOptions, DatasetIndexExt, IndexType};
+use lance_index::{optimize::OptimizeOptions, DatasetIndexExt};
+use log::info;
 
 use crate::error::{Error, Result};
 use crate::index::vector::{VectorIndex, VectorIndexStatistics};
@@ -38,7 +38,46 @@ use crate::query::Query;
 use crate::utils::{PatchReadParam, PatchWriteParam};
 use crate::WriteMode;
 
-pub const VECTOR_COLUMN_NAME: &str = "vector";
+/// Optimize the dataset.
+///
+/// Similar to `VACUUM` in PostgreSQL, it offers different options to
+/// optimize different parts of the table on disk.
+///
+/// By default, it optimizes everything, as [`OptimizeAction::All`].
+pub enum OptimizeAction {
+    /// Run optimization on every, with default options.
+    All,
+    /// Compact files in the dataset
+    Compact {
+        options: CompactionOptions,
+        remap_options: Option<Arc<dyn IndexRemapperOptions>>,
+    },
+    /// Prune old version of datasets.
+    Prune {
+        /// The duration of time to keep versions of the dataset.
+        older_than: Duration,
+        /// Because they may be part of an in-progress transaction, files newer than 7 days old are not deleted by default.
+        /// If you are sure that there are no in-progress transactions, then you can set this to True to delete all files older than `older_than`.
+        delete_unverified: Option<bool>,
+    },
+    /// Optimize index.
+    Index(OptimizeOptions),
+}
+
+impl Default for OptimizeAction {
+    fn default() -> Self {
+        Self::All
+    }
+}
+
+/// Statistics about the optimization.
+pub struct OptimizeStats {
+    /// Stats of the file compaction.
+    pub compaction: Option<CompactionMetrics>,
+
+    /// Stats of the version pruning
+    pub prune: Option<RemovalStats>,
+}
 
 /// A Table is a collection of strong typed Rows.
 ///
@@ -194,6 +233,14 @@ pub trait Table: std::fmt::Display + Send + Sync {
     /// # });
     /// ```
     fn query(&self) -> Query;
+
+    /// Optimize the on-disk data and indices for better performance.
+    ///
+    /// <section class="warning">Experimental API</section>
+    ///
+    /// Modeled after ``VACCUM`` in PostgreSQL.
+    /// Not all implementations support explicit optimization.
+    async fn optimize(&self, action: OptimizeAction) -> Result<OptimizeStats>;
 }
 
 /// Reference to a Table pointer.
@@ -396,17 +443,8 @@ impl NativeTable {
         self.dataset.lock().expect("lock poison").version().version
     }
 
-    /// Create a scalar index on the table
+    async fn optimize_indices(&self, options: &OptimizeOptions) -> Result<()> {
-    pub async fn create_scalar_index(&self, column: &str, replace: bool) -> Result<()> {
+        info!("LanceDB: optimizing indices: {:?}", options);
-        let mut dataset = self.clone_inner_dataset();
-        let params = ScalarIndexParams::default();
-        dataset
-            .create_index(&[column], IndexType::Scalar, None, &params, replace)
-            .await?;
-        Ok(())
-    }
-
-    pub async fn optimize_indices(&mut self, options: &OptimizeOptions) -> Result<()> {
         let mut dataset = self.clone_inner_dataset();
         dataset.optimize_indices(options).await?;
 
@@ -463,7 +501,7 @@ impl NativeTable {
     ///
     /// This calls into [lance::dataset::Dataset::cleanup_old_versions] and
     /// returns the result.
-    pub async fn cleanup_old_versions(
+    async fn cleanup_old_versions(
         &self,
         older_than: Duration,
         delete_unverified: Option<bool>,
@@ -480,7 +518,7 @@ impl NativeTable {
     /// for faster reads.
     ///
     /// This calls into [lance::dataset::optimize::compact_files].
-    pub async fn compact_files(
+    async fn compact_files(
         &self,
         options: CompactionOptions,
         remap_options: Option<Arc<dyn IndexRemapperOptions>>,
@@ -614,6 +652,52 @@ impl Table for NativeTable {
         self.reset_dataset(dataset);
         Ok(())
     }
+
+    async fn optimize(&self, action: OptimizeAction) -> Result<OptimizeStats> {
+        let mut stats = OptimizeStats {
+            compaction: None,
+            prune: None,
+        };
+        match action {
+            OptimizeAction::All => {
+                stats.compaction = self
+                    .optimize(OptimizeAction::Compact {
+                        options: CompactionOptions::default(),
+                        remap_options: None,
+                    })
+                    .await?
+                    .compaction;
+                stats.prune = self
+                    .optimize(OptimizeAction::Prune {
+                        older_than: Duration::days(7),
+                        delete_unverified: None,
+                    })
+                    .await?
+                    .prune;
+                self.optimize(OptimizeAction::Index(OptimizeOptions::default()))
+                    .await?;
+            }
+            OptimizeAction::Compact {
+                options,
+                remap_options,
+            } => {
+                stats.compaction = Some(self.compact_files(options, remap_options).await?);
+            }
+            OptimizeAction::Prune {
+                older_than,
+                delete_unverified,
+            } => {
+                stats.prune = Some(
+                    self.cleanup_old_versions(older_than, delete_unverified)
+                        .await?,
+                );
+            }
+            OptimizeAction::Index(options) => {
+                self.optimize_indices(&options).await?;
+            }
+        }
+        Ok(stats)
+    }
 }
 
 #[cfg(test)]