[python] Bump version: 0.3.4 → 0.3.5

Closes #69

Will not pass until https://github.com/lancedb/lance/pull/1585 is
released

node/src/index.ts

@@ -21,15 +21,7 @@ import type { EmbeddingFunction } from './embedding/embedding_function'
 import { RemoteConnection } from './remote'
 import { Query } from './query'
 import { isEmbeddingFunction } from './embedding/embedding_function'
-import {
-  type Connection, type CreateTableOptions, type Table,
-  type VectorIndexParams, type UpdateArgs, type UpdateSqlArgs,
-  type VectorIndex, type IndexStats,
-  type ConnectionOptions, WriteMode, type WriteOptions
-} from './types'
-import { toSQL } from './util'
-
-export { type WriteMode }
+import { type Literal, toSQL } from './util'
 
 // eslint-disable-next-line @typescript-eslint/no-var-requires
 const { databaseNew, databaseTableNames, databaseOpenTable, databaseDropTable, tableCreate, tableAdd, tableCreateVectorIndex, tableCountRows, tableDelete, tableUpdate, tableCleanupOldVersions, tableCompactFiles, tableListIndices, tableIndexStats } = require('../native.js')
@@ -38,6 +30,30 @@ export { Query }
 export type { EmbeddingFunction }
 export { OpenAIEmbeddingFunction } from './embedding/openai'
 
+export interface AwsCredentials {
+  accessKeyId: string
+
+  secretKey: string
+
+  sessionToken?: string
+}
+
+export interface ConnectionOptions {
+  uri: string
+
+  awsCredentials?: AwsCredentials
+
+  awsRegion?: string
+
+  // API key for the remote connections
+  apiKey?: string
+  // Region to connect
+  region?: string
+
+  // override the host for the remote connections
+  hostOverride?: string
+}
+
 function getAwsArgs (opts: ConnectionOptions): any[] {
   const callArgs = []
   const awsCredentials = opts.awsCredentials
@@ -55,6 +71,23 @@ function getAwsArgs (opts: ConnectionOptions): any[] {
   return callArgs
 }
 
+export interface CreateTableOptions<T> {
+  // Name of Table
+  name: string
+
+  // Data to insert into the Table
+  data?: Array<Record<string, unknown>> | ArrowTable | undefined
+
+  // Optional Arrow Schema for this table
+  schema?: Schema | undefined
+
+  // Optional embedding function used to create embeddings
+  embeddingFunction?: EmbeddingFunction<T> | undefined
+
+  // WriteOptions for this operation
+  writeOptions?: WriteOptions | undefined
+}
+
 /**
  * Connect to a LanceDB instance at the given URI
  * @param uri The uri of the database.
@@ -83,6 +116,235 @@ export async function connect (arg: string | Partial<ConnectionOptions>): Promis
   return new LocalConnection(db, opts)
 }
 
+/**
+ * A LanceDB Connection that allows you to open tables and create new ones.
+ *
+ * Connection could be local against filesystem or remote against a server.
+ */
+export interface Connection {
+  uri: string
+
+  tableNames(): Promise<string[]>
+
+  /**
+   * Open a table in the database.
+   *
+   * @param name The name of the table.
+   * @param embeddings An embedding function to use on this table
+   */
+  openTable<T>(name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
+
+  /**
+   * Creates a new Table, optionally initializing it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Array of Records to be inserted into the table
+   * @param schema - An Arrow Schema that describe this table columns
+   * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
+   * @param {WriteOptions} writeOptions - The write options to use when creating the table.
+   */
+  createTable<T> ({ name, data, schema, embeddingFunction, writeOptions }: CreateTableOptions<T>): Promise<Table<T>>
+
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Non-empty Array of Records to be inserted into the table
+   */
+  createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table>
+
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Non-empty Array of Records to be inserted into the table
+   * @param {WriteOptions} options - The write options to use when creating the table.
+   */
+  createTable (name: string, data: Array<Record<string, unknown>>, options: WriteOptions): Promise<Table>
+
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Non-empty Array of Records to be inserted into the table
+   * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
+   */
+  createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param {string} name - The name of the table.
+   * @param data - Non-empty Array of Records to be inserted into the table
+   * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
+   * @param {WriteOptions} options - The write options to use when creating the table.
+   */
+  createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>, options: WriteOptions): Promise<Table<T>>
+
+  /**
+   * Drop an existing table.
+   * @param name The name of the table to drop.
+   */
+  dropTable(name: string): Promise<void>
+
+}
+
+/**
+ * A LanceDB Table is the collection of Records. Each Record has one or more vector fields.
+ */
+export interface Table<T = number[]> {
+  name: string
+
+  /**
+   * Creates a search query to find the nearest neighbors of the given search term
+   * @param query The query search term
+   */
+  search: (query: T) => Query<T>
+
+  /**
+   * Insert records into this Table.
+   *
+   * @param data Records to be inserted into the Table
+   * @return The number of rows added to the table
+   */
+  add: (data: Array<Record<string, unknown>>) => Promise<number>
+
+  /**
+   * Insert records into this Table, replacing its contents.
+   *
+   * @param data Records to be inserted into the Table
+   * @return The number of rows added to the table
+   */
+  overwrite: (data: Array<Record<string, unknown>>) => Promise<number>
+
+  /**
+   * Create an ANN index on this Table vector index.
+   *
+   * @param indexParams The parameters of this Index, @see VectorIndexParams.
+   */
+  createIndex: (indexParams: VectorIndexParams) => Promise<any>
+
+  /**
+   * Returns the number of rows in this table.
+   */
+  countRows: () => Promise<number>
+
+  /**
+   * Delete rows from this table.
+   *
+   * This can be used to delete a single row, many rows, all rows, or
+   * sometimes no rows (if your predicate matches nothing).
+   *
+   * @param filter  A filter in the same format used by a sql WHERE clause. The
+   *                filter must not be empty.
+   *
+   * @examples
+   *
+   * ```ts
+   * const con = await lancedb.connect("./.lancedb")
+   * const data = [
+   *    {id: 1, vector: [1, 2]},
+   *    {id: 2, vector: [3, 4]},
+   *    {id: 3, vector: [5, 6]},
+   * ];
+   * const tbl = await con.createTable("my_table", data)
+   * await tbl.delete("id = 2")
+   * await tbl.countRows() // Returns 2
+   * ```
+   *
+   * If you have a list of values to delete, you can combine them into a
+   * stringified list and use the `IN` operator:
+   *
+   * ```ts
+   * const to_remove = [1, 5];
+   * await tbl.delete(`id IN (${to_remove.join(",")})`)
+   * await tbl.countRows() // Returns 1
+   * ```
+   */
+  delete: (filter: string) => Promise<void>
+
+  /**
+   * Update rows in this table.
+   *
+   * This can be used to update a single row, many rows, all rows, or
+   * sometimes no rows (if your predicate matches nothing).
+   *
+   * @param args see {@link UpdateArgs} and {@link UpdateSqlArgs} for more details
+   *
+   * @examples
+   *
+   * ```ts
+   * const con = await lancedb.connect("./.lancedb")
+   * const data = [
+   *    {id: 1, vector: [3, 3], name: 'Ye'},
+   *    {id: 2, vector: [4, 4], name: 'Mike'},
+   * ];
+   * const tbl = await con.createTable("my_table", data)
+   *
+   * await tbl.update({
+   *   filter: "id = 2",
+   *   updates: { vector: [2, 2], name: "Michael" },
+   * })
+   *
+   * let results = await tbl.search([1, 1]).execute();
+   * // Returns [
+   * //   {id: 2, vector: [2, 2], name: 'Michael'}
+   * //   {id: 1, vector: [3, 3], name: 'Ye'}
+   * // ]
+   * ```
+   *
+   */
+  update: (args: UpdateArgs | UpdateSqlArgs) => Promise<void>
+
+  /**
+   * List the indicies on this table.
+   */
+  listIndices: () => Promise<VectorIndex[]>
+
+  /**
+   * Get statistics about an index.
+   */
+  indexStats: (indexUuid: string) => Promise<IndexStats>
+}
+
+export interface UpdateArgs {
+  /**
+   * A filter in the same format used by a sql WHERE clause. The filter may be empty,
+   * in which case all rows will be updated.
+   */
+  where?: string
+
+  /**
+   * A key-value map of updates. The keys are the column names, and the values are the
+   * new values to set
+   */
+  values: Record<string, Literal>
+}
+
+export interface UpdateSqlArgs {
+  /**
+   * A filter in the same format used by a sql WHERE clause. The filter may be empty,
+   * in which case all rows will be updated.
+   */
+  where?: string
+
+  /**
+   * A key-value map of updates. The keys are the column names, and the values are the
+   * new values to set as SQL expressions.
+   */
+  valuesSql: Record<string, string>
+}
+
+export interface VectorIndex {
+  columns: string[]
+  name: string
+  uuid: string
+}
+
+export interface IndexStats {
+  numIndexedRows: number | null
+  numUnindexedRows: number | null
+}
+
 /**
  * A connection to a LanceDB database.
  */
@@ -430,6 +692,83 @@ export interface CompactionMetrics {
   filesAdded: number
 }
 
+/// Config to build IVF_PQ index.
+///
+export interface IvfPQIndexConfig {
+  /**
+   * The column to be indexed
+   */
+  column?: string
+
+  /**
+   * A unique name for the index
+   */
+  index_name?: string
+
+  /**
+   * Metric type, L2 or Cosine
+   */
+  metric_type?: MetricType
+
+  /**
+   * The number of partitions this index
+   */
+  num_partitions?: number
+
+  /**
+   * The max number of iterations for kmeans training.
+   */
+  max_iters?: number
+
+  /**
+   * Train as optimized product quantization.
+   */
+  use_opq?: boolean
+
+  /**
+   * Number of subvectors to build PQ code
+   */
+  num_sub_vectors?: number
+  /**
+   * The number of bits to present one PQ centroid.
+   */
+  num_bits?: number
+
+  /**
+   * Max number of iterations to train OPQ, if `use_opq` is true.
+   */
+  max_opq_iters?: number
+
+  /**
+   * Replace an existing index with the same name if it exists.
+   */
+  replace?: boolean
+
+  type: 'ivf_pq'
+}
+
+export type VectorIndexParams = IvfPQIndexConfig
+
+/**
+ * Write mode for writing a table.
+ */
+export enum WriteMode {
+  /** Create a new {@link Table}. */
+  Create = 'create',
+  /** Overwrite the existing {@link Table} if presented. */
+  Overwrite = 'overwrite',
+  /** Append new data to the table. */
+  Append = 'append'
+}
+
+/**
+ * Write options when creating a Table.
+ */
+export interface WriteOptions {
+  /** A {@link WriteMode} to use on this operation */
+  writeMode?: WriteMode
+}
+
 export class DefaultWriteOptions implements WriteOptions {
   writeMode = WriteMode.Create
 }
@@ -438,3 +777,23 @@ export function isWriteOptions (value: any): value is WriteOptions {
   return Object.keys(value).length === 1 &&
       (value.writeMode === undefined || typeof value.writeMode === 'string')
 }
+
+/**
+ * Distance metrics type.
+ */
+export enum MetricType {
+  /**
+   * Euclidean distance
+   */
+  L2 = 'l2',
+
+  /**
+   * Cosine distance
+   */
+  Cosine = 'cosine',
+
+  /**
+   * Dot product
+   */
+  Dot = 'dot'
+}

node/src/integration_test/test.ts

@@ -0,0 +1,180 @@
+// Copyright 2023 LanceDB Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { describe } from 'mocha'
+import * as chai from 'chai'
+import * as chaiAsPromised from 'chai-as-promised'
+import { v4 as uuidv4 } from 'uuid'
+
+import * as lancedb from '../index'
+import { tmpdir } from 'os'
+import * as fs from 'fs'
+import * as path from 'path'
+
+const assert = chai.assert
+chai.use(chaiAsPromised)
+
+describe('LanceDB AWS Integration test', function () {
+  it('s3+ddb schema is processed correctly', async function () {
+    this.timeout(15000)
+
+    // WARNING: specifying engine is NOT a publicly supported feature in lancedb yet
+    // THE API WILL CHANGE
+    const conn = await lancedb.connect('s3://lancedb-integtest?engine=ddb&ddbTableName=lancedb-integtest')
+    const data = [{ vector: Array(128).fill(1.0) }]
+
+    const tableName = uuidv4()
+    let table = await conn.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite })
+
+    const futs = [table.add(data), table.add(data), table.add(data), table.add(data), table.add(data)]
+    await Promise.allSettled(futs)
+
+    table = await conn.openTable(tableName)
+    assert.equal(await table.countRows(), 6)
+  })
+})
+
+describe('LanceDB Mirrored Store Integration test', function () {
+  it('s3://...?mirroredStore=... param is processed correctly', async function () {
+    this.timeout(600000)
+
+    const dir = tmpdir()
+    console.log(dir)
+    const conn = await lancedb.connect(`s3://lancedb-integtest?mirroredStore=${dir}`)
+    const data = Array(200).fill({ vector: Array(128).fill(1.0), id: 0 })
+    data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 1 }))
+    data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 2 }))
+    data.push(...Array(200).fill({ vector: Array(128).fill(1.0), id: 3 }))
+
+    const tableName = uuidv4()
+
+    // try create table and check if it's mirrored
+    const t = await conn.createTable(tableName, data, { writeMode: lancedb.WriteMode.Overwrite })
+
+    const mirroredPath = path.join(dir, `${tableName}.lance`)
+    fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
+      if (err != null) throw err
+      // there should be three dirs
+      assert.equal(files.length, 3)
+      assert.isTrue(files[0].isDirectory())
+      assert.isTrue(files[1].isDirectory())
+
+      fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.txn'))
+      })
+
+      fs.readdir(path.join(mirroredPath, '_versions'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.manifest'))
+      })
+
+      fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.lance'))
+      })
+    })
+
+    // try create index and check if it's mirrored
+    await t.createIndex({ column: 'vector', type: 'ivf_pq' })
+
+    fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
+      if (err != null) throw err
+      // there should be four dirs
+      assert.equal(files.length, 4)
+      assert.isTrue(files[0].isDirectory())
+      assert.isTrue(files[1].isDirectory())
+      assert.isTrue(files[2].isDirectory())
+
+      // Two TXs now
+      fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 2)
+        assert.isTrue(files[0].name.endsWith('.txn'))
+        assert.isTrue(files[1].name.endsWith('.txn'))
+      })
+
+      fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.lance'))
+      })
+
+      fs.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].isDirectory())
+
+        fs.readdir(path.join(mirroredPath, '_indices', files[0].name), { withFileTypes: true }, (err, files) => {
+          if (err != null) throw err
+
+          assert.equal(files.length, 1)
+          assert.isTrue(files[0].isFile())
+          assert.isTrue(files[0].name.endsWith('.idx'))
+        })
+      })
+    })
+
+    // try delete and check if it's mirrored
+    await t.delete('id = 0')
+
+    fs.readdir(mirroredPath, { withFileTypes: true }, (err, files) => {
+      if (err != null) throw err
+      // there should be five dirs
+      assert.equal(files.length, 5)
+      assert.isTrue(files[0].isDirectory())
+      assert.isTrue(files[1].isDirectory())
+      assert.isTrue(files[2].isDirectory())
+      assert.isTrue(files[3].isDirectory())
+      assert.isTrue(files[4].isDirectory())
+
+      // Three TXs now
+      fs.readdir(path.join(mirroredPath, '_transactions'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 3)
+        assert.isTrue(files[0].name.endsWith('.txn'))
+        assert.isTrue(files[1].name.endsWith('.txn'))
+      })
+
+      fs.readdir(path.join(mirroredPath, 'data'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.lance'))
+      })
+
+      fs.readdir(path.join(mirroredPath, '_indices'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].isDirectory())
+
+        fs.readdir(path.join(mirroredPath, '_indices', files[0].name), { withFileTypes: true }, (err, files) => {
+          if (err != null) throw err
+
+          assert.equal(files.length, 1)
+          assert.isTrue(files[0].isFile())
+          assert.isTrue(files[0].name.endsWith('.idx'))
+        })
+      })
+
+      fs.readdir(path.join(mirroredPath, '_deletions'), { withFileTypes: true }, (err, files) => {
+        if (err != null) throw err
+        assert.equal(files.length, 1)
+        assert.isTrue(files[0].name.endsWith('.arrow'))
+      })
+    })
+  })
+})

node/src/query.ts

@@ -14,16 +14,10 @@
 
 import { Vector, tableFromIPC } from 'apache-arrow'
 import { type EmbeddingFunction } from './embedding/embedding_function'
-import { type MetricType } from './types'
+import { type MetricType } from '.'
 
 // eslint-disable-next-line @typescript-eslint/no-var-requires
-// const { tableSearch } = require('../native.js')
-
-const tableSearch = async function (args: any, arg2: any): Promise<any> {
-  return await new Promise((resolve, reject) => {
-    resolve('')
-  })
-}
+const { tableSearch } = require('../native.js')
 
 /**
  * A builder for nearest neighbor queries for LanceDB.

node/src/remote/index.ts

@@ -13,15 +13,12 @@
 // limitations under the License.
 
 import {
-  type Table, type VectorIndexParams,
-  type VectorIndex,
+  type EmbeddingFunction, type Table, type VectorIndexParams, type Connection,
+  type ConnectionOptions, type CreateTableOptions, type VectorIndex,
+  type WriteOptions,
   type IndexStats,
-  type UpdateArgs, type UpdateSqlArgs,
-  type Connection,
-  type ConnectionOptions, type CreateTableOptions,
-  type WriteOptions
-} from '../types'
-import { type EmbeddingFunction } from '../embedding/embedding_function'
+  type UpdateArgs, type UpdateSqlArgs
+} from '../index'
 import { Query } from '../query'
 
 import { Vector, Table as ArrowTable } from 'apache-arrow'

node/src/test/embedding/openai.ts

@@ -0,0 +1,57 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { describe } from 'mocha'
+import { assert } from 'chai'
+
+import { OpenAIEmbeddingFunction } from '../../embedding/openai'
+import { isEmbeddingFunction } from '../../embedding/embedding_function'
+
+// eslint-disable-next-line @typescript-eslint/no-var-requires
+const { OpenAIApi } = require('openai')
+// eslint-disable-next-line @typescript-eslint/no-var-requires
+const { stub } = require('sinon')
+
+describe('OpenAPIEmbeddings', function () {
+  const stubValue = {
+    data: {
+      data: [
+        {
+          embedding: Array(1536).fill(1.0)
+        },
+        {
+          embedding: Array(1536).fill(2.0)
+        }
+      ]
+    }
+  }
+
+  describe('#embed', function () {
+    it('should create vector embeddings', async function () {
+      const openAIStub = stub(OpenAIApi.prototype, 'createEmbedding').returns(stubValue)
+      const f = new OpenAIEmbeddingFunction('text', 'sk-key')
+      const vectors = await f.embed(['abc', 'def'])
+      assert.isTrue(openAIStub.calledOnce)
+      assert.equal(vectors.length, 2)
+      assert.deepEqual(vectors[0], stubValue.data.data[0].embedding)
+      assert.deepEqual(vectors[1], stubValue.data.data[1].embedding)
+    })
+  })
+
+  describe('isEmbeddingFunction', function () {
+    it('should match the isEmbeddingFunction guard', function () {
+      assert.isTrue(isEmbeddingFunction(new OpenAIEmbeddingFunction('text', 'sk-key')))
+    })
+  })
+})

node/src/test/io.ts

@@ -0,0 +1,76 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+// IO tests
+
+import { describe } from 'mocha'
+import { assert } from 'chai'
+
+import * as lancedb from '../index'
+import { type ConnectionOptions } from '../index'
+
+describe('LanceDB S3 client', function () {
+  if (process.env.TEST_S3_BASE_URL != null) {
+    const baseUri = process.env.TEST_S3_BASE_URL
+    it('should have a valid url', async function () {
+      const opts = { uri: `${baseUri}/valid_url` }
+      const table = await createTestDB(opts, 2, 20)
+      const con = await lancedb.connect(opts)
+      assert.equal(con.uri, opts.uri)
+
+      const results = await table.search([0.1, 0.3]).limit(5).execute()
+      assert.equal(results.length, 5)
+    }).timeout(10_000)
+  } else {
+    describe.skip('Skip S3 test', function () {})
+  }
+
+  if (process.env.TEST_S3_BASE_URL != null && process.env.TEST_AWS_ACCESS_KEY_ID != null && process.env.TEST_AWS_SECRET_ACCESS_KEY != null) {
+    const baseUri = process.env.TEST_S3_BASE_URL
+    it('use custom credentials', async function () {
+      const opts: ConnectionOptions = {
+        uri: `${baseUri}/custom_credentials`,
+        awsCredentials: {
+          accessKeyId: process.env.TEST_AWS_ACCESS_KEY_ID as string,
+          secretKey: process.env.TEST_AWS_SECRET_ACCESS_KEY as string
+        }
+      }
+      const table = await createTestDB(opts, 2, 20)
+      console.log(table)
+      const con = await lancedb.connect(opts)
+      console.log(con)
+      assert.equal(con.uri, opts.uri)
+
+      const results = await table.search([0.1, 0.3]).limit(5).execute()
+      assert.equal(results.length, 5)
+    }).timeout(10_000)
+  } else {
+    describe.skip('Skip S3 test', function () {})
+  }
+})
+
+async function createTestDB (opts: ConnectionOptions, numDimensions: number = 2, numRows: number = 2): Promise<lancedb.Table> {
+  const con = await lancedb.connect(opts)
+
+  const data = []
+  for (let i = 0; i < numRows; i++) {
+    const vector = []
+    for (let j = 0; j < numDimensions; j++) {
+      vector.push(i + (j * 0.1))
+    }
+    data.push({ id: i + 1, name: `name_${i}`, price: i + 10, is_active: (i % 2 === 0), vector })
+  }
+
+  return await con.createTable('vectors_2', data)
+}

node/src/test/test.ts

@@ -0,0 +1,616 @@
+// Copyright 2023 LanceDB Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { describe } from 'mocha'
+import { track } from 'temp'
+import * as chai from 'chai'
+import * as chaiAsPromised from 'chai-as-promised'
+
+import * as lancedb from '../index'
+import { type AwsCredentials, type EmbeddingFunction, MetricType, Query, WriteMode, DefaultWriteOptions, isWriteOptions, type LocalTable } from '../index'
+import { FixedSizeList, Field, Int32, makeVector, Schema, Utf8, Table as ArrowTable, vectorFromArray, Float32 } from 'apache-arrow'
+
+const expect = chai.expect
+const assert = chai.assert
+chai.use(chaiAsPromised)
+
+describe('LanceDB client', function () {
+  describe('when creating a connection to lancedb', function () {
+    it('should have a valid url', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+      assert.equal(con.uri, uri)
+    })
+
+    it('should accept an options object', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect({ uri })
+      assert.equal(con.uri, uri)
+    })
+
+    it('should accept custom aws credentials', async function () {
+      const uri = await createTestDB()
+      const awsCredentials: AwsCredentials = {
+        accessKeyId: '',
+        secretKey: ''
+      }
+      const con = await lancedb.connect({ uri, awsCredentials })
+      assert.equal(con.uri, uri)
+    })
+
+    it('should return the existing table names', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+  })
+
+  describe('when querying an existing dataset', function () {
+    it('should open a table', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      assert.equal(table.name, 'vectors')
+    })
+
+    it('execute a query', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      const results = await table.search([0.1, 0.3]).execute()
+
+      assert.equal(results.length, 2)
+      assert.equal(results[0].price, 10)
+      const vector = results[0].vector as Float32Array
+      assert.approximately(vector[0], 0.0, 0.2)
+      assert.approximately(vector[0], 0.1, 0.3)
+    })
+
+    it('limits # of results', async function () {
+      const uri = await createTestDB(2, 100)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      let results = await table.search([0.1, 0.3]).limit(1).execute()
+      assert.equal(results.length, 1)
+      assert.equal(results[0].id, 1)
+
+      // there is a default limit if unspecified
+      results = await table.search([0.1, 0.3]).execute()
+      assert.equal(results.length, 10)
+    })
+
+    it('uses a filter / where clause without vector search', async function () {
+      // eslint-disable-next-line @typescript-eslint/explicit-function-return-type
+      const assertResults = (results: Array<Record<string, unknown>>) => {
+        assert.equal(results.length, 50)
+      }
+
+      const uri = await createTestDB(2, 100)
+      const con = await lancedb.connect(uri)
+      const table = (await con.openTable('vectors')) as LocalTable
+      let results = await table.filter('id % 2 = 0').execute()
+      assertResults(results)
+      results = await table.where('id % 2 = 0').execute()
+      assertResults(results)
+    })
+
+    it('uses a filter / where clause', async function () {
+      // eslint-disable-next-line @typescript-eslint/explicit-function-return-type
+      const assertResults = (results: Array<Record<string, unknown>>) => {
+        assert.equal(results.length, 1)
+        assert.equal(results[0].id, 2)
+      }
+
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      let results = await table.search([0.1, 0.1]).filter('id == 2').execute()
+      assertResults(results)
+      results = await table.search([0.1, 0.1]).where('id == 2').execute()
+      assertResults(results)
+    })
+
+    it('should correctly process prefilter/postfilter', async function () {
+      const uri = await createTestDB(16, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
+      // post filter should return less than the limit
+      let results = await table.search(new Array(16).fill(0.1)).limit(10).filter('id >= 10').prefilter(false).execute()
+      assert.isTrue(results.length < 10)
+
+      // pre filter should return exactly the limit
+      results = await table.search(new Array(16).fill(0.1)).limit(10).filter('id >= 10').prefilter(true).execute()
+      assert.isTrue(results.length === 10)
+    })
+
+    it('select only a subset of columns', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      const results = await table.search([0.1, 0.1]).select(['is_active']).execute()
+      assert.equal(results.length, 2)
+      // vector and _distance are always returned
+      assert.isDefined(results[0].vector)
+      assert.isDefined(results[0]._distance)
+      assert.isDefined(results[0].is_active)
+
+      assert.isUndefined(results[0].id)
+      assert.isUndefined(results[0].name)
+      assert.isUndefined(results[0].price)
+    })
+  })
+
+  describe('when creating a new dataset', function () {
+    it('create an empty table', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('id', new Int32()), new Field('name', new Utf8())]
+      )
+      const table = await con.createTable({ name: 'vectors', schema })
+      assert.equal(table.name, 'vectors')
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+
+    it('create a table with a empty data array', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('id', new Int32()), new Field('name', new Utf8())]
+      )
+      const table = await con.createTable({ name: 'vectors', schema, data: [] })
+      assert.equal(table.name, 'vectors')
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+
+    it('create a table from an Arrow Table', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const i32s = new Int32Array(new Array<number>(10))
+      const i32 = makeVector(i32s)
+
+      const data = new ArrowTable({ vector: i32 })
+
+      const table = await con.createTable({ name: 'vectors', data })
+      assert.equal(table.name, 'vectors')
+      assert.equal(await table.countRows(), 10)
+      assert.deepEqual(await con.tableNames(), ['vectors'])
+    })
+
+    it('creates a new table from javascript objects', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const data = [
+        { id: 1, vector: [0.1, 0.2], price: 10 },
+        { id: 2, vector: [1.1, 1.2], price: 50 }
+      ]
+
+      const tableName = `vectors_${Math.floor(Math.random() * 100)}`
+      const table = await con.createTable(tableName, data)
+      assert.equal(table.name, tableName)
+      assert.equal(await table.countRows(), 2)
+    })
+
+    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)
+
+      const data = [
+        { id: 1, vector: [0.1, 0.2], price: 10 },
+        { id: 2, vector: [1.1, 1.2], price: 50 }
+      ]
+
+      const tableName = 'overwrite'
+      await con.createTable(tableName, data, { writeMode: WriteMode.Create })
+
+      const newData = [
+        { id: 1, vector: [0.1, 0.2], price: 10 },
+        { id: 2, vector: [1.1, 1.2], price: 50 },
+        { id: 3, vector: [1.1, 1.2], price: 50 }
+      ]
+
+      await expect(con.createTable(tableName, newData)).to.be.rejectedWith(Error, 'already exists')
+
+      const table = await con.createTable(tableName, newData, { writeMode: WriteMode.Overwrite })
+      assert.equal(table.name, tableName)
+      assert.equal(await table.countRows(), 3)
+    })
+
+    it('appends records to an existing table ', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const data = [
+        { id: 1, vector: [0.1, 0.2], price: 10, name: 'a' },
+        { id: 2, vector: [1.1, 1.2], price: 50, name: 'b' }
+      ]
+
+      const table = await con.createTable('vectors', data)
+      assert.equal(await table.countRows(), 2)
+
+      const dataAdd = [
+        { id: 3, vector: [2.1, 2.2], price: 10, name: 'c' },
+        { id: 4, vector: [3.1, 3.2], price: 50, name: 'd' }
+      ]
+      await table.add(dataAdd)
+      assert.equal(await table.countRows(), 4)
+    })
+
+    it('overwrite all records in a table', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+
+      const table = await con.openTable('vectors')
+      assert.equal(await table.countRows(), 2)
+
+      const dataOver = [
+        { vector: [2.1, 2.2], price: 10, name: 'foo' },
+        { vector: [3.1, 3.2], price: 50, name: 'bar' }
+      ]
+      await table.overwrite(dataOver)
+      assert.equal(await table.countRows(), 2)
+    })
+
+    it('can update records in the table', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+
+      const table = await con.openTable('vectors')
+      assert.equal(await table.countRows(), 2)
+
+      await table.update({ where: 'price = 10', valuesSql: { price: '100' } })
+      const results = await table.search([0.1, 0.2]).execute()
+      assert.equal(results[0].price, 100)
+      assert.equal(results[1].price, 11)
+    })
+
+    it('can update the records using a literal value', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+
+      const table = await con.openTable('vectors')
+      assert.equal(await table.countRows(), 2)
+
+      await table.update({ where: 'price = 10', values: { price: 100 } })
+      const results = await table.search([0.1, 0.2]).execute()
+      assert.equal(results[0].price, 100)
+      assert.equal(results[1].price, 11)
+    })
+
+    it('can update every record in the table', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+
+      const table = await con.openTable('vectors')
+      assert.equal(await table.countRows(), 2)
+
+      await table.update({ valuesSql: { price: '100' } })
+      const results = await table.search([0.1, 0.2]).execute()
+
+      assert.equal(results[0].price, 100)
+      assert.equal(results[1].price, 100)
+    })
+
+    it('can delete records from a table', async function () {
+      const uri = await createTestDB()
+      const con = await lancedb.connect(uri)
+
+      const table = await con.openTable('vectors')
+      assert.equal(await table.countRows(), 2)
+
+      await table.delete('price = 10')
+      assert.equal(await table.countRows(), 1)
+    })
+  })
+
+  describe('when searching an empty dataset', function () {
+    it('should not fail', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('vector', new FixedSizeList(128, new Field('float32', new Float32())))]
+      )
+      const table = await con.createTable({ name: 'vectors', schema })
+      const result = await table.search(Array(128).fill(0.1)).execute()
+      assert.isEmpty(result)
+    })
+  })
+
+  describe('when searching an empty-after-delete dataset', function () {
+    it('should not fail', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+
+      const schema = new Schema(
+        [new Field('vector', new FixedSizeList(128, new Field('float32', new Float32())))]
+      )
+      const table = await con.createTable({ name: 'vectors', schema })
+      await table.add([{ vector: Array(128).fill(0.1) }])
+      // https://github.com/lancedb/lance/issues/1635
+      await table.delete('true')
+      const result = await table.search(Array(128).fill(0.1)).execute()
+      assert.isEmpty(result)
+    })
+  })
+
+  describe('when creating a vector index', function () {
+    it('overwrite all records in a table', async function () {
+      const uri = await createTestDB(32, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
+    }).timeout(10_000) // Timeout is high partially because GH macos runner is pretty slow
+
+    it('replace an existing index', async function () {
+      const uri = await createTestDB(16, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+
+      await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
+
+      // Replace should fail if the index already exists
+      await expect(table.createIndex({
+        type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2, replace: false
+      })
+      ).to.be.rejectedWith('LanceError(Index)')
+
+      // Default replace = true
+      await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
+    }).timeout(50_000)
+
+    it('it should fail when the column is not a vector', async function () {
+      const uri = await createTestDB(32, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      const createIndex = table.createIndex({ type: 'ivf_pq', column: 'name', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
+      await expect(createIndex).to.be.rejectedWith(/VectorIndex requires the column data type to be fixed size list of float32s/)
+    })
+
+    it('it should fail when the column is not a vector', async function () {
+      const uri = await createTestDB(32, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      const createIndex = table.createIndex({ type: 'ivf_pq', column: 'name', num_partitions: -1, max_iters: 2, num_sub_vectors: 2 })
+      await expect(createIndex).to.be.rejectedWith('num_partitions: must be > 0')
+    })
+
+    it('should be able to list index and stats', async function () {
+      const uri = await createTestDB(32, 300)
+      const con = await lancedb.connect(uri)
+      const table = await con.openTable('vectors')
+      await table.createIndex({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2, num_sub_vectors: 2 })
+
+      const indices = await table.listIndices()
+      expect(indices).to.have.lengthOf(1)
+      expect(indices[0].name).to.equal('vector_idx')
+      expect(indices[0].uuid).to.not.be.equal(undefined)
+      expect(indices[0].columns).to.have.lengthOf(1)
+      expect(indices[0].columns[0]).to.equal('vector')
+
+      const stats = await table.indexStats(indices[0].uuid)
+      expect(stats.numIndexedRows).to.equal(300)
+      expect(stats.numUnindexedRows).to.equal(0)
+    }).timeout(50_000)
+  })
+
+  describe('when using a custom embedding function', function () {
+    class TextEmbedding implements EmbeddingFunction<string> {
+      sourceColumn: string
+
+      constructor (targetColumn: string) {
+        this.sourceColumn = targetColumn
+      }
+
+      _embedding_map = new Map<string, number[]>([
+        ['foo', [2.1, 2.2]],
+        ['bar', [3.1, 3.2]]
+      ])
+
+      async embed (data: string[]): Promise<number[][]> {
+        return data.map(datum => this._embedding_map.get(datum) ?? [0.0, 0.0])
+      }
+    }
+
+    it('should encode the original data into embeddings', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+      const embeddings = new TextEmbedding('name')
+
+      const data = [
+        { price: 10, name: 'foo' },
+        { price: 50, name: 'bar' }
+      ]
+      const table = await con.createTable('vectors', data, embeddings, { writeMode: WriteMode.Create })
+      const results = await table.search('foo').execute()
+      assert.equal(results.length, 2)
+    })
+
+    it('should create embeddings for Arrow Table', async function () {
+      const dir = await track().mkdir('lancejs')
+      const con = await lancedb.connect(dir)
+      const embeddingFunction = new TextEmbedding('name')
+
+      const names = vectorFromArray(['foo', 'bar'], new Utf8())
+      const data = new ArrowTable({ name: names })
+
+      const table = await con.createTable({ name: 'vectors', data, embeddingFunction })
+      assert.equal(table.name, 'vectors')
+      const results = await table.search('foo').execute()
+      assert.equal(results.length, 2)
+    })
+  })
+})
+
+describe('Remote LanceDB client', function () {
+  describe('when the server is not reachable', function () {
+    it('produces a network error', async function () {
+      const con = await lancedb.connect({
+        uri: 'db://test-1234',
+        region: 'asdfasfasfdf',
+        apiKey: 'some-api-key'
+      })
+
+      // GET
+      try {
+        await con.tableNames()
+      } catch (err) {
+        expect(err).to.have.property('message', 'Network Error: getaddrinfo ENOTFOUND test-1234.asdfasfasfdf.api.lancedb.com')
+      }
+
+      // POST
+      try {
+        await con.createTable({ name: 'vectors', schema: new Schema([]) })
+      } catch (err) {
+        expect(err).to.have.property('message', 'Network Error: getaddrinfo ENOTFOUND test-1234.asdfasfasfdf.api.lancedb.com')
+      }
+
+      // Search
+      const table = await con.openTable('vectors')
+      try {
+        await table.search([0.1, 0.3]).execute()
+      } catch (err) {
+        expect(err).to.have.property('message', 'Network Error: getaddrinfo ENOTFOUND test-1234.asdfasfasfdf.api.lancedb.com')
+      }
+    })
+  })
+})
+
+describe('Query object', function () {
+  it('sets custom parameters', async function () {
+    const query = new Query([0.1, 0.3])
+      .limit(1)
+      .metricType(MetricType.Cosine)
+      .refineFactor(100)
+      .select(['a', 'b'])
+      .nprobes(20) as Record<string, any>
+    assert.equal(query._limit, 1)
+    assert.equal(query._metricType, MetricType.Cosine)
+    assert.equal(query._refineFactor, 100)
+    assert.equal(query._nprobes, 20)
+    assert.deepEqual(query._select, ['a', 'b'])
+  })
+})
+
+async function createTestDB (numDimensions: number = 2, numRows: number = 2): Promise<string> {
+  const dir = await track().mkdir('lancejs')
+  const con = await lancedb.connect(dir)
+
+  const data = []
+  for (let i = 0; i < numRows; i++) {
+    const vector = []
+    for (let j = 0; j < numDimensions; j++) {
+      vector.push(i + (j * 0.1))
+    }
+    data.push({ id: i + 1, name: `name_${i}`, price: i + 10, is_active: (i % 2 === 0), vector })
+  }
+
+  await con.createTable('vectors', data)
+  return dir
+}
+
+describe('Drop table', function () {
+  it('drop a table', async function () {
+    const dir = await track().mkdir('lancejs')
+    const con = await lancedb.connect(dir)
+
+    const data = [
+      { price: 10, name: 'foo', vector: [1, 2, 3] },
+      { price: 50, name: 'bar', vector: [4, 5, 6] }
+    ]
+    await con.createTable('t1', data)
+    await con.createTable('t2', data)
+
+    assert.deepEqual(await con.tableNames(), ['t1', 't2'])
+
+    await con.dropTable('t1')
+    assert.deepEqual(await con.tableNames(), ['t2'])
+  })
+})
+
+describe('WriteOptions', function () {
+  context('#isWriteOptions', function () {
+    it('should not match empty object', function () {
+      assert.equal(isWriteOptions({}), false)
+    })
+    it('should match write options', function () {
+      assert.equal(isWriteOptions({ writeMode: WriteMode.Create }), true)
+    })
+    it('should match undefined write mode', function () {
+      assert.equal(isWriteOptions({ writeMode: undefined }), true)
+    })
+    it('should match default write options', function () {
+      assert.equal(isWriteOptions(new DefaultWriteOptions()), true)
+    })
+  })
+})
+
+describe('Compact and cleanup', function () {
+  it('can cleanup after compaction', async function () {
+    const dir = await track().mkdir('lancejs')
+    const con = await lancedb.connect(dir)
+
+    const data = [
+      { price: 10, name: 'foo', vector: [1, 2, 3] },
+      { price: 50, name: 'bar', vector: [4, 5, 6] }
+    ]
+    const table = await con.createTable('t1', data) as LocalTable
+
+    const newData = [
+      { price: 30, name: 'baz', vector: [7, 8, 9] }
+    ]
+    await table.add(newData)
+
+    const compactionMetrics = await table.compactFiles({
+      numThreads: 2
+    })
+    assert.equal(compactionMetrics.fragmentsRemoved, 2)
+    assert.equal(compactionMetrics.fragmentsAdded, 1)
+    assert.equal(await table.countRows(), 3)
+
+    await table.cleanupOldVersions()
+    assert.equal(await table.countRows(), 3)
+
+    // should have no effect, but this validates the arguments are parsed.
+    await table.compactFiles({
+      targetRowsPerFragment: 102410,
+      maxRowsPerGroup: 1024,
+      materializeDeletions: true,
+      materializeDeletionsThreshold: 0.5,
+      numThreads: 2
+    })
+
+    const cleanupMetrics = await table.cleanupOldVersions(0, true)
+    assert.isAtLeast(cleanupMetrics.bytesRemoved, 1)
+    assert.isAtLeast(cleanupMetrics.oldVersions, 1)
+    assert.equal(await table.countRows(), 3)
+  })
+})

node/src/test/util.ts

@@ -0,0 +1,45 @@
+// Copyright 2023 LanceDB Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+import { toSQL } from '../util'
+import * as chai from 'chai'
+
+const expect = chai.expect
+
+describe('toSQL', function () {
+  it('should turn string to SQL expression', function () {
+    expect(toSQL('foo')).to.equal("'foo'")
+  })
+
+  it('should turn number to SQL expression', function () {
+    expect(toSQL(123)).to.equal('123')
+  })
+
+  it('should turn boolean to SQL expression', function () {
+    expect(toSQL(true)).to.equal('TRUE')
+  })
+
+  it('should turn null to SQL expression', function () {
+    expect(toSQL(null)).to.equal('NULL')
+  })
+
+  it('should turn Date to SQL expression', function () {
+    const date = new Date('05 October 2011 14:48 UTC')
+    expect(toSQL(date)).to.equal("'2011-10-05T14:48:00.000Z'")
+  })
+
+  it('should turn array to SQL expression', function () {
+    expect(toSQL(['foo', 'bar', true, 1])).to.equal("['foo', 'bar', TRUE, 1]")
+  })
+})

node/src/types.ts

@@ -1,375 +0,0 @@
-
-import {
-  type Schema,
-  type Table as ArrowTable
-} from 'apache-arrow'
-
-import { type Literal } from './util'
-import type { EmbeddingFunction } from './embedding/embedding_function'
-import { type Query } from './query'
-
-export interface AwsCredentials {
-  accessKeyId: string
-
-  secretKey: string
-
-  sessionToken?: string
-}
-
-/**
- * Write options when creating a Table.
- */
-export interface WriteOptions {
-  /** A {@link WriteMode} to use on this operation */
-  writeMode?: WriteMode
-}
-
-/**
- * Write mode for writing a table.
- */
-export enum WriteMode {
-  /** Create a new {@link Table}. */
-  Create = 'create',
-  /** Overwrite the existing {@link Table} if presented. */
-  Overwrite = 'overwrite',
-  /** Append new data to the table. */
-  Append = 'append'
-}
-
-/**
- * A LanceDB Connection that allows you to open tables and create new ones.
- *
- * Connection could be local against filesystem or remote against a server.
- */
-export interface Connection {
-  uri: string
-
-  tableNames(): Promise<string[]>
-
-  /**
-    * Open a table in the database.
-    *
-    * @param name The name of the table.
-    * @param embeddings An embedding function to use on this table
-    */
-  openTable<T>(name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>>
-
-  /**
-    * Creates a new Table, optionally initializing it with new data.
-    *
-    * @param {string} name - The name of the table.
-    * @param data - Array of Records to be inserted into the table
-    * @param schema - An Arrow Schema that describe this table columns
-    * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
-    * @param {WriteOptions} writeOptions - The write options to use when creating the table.
-    */
-  createTable<T> ({ name, data, schema, embeddingFunction, writeOptions }: CreateTableOptions<T>): Promise<Table<T>>
-
-  /**
-    * Creates a new Table and initialize it with new data.
-    *
-    * @param {string} name - The name of the table.
-    * @param data - Non-empty Array of Records to be inserted into the table
-    */
-  createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table>
-
-  /**
-    * Creates a new Table and initialize it with new data.
-    *
-    * @param {string} name - The name of the table.
-    * @param data - Non-empty Array of Records to be inserted into the table
-    * @param {WriteOptions} options - The write options to use when creating the table.
-    */
-  createTable (name: string, data: Array<Record<string, unknown>>, options: WriteOptions): Promise<Table>
-
-  /**
-    * Creates a new Table and initialize it with new data.
-    *
-    * @param {string} name - The name of the table.
-    * @param data - Non-empty Array of Records to be inserted into the table
-    * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
-    */
-  createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
-  /**
-    * Creates a new Table and initialize it with new data.
-    *
-    * @param {string} name - The name of the table.
-    * @param data - Non-empty Array of Records to be inserted into the table
-    * @param {EmbeddingFunction} embeddings - An embedding function to use on this table
-    * @param {WriteOptions} options - The write options to use when creating the table.
-    */
-  createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>, options: WriteOptions): Promise<Table<T>>
-
-  /**
-    * Drop an existing table.
-    * @param name The name of the table to drop.
-    */
-  dropTable(name: string): Promise<void>
-
-}
-
-export interface CreateTableOptions<T> {
-  // Name of Table
-  name: string
-
-  // Data to insert into the Table
-  data?: Array<Record<string, unknown>> | ArrowTable | undefined
-
-  // Optional Arrow Schema for this table
-  schema?: Schema | undefined
-
-  // Optional embedding function used to create embeddings
-  embeddingFunction?: EmbeddingFunction<T> | undefined
-
-  // WriteOptions for this operation
-  writeOptions?: WriteOptions | undefined
-}
-
-export interface ConnectionOptions {
-  uri: string
-
-  awsCredentials?: AwsCredentials
-
-  awsRegion?: string
-
-  // API key for the remote connections
-  apiKey?: string
-  // Region to connect
-  region?: string
-
-  // override the host for the remote connections
-  hostOverride?: string
-}
-
-/**
- * Distance metrics type.
- */
-export enum MetricType {
-  /**
-    * Euclidean distance
-    */
-  L2 = 'l2',
-
-  /**
-    * Cosine distance
-    */
-  Cosine = 'cosine',
-
-  /**
-    * Dot product
-    */
-  Dot = 'dot'
-}
-
-/// Config to build IVF_PQ index.
-///
-export interface IvfPQIndexConfig {
-  /**
-    * The column to be indexed
-    */
-  column?: string
-
-  /**
-    * A unique name for the index
-    */
-  index_name?: string
-
-  /**
-    * Metric type, L2 or Cosine
-    */
-  metric_type?: MetricType
-
-  /**
-    * The number of partitions this index
-    */
-  num_partitions?: number
-
-  /**
-    * The max number of iterations for kmeans training.
-    */
-  max_iters?: number
-
-  /**
-    * Train as optimized product quantization.
-    */
-  use_opq?: boolean
-
-  /**
-    * Number of subvectors to build PQ code
-    */
-  num_sub_vectors?: number
-  /**
-    * The number of bits to present one PQ centroid.
-    */
-  num_bits?: number
-
-  /**
-    * Max number of iterations to train OPQ, if `use_opq` is true.
-    */
-  max_opq_iters?: number
-
-  /**
-    * Replace an existing index with the same name if it exists.
-    */
-  replace?: boolean
-
-  type: 'ivf_pq'
-}
-
-export type VectorIndexParams = IvfPQIndexConfig
-
-/**
- * A LanceDB Table is the collection of Records. Each Record has one or more vector fields.
- */
-export interface Table<T = number[]> {
-  name: string
-
-  /**
-     * Creates a search query to find the nearest neighbors of the given search term
-     * @param query The query search term
-     */
-  search: (query: T) => Query<T>
-
-  /**
-     * Insert records into this Table.
-     *
-     * @param data Records to be inserted into the Table
-     * @return The number of rows added to the table
-     */
-  add: (data: Array<Record<string, unknown>>) => Promise<number>
-
-  /**
-     * Insert records into this Table, replacing its contents.
-     *
-     * @param data Records to be inserted into the Table
-     * @return The number of rows added to the table
-     */
-  overwrite: (data: Array<Record<string, unknown>>) => Promise<number>
-
-  /**
-     * Create an ANN index on this Table vector index.
-     *
-     * @param indexParams The parameters of this Index, @see VectorIndexParams.
-     */
-  createIndex: (indexParams: VectorIndexParams) => Promise<any>
-
-  /**
-     * Returns the number of rows in this table.
-     */
-  countRows: () => Promise<number>
-
-  /**
-     * Delete rows from this table.
-     *
-     * This can be used to delete a single row, many rows, all rows, or
-     * sometimes no rows (if your predicate matches nothing).
-     *
-     * @param filter  A filter in the same format used by a sql WHERE clause. The
-     *                filter must not be empty.
-     *
-     * @examples
-     *
-     * ```ts
-     * const con = await lancedb.connect("./.lancedb")
-     * const data = [
-     *    {id: 1, vector: [1, 2]},
-     *    {id: 2, vector: [3, 4]},
-     *    {id: 3, vector: [5, 6]},
-     * ];
-     * const tbl = await con.createTable("my_table", data)
-     * await tbl.delete("id = 2")
-     * await tbl.countRows() // Returns 2
-     * ```
-     *
-     * If you have a list of values to delete, you can combine them into a
-     * stringified list and use the `IN` operator:
-     *
-     * ```ts
-     * const to_remove = [1, 5];
-     * await tbl.delete(`id IN (${to_remove.join(",")})`)
-     * await tbl.countRows() // Returns 1
-     * ```
-     */
-  delete: (filter: string) => Promise<void>
-
-  /**
-     * Update rows in this table.
-     *
-     * This can be used to update a single row, many rows, all rows, or
-     * sometimes no rows (if your predicate matches nothing).
-     *
-     * @param args see {@link UpdateArgs} and {@link UpdateSqlArgs} for more details
-     *
-     * @examples
-     *
-     * ```ts
-     * const con = await lancedb.connect("./.lancedb")
-     * const data = [
-     *    {id: 1, vector: [3, 3], name: 'Ye'},
-     *    {id: 2, vector: [4, 4], name: 'Mike'},
-     * ];
-     * const tbl = await con.createTable("my_table", data)
-     *
-     * await tbl.update({
-     *   filter: "id = 2",
-     *   updates: { vector: [2, 2], name: "Michael" },
-     * })
-     *
-     * let results = await tbl.search([1, 1]).execute();
-     * // Returns [
-     * //   {id: 2, vector: [2, 2], name: 'Michael'}
-     * //   {id: 1, vector: [3, 3], name: 'Ye'}
-     * // ]
-     * ```
-     *
-     */
-  update: (args: UpdateArgs | UpdateSqlArgs) => Promise<void>
-
-  /**
-     * List the indicies on this table.
-     */
-  listIndices: () => Promise<VectorIndex[]>
-
-  /**
-     * Get statistics about an index.
-     */
-  indexStats: (indexUuid: string) => Promise<IndexStats>
-}
-export interface UpdateArgs {
-  /**
-     * A filter in the same format used by a sql WHERE clause. The filter may be empty,
-     * in which case all rows will be updated.
-     */
-  where?: string
-
-  /**
-     * A key-value map of updates. The keys are the column names, and the values are the
-     * new values to set
-     */
-  values: Record<string, Literal>
-}
-
-export interface UpdateSqlArgs {
-  /**
-     * A filter in the same format used by a sql WHERE clause. The filter may be empty,
-     * in which case all rows will be updated.
-     */
-  where?: string
-
-  /**
-     * A key-value map of updates. The keys are the column names, and the values are the
-     * new values to set as SQL expressions.
-     */
-  valuesSql: Record<string, string>
-}
-
-export interface VectorIndex {
-  columns: string[]
-  name: string
-  uuid: string
-}
-
-export interface IndexStats {
-  numIndexedRows: number | null
-  numUnindexedRows: number | null
-}

python/.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.3.4
+current_version = 0.3.5
 commit = True
 message = [python] Bump version: {current_version} → {new_version}
 tag = True

python/lancedb/table.py

@@ -17,7 +17,7 @@ import inspect
 import os
 from abc import ABC, abstractmethod
 from functools import cached_property
-from typing import TYPE_CHECKING, Any, Iterable, List, Optional, Union
+from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Union
 
 import lance
 import numpy as np
@@ -30,7 +30,7 @@ from .common import DATA, VEC, VECTOR_COLUMN_NAME
 from .embeddings import EmbeddingFunctionConfig, EmbeddingFunctionRegistry
 from .pydantic import LanceModel
 from .query import LanceQueryBuilder, Query
-from .util import fs_from_uri, safe_import_pandas
+from .util import fs_from_uri, safe_import_pandas, value_to_sql
 from .utils.events import register_event
 
 if TYPE_CHECKING:
@@ -913,30 +913,35 @@ class LanceTable(Table):
     def delete(self, where: str):
         self._dataset.delete(where)
 
-    def update(self, where: str, values: dict):
+    def update(
+        self,
+        where: Optional[str] = None,
+        values: Optional[dict] = None,
+        *,
+        values_sql: Optional[Dict[str, str]] = None,
+    ):
         """
-        EXPERIMENTAL: Update rows in the table (not threadsafe).
-
         This can be used to update zero to all rows depending on how many
         rows match the where clause.
 
         Parameters
         ----------
-        where: str
+        where: str, optional
             The SQL where clause to use when updating rows. For example, 'x = 2'
             or 'x IN (1, 2, 3)'. The filter must not be empty, or it will error.
-        values: dict
+        values: dict, optional
             The values to update. The keys are the column names and the values
             are the values to set.
+        values_sql: dict, optional
+            The values to update, expressed as SQL expression strings. These can
+            reference existing columns. For example, {"x": "x + 1"} will increment
+            the x column by 1.
 
         Examples
         --------
         >>> import lancedb
-        >>> data = [
-        ...    {"x": 1, "vector": [1, 2]},
-        ...    {"x": 2, "vector": [3, 4]},
-        ...    {"x": 3, "vector": [5, 6]}
-        ... ]
+        >>> import pandas as pd
+        >>> data = pd.DataFrame({"x": [1, 2, 3], "vector": [[1, 2], [3, 4], [5, 6]]})
         >>> db = lancedb.connect("./.lancedb")
         >>> table = db.create_table("my_table", data)
         >>> table.to_pandas()
@@ -952,18 +957,15 @@ class LanceTable(Table):
         2  2  [10.0, 10.0]
 
         """
-        orig_data = self._dataset.to_table(filter=where).combine_chunks()
-        if len(orig_data) == 0:
-            return
-        for col, val in values.items():
-            i = orig_data.column_names.index(col)
-            if i < 0:
-                raise ValueError(f"Column {col} does not exist")
-            orig_data = orig_data.set_column(
-                i, col, pa.array([val] * len(orig_data), type=orig_data[col].type)
-            )
-        self.delete(where)
-        self.add(orig_data, mode="append")
+        if values is not None and values_sql is not None:
+            raise ValueError("Only one of values or values_sql can be provided")
+        if values is None and values_sql is None:
+            raise ValueError("Either values or values_sql must be provided")
+
+        if values is not None:
+            values_sql = {k: value_to_sql(v) for k, v in values.items()}
+
+        self.to_lance().update(values_sql, where)
         self._reset_dataset()
         register_event("update")
 

python/lancedb/util.py

@@ -12,9 +12,12 @@
 #  limitations under the License.
 
 import os
+from datetime import date, datetime
+from functools import singledispatch
 from typing import Tuple
 from urllib.parse import urlparse
 
+import numpy as np
 import pyarrow.fs as pa_fs
 
 
@@ -88,3 +91,53 @@ def safe_import_pandas():
         return pd
     except ImportError:
         return None
+
+
+@singledispatch
+def value_to_sql(value):
+    raise NotImplementedError("SQL conversion is not implemented for this type")
+
+
+@value_to_sql.register(str)
+def _(value: str):
+    return f"'{value}'"
+
+
+@value_to_sql.register(int)
+def _(value: int):
+    return str(value)
+
+
+@value_to_sql.register(float)
+def _(value: float):
+    return str(value)
+
+
+@value_to_sql.register(bool)
+def _(value: bool):
+    return str(value).upper()
+
+
+@value_to_sql.register(type(None))
+def _(value: type(None)):
+    return "NULL"
+
+
+@value_to_sql.register(datetime)
+def _(value: datetime):
+    return f"'{value.isoformat()}'"
+
+
+@value_to_sql.register(date)
+def _(value: date):
+    return f"'{value.isoformat()}'"
+
+
+@value_to_sql.register(list)
+def _(value: list):
+    return "[" + ", ".join(map(value_to_sql, value)) + "]"
+
+
+@value_to_sql.register(np.ndarray)
+def _(value: np.ndarray):
+    return value_to_sql(value.tolist())

python/pyproject.toml

@@ -1,12 +1,12 @@
 [project]
 name = "lancedb"
-version = "0.3.4"
+version = "0.3.5"
 dependencies = [
     "deprecation",
-    "pylance==0.8.17",
+    "pylance==0.8.21",
     "ratelimiter~=1.0",
     "retry>=0.9.2",
-    "tqdm>=4.1.0",
+    "tqdm>=4.27.0",
     "aiohttp",
     "pydantic>=1.10",
     "attrs>=21.3.0",

python/tests/test_table.py

@@ -12,7 +12,7 @@
 #  limitations under the License.
 
 import functools
-from datetime import timedelta
+from datetime import date, datetime, timedelta
 from pathlib import Path
 from typing import List
 from unittest.mock import PropertyMock, patch
@@ -348,14 +348,79 @@ def test_update(db):
     assert len(table) == 2
     assert len(table.list_versions()) == 2
     table.update(where="id=0", values={"vector": [1.1, 1.1]})
-    assert len(table.list_versions()) == 4
-    assert table.version == 4
+    assert len(table.list_versions()) == 3
+    assert table.version == 3
     assert len(table) == 2
     v = table.to_arrow()["vector"].combine_chunks()
     v = v.values.to_numpy().reshape(2, 2)
     assert np.allclose(v, np.array([[1.2, 1.9], [1.1, 1.1]]))
 
 
+def test_update_types(db):
+    table = LanceTable.create(
+        db,
+        "my_table",
+        data=[
+            {
+                "id": 0,
+                "str": "foo",
+                "float": 1.1,
+                "timestamp": datetime(2021, 1, 1),
+                "date": date(2021, 1, 1),
+                "vector1": [1.0, 0.0],
+                "vector2": [1.0, 1.0],
+            }
+        ],
+    )
+    # Update with SQL
+    table.update(
+        values_sql=dict(
+            id="1",
+            str="'bar'",
+            float="2.2",
+            timestamp="TIMESTAMP '2021-01-02 00:00:00'",
+            date="DATE '2021-01-02'",
+            vector1="[2.0, 2.0]",
+            vector2="[3.0, 3.0]",
+        )
+    )
+    actual = table.to_arrow().to_pylist()[0]
+    expected = dict(
+        id=1,
+        str="bar",
+        float=2.2,
+        timestamp=datetime(2021, 1, 2),
+        date=date(2021, 1, 2),
+        vector1=[2.0, 2.0],
+        vector2=[3.0, 3.0],
+    )
+    assert actual == expected
+
+    # Update with values
+    table.update(
+        values=dict(
+            id=2,
+            str="baz",
+            float=3.3,
+            timestamp=datetime(2021, 1, 3),
+            date=date(2021, 1, 3),
+            vector1=[3.0, 3.0],
+            vector2=np.array([4.0, 4.0]),
+        )
+    )
+    actual = table.to_arrow().to_pylist()[0]
+    expected = dict(
+        id=2,
+        str="baz",
+        float=3.3,
+        timestamp=datetime(2021, 1, 3),
+        date=date(2021, 1, 3),
+        vector1=[3.0, 3.0],
+        vector2=[4.0, 4.0],
+    )
+    assert actual == expected
+
+
 def test_create_with_embedding_function(db):
     class MyTable(LanceModel):
         text: str