python v0.1.4

pypi does not allow packages to be uploaded that has a direct reference

for now we'll just ask the user to install tantivy separately

---------

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

.github/workflows/python.yml

@@ -31,6 +31,7 @@ jobs:
     - name: Install lancedb
       run: |
         pip install -e .
+        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
         pip install pytest
     - name: Run tests
       run: pytest -x -v --durations=30 tests
@@ -49,10 +50,11 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v4
       with:
-        python-version: "3.10"
+        python-version: "3.11"
     - name: Install lancedb
       run: |
         pip install -e .
+        pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
         pip install pytest
     - name: Run tests
       run: pytest -x -v --durations=30 tests

Cargo.lock

@@ -1052,6 +1052,7 @@ dependencies = [
  "paste",
  "petgraph",
  "rand",
+ "regex",
  "uuid",
 ]
 
@@ -1645,9 +1646,9 @@ dependencies = [
 
 [[package]]
 name = "lance"
-version = "0.4.12"
+version = "0.4.17"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "fc96cf89139af6f439a0e28ccd04ddf81be795b79fda3105b7a8952fadeb778e"
+checksum = "86dda8185bd1ffae7b910c1f68035af23be9b717c52e9cc4de176cd30b47f772"
 dependencies = [
  "accelerate-src",
  "arrow",
@@ -1684,6 +1685,7 @@ dependencies = [
  "rand",
  "reqwest",
  "shellexpand",
+ "snafu",
  "sqlparser-lance",
  "tokio",
  "url",
@@ -3359,8 +3361,11 @@ name = "vectordb"
 version = "0.0.1"
 dependencies = [
  "arrow-array",
+ "arrow-data",
  "arrow-schema",
  "lance",
+ "object_store",
+ "rand",
  "tempfile",
  "tokio",
 ]

docs/mkdocs.yml

@@ -19,6 +19,7 @@ nav:
 - Basics: basic.md
 - Embeddings: embedding.md
 - Indexing: ann_indexes.md
+- Full-text search: fts.md
 - Integrations: integrations.md
 - Python API: python.md
 

docs/src/fts.md

@@ -0,0 +1,51 @@
+# [EXPERIMENTAL] Full text search
+
+LanceDB now provides experimental support for full text search.
+This is currently Python only. We plan to push the integration down to Rust in the future
+to make this available for JS as well.
+
+## Installation
+
+To use full text search, you must install optional dependency tantivy-py:
+
+# tantivy 0.19.2
+pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985
+
+
+## Quickstart
+
+Assume:
+1. `table` is a LanceDB Table
+2. `text` is the name of the Table column that we want to index
+
+To create the index:
+
+```python
+table.create_fts_index("text")
+```
+
+To search:
+
+```python
+df = table.search("puppy").limit(10).select(["text"]).to_df()
+```
+
+LanceDB automatically looks for an FTS index if the input is str.
+
+## Multiple text columns
+
+If you have multiple columns to index, pass them all as a list to `create_fts_index`:
+
+```python
+table.create_fts_index(["text1", "text2"])
+```
+
+Note that the search API call does not change - you can search over all indexed columns at once.
+
+## Current limitations
+
+1. Currently we do not yet support incremental writes.
+If you add data after fts index creation, it won't be reflected
+in search results until you do a full reindex.
+
+2. We currently only support local filesystem paths for the fts index.

docs/src/index.md

@@ -45,5 +45,6 @@ We will be adding completed demo apps built using LanceDB.
 * [`Basic Operations`](basic.md) - basic functionality of LanceDB.
 * [`Embedding Functions`](embedding.md) - functions for working with embeddings.
 * [`Indexing`](ann_indexes.md) - create vector indexes to speed up queries.
+* [`Full text search`](fts.md) - [EXPERIMENTAL] full-text search API
 * [`Ecosystem Integrations`](integrations.md) - integrating LanceDB with python data tooling ecosystem.
 * [`API Reference`](python.md) - detailed documentation for the LanceDB Python SDK.

node/src/arrow.ts

@@ -15,15 +15,16 @@
 import {
   Field,
   Float32,
-  List,
+  List, type ListBuilder,
   makeBuilder,
   RecordBatchFileWriter,
-  Table,
+  Table, Utf8,
   type Vector,
   vectorFromArray
 } from 'apache-arrow'
+import { type EmbeddingFunction } from './index'
 
-export function convertToTable (data: Array<Record<string, unknown>>): Table {
+export function convertToTable<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Table {
   if (data.length === 0) {
     throw new Error('At least one record needs to be provided')
   }
@@ -33,11 +34,7 @@ export function convertToTable (data: Array<Record<string, unknown>>): Table {
 
   for (const columnsKey of columns) {
     if (columnsKey === 'vector') {
-      const children = new Field<Float32>('item', new Float32())
+      const listBuilder = newVectorListBuilder()
-      const list = new List(children)
-      const listBuilder = makeBuilder({
-        type: list
-      })
       const vectorSize = (data[0].vector as any[]).length
       for (const datum of data) {
         if ((datum[columnsKey] as any[]).length !== vectorSize) {
@@ -52,15 +49,37 @@ export function convertToTable (data: Array<Record<string, unknown>>): Table {
       for (const datum of data) {
         values.push(datum[columnsKey])
       }
+
+      if (columnsKey === embeddings?.sourceColumn) {
+        const vectors = embeddings.embed(values as T[])
+        const listBuilder = newVectorListBuilder()
+        vectors.map(v => listBuilder.append(v))
+        records.vector = listBuilder.finish().toVector()
+      }
+
+      if (typeof values[0] === 'string') {
+        // `vectorFromArray` converts strings into dictionary vectors, forcing it back to a string column
+        records[columnsKey] = vectorFromArray(values, new Utf8())
+      } else {
         records[columnsKey] = vectorFromArray(values)
       }
     }
+  }
 
   return new Table(records)
 }
 
-export async function fromRecordsToBuffer (data: Array<Record<string, unknown>>): Promise<Buffer> {
+// Creates a new Arrow ListBuilder that stores a Vector column
-  const table = convertToTable(data)
+function newVectorListBuilder (): ListBuilder<Float32, any> {
+  const children = new Field<Float32>('item', new Float32())
+  const list = new List(children)
+  return makeBuilder({
+    type: list
+  })
+}
+
+export async function fromRecordsToBuffer<T> (data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Buffer> {
+  const table = convertToTable(data, embeddings)
   const writer = RecordBatchFileWriter.writeAll(table)
   return Buffer.from(await writer.toUint8Array())
 }

node/src/index.ts

@@ -21,14 +21,15 @@ import {
 import { fromRecordsToBuffer } from './arrow'
 
 // eslint-disable-next-line @typescript-eslint/no-var-requires
-const { databaseNew, databaseTableNames, databaseOpenTable, tableCreate, tableSearch, tableAdd } = require('../native.js')
+const { databaseNew, databaseTableNames, databaseOpenTable, tableCreate, tableSearch, tableAdd, tableCreateVectorIndex } = require('../native.js')
 
 /**
  * Connect to a LanceDB instance at the given URI
  * @param uri The uri of the database.
  */
 export async function connect (uri: string): Promise<Connection> {
-  return new Connection(uri)
+  const db = await databaseNew(uri)
+  return new Connection(db, uri)
 }
 
 /**
@@ -38,9 +39,9 @@ export class Connection {
   private readonly _uri: string
   private readonly _db: any
 
-  constructor (uri: string) {
+  constructor (db: any, uri: string) {
     this._uri = uri
-    this._db = databaseNew(uri)
+    this._db = db
   }
 
   get uri (): string {
@@ -56,16 +57,49 @@ export class Connection {
 
   /**
    * Open a table in the database.
+   *
    * @param name The name of the table.
    */
-  async openTable (name: string): Promise<Table> {
+  async openTable (name: string): Promise<Table>
+  /**
+   * Open a table in the database.
+   *
+   * @param name The name of the table.
+   * @param embeddings An embedding function to use on this Table
+   */
+  async openTable<T> (name: string, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
+  async openTable<T> (name: string, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
     const tbl = await databaseOpenTable.call(this._db, name)
+    if (embeddings !== undefined) {
+      return new Table(tbl, name, embeddings)
+    } else {
       return new Table(tbl, name)
     }
+  }
 
-  async createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table> {
+  /**
-    await tableCreate.call(this._db, name, await fromRecordsToBuffer(data))
+   * Creates a new Table and initialize it with new data.
-    return await this.openTable(name)
+   *
+   * @param name The name of the table.
+   * @param data Non-empty Array of Records to be inserted into the Table
+   */
+
+  async createTable (name: string, data: Array<Record<string, unknown>>): Promise<Table>
+  /**
+   * Creates a new Table and initialize it with new data.
+   *
+   * @param name The name of the table.
+   * @param data Non-empty Array of Records to be inserted into the Table
+   * @param embeddings An embedding function to use on this Table
+   */
+  async createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings: EmbeddingFunction<T>): Promise<Table<T>>
+  async createTable<T> (name: string, data: Array<Record<string, unknown>>, embeddings?: EmbeddingFunction<T>): Promise<Table<T>> {
+    const tbl = await tableCreate.call(this._db, name, await fromRecordsToBuffer(data, embeddings))
+    if (embeddings !== undefined) {
+      return new Table(tbl, name, embeddings)
+    } else {
+      return new Table(tbl, name)
+    }
   }
 
   async createTableArrow (name: string, table: ArrowTable): Promise<Table> {
@@ -75,16 +109,22 @@ export class Connection {
   }
 }
 
-/**
+export class Table<T = number[]> {
- * A table in a LanceDB database.
- */
-export class Table {
   private readonly _tbl: any
   private readonly _name: string
+  private readonly _embeddings?: EmbeddingFunction<T>
 
-  constructor (tbl: any, name: string) {
+  constructor (tbl: any, name: string)
+  /**
+   * @param tbl
+   * @param name
+   * @param embeddings An embedding function to use when interacting with this table
+   */
+  constructor (tbl: any, name: string, embeddings: EmbeddingFunction<T>)
+  constructor (tbl: any, name: string, embeddings?: EmbeddingFunction<T>) {
     this._tbl = tbl
     this._name = name
+    this._embeddings = embeddings
   }
 
   get name (): string {
@@ -92,72 +132,173 @@ export class Table {
   }
 
   /**
-     * Create a search query to find the nearest neighbors of the given query vector.
+   * Creates a search query to find the nearest neighbors of the given search term
-     * @param queryVector The query vector.
+   * @param query The query search term
    */
-  search (queryVector: number[]): Query {
+  search (query: T): Query {
+    let queryVector: number[]
+    if (this._embeddings !== undefined) {
+      queryVector = this._embeddings.embed([query])[0]
+    } else {
+      queryVector = query as number[]
+    }
     return new Query(this._tbl, queryVector)
   }
 
   /**
-   * Insert records into this Table
+   * Insert records into this Table.
-   * @param data Records to be inserted into the Table
    *
-   * @param mode Append / Overwrite existing records. Default: Append
+   * @param data Records to be inserted into the Table
    * @return The number of rows added to the table
    */
   async add (data: Array<Record<string, unknown>>): Promise<number> {
-    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data), WriteMode.Append.toString())
+    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Append.toString())
   }
 
+  /**
+   * 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
+   */
   async overwrite (data: Array<Record<string, unknown>>): Promise<number> {
-    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data), WriteMode.Overwrite.toString())
+    return tableAdd.call(this._tbl, await fromRecordsToBuffer(data, this._embeddings), WriteMode.Overwrite.toString())
+  }
+
+  /**
+   * Create an ANN index on this Table vector index.
+   *
+   * @param indexParams The parameters of this Index, @see VectorIndexParams.
+   */
+  async create_index (indexParams: VectorIndexParams): Promise<any> {
+    return tableCreateVectorIndex.call(this._tbl, indexParams)
   }
 }
 
+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
+
+  type: 'ivf_pq'
+}
+
+export type VectorIndexParams = IvfPQIndexConfig
+
 /**
  * A builder for nearest neighbor queries for LanceDB.
  */
 export class Query {
   private readonly _tbl: any
-  private readonly _query_vector: number[]
+  private readonly _queryVector: number[]
   private _limit: number
-  private readonly _refine_factor?: number
+  private _refineFactor?: number
-  private readonly _nprobes: number
+  private _nprobes: number
   private readonly _columns?: string[]
   private _filter?: string
-  private readonly _metric = 'L2'
+  private _metricType?: MetricType
 
   constructor (tbl: any, queryVector: number[]) {
     this._tbl = tbl
-    this._query_vector = queryVector
+    this._queryVector = queryVector
     this._limit = 10
     this._nprobes = 20
-    this._refine_factor = undefined
+    this._refineFactor = undefined
     this._columns = undefined
     this._filter = undefined
+    this._metricType = undefined
   }
 
+  /***
+   * Sets the number of results that will be returned
+   * @param value number of results
+   */
   limit (value: number): Query {
     this._limit = value
     return this
   }
 
+  /**
+   * Refine the results by reading extra elements and re-ranking them in memory.
+   * @param value refine factor to use in this query.
+   */
+  refineFactor (value: number): Query {
+    this._refineFactor = value
+    return this
+  }
+
+  /**
+   * The number of probes used. A higher number makes search more accurate but also slower.
+   * @param value The number of probes used.
+   */
+  nprobes (value: number): Query {
+    this._nprobes = value
+    return this
+  }
+
+  /**
+   * A filter statement to be applied to this query.
+   * @param value A filter in the same format used by a sql WHERE clause.
+   */
   filter (value: string): Query {
     this._filter = value
     return this
   }
 
+  /**
+   * The MetricType used for this Query.
+   * @param value The metric to the. @see MetricType for the different options
+   */
+  metricType (value: MetricType): Query {
+    this._metricType = value
+    return this
+  }
+
   /**
    * Execute the query and return the results as an Array of Objects
    */
   async execute<T = Record<string, unknown>> (): Promise<T[]> {
-    let buffer
+    const buffer = await tableSearch.call(this._tbl, this)
-    if (this._filter != null) {
-      buffer = await tableSearch.call(this._tbl, this._query_vector, this._limit, this._filter)
-    } else {
-      buffer = await tableSearch.call(this._tbl, this._query_vector, this._limit)
-    }
     const data = tableFromIPC(buffer)
     return data.toArray().map((entry: Record<string, unknown>) => {
       const newObject: Record<string, unknown> = {}
@@ -177,3 +318,33 @@ export enum WriteMode {
   Overwrite = 'overwrite',
   Append = 'append'
 }
+
+/**
+ * An embedding function that automatically creates vector representation for a given column.
+ */
+export interface EmbeddingFunction<T> {
+  /**
+   * The name of the column that will be used as input for the Embedding Function.
+   */
+  sourceColumn: string
+
+  /**
+   * Creates a vector representation for the given values.
+   */
+  embed: (data: T[]) => number[][]
+}
+
+/**
+ * Distance metrics type.
+ */
+export enum MetricType {
+  /**
+   * Euclidean distance
+   */
+  L2 = 'l2',
+
+  /**
+   * Cosine distance
+   */
+  Cosine = 'cosine'
+}

node/src/test/io.ts

@@ -0,0 +1,52 @@
+// 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'
+
+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 uri = `${baseUri}/valid_url`
+      const table = await createTestDB(uri, 2, 20)
+      const con = await lancedb.connect(uri)
+      assert.equal(con.uri, uri)
+
+      const results = await table.search([0.1, 0.3]).limit(5).execute()
+      assert.equal(results.length, 5)
+    })
+  } else {
+    describe.skip('Skip S3 test', function () {})
+  }
+})
+
+async function createTestDB (uri: string, numDimensions: number = 2, numRows: number = 2): Promise<lancedb.Table> {
+  const con = await lancedb.connect(uri)
+
+  const data = []
+  for (let i = 0; i < numRows; i++) {
+    const vector = []
+    for (let j = 0; j < numDimensions; j++) {
+      vector.push(i + (j * 0.1))
+    }
+    data.push({ id: i + 1, name: `name_${i}`, price: i + 10, is_active: (i % 2 === 0), vector })
+  }
+
+  return await con.createTable('vectors', data)
+}

node/src/test/test.ts

@@ -17,6 +17,7 @@ import { assert } from 'chai'
 import { track } from 'temp'
 
 import * as lancedb from '../index'
+import { type EmbeddingFunction, MetricType, Query } from '../index'
 
 describe('LanceDB client', function () {
   describe('when creating a connection to lancedb', function () {
@@ -67,7 +68,7 @@ describe('LanceDB client', 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]).filter('id == 2').execute()
+      const results = await table.search([0.1, 0.1]).filter('id == 2').execute()
       assert.equal(results.length, 1)
       assert.equal(results[0].id, 2)
     })
@@ -96,8 +97,8 @@ describe('LanceDB client', function () {
       const con = await lancedb.connect(dir)
 
       const data = [
-        { id: 1, vector: [0.1, 0.2], price: 10 },
+        { id: 1, vector: [0.1, 0.2], price: 10, name: 'a' },
-        { id: 2, vector: [1.1, 1.2], price: 50 }
+        { id: 2, vector: [1.1, 1.2], price: 50, name: 'b' }
       ]
 
       const table = await con.createTable('vectors', data)
@@ -105,8 +106,8 @@ describe('LanceDB client', function () {
       assert.equal(results.length, 2)
 
       const dataAdd = [
-        { id: 3, vector: [2.1, 2.2], price: 10 },
+        { id: 3, vector: [2.1, 2.2], price: 10, name: 'c' },
-        { id: 4, vector: [3.1, 3.2], price: 50 }
+        { id: 4, vector: [3.1, 3.2], price: 50, name: 'd' }
       ]
       await table.add(dataAdd)
       const resultsAdd = await table.search([0.1, 0.3]).execute()
@@ -130,16 +131,76 @@ describe('LanceDB client', function () {
       assert.equal(resultsAdd.length, 2)
     })
   })
+
+  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.create_index({ type: 'ivf_pq', column: 'vector', num_partitions: 2, max_iters: 2 })
+    }).timeout(10_000) // Timeout is high partially because GH macos runner is pretty slow
+  })
+
+  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]]
+      ])
+
+      embed (data: string[]): 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)
+      const results = await table.search('foo').execute()
+      assert.equal(results.length, 2)
+    })
+  })
 })
 
-async function createTestDB (): Promise<string> {
+describe('Query object', function () {
+  it('sets custom parameters', async function () {
+    const query = new Query(undefined, [0.1, 0.3])
+      .limit(1)
+      .metricType(MetricType.Cosine)
+      .refineFactor(100)
+      .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)
+  })
+})
+
+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 = [
+  const data = []
-    { id: 1, vector: [0.1, 0.2], name: 'foo', price: 10, is_active: true },
+  for (let i = 0; i < numRows; i++) {
-    { id: 2, vector: [1.1, 1.2], name: 'bar', price: 50, is_active: false }
+    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

python/lancedb/fts.py

@@ -0,0 +1,128 @@
+#  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.
+
+"""Full text search index using tantivy-py"""
+import os
+from typing import List, Tuple
+
+import pyarrow as pa
+
+try:
+    import tantivy
+except ImportError:
+    raise ImportError(
+        "Please install tantivy-py `pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985` to use the full text search feature."
+    )
+
+from .table import LanceTable
+
+
+def create_index(index_path: str, text_fields: List[str]) -> tantivy.Index:
+    """
+    Create a new Index (not populated)
+
+    Parameters
+    ----------
+    index_path : str
+        Path to the index directory
+    text_fields : List[str]
+        List of text fields to index
+
+    Returns
+    -------
+    index : tantivy.Index
+        The index object (not yet populated)
+    """
+    # Declaring our schema.
+    schema_builder = tantivy.SchemaBuilder()
+    # special field that we'll populate with row_id
+    schema_builder.add_integer_field("doc_id", stored=True)
+    # data fields
+    for name in text_fields:
+        schema_builder.add_text_field(name, stored=True)
+    schema = schema_builder.build()
+    os.makedirs(index_path, exist_ok=True)
+    index = tantivy.Index(schema, path=index_path)
+    return index
+
+
+def populate_index(index: tantivy.Index, table: LanceTable, fields: List[str]) -> int:
+    """
+    Populate an index with data from a LanceTable
+
+    Parameters
+    ----------
+    index : tantivy.Index
+        The index object
+    table : LanceTable
+        The table to index
+    fields : List[str]
+        List of fields to index
+    """
+    # first check the fields exist and are string or large string type
+    for name in fields:
+        f = table.schema.field(name)  # raises KeyError if not found
+        if not pa.types.is_string(f.type) and not pa.types.is_large_string(f.type):
+            raise TypeError(f"Field {name} is not a string type")
+
+    # create a tantivy writer
+    writer = index.writer()
+    # write data into index
+    dataset = table.to_lance()
+    row_id = 0
+    for b in dataset.to_batches(columns=fields):
+        for i in range(b.num_rows):
+            doc = tantivy.Document()
+            doc.add_integer("doc_id", row_id)
+            for name in fields:
+                doc.add_text(name, b[name][i].as_py())
+            writer.add_document(doc)
+            row_id += 1
+    # commit changes
+    writer.commit()
+    return row_id
+
+
+def search_index(
+    index: tantivy.Index, query: str, limit: int = 10
+) -> Tuple[Tuple[int], Tuple[float]]:
+    """
+    Search an index for a query
+
+    Parameters
+    ----------
+    index : tantivy.Index
+        The index object
+    query : str
+        The query string
+    limit : int
+        The maximum number of results to return
+
+    Returns
+    -------
+    ids_and_score: list[tuple[int], tuple[float]]
+        A tuple of two tuples, the first containing the document ids
+        and the second containing the scores
+    """
+    searcher = index.searcher()
+    query = index.parse_query(query)
+    # get top results
+    results = searcher.search(query, limit)
+    return tuple(
+        zip(
+            *[
+                (searcher.doc(doc_address)["doc_id"][0], score)
+                for score, doc_address in results.hits
+            ]
+        )
+    )

python/lancedb/query.py

@@ -14,6 +14,7 @@ from __future__ import annotations
 
 import numpy as np
 import pandas as pd
+import pyarrow as pa
 
 from .common import VECTOR_COLUMN_NAME
 
@@ -131,7 +132,6 @@ class LanceQueryBuilder:
         vector and the returned vector.
         """
         ds = self._table.to_lance()
-        # TODO indexed search
         tbl = ds.to_table(
             columns=self._columns,
             filter=self._where,
@@ -145,3 +145,26 @@ class LanceQueryBuilder:
             },
         )
         return tbl.to_pandas()
+
+
+class LanceFtsQueryBuilder(LanceQueryBuilder):
+    def to_df(self) -> pd.DataFrame:
+        try:
+            import tantivy
+        except ImportError:
+            raise ImportError(
+                "Please install tantivy-py `pip install tantivy@git+https://github.com/quickwit-oss/tantivy-py#164adc87e1a033117001cf70e38c82a53014d985` to use the full text search feature."
+            )
+
+        from .fts import search_index
+
+        # get the index path
+        index_path = self._table._get_fts_index_path()
+        # open the index
+        index = tantivy.Index.open(index_path)
+        # get the scores and doc ids
+        row_ids, scores = search_index(index, self._query, self._limit)
+        scores = pa.array(scores)
+        output_tbl = self._table.to_lance().take(row_ids, columns=self._columns)
+        output_tbl = output_tbl.append_column("score", scores)
+        return output_tbl.to_pandas()

python/lancedb/table.py

@@ -14,7 +14,9 @@
 from __future__ import annotations
 
 import os
+import shutil
 from functools import cached_property
+from typing import List, Union
 
 import lance
 import numpy as np
@@ -24,7 +26,8 @@ from lance import LanceDataset
 from lance.vector import vec_to_table
 
 from .common import DATA, VEC, VECTOR_COLUMN_NAME
-from .query import LanceQueryBuilder
+from .query import LanceFtsQueryBuilder, LanceQueryBuilder
+from .util import get_uri_scheme
 
 
 def _sanitize_data(data, schema):
@@ -130,6 +133,27 @@ class LanceTable:
         )
         self._reset_dataset()
 
+    def create_fts_index(self, field_names: Union[str, List[str]]):
+        """Create a full-text search index on the table.
+
+        Warning - this API is highly experimental and is highly likely to change
+        in the future.
+
+        Parameters
+        ----------
+        field_names: str or list of str
+            The name(s) of the field to index.
+        """
+        from .fts import create_index, populate_index
+
+        if isinstance(field_names, str):
+            field_names = [field_names]
+        index = create_index(self._get_fts_index_path(), field_names)
+        populate_index(index, self, field_names)
+
+    def _get_fts_index_path(self):
+        return os.path.join(self._dataset_uri, "_indices", "tantivy")
+
     @cached_property
     def _dataset(self) -> LanceDataset:
         return lance.dataset(self._dataset_uri, version=self._version)
@@ -158,7 +182,7 @@ class LanceTable:
         self._reset_dataset()
         return len(self)
 
-    def search(self, query: VEC) -> LanceQueryBuilder:
+    def search(self, query: Union[VEC, str]) -> LanceQueryBuilder:
         """Create a search query to find the nearest neighbors
         of the given query vector.
 
@@ -174,6 +198,10 @@ class LanceTable:
         and also the "score" column which is the distance between the query
         vector and the returned vector.
         """
+        if isinstance(query, str):
+            # fts
+            return LanceFtsQueryBuilder(self, query)
+
         if isinstance(query, list):
             query = np.array(query)
         if isinstance(query, np.ndarray):

python/pyproject.toml

@@ -1,7 +1,7 @@
 [project]
 name = "lancedb"
-version = "0.1.2"
+version = "0.1.4"
-dependencies = ["pylance>=0.4.6", "ratelimiter", "retry", "tqdm"]
+dependencies = ["pylance>=0.4.17", "ratelimiter", "retry", "tqdm"]
 description = "lancedb"
 authors = [
     { name = "LanceDB Devs", email = "dev@lancedb.com" },

python/tests/test_embeddings.py

@@ -14,7 +14,6 @@ import sys
 
 import numpy as np
 import pyarrow as pa
-
 from lancedb.embeddings import with_embeddings
 
 

python/tests/test_fts.py

@@ -0,0 +1,84 @@
+# 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 os
+import random
+
+import lancedb.fts
+import numpy as np
+import pandas as pd
+import pytest
+import tantivy
+
+import lancedb as ldb
+
+
+@pytest.fixture
+def table(tmp_path) -> ldb.table.LanceTable:
+    db = ldb.connect(tmp_path)
+    vectors = [np.random.randn(128) for _ in range(100)]
+
+    nouns = ("puppy", "car", "rabbit", "girl", "monkey")
+    verbs = ("runs", "hits", "jumps", "drives", "barfs")
+    adv = ("crazily.", "dutifully.", "foolishly.", "merrily.", "occasionally.")
+    adj = ("adorable", "clueless", "dirty", "odd", "stupid")
+    text = [
+        " ".join(
+            [
+                nouns[random.randrange(0, 5)],
+                verbs[random.randrange(0, 5)],
+                adv[random.randrange(0, 5)],
+                adj[random.randrange(0, 5)],
+            ]
+        )
+        for _ in range(100)
+    ]
+    table = db.create_table(
+        "test", data=pd.DataFrame({"vector": vectors, "text": text, "text2": text})
+    )
+    return table
+
+
+def test_create_index(tmp_path):
+    index = ldb.fts.create_index(str(tmp_path / "index"), ["text"])
+    assert isinstance(index, tantivy.Index)
+    assert os.path.exists(str(tmp_path / "index"))
+
+
+def test_populate_index(tmp_path, table):
+    index = ldb.fts.create_index(str(tmp_path / "index"), ["text"])
+    assert ldb.fts.populate_index(index, table, ["text"]) == len(table)
+
+
+def test_search_index(tmp_path, table):
+    index = ldb.fts.create_index(str(tmp_path / "index"), ["text"])
+    ldb.fts.populate_index(index, table, ["text"])
+    index.reload()
+    results = ldb.fts.search_index(index, query="puppy", limit=10)
+    assert len(results) == 2
+    assert len(results[0]) == 10  # row_ids
+    assert len(results[1]) == 10  # scores
+
+
+def test_create_index_from_table(tmp_path, table):
+    table.create_fts_index("text")
+    df = table.search("puppy").limit(10).select(["text"]).to_df()
+    assert len(df) == 10
+    assert "text" in df.columns
+
+
+def test_create_index_multiple_columns(tmp_path, table):
+    table.create_fts_index(["text", "text2"])
+    df = table.search("puppy").limit(10).to_df()
+    assert len(df) == 10
+    assert "text" in df.columns
+    assert "text2" in df.columns

python/tests/test_query.py

@@ -17,7 +17,6 @@ import pandas as pd
 import pandas.testing as tm
 import pyarrow as pa
 import pytest
-
 from lancedb.query import LanceQueryBuilder
 
 

python/tests/test_table.py

@@ -16,7 +16,6 @@ from pathlib import Path
 import pandas as pd
 import pyarrow as pa
 import pytest
-
 from lancedb.table import LanceTable
 
 

rust/ffi/node/Cargo.toml

@@ -15,7 +15,7 @@ arrow-ipc = "37.0"
 arrow-schema = "37.0"
 once_cell = "1"
 futures = "0.3"
-lance = "0.4.3"
+lance = "0.4.17"
 vectordb = { path = "../../vectordb" }
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
 neon = {version = "0.10.1", default-features = false, features = ["channel-api", "napi-6", "promise-api", "task-api"] }

rust/ffi/node/src/index.rs

@@ -0,0 +1,15 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+pub mod vector;

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

@@ -0,0 +1,128 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+use std::convert::TryFrom;
+
+use lance::index::vector::ivf::IvfBuildParams;
+use lance::index::vector::pq::PQBuildParams;
+use lance::index::vector::MetricType;
+use neon::context::FunctionContext;
+use neon::prelude::*;
+
+use vectordb::index::vector::{IvfPQIndexBuilder, VectorIndexBuilder};
+
+use crate::{runtime, JsTable};
+
+pub(crate) fn table_create_vector_index(mut cx: FunctionContext) -> JsResult<JsPromise> {
+    let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
+    let index_params = cx.argument::<JsObject>(0)?;
+    let index_params_builder = get_index_params_builder(&mut cx, index_params).unwrap();
+
+    let rt = runtime(&mut cx)?;
+    let channel = cx.channel();
+
+    let (deferred, promise) = cx.promise();
+    let table = js_table.table.clone();
+
+    rt.block_on(async move {
+        let add_result = table
+            .lock()
+            .unwrap()
+            .create_index(&index_params_builder)
+            .await;
+
+        deferred.settle_with(&channel, move |mut cx| {
+            add_result
+                .map(|_| cx.undefined())
+                .or_else(|err| cx.throw_error(err.to_string()))
+        });
+    });
+    Ok(promise)
+}
+
+fn get_index_params_builder(
+    cx: &mut FunctionContext,
+    obj: Handle<JsObject>,
+) -> Result<impl VectorIndexBuilder, String> {
+    let idx_type = obj
+        .get::<JsString, _, _>(cx, "type")
+        .map_err(|t| t.to_string())?
+        .value(cx);
+
+    match idx_type.as_str() {
+        "ivf_pq" => {
+            let mut index_builder: IvfPQIndexBuilder = IvfPQIndexBuilder::new();
+            let mut pq_params = PQBuildParams::default();
+
+            obj.get_opt::<JsString, _, _>(cx, "column")
+                .map_err(|t| t.to_string())?
+                .map(|s| index_builder.column(s.value(cx)));
+
+            obj.get_opt::<JsString, _, _>(cx, "index_name")
+                .map_err(|t| t.to_string())?
+                .map(|s| index_builder.index_name(s.value(cx)));
+
+            obj.get_opt::<JsString, _, _>(cx, "metric_type")
+                .map_err(|t| t.to_string())?
+                .map(|s| MetricType::try_from(s.value(cx).as_str()))
+                .map(|mt| {
+                    let metric_type = mt.unwrap();
+                    index_builder.metric_type(metric_type);
+                    pq_params.metric_type = metric_type;
+                });
+
+            let num_partitions = obj
+                .get_opt::<JsNumber, _, _>(cx, "num_partitions")
+                .map_err(|t| t.to_string())?
+                .map(|s| s.value(cx) as usize);
+
+            let max_iters = obj
+                .get_opt::<JsNumber, _, _>(cx, "max_iters")
+                .map_err(|t| t.to_string())?
+                .map(|s| s.value(cx) as usize);
+
+            num_partitions.map(|np| {
+                let max_iters = max_iters.unwrap_or(50);
+                let ivf_params = IvfBuildParams {
+                    num_partitions: np,
+                    max_iters,
+                };
+                index_builder.ivf_params(ivf_params)
+            });
+
+            obj.get_opt::<JsBoolean, _, _>(cx, "use_opq")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.use_opq = s.value(cx));
+
+            obj.get_opt::<JsNumber, _, _>(cx, "num_sub_vectors")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.num_sub_vectors = s.value(cx) as usize);
+
+            obj.get_opt::<JsNumber, _, _>(cx, "num_bits")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.num_bits = s.value(cx) as usize);
+
+            obj.get_opt::<JsNumber, _, _>(cx, "max_iters")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.max_iters = s.value(cx) as usize);
+
+            obj.get_opt::<JsNumber, _, _>(cx, "max_opq_iters")
+                .map_err(|t| t.to_string())?
+                .map(|s| pq_params.max_opq_iters = s.value(cx) as usize);
+
+            Ok(index_builder)
+        }
+        t => Err(format!("{} is not a valid index type", t).to_string()),
+    }
+}

rust/ffi/node/src/lib.rs

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 use std::collections::HashMap;
+use std::convert::TryFrom;
 use std::ops::Deref;
 use std::sync::{Arc, Mutex};
 
@@ -21,6 +22,7 @@ use arrow_ipc::writer::FileWriter;
 use futures::{TryFutureExt, TryStreamExt};
 use lance::arrow::RecordBatchBuffer;
 use lance::dataset::WriteMode;
+use lance::index::vector::MetricType;
 use neon::prelude::*;
 use neon::types::buffer::TypedArray;
 use once_cell::sync::OnceCell;
@@ -34,17 +36,18 @@ use crate::arrow::arrow_buffer_to_record_batch;
 
 mod arrow;
 mod convert;
+mod index;
 
 struct JsDatabase {
     database: Arc<Database>,
 }
 
+impl Finalize for JsDatabase {}
+
 struct JsTable {
     table: Arc<Mutex<Table>>,
 }
 
-impl Finalize for JsDatabase {}
-
 impl Finalize for JsTable {}
 
 fn runtime<'a, C: Context<'a>>(cx: &mut C) -> NeonResult<&'static Runtime> {
@@ -53,23 +56,46 @@ fn runtime<'a, C: Context<'a>>(cx: &mut C) -> NeonResult<&'static Runtime> {
     RUNTIME.get_or_try_init(|| Runtime::new().or_else(|err| cx.throw_error(err.to_string())))
 }
 
-fn database_new(mut cx: FunctionContext) -> JsResult<JsBox<JsDatabase>> {
+fn database_new(mut cx: FunctionContext) -> JsResult<JsPromise> {
     let path = cx.argument::<JsString>(0)?.value(&mut cx);
+
+    let rt = runtime(&mut cx)?;
+    let channel = cx.channel();
+    let (deferred, promise) = cx.promise();
+
+    rt.spawn(async move {
+        let database = Database::connect(&path).await;
+
+        deferred.settle_with(&channel, move |mut cx| {
             let db = JsDatabase {
-        database: Arc::new(Database::connect(path).or_else(|err| cx.throw_error(err.to_string()))?),
+                database: Arc::new(database.or_else(|err| cx.throw_error(err.to_string()))?),
             };
             Ok(cx.boxed(db))
+        });
+    });
+    Ok(promise)
 }
 
-fn database_table_names(mut cx: FunctionContext) -> JsResult<JsArray> {
+fn database_table_names(mut cx: FunctionContext) -> JsResult<JsPromise> {
     let db = cx
         .this()
         .downcast_or_throw::<JsBox<JsDatabase>, _>(&mut cx)?;
-    let tables = db
+
-        .database
+    let rt = runtime(&mut cx)?;
-        .table_names()
+    let (deferred, promise) = cx.promise();
-        .or_else(|err| cx.throw_error(err.to_string()))?;
+    let channel = cx.channel();
-    convert::vec_str_to_array(&tables, &mut cx)
+    let database = db.database.clone();
+
+    rt.spawn(async move {
+        let tables_rst = database.table_names().await;
+
+        deferred.settle_with(&channel, move |mut cx| {
+            let tables = tables_rst.or_else(|err| cx.throw_error(err.to_string()))?;
+            let table_names = convert::vec_str_to_array(&tables, &mut cx);
+            table_names
+        });
+    });
+    Ok(promise)
 }
 
 fn database_open_table(mut cx: FunctionContext) -> JsResult<JsPromise> {
@@ -84,10 +110,12 @@ fn database_open_table(mut cx: FunctionContext) -> JsResult<JsPromise> {
 
     let (deferred, promise) = cx.promise();
     rt.spawn(async move {
-        let table_rst = database.open_table(table_name).await;
+        let table_rst = database.open_table(&table_name).await;
 
         deferred.settle_with(&channel, move |mut cx| {
-            let table = Arc::new(Mutex::new(table_rst.or_else(|err| cx.throw_error(err.to_string()))?));
+            let table = Arc::new(Mutex::new(
+                table_rst.or_else(|err| cx.throw_error(err.to_string()))?,
+            ));
             Ok(cx.boxed(JsTable { table }))
         });
     });
@@ -96,15 +124,32 @@ fn database_open_table(mut cx: FunctionContext) -> JsResult<JsPromise> {
 
 fn table_search(mut cx: FunctionContext) -> JsResult<JsPromise> {
     let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
-    let query_vector = cx.argument::<JsArray>(0)?; //. .as_value(&mut cx);
+    let query_obj = cx.argument::<JsObject>(0)?;
-    let limit = cx.argument::<JsNumber>(1)?.value(&mut cx);
+
-    let filter = cx.argument_opt(2).map(|f| f.downcast_or_throw::<JsString, _>(&mut cx).unwrap().value(&mut cx));
+    let limit = query_obj
+        .get::<JsNumber, _, _>(&mut cx, "_limit")?
+        .value(&mut cx);
+    let filter = query_obj
+        .get_opt::<JsString, _, _>(&mut cx, "_filter")?
+        .map(|s| s.value(&mut cx));
+    let refine_factor = query_obj
+        .get_opt::<JsNumber, _, _>(&mut cx, "_refineFactor")?
+        .map(|s| s.value(&mut cx))
+        .map(|i| i as u32);
+    let nprobes = query_obj
+        .get::<JsNumber, _, _>(&mut cx, "_nprobes")?
+        .value(&mut cx) as usize;
+    let metric_type = query_obj
+        .get_opt::<JsString, _, _>(&mut cx, "_metricType")?
+        .map(|s| s.value(&mut cx))
+        .map(|s| MetricType::try_from(s.as_str()).unwrap());
 
     let rt = runtime(&mut cx)?;
     let channel = cx.channel();
 
     let (deferred, promise) = cx.promise();
     let table = js_table.table.clone();
+    let query_vector = query_obj.get::<JsArray, _, _>(&mut cx, "_queryVector")?;
     let query = convert::js_array_to_vec(query_vector.deref(), &mut cx);
 
     rt.spawn(async move {
@@ -113,7 +158,10 @@ fn table_search(mut cx: FunctionContext) -> JsResult<JsPromise> {
             .unwrap()
             .search(Float32Array::from(query))
             .limit(limit as usize)
-            .filter(filter);
+            .refine_factor(refine_factor)
+            .nprobes(nprobes)
+            .filter(filter)
+            .metric_type(metric_type);
         let record_batch_stream = builder.execute();
         let results = record_batch_stream
             .and_then(|stream| stream.try_collect::<Vec<_>>().map_err(Error::from))
@@ -161,10 +209,12 @@ fn table_create(mut cx: FunctionContext) -> JsResult<JsPromise> {
 
     rt.block_on(async move {
         let batch_reader: Box<dyn RecordBatchReader> = Box::new(RecordBatchBuffer::new(batches));
-        let table_rst = database.create_table(table_name, batch_reader).await;
+        let table_rst = database.create_table(&table_name, batch_reader).await;
 
         deferred.settle_with(&channel, move |mut cx| {
-            let table = Arc::new(Mutex::new(table_rst.or_else(|err| cx.throw_error(err.to_string()))?));
+            let table = Arc::new(Mutex::new(
+                table_rst.or_else(|err| cx.throw_error(err.to_string()))?,
+            ));
             Ok(cx.boxed(JsTable { table }))
         });
     });
@@ -178,9 +228,7 @@ fn table_add(mut cx: FunctionContext) -> JsResult<JsPromise> {
         ("overwrite", WriteMode::Overwrite),
     ]);
 
-    let js_table = cx
+    let js_table = cx.this().downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
-        .this()
-        .downcast_or_throw::<JsBox<JsTable>, _>(&mut cx)?;
     let buffer = cx.argument::<JsBuffer>(0)?;
     let write_mode = cx.argument::<JsString>(1)?.value(&mut cx);
     let batches = arrow_buffer_to_record_batch(buffer.as_slice(&mut cx));
@@ -204,7 +252,6 @@ fn table_add(mut cx: FunctionContext) -> JsResult<JsPromise> {
     Ok(promise)
 }
 
-
 #[neon::main]
 fn main(mut cx: ModuleContext) -> NeonResult<()> {
     cx.export_function("databaseNew", database_new)?;
@@ -213,5 +260,9 @@ fn main(mut cx: ModuleContext) -> NeonResult<()> {
     cx.export_function("tableSearch", table_search)?;
     cx.export_function("tableCreate", table_create)?;
     cx.export_function("tableAdd", table_add)?;
+    cx.export_function(
+        "tableCreateVectorIndex",
+        index::vector::table_create_vector_index,
+    )?;
     Ok(())
 }

rust/vectordb/Cargo.toml

@@ -10,9 +10,13 @@ repository = "https://github.com/lancedb/lancedb"
 
 [dependencies]
 arrow-array = "37.0"
+arrow-data = "37.0"
 arrow-schema = "37.0"
-lance = "0.4.3"
+object_store = "0.5.6"
+
+lance = "0.4.17"
 tokio = { version = "1.23", features = ["rt-multi-thread"] }
 
 [dev-dependencies]
 tempfile = "3.5.0"
+rand = { version = "0.8.3", features = ["small_rng"] }

rust/vectordb/src/database.rs

@@ -12,16 +12,19 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use arrow_array::RecordBatchReader;
 use std::fs::create_dir_all;
-use std::path::{Path, PathBuf};
+use std::path::Path;
-use std::sync::Arc;
+
+use arrow_array::RecordBatchReader;
+use lance::io::object_store::ObjectStore;
 
 use crate::error::Result;
 use crate::table::Table;
 
 pub struct Database {
-    pub(crate) path: Arc<PathBuf>,
+    object_store: ObjectStore,
+
+    pub(crate) uri: String,
 }
 
 const LANCE_EXTENSION: &str = "lance";
@@ -37,12 +40,17 @@ impl Database {
     /// # Returns
     ///
     /// * A [Database] object.
-    pub fn connect<P: AsRef<Path>>(path: P) -> Result<Database> {
+    pub async fn connect(uri: &str) -> Result<Database> {
-        if !path.as_ref().try_exists()? {
+        let object_store = ObjectStore::new(uri).await?;
+        if object_store.is_local() {
+            let path = Path::new(uri);
+            if !path.try_exists()? {
                 create_dir_all(&path)?;
             }
+        }
         Ok(Database {
-            path: Arc::new(path.as_ref().to_path_buf()),
+            uri: uri.to_string(),
+            object_store,
         })
     }
 
@@ -51,12 +59,13 @@ impl Database {
     /// # Returns
     ///
     /// * A [Vec<String>] with all table names.
-    pub fn table_names(&self) -> Result<Vec<String>> {
+    pub async fn table_names(&self) -> Result<Vec<String>> {
         let f = self
-            .path
+            .object_store
-            .read_dir()?
+            .read_dir("/")
-            .flatten()
+            .await?
-            .map(|dir_entry| dir_entry.path())
+            .iter()
+            .map(|fname| Path::new(fname))
             .filter(|path| {
                 let is_lance = path
                     .extension()
@@ -76,10 +85,10 @@ impl Database {
 
     pub async fn create_table(
         &self,
-        name: String,
+        name: &str,
         batches: Box<dyn RecordBatchReader>,
     ) -> Result<Table> {
-        Table::create(self.path.clone(), name, batches).await
+        Table::create(&self.uri, name, batches).await
     }
 
     /// Open a table in the database.
@@ -90,8 +99,8 @@ impl Database {
     /// # Returns
     ///
     /// * A [Table] object.
-    pub async fn open_table(&self, name: String) -> Result<Table> {
+    pub async fn open_table(&self, name: &str) -> Result<Table> {
-        Table::open(self.path.clone(), name).await
+        Table::open(&self.uri, name).await
     }
 }
 
@@ -105,10 +114,10 @@ mod tests {
     #[tokio::test]
     async fn test_connect() {
         let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let uri = tmp_dir.path().to_str().unwrap();
-        let db = Database::connect(&path_buf);
+        let db = Database::connect(uri).await.unwrap();
 
-        assert_eq!(db.unwrap().path.as_path(), path_buf.as_path())
+        assert_eq!(db.uri, uri);
     }
 
     #[tokio::test]
@@ -118,10 +127,16 @@ mod tests {
         create_dir_all(tmp_dir.path().join("table2.lance")).unwrap();
         create_dir_all(tmp_dir.path().join("invalidlance")).unwrap();
 
-        let db = Database::connect(&tmp_dir.into_path()).unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();
-        let tables = db.table_names().unwrap();
+        let db = Database::connect(uri).await.unwrap();
+        let tables = db.table_names().await.unwrap();
         assert_eq!(tables.len(), 2);
         assert!(tables.contains(&String::from("table1")));
         assert!(tables.contains(&String::from("table2")));
     }
+
+    #[tokio::test]
+    async fn test_connect_s3() {
+        // let db = Database::connect("s3://bucket/path/to/database").await.unwrap();
+    }
 }

rust/vectordb/src/error.rs

@@ -41,3 +41,15 @@ impl From<lance::Error> for Error {
         Self::Lance(e.to_string())
     }
 }
+
+impl From<object_store::Error> for Error {
+    fn from(e: object_store::Error) -> Self {
+        Self::IO(e.to_string())
+    }
+}
+
+impl From<object_store::path::Error> for Error {
+    fn from(e: object_store::path::Error) -> Self {
+        Self::IO(e.to_string())
+    }
+}

rust/vectordb/src/index.rs

@@ -0,0 +1,15 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+pub mod vector;

rust/vectordb/src/index/vector.rs

@@ -0,0 +1,163 @@
+// Copyright 2023 Lance Developers.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+// See the License for the specific language governing permissions and
+// limitations under the License.
+
+use lance::index::vector::ivf::IvfBuildParams;
+use lance::index::vector::pq::PQBuildParams;
+use lance::index::vector::{MetricType, VectorIndexParams};
+
+pub trait VectorIndexBuilder {
+    fn get_column(&self) -> Option<String>;
+    fn get_index_name(&self) -> Option<String>;
+    fn build(&self) -> VectorIndexParams;
+}
+
+pub struct IvfPQIndexBuilder {
+    column: Option<String>,
+    index_name: Option<String>,
+    metric_type: Option<MetricType>,
+    ivf_params: Option<IvfBuildParams>,
+    pq_params: Option<PQBuildParams>,
+}
+
+impl IvfPQIndexBuilder {
+    pub fn new() -> IvfPQIndexBuilder {
+        IvfPQIndexBuilder {
+            column: None,
+            index_name: None,
+            metric_type: None,
+            ivf_params: None,
+            pq_params: None,
+        }
+    }
+}
+
+impl IvfPQIndexBuilder {
+    pub fn column(&mut self, column: String) -> &mut IvfPQIndexBuilder {
+        self.column = Some(column);
+        self
+    }
+
+    pub fn index_name(&mut self, index_name: String) -> &mut IvfPQIndexBuilder {
+        self.index_name = Some(index_name);
+        self
+    }
+
+    pub fn metric_type(&mut self, metric_type: MetricType) -> &mut IvfPQIndexBuilder {
+        self.metric_type = Some(metric_type);
+        self
+    }
+
+    pub fn ivf_params(&mut self, ivf_params: IvfBuildParams) -> &mut IvfPQIndexBuilder {
+        self.ivf_params = Some(ivf_params);
+        self
+    }
+
+    pub fn pq_params(&mut self, pq_params: PQBuildParams) -> &mut IvfPQIndexBuilder {
+        self.pq_params = Some(pq_params);
+        self
+    }
+}
+
+impl VectorIndexBuilder for IvfPQIndexBuilder {
+    fn get_column(&self) -> Option<String> {
+        self.column.clone()
+    }
+
+    fn get_index_name(&self) -> Option<String> {
+        self.index_name.clone()
+    }
+
+    fn build(&self) -> VectorIndexParams {
+        let ivf_params = self.ivf_params.clone().unwrap_or(IvfBuildParams::default());
+        let pq_params = self.pq_params.clone().unwrap_or(PQBuildParams::default());
+
+        VectorIndexParams::with_ivf_pq_params(pq_params.metric_type, ivf_params, pq_params)
+    }
+}
+
+#[cfg(test)]
+mod tests {
+    use lance::index::vector::ivf::IvfBuildParams;
+    use lance::index::vector::pq::PQBuildParams;
+    use lance::index::vector::{MetricType, StageParams};
+
+    use crate::index::vector::{IvfPQIndexBuilder, VectorIndexBuilder};
+
+    #[test]
+    fn test_builder_no_params() {
+        let index_builder = IvfPQIndexBuilder::new();
+        assert!(index_builder.get_column().is_none());
+        assert!(index_builder.get_index_name().is_none());
+
+        let index_params = index_builder.build();
+        assert_eq!(index_params.stages.len(), 2);
+        if let StageParams::Ivf(ivf_params) = index_params.stages.get(0).unwrap() {
+            let default = IvfBuildParams::default();
+            assert_eq!(ivf_params.num_partitions, default.num_partitions);
+            assert_eq!(ivf_params.max_iters, default.max_iters);
+        } else {
+            panic!("Expected first stage to be ivf")
+        }
+
+        if let StageParams::PQ(pq_params) = index_params.stages.get(1).unwrap() {
+            assert_eq!(pq_params.use_opq, false);
+        } else {
+            panic!("Expected second stage to be pq")
+        }
+    }
+
+    #[test]
+    fn test_builder_all_params() {
+        let mut index_builder = IvfPQIndexBuilder::new();
+
+        index_builder
+            .column("c".to_owned())
+            .metric_type(MetricType::Cosine)
+            .index_name("index".to_owned());
+
+        assert_eq!(index_builder.column.clone().unwrap(), "c");
+        assert_eq!(index_builder.metric_type.unwrap(), MetricType::Cosine);
+        assert_eq!(index_builder.index_name.clone().unwrap(), "index");
+
+        let ivf_params = IvfBuildParams::new(500);
+        let mut pq_params = PQBuildParams::default();
+        pq_params.use_opq = true;
+        pq_params.max_iters = 1;
+        pq_params.num_bits = 8;
+        pq_params.num_sub_vectors = 50;
+        pq_params.metric_type = MetricType::Cosine;
+        pq_params.max_opq_iters = 2;
+        index_builder.ivf_params(ivf_params);
+        index_builder.pq_params(pq_params);
+
+        let index_params = index_builder.build();
+        assert_eq!(index_params.stages.len(), 2);
+        if let StageParams::Ivf(ivf_params) = index_params.stages.get(0).unwrap() {
+            assert_eq!(ivf_params.num_partitions, 500);
+        } else {
+            assert!(false, "Expected first stage to be ivf")
+        }
+
+        if let StageParams::PQ(pq_params) = index_params.stages.get(1).unwrap() {
+            assert_eq!(pq_params.use_opq, true);
+            assert_eq!(pq_params.max_iters, 1);
+            assert_eq!(pq_params.num_bits, 8);
+            assert_eq!(pq_params.num_sub_vectors, 50);
+            assert_eq!(pq_params.metric_type, MetricType::Cosine);
+            assert_eq!(pq_params.max_opq_iters, 2);
+        } else {
+            assert!(false, "Expected second stage to be pq")
+        }
+    }
+}

rust/vectordb/src/lib.rs

@@ -14,5 +14,6 @@
 
 pub mod database;
 pub mod error;
+pub mod index;
 pub mod query;
 pub mod table;

rust/vectordb/src/query.rs

@@ -29,7 +29,7 @@ pub struct Query {
     pub filter: Option<String>,
     pub nprobes: usize,
     pub refine_factor: Option<u32>,
-    pub metric_type: MetricType,
+    pub metric_type: Option<MetricType>,
     pub use_index: bool,
 }
 
@@ -51,9 +51,9 @@ impl Query {
             limit: 10,
             nprobes: 20,
             refine_factor: None,
-            metric_type: MetricType::L2,
+            metric_type: None,
             use_index: false,
-            filter: None
+            filter: None,
         }
     }
 
@@ -71,10 +71,10 @@ impl Query {
             self.limit,
         )?;
         scanner.nprobs(self.nprobes);
-        scanner.distance_metric(self.metric_type);
         scanner.use_index(self.use_index);
         self.filter.as_ref().map(|f| scanner.filter(f));
         self.refine_factor.map(|rf| scanner.refine(rf));
+        self.metric_type.map(|mt| scanner.distance_metric(mt));
         Ok(scanner.try_into_stream().await?)
     }
 
@@ -123,7 +123,7 @@ impl Query {
     /// # Arguments
     ///
     /// * `metric_type` - The distance metric to use. By default [MetricType::L2] is used.
-    pub fn metric_type(mut self, metric_type: MetricType) -> Query {
+    pub fn metric_type(mut self, metric_type: Option<MetricType>) -> Query {
         self.metric_type = metric_type;
         self
     }
@@ -174,14 +174,14 @@ mod tests {
             .limit(100)
             .nprobes(1000)
             .use_index(true)
-            .metric_type(MetricType::Cosine)
+            .metric_type(Some(MetricType::Cosine))
             .refine_factor(Some(999));
 
         assert_eq!(query.query_vector, new_vector);
         assert_eq!(query.limit, 100);
         assert_eq!(query.nprobes, 1000);
         assert_eq!(query.use_index, true);
-        assert_eq!(query.metric_type, MetricType::Cosine);
+        assert_eq!(query.metric_type, Some(MetricType::Cosine));
         assert_eq!(query.refine_factor, Some(999));
     }
 

rust/vectordb/src/table.rs

@@ -12,26 +12,33 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-use std::path::PathBuf;
+use std::path::Path;
 use std::sync::Arc;
 
 use arrow_array::{Float32Array, RecordBatchReader};
 use lance::dataset::{Dataset, WriteMode, WriteParams};
+use lance::index::IndexType;
 
 use crate::error::{Error, Result};
+use crate::index::vector::VectorIndexBuilder;
 use crate::query::Query;
 
 pub const VECTOR_COLUMN_NAME: &str = "vector";
-
 pub const LANCE_FILE_EXTENSION: &str = "lance";
 
 /// A table in a LanceDB database.
 pub struct Table {
     name: String,
-    path: String,
+    uri: String,
     dataset: Arc<Dataset>,
 }
 
+impl std::fmt::Display for Table {
+    fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
+        write!(f, "Table({})", self.name)
+    }
+}
+
 impl Table {
     /// Opens an existing Table
     ///
@@ -43,18 +50,21 @@ impl Table {
     /// # Returns
     ///
     /// * A [Table] object.
-    pub async fn open(base_path: Arc<PathBuf>, name: String) -> Result<Self> {
+    pub async fn open(base_uri: &str, name: &str) -> Result<Self> {
-        let ds_path = base_path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
+        let path = Path::new(base_uri);
-        let ds_uri = ds_path
+
+        let table_uri = path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
+        let uri = table_uri
+            .as_path()
             .to_str()
-            .ok_or(Error::IO(format!("Unable to find table {}", name)))?;
+            .ok_or(Error::IO(format!("Invalid table name: {}", name)))?;
-        let dataset = Dataset::open(ds_uri).await?;
+
-        let table = Table {
+        let dataset = Dataset::open(&uri).await?;
-            name,
+        Ok(Table {
-            path: ds_uri.to_string(),
+            name: name.to_string(),
+            uri: uri.to_string(),
             dataset: Arc::new(dataset),
-        };
+        })
-        Ok(table)
     }
 
     /// Creates a new Table
@@ -69,18 +79,44 @@ impl Table {
     ///
     /// * A [Table] object.
     pub async fn create(
-        base_path: Arc<PathBuf>,
+        base_uri: &str,
-        name: String,
+        name: &str,
         mut batches: Box<dyn RecordBatchReader>,
     ) -> Result<Self> {
-        let ds_path = base_path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
+        let base_path = Path::new(base_uri);
-        let path = ds_path
+        let table_uri = base_path.join(format!("{}.{}", name, LANCE_FILE_EXTENSION));
+        let uri = table_uri
+            .as_path()
             .to_str()
-            .ok_or(Error::IO(format!("Unable to find table {}", name)))?;
+            .ok_or(Error::IO(format!("Invalid table name: {}", name)))?
-
+            .to_string();
         let dataset =
-            Arc::new(Dataset::write(&mut batches, path, Some(WriteParams::default())).await?);
+            Arc::new(Dataset::write(&mut batches, &uri, Some(WriteParams::default())).await?);
-        Ok(Table { name, path: path.to_string(), dataset })
+        Ok(Table {
+            name: name.to_string(),
+            uri,
+            dataset,
+        })
+    }
+
+    /// Create index on the table.
+    pub async fn create_index(&mut self, index_builder: &impl VectorIndexBuilder) -> Result<()> {
+        use lance::index::DatasetIndexExt;
+
+        let dataset = self
+            .dataset
+            .create_index(
+                &[index_builder
+                    .get_column()
+                    .unwrap_or(VECTOR_COLUMN_NAME.to_string())
+                    .as_str()],
+                IndexType::Vector,
+                index_builder.get_index_name(),
+                &index_builder.build(),
+            )
+            .await?;
+        self.dataset = Arc::new(dataset);
+        Ok(())
     }
 
     /// Insert records into this Table
@@ -95,12 +131,12 @@ impl Table {
     pub async fn add(
         &mut self,
         mut batches: Box<dyn RecordBatchReader>,
-        write_mode: Option<WriteMode>
+        write_mode: Option<WriteMode>,
     ) -> Result<usize> {
         let mut params = WriteParams::default();
         params.mode = write_mode.unwrap_or(WriteMode::Append);
 
-        self.dataset = Arc::new(Dataset::write(&mut batches, self.path.as_str(), Some(params)).await?);
+        self.dataset = Arc::new(Dataset::write(&mut batches, &self.uri, Some(params)).await?);
         Ok(batches.count())
     }
 
@@ -125,56 +161,68 @@ impl Table {
 
 #[cfg(test)]
 mod tests {
-    use arrow_array::{Float32Array, Int32Array, RecordBatch, RecordBatchReader};
+    use std::sync::Arc;
+
+    use arrow_array::{
+        Array, FixedSizeListArray, Float32Array, Int32Array, RecordBatch, RecordBatchReader,
+    };
+    use arrow_data::ArrayDataBuilder;
     use arrow_schema::{DataType, Field, Schema};
     use lance::arrow::RecordBatchBuffer;
     use lance::dataset::{Dataset, WriteMode};
-    use std::sync::Arc;
+    use lance::index::vector::ivf::IvfBuildParams;
+    use lance::index::vector::pq::PQBuildParams;
+    use rand::Rng;
     use tempfile::tempdir;
 
-    use crate::table::Table;
+    use super::*;
+    use crate::index::vector::IvfPQIndexBuilder;
 
     #[tokio::test]
     async fn test_new_table_not_exists() {
         let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let uri = tmp_dir.path().to_str().unwrap();
 
-        let table = Table::open(Arc::new(path_buf), "test".to_string()).await;
+        let table = Table::open(&uri, "test").await;
         assert!(table.is_err());
     }
 
     #[tokio::test]
     async fn test_open() {
         let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = tmp_dir.path().to_str().unwrap();
 
         let mut batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
-        Dataset::write(
+        Dataset::write(&mut batches, dataset_path.to_str().unwrap(), None)
-            &mut batches,
-            path_buf.join("test.lance").to_str().unwrap(),
-            None,
-        )
             .await
             .unwrap();
 
-        let table = Table::open(Arc::new(path_buf), "test".to_string())
+        let table = Table::open(uri, "test").await.unwrap();
-            .await
-            .unwrap();
 
         assert_eq!(table.name, "test")
     }
 
+    #[test]
+    fn test_object_store_path() {
+        use std::path::Path as StdPath;
+        let p = StdPath::new("s3://bucket/path/to/file");
+        let c = p.join("subfile");
+        assert_eq!(c.to_str().unwrap(), "s3://bucket/path/to/file/subfile");
+    }
+
     #[tokio::test]
     async fn test_add() {
         let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let uri = tmp_dir.path().to_str().unwrap();
 
         let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
         let schema = batches.schema().clone();
-        let mut table = Table::create(Arc::new(path_buf), "test".to_string(), batches).await.unwrap();
+        let mut table = Table::create(&uri, "test", batches).await.unwrap();
         assert_eq!(table.count_rows().await.unwrap(), 10);
 
-        let new_batches: Box<dyn RecordBatchReader> = Box::new(RecordBatchBuffer::new(vec![RecordBatch::try_new(
+        let new_batches: Box<dyn RecordBatchReader> =
+            Box::new(RecordBatchBuffer::new(vec![RecordBatch::try_new(
                 schema,
                 vec![Arc::new(Int32Array::from_iter_values(100..110))],
             )
@@ -188,19 +236,24 @@ mod tests {
     #[tokio::test]
     async fn test_add_overwrite() {
         let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let uri = tmp_dir.path().to_str().unwrap();
 
         let batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
         let schema = batches.schema().clone();
-        let mut table = Table::create(Arc::new(path_buf), "test".to_string(), batches).await.unwrap();
+        let mut table = Table::create(uri, "test", batches).await.unwrap();
         assert_eq!(table.count_rows().await.unwrap(), 10);
 
-        let new_batches: Box<dyn RecordBatchReader> = Box::new(RecordBatchBuffer::new(vec![RecordBatch::try_new(
+        let new_batches: Box<dyn RecordBatchReader> =
+            Box::new(RecordBatchBuffer::new(vec![RecordBatch::try_new(
                 schema,
                 vec![Arc::new(Int32Array::from_iter_values(100..110))],
-        ).unwrap()]));
+            )
+            .unwrap()]));
 
-        table.add(new_batches, Some(WriteMode::Overwrite)).await.unwrap();
+        table
+            .add(new_batches, Some(WriteMode::Overwrite))
+            .await
+            .unwrap();
         assert_eq!(table.count_rows().await.unwrap(), 10);
         assert_eq!(table.name, "test");
     }
@@ -208,20 +261,15 @@ mod tests {
     #[tokio::test]
     async fn test_search() {
         let tmp_dir = tempdir().unwrap();
-        let path_buf = tmp_dir.into_path();
+        let dataset_path = tmp_dir.path().join("test.lance");
+        let uri = tmp_dir.path().to_str().unwrap();
 
         let mut batches: Box<dyn RecordBatchReader> = Box::new(make_test_batches());
-        Dataset::write(
+        Dataset::write(&mut batches, dataset_path.to_str().unwrap(), None)
-            &mut batches,
-            path_buf.join("test.lance").to_str().unwrap(),
-            None,
-        )
             .await
             .unwrap();
 
-        let table = Table::open(Arc::new(path_buf), "test".to_string())
+        let table = Table::open(uri, "test").await.unwrap();
-            .await
-            .unwrap();
 
         let vector = Float32Array::from_iter_values([0.1, 0.2]);
         let query = table.search(vector.clone());
@@ -236,4 +284,72 @@ mod tests {
         )
         .unwrap()])
     }
+
+    #[tokio::test]
+    async fn test_create_index() {
+        use arrow_array::RecordBatch;
+        use arrow_schema::{DataType, Field, Schema as ArrowSchema};
+        use rand;
+        use std::iter::repeat_with;
+
+        use arrow_array::Float32Array;
+
+        let tmp_dir = tempdir().unwrap();
+        let uri = tmp_dir.path().to_str().unwrap();
+
+        let dimension = 16;
+        let schema = Arc::new(ArrowSchema::new(vec![Field::new(
+            "embeddings",
+            DataType::FixedSizeList(
+                Arc::new(Field::new("item", DataType::Float32, true)),
+                dimension,
+            ),
+            false,
+        )]));
+
+        let mut rng = rand::thread_rng();
+        let float_arr = Float32Array::from(
+            repeat_with(|| rng.gen::<f32>())
+                .take(512 * dimension as usize)
+                .collect::<Vec<f32>>(),
+        );
+
+        let vectors = Arc::new(create_fixed_size_list(float_arr, dimension).unwrap());
+        let batches = RecordBatchBuffer::new(vec![RecordBatch::try_new(
+            schema.clone(),
+            vec![vectors.clone()],
+        )
+        .unwrap()]);
+
+        let reader: Box<dyn RecordBatchReader + Send> = Box::new(batches);
+        let mut table = Table::create(uri, "test", reader).await.unwrap();
+
+        let mut i = IvfPQIndexBuilder::new();
+
+        let index_builder = i
+            .column("embeddings".to_string())
+            .index_name("my_index".to_string())
+            .ivf_params(IvfBuildParams::new(256))
+            .pq_params(PQBuildParams::default());
+
+        table.create_index(index_builder).await.unwrap();
+
+        assert_eq!(table.dataset.load_indices().await.unwrap().len(), 1);
+        assert_eq!(table.count_rows().await.unwrap(), 512);
+        assert_eq!(table.name, "test");
+    }
+
+    fn create_fixed_size_list<T: Array>(values: T, list_size: i32) -> Result<FixedSizeListArray> {
+        let list_type = DataType::FixedSizeList(
+            Arc::new(Field::new("item", values.data_type().clone(), true)),
+            list_size,
+        );
+        let data = ArrayDataBuilder::new(list_type)
+            .len(values.len() / list_size as usize)
+            .add_child_data(values.into_data())
+            .build()
+            .unwrap();
+
+        Ok(FixedSizeListArray::from(data))
+    }
 }