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*   LanceDB

On this page

LanceDB
=======

LanceDB is an embedded vector database for AI applications. It is open source and distributed with an Apache-2.0 license.

LanceDB datasets are persisted to disk and can be shared between Node.js and Python.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

Install the [LanceDB](https://github.com/lancedb/lancedb) [Node.js bindings](https://www.npmjs.com/package/vectordb):

*   npm
*   Yarn
*   pnpm

    npm install -S vectordb

    yarn add vectordb

    pnpm add vectordb

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai @langchain/community

    yarn add @langchain/openai @langchain/community

    pnpm add @langchain/openai @langchain/community

Usage[​](#usage "Direct link to Usage")
---------------------------------------

### Create a new index from texts[​](#create-a-new-index-from-texts "Direct link to Create a new index from texts")

    import { LanceDB } from "@langchain/community/vectorstores/lancedb";import { OpenAIEmbeddings } from "@langchain/openai";import { connect } from "vectordb";import * as fs from "node:fs/promises";import * as path from "node:path";import os from "node:os";export const run = async () => {  const dir = await fs.mkdtemp(path.join(os.tmpdir(), "lancedb-"));  const db = await connect(dir);  const table = await db.createTable("vectors", [    { vector: Array(1536), text: "sample", id: 1 },  ]);  const vectorStore = await LanceDB.fromTexts(    ["Hello world", "Bye bye", "hello nice world"],    [{ id: 2 }, { id: 1 }, { id: 3 }],    new OpenAIEmbeddings(),    { table }  );  const resultOne = await vectorStore.similaritySearch("hello world", 1);  console.log(resultOne);  // [ Document { pageContent: 'hello nice world', metadata: { id: 3 } } ]};

#### API Reference:

*   [LanceDB](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_lancedb.LanceDB.html) from `@langchain/community/vectorstores/lancedb`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

### Create a new index from a loader[​](#create-a-new-index-from-a-loader "Direct link to Create a new index from a loader")

    import { LanceDB } from "@langchain/community/vectorstores/lancedb";import { OpenAIEmbeddings } from "@langchain/openai";import { TextLoader } from "langchain/document_loaders/fs/text";import fs from "node:fs/promises";import path from "node:path";import os from "node:os";import { connect } from "vectordb";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await loader.load();export const run = async () => {  const dir = await fs.mkdtemp(path.join(os.tmpdir(), "lancedb-"));  const db = await connect(dir);  const table = await db.createTable("vectors", [    { vector: Array(1536), text: "sample", source: "a" },  ]);  const vectorStore = await LanceDB.fromDocuments(    docs,    new OpenAIEmbeddings(),    { table }  );  const resultOne = await vectorStore.similaritySearch("hello world", 1);  console.log(resultOne);  // [  //   Document {  //     pageContent: 'Foo\nBar\nBaz\n\n',  //     metadata: { source: 'src/document_loaders/example_data/example.txt' }  //   }  // ]};

#### API Reference:

*   [LanceDB](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_lancedb.LanceDB.html) from `@langchain/community/vectorstores/lancedb`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text`

### Open an existing dataset[​](#open-an-existing-dataset "Direct link to Open an existing dataset")

    import { LanceDB } from "@langchain/community/vectorstores/lancedb";import { OpenAIEmbeddings } from "@langchain/openai";import { connect } from "vectordb";import * as fs from "node:fs/promises";import * as path from "node:path";import os from "node:os";////  You can open a LanceDB dataset created elsewhere, such as LangChain Python, by opening//     an existing table//export const run = async () => {  const uri = await createdTestDb();  const db = await connect(uri);  const table = await db.openTable("vectors");  const vectorStore = new LanceDB(new OpenAIEmbeddings(), { table });  const resultOne = await vectorStore.similaritySearch("hello world", 1);  console.log(resultOne);  // [ Document { pageContent: 'Hello world', metadata: { id: 1 } } ]};async function createdTestDb(): Promise<string> {  const dir = await fs.mkdtemp(path.join(os.tmpdir(), "lancedb-"));  const db = await connect(dir);  await db.createTable("vectors", [    { vector: Array(1536), text: "Hello world", id: 1 },    { vector: Array(1536), text: "Bye bye", id: 2 },    { vector: Array(1536), text: "hello nice world", id: 3 },  ]);  return dir;}

#### API Reference:

*   [LanceDB](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_lancedb.LanceDB.html) from `@langchain/community/vectorstores/lancedb`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

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*   [Setup](#setup)
*   [Usage](#usage)
    *   [Create a new index from texts](#create-a-new-index-from-texts)
    *   [Create a new index from a loader](#create-a-new-index-from-a-loader)
    *   [Open an existing dataset](#open-an-existing-dataset)

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