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*   PGVector

On this page

PGVector
========

To enable vector search in a generic PostgreSQL database, LangChain.js supports using the [`pgvector`](https://github.com/pgvector/pgvector) Postgres extension.

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

To work with PGVector, you need to install the `pg` package:

*   npm
*   Yarn
*   pnpm

    npm install pg

    yarn add pg

    pnpm add pg

### Setup a `pgvector` self hosted instance with `docker-compose`[​](#setup-a-pgvector-self-hosted-instance-with-docker-compose "Direct link to setup-a-pgvector-self-hosted-instance-with-docker-compose")

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

`pgvector` provides a prebuilt Docker image that can be used to quickly setup a self-hosted Postgres instance. Create a file below named `docker-compose.yml`:

    # Run this command to start the database:# docker-compose up --buildversion: "3"services:  db:    hostname: 127.0.0.1    image: ankane/pgvector    ports:      - 5432:5432    restart: always    environment:      - POSTGRES_DB=api      - POSTGRES_USER=myuser      - POSTGRES_PASSWORD=ChangeMe    volumes:      - ./init.sql:/docker-entrypoint-initdb.d/init.sql

And then in the same directory, run `docker compose up` to start the container.

You can find more information on how to setup `pgvector` in the [official repository](https://github.com/pgvector/pgvector).

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

Security

User-generated data such as usernames should not be used as input for table and column names.  
**This may lead to SQL Injection!**

One complete example of using `PGVectorStore` is the following:

    import { OpenAIEmbeddings } from "@langchain/openai";import {  DistanceStrategy,  PGVectorStore,} from "@langchain/community/vectorstores/pgvector";import { PoolConfig } from "pg";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/pgvectorconst config = {  postgresConnectionOptions: {    type: "postgres",    host: "127.0.0.1",    port: 5433,    user: "myuser",    password: "ChangeMe",    database: "api",  } as PoolConfig,  tableName: "testlangchain",  columns: {    idColumnName: "id",    vectorColumnName: "vector",    contentColumnName: "content",    metadataColumnName: "metadata",  },  // supported distance strategies: cosine (default), innerProduct, or euclidean  distanceStrategy: "cosine" as DistanceStrategy,};const pgvectorStore = await PGVectorStore.initialize(  new OpenAIEmbeddings(),  config);await pgvectorStore.addDocuments([  { pageContent: "what's this", metadata: { a: 2 } },  { pageContent: "Cat drinks milk", metadata: { a: 1 } },]);const results = await pgvectorStore.similaritySearch("water", 1);console.log(results);/*  [ Document { pageContent: 'Cat drinks milk', metadata: { a: 1 } } ]*/// Filtering is supportedconst results2 = await pgvectorStore.similaritySearch("water", 1, {  a: 2,});console.log(results2);/*  [ Document { pageContent: 'what's this', metadata: { a: 2 } } ]*/// Filtering on multiple values using "in" is supported tooconst results3 = await pgvectorStore.similaritySearch("water", 1, {  a: {    in: [2],  },});console.log(results3);/*  [ Document { pageContent: 'what's this', metadata: { a: 2 } } ]*/await pgvectorStore.delete({  filter: {    a: 1,  },});const results4 = await pgvectorStore.similaritySearch("water", 1);console.log(results4);/*  [ Document { pageContent: 'what's this', metadata: { a: 2 } } ]*/await pgvectorStore.end();

#### API Reference:

*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [DistanceStrategy](https://v02.api.js.langchain.com/types/langchain_community_vectorstores_pgvector.DistanceStrategy.html) from `@langchain/community/vectorstores/pgvector`
*   [PGVectorStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_pgvector.PGVectorStore.html) from `@langchain/community/vectorstores/pgvector`

You can also specify a `collectionTableName` and a `collectionName` to partition vectors between multiple users or namespaces.

### Advanced: reusing connections[​](#advanced-reusing-connections "Direct link to Advanced: reusing connections")

You can reuse connections by creating a pool, then creating new `PGVectorStore` instances directly via the constructor.

Note that you should call `.initialize()` to set up your database at least once to set up your tables properly before using the constructor.

    import { OpenAIEmbeddings } from "@langchain/openai";import { PGVectorStore } from "@langchain/community/vectorstores/pgvector";import pg from "pg";// First, follow set-up instructions at// https://js.langchain.com/docs/modules/indexes/vector_stores/integrations/pgvectorconst reusablePool = new pg.Pool({  host: "127.0.0.1",  port: 5433,  user: "myuser",  password: "ChangeMe",  database: "api",});const originalConfig = {  pool: reusablePool,  tableName: "testlangchain",  collectionName: "sample",  collectionTableName: "collections",  columns: {    idColumnName: "id",    vectorColumnName: "vector",    contentColumnName: "content",    metadataColumnName: "metadata",  },};// Set up the DB.// Can skip this step if you've already initialized the DB.// await PGVectorStore.initialize(new OpenAIEmbeddings(), originalConfig);const pgvectorStore = new PGVectorStore(new OpenAIEmbeddings(), originalConfig);await pgvectorStore.addDocuments([  { pageContent: "what's this", metadata: { a: 2 } },  { pageContent: "Cat drinks milk", metadata: { a: 1 } },]);const results = await pgvectorStore.similaritySearch("water", 1);console.log(results);/*  [ Document { pageContent: 'Cat drinks milk', metadata: { a: 1 } } ]*/const pgvectorStore2 = new PGVectorStore(new OpenAIEmbeddings(), {  pool: reusablePool,  tableName: "testlangchain",  collectionTableName: "collections",  collectionName: "some_other_collection",  columns: {    idColumnName: "id",    vectorColumnName: "vector",    contentColumnName: "content",    metadataColumnName: "metadata",  },});const results2 = await pgvectorStore2.similaritySearch("water", 1);console.log(results2);/*  []*/await reusablePool.end();

#### API Reference:

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

* * *

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*   [Setup](#setup)
    *   [Setup a `pgvector` self hosted instance with `docker-compose`](#setup-a-pgvector-self-hosted-instance-with-docker-compose)
*   [Usage](#usage)
    *   [Advanced: reusing connections](#advanced-reusing-connections)

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