PGVectorStore integration

Compatibility: Only available on Node.js.

To enable vector search in generic PostgreSQL databases, LangChain.js supports using the pgvector Postgres extension. This guide provides a quick overview for getting started with PGVector vector stores. For detailed documentation of all PGVectorStore features and configurations head to the API reference.

PGVectorStore vs PGVector: Which one to use?

LangChain has two Postgres vector store integrations. Here’s how to choose the right one:

Feature	PGVector (Legacy)	PGVectorStore (Modern)
Package	`langchain-community`	`langchain-postgres` (Python) / `@langchain/pgvector` (JS)
Maintenance	Community-maintained	Official Partner Package
Connection	Simple connection string	`PGEngine` (better connection pooling)
Search Types	Standard Vector Search	Hybrid Search (Vector + BM25)
Async Support	Limited	Full Native Async
Schema	Fixed table structure	Customizable columns and schemas

Use PGVectorStore (Modern—recommended) when:

Starting any new project
You need Hybrid Search (vector + keyword combined)
You want better performance via connection pooling
You are building async applications (e.g., FastAPI, async Node.js)

Use PGVector (Legacy) only when:

Maintaining an existing codebase that already uses it
Following an older tutorial that has not been updated yet

For all new projects, we recommend using PGVectorStore from langchain-postgres (Python) or @langchain/community (JavaScript).

Overview

Integration details

Class	Package	PY support	Downloads	Version
`PGVectorStore`	`@langchain/pgvector`	✅

Setup

To use PGVector vector stores, set up a Postgres instance with the pgvector extension enabled, then install @langchain/pgvector, the pg driver, and @langchain/core. This guide uses OpenAI embeddings as an example. You can use other supported embeddings models instead.

npm install @langchain/pgvector @langchain/openai @langchain/core pg

yarn add @langchain/pgvector @langchain/openai @langchain/core pg

pnpm add @langchain/pgvector @langchain/openai @langchain/core pg

Setting up an instance

There are many ways to connect to Postgres depending on how you’ve set up your instance. Here’s one example of a local setup using a prebuilt Docker image provided by the pgvector team. Create a file with the below content named docker-compose.yml:

# Run this command to start the database:
# docker compose up
services:
  db:
    hostname: 127.0.0.1
    image: pgvector/pgvector:pg16
    ports:
      - 5432:5432
    restart: always
    environment:
      - POSTGRES_DB=api
      - POSTGRES_USER=myuser
      - POSTGRES_PASSWORD=ChangeMe

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.

Credentials

To connect to your Postgres instance, you’ll need corresponding credentials. For a full list of supported options, see the node-postgres docs. If you are using OpenAI embeddings for this guide, you’ll need to set your OpenAI key as well:

process.env.OPENAI_API_KEY = "YOUR_API_KEY";

If you want to get automated tracing of your model calls you can also set your LangSmith API key by uncommenting below:

// process.env.LANGSMITH_TRACING="true"
// process.env.LANGSMITH_API_KEY="your-api-key"

Instantiation

To instantiate the vector store, call the .initialize() static method. This will automatically check for the presence of a table, given by tableName in the passed config. If it is not there, it will create it with the required columns.

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

import { PGVectorStore } from "@langchain/pgvector";
import type { DistanceStrategy } from "@langchain/pgvector";
import { OpenAIEmbeddings } from "@langchain/openai";
import type { PoolConfig } from "pg";

const embeddings = new OpenAIEmbeddings({
  model: "text-embedding-3-small",
});

// Sample config
const config = {
  postgresConnectionOptions: {
    type: "postgres",
    host: "127.0.0.1",
    port: 5433,
    user: "myuser",
    password: "ChangeMe",
    database: "api",
  } as PoolConfig,
  tableName: "testlangchainjs",
  columns: {
    idColumnName: "id",
    vectorColumnName: "vector",
    contentColumnName: "content",
    metadataColumnName: "metadata",
  },
  // supported distance strategies: cosine (default), innerProduct, or euclidean
  distanceStrategy: "cosine" as DistanceStrategy,
};

const vectorStore = await PGVectorStore.initialize(
  embeddings,
  config
);

Manage vector store

Add items to vector store

import type { Document } from "@langchain/core/documents";

const document1: Document = {
  pageContent: "The powerhouse of the cell is the mitochondria",
  metadata: { source: "https://example.com" }
};

const document2: Document = {
  pageContent: "Buildings are made out of brick",
  metadata: { source: "https://example.com" }
};

const document3: Document = {
  pageContent: "Mitochondria are made out of lipids",
  metadata: { source: "https://example.com" }
};

const document4: Document = {
  pageContent: "The 2024 Olympics are in Paris",
  metadata: { source: "https://example.com" }
}

const documents = [document1, document2, document3, document4];

const ids = [crypto.randomUUID(), crypto.randomUUID(), crypto.randomUUID(), crypto.randomUUID()]

await vectorStore.addDocuments(documents, { ids: ids });

Delete items from vector store

const id4 = ids[ids.length - 1];

await vectorStore.delete({ ids: [id4] });

Query vector store

Once your vector store has been created and the relevant documents have been added you will most likely wish to query it during the running of your chain or agent.

Query directly

Performing a simple similarity search can be done as follows:

const filter = { source: "https://example.com" };

const similaritySearchResults = await vectorStore.similaritySearch("biology", 2, filter);

for (const doc of similaritySearchResults) {
  console.log(`* ${doc.pageContent} [${JSON.stringify(doc.metadata, null)}]`);
}

* The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* Mitochondria are made out of lipids [{"source":"https://example.com"}]

The above filter syntax supports exact match, but the following are also supported:

Using the `in` operator

{
  "field": {
    "in": ["value1", "value2"],
  }
}

Using the `notIn` operator

{
  "field": {
    "notIn": ["value1", "value2"],
  }
}

Using the `arrayContains` operator

{
  "field": {
    "arrayContains": ["value1", "value2"],
  }
}

If you want to execute a similarity search and receive the corresponding scores you can run:

const similaritySearchWithScoreResults = await vectorStore.similaritySearchWithScore("biology", 2, filter)

for (const [doc, score] of similaritySearchWithScoreResults) {
  console.log(`* [SIM=${score.toFixed(3)}] ${doc.pageContent} [${JSON.stringify(doc.metadata)}]`);
}

* [SIM=0.835] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.852] Mitochondria are made out of lipids [{"source":"https://example.com"}]

Query by turning into retriever

You can also transform the vector store into a retriever for easier usage in your chains.

const retriever = vectorStore.asRetriever({
  // Optional filter
  filter: filter,
  k: 2,
});
await retriever.invoke("biology");

[
  Document {
    pageContent: 'The powerhouse of the cell is the mitochondria',
    metadata: { source: 'https://example.com' },
    id: undefined
  },
  Document {
    pageContent: 'Mitochondria are made out of lipids',
    metadata: { source: 'https://example.com' },
    id: undefined
  }
]

Usage for retrieval-augmented generation

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

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/pgvector";
import pg from "pg";

const reusablePool = new pg.Pool({
  host: "127.0.0.1",
  port: 5433,
  user: "myuser",
  password: "ChangeMe",
  database: "api",
});

const originalConfig = {
  pool: reusablePool,
  tableName: "testlangchainjs",
  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: "testlangchainjs",
  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();

Create HNSW index

By default, the extension performs a sequential scan search, with 100% recall. You might consider creating an HNSW index for approximate nearest neighbor (ANN) search to speed up similaritySearchVectorWithScore execution time. To create the HNSW index on your vector column, use the createHnswIndex() method. The method parameters include:

dimensions: Defines the number of dimensions in your vector data type, up to 2000. For example, use 1536 for OpenAI’s text-embedding-ada-002 and Amazon’s amazon.titan-embed-text-v1 models.
m?: The max number of connections per layer (16 by default). Index build time improves with smaller values, while higher values can speed up search queries.
efConstruction?: The size of the dynamic candidate list for constructing the graph (64 by default). A higher value can potentially improve the index quality at the cost of index build time.
distanceFunction?: The distance function name you want to use, is automatically selected based on the distanceStrategy.

For more info, see the Pgvector GitHub repo and the HNSW paper from Malkov Yu A. and Yashunin D. A.. 2020. Efficient and robust approximate nearest neighbor search using hierarchical navigable small world graphs

import { OpenAIEmbeddings } from "@langchain/openai";
import { PGVectorStore } from "@langchain/pgvector";
import type { DistanceStrategy } from "@langchain/pgvector";
import type { PoolConfig } from "pg";


const hnswConfig = {
  postgresConnectionOptions: {
    type: "postgres",
    host: "127.0.0.1",
    port: 5433,
    user: "myuser",
    password: "ChangeMe",
    database: "api",
  } as PoolConfig,
  tableName: "testlangchainjs",
  columns: {
    idColumnName: "id",
    vectorColumnName: "vector",
    contentColumnName: "content",
    metadataColumnName: "metadata",
  },
  // supported distance strategies: cosine (default), innerProduct, or euclidean
  distanceStrategy: "cosine" as DistanceStrategy,
};

const hnswPgVectorStore = await PGVectorStore.initialize(
  new OpenAIEmbeddings(),
  hnswConfig
);

// create the index
await hnswPgVectorStore.createHnswIndex({
  dimensions: 1536,
  efConstruction: 64,
  m: 16,
});

await hnswPgVectorStore.addDocuments([
  { pageContent: "what's this", metadata: { a: 2, b: ["tag1", "tag2"] } },
  { pageContent: "Cat drinks milk", metadata: { a: 1, b: ["tag2"] } },
]);

const model = new OpenAIEmbeddings();
const query = await model.embedQuery("water");
const hnswResults = await hnswPgVectorStore.similaritySearchVectorWithScore(query, 1);

console.log(hnswResults);

await hnswPgVectorStore.end();

Closing connections

Make sure you close the connection when you are finished to avoid excessive resource consumption:

await vectorStore.end();

API reference

For detailed documentation of all PGVectorStore features and configurations head to the API reference.

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Popular Providers

General integrations

RAG integrations

PGVectorStore vs PGVector: Which one to use?

Overview

Integration details

Setup

Setting up an instance

Credentials

Instantiation

Manage vector store

Add items to vector store

Delete items from vector store

Query vector store

Query directly

Using the `in` operator

Using the `notIn` operator

Using the `arrayContains` operator

Query by turning into retriever

Usage for retrieval-augmented generation

Advanced: reusing connections

Create HNSW index

Closing connections

API reference

​PGVectorStore vs PGVector: Which one to use?

​Overview

​Integration details

​Setup

​Setting up an instance

​Credentials

​Instantiation

​Manage vector store

​Add items to vector store

​Delete items from vector store

​Query vector store

​Query directly

​Using the in operator

​Using the notIn operator

​Using the arrayContains operator

​Query by turning into retriever

​Usage for retrieval-augmented generation

​Advanced: reusing connections

​Create HNSW index

​Closing connections

​API reference

PGVectorStore vs PGVector: Which one to use?

Overview

Integration details

Setup

Setting up an instance

Credentials

Instantiation

Manage vector store

Add items to vector store

Delete items from vector store

Query vector store

Query directly

Using the `in` operator

Using the `notIn` operator

Using the `arrayContains` operator

Query by turning into retriever

Usage for retrieval-augmented generation

Advanced: reusing connections

Create HNSW index

Closing connections

API reference