Vector stores

A vector store stores embedded data and performs similarity search.

Pick your embedding model:

OpenAI

Install dependencies

npm

npm i @langchain/openai

yarn

yarn add @langchain/openai

pnpm

pnpm add @langchain/openai

OPENAI_API_KEY=your-api-key

import { OpenAIEmbeddings } from "@langchain/openai";

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

Azure

Install dependencies

npm

npm i @langchain/openai

yarn

yarn add @langchain/openai

pnpm

pnpm add @langchain/openai

AZURE_OPENAI_API_INSTANCE_NAME=<YOUR_INSTANCE_NAME>
AZURE_OPENAI_API_KEY=<YOUR_KEY>
AZURE_OPENAI_API_VERSION="2024-02-01"

import { AzureOpenAIEmbeddings } from "@langchain/openai";

const embeddings = new AzureOpenAIEmbeddings({
  azureOpenAIApiEmbeddingsDeploymentName: "text-embedding-ada-002"
});

AWS

Install dependencies

npm

npm i @langchain/aws

yarn

yarn add @langchain/aws

pnpm

pnpm add @langchain/aws

BEDROCK_AWS_REGION=your-region

import { BedrockEmbeddings } from "@langchain/aws";

const embeddings = new BedrockEmbeddings({
  model: "amazon.titan-embed-text-v1"
});

VertexAI

Install dependencies

npm

npm i @langchain/google-vertexai

yarn

yarn add @langchain/google-vertexai

pnpm

pnpm add @langchain/google-vertexai

GOOGLE_APPLICATION_CREDENTIALS=credentials.json

import { VertexAIEmbeddings } from "@langchain/google-vertexai";

const embeddings = new VertexAIEmbeddings({
  model: "text-embedding-004"
});

MistralAI

Install dependencies

npm

npm i @langchain/mistralai

yarn

yarn add @langchain/mistralai

pnpm

pnpm add @langchain/mistralai

MISTRAL_API_KEY=your-api-key

import { MistralAIEmbeddings } from "@langchain/mistralai";

const embeddings = new MistralAIEmbeddings({
  model: "mistral-embed"
});

Cohere

Install dependencies

npm

npm i @langchain/cohere

yarn

yarn add @langchain/cohere

pnpm

pnpm add @langchain/cohere

COHERE_API_KEY=your-api-key

import { CohereEmbeddings } from "@langchain/cohere";

const embeddings = new CohereEmbeddings({
  model: "embed-english-v3.0"
});

Pick your vector store:

Memory

Install dependencies

npm

npm i langchain

yarn

yarn add langchain

pnpm

pnpm add langchain

import { MemoryVectorStore } from "langchain/vectorstores/memory";

const vectorStore = new MemoryVectorStore(embeddings);

Chroma

Install dependencies

npm

npm i @langchain/community

yarn

yarn add @langchain/community

pnpm

pnpm add @langchain/community

import { Chroma } from "@langchain/community/vectorstores/chroma";

const vectorStore = new Chroma(embeddings, {
  collectionName: "a-test-collection",
});

FAISS

Install dependencies

npm

npm i @langchain/community

yarn

yarn add @langchain/community

pnpm

pnpm add @langchain/community

import { FaissStore } from "@langchain/community/vectorstores/faiss";

const vectorStore = new FaissStore(embeddings, {});

MongoDB

Install dependencies

npm

npm i @langchain/mongodb

yarn

yarn add @langchain/mongodb

pnpm

pnpm add @langchain/mongodb

import { MongoDBAtlasVectorSearch } from "@langchain/mongodb"
import { MongoClient } from "mongodb";

const client = new MongoClient(process.env.MONGODB_ATLAS_URI || "");
const collection = client
  .db(process.env.MONGODB_ATLAS_DB_NAME)
  .collection(process.env.MONGODB_ATLAS_COLLECTION_NAME);

const vectorStore = new MongoDBAtlasVectorSearch(embeddings, {
  collection: collection,
  indexName: "vector_index",
  textKey: "text",
  embeddingKey: "embedding",
});

PGVector

Install dependencies

npm

npm i @langchain/community

yarn

yarn add @langchain/community

pnpm

pnpm add @langchain/community

import PGVectorStore from "@langchain/community/vectorstores/pgvector";

const vectorStore = await PGVectorStore.initialize(embeddings, {})

Pinecone

Install dependencies

npm

npm i @langchain/pinecone

yarn

yarn add @langchain/pinecone

pnpm

pnpm add @langchain/pinecone

import { PineconeStore } from "@langchain/pinecone";
import { Pinecone as PineconeClient } from "@pinecone-database/pinecone";

const pinecone = new PineconeClient();
const vectorStore = new PineconeStore(embeddings, {
  pineconeIndex,
  maxConcurrency: 5,
});

Qdrant

Install dependencies

npm

npm i @langchain/qdrant

yarn

yarn add @langchain/qdrant

pnpm

pnpm add @langchain/qdrant

import { QdrantVectorStore } from "@langchain/qdrant";

const vectorStore = await QdrantVectorStore.fromExistingCollection(embeddings, {
  url: process.env.QDRANT_URL,
  collectionName: "langchainjs-testing",
});

LangChain.js integrates with a variety of vector stores. You can check out a full list below:

Name	Description
AnalyticDB	AnalyticDB for PostgreSQL is a massively parallel processing (MPP) da...
Astra DB	Only available on Node.js.
Azure AI Search	Azure AI Search (formerly known as Azure Search and Azure Cognitive S...
Azure Cosmos DB for MongoDB vCore	Azure Cosmos DB for MongoDB vCore makes it easy to create a database ...
Azure Cosmos DB for NoSQL	Azure Cosmos DB for NoSQL provides support for querying items with fl...
Cassandra	Only available on Node.js.
Chroma	Chroma is a AI-native
ClickHouse	Only available on Node.js.
CloseVector	available on both browser and Node.js
Cloudflare Vectorize	If you're deploying your project in a Cloudflare worker, you can use ...
Convex	LangChain.js supports Convex as a vector store, and supports the stan...
Couchbase	Couchbase is an award-winning distributed NoSQL cloud database that d...
Elasticsearch	Elasticsearch is a
Faiss	Faiss is a library for
Google Vertex AI Matching Engine	Only available on Node.js.
SAP HANA Cloud Vector Engine	SAP HANA Cloud Vector Engine is a vector store fully integrated into ...
HNSWLib	HNSWLib is an in-memory vector store that can be saved to a file. It
LanceDB	LanceDB is an embedded vector database for AI applications. It is ope...
libSQL	Turso is a SQLite-compatible database built on libSQL, the Open Contr...
In-memory	LangChain offers is an in-memory, ephemeral vectorstore that stores
Milvus	Milvus is a vector database built for embeddings similarity search an...
Momento Vector Index (MVI)	MVI: the most productive, easiest to use, serverless vector index for...
MongoDB Atlas	This guide provides a quick overview for getting started with MongoDB
MyScale	Only available on Node.js.
Neo4j Vector Index	Neo4j is an open-source graph database with integrated support for ve...
Neon Postgres	Neon is a fully managed serverless PostgreSQL database. It separates ...
OpenSearch	Only available on Node.js.
PGVector	To enable vector search in generic PostgreSQL databases, LangChain.js
Pinecone	Pinecone is a vector database that helps
Prisma	For augmenting existing models in PostgreSQL database with vector sea...
Qdrant	Qdrant is a vector similarity search engine. It
Redis	Redis is a fast open source, in-memory data store.
Rockset	Rockset is a real-time analyitics SQL database that runs in the cloud.
SingleStore	SingleStoreDB is a robust, high-performance distributed SQL database ...
Supabase	Supabase is an open-source Firebase
Tigris	Tigris makes it easy to build AI applications with vector embeddings.
Turbopuffer	Setup
TypeORM	To enable vector search in a generic PostgreSQL database, LangChain.j...
Typesense	Vector store that utilizes the Typesense search engine.
Upstash Vector	Upstash Vector is a REST based serverless vector
USearch	Only available on Node.js.
Vectara	Vectara is a platform for building GenAI applications. It provides an...
Vercel Postgres	LangChain.js supports using the @vercel/postgres package to use gener...
Voy	Voy is a WASM vector similarity search engine written in Rust.
Weaviate	Weaviate is an open source vector database that
Xata	Xata is a serverless data platform, based on PostgreSQL. It provides ...
Zep Open Source	Zep is a long-term memory service for AI Assistant apps.
Zep Cloud	Zep is a long-term memory service for AI Assistant apps.