Vector stores - Docs by LangChain

A vector store stores embedded data and performs similarity search.

OpenAI

Install dependencies:

npm i @langchain/openai

Add environment variables:

OPENAI_API_KEY=your-api-key

Instantiate the model:

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

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

Azure

Install dependencies

npm i @langchain/openai

Add environment variables:

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

Instantiate the model:

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

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

AWS

Install dependencies:

npm i @langchain/aws

Add environment variables:

BEDROCK_AWS_REGION=your-region

Instantiate the model:

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

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

VertexAI

Install dependencies:

npm i @langchain/google-vertexai

Add environment variables:

GOOGLE_APPLICATION_CREDENTIALS=credentials.json

Instantiate the model:

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

const embeddings = new VertexAIEmbeddings({
  model: "gemini-embedding-001"
});

MistralAI

Install dependencies:

npm i @langchain/mistralai

Add environment variables:

MISTRAL_API_KEY=your-api-key

Instantiate the model:

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

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

Cohere

Install dependencies:

npm i @langchain/cohere

Add environment variables:

COHERE_API_KEY=your-api-key

Instantiate the model:

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

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

Memory

Install dependencies:

npm i langchain

Instantiate the model:

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

const vectorStore = new MemoryVectorStore(embeddings);

Chroma

Install dependencies:

npm i @langchain/community

Instantiate the model:

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

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

FAISS

Install dependencies:

npm i @langchain/community

Instantiate the model:

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

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

MongoDB

Install dependencies:

npm i @langchain/mongodb

Instantiate the model:

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,
  indexName: "vector_index",
  textKey: "text",
  embeddingKey: "embedding",
});

PGVector

Install dependencies:

npm i @langchain/community

Instantiate the model:

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

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

Pinecone

Install dependencies:

npm i @langchain/pinecone

Instantiate the model:

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 i @langchain/qdrant

Instantiate the model:

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:

Providers

General integrations

RAG integrations

​All vector stores

AnalyticDB

Astra DB

Azion EdgeSQL

Azure AI Search

Azure Cosmos DB for MongoDB vCore

Azure Cosmos DB for NoSQL

Cassandra

Chroma

ClickHouse

CloseVector

Cloudflare Vectorize

Convex

Couchbase

Elasticsearch

Faiss

Google Cloud SQL for PostgreSQL

Google Vertex AI Matching Engine

SAP HANA Cloud Vector Engine

HNSWLib

LanceDB

libSQL

MariaDB

In-memory

Milvus

Momento Vector Index (MVI)

MongoDB Atlas

MyScale

Neo4j Vector Index

Neon Postgres

OpenSearch

PGVector

Pinecone

Prisma

Qdrant

Redis

Rockset

SingleStore

Supabase

Tigris

Turbopuffer

TypeORM

Typesense

Upstash Vector

USearch

Vectara

Vercel Postgres

Voy

Weaviate

Xata

Zep Open Source

Zep Cloud

All vector stores