Vector store integrations - Docs by LangChain

Overview

A vector store stores embedded data and performs similarity search.

Interface

LangChain provides a unified interface for vector stores, allowing you to:

addDocuments - Add documents to the store.
delete - Remove stored documents by ID.
similaritySearch - Query for semantically similar documents.

This abstraction lets you switch between different implementations without altering your application logic.

Initialization

Most vectorstores in LangChain accept an embedding model as an argument when initializing the vector store.

import { OpenAIEmbeddings } from "@langchain/openai";
import { MemoryVectorStore } from "@langchain/classic/vectorstores/memory";

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

Adding documents

You can add documents to the vector store by using the addDocuments function.

import { Document } from "@langchain/core/documents";
const document = new Document({
  pageContent: "Hello world",
});
await vectorStore.addDocuments([document]);

Deleting documents

You can delete documents from the vector store by using the delete function.

await vectorStore.delete({
  filter: {
    pageContent: "Hello world",
  },
});

Similarity search

Issue a semantic query using similaritySearch, which returns the closest embedded documents:

const results = await vectorStore.similaritySearch("Hello world", 10);

Many vector stores support parameters like:

k — number of results to return
filter — conditional filtering based on metadata

Similarity metrics & indexing

Embedding similarity may be computed using:

Cosine similarity
Euclidean distance
Dot product

Efficient search often employs indexing methods such as HNSW (Hierarchical Navigable Small World), though specifics depend on the vector store.

Metadata filtering

Filtering by metadata (e.g., source, date) can refine search results:

vectorStore.similaritySearch("query", 2, { source: "tweets" });

Top integrations

Select embedding model:

OpenAI

Install dependencies:

npm i @langchain/openai

yarn add @langchain/openai

pnpm add @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

yarn add @langchain/openai

pnpm add @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

yarn add @langchain/aws

pnpm add @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"
});

Google Gemini

Install dependencies:

npm i @langchain/google-genai

yarn add @langchain/google-genai

pnpm add @langchain/google-genai

Add environment variables:

GOOGLE_API_KEY=your-api-key

Instantiate the model:

import { GoogleGenerativeAIEmbeddings } from "@langchain/google-genai";

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

Google Vertex

Install dependencies:

npm i @langchain/google-vertexai

yarn add @langchain/google-vertexai

pnpm add @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

yarn add @langchain/mistralai

pnpm add @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

yarn add @langchain/cohere

pnpm add @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"
});

Ollama

Install dependencies:

npm i @langchain/ollama

yarn add @langchain/ollama

pnpm add @langchain/ollama

Instantiate the model:

import { OllamaEmbeddings } from "@langchain/ollama";

const embeddings = new OllamaEmbeddings({
  model: "llama2",
  baseUrl: "http://localhost:11434", // Default value
});

Select vector store:

Memory

npm i langchain

yarn add langchain

pnpm add langchain

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

const vectorStore = new MemoryVectorStore(embeddings);

Chroma

npm i @langchain/community

yarn add @langchain/community

pnpm add @langchain/community

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

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

FAISS

npm i @langchain/community

yarn add @langchain/community

pnpm add @langchain/community

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

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

MongoDB

Manual embedding
Automated embedding

npm i @langchain/mongodb

yarn add @langchain/mongodb

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

npm i @langchain/mongodb

yarn add @langchain/mongodb

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({ collection });

PGVector

npm i @langchain/community

yarn add @langchain/community

pnpm add @langchain/community

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

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

Pinecone

npm i @langchain/pinecone

yarn add @langchain/pinecone

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,
});

Redis

npm i @langchain/redis

yarn add @langchain/redis

pnpm add @langchain/redis

import { RedisVectorStore } from "@langchain/redis";

const vectorStore = new RedisVectorStore(embeddings, {
  redisClient: client,
  indexName: "langchainjs-testing",
});

Qdrant

npm i @langchain/qdrant

yarn add @langchain/qdrant

pnpm add @langchain/qdrant

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

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

Oracle AI Database

npm i @oracle/langchain-oracledb @langchain/core

yarn add @oracle/langchain-oracledb @langchain/core

pnpm add @oracle/langchain-oracledb @langchain/core

import oracledb from "oracledb";
import { OracleEmbeddings, OracleVS } from "@oracle/langchain-oracledb";

const connection = await oracledb.getConnection({
  user: process.env.ORACLE_USER,
  password: process.env.ORACLE_PASSWORD,
  connectionString: process.env.ORACLE_DSN,
});

const embeddings = new OracleEmbeddings(connection, {
  provider: "database",
  model: process.env.DEMO_ONNX_MODEL ?? "DEMO_MODEL",
});

const vectorStore = new OracleVS(embeddings, {
  client: connection,
  tableName: "DEMO_VECTORS",
  query: "Find support tickets mentioning service outages.",
  distanceStrategy: "DOT",
});
await vectorStore.initialize();

Weaviate

npm i @langchain/weaviate

yarn add @langchain/weaviate

pnpm add @langchain/weaviate

import { WeaviateStore } from "@langchain/weaviate";

const vectorStore = new WeaviateStore(embeddings, {
    client: weaviateClient,
    indexName: "Langchainjs_test",
});

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

​Overview

​Interface

​Initialization

​Adding documents

​Deleting documents

​Similarity search

​Similarity metrics & indexing

​Metadata filtering

​Top integrations

​All vector stores

AnalyticDB

Astra DB

Azion EdgeSQL

Azure AI Search

Azure DocumentDB

Azure Cosmos DB for NoSQL

Cassandra

Chroma

ClickHouse

CloseVector

Cloudflare Vectorize

Convex

Couchbase Query

Couchbase Search

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

Oracle AI Database

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

Overview

Interface

Initialization

Adding documents

Deleting documents

Similarity search

Similarity metrics & indexing

Metadata filtering

Top integrations

All vector stores