Vector store integrations

Integrate with vector stores using LangChain JavaScript.

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

npm install @langchain/openai @langchain/core

yarn add @langchain/openai @langchain/core

pnpm add @langchain/openai @langchain/core

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

npm install @langchain/openai @langchain/core

yarn add @langchain/openai @langchain/core

pnpm add @langchain/openai @langchain/core

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

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

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

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

npm install @langchain/mistralai @langchain/core

yarn add @langchain/mistralai @langchain/core

pnpm add @langchain/mistralai @langchain/core

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

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

npm install @langchain/ollama @langchain/core

yarn add @langchain/ollama @langchain/core

pnpm add @langchain/ollama @langchain/core

Instantiate the model:

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

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

Voyage AI

Install dependencies:

npm

npm install @langchain/mongodb @langchain/core

yarn add @langchain/mongodb @langchain/core

pnpm add @langchain/mongodb @langchain/core

Add environment variables:

VOYAGE_API_KEY=your-api-key

Instantiate the model:

import { VoyageEmbeddings } from "@langchain/mongodb";

const embeddings = new VoyageEmbeddings({
  model: "voyage-4"
});

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);

MongoDB

Manual embedding

npm

npm install @langchain/mongodb mongodb @langchain/core

yarn add @langchain/mongodb mongodb @langchain/core

pnpm add @langchain/mongodb mongodb @langchain/core

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",
});

Automated embedding

npm

npm install @langchain/mongodb mongodb @langchain/core

yarn add @langchain/mongodb mongodb @langchain/core

pnpm add @langchain/mongodb mongodb @langchain/core

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 });

Pinecone

npm

npm install @langchain/pinecone @langchain/core @pinecone-database/pinecone

yarn add @langchain/pinecone @langchain/core @pinecone-database/pinecone

pnpm add @langchain/pinecone @langchain/core @pinecone-database/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

npm install @langchain/redis @langchain/core redis

yarn add @langchain/redis @langchain/core redis

pnpm add @langchain/redis @langchain/core redis

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

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

Qdrant

npm

npm install @langchain/qdrant @langchain/core

yarn add @langchain/qdrant @langchain/core

pnpm add @langchain/qdrant @langchain/core

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

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

Oracle AI Database

npm

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

npm install @langchain/weaviate @langchain/core weaviate-client

yarn add @langchain/weaviate @langchain/core weaviate-client

pnpm add @langchain/weaviate @langchain/core weaviate-client

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:

All vector stores

Azure DocumentDB

View guide

Azure Cosmos DB for NoSQL

View guide

Cloudflare Vectorize

View guide

Google Cloud SQL for PostgreSQL

View guide

In-memory

View guide

Milvus

View guide

MongoDB Atlas

View guide

Oracle AI Database

View guide

Pinecone

View guide

Qdrant

View guide

Redis

View guide

Weaviate

View guide

Neo4j Vector Index

View guide

PGVector

View guide

Turbopuffer

View guide

YDB

View guide

---

Connect these docs to Claude, VSCode, and more via MCP for real-time answers.

Edit this page on GitHub or file an issue.