Elasticsearch

<Tip>
**Compatibility**

Only available on Node.js.
</Tip>

Elasticsearch is a distributed, RESTful search engine optimized for speed and relevance on production-scale workloads. It supports also vector search using the k-nearest neighbor (kNN) algorithm and also custom models for Natural Language Processing (NLP). You can read more about the support of vector search in Elasticsearch here.

This guide provides a quick overview for getting started with Elasticsearch vector stores. For detailed documentation of all ElasticVectorSearch features and configurations head to the API reference.

Overview

Integration details

Class	Package	PY support	Version
ElasticVectorSearch	@langchain/community	✅

Setup

To use Elasticsearch vector stores, you’ll need to install the @langchain/community integration package.

LangChain.js accepts @elastic/elasticsearch as the client for Elasticsearch vectorstore. You’ll need to install it as a peer dependency.

This guide will also use OpenAI embeddings, which require you to install the @langchain/openai integration package. You can also use other supported embeddings models if you wish.

import IntegrationInstallTooltip from "@mdx_components/integration_install_tooltip.mdx";
<IntegrationInstallTooltip></IntegrationInstallTooltip>

<Npm2Yarn>
  @langchain/community @elastic/elasticsearch @langchain/openai @langchain/core
</Npm2Yarn>

Credentials

To use Elasticsearch vector stores, you’ll need to have an Elasticsearch instance running.

You can use the official Docker image to get started, or you can use Elastic Cloud, Elastic’s official cloud service.

For connecting to Elastic Cloud you can read the documentation reported here for obtaining an API key.

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

Instatiating Elasticsearch will vary depending on where your instance is hosted.

import {
  ElasticVectorSearch,
  type ElasticClientArgs,
} from "@langchain/community/vectorstores/elasticsearch";
import { OpenAIEmbeddings } from "@langchain/openai";

import { Client, type ClientOptions } from "@elastic/elasticsearch";

import * as fs from "node:fs";

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

const config: ClientOptions = {
  node: process.env.ELASTIC_URL ?? "https://127.0.0.1:9200",
};

if (process.env.ELASTIC_API_KEY) {
  config.auth = {
    apiKey: process.env.ELASTIC_API_KEY,
  };
} else if (process.env.ELASTIC_USERNAME && process.env.ELASTIC_PASSWORD) {
  config.auth = {
    username: process.env.ELASTIC_USERNAME,
    password: process.env.ELASTIC_PASSWORD,
  };
}
// Local Docker deploys require a TLS certificate
if (process.env.ELASTIC_CERT_PATH) {
  config.tls = {
    ca: fs.readFileSync(process.env.ELASTIC_CERT_PATH),
    rejectUnauthorized: false,
  }
}
const clientArgs: ElasticClientArgs = {
  client: new Client(config),
  indexName: process.env.ELASTIC_INDEX ?? "test_vectorstore",
};

const vectorStore = new ElasticVectorSearch(embeddings, clientArgs);

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];

await vectorStore.addDocuments(documents, { ids: ["1", "2", "3", "4"] });

[ '1', '2', '3', '4' ]

Delete items from vector store

You can delete values from the store by passing the same id you passed in:

await vectorStore.delete({ ids: ["4"] });

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 = [{
  operator: "match",
  field: "source",
  value: "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 vector store supports Elasticsearch filter syntax operators.

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=0.374] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.370] 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:

• Build a RAG app with LangChain.
• Agentic RAG
• Retrieval docs

API reference

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