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*   Elasticsearch

On this page

Elasticsearch
=============

Compatibility

Only available on Node.js.

[Elasticsearch](https://github.com/elastic/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](https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm) (kNN) algorithm and also [custom models for Natural Language Processing](https://www.elastic.co/blog/how-to-deploy-nlp-text-embeddings-and-vector-search) (NLP). You can read more about the support of vector search in Elasticsearch [here](https://www.elastic.co/guide/en/elasticsearch/reference/current/knn-search.html).

LangChain.js accepts [@elastic/elasticsearch](https://github.com/elastic/elasticsearch-js) as the client for Elasticsearch vectorstore.

Setup[​](#setup "Direct link to Setup")
---------------------------------------

*   npm
*   Yarn
*   pnpm

    npm install -S @elastic/elasticsearch

    yarn add @elastic/elasticsearch

    pnpm add @elastic/elasticsearch

You'll also need to have an Elasticsearch instance running. You can use the [official Docker image](https://www.elastic.co/guide/en/elasticsearch/reference/current/docker.html) to get started, or you can use [Elastic Cloud](https://www.elastic.co/cloud/), Elastic's official cloud service.

For connecting to Elastic Cloud you can read the documentation reported [here](https://www.elastic.co/guide/en/kibana/current/api-keys.html) for obtaining an API key.

Example: index docs, vector search and LLM integration[​](#example-index-docs-vector-search-and-llm-integration "Direct link to Example: index docs, vector search and LLM integration")
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Below is an example that indexes 4 documents in Elasticsearch, runs a vector search query, and finally uses an LLM to answer a question in natural language based on the retrieved documents.

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/installation#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai

    yarn add @langchain/openai

    pnpm add @langchain/openai

    import { Client, ClientOptions } from "@elastic/elasticsearch";import { OpenAI, OpenAIEmbeddings } from "@langchain/openai";import { VectorDBQAChain } from "langchain/chains";import {  ElasticClientArgs,  ElasticVectorSearch,} from "@langchain/community/vectorstores/elasticsearch";import { Document } from "@langchain/core/documents";// to run this first run Elastic's docker-container with `docker-compose up -d --build`export async function run() {  const config: ClientOptions = {    node: process.env.ELASTIC_URL ?? "http://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,    };  }  const clientArgs: ElasticClientArgs = {    client: new Client(config),    indexName: process.env.ELASTIC_INDEX ?? "test_vectorstore",  };  // Index documents  const docs = [    new Document({      metadata: { foo: "bar" },      pageContent: "Elasticsearch is a powerful vector db",    }),    new Document({      metadata: { foo: "bar" },      pageContent: "the quick brown fox jumped over the lazy dog",    }),    new Document({      metadata: { baz: "qux" },      pageContent: "lorem ipsum dolor sit amet",    }),    new Document({      metadata: { baz: "qux" },      pageContent:        "Elasticsearch a distributed, RESTful search engine optimized for speed and relevance on production-scale workloads.",    }),  ];  const embeddings = new OpenAIEmbeddings();  // await ElasticVectorSearch.fromDocuments(docs, embeddings, clientArgs);  const vectorStore = new ElasticVectorSearch(embeddings, clientArgs);  // Also supports an additional {ids: []} parameter for upsertion  const ids = await vectorStore.addDocuments(docs);  /* Search the vector DB independently with meta filters */  const results = await vectorStore.similaritySearch("fox jump", 1);  console.log(JSON.stringify(results, null, 2));  /* [        {          "pageContent": "the quick brown fox jumped over the lazy dog",          "metadata": {            "foo": "bar"          }        }    ]  */  /* Use as part of a chain (currently no metadata filters) for LLM query */  const model = new OpenAI();  const chain = VectorDBQAChain.fromLLM(model, vectorStore, {    k: 1,    returnSourceDocuments: true,  });  const response = await chain.invoke({ query: "What is Elasticsearch?" });  console.log(JSON.stringify(response, null, 2));  /*    {      "text": " Elasticsearch is a distributed, RESTful search engine optimized for speed and relevance on production-scale workloads.",      "sourceDocuments": [        {          "pageContent": "Elasticsearch a distributed, RESTful search engine optimized for speed and relevance on production-scale workloads.",          "metadata": {            "baz": "qux"          }        }      ]    }    */  await vectorStore.delete({ ids });  const response2 = await chain.invoke({ query: "What is Elasticsearch?" });  console.log(JSON.stringify(response2, null, 2));  /*    []  */}

#### API Reference:

*   [OpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAI.html) from `@langchain/openai`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [VectorDBQAChain](https://v02.api.js.langchain.com/classes/langchain_chains.VectorDBQAChain.html) from `langchain/chains`
*   [ElasticClientArgs](https://v02.api.js.langchain.com/interfaces/langchain_community_vectorstores_elasticsearch.ElasticClientArgs.html) from `@langchain/community/vectorstores/elasticsearch`
*   [ElasticVectorSearch](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_elasticsearch.ElasticVectorSearch.html) from `@langchain/community/vectorstores/elasticsearch`
*   [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`

* * *

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*   [Setup](#setup)
*   [Example: index docs, vector search and LLM integration](#example-index-docs-vector-search-and-llm-integration)

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