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*   OpenSearch

On this page

OpenSearch
==========

Compatibility

Only available on Node.js.

[OpenSearch](https://opensearch.org/) is a fork of [Elasticsearch](https://www.elastic.co/elasticsearch/) that is fully compatible with the Elasticsearch API. Read more about their support for Approximate Nearest Neighbors [here](https://opensearch.org/docs/latest/search-plugins/knn/approximate-knn/).

Langchain.js accepts [@opensearch-project/opensearch](https://opensearch.org/docs/latest/clients/javascript/index/) as the client for OpenSearch vectorstore.

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

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install -S @langchain/openai @opensearch-project/opensearch

    yarn add @langchain/openai @opensearch-project/opensearch

    pnpm add @langchain/openai @opensearch-project/opensearch

You'll also need to have an OpenSearch instance running. You can use the [official Docker image](https://opensearch.org/docs/latest/opensearch/install/docker/) to get started. You can also find an example docker-compose file [here](https://github.com/langchain-ai/langchainjs/blob/main/examples/src/indexes/vector_stores/opensearch/docker-compose.yml).

Index docs[​](#index-docs "Direct link to Index docs")
------------------------------------------------------

    import { Client } from "@opensearch-project/opensearch";import { Document } from "langchain/document";import { OpenAIEmbeddings } from "@langchain/openai";import { OpenSearchVectorStore } from "langchain/vectorstores/opensearch";const client = new Client({  nodes: [process.env.OPENSEARCH_URL ?? "http://127.0.0.1:9200"],});const docs = [  new Document({    metadata: { foo: "bar" },    pageContent: "opensearch is also a 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:      "OpenSearch is a scalable, flexible, and extensible open-source software suite for search, analytics, and observability applications",  }),];await OpenSearchVectorStore.fromDocuments(docs, new OpenAIEmbeddings(), {  client,  indexName: process.env.OPENSEARCH_INDEX, // Will default to `documents`});

Query docs[​](#query-docs "Direct link to Query docs")
------------------------------------------------------

    import { Client } from "@opensearch-project/opensearch";import { VectorDBQAChain } from "langchain/chains";import { OpenAIEmbeddings } from "@langchain/openai";import { OpenAI } from "@langchain/openai";import { OpenSearchVectorStore } from "langchain/vectorstores/opensearch";const client = new Client({  nodes: [process.env.OPENSEARCH_URL ?? "http://127.0.0.1:9200"],});const vectorStore = new OpenSearchVectorStore(new OpenAIEmbeddings(), {  client,});/* Search the vector DB independently with meta filters */const results = await vectorStore.similaritySearch("hello world", 1);console.log(JSON.stringify(results, null, 2));/* [    {      "pageContent": "Hello world",      "metadata": {        "id": 2      }    }  ] *//* Use as part of a chain (currently no metadata filters) */const model = new OpenAI();const chain = VectorDBQAChain.fromLLM(model, vectorStore, {  k: 1,  returnSourceDocuments: true,});const response = await chain.call({ query: "What is opensearch?" });console.log(JSON.stringify(response, null, 2));/*  {    "text": " Opensearch is a collection of technologies that allow search engines to publish search results in a standard format, making it easier for users to search across multiple sites.",    "sourceDocuments": [      {        "pageContent": "What's this?",        "metadata": {          "id": 3        }      }    ]  }  */

* * *

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