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*   HNSWLib

On this page

HNSWLib
=======

Compatibility

Only available on Node.js.

HNSWLib is an in-memory vectorstore that can be saved to a file. It uses [HNSWLib](https://github.com/nmslib/hnswlib).

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

caution

**On Windows**, you might need to install [Visual Studio](https://visualstudio.microsoft.com/downloads/) first in order to properly build the `hnswlib-node` package.

You can install it with

*   npm
*   Yarn
*   pnpm

    npm install hnswlib-node

    yarn add hnswlib-node

    pnpm add hnswlib-node

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 @langchain/community

    yarn add @langchain/openai @langchain/community

    pnpm add @langchain/openai @langchain/community

Usage[​](#usage "Direct link to Usage")
---------------------------------------

### Create a new index from texts[​](#create-a-new-index-from-texts "Direct link to Create a new index from texts")

    import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";import { OpenAIEmbeddings } from "@langchain/openai";const vectorStore = await HNSWLib.fromTexts(  ["Hello world", "Bye bye", "hello nice world"],  [{ id: 2 }, { id: 1 }, { id: 3 }],  new OpenAIEmbeddings());const resultOne = await vectorStore.similaritySearch("hello world", 1);console.log(resultOne);

#### API Reference:

*   [HNSWLib](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hnswlib.HNSWLib.html) from `@langchain/community/vectorstores/hnswlib`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

### Create a new index from a loader[​](#create-a-new-index-from-a-loader "Direct link to Create a new index from a loader")

    import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";import { OpenAIEmbeddings } from "@langchain/openai";import { TextLoader } from "langchain/document_loaders/fs/text";// Create docs with a loaderconst loader = new TextLoader("src/document_loaders/example_data/example.txt");const docs = await loader.load();// Load the docs into the vector storeconst vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());// Search for the most similar documentconst result = await vectorStore.similaritySearch("hello world", 1);console.log(result);

#### API Reference:

*   [HNSWLib](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hnswlib.HNSWLib.html) from `@langchain/community/vectorstores/hnswlib`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text`

### Save an index to a file and load it again[​](#save-an-index-to-a-file-and-load-it-again "Direct link to Save an index to a file and load it again")

    import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";import { OpenAIEmbeddings } from "@langchain/openai";// Create a vector store through any method, here from texts as an exampleconst vectorStore = await HNSWLib.fromTexts(  ["Hello world", "Bye bye", "hello nice world"],  [{ id: 2 }, { id: 1 }, { id: 3 }],  new OpenAIEmbeddings());// Save the vector store to a directoryconst directory = "your/directory/here";await vectorStore.save(directory);// Load the vector store from the same directoryconst loadedVectorStore = await HNSWLib.load(directory, new OpenAIEmbeddings());// vectorStore and loadedVectorStore are identicalconst result = await loadedVectorStore.similaritySearch("hello world", 1);console.log(result);

#### API Reference:

*   [HNSWLib](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hnswlib.HNSWLib.html) from `@langchain/community/vectorstores/hnswlib`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

### Filter documents[​](#filter-documents "Direct link to Filter documents")

    import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";import { OpenAIEmbeddings } from "@langchain/openai";const vectorStore = await HNSWLib.fromTexts(  ["Hello world", "Bye bye", "hello nice world"],  [{ id: 2 }, { id: 1 }, { id: 3 }],  new OpenAIEmbeddings());const result = await vectorStore.similaritySearch(  "hello world",  10,  (document) => document.metadata.id === 3);// only "hello nice world" will be returnedconsole.log(result);

#### API Reference:

*   [HNSWLib](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hnswlib.HNSWLib.html) from `@langchain/community/vectorstores/hnswlib`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

### Delete index[​](#delete-index "Direct link to Delete index")

    import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";import { OpenAIEmbeddings } from "@langchain/openai";// Save the vector store to a directoryconst directory = "your/directory/here";// Load the vector store from the same directoryconst loadedVectorStore = await HNSWLib.load(directory, new OpenAIEmbeddings());await loadedVectorStore.delete({ directory });

#### API Reference:

*   [HNSWLib](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hnswlib.HNSWLib.html) from `@langchain/community/vectorstores/hnswlib`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`

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](/v0.2/docs/integrations/vectorstores/lancedb)

*   [Setup](#setup)
*   [Usage](#usage)
    *   [Create a new index from texts](#create-a-new-index-from-texts)
    *   [Create a new index from a loader](#create-a-new-index-from-a-loader)
    *   [Save an index to a file and load it again](#save-an-index-to-a-file-and-load-it-again)
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