<Tip>
**Compatibility**

Only available on Node.js.
</Tip>
HNSWLib is an in-memory vector store that can be saved to a file. It uses the HNSWLib library. This guide provides a quick overview for getting started with HNSWLib vector stores. For detailed documentation of all HNSWLib features and configurations head to the API reference.

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Overview

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Integration details

ClassPackagePY supportPackage latest
HNSWLib@langchain/community❌NPM - Version

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Setup

To use HNSWLib vector stores, you’ll need to install the @langchain/community integration package with the hnswlib-node package 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 hnswlib-node @langchain/openai @langchain/core
</Npm2Yarn>

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


</Warning>

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Credentials

Because HNSWLib runs locally, you do not need any credentials to use it. 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"

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Instantiation

import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";
import { OpenAIEmbeddings } from "@langchain/openai";

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

const vectorStore = await HNSWLib.fromDocuments([], embeddings);

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Manage vector store

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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);
Deletion and ids for individual documents are not currently supported.

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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.

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Query directly

Performing a simple similarity search can be done as follows: 
const filter = (doc) => doc.metadata.source === "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 filter is optional, and must be a predicate function that takes a document as input, and returns true or false depending on whether the document should be returned. 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=0.835] The powerhouse of the cell is the mitochondria [{"source":"https://example.com"}]
* [SIM=0.852] Mitochondria are made out of lipids [{"source":"https://example.com"}]

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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");

[
  {
    pageContent: 'The powerhouse of the cell is the mitochondria',
    metadata: { source: 'https://example.com' }
  },
  {
    pageContent: 'Mitochondria are made out of lipids',
    metadata: { source: 'https://example.com' }
  }
]

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Usage for retrieval-augmented generation

For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:

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Save to/load from file

HNSWLib supports saving your index to a file, then reloading it at a later date: 
// Save the vector store to a directory
const directory = "your/directory/here";
await vectorStore.save(directory);

// Load the vector store from the same directory
const loadedVectorStore = await HNSWLib.load(directory, new OpenAIEmbeddings());

// vectorStore and loadedVectorStore are identical
await loadedVectorStore.similaritySearch("hello world", 1);

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Delete a saved index

You can use the .delete method to clear an index saved to a given directory: 
// Load the vector store from the same directory
const savedVectorStore = await HNSWLib.load(directory, new OpenAIEmbeddings());

await savedVectorStore.delete({ directory });

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API reference

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