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HNSWLib

Compatibility

Only available on Node.js.

HNSWLib is an in-memory vectorstore that can be saved to a file. It uses HNSWLib.

Setup​

You can install it with

npm install hnswlib-node

Usage​

Create a new index from texts​

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

export const run = async () => {
  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);
};

Create a new index from a loader​

import { HNSWLib } from "langchain/vectorstores/hnswlib";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { TextLoader } from "langchain/document_loaders/fs/text";

export const run = async () => {
  // Create docs with a loader
  const loader = new TextLoader(
    "src/document_loaders/example_data/example.txt"
  );
  const docs = await loader.load();

  // Load the docs into the vector store
  const vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());

  // Search for the most similar document
  const resultOne = await vectorStore.similaritySearch("hello world", 1);
  console.log(resultOne);
};

Save an index to a file and load it again​

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

export const run = async () => {
  // Create a vector store through any method, here from texts as an example
  const 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 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

  const result = await loadedVectorStore.similaritySearch("hello world", 1);
  console.log(result);
};