Skip to main content

Faiss

Compatibility

Only available on Node.js.

Faiss is a library for efficient similarity search and clustering of dense vectors.

Langchainjs supports using Faiss as a vectorstore that can be saved to file. It also provides the ability to read the saved file from Python's implementation.

Setup​

Install the faiss-node, which is a Node.js bindings for Faiss.

npm install -S faiss-node

To enable the ability to read the saved file from Python's implementation, the pickleparser also needs to install.

npm install -S pickleparser

Usage​

tip

See this section for general instructions on installing integration packages.

npm install @langchain/openai @langchain/community

Create a new index from texts​

import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { OpenAIEmbeddings } from "@langchain/openai";

export const run = async () => {
  const vectorStore = await FaissStore.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:

Create a new index from a loader​

import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { OpenAIEmbeddings } from "@langchain/openai";
import { TextLoader } from "langchain/document_loaders/fs/text";

// 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 FaissStore.fromDocuments(
  docs,
  new OpenAIEmbeddings()
);

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

API Reference:

Deleting vectors​

import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { OpenAIEmbeddings } from "@langchain/openai";
import { Document } from "@langchain/core/documents";

const vectorStore = new FaissStore(new OpenAIEmbeddings(), {});
const ids = ["2", "1", "4"];
const idsReturned = await vectorStore.addDocuments(
  [
    new Document({
      pageContent: "my world",
      metadata: { tag: 2 },
    }),
    new Document({
      pageContent: "our world",
      metadata: { tag: 1 },
    }),
    new Document({
      pageContent: "your world",
      metadata: { tag: 4 },
    }),
  ],
  {
    ids,
  }
);

console.log(idsReturned);

/*
  [ '2', '1', '4' ]
*/

const docs = await vectorStore.similaritySearch("my world", 3);

console.log(docs);

/*
[
  Document { pageContent: 'my world', metadata: { tag: 2 } },
  Document { pageContent: 'your world', metadata: { tag: 4 } },
  Document { pageContent: 'our world', metadata: { tag: 1 } }
]
*/

await vectorStore.delete({ ids: [ids[0], ids[1]] });

const docs2 = await vectorStore.similaritySearch("my world", 3);

console.log(docs2);

/*
[ Document { pageContent: 'your world', metadata: { tag: 4 } } ]
*/

API Reference:

Merging indexes and creating new index from another instance​

import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { OpenAIEmbeddings } from "@langchain/openai";

export const run = async () => {
  // Create an initial vector store
  const vectorStore = await FaissStore.fromTexts(
    ["Hello world", "Bye bye", "hello nice world"],
    [{ id: 2 }, { id: 1 }, { id: 3 }],
    new OpenAIEmbeddings()
  );

  // Create another vector store from texts
  const vectorStore2 = await FaissStore.fromTexts(
    ["Some text"],
    [{ id: 1 }],
    new OpenAIEmbeddings()
  );

  // merge the first vector store into vectorStore2
  await vectorStore2.mergeFrom(vectorStore);

  const resultOne = await vectorStore2.similaritySearch("hello world", 1);
  console.log(resultOne);

  // You can also create a new vector store from another FaissStore index
  const vectorStore3 = await FaissStore.fromIndex(
    vectorStore2,
    new OpenAIEmbeddings()
  );
  const resultTwo = await vectorStore3.similaritySearch("Bye bye", 1);
  console.log(resultTwo);
};

API Reference:

Save an index to file and load it again​

import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { OpenAIEmbeddings } from "@langchain/openai";

// Create a vector store through any method, here from texts as an example
const vectorStore = await FaissStore.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 FaissStore.load(
  directory,
  new OpenAIEmbeddings()
);

// vectorStore and loadedVectorStore are identical
const result = await loadedVectorStore.similaritySearch("hello world", 1);
console.log(result);

API Reference:

Load the saved file from Python's implementation​

import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { OpenAIEmbeddings } from "@langchain/openai";

// The directory of data saved from Python
const directory = "your/directory/here";

// Load the vector store from the directory
const loadedVectorStore = await FaissStore.loadFromPython(
  directory,
  new OpenAIEmbeddings()
);

// Search for the most similar document
const result = await loadedVectorStore.similaritySearch("test", 2);
console.log("result", result);

API Reference:

Was this page helpful?


You can leave detailed feedback on GitHub.