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Faiss

兼容性

仅适用于Node.js环境。

Faiss 是用于稠密向量的高效相似度搜索和聚类的库。

Langchainjs支持使用Faiss作为向量库，并可将其保存到文件。同时，它还提供从Python实现读取保存的文件的功能。

安装​

安装faiss-node,它是Faiss的Node.js绑定。

npm install -S faiss-node

要启用从Python实现读取保存的文件的功能，还需要安装pickleparser。

npm install -S pickleparser

使用​

从文本创建新索引​

import { FaissStore } from "langchain/vectorstores/faiss";
import { OpenAIEmbeddings } from "langchain/embeddings/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);
};

从加载器创建新索引​

import { FaissStore } from "langchain/vectorstores/faiss";
import { OpenAIEmbeddings } from "langchain/embeddings/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);

将索引保存到文件并再次加载​

import { FaissStore } from "langchain/vectorstores/faiss";
import { OpenAIEmbeddings } from "langchain/embeddings/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);

从Python实现中加载保存的文件​

import { FaissStore } from "langchain/vectorstores/faiss";
import { OpenAIEmbeddings } from "langchain/embeddings/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);