MemoryVectorStore

MemoryVectorStore是一个内存中的暂存向量存储器，用于在内存中存储嵌入，并做精确的线性搜索以找到最相似的嵌入。默认的相似度度量是余弦相似度，但可以更改为ml-distance支持的任何相似度度量方式。

用法

从文本创建新索引

import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";

export const run = async () => {
  const vectorStore = await MemoryVectorStore.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);
};

从加载程序创建新索引

import { MemoryVectorStore } from "langchain/vectorstores/memory";
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 MemoryVectorStore.fromDocuments(
    docs,
    new OpenAIEmbeddings()
  );

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

  console.log(resultOne);
};

使用自定义相似度度量

import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "langchain/embeddings/openai";
import { similarity } from "ml-distance";

export const run = async () => {
  const vectorStore = await MemoryVectorStore.fromTexts(
    ["Hello world", "Bye bye", "hello nice world"],
    [{ id: 2 }, { id: 1 }, { id: 3 }],
    new OpenAIEmbeddings(),
    { similarity: similarity.pearson }
  );

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