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MemoryVectorStore

MemoryVectorStore is an in-memory, ephemeral vectorstore that stores embeddings in-memory and does an exact, linear search for the most similar embeddings. The default similarity metric is cosine similarity, but can be changed to any of the similarity metrics supported by ml-distance.

Usage​

Create a new index from texts​

tip

See this section for general instructions on installing integration packages.

npm install @langchain/openai
import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { OpenAIEmbeddings } from "@langchain/openai";

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);

/*
  [
    Document {
      pageContent: "Hello world",
      metadata: { id: 2 }
    }
  ]
*/

API Reference:

Create a new index from a loader​

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

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

console.log(resultOne);

/*
  [
    Document {
      pageContent: "Hello world",
      metadata: { id: 2 }
    }
  ]
*/

API Reference:

Use a custom similarity metric​

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

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);

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