Conversation buffer memory

This notebook shows how to use BufferMemory. This memory allows for storing of messages, then later formats the messages into a prompt input variable.

We can first extract it as a string.

tip

See this section for general instructions on installing integration packages.

npm

npm install @langchain/openai

Yarn

yarn add @langchain/openai

pnpm

pnpm add @langchain/openai

import { OpenAI } from "@langchain/openai";
import { BufferMemory } from "langchain/memory";
import { ConversationChain } from "langchain/chains";

const model = new OpenAI({});
const memory = new BufferMemory();
const chain = new ConversationChain({ llm: model, memory: memory });
const res1 = await chain.call({ input: "Hi! I'm Jim." });
console.log({ res1 });

{response: " Hi Jim! It's nice to meet you. My name is AI. What would you like to talk about?"}

const res2 = await chain.call({ input: "What's my name?" });
console.log({ res2 });

{response: ' You said your name is Jim. Is there anything else you would like to talk about?'}

You can also load messages into a BufferMemory instance by creating and passing in a ChatHistory object. This lets you easily pick up state from past conversations:

import { BufferMemory, ChatMessageHistory } from "langchain/memory";
import { HumanMessage, AIMessage } from "langchain/schema";

const pastMessages = [
  new HumanMessage("My name's Jonas"),
  new AIMessage("Nice to meet you, Jonas!"),
];

const memory = new BufferMemory({
  chatHistory: new ChatMessageHistory(pastMessages),
});