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Astra 数据库聊天内存

¥Astra DB Chat Memory

为了在聊天会话中实现更长期的持久化，你可以将支持聊天内存类（如 BufferMemory）的默认内存 chatHistory 替换为 Astra DB。

¥For longer-term persistence across chat sessions, you can swap out the default in-memory chatHistory that backs chat memory classes like BufferMemory for Astra DB.

设置​

¥Setup

你需要安装 Astra DB TS 客户端：

¥You need to install the Astra DB TS client:

npm install @datastax/astra-db-ts
tip

请参阅 此部分包含有关安装集成包的一般说明。

¥See this section for general instructions on installing integration packages.

npm install @langchain/openai @langchain/community @langchain/core

配置和初始化​

¥Configuration and Initalization

初始化 AstraDBChatMessageHistory 有两种方法

¥There are two ways to inialize your AstraDBChatMessageHistory

如果你已经定义了 AstraDB 客户端实例，你可以连接到你的集合并使用构造函数初始化 ChatMessageHistory 实例。

¥If you already have an instance of the AstraDB client defined you can connect to your collection and initialize an instance of the ChatMessageHistory using the constuctor.

const client = (client = new AstraDB(
  process.env.ASTRA_DB_APPLICATION_TOKEN,
  process.env.ASTRA_DB_ENDPOINT,
  process.env.ASTRA_DB_NAMESPACE
));

const collection = await client.collection("YOUR_COLLECTION_NAME");

const chatHistory = new AstraDBChatMessageHistory({
  collection,
  sessionId: "YOUR_SESSION_ID",
});

如果你尚无 AstraDB 客户端实例，则可以使用 initialize 方法。

¥If you don't already have an instance of an AstraDB client you can use the initialize method.

const chatHistory = await AstraDBChatMessageHistory.initialize({
  token: process.env.ASTRA_DB_APPLICATION_TOKEN ?? "token",
  endpoint: process.env.ASTRA_DB_ENDPOINT ?? "endpoint",
  namespace: process.env.ASTRA_DB_NAMESPACE,
  collectionName: "YOUR_COLLECTION_NAME",
  sessionId: "YOUR_SESSION_ID",
});

用法​

¥Usage

Tip

你的集合必须已经存在

¥Your collection must already exist

import { RunnableWithMessageHistory } from "@langchain/core/runnables";
import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatOpenAI } from "@langchain/openai";
import { AstraDBChatMessageHistory } from "@langchain/community/stores/message/astradb";

const model = new ChatOpenAI({
  model: "gpt-3.5-turbo",
  temperature: 0,
});

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    "You are a helpful assistant. Answer all questions to the best of your ability.",
  ],
  new MessagesPlaceholder("chat_history"),
  ["human", "{input}"],
]);

const chain = prompt.pipe(model).pipe(new StringOutputParser());

const chainWithHistory = new RunnableWithMessageHistory({
  runnable: chain,
  inputMessagesKey: "input",
  historyMessagesKey: "chat_history",
  getMessageHistory: async (sessionId) => {
    const chatHistory = await AstraDBChatMessageHistory.initialize({
      token: process.env.ASTRA_DB_APPLICATION_TOKEN as string,
      endpoint: process.env.ASTRA_DB_ENDPOINT as string,
      namespace: process.env.ASTRA_DB_NAMESPACE,
      collectionName: "YOUR_COLLECTION_NAME",
      sessionId,
    });
    return chatHistory;
  },
});

const res1 = await chainWithHistory.invoke(
  {
    input: "Hi! I'm Jim.",
  },
  { configurable: { sessionId: "langchain-test-session" } }
);
console.log({ res1 });
/*
{
  res1: {
    text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?"
  }
}
*/

const res2 = await chainWithHistory.invoke(
  { input: "What did I just say my name was?" },
  { configurable: { sessionId: "langchain-test-session" } }
);
console.log({ res2 });

/*
{
  res2: {
    text: "You said your name was Jim."
  }
}
*/

API Reference: