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Aurora DSQL 聊天记忆

为了在聊天会话之间实现更长期的持久化，您可以将默认的内存中 chatHistory 替换为无服务器且兼容 PostgreSQL 的 Amazon Aurora DSQL 数据库。

这与 PostgreSQL 的集成非常相似，但有一些差异以使其兼容 DSQL：

  1. PostgreSQL 中的 id 列是 SERIAL 自增列，而 DSQL 中使用的是 UUID，并通过数据库函数 gen_random_uuid 生成。
  2. 创建了一个 created_at 列来跟踪消息的顺序和历史。
  3. PostgreSQL 中的 message 列是 JSONB 类型，而 DSQL 中是 TEXT 类型，并通过 JavaScript 解析来处理。

配置​

前往 AWS 控制台并创建一个 Aurora DSQL 集群：https://console.aws.amazon.com/dsql/clusters

:::提示 请参阅安装集成包的一般说明部分。 :::

npm install @langchain/openai @langchain/community @langchain/core pg @aws-sdk/dsql-signer

使用方法​

每个聊天历史会话都存储在一个 Aurora DSQL（兼容 PostgreSQL）数据库中，并需要一个会话 ID。

与 Aurora DSQL 的连接是通过一个 PostgreSQL 连接池处理的。您可以通过 pool 参数传递一个连接池实例，或者通过 poolConfig 参数传递一个连接池配置。更多信息请参见 pg-node 的连接池文档。如果同时提供了连接池实例和连接池配置，则优先使用连接池实例。

有关 DSQL 的身份验证和授权方法，请参阅 https://docs.aws.amazon.com/aurora-dsql/latest/userguide/authentication-authorization.html。

以下示例使用 AWS SDK 生成一个认证令牌，并将其传递给连接池配置：

import pg from "pg";

import { DsqlSigner } from "@aws-sdk/dsql-signer";
import { AuroraDsqlChatMessageHistory } from "@langchain/community/stores/message/aurora_dsql";
import { ChatOpenAI } from "@langchain/openai";
import { RunnableWithMessageHistory } from "@langchain/core/runnables";

import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";
import { StringOutputParser } from "@langchain/core/output_parsers";

async function getPostgresqlPool() {
  const signer = new DsqlSigner({
    hostname: process.env.DSQL_ENDPOINT!,
  });

  const token = await signer.getDbConnectAdminAuthToken();

  if (!token) throw new Error("Auth token error for DSQL");

  const poolConfig: pg.PoolConfig = {
    host: process.env.DSQL_ENDPOINT,
    port: 5432,
    user: "admin",
    password: token,
    ssl: true,
    database: "postgres",
  };

  const pool = new pg.Pool(poolConfig);
  return pool;
}

const pool = await getPostgresqlPool();

const model = new ChatOpenAI({
  model: "gpt-4o-mini",
});

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 = new AuroraDsqlChatMessageHistory({
      sessionId,
      pool,
      // Can also pass `poolConfig` to initialize the pool internally,
      // but easier to call `.end()` at the end later.
    });
    return chatHistory;
  },
});

const res1 = await chainWithHistory.invoke(
  {
    input: "Hi! I'm MJDeligan.",
  },
  { configurable: { sessionId: "langchain-test-session" } }
);
console.log(res1);
/*
  "Hello MJDeligan! 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);

/*
  "You said your name was MJDeligan."
*/

// If you provided a pool config you should close the created pool when you are done
await pool.end();

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