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基于 Cloudflare D1 的聊天记忆

info

此集成仅支持在 Cloudflare Workers 中使用。

为了在多个聊天会话中实现更长期的数据持久化，您可以将用于聊天记忆类（如 BufferMemory）的默认内存 chatHistory 替换为 Cloudflare D1 实例。

安装与配置​

您需要安装 LangChain Cloudflare 集成包。
在以下示例中，我们同时使用了 Anthropic，但您也可以使用任意模型：

:::提示 请参阅安装集成包的一般说明部分。 :::

npm install @langchain/cloudflare @langchain/anthropic @langchain/core

按照 官方文档 为您的 Worker 设置 D1 实例。您项目的 wrangler.toml 文件应类似于以下内容：

name = "YOUR_PROJECT_NAME"
main = "src/index.ts"
compatibility_date = "2024-01-10"

[vars]
ANTHROPIC_API_KEY = "YOUR_ANTHROPIC_KEY"

[[d1_databases]]
binding = "DB"                                       # 在 Worker 中可通过 env.DB 使用
database_name = "YOUR_D1_DB_NAME"
database_id = "YOUR_D1_DB_ID"

使用方法​

然后，您可以使用 D1 来存储聊天历史记录，如下所示：

import type { D1Database } from "@cloudflare/workers-types";
import { BufferMemory } from "langchain/memory";
import { CloudflareD1MessageHistory } from "@langchain/cloudflare";
import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";
import { RunnableSequence } from "@langchain/core/runnables";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { ChatAnthropic } from "@langchain/anthropic";

export interface Env {
  DB: D1Database;

  ANTHROPIC_API_KEY: string;
}

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    try {
      const { searchParams } = new URL(request.url);
      const input = searchParams.get("input");
      if (!input) {
        throw new Error(`Missing "input" parameter`);
      }
      const memory = new BufferMemory({
        returnMessages: true,
        chatHistory: new CloudflareD1MessageHistory({
          tableName: "stored_message",
          sessionId: "example",
          database: env.DB,
        }),
      });
      const prompt = ChatPromptTemplate.fromMessages([
        ["system", "You are a helpful chatbot"],
        new MessagesPlaceholder("history"),
        ["human", "{input}"],
      ]);
      const model = new ChatAnthropic({
        apiKey: env.ANTHROPIC_API_KEY,
      });

      const chain = RunnableSequence.from([
        {
          input: (initialInput) => initialInput.input,
          memory: () => memory.loadMemoryVariables({}),
        },
        {
          input: (previousOutput) => previousOutput.input,
          history: (previousOutput) => previousOutput.memory.history,
        },
        prompt,
        model,
        new StringOutputParser(),
      ]);

      const chainInput = { input };

      const res = await chain.invoke(chainInput);
      await memory.saveContext(chainInput, {
        output: res,
      });

      return new Response(JSON.stringify(res), {
        headers: { "content-type": "application/json" },
      });
    } catch (err: any) {
      console.log(err.message);
      return new Response(err.message, { status: 500 });
    }
  },
};

API Reference:

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