// summary-middleware.ts
import { createMiddleware } from "langchain";
import { ChatAnthropic } from "@langchain/anthropic";
import {
  BaseMessage,
  SystemMessage,
  RemoveMessage,
} from "@langchain/core/messages";
 
const summarizer = new ChatAnthropic({
  model: "claude-haiku-4-5", // 摘要用便宜模型
  temperature: 0,
});
 
// 触发阈值与窗口大小
const TRIGGER_AT = 20;  // 消息条数超过 20 触发
const KEEP_RECENT = 6;  // 保留最近 6 条原始消息
 
async function summarize(messages: BaseMessage[], oldSummary: string): Promise<string> {
  const dialog = messages
    .map((m) => {
      const role = m.getType() === "human" ? "用户" : m.getType() === "ai" ? "助手" : m.getType();
      const text = typeof m.text === "string" ? m.text : "";
      return `${role}: ${text}`;
    })
    .join("\n");
 
  const res = await summarizer.invoke([
    new SystemMessage(
      `你是对话摘要器。请把下面这段对话压缩成一段简洁摘要，保留：用户身份、偏好、关键决定、待办。
当前已有的旧摘要（可能为空）：
${oldSummary || "（无）"}
 
新的对话内容：
${dialog}
 
输出更新后的完整摘要（不要寒暄，直接给摘要正文）：`
    ),
  ]);
 
  return typeof res.text === "string" ? res.text : "";
}
 
export const summaryMiddleware = createMiddleware({
  name: "summary",
  stateSchema: {
    // 把"当前摘要"作为自定义 state 字段
    summary: { value: (a: string, b: string) => b ?? a, default: () => "" },
  },
  beforeModel: async (state) => {
    const messages = state.messages as BaseMessage[];
    if (messages.length < TRIGGER_AT) return;
 
    // 拿出要被压缩的旧消息
    const toCompress = messages.slice(0, messages.length - KEEP_RECENT);
    const newSummary = await summarize(toCompress, state.summary ?? "");
 
    // 用 RemoveMessage 标记把旧消息从历史里移除
    const removals = toCompress.map((m) => new RemoveMessage({ id: m.id! }));
 
    // 把摘要作为一条 system message 拼到最前面
    const summaryMessage = new SystemMessage(`历史对话摘要：\n${newSummary}`);
 
    return {
      messages: [...removals, summaryMessage],
      summary: newSummary,
    };
  },
});

import { createAgent } from "langchain";
import { MemorySaver } from "@langchain/langgraph";
import { summaryMiddleware } from "./summary-middleware";
 
const agent = createAgent({
  model: "anthropic:claude-sonnet-4-6",
  tools: [],
  systemPrompt: "你是一个会记住用户信息的助手。",
  middleware: [summaryMiddleware],
  checkpointer: new MemorySaver(),
});
 
const config = { configurable: { thread_id: "long-chat" } };
 
// 模拟一段超长对话
const turns = [
  "我叫张三，在北京做后端开发",
  "技术栈是 Go 和 Python",
  "最近在学 LangChain.js",
  "想做一个客服 bot",
  "数据源是飞书云文档",
  // ... 中间省略 30 轮 ...
  "回顾一下我的情况，给个学习路线",
];
 
for (const t of turns) {
  await agent.invoke({ messages: [{ role: "user", content: t }] }, config);
}
 
// 检查最终 state
const final = await agent.getState(config);
console.log("当前消息数:", final.values.messages.length);
console.log("当前摘要:", final.values.summary);

const summarizer = new ChatAnthropic({ model: "claude-haiku-4-5", temperature: 0 });
const chatModel = "anthropic:claude-sonnet-4-6";

保留以下类别，不允许遗漏：
- 用户身份信息（姓名、职业、城市、公司）
- 明确表达的偏好与限制
- 尚未完成的任务、承诺、TODO
- 用户做过的关键决定

可以丢弃：寒暄、重复确认、客套话、过时的临时情绪。

const STRUCTURED_PROMPT = `输出格式（JSON）：
{
  "user_profile": { "name": "", "role": "" },
  "topics": [],
  "decisions": [],
  "pending": []
}`;

beforeModel: async (state) => {
  const totalTokens = estimateTokens(state.messages);
  if (totalTokens < 6000) return;
  // ...
}

// support-bot.ts
import { createAgent, createMiddleware } from "langchain";
import { MemorySaver } from "@langchain/langgraph";
import { ChatAnthropic } from "@langchain/anthropic";
import {
  BaseMessage,
  SystemMessage,
  RemoveMessage,
} from "@langchain/core/messages";
 
const summarizer = new ChatAnthropic({ model: "claude-haiku-4-5", temperature: 0 });
 
const TRIGGER = 16;
const KEEP = 6;
 
async function buildSummary(toCompress: BaseMessage[], oldSummary: string) {
  const dialog = toCompress
    .map((m) => `${m.getType()}: ${typeof m.text === "string" ? m.text : ""}`)
    .join("\n");
 
  const res = await summarizer.invoke([
    new SystemMessage(
      `你是客服对话摘要器。压缩下面对话，按以下结构输出：
- 客户身份与诉求
- 已提供的解决方案
- 客户情绪
- 未解决事项
 
旧摘要：${oldSummary || "（无）"}
 
新对话：
${dialog}`
    ),
  ]);
  return typeof res.text === "string" ? res.text : "";
}
 
const summaryMw = createMiddleware({
  name: "summary",
  stateSchema: {
    summary: { value: (a: string, b: string) => b ?? a, default: () => "" },
  },
  beforeModel: async (state) => {
    const msgs = state.messages as BaseMessage[];
    if (msgs.length < TRIGGER) return;
 
    const cut = msgs.length - KEEP;
    const toCompress = msgs.slice(0, cut);
    const newSummary = await buildSummary(toCompress, state.summary ?? "");
 
    return {
      messages: [
        ...toCompress.map((m) => new RemoveMessage({ id: m.id! })),
        new SystemMessage(`【客服上下文摘要】\n${newSummary}`),
      ],
      summary: newSummary,
    };
  },
});
 
const agent = createAgent({
  model: "anthropic:claude-sonnet-4-6",
  tools: [],
  systemPrompt: "你是一名耐心的客服。优先调用历史摘要里的信息。",
  middleware: [summaryMw],
  checkpointer: new MemorySaver(),
});
 
const config = { configurable: { thread_id: "customer-A-001" } };
 
const turns = [
  "你好，我是用户 A，订单号 12345，发货一周还没收到",
  "上面写的物流单号是 SF1234567890",
  "我之前催过两次都没人理",
  "我有点生气了，要求赔偿",
  "我希望明天就能拿到货",
  "我可以接受发顺丰特快",
  "另外，我下周要出差，得在周三之前收到",
  "我家地址是北京朝阳望京 SOHO 1 号楼",
  "电话是 138 0000 0000",
  "你能帮我升级处理吗？",
  "好的，那你回复我一下进度",
  "我先去吃午饭",
  "下午我会盯着这个工单",
  "如果有问题随时联系我",
  "现在能告诉我一下我的诉求总结吗？",
  "我之前提到的物流单号是多少？",
];
 
for (const t of turns) {
  const r = await agent.invoke({ messages: [{ role: "user", content: t }] }, config);
  console.log(`\n用户: ${t}`);
  console.log(`客服: ${r.messages.at(-1)?.text}`);
}
 
const finalState = await agent.getState(config);
console.log("\n=== 最终 state ===");
console.log("消息条数:", finalState.values.messages.length);
console.log("摘要:\n", finalState.values.summary);