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import { StateSchema, MessagesValue } from '@langchain/langgraph'
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import { z } from 'zod'
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const MyState = new StateSchema({
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  // MessagesValue：专门用于消息列表的特殊类型
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  // 内置了合并逻辑——新消息会追加到列表末尾，而不是覆盖
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  messages: MessagesValue,
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  // 普通 Zod schema：每次更新直接覆盖旧值
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  currentStep: z.string().default('init'),
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})

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import {
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  StateGraph,
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  StateSchema,
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  MessagesValue,
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  START,
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  END,
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} from '@langchain/langgraph'
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import type { GraphNode } from '@langchain/langgraph'
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import { z } from 'zod'
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// 1. 定义状态
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const MyState = new StateSchema({
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  messages: MessagesValue,
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  currentStep: z.string().default('init'),
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})
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// 2. 定义节点
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// 每个节点是一个函数，接收当前状态，返回状态的部分更新
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const greet: GraphNode<typeof MyState> = (state) => {
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  const userName = state.messages.at(-1)?.content ?? '朋友'
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  return {
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    messages: [{ role: 'assistant', content: `你好，${userName}！` }],
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    currentStep: 'greeted',
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  }
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}
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const farewell: GraphNode<typeof MyState> = (state) => {
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  return {
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    messages: [{ role: 'assistant', content: '再见，有问题随时来找我。' }],
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    currentStep: 'done',
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  }
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}
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// 3. 构建图
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const graph = new StateGraph(MyState)
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  // 添加节点：名称 + 处理函数
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  .addNode('greet', greet)
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  .addNode('farewell', farewell)
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  // 添加边：定义流转关系
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  .addEdge(START, 'greet')       // 入口 → greet
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  .addEdge('greet', 'farewell')  // greet → farewell
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  .addEdge('farewell', END)      // farewell → 结束
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  // 编译：把图定义转化为可运行的实例
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  .compile()
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// 4. 运行
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const result = await graph.invoke({
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  messages: [{ role: 'user', content: '小明' }],
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})
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console.log(result.currentStep)
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// → 'done'
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for (const msg of result.messages) {
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  console.log(`[${msg.getType()}]: ${msg.content}`)
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}
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// → [human]: 小明
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// → [ai]: 你好，小明！
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// → [ai]: 再见，有问题随时来找我。

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import { createAgent } from 'langchain'
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// LangChain 的方式：把逻辑全部塞进 system prompt，由模型自己决定怎么走
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const agent = createAgent({
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  model: 'openai:gpt-4.1-mini',
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  tools: [],
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  systemPrompt: '先打招呼，然后说再见。',
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})

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import {
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  StateGraph,
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  StateSchema,
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  MessagesValue,
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  START,
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  END,
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} from '@langchain/langgraph'
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import type { GraphNode, ConditionalEdgeRouter } from '@langchain/langgraph'
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import { z } from 'zod'
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const State = new StateSchema({
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  messages: MessagesValue,
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  mood: z.enum(['happy', 'sad', 'neutral']).default('neutral'),
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})
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// 分析情绪的节点
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const analyzeMood: GraphNode<typeof State> = (state) => {
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  const lastMsg = state.messages.at(-1)?.content?.toString() ?? ''
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  let mood: 'happy' | 'sad' | 'neutral' = 'neutral'
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  if (lastMsg.includes('开心') || lastMsg.includes('高兴')) mood = 'happy'
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  if (lastMsg.includes('难过') || lastMsg.includes('伤心')) mood = 'sad'
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  return { mood }
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}
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// 不同情绪对应不同回复节点
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const happyReply: GraphNode<typeof State> = () => ({
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  messages: [{ role: 'assistant', content: '很高兴听到你这么开心！继续保持好心情 😊' }],
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})
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const sadReply: GraphNode<typeof State> = () => ({
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  messages: [{ role: 'assistant', content: '别难过，有什么我能帮你的吗？' }],
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})
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const neutralReply: GraphNode<typeof State> = () => ({
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  messages: [{ role: 'assistant', content: '你好，有什么可以帮你的？' }],
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})
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// 路由函数：根据状态中的 mood 字段决定走哪条边
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const moodRouter: ConditionalEdgeRouter<
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  typeof State,
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  'happyReply' | 'sadReply' | 'neutralReply'
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> = (state) => {
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  switch (state.mood) {
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    case 'happy': return 'happyReply'
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    case 'sad': return 'sadReply'
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    default: return 'neutralReply'
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  }
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}
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const graph = new StateGraph(State)
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  .addNode('analyzeMood', analyzeMood)
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  .addNode('happyReply', happyReply)
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  .addNode('sadReply', sadReply)
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  .addNode('neutralReply', neutralReply)
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  .addEdge(START, 'analyzeMood')
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  // 条件边：analyzeMood 执行完后，根据 moodRouter 的返回值决定走哪个节点
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  // 第三个参数列出这条边可能到达的目标，方便做结构校验和类型收窄。
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  .addConditionalEdges('analyzeMood', moodRouter, [
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    'happyReply', 'sadReply', 'neutralReply',
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  ])
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  .addEdge('happyReply', END)
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  .addEdge('sadReply', END)
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  .addEdge('neutralReply', END)
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  .compile()
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const result = await graph.invoke({
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  messages: [{ role: 'user', content: '今天好开心啊' }],
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})
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console.log(result.messages.at(-1)?.content)
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// → '很高兴听到你这么开心！继续保持好心情 😊'

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import { ChatOpenAI } from '@langchain/openai'
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import { tool } from '@langchain/core/tools'
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import { StateGraph, StateSchema, MessagesValue, START, END } from '@langchain/langgraph'
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import type { GraphNode } from '@langchain/langgraph'
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import { z } from 'zod'
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// LangChain 的组件：模型和工具
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const model = new ChatOpenAI({ model: 'gpt-4.1-mini' })
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const getWeather = tool(
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  async ({ city }) => `${city}：明天小雨，17-22 度`,
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  {
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    name: 'get_weather',
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    description: '查询天气',
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    schema: z.object({ city: z.string() }),
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  },
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)
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const modelWithTools = model.bindTools([getWeather])
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const State = new StateSchema({
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  messages: MessagesValue,
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})
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// LangGraph 的节点：内部使用 LangChain 组件
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const callModel: GraphNode<typeof State> = async (state) => {
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  const response = await modelWithTools.invoke(state.messages)
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  return { messages: [response] }
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}
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// LangGraph 负责编排
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const graph = new StateGraph(State)
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  .addNode('callModel', callModel)
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  .addEdge(START, 'callModel')
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  .addEdge('callModel', END)
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  .compile()