Agent与普通Chatbot区别

// src/chatbot/simple.ts
import { Hono } from 'hono'
 
type Env = {
  AI: Ai
}
 
const app = new Hono<{ Bindings: Env }>()
 
app.post('/chat', async (c) => {
  const { message } = await c.req.json<{ message: string }>()
 
  const response = await c.env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
    messages: [
      { role: 'system', content: '你是一个有用的助手。' },
      { role: 'user', content: message },
    ],
  })
 
  return c.json({ reply: response })
})
 
export default app

// src/agent/index.ts
import { Hono } from 'hono'
 
type Env = {
  AI: Ai
  DB: D1Database
}
 
// 工具定义：告诉 LLM 有哪些工具可用
const toolDefinitions = [
  {
    name: 'get_weather',
    description: '查询指定城市指定日期的天气',
    parameters: {
      type: 'object' as const,
      properties: {
        city: { type: 'string', description: '城市名称' },
        date: { type: 'string', description: '日期，格式 YYYY-MM-DD' },
      },
      required: ['city', 'date'],
    },
  },
  {
    name: 'get_travel_recommendation',
    description: '根据天气情况推荐旅行目的地',
    parameters: {
      type: 'object' as const,
      properties: {
        temperature: { type: 'number', description: '温度（摄氏度）' },
        condition: { type: 'string', description: '天气状况' },
      },
      required: ['temperature', 'condition'],
    },
  },
]
 
// 工具实现：实际执行工具逻辑
async function executeTool(
  name: string,
  args: Record<string, unknown>,
  env: Env
): Promise<unknown> {
  if (name === 'get_weather') {
    const record = await env.DB
      .select()
      .from(weatherTable)
      .where(eq(weatherTable.city, args.city as string))
      .get()
    return record ?? { temperature: 25, condition: '晴' }
  }
 
  if (name === 'get_travel_recommendation') {
    const temp = args.temperature as number
    if (temp > 30) {
      return { suggestion: '推荐去海边或山区避暑，如青岛、莫干山' }
    }
    return { suggestion: '适合城市游览，推荐西湖、乌镇' }
  }
 
  return { error: `未知工具：${name}` }
}
 
// Agent 核心路由
const app = new Hono<{ Bindings: Env }>()
 
app.post('/agent', async (c) => {
  const { message } = await c.req.json<{ message: string }>()
 
  const toolMap: Record<string, (args: Record<string, unknown>) => Promise<unknown>> = {
    get_weather: (args) => executeTool('get_weather', args, c.env),
    get_travel_recommendation: (args) => executeTool('get_travel_recommendation', args, c.env),
  }
 
  const messages: Array<{ role: string; content: string; tool_calls?: unknown[]; tool_call_id?: string; name?: string }> = [
    { role: 'system', content: '你是一个天气和旅行助手。需要查天气或推荐时，请使用工具。' },
    { role: 'user', content: message },
  ]
 
  const maxIterations = 10
  for (let i = 0; i < maxIterations; i++) {
    const response = await c.env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
      messages,
      tools: toolDefinitions,
    })
 
    // 没有工具调用 → 模型认为已有足够信息，返回最终回答
    if (!response.tool_calls || response.tool_calls.length === 0) {
      return c.json({ reply: response })
    }
 
    // 有工具调用 → 逐个执行，把结果加进 messages
    const assistantMessage = response as { role: string; content: string; tool_calls: Array<{ id: string; name: string; arguments: Record<string, unknown> }> }
    messages.push({
      role: 'assistant',
      content: assistantMessage.content ?? '',
      tool_calls: assistantMessage.tool_calls,
    })
 
    for (const toolCall of assistantMessage.tool_calls) {
      const fn = toolMap[toolCall.name]
      if (!fn) {
        messages.push({
          role: 'tool',
          tool_call_id: toolCall.id,
          name: toolCall.name,
          content: JSON.stringify({ error: `未知工具：${toolCall.name}` }),
        })
        continue
      }
 
      const result = await fn(toolCall.arguments)
      messages.push({
        role: 'tool',
        tool_call_id: toolCall.id,
        name: toolCall.name,
        content: JSON.stringify(result),
      })
    }
 
    // 循环继续：LLM 会看到工具调用结果，决定下一步
  }
 
  return c.json({ reply: '达到最大迭代次数，停止处理。' }, 500)
})
 
export default app

// src/chatbot/core.ts
app.post('/chat', async (c) => {
  const { message } = await c.req.json<{ message: string }>()
 
  // 一次 LLM 调用
  const response = await c.env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
    messages: [
      { role: 'system', content: '你是一个有用的助手。' },
      { role: 'user', content: message },
    ],
  })
 
  // 直接返回
  return c.json({ reply: response })
})

// src/agent/core.ts
app.post('/agent', async (c) => {
  const { message } = await c.req.json<{ message: string }>()
 
  const messages = [
    { role: 'system', content: '你是一个天气和旅行助手。' },
    { role: 'user', content: message },
  ]
 
  // 循环调用 LLM
  for (let i = 0; i < maxIterations; i++) {
    const response = await c.env.AI.run('@cf/meta/llama-3.1-8b-instruct', {
      messages,
      tools: toolDefinitions,
    })
 
    // 无工具调用 → 结束
    if (!response.tool_calls?.length) {
      return c.json({ reply: response })
    }
 
    // 有工具调用 → 执行并把结果加进 messages
    messages.push({ role: 'assistant', content: '', tool_calls: response.tool_calls })
    for (const tc of response.tool_calls) {
      const result = await executeTool(tc.name, tc.arguments, c.env)
      messages.push({ role: 'tool', tool_call_id: tc.id, name: tc.name, content: JSON.stringify(result) })
    }
  }
})