import { ContextManager } from './context.js';
import { chat } from './llm.js';
import { PROMPTS } from './prompts/system.js';
import { getOpenAITools, findTool } from './tools/registry.js';
import * as readline from 'node:readline/promises';

const promptName = process.argv[2] || 'agent';
const systemPrompt = PROMPTS[promptName as keyof typeof PROMPTS];

if (!systemPrompt) {
  console.error(`未知提示词: ${promptName}，可选: ${Object.keys(PROMPTS).join(', ')}`);
  process.exit(1);
}

const context = new ContextManager();
context.add({ role: 'system', content: systemPrompt });

const rl = readline.createInterface({
  input: process.stdin,
  output: process.stdout,
});

console.log(`提示词模式: ${promptName}，输入 "exit" 退出。\n`);

while (true) {
  const userInput = await rl.question('我: ');
  if (userInput.toLowerCase() === 'exit') break;

  context.add({ role: 'user', content: userInput });
  
  // 第一轮：带工具定义调用
  const tools = getOpenAITools();
  let reply = await chat(context.getMessages(), tools);

  // 如果模型要求调用工具，执行并在上下文中构造 tool 消息
  while (reply.tool_calls && reply.tool_calls.length > 0) {
    // 先把 assistant 消息（含 tool_calls）加入上下文
    context.add({ role: 'assistant', content: '', tool_calls: reply.tool_calls } as any);

    for (const tc of reply.tool_calls) {
      const tool = findTool(tc.function.name);
      const args = JSON.parse(tc.function.arguments);
      const result = await tool!.execute(args);
      console.log(`  [工具] ${tc.function.name}(${tc.function.arguments}) -> ${result}`);

      // tool 消息：必须回传 tool_call_id
      context.add({
        role: 'tool',
        tool_call_id: tc.id,
        content: result,
      } as any);
    }

    // 继续调用，看模型还需要不需要更多工具
    reply = await chat(context.getMessages(), tools);
  }
  
  console.log('助手:', reply.content);
  context.add({ role: 'assistant', content: reply.content! });
}

rl.close();