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src/agent/react_agent.py
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345
src/agent/react_agent.py
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# -*- coding: utf-8 -*-
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"""
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ReAct Agent - 基于 ReAct 模式的智能代理
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用单次 LLM 调用 + 工具循环替代原有的多步流水线
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"""
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import logging
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import json
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import re
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from typing import Dict, Any, List, Optional
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from datetime import datetime
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from src.agent.llm_client import LLMManager
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from src.config.unified_config import get_config
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logger = logging.getLogger(__name__)
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# ── 工具定义（供 LLM 理解可用能力） ──────────────────────────
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TOOL_DEFINITIONS = [
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{
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"name": "search_knowledge",
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"description": "搜索知识库，根据关键词查找相关的问题和答案",
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"parameters": {
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"query": {"type": "string", "description": "搜索关键词", "required": True},
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"top_k": {"type": "integer", "description": "返回结果数量，默认3", "required": False}
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}
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},
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{
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"name": "add_knowledge",
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"description": "向知识库添加新的问答条目",
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"parameters": {
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"question": {"type": "string", "description": "问题", "required": True},
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"answer": {"type": "string", "description": "答案", "required": True},
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"category": {"type": "string", "description": "分类", "required": False}
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}
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},
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{
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"name": "query_vehicle",
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"description": "查询车辆信息，支持按VIN码或车牌号查询",
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"parameters": {
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"vin": {"type": "string", "description": "VIN码", "required": False},
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"plate_number": {"type": "string", "description": "车牌号", "required": False}
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}
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},
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{
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"name": "get_analytics",
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"description": "获取系统数据分析报告，如每日统计、分类统计等",
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"parameters": {
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"report_type": {
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"type": "string",
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"description": "报告类型: daily_analytics / summary / category_performance",
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"required": True
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}
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}
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},
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{
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"name": "send_feishu_message",
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"description": "通过飞书发送消息通知",
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"parameters": {
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"message": {"type": "string", "description": "消息内容", "required": True},
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"chat_id": {"type": "string", "description": "飞书群聊ID（可选）", "required": False}
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}
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},
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]
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def _build_tools_prompt() -> str:
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"""构建工具描述文本供 system prompt 使用"""
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lines = []
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for t in TOOL_DEFINITIONS:
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params_desc = []
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for pname, pinfo in t["parameters"].items():
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req = "必填" if pinfo.get("required") else "可选"
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params_desc.append(f" - {pname} ({pinfo['type']}, {req}): {pinfo['description']}")
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lines.append(f"- {t['name']}: {t['description']}\n 参数:\n" + "\n".join(params_desc))
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return "\n".join(lines)
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SYSTEM_PROMPT = f"""你是 TSP 智能客服助手，帮助用户解决车辆售后问题、查询知识库、管理客诉信息。
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你可以使用以下工具来完成任务：
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{_build_tools_prompt()}
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## 回复规则
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1. 如果你需要使用工具，请严格按以下 JSON 格式回复（不要包含其他内容）：
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```json
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{{"tool": "工具名", "parameters": {{"参数名": "参数值"}}}}
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```
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2. 如果你不需要使用工具，可以直接用自然语言回复用户。
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3. 每次只调用一个工具。
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4. 根据工具返回的结果，综合生成最终回复。
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5. 回复要简洁专业，使用中文。
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"""
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class ReactAgent:
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"""基于 ReAct 模式的 Agent"""
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MAX_TOOL_ROUNDS = 5 # 最多工具调用轮次，防止死循环
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def __init__(self):
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config = get_config()
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self.llm = LLMManager(config.llm)
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self._tool_handlers = self._register_tool_handlers()
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self.execution_history: List[Dict[str, Any]] = []
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logger.info("ReactAgent 初始化完成")
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# ── 工具处理器注册 ──────────────────────────────────────
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def _register_tool_handlers(self) -> Dict[str, Any]:
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return {
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"search_knowledge": self._tool_search_knowledge,
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"add_knowledge": self._tool_add_knowledge,
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"query_vehicle": self._tool_query_vehicle,
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"get_analytics": self._tool_get_analytics,
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"send_feishu_message": self._tool_send_feishu_message,
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}
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# ── 主处理入口 ──────────────────────────────────────────
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async def chat(
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self,
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message: str,
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user_id: str = "anonymous",
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conversation_history: Optional[List[Dict[str, str]]] = None,
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) -> Dict[str, Any]:
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"""处理用户消息，返回最终回复"""
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messages = [{"role": "system", "content": SYSTEM_PROMPT}]
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# 加入历史对话（最近 10 轮）
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if conversation_history:
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messages.extend(conversation_history[-10:])
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messages.append({"role": "user", "content": message})
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tool_calls_log = []
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for round_idx in range(self.MAX_TOOL_ROUNDS):
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# 调用 LLM
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try:
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response_text = await self.llm.chat(messages, temperature=0.3, max_tokens=2000)
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except Exception as e:
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logger.error(f"LLM 调用失败: {e}")
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return self._error_response(str(e))
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# 尝试解析工具调用
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tool_call = self._parse_tool_call(response_text)
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if tool_call is None:
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# 没有工具调用 → 这是最终回复
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self._record_execution(message, user_id, tool_calls_log, response_text)
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return {
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"success": True,
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"response": response_text,
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"tool_calls": tool_calls_log,
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"rounds": round_idx + 1,
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}
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# 执行工具
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tool_name = tool_call["tool"]
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tool_params = tool_call.get("parameters", {})
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logger.info(f"[Round {round_idx+1}] 调用工具: {tool_name}, 参数: {tool_params}")
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tool_result = await self._execute_tool(tool_name, tool_params)
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tool_calls_log.append({
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"tool": tool_name,
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"parameters": tool_params,
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"result": tool_result,
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"round": round_idx + 1,
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})
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# 把工具调用和结果加入对话上下文
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messages.append({"role": "assistant", "content": response_text})
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messages.append({
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"role": "user",
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"content": f"工具 `{tool_name}` 返回结果:\n```json\n{json.dumps(tool_result, ensure_ascii=False, default=str)}\n```\n请根据以上结果回复用户。"
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})
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# 超过最大轮次
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self._record_execution(message, user_id, tool_calls_log, "[达到最大工具调用轮次]")
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return {
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"success": True,
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"response": "抱歉，处理过程较复杂，请稍后重试或换个方式描述您的问题。",
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"tool_calls": tool_calls_log,
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"rounds": self.MAX_TOOL_ROUNDS,
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}
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# ── 工具调用解析 ──────────────────────────────────────
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def _parse_tool_call(self, text: str) -> Optional[Dict[str, Any]]:
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"""从 LLM 回复中解析工具调用 JSON"""
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if not text:
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return None
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# 尝试从 ```json ... ``` 代码块中提取
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code_block = re.search(r'```json\s*(\{.*?\})\s*```', text, re.DOTALL)
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if code_block:
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try:
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data = json.loads(code_block.group(1))
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if "tool" in data:
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return data
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except json.JSONDecodeError:
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pass
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# 尝试直接解析整段文本为 JSON
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try:
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data = json.loads(text.strip())
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if isinstance(data, dict) and "tool" in data:
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return data
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except json.JSONDecodeError:
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pass
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# 尝试从文本中找到第一个 JSON 对象
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json_match = re.search(r'\{[^{}]*"tool"\s*:\s*"[^"]+?"[^{}]*\}', text, re.DOTALL)
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if json_match:
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try:
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data = json.loads(json_match.group())
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if "tool" in data:
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return data
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except json.JSONDecodeError:
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pass
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return None
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# ── 工具执行 ──────────────────────────────────────────
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async def _execute_tool(self, tool_name: str, params: Dict[str, Any]) -> Dict[str, Any]:
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"""执行指定工具"""
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handler = self._tool_handlers.get(tool_name)
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if not handler:
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return {"error": f"未知工具: {tool_name}"}
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try:
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return await handler(**params)
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except Exception as e:
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logger.error(f"工具 {tool_name} 执行失败: {e}")
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return {"error": str(e)}
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# ── 具体工具实现 ──────────────────────────────────────
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async def _tool_search_knowledge(self, query: str, top_k: int = 3, **kw) -> Dict[str, Any]:
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"""搜索知识库"""
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try:
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from src.knowledge_base.knowledge_manager import KnowledgeManager
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km = KnowledgeManager()
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results = km.search_knowledge(query, top_k)
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return {"results": results, "count": len(results)}
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except Exception as e:
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return {"error": str(e)}
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async def _tool_add_knowledge(self, question: str, answer: str, category: str = "通用", **kw) -> Dict[str, Any]:
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"""添加知识库条目"""
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try:
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from src.knowledge_base.knowledge_manager import KnowledgeManager
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km = KnowledgeManager()
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success = km.add_knowledge_entry(question=question, answer=answer, category=category)
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return {"success": success}
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except Exception as e:
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return {"error": str(e)}
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async def _tool_query_vehicle(self, vin: str = None, plate_number: str = None, **kw) -> Dict[str, Any]:
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"""查询车辆信息"""
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try:
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from src.vehicle.vehicle_data_manager import VehicleDataManager
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vm = VehicleDataManager()
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if vin:
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result = vm.get_latest_vehicle_data_by_vin(vin)
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return {"vehicle_data": result} if result else {"error": "未找到该VIN的车辆数据"}
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elif plate_number:
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return {"error": "暂不支持按车牌号查询，请使用VIN码"}
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else:
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return {"error": "请提供 VIN 码"}
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except Exception as e:
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return {"error": str(e)}
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async def _tool_get_analytics(self, report_type: str = "summary", **kw) -> Dict[str, Any]:
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"""获取分析报告"""
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try:
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from src.analytics.analytics_manager import AnalyticsManager
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am = AnalyticsManager()
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if report_type == "daily_analytics":
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return am.generate_daily_analytics()
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elif report_type == "summary":
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return am.get_analytics_summary()
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elif report_type == "category_performance":
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return am.get_category_performance()
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else:
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return {"error": f"不支持的报告类型: {report_type}"}
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except Exception as e:
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return {"error": str(e)}
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async def _tool_send_feishu_message(self, message: str, chat_id: str = None, **kw) -> Dict[str, Any]:
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"""发送飞书消息"""
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try:
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from src.integrations.feishu_service import FeishuService
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fs = FeishuService()
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if not chat_id:
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return {"error": "请提供飞书群聊 chat_id"}
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success = fs.send_message(receive_id=chat_id, content=message, receive_id_type="chat_id")
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return {"success": success}
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except Exception as e:
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return {"error": str(e)}
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# ── 辅助方法 ──────────────────────────────────────────
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def _record_execution(self, message: str, user_id: str, tool_calls: list, response: str):
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"""记录执行历史"""
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record = {
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"timestamp": datetime.now().isoformat(),
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"user_id": user_id,
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"message": message,
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"tool_calls": tool_calls,
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"response": response[:500],
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}
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self.execution_history.append(record)
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if len(self.execution_history) > 500:
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self.execution_history = self.execution_history[-500:]
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def _error_response(self, error_msg: str) -> Dict[str, Any]:
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return {
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"success": False,
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"response": "抱歉，系统处理出现问题，请稍后重试。",
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"error": error_msg,
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"tool_calls": [],
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"rounds": 0,
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}
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def get_tool_definitions(self) -> List[Dict[str, Any]]:
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"""返回工具定义列表（供 API 展示）"""
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return TOOL_DEFINITIONS
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def get_execution_history(self, limit: int = 50) -> List[Dict[str, Any]]:
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"""获取执行历史"""
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return self.execution_history[-limit:]
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def get_status(self) -> Dict[str, Any]:
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"""获取 Agent 状态"""
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return {
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"status": "active",
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"available_tools": [t["name"] for t in TOOL_DEFINITIONS],
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"tool_count": len(TOOL_DEFINITIONS),
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"history_count": len(self.execution_history),
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"max_tool_rounds": self.MAX_TOOL_ROUNDS,
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}
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