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@@ -0,0 +1,524 @@
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+from pdb import run
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+import sys
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+
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+from langchain.tools import tool
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+from langchain.agents import create_agent
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+from langchain_openai import ChatOpenAI
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+from typing import Dict, Any, Optional
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+
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+from openai import chat
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+from config.settings import settings
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+from langchain_core.messages import HumanMessage, SystemMessage
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+import datetime
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+from utils.context_helper import safe_context_param
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+from utils.logger import chat_logger
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+
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+"""
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+增加worker需要修改:
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+worker_type_mapping
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+"""
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+
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+# WorkerAgent缓存字典
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+_worker_cache = {}
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+# 工具缓存字典
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+_tools_cache = {}
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+
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+# worker_type, tool_types
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+worker_type_mapping = {
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+ "sale_worker": ("销售报表查询", ["sale"]),
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+ "ware_worker": ("库存查询", ["ware"]),
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+ "money_worker": ("财务查询", ["money"]),
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+ "price_worker": ("价格查询", ["price"]),
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+ "salebill_worker": ("销售类单据查询", ["salebill"]),
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+ "base_data_worker": ("基础资料查询", ["mtrl_data", "cust_data"]),
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+}
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+
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+
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+class WorkerAgent:
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+ """Worker Agent - 专门处理特定领域的任务"""
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+
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+ def __init__(self, worker_type: str, tools: list):
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+ self.worker_type = worker_type
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+ self.tools = tools
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+ self.llm = ChatOpenAI(
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+ model=settings.LLM_MODEL,
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+ temperature=settings.LLM_TEMPERATURE,
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+ api_key=settings.DEEPSEEK_API_KEY,
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+ base_url=settings.DEEPSEEK_BASE_URL,
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+ max_tokens=settings.LLM_MAX_TOKENS,
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+ )
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+ self.agent = self._create_agent()
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+
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+ def _create_agent(self):
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+ """创建Agent实例"""
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+
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+ return create_agent(
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+ self.llm,
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+ tools=self.tools,
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+ )
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+
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+ def execute(self, query: str, backend_url: str, token: str) -> any:
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+ """执行任务"""
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+ try:
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+ system_prompt = f"""
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+ 你是{self.worker_type}专家，专门处理{self.worker_type}相关任务。
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+
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+ 职责：
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+ - 接收主Agent分配的具体任务
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+ - 智能选择最合适的工具执行任务
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+ - 理解工具返回的数据结构
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+ - 组织完整的答案向用户回答
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+
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+ 工具用到的参数：
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+ - backend_url: {backend_url}
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+ - token: {token}
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+
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+ 当前时间：{datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")}
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+
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+ 数据查询结果尽量以 Markdown 表格格式输出
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+
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+ 如果工具有输出格式要求，必须严格按照要求输出。
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+ """
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+
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+ if settings.ECHARTS_ENABLED:
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+ system_prompt = (
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+ system_prompt
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+ + """
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+ 你可以输出echarts柱状图、折线图、饼图。
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+
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+ 饼图格式范例如下:
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+ ```echarts
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+ {{
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+ "title": {{
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+ "text": "浏览器份额", "left": "center" }}
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+ "tooltip": {{
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+ "trigger": "item" }},
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+ "legend": {{
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+ "orient": "vertical", "left": "left" }},
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+ "series": [
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+ {{
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+ "name": "Share",
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+ "type": "pie",
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+ "radius": "55%",
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+ "center": ["50%", "60%"],
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+ "data": [
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+ {{"value": 1048, "name": "Chrome" }}
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+ {{"value": 735, "name": "Firefox" }}
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+ {{"value": 580, "name": "Edge" }}
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+ ]
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+ }}
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+ ]
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+ }}
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+ ```
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+
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+ 柱状图格式范例如下:
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+ ```echarts
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+ {{
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+ "title": {{"text": "每周销量" }}
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+ "tooltip": {{}},
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+ "xAxis": {{"type": "category", "data": ["Mon","Tue","Wed","Thu","Fri","Sat","Sun"] }}
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+ "yAxis": {{"type": "value" }}
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+ "series": [
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+ {{"type": "bar", "data": [120, 200, 150, 80, 70, 110, 130] }}
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+ ]
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+ }}
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+ ```
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+
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+ 折线图,格式范例如下:
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+ ```echarts
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+ {{
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+ "title": {{ "text": "温度趋势" }},
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+ "tooltip": {{ "trigger": "axis" }},
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+ "legend": {{ "data": ["最高", "最低"] }},
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+ "xAxis": {{
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+ "type": "category",
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+ "boundaryGap": false,
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+ "data": ["Mon","Tue","Wed","Thu","Fri","Sat","Sun"]
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+ }},
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+ "yAxis": {{"type": "value" }},
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+ "series": [
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+ {{"name": "最高", "type": "line", "data": [11, 11, 15, 13, 12, 13, 10], "smooth": true }}
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+ {{"name": "最低", "type": "line", "data": [1, -2, 2, 5, 3, 2, 0], "smooth": true }}
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+ ]
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+ }}
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+ ```
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+ """
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+ )
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+
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+ inputs = {
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+ "messages": [
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+ SystemMessage(content=system_prompt),
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+ HumanMessage(content=query),
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+ ]
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+ }
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+ response = self.agent.invoke(inputs)
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+ return response
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+
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+ except Exception as e:
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+ return f"Worker执行失败: {str(e)}"
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+
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+
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+# 自动发现指定类型的工具
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+def _get_tools_by_type(tool_type: str) -> list:
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+ """根据类型自动发现工具 - 完整支持编译前后的文件"""
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+
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+ # 检查缓存
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+ if tool_type in _tools_cache:
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+ chat_logger.info(f"使用缓存的工具: {tool_type}")
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+ return _tools_cache[tool_type]
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+
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+ chat_logger.info(f"发现并加载工具: {tool_type}")
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+
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+ import importlib
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+ import inspect
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+ from pathlib import Path
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+ import sys
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+ from langchain.tools import BaseTool
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+
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+ tools = []
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+
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+ # 获取项目根目录并添加到Python路径
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+ project_root = Path(__file__).parent.parent
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+ if str(project_root) not in sys.path:
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+ sys.path.insert(0, str(project_root))
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+
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+ tools_dir = project_root / "tools"
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+ # 导入配置以检查知识库开关
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+ from config.settings import settings
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+
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+ # 扫描工具文件
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+ tool_files = []
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+
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+ # 模式1: 编译前的.py文件
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+ for file_path in tools_dir.glob("*_tools.py"):
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+ if file_path.is_file():
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+ module_name = file_path.stem
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+
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+ # 检查文件名是否以指定类型开头（前缀匹配）
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+ if module_name.startswith(f"{tool_type}_"):
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+ # 如果知识库被禁用，跳过知识库工具
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+ if (
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+ module_name == "knowledge_tools"
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+ and not settings.KNOWLEDGE_BASE_ENABLED
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+ ):
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+ print("知识库功能已禁用，跳过知识库工具")
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+ continue
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+ tool_files.append(module_name)
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+ print(f"发现工具文件: {module_name}")
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+
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+ # 模式2: 编译后的.pyd文件
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+ for file_path in tools_dir.glob("*_tools.cp*.pyd"):
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+ if file_path.is_file():
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+ # 从文件名中提取模块名，如: ware_tools.cp313-win_amd64.pyd -> ware_tools
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+ module_name = file_path.stem.split(".")[0]
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+
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+ # 检查文件名是否以指定类型开头（前缀匹配）
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+ if module_name.startswith(f"{tool_type}_"):
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+ # 如果知识库被禁用，跳过知识库工具
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+ if (
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+ module_name == "knowledge_tools"
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+ and not settings.KNOWLEDGE_BASE_ENABLED
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+ ):
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+ print("知识库功能已禁用，跳过知识库工具")
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+ continue
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+ if module_name not in tool_files: # 避免重复添加
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+ tool_files.append(module_name)
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+ print(f"发现编译后工具文件: {module_name}")
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+
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+ # 如果没有找到指定类型的工具文件，使用默认列表
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+ if not tool_files:
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+ # 根据类型提供默认工具列表
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+ default_tool_mapping = {
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+ "sale": ["sale_tools"],
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+ "ware": ["ware_tools"],
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+ "money": ["money_tools"],
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+ "price": ["price_tools"],
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+ "knowledge": ["knowledge_tools"] if settings.KNOWLEDGE_BASE_ENABLED else [],
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+ }
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+
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+ if tool_type in default_tool_mapping:
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+ tool_files = default_tool_mapping[tool_type]
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+ print(f"使用默认工具列表: {tool_files}")
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+
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+ # 导入模块并获取工具
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+ for module_name in tool_files:
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+ try:
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+ full_module_path = f"tools.{module_name}"
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+ module = importlib.import_module(full_module_path)
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+
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+ # 使用更全面的工具发现方法（参考tool_factory.py）
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+ tool_count = 0
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+
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+ # 方法1: 检查模块的所有属性
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+ for attr_name in dir(module):
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+ if attr_name.startswith("_"):
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+ continue
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+
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+ attr = getattr(module, attr_name)
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+
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+ # 检查是否是BaseTool实例
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+ if isinstance(attr, BaseTool):
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+ tools.append(attr)
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+ tool_count += 1
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+ continue
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+
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+ # 检查是否是函数且具有工具属性
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+ if callable(attr) and hasattr(attr, "name"):
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+ tools.append(attr)
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+ tool_count += 1
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+
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+ # 方法2: 检查模块中是否有get_all_tools函数
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+ if hasattr(module, "get_all_tools"):
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+ module_tools = module.get_all_tools()
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+ if isinstance(module_tools, list):
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+ tools.extend(module_tools)
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+ tool_count += len(module_tools)
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+
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+ if tool_count > 0:
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+ print(f"✅ 从 {module_name} 加载了 {tool_count} 个工具")
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+ else:
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+ print(f"⚠️ {module_name} 中未发现工具函数")
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+
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+ except Exception as e:
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+ print(f"❌ 导入模块 {module_name} 失败: {e}")
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+
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+ # 缓存工具
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+ _tools_cache[tool_type] = tools
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+
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+ return tools
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+
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+
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+def _get_worker_agent(worker_type: str, tools: list) -> WorkerAgent:
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+ """获取或创建WorkerAgent实例（使用缓存）"""
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+ cache_key = f"{worker_type}" # 使用worker类型作为缓存键
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+
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+ if cache_key not in _worker_cache:
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+ chat_logger.info(f"创建新的WorkerAgent缓存: {worker_type}")
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+ _worker_cache[cache_key] = WorkerAgent(worker_type, tools)
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+ else:
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+ chat_logger.info(f"使用缓存的WorkerAgent: {worker_type}")
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+
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+ return _worker_cache[cache_key]
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+
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+
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+def _execute_and_get_result(
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+ worker_type: str,
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+ tools: list,
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+ token: str,
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+ backend_url: str,
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+ query: str,
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+ context: str = "",
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+) -> str:
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+ """执行worker并获取结果"""
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+ try:
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+ chat_logger.info(f"开始执行worker: {worker_type}")
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+ # worker = WorkerAgent(worker_type, tools)
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+ worker = _get_worker_agent(worker_type, tools)
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+ # 如果有上下文，合并到查询中
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+ # chat_logger.info(f"原始上下文: {context}")
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+
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+ safe_context = safe_context_param(context)
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+ if safe_context:
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+ enhanced_query = f"对话上下文：{safe_context}\n当前问题：{query}"
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+ else:
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+ enhanced_query = query
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+
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+ # chat_logger.info(f"处理后的上下文: {safe_context}")
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+
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+ result = worker.execute(enhanced_query, backend_url, token)
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+ chat_logger.info(f"Worker{worker_type}执行成功:问题{query}")
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+ ai_messages = [
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+ msg
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+ for msg in result.get("messages", [])
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+ if hasattr(msg, "type") and msg.type == "ai"
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+ ]
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+
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+ if ai_messages:
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+ # 获取最后一条 AI 消息的内容
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+ last_ai_content = ai_messages[-1].content
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+ return last_ai_content
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+ else:
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+ return ""
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+
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+ except Exception as e:
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+ chat_logger.error(
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+ f"Worker{worker_type}执行失败:问题{query}，错误信息: {str(e)}"
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+ )
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+ return f"Worker{worker_type}执行失败:问题{query}，错误信息: {str(e)}"
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+
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+
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+def init_worker_cache() -> None:
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+ """初始化Worker缓存（在服务启动时调用）"""
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+ chat_logger.info("开始初始化Worker缓存...")
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+
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+ # 直接遍历worker_type_mapping字典来初始化缓存
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+ for worker_name, (worker_type, tool_types) in worker_type_mapping.items():
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+ try:
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+ # 组合工具
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+ tools = []
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+ for tool_type in tool_types:
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+ tools.extend(_get_tools_by_type(tool_type))
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+
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+ if tools:
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+ # 创建WorkerAgent实例并缓存
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+ cache_key = f"{worker_type}"
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+ if cache_key not in _worker_cache:
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+ _worker_cache[cache_key] = WorkerAgent(worker_type, tools)
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+ chat_logger.info(
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+ f"预缓存WorkerAgent: {worker_type} ({worker_name})"
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+ )
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+ else:
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+ chat_logger.info(f"WorkerAgent已缓存: {worker_type}")
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+ else:
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+ chat_logger.info(f"未发现工具，跳过缓存: {worker_type}")
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+
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+ except Exception as e:
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+ chat_logger.info(f"预缓存WorkerAgent失败 {worker_name}: {e}")
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+ chat_logger.info(f"Worker缓存初始化完成，共缓存 {len(_worker_cache)} 个WorkerAgent")
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+
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+
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+def clear_worker_cache() -> None:
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+ """清除Worker缓存"""
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+ global _worker_cache, _tools_cache
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+ _worker_cache.clear()
|
|
|
+ _tools_cache.clear()
|
|
|
+ chat_logger.info("Worker缓存已清除")
|
|
|
+
|
|
|
+
|
|
|
+def get_worker_cache_stats() -> dict:
|
|
|
+ """获取Worker缓存统计信息"""
|
|
|
+ return {
|
|
|
+ "worker_agents_cached": len(_worker_cache),
|
|
|
+ "tools_cached": len(_tools_cache),
|
|
|
+ "cached_worker_types": list(_worker_cache.keys()),
|
|
|
+ "cached_tool_types": list(_tools_cache.keys()),
|
|
|
+ }
|
|
|
+
|
|
|
+
|
|
|
+def get_worker_config(worker_name: str) -> tuple[str, list[str]]:
|
|
|
+ """根据worker名称获取配置信息"""
|
|
|
+ if worker_name not in worker_type_mapping:
|
|
|
+ raise ValueError(f"未知的worker名称: {worker_name}")
|
|
|
+
|
|
|
+ return worker_type_mapping[worker_name]
|
|
|
+
|
|
|
+
|
|
|
+def run_worker(
|
|
|
+ worker_name: str,
|
|
|
+ token: str,
|
|
|
+ backend_url: str,
|
|
|
+ query: str,
|
|
|
+ context: str = "",
|
|
|
+) -> str:
|
|
|
+ worker_type, tool_types = get_worker_config(worker_name)
|
|
|
+ tools = []
|
|
|
+ for tool_type in tool_types:
|
|
|
+ tools.extend(_get_tools_by_type(tool_type))
|
|
|
+
|
|
|
+ return _execute_and_get_result(
|
|
|
+ worker_type, tools, token, backend_url, query, context
|
|
|
+ )
|
|
|
+
|
|
|
+
|
|
|
+# Worker定义
|
|
|
+@tool
|
|
|
+def sale_worker(query: str, backend_url: str, token: str, context: str = "") -> str:
|
|
|
+ """
|
|
|
+ 销售报表查询专家
|
|
|
+ 职责：销售金额、销售数量、客户销售排名、业务员销售排名、产品型号销售数据等
|
|
|
+ 使用场景："2024客户销售业绩排名"、"查2024年产品型号销售排名"、"查看2023年1月1日至2023年12月31日的销售金额"等
|
|
|
+ """
|
|
|
+ # tools = _get_tools_by_type("sale") # 即 sale_tools.py下的所有工具
|
|
|
+ # return _execute_and_get_result(
|
|
|
+ # "销售报表查询", tools, token, backend_url, query, context
|
|
|
+ # )
|
|
|
+ return run_worker("sale_worker", token, backend_url, query, context)
|
|
|
+
|
|
|
+
|
|
|
+@tool
|
|
|
+def ware_worker(query: str, backend_url: str, token: str, context: str = "") -> str:
|
|
|
+ """
|
|
|
+ 库存查询专家
|
|
|
+ 职责：库存管理查询相关问题解答
|
|
|
+ 使用场景："查看铜管的库存"、"安吉仓库最多的库存哪个型号"
|
|
|
+ """
|
|
|
+ # tools = _get_tools_by_type("ware") # 即 ware_tools.py下的所有工具
|
|
|
+ # return _execute_and_get_result(
|
|
|
+ # "库存查询", tools, token, backend_url, query, context
|
|
|
+ # )
|
|
|
+ return run_worker("ware_worker", token, backend_url, query, context)
|
|
|
+
|
|
|
+
|
|
|
+@tool
|
|
|
+def money_worker(query: str, backend_url: str, token: str, context: str = "") -> str:
|
|
|
+ """
|
|
|
+ 财务查询专家
|
|
|
+
|
|
|
+ 职责：财务查询相关问题解答
|
|
|
+ 使用场景："查询客户A的应收帐情况"、"查询客户A,2025年的收款情况"
|
|
|
+ """
|
|
|
+ # 图表工具可以复用数据查询工具的结果
|
|
|
+ # tools = _get_tools_by_type("money") # 即 money_tools.py下的所有工具
|
|
|
+ # return _execute_and_get_result(
|
|
|
+ # "财务查询", tools, token, backend_url, query, context
|
|
|
+ # )
|
|
|
+ return run_worker("money_worker", token, backend_url, query, context)
|
|
|
+
|
|
|
+
|
|
|
+@tool
|
|
|
+def price_worker(query: str, backend_url: str, token: str, context: str = "") -> str:
|
|
|
+ """
|
|
|
+ 价格查询专家
|
|
|
+
|
|
|
+ 职责：价格查询相关问题解答
|
|
|
+ 使用场景："查询铜管的销售价格"
|
|
|
+ """
|
|
|
+ # tools = _get_tools_by_type("price")
|
|
|
+ # return _execute_and_get_result(
|
|
|
+ # "价格查询", tools, token, backend_url, query, context
|
|
|
+ # )
|
|
|
+ return run_worker("price_worker", token, backend_url, query, context)
|
|
|
+
|
|
|
+
|
|
|
+@tool
|
|
|
+def salebill_worker(query: str, backend_url: str, token: str, context: str = "") -> str:
|
|
|
+ """
|
|
|
+ 销售类单据查询专家
|
|
|
+
|
|
|
+ 职责：销售类单据查询相关问题解答
|
|
|
+ 使用场景："查询客户A的订单进度"、"查询销售订单SG251119001"
|
|
|
+ """
|
|
|
+ # tools = _get_tools_by_type("salebill")
|
|
|
+ # return _execute_and_get_result(
|
|
|
+ # "销售类单据查询", tools, token, backend_url, query, context
|
|
|
+ # )
|
|
|
+ return run_worker("salebill_worker", token, backend_url, query, context)
|
|
|
+
|
|
|
+
|
|
|
+@tool
|
|
|
+def base_data_worker(
|
|
|
+ query: str, backend_url: str, token: str, context: str = ""
|
|
|
+) -> str:
|
|
|
+ """
|
|
|
+ 基础资料查询专家
|
|
|
+
|
|
|
+ 职责：基础资料查询相关问题解答
|
|
|
+ 使用场景："查产品 A388餐椅"、"查一下客户 南华家具"
|
|
|
+ """
|
|
|
+ # tools = _get_tools_by_type("mtrl_data") + _get_tools_by_type("cust_data")
|
|
|
+ # return _execute_and_get_result(
|
|
|
+ # "基础资料查询", tools, token, backend_url, query, context
|
|
|
+ # )
|
|
|
+ return run_worker("base_data_worker", token, backend_url, query, context)
|
|
|
+
|
|
|
+
|
|
|
+def get_worker_tools() -> list:
|
|
|
+ """获取所有Worker工具"""
|
|
|
+ return [
|
|
|
+ sale_worker,
|
|
|
+ ware_worker,
|
|
|
+ money_worker,
|
|
|
+ price_worker,
|
|
|
+ salebill_worker,
|
|
|
+ base_data_worker,
|
|
|
+ ]
|
