工具选择与执行分开，由程序管控流程方案验证

src/controller/api.py

@@ -9,7 +9,7 @@ from fastapi.middleware.cors import CORSMiddleware
 import uvicorn
 
 
-from src.server.agent_rate import new_rate_agent
+from src.server.agent_rate import new_rate_agent, RateAgentV3
 from src.server.classify import new_router_llm
 from src.controller.request import GeoAgentRateRequest
 
@@ -77,6 +77,7 @@ class AgentManager:
             verbose=True
         )
         self.agent = new_rate_agent(self.llm, verbose=True, tool_base_url=tool_base_url)
+        self.rate_agent = RateAgentV3(self.llm, tool_base_url=tool_base_url)
         self.router_llm = new_router_llm(self.llm)
     
     def get_llm(self):
@@ -85,6 +86,9 @@ class AgentManager:
     def get_agent(self):
         return self.agent
 
+    def get_rate_agent(self):
+        return self.rate_agent
+
     def get_router_llm(self):
         return self.router_llm
 
@@ -93,8 +97,10 @@ agent_manager = AgentManager()
 @app.post('/api/agent/rate')
 def rate(chat_request: GeoAgentRateRequest, token: str = Header(None)):
     agent = agent_manager.get_agent()
+    rate_agent = agent_manager.get_rate_agent()
     try:
         res = agent.exec(prompt_args={"input": chat_request.query})
+        # res = rate_agent.run(chat_request.query)
     except Exception as e:
         print(f"处理请求失败, 错误信息: {str(e)}，请重新提问")
         return {

src/server/agent_rate.py

@@ -15,6 +15,7 @@ from langchain import hub
 
 from src.agent.tool_rate import RegionRateTool,RankingRateTool
 from src.agent.tool_monitor import MonitorPointTool
+from src.server.tool_picker import ToolPicker, ToolRunner
 
 # def create_rate_agent(llm, tools: List[BaseTool],prompt: PromptTemplate = None, 
 #     tools_renderer: ToolsRenderer = render_text_description_and_args,
@@ -168,4 +169,20 @@ def new_rate_agent(llm, verbose: bool = False,**args):
 
 
     
+class RateAgentV3:
+    def __init__(self, llm, tool_base_url: str):
+        tools = [
+            RegionRateTool(base_url=tool_base_url),
+            RankingRateTool(base_url=tool_base_url),
+            MonitorPointTool(base_url=tool_base_url)
+        ]
+        self.picker = ToolPicker(llm, tools)
+        tools_dict = {}
+        for t in tools:
+            tools_dict[t.name] = t
+        self.runner = ToolRunner(llm, tools_dict)
+
+    def run(self, input: str):
+        picker_result = self.picker.pick(input)
+        return self.runner.run(input, picker_result["tool"], picker_result["params"])
 

src/server/tool_picker.py

+from typing import List, Dict
+from datetime import datetime
+
+from langchain_core.prompts import PromptTemplate,ChatPromptTemplate,SystemMessagePromptTemplate,MessagesPlaceholder,HumanMessagePromptTemplate
+from langchain.tools.render import ToolsRenderer, render_text_description_and_args
+from langchain_core.output_parsers import JsonOutputParser as JSONOutputParser
+from langchain_core.tools import BaseTool
+
+
+PICKER_SYSTEM_PROMPT = """
+你是一个智能工具选择助手，你需要根据用户的问题选择最合适的工具，并提取出工具所需的参数。
+
+工作流程：
+1. 分析用户问题，确定所需工具
+2. 提取并整理工具所需的参数
+3. 返回工具名称和参数
+
+请遵循以下规则：
+- 工具名称必须从工具列表中选择: {{tool_names}}
+- 返回格式：
+    ```json
+    {{
+        "tool": "工具名称", 
+        "params": {{"参数1": "值1", "参数2": "值2"}}
+    }}
+    ```
+
+工具列表详情：
+{tools}
+
+""" 
+
+PICKER_HUMAN_PROMPT = """
+用户的问题是：{input}
+"""
+
+class ToolPicker:
+    def __init__(self, llm, tools: List):
+        self.tools = tools
+        self.llm = llm
+        date_now = datetime.now().strftime("%Y-%m-%d")
+        picker_human = f"今天是{date_now}\n\n{PICKER_HUMAN_PROMPT}"
+        prompt = ChatPromptTemplate.from_messages([
+            SystemMessagePromptTemplate.from_template(PICKER_SYSTEM_PROMPT),
+            MessagesPlaceholder(variable_name="chat_history", optional=True),
+            HumanMessagePromptTemplate.from_template(picker_human)
+        ])
+        prompt = prompt.partial(tools=render_text_description_and_args(tools))
+
+        self.chain = prompt | self.llm | JSONOutputParser()
+
+    def pick(self, input: str):
+        print(input)
+        return self.chain.invoke({"input": input})
+
+
+RUNNER_SYSTEM_PROMPT = """
+你是一个擅长根据工具执行结果回答用户问题的助手。
+
+工作流程：
+1. 分析用户的问题
+2. 根据用户的问题，解读工具执行结果，进行简要的分析说明
+3. 返回用户问题的答案
+
+请遵循以下规则：
+- 工具执行结果中的数据必须使用 markdown 表格展示
+- 确保数据的完整性, 不要遗漏数据
+- 表格中的数据只能来源于工具执行结果
+"""
+
+
+RUNNER_HUMAN_PROMPT = """
+用户的问题是：{input}
+工具的执行结果是：{result}
+"""
+
+
+class ToolRunner:
+    def __init__(self, llm, tools: Dict[str, BaseTool]):
+        self.tools = tools
+        self.llm = llm
+
+        prompt = ChatPromptTemplate.from_messages([
+            SystemMessagePromptTemplate.from_template(RUNNER_SYSTEM_PROMPT),
+            HumanMessagePromptTemplate.from_template(RUNNER_HUMAN_PROMPT)
+        ])
+        self.chain = prompt | self.llm 
+
+
+    def run(self, input: str, tool_name: str, params: Dict):
+        if tool_name not in self.tools:
+            raise ValueError(f"Tool {tool_name} not found")
+        tool = self.tools[tool_name]
+        result = tool.invoke(params)
+        return self.chain.invoke({"input": input, "result": result})
+

test/test_tool_picker.py

+import sys,os
+sys.path.append("../")
+
+import pytest
+from unittest.mock import Mock
+
+from langchain_openai import ChatOpenAI
+
+from src.server.tool_picker import ToolPicker
+from src.agent.tool_rate import RegionRateTool,RankingRateTool
+from src.agent.tool_monitor import MonitorPointTool
+
+@pytest.fixture
+def mock_llm():
+    # llm = Mock(spec=ChatOpenAI)
+    # # 模拟 LLM 返回结果
+    # llm.invoke.return_value = {"content": """{"tool": "RegionRateTool", "params": {"start_time": "2024-11-13", "end_time": "2024-11-13", "region_name": ""}}"""}
+    # return llm
+
+    llm = ChatOpenAI(
+        openai_api_key="xxxxxx",
+        openai_api_base="http://192.168.10.14:8000/v1",
+        model_name="Qwen2-7B",
+        verbose=True
+    )
+    return llm
+
+# 使用参数化测试不同的场景
+@pytest.mark.parametrize("query, expected_response, expected_tool", [
+    (
+        "请分析下今天全国各地区在线率情况",
+        {"tool": "region_online_rate", "params": {"start_time": "2024-11-13", "end_time": "2024-11-13", "region_name": ""}},
+        "region_online_rate"
+    ),
+    (
+        "请分析下今天甘肃省设备在线率情况",
+        {"tool": "region_online_rate", "params": {"start_time": "2024-11-13", "end_time": "2024-11-13", "region_name": "甘肃省"}},
+        "region_online_rate"
+    ),
+    (
+        "查询今年三季度甘肃省设备在线率情况",
+        {"tool": "region_online_rate", "params": {"start_time": "2024-07-01", "end_time": "2024-09-30", "region_name": "甘肃省"}},
+        "region_online_rate"
+    ),
+    (
+        "查询2024年11月13日各地区排名情况",
+        {"tool": "online_rate_ranking", "params": {"rate_type": "1"}},
+        "online_rate_ranking"
+    ),
+    (
+        "查询各厂商在线率排名情况",
+        {"tool": "online_rate_ranking", "params": {"rate_type": "2"}},
+        "online_rate_ranking"
+    ),
+    (
+        "甘肃省监控点的状态如何？",
+        {"tool": "monitor_points_query", "params": {"key": "甘肃省"}},
+        "monitor_points_query"
+    ),
+])
+
+def test_tool_picker_scenarios(mock_llm, query, expected_response, expected_tool):
+    
+    # 创建测试用的工具
+    test_tools = [
+        RegionRateTool(),
+        RankingRateTool(),
+        MonitorPointTool()
+    ]
+    
+    picker = ToolPicker(mock_llm, test_tools)
+    result = picker.pick(query)
+    
+    # 验证结果
+    assert isinstance(result, dict)
+    assert result["tool"] == expected_tool
+    assert "params" in result