update

src/multi_agent/weather_agent.py

@@ -0,0 +1,57 @@
+import operator
+
+from typing import TypedDict, List, Annotated
+from langgraph.graph import END, StateGraph
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
+
+from src.multi_agent.config import LLM
+from src.multi_agent.util.weather_search import search_weather
+
+llm = LLM
+
+
+class WeatherState(TypedDict):
+    user_request: str
+    city: str
+    result: str
+
+
+def requirement_analysis(state: WeatherState):
+    user_request = state.get('user_request')
+    analysis_prompt = ChatPromptTemplate([
+        ('system', '你是一个天气查询agent，你负责从用户的需求中提取出需要查询天气的城市或地区，你只需要输出这个地名，不需要输出其他任何内容'),
+        ('user', f'当前的用户需求是:'),
+        MessagesPlaceholder(variable_name="user_request"),
+    ])
+    analysis_chain = analysis_prompt | llm
+    llm_resp = analysis_chain.invoke({'user_request': [("user", user_request)]})
+    return {'city': llm_resp.content}
+
+
+def get_weather_data(state: WeatherState):
+    city = state.get('city')
+    weather_data = search_weather(city)
+    return {'result': weather_data}
+
+
+def enter_chain(input_data: dict):
+    return {'user_request': input_data['next_plan']}
+
+
+workflow = StateGraph(WeatherState)
+# 其实完全没必要使用子agent，但演示一下子agent如何并网
+
+workflow.add_node('需求分析', requirement_analysis)
+workflow.add_node('获取天气', get_weather_data)
+
+workflow.set_entry_point('需求分析')
+
+workflow.add_edge('需求分析', '获取天气')
+workflow.add_edge('获取天气', END)
+
+weather_graph = workflow.compile()
+weather_chain = enter_chain | weather_graph
+
+
+if __name__ == '__main__':
+    requirement_analysis({})