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This commit is contained in:
@@ -0,0 +1,10 @@
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from langchain_ollama import ChatOllama
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LLM = ChatOllama(model='qwen2.5:14b', api_key="1145141919810", base_url='http://192.168.195.158:11434')
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# LLM = ChatOpenAI(model="gpt-4o", openai_api_key='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')
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tavily_key = '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'
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gaode_key = '00fd082df2414f75c6efb64896819451'
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@@ -1,3 +1,4 @@
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import json
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import os
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import time
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import operator
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@@ -11,7 +12,12 @@ from langchain_core.messages import HumanMessage
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from pydantic import BaseModel, Field
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llm = ChatOpenAI(model="gpt-4o", temperature=0.1, openai_api_key='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')
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from src.multi_agent.util.tools import formatted_json
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from src.multi_agent.util.web_search import do_search
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from src.multi_agent.config import LLM
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from src.multi_agent.weather_agent import weather_chain
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llm = LLM
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MEMBERS = {'天气查询模块': '输入一个城市和日期，查询当日的天气状况',
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'网络查询模块': '在网上搜索指定的内容',
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@@ -41,31 +47,53 @@ def planner(state: AgentState):
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])
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planner_chain = (planner_prompt | llm)
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result_dict = {'plan': '为了完成这个任务，我们需要按照以下步骤进行：\n\n1. **网络查询模块**：\n - 任务：查询2024年中国GDP最高的城市。\n - 行动：在网上搜索2024年中国GDP最高的城市的信息。\n\n2. **天气查询模块**：\n - 任务：查询该城市今天的天气状况。\n - 行动：使用天气查询模块，输入城市名称和当前日期，获取该城市的天气信息。\n\n3. **诗歌创作模块**：\n - 任务：根据查询到的城市名称和天气状况，创作一首诗。\n - 行动：利用诗歌创作模块，结合城市的特点和天气状况，创作一首诗。\n\n请各模块按照上述计划执行任务，并在完成后汇总信息。'}
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return result_dict
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# llm_resp = planner_chain.invoke({'query': [('user', user_query)]})
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# return {'plan': llm_resp.content}
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llm_resp = planner_chain.invoke({'query': [('user', user_query)]})
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return {'plan': llm_resp.content}
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def decision_node(state: AgentState):
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class NextStep(BaseModel):
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next_agent: str = Field(description='下一步应该交给哪个模块来完成')
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action: str = Field(description='下一步应该执行的行动计划是什么')
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members = state.get('members', {})
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member_keys = list(members.keys())
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if isinstance(llm, ChatOpenAI):
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class NextStep(BaseModel):
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next_agent: str = Field(description='下一步应该交给哪个模块来完成')
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action: str = Field(description='下一步应该执行的行动计划是什么。如果已经执行所有的任务，则填入执行结果')
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decision_prompt = ChatPromptTemplate.from_messages([
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('system', f'你负责针对一个给定的任务计划，以及当前的任务执行状态，制定下一步应该由谁来执行什么内容。可以在这些模块中进行选择：{", ".join(member_keys)}。'),
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('system', f'你负责针对一个给定的任务计划，以及当前的任务执行状态，制定下一步应该由谁来执行什么内容。'
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f'可以在这些模块中进行选择：{", ".join(member_keys)}。'),
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('user', f'当前的计划是:'),
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MessagesPlaceholder(variable_name="plan"),
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('user', '以下是各个模块之间的消息记录'),
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MessagesPlaceholder(variable_name="messages")
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])
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decision_chain = (decision_prompt | llm.with_structured_output(NextStep))
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llm_resp = decision_chain.invoke({'plan': [('user', state.get('plan', ''))], 'messages': state.get('messages', [])})
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llm_resp = decision_chain.invoke({'plan': [('user', state.get('plan', ''))],
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'messages': state.get('messages', [])})
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next_agent = llm_resp.next_agent
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action = llm_resp.action
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else:
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decision_prompt = ChatPromptTemplate.from_messages([
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('system',
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f'你负责针对一个给定的任务计划，以及当前的任务执行状态，制定下一步应该由谁来执行什么内容。'
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f'可以在这些模块中进行选择：{", ".join(member_keys)}。你的输出必须只能是一个json字符串，'
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f'必须包含next_agent和action两个字段。其中next_agent是下一步应该交给哪个模块来完成，'
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f'action是下一步应该执行的行动计划是什么，如果已经执行完成所有的任务，则在action中填入执行结果'
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f'你只需要输出这个json即可，不需要给出任何解释'),
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('user', f'当前的计划是:'),
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MessagesPlaceholder(variable_name="plan"),
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('user', '以下是各个模块之间的消息记录'),
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MessagesPlaceholder(variable_name="messages")
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])
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decision_chain = (decision_prompt | llm)
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llm_resp = decision_chain.invoke(
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{'plan': [('user', state.get('plan', ''))], 'messages': state.get('messages', [])})
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llm_resp_str = formatted_json(llm_resp.content)
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resp_dict = json.loads(llm_resp_str)
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next_agent = resp_dict.get('next_agent')
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action = resp_dict.get('action')
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if not (next_agent and action):
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raise ValueError('模型返回异常')
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return {'messages': [HumanMessage(content=f'接下来由{next_agent}来执行{action}')], 'next_agent': next_agent, 'next_plan': action}
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@@ -79,13 +107,25 @@ def router_node(state: AgentState):
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def weather_node(state: AgentState):
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result = '上海的天气是25℃，下雨'
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# result = '上海的天气是25℃，下雨'
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agent_resp = weather_chain.invoke({'next_plan': state.get('next_plan')})
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result = agent_resp.get('result')
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return {'messages': [HumanMessage(content=result)]}
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def websearch_node(state: AgentState):
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result = '2024年中国GDP最高的城市是上海'
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return {'messages': [HumanMessage(content=result)]}
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next_plan = state.get('next_plan', '')
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websearch_prompt = ChatPromptTemplate.from_messages([
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('system', f'你是一个网络搜索Agent，你需要根据用户的问题，从中提取出用于在网络搜索引擎上搜索的关键字。只需要回答关键字即可，不需要进行解释'),
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('user', f'当前的用户问题是：'),
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MessagesPlaceholder(variable_name="next_plan"),
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])
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websearch_chain = (websearch_prompt | llm)
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llm_resp = websearch_chain.invoke({'next_plan': [('user', next_plan)]})
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search_result = do_search(llm_resp.content)
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result_resp = llm.invoke(f'请根据搜索结果进行回答我的问题。\n我的问题是:{next_plan}\n\n搜索结果为:{search_result}')
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return {'messages': [HumanMessage(content=result_resp.content)]}
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def composer_node(state: AgentState):
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@@ -100,7 +140,7 @@ def composer_node(state: AgentState):
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def end_node(state: AgentState):
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print('placeholder')
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print('任务完成')
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workflow = StateGraph(AgentState)
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@@ -134,12 +174,14 @@ workflow.add_edge('结束节点', END)
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workflow.set_entry_point('planner')
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graph = workflow.compile()
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print(graph.get_graph().draw_ascii())
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enter = {'user_query': "查询2024年中国GDP最高的城市，以这个城市今天的天气，创作一首诗",
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"members": MEMBERS}
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for s in graph.stream(enter):
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if "__end__" not in s:
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if '__main__' == __name__:
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print(graph.get_graph().draw_ascii())
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user_query = '查询2024年中国GDP最高的城市，以这个城市今天的天气，创作一首诗'
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enter = {'user_query': user_query, "members": MEMBERS}
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for s in graph.stream(enter):
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if "__end__" not in s and 'router' not in s:
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print(s)
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print('-'*80)
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@@ -0,0 +1,17 @@
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import json
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def formatted_json(input_str: str):
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if input_str.startswith("```"):
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input_str = input_str[3::]
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if input_str.endswith("```"):
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input_str = input_str[:-3:]
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if input_str.startswith("'''"):
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input_str = input_str[3::]
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if input_str.endswith("'''"):
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input_str = input_str[:-3:]
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while input_str.startswith('\n'):
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input_str = input_str[1::]
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if input_str.startswith('json'):
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input_str = input_str[4::]
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return input_str
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@@ -0,0 +1,28 @@
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import requests
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from src.multi_agent.config import gaode_key
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def search_weather(area_str):
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district_code_url = 'https://restapi.amap.com/v3/config/district?parameters'
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weather_url = 'https://restapi.amap.com/v3/weather/weatherInfo?parameters'
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# area_str = '上海市'
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response = requests.get(district_code_url, params={'key': gaode_key, 'keywords': area_str})
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district_code_list = response.json().get('districts')
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if not district_code_list:
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raise Exception('查询失败')
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district_code = district_code_list[0].get('adcode')
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response = requests.get(weather_url, params={'key': gaode_key, 'city': district_code})
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weather_list = response.json().get('lives')
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if not weather_list:
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raise Exception('查询失败')
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weather = weather_list[0]
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result = f'{area_str}现在{weather["weather"]}，{weather["temperature"]}摄氏度，湿度{weather["humidity"]}%，{weather["winddirection"]}风{weather["windpower"]}级'
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return result
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@@ -1,12 +1,14 @@
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from tavily import TavilyClient
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import requests
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from src.multi_agent.config import tavily_key
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query = '乔布斯是谁'
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base_url = 'https://api.tavily.com/search'
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json = {
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'api_key': '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',
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def do_search(query):
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# query = '乔布斯是谁'
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base_url = 'https://api.tavily.com/search'
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json = {
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'api_key': tavily_key,
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'query': query,
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'search_depth': 'basic',
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'include_answer': False,
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@@ -15,13 +17,14 @@ json = {
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'max_results': 5,
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'include_domains': [],
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'exclude_domains': []
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}
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}
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response = requests.post(base_url, json=json, verify=False)
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response = requests.post(base_url, json=json, verify=False)
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if response.status_code == 200:
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if response.status_code == 200:
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search_result = response.json()
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else:
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else:
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raise Exception(f'Error: {response.status_code}: {response.reason}')
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return search_result
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print('done')
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@@ -0,0 +1,57 @@
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import operator
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from typing import TypedDict, List, Annotated
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from langgraph.graph import END, StateGraph
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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from src.multi_agent.config import LLM
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from src.multi_agent.util.weather_search import search_weather
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llm = LLM
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class WeatherState(TypedDict):
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user_request: str
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city: str
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result: str
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def requirement_analysis(state: WeatherState):
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user_request = state.get('user_request')
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analysis_prompt = ChatPromptTemplate([
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('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'),
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('user', f'当前的用户需求是:'),
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MessagesPlaceholder(variable_name="user_request"),
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])
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analysis_chain = analysis_prompt | llm
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llm_resp = analysis_chain.invoke({'user_request': [("user", user_request)]})
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return {'city': llm_resp.content}
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def get_weather_data(state: WeatherState):
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city = state.get('city')
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weather_data = search_weather(city)
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return {'result': weather_data}
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def enter_chain(input_data: dict):
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return {'user_request': input_data['next_plan']}
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workflow = StateGraph(WeatherState)
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# 其实完全没必要使用子agent，但演示一下子agent如何并网
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workflow.add_node('需求分析', requirement_analysis)
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workflow.add_node('获取天气', get_weather_data)
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workflow.set_entry_point('需求分析')
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workflow.add_edge('需求分析', '获取天气')
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workflow.add_edge('获取天气', END)
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weather_graph = workflow.compile()
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weather_chain = enter_chain | weather_graph
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if __name__ == '__main__':
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requirement_analysis({})
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