# LangChain 單智能體模式示例import os
from typing import Anyfrom langchain.agents import AgentType, initialize_agent, Tool
from langchain_openai import ChatOpenAI
from langchain.tools import BaseTool
from langchain_experimental.tools.python.tool import PythonREPLTool
from langchain.memory import ConversationBufferMemory
from langchain_community.utilities import WikipediaAPIWrapper
from langgraph.prebuilt import create_react_agent# 確保設置環境變量
os.environ["OPENAI_API_KEY"] = "sk-cRMC2m0GsE18vYaWdAMj"
os.environ["OPENAI_BASE_URL"] = "https://aigptx.top/v1/"# 1. ReAct 智能體示例 - 結合推理和行動的智能體
def create_init_tool_agent():"""創建基本的ReAct智能體"""# 定義工具集wikipedia = WikipediaAPIWrapper()python_repl = PythonREPLTool()tools = [Tool(name="維基百科",func=wikipedia.run,description="用于查詢維基百科文章的工具"),Tool(name="Python解釋器",func=python_repl.run,description="用于執行Python代碼的工具，可以進行計算或數據分析")]# 創建LLMllm = ChatOpenAI(temperature=1, max_tokens=2000, model='gpt-3.5-turbo-0125')# 創建記憶組件memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)# langgraph_agent_executor = create_react_agent(model=llm, tools=tools)# 初始化ReAct智能體langgraph_agent_executor = initialize_agent(tools,llm,agent=AgentType.CHAT_CONVERSATIONAL_REACT_DESCRIPTION,verbose=True,memory=memory,handle_parsing_errors=True)return langgraph_agent_executor# 2. OpenAI函數智能體示例 - 專為函數調用設計的智能體
def create_openai_functions_agent():"""創建基于OpenAI函數調用的智能體"""# 定義工具集wikipedia = WikipediaAPIWrapper()python_repl = PythonREPLTool()tools = [Tool(name="Python執行器",func=python_repl.run,description="執行Python代碼的工具，適合進行計算、數據處理"),Tool(name="維基百科",func=wikipedia.run,description="搜索維基百科文章的工具，適合查詢事實性信息")]# 創建LLMllm = ChatOpenAI(temperature=0)# 初始化OpenAI函數智能體agent = initialize_agent(tools,llm,agent=AgentType.OPENAI_FUNCTIONS,verbose=True)return agent# 3. 自定義智能體工具示例
class WeatherTool(BaseTool):name: str = "天氣查詢"description: str = "查詢指定城市的天氣情況"def _run(self, city: str) -> str:# 模擬天氣API調用return f"{city}的天氣: 晴朗, 25°C, 濕度50%"async def _arun(self, city: str) -> str:return self._run(city)class CalculatorTool(BaseTool):name: str = "計算器"description: str = "進行數學計算，輸入應為數學表達式"def _run(self, expression: str) -> str:try:result = eval(expression)return f"計算結果: {result}"except Exception as e:return f"計算錯誤: {str(e)}"async def _arun(self, expression: str) -> str:return self._run(expression)def create_custom_tool_agent():"""創建帶有自定義工具的智能體"""tools = [WeatherTool(),CalculatorTool(),PythonREPLTool()]llm = ChatOpenAI(temperature=0)agents = initialize_agent(tools,llm,agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,verbose=True)return agents# 使用示例
if __name__ == "__main__":print("=== LangChain 單智能體模式示例 ===")# 選擇要演示的智能體類型 agent_type = "openai_functions"  # 可選: "react", "openai_functions", "custom"response: Any = ''if agent_type == "react":agent = create_init_tool_agent()response = agent.invoke({'input': '誰是阿爾伯特·愛因斯坦? 他出生于哪一年? 計算從他出生到現在過了多少年。回答的時候請使用中文輸出', 'chat_history': []})elif agent_type == "openai_functions":agent = create_openai_functions_agent()response = agent.invoke({'input': '計算 2345 + 5678 的結果，并解釋這兩個數字的數學特性。', 'chat_history': []})elif agent_type == "custom":agent = create_custom_tool_agent()response = agent.invoke({'input': '北京今天的時間和今天的天氣如何？然后計算25乘以4的結果。', 'chat_history': []})print(f"\n最終回答: {response}")