Shipmaster1/Agent_Workout_531

1from typing import List, Dict, Any2from langchain.agents import create_openai_functions_agent, AgentExecutor3from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder4from langchain_core.tools import BaseTool5from langchain_openai import ChatOpenAI6 7class ResearchAgent:8    def __init__(self, tools: List[BaseTool], openai_api_key: str):9        self.tools = tools10        self.llm = ChatOpenAI(11            temperature=0,12            model="gpt-4-turbo-preview",13            openai_api_key=openai_api_key14        )15        16        # Define the system prompt17        system_prompt = """You are a specialized research assistant focused on scientific literature analysis.18        Your goal is to help users find, analyze, and understand scientific papers and research findings.19        You have access to tools that can:20        1. Search for relevant papers and research21        2. Analyze PDF documents22        3. Track citations and research impact23        24        Always be thorough in your analysis and provide clear, well-structured responses.25        If you're unsure about something, be honest and ask for clarification."""26        27        # Create the prompt template28        prompt = ChatPromptTemplate.from_messages([29            ("system", system_prompt),30            MessagesPlaceholder(variable_name="chat_history"),31            ("human", "{input}"),32            MessagesPlaceholder(variable_name="agent_scratchpad"),33        ])34        35        # Create the agent36        self.agent = create_openai_functions_agent(37            llm=self.llm,38            prompt=prompt,39            tools=self.tools40        )41        42        # Create the agent executor43        self.agent_executor = AgentExecutor(44            agent=self.agent,45            tools=self.tools,46            verbose=False47        )48    49    def run(self, query: str, chat_history: List[Dict[str, Any]] = None) -> str:50        """Run the agent with the given query and chat history."""51        if chat_history is None:52            chat_history = []53            54        result = self.agent_executor.invoke({55            "input": query,56            "chat_history": chat_history57        })58        59        return result["output"]