Delete agent.py

agent.py

@@ -1,244 +0,0 @@
-import os
-import time
-import json
-from dotenv import load_dotenv
-from langgraph.graph import StateGraph, END
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_community.tools import DuckDuckGoSearchRun
-from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
-from langchain_core.messages import SystemMessage, AIMessage, HumanMessage
-from langchain_core.tools import tool
-from tenacity import retry, stop_after_attempt, wait_exponential
-
-# Load environment variables
-load_dotenv()
-google_api_key = os.getenv("GOOGLE_API_KEY") or os.environ.get("GOOGLE_API_KEY")
-if not google_api_key:
-    raise ValueError("Missing GOOGLE_API_KEY environment variable")
-
-# --- Math Tools ---
-@tool
-def multiply(a: int, b: int) -> int:
-    """Multiply two integers."""
-    return a * b
-
-@tool
-def add(a: int, b: int) -> int:
-    """Add two integers."""
-    return a + b
-
-@tool
-def subtract(a: int, b: int) -> int:
-    """Subtract b from a."""
-    return a - b
-
-@tool
-def divide(a: int, b: int) -> float:
-    """Divide a by b, error on zero."""
-    if b == 0:
-        raise ValueError("Cannot divide by zero.")
-    return a / b
-
-@tool
-def modulus(a: int, b: int) -> int:
-    """Compute a mod b."""
-    return a % b
-
-# --- Browser Tools ---
-@tool
-def wiki_search(query: str) -> str:
-    """Search Wikipedia and return up to 3 relevant documents."""
-    try:
-        docs = WikipediaLoader(query=query, load_max_docs=3).load()
-        if not docs:
-            return "No Wikipedia results found."
-        
-        results = []
-        for doc in docs:
-            title = doc.metadata.get('title', 'Unknown Title')
-            content = doc.page_content[:2000]  # Limit content length
-            results.append(f"Title: {title}\nContent: {content}")
-        
-        return "\n\n---\n\n".join(results)
-    except Exception as e:
-        return f"Wikipedia search error: {str(e)}"
-
-@tool
-def arxiv_search(query: str) -> str:
-    """Search Arxiv and return up to 3 relevant papers."""
-    try:
-        docs = ArxivLoader(query=query, load_max_docs=3).load()
-        if not docs:
-            return "No arXiv papers found."
-        
-        results = []
-        for doc in docs:
-            title = doc.metadata.get('Title', 'Unknown Title')
-            authors = ", ".join(doc.metadata.get('Authors', []))
-            content = doc.page_content[:2000]  # Limit content length
-            results.append(f"Title: {title}\nAuthors: {authors}\nContent: {content}")
-        
-        return "\n\n---\n\n".join(results)
-    except Exception as e:
-        return f"arXiv search error: {str(e)}"
-
-@tool
-def web_search(query: str) -> str:
-    """Search the web using DuckDuckGo and return top results."""
-    try:
-        search = DuckDuckGoSearchRun()
-        result = search.run(query)
-        return f"Web search results for '{query}':\n{result[:2000]}"  # Limit content length
-    except Exception as e:
-        return f"Web search error: {str(e)}"
-
-# --- Load system prompt ---
-with open("system_prompt.txt", "r", encoding="utf-8") as f:
-    system_prompt = f.read()
-
-# --- System message ---
-sys_msg = SystemMessage(content=system_prompt)
-
-# --- Tool Setup ---
-tools = [
-    multiply,
-    add,
-    subtract,
-    divide,
-    modulus,
-    wiki_search,
-    arxiv_search,
-    web_search,
-]
-
-# --- Graph Builder ---
-def build_graph():
-    # Initialize model with Gemini 2.5 Flash
-    llm = ChatGoogleGenerativeAI(
-        model="gemini-2.5-flash",
-        temperature=0.3,
-        google_api_key=google_api_key,
-        max_retries=3
-    )
-    
-    # Bind tools to LLM
-    llm_with_tools = llm.bind_tools(tools)
-    
-    # 使用 TypedDict 定义状态而不是自定义类
-    from typing import TypedDict, Annotated, Sequence
-    import operator
-    
-    class AgentState(TypedDict):
-        messages: Annotated[Sequence[dict], operator.add]
-    
-    # Node definitions with error handling
-    def agent_node(state: AgentState):
-        """Main agent node that processes messages with retry logic"""
-        try:
-            # Add rate limiting
-            time.sleep(1)  # 1 second delay between requests
-            
-            # Add retry logic for API quota issues
-            @retry(stop=stop_after_attempt(3), 
-                   wait=wait_exponential(multiplier=1, min=4, max=10))
-            def invoke_llm_with_retry():
-                return llm_with_tools.invoke(state["messages"])
-            
-            response = invoke_llm_with_retry()
-            return {"messages": [response]}
-        
-        except Exception as e:
-            # Handle specific errors
-            error_type = "UNKNOWN"
-            if "429" in str(e):
-                error_type = "QUOTA_EXCEEDED"
-            elif "400" in str(e):
-                error_type = "INVALID_REQUEST"
-                
-            error_msg = f"AGENT ERROR ({error_type}): {str(e)[:200]}"
-            return {"messages": [AIMessage(content=error_msg)]}
-
-    # Tool node
-    def tool_node(state: AgentState):
-        """Execute tools based on agent's request"""
-        last_message = state["messages"][-1]
-        tool_calls = last_message.additional_kwargs.get("tool_calls", [])
-        
-        tool_responses = []
-        for tool_call in tool_calls:
-            tool_name = tool_call["function"]["name"]
-            tool_args = tool_call["function"].get("arguments", {})
-            
-            # Find the tool
-            tool_func = next((t for t in tools if t.name == tool_name), None)
-            if not tool_func:
-                tool_responses.append(f"Tool {tool_name} not found")
-                continue
-            
-            try:
-                # Execute the tool
-                if isinstance(tool_args, str):
-                    # Parse JSON if arguments are in string format
-                    tool_args = json.loads(tool_args)
-                
-                result = tool_func.invoke(tool_args)
-                tool_responses.append(f"Tool {tool_name} result: {result}")
-            except Exception as e:
-                tool_responses.append(f"Tool {tool_name} error: {str(e)}")
-        
-        tool_response_content = "\n".join(tool_responses)
-        return {"messages": [AIMessage(content=tool_response_content)]}
-
-    # Custom condition function
-    def should_continue(state: AgentState):
-        last_message = state["messages"][-1]
-        
-        # If there was an error, end
-        if "AGENT ERROR" in last_message.content:
-            return "end"
-        
-        # Check for tool calls
-        if hasattr(last_message, "tool_calls") and last_message.tool_calls:
-            return "tools"
-        
-        # Check for final answer
-        if "FINAL ANSWER" in last_message.content:
-            return "end"
-            
-        # Otherwise, continue to agent
-        return "agent"
-
-    # Build the graph
-    workflow = StateGraph(AgentState)
-    
-    # Add nodes
-    workflow.add_node("agent", agent_node)
-    workflow.add_node("tools", tool_node)
-    
-    # Set entry point
-    workflow.set_entry_point("agent")
-    
-    # Define edges
-    workflow.add_conditional_edges(
-        "agent",
-        should_continue,
-        {
-            "agent": "agent",
-            "tools": "tools",
-            "end": END
-        }
-    )
-    
-    workflow.add_conditional_edges(
-        "tools",
-        # Always go back to agent after using tools
-        lambda state: "agent",
-        {
-            "agent": "agent"
-        }
-    )
-    
-    return workflow.compile()
-
-# Initialize the agent graph
-agent_graph = build_graph()