agent.py

import os
import time
import json
import logging
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
from typing import TypedDict, Annotated, Sequence
import operator

# 配置日志
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("GAIA_Agent")

# 加载环境变量
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:
        # 确保查询包含"discography"关键词
        if "discography" not in query.lower():
            query = f"{query} discography"
            
        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]  # 限制内容长度
            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]  # 限制内容长度
            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]}"  # 限制内容长度
    except Exception as e:
        return f"Web search error: {str(e)}"

# --- 加载系统提示 ---
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()

# --- 工具设置 ---
tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    arxiv_search,
    web_search,
]

# --- 图构建器 ---
def build_graph():
    # 初始化模型 - 使用指定的 gemini-2.5-flash
    llm = ChatGoogleGenerativeAI(
        model="gemini-2.5-flash",  # 保持使用指定的模型
        temperature=0.3,
        google_api_key=google_api_key,
        max_retries=5,
        request_timeout=60
    )
    
    # 绑定工具到LLM
    llm_with_tools = llm.bind_tools(tools)
    
    # 1. 定义状态结构
    class AgentState(TypedDict):
        messages: Annotated[Sequence, operator.add]
        retry_count: int
    
    # 2. 创建图
    workflow = StateGraph(AgentState)
    
    # 3. 定义节点函数
    def agent_node(state: AgentState):
        """主代理节点"""
        try:
            # 添加请求间隔
            time.sleep(2)
            
            # 带重试的调用
            @retry(stop=stop_after_attempt(5),
                   wait=wait_exponential(multiplier=1, min=4, max=30))
            def invoke_with_retry():
                return llm_with_tools.invoke(state["messages"])
            
            response = invoke_with_retry()
            return {"messages": [response], "retry_count": 0}
        
        except Exception as e:
            error_details = f"Gemini API Error: {type(e).__name__}: {str(e)}"
            logger.error(error_details)
            
            error_type = "UNKNOWN"
            if "429" in str(e):
                error_type = "QUOTA_EXCEEDED"
            elif "400" in str(e):
                error_type = "INVALID_REQUEST"
            elif "503" in str(e):
                error_type = "SERVICE_UNAVAILABLE"
                
            new_retry_count = state.get("retry_count", 0) + 1
            error_msg = f"AGENT ERROR ({error_type}): {error_details[:300]}"
            
            if new_retry_count < 3:
                error_msg += "\n\nWill retry after delay..."
            else:
                error_msg += "\n\nMax retries exceeded. Please try again later."
            
            return {"messages": [AIMessage(content=error_msg)], "retry_count": new_retry_count}
    
    def tool_node(state: AgentState):
        """工具执行节点"""
        last_msg = state["messages"][-1]
        tool_calls = last_msg.additional_kwargs.get("tool_calls", [])
        
        responses = []
        for call in tool_calls:
            tool_name = call["function"]["name"]
            tool_args = call["function"].get("arguments", {})
            
            tool_func = next((t for t in tools if t.name == tool_name), None)
            if not tool_func:
                responses.append(f"Tool {tool_name} not available")
                continue
            
            try:
                if isinstance(tool_args, str):
                    try:
                        tool_args = json.loads(tool_args)
                    except json.JSONDecodeError:
                        if "query" in tool_args:
                            tool_args = {"query": tool_args}
                        else:
                            tool_args = {"query": tool_args}
                
                result = tool_func.invoke(tool_args)
                responses.append(f"{tool_name} result: {str(result)[:1000]}")
            except Exception as e:
                responses.append(f"{tool_name} error: {str(e)}")
        
        tool_response_content = "\n".join(responses)
        return {"messages": [AIMessage(content=tool_response_content)], "retry_count": 0}
    
    # 4. 添加节点到工作流
    workflow.add_node("agent", agent_node)
    workflow.add_node("tools", tool_node)
    
    # 5. 设置入口点
    workflow.set_entry_point("agent")
    
    # 6. 定义条件边
    def should_continue(state: AgentState):
        last_msg = state["messages"][-1]
        retry_count = state.get("retry_count", 0)
        
        if "AGENT ERROR" in last_msg.content:
            if retry_count < 3:
                return "agent"
            return "end"
        
        if hasattr(last_msg, "tool_calls") and last_msg.tool_calls:
            return "tools"
        
        if "FINAL ANSWER" in last_msg.content:
            return "end"
            
        return "agent"
    
    workflow.add_conditional_edges(
        "agent",
        should_continue,
        {
            "agent": "agent",
            "tools": "tools",
            "end": END
        }
    )
    
    # 7. 定义工具节点后的流向
    workflow.add_edge("tools", "agent")
    
    # 8. 编译图
    return workflow.compile()

# 初始化代理图
agent_graph = build_graph()