agent.py

import os
import time
from dotenv import load_dotenv
from langgraph.graph import StateGraph, END
from langgraph.prebuilt import ToolNode, tools_condition
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 (Gemini 2.0 Flash)
    llm = ChatGoogleGenerativeAI(
        model="gemini-2.0-flash-exp",
        temperature=0.3,
        google_api_key=google_api_key,
        max_retries=3
    )
    
    # Bind tools to LLM
    llm_with_tools = llm.bind_tools(tools)
    
    # Define state
    class AgentState:
        def __init__(self, messages):
            self.messages = messages
    
    # 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 AgentState(state.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 AgentState(state.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
                    import json
                    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)}")
        
        return AgentState(state.messages + [AIMessage(content="\n".join(tool_responses)])

    # 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",
        lambda state: "agent",
        {
            "agent": "agent"
        }
    )
    
    return workflow.compile()

# Initialize the agent graph
agent_graph = build_graph()