agent.py

"""LangGraph Agent - Complete bypass of problematic vector store"""
import os
import json
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
from langchain_groq import ChatGroq
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader
from langchain_community.document_loaders import ArxivLoader
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_core.tools import tool
from supabase.client import Client, create_client

load_dotenv()

@tool
def multiply(a: int, b: int) -> int:
    """Multiply two numbers."""
    return a * b

@tool
def add(a: int, b: int) -> int:
    """Add two numbers."""
    return a + b

@tool
def subtract(a: int, b: int) -> int:
    """Subtract two numbers."""
    return a - b

@tool
def divide(a: int, b: int) -> int:
    """Divide two numbers."""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b

@tool
def modulus(a: int, b: int) -> int:
    """Get the modulus of two numbers."""
    return a % b

@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia for a query and return maximum 2 results."""
    try:
        search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
        formatted_docs = []
        for doc in search_docs:
            source = "Wikipedia"
            if hasattr(doc, 'metadata') and isinstance(doc.metadata, dict):
                source = doc.metadata.get('source', 'Wikipedia')
            formatted_docs.append(f"Source: {source}\n{doc.page_content[:1000]}...")
        
        return "\n\n---\n\n".join(formatted_docs)
    except Exception as e:
        return f"Error searching Wikipedia: {str(e)}"

@tool
def web_search(query: str) -> str:
    """Search the web using Tavily."""
    try:
        search_tool = TavilySearchResults(max_results=3)
        results = search_tool.invoke(query)
        
        if isinstance(results, list):
            formatted_results = []
            for result in results:
                if isinstance(result, dict):
                    url = result.get('url', 'Unknown')
                    content = result.get('content', '')[:1000]
                    formatted_results.append(f"Source: {url}\n{content}...")
            return "\n\n---\n\n".join(formatted_results)
        return str(results)
    except Exception as e:
        return f"Error searching web: {str(e)}"

@tool
def arxiv_search(query: str) -> str:
    """Search Arxiv for academic papers."""
    try:
        search_docs = ArxivLoader(query=query, load_max_docs=3).load()
        formatted_docs = []
        for doc in search_docs:
            source = "ArXiv"
            if hasattr(doc, 'metadata') and isinstance(doc.metadata, dict):
                source = doc.metadata.get('source', 'ArXiv')
            formatted_docs.append(f"Source: {source}\n{doc.page_content[:1000]}...")
        
        return "\n\n---\n\n".join(formatted_docs)
    except Exception as e:
        return f"Error searching ArXiv: {str(e)}"

# Raw Supabase search function that bypasses LangChain entirely
def raw_supabase_search(query: str, supabase_client):
    """Direct Supabase search without any LangChain components"""
    try:
        # Simple text-based search using Supabase's built-in functions
        # This assumes you have a simple text search function in your database
        result = supabase_client.table('documents').select('content').text_search('content', query).limit(1).execute()
        
        if result.data:
            return result.data[0]['content']
        else:
            # Fallback: get any document (for testing)
            result = supabase_client.table('documents').select('content').limit(1).execute()
            if result.data:
                return result.data[0]['content']
            return "No documents found in database"
            
    except Exception as e:
        return f"Database search error: {str(e)}"

# Alternative: Use simple SQL query
def simple_sql_search(query: str, supabase_client):
    """Simple SQL-based search"""
    try:
        # Use a simple SQL query to avoid metadata issues
        sql_query = f"""
        SELECT content 
        FROM documents 
        WHERE content ILIKE '%{query}%' 
        LIMIT 1
        """
        result = supabase_client.rpc('execute_sql', {'query': sql_query}).execute()
        
        if result.data:
            return result.data[0]['content']
        return "No matching documents found"
        
    except Exception as e:
        return f"SQL search error: {str(e)}"

# Load system prompt
try:
    with open("system_prompt.txt", "r", encoding="utf-8") as f:
        system_prompt = f.read()
except FileNotFoundError:
    system_prompt = "You are a helpful AI assistant."

sys_msg = SystemMessage(content=system_prompt)

# Initialize Supabase without vector store
supabase_url = "https://ajnakgegqblhwltzkzbz.supabase.co"
supabase_key = "eyJhbGciOiJIUzI1NiIsInR5cCI6IkpXVCJ9.eyJpc3MiOiJzdXBhYmFzZSIsInJlZiI6ImFqbmFrZ2VncWJsaHdsdHpremJ6Iiwicm9sZSI6ImFub24iLCJpYXQiOjE3NDkyMDgxODgsImV4cCI6MjA2NDc4NDE4OH0.b9RPF-5otedg4yiaQu_uhOgYpXVXd9D_0oR-9cluUjo"

try:
    supabase_client = create_client(supabase_url, supabase_key)
except Exception as e:
    print(f"Warning: Could not initialize Supabase client: {e}")
    supabase_client = None

tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    web_search,
    arxiv_search,
]

def build_graph(provider: str = "groq"):
    """Build the graph without problematic vector store operations"""
    if provider == "groq":
        llm = ChatGroq(
            model="qwen-qwq-32b",
            api_key="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", 
            temperature=0
        )
    else:
        raise ValueError("Invalid provider. Choose 'groq'.")
    
    def retriever(state: MessagesState):
        """Retriever that actually searches based on query"""
        try:
            query = state["messages"][-1].content.lower()
            
            if supabase_client is None:
                return {"messages": [AIMessage(content="I don't have access to my knowledge base right now. Let me help you using my general knowledge or search tools instead. What would you like to know?")]}
            
            print(f"Searching for: {query}")  # Debug print
            
            # Try text-based search in the content
            try:
                # Search for documents containing query terms
                result = supabase_client.table('documents').select('content')\
                    .ilike('content', f'%{query}%')\
                    .limit(3).execute()
                
                if result.data and len(result.data) > 0:
                    print(f"Found {len(result.data)} results")  # Debug print
                    
                    # Get the most relevant result
                    content = result.data[0].get('content', '')
                    
                    # Look for final answer pattern
                    if "Final answer :" in content:
                        answer = content.split("Final answer :")[-1].strip()
                    else:
                        # Take relevant portion
                        answer = content.strip()[:800]
                        if len(content) > 800:
                            answer += "..."
                    
                    return {"messages": [AIMessage(content=answer)]}
                else:
                    print("No matching documents found")  # Debug print
                    
            except Exception as e:
                print(f"Text search failed: {e}")
            
            # Fallback: Instead of returning same document, provide helpful response
            return {"messages": [AIMessage(content=f"I couldn't find specific information about '{query}' in my knowledge base. Let me try to help you with my general knowledge, or would you like me to search the web for current information?")]}
                
        except Exception as e:
            return {"messages": [AIMessage(content=f"I'm having trouble accessing my knowledge base right now. How can I help you using web search or my general knowledge instead?")]}

    # Build simple graph
    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    builder.set_entry_point("retriever")
    builder.set_finish_point("retriever")
    
    return builder.compile()

# RECOMMENDED: Use this function instead of build_graph()
def build_working_graph(provider: str = "groq"):
    """Build a fully functional graph that actually works for different questions"""
    if provider == "groq":
        llm = ChatGroq(
            model="qwen-qwq-32b",
            api_key="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", 
            temperature=0
        )
    else:
        raise ValueError("Invalid provider.")
    
    llm_with_tools = llm.bind_tools(tools)

    def assistant(state: MessagesState):
        """Assistant that can provide different answers for different questions"""
        # Add system message to the conversation
        messages = [sys_msg] + state["messages"]
        response = llm_with_tools.invoke(messages)
        return {"messages": [response]}

    # Build the graph
    builder = StateGraph(MessagesState)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))
    
    builder.set_entry_point("assistant")
    builder.add_conditional_edges("assistant", tools_condition)
    builder.add_edge("tools", "assistant")
    
    return builder.compile()

# Test function
def test_graph():
    """Test the graph builds successfully"""
    print("Building working graph (recommended)...")
    try:
        graph = build_working_graph()
        print("✓ Working graph built successfully!")
        return graph
    except Exception as e:
        print(f"✗ Working graph failed: {e}")
        
    print("Testing retriever-based graph...")
    try:
        graph1 = build_graph()
        print("✓ Retriever graph built successfully!")
        return graph1
    except Exception as e:
        print(f"✗ Retriever graph failed: {e}")
        return None

if __name__ == "__main__":
    question = "When was a picture of St. Thomas Aquinas first added to the Wikipedia page on the Principle of double effect?"
    
    graph = test_graph()
    
    messages = [HumanMessage(content=question)]
    messages = graph.invoke({"messages": messages})
    for m in messages["messages"]:
        m.pretty_print()