agent.py

import cmath
import os
from typing import Dict, List, Sequence, TypedDict, cast

from dotenv import load_dotenv
from langchain.tools.retriever import create_retriever_tool
from langchain_community.document_loaders import ArxivLoader, WikipediaLoader
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_core.messages import AIMessage, HumanMessage, SystemMessage
from langchain_core.tools import tool
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import (
    ChatHuggingFace,
    HuggingFaceEmbeddings,
    HuggingFaceEndpoint,
)
from langchain_tavily import TavilySearch
from langgraph.graph import END, START, MessagesState, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition
from pydantic import BaseModel
from supabase.client import Client, create_client

# Load environment variables from .env file
load_dotenv()


class WebSearchInput(BaseModel):
    query: str


class WikipediaSearchInput(BaseModel):
    query: str


class ArxivSearchInput(BaseModel):
    query: str


@tool
def search_web(query: str) -> str:
    """Search the web using Tavily and return relevant results."""

    """Search Tavily for a query and return maximum 3 results.
    
    Args:
        query: The search query."""
    search_docs = TavilySearch(max_results=3).invoke({"query": query})
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"web_results": formatted_search_docs}


@tool
def search_wikipedia(query: str) -> str:
    """Search Wikipedia using LangChain's loader and return the first document summary."""
    try:
        loader = WikipediaLoader(query=query, lang="en", load_max_docs=2)
        docs = loader.load()
        if not docs:
            return {"error": f"No Wikipedia articles found for query: {query}"}
        formatted_docs = "\n\n---\n\n".join(
            [f"Wikipedia Article: {query}\n\n{doc.page_content}" for doc in docs]
        )
        return {"wiki_results": formatted_docs}
    except Exception as e:
        return {"error": f"Error searching Wikipedia: {str(e)}"}


@tool
def arxiv_search(query: str) -> str:
    """Search Arxiv for a query and return maximum 3 result.
    Args:
        query: The search query."""
    search_docs = ArxivLoader(query=query, load_max_docs=3).load()
    formatted_search_docs = "\n\n---\n\n".join(
        [
            f'<Document source="{doc.metadata["source"]}" page="{doc.metadata.get("page", "")}"/>\n{doc.page_content[:1000]}\n</Document>'
            for doc in search_docs
        ]
    )
    return {"arxiv_results": formatted_search_docs}


@tool
def power(a: float, b: float) -> float:
    """
    Get the power of two numbers.
    Args:
        a (float): the first number
        b (float): the second number
    """
    return a**b


@tool
def square_root(a: float) -> float | complex:
    """
    Get the square root of a number.
    Args:
        a (float): the number to get the square root of
    """
    if a >= 0:
        return a**0.5
    return cmath.sqrt(a)


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


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

    Args:
        a: first int
        b: second int
    """
    return a + b


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

    Args:
        a: first int
        b: second int
    """
    return a - b


@tool
def divide(a: float, b: float) -> float:
    """
    Divides two numbers.
    Args:
        a (float): the first float number
        b (float): the second float number
    """
    if b == 0:
        raise ValueError("Cannot divided by zero.")
    return a / b


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

    Args:
        a: first int
        b: second int
    """
    return a % b


# System prompt
system_prompt = SystemMessage(
    content="""You are a helpful assistant tasked with answering questions using a set of tools. 
Now, I will ask you a question. Report your thoughts, and finish your answer with the following template: 
FINAL ANSWER: [YOUR FINAL ANSWER]. 
YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, Apply the rules above for each element (number or string), ensure there is exactly one space after each comma.
Your answer should only start with "FINAL ANSWER: ", then follows with the answer. """
)

supabase_url = os.environ.get("SUPABASE_URL")
supabase_service_key = os.environ.get("SUPABASE_SERVICE_KEY")
# build a retriever
embeddings = HuggingFaceEmbeddings(
    model_name="sentence-transformers/all-mpnet-base-v2"
)  #  dim=768
supabase: Client = create_client(supabase_url, supabase_service_key)
vector_store = SupabaseVectorStore(
    client=supabase,
    embedding=embeddings,
    table_name="documents",
    query_name="match_documents_langchain",
)
create_retriever_tool = create_retriever_tool(
    retriever=vector_store.as_retriever(),
    name="Question Search",
    description="A tool to retrieve similar questions from a vector store.",
)

# Initialize tools
tools = [
    search_wikipedia,
    search_web,
    arxiv_search,
    power,
    square_root,
    multiply,
    divide,
    subtract,
    add,
    modulus,
]


def build_agent_graph(provider: str = "groq"):
    """Build the graph"""

    # Initialize LLM class
    try:
        gemini_api_key = os.getenv("GEMINI_API_KEY")
        if provider == "groq":
            # Groq https://console.groq.com/docs/models
            chat_model = ChatGroq(
                model="qwen-qwq-32b", temperature=0
            )  # optional : qwen-qwq-32b gemma2-9b-it
        elif provider == "gemini":
            chat_model = ChatGoogleGenerativeAI(
                model="gemini-2.5-pro",
                temperature=1.0,
                max_retries=2,
                google_api_key=gemini_api_key,
            )
        elif provider == "huggingface":
            llm = HuggingFaceEndpoint(
                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                temperature=0,
            )
            chat_model = ChatHuggingFace(llm=llm, verbose=True)
        else:
            raise ValueError("Invalid provider.")
    except Exception as e:
        raise Exception(f"Failed to initialize LLM: {str(e)}")

    llm_with_tools = chat_model.bind_tools(tools)

    # Create nodes
    def assistant(state: MessagesState):
        """Assistant node"""
        return {"messages": [llm_with_tools.invoke(state["messages"])]}

    def retriever(state: MessagesState):
        query = state["messages"][-1].content
        results = vector_store.similarity_search(query, k=1)

        if not results:
            print(f"[retriever] No similar documents found for query: {query}")
            return {
                "messages": [
                    AIMessage(content="I couldn't find any similar content in memory.")
                ]
            }

        similar_doc = results[0]
        content = similar_doc.page_content

        if "Final answer :" in content:
            answer = content.split("Final answer :")[-1].strip()
        else:
            answer = content.strip()

        return {"messages": [AIMessage(content=answer)]}

    # Build graph
    builder = StateGraph(MessagesState)
    builder.add_node("retriever", retriever)
    # builder.add_node("assistant", assistant)
    # builder.add_node("tools", ToolNode(tools))
    # builder.add_edge(START, "retriever")
    # builder.add_edge("retriever", "assistant")
    # builder.add_conditional_edges(
    #     "assistant",
    #     tools_condition,
    # )
    # builder.add_edge("tools", "assistant")

    builder.set_entry_point("retriever")
    builder.set_finish_point("retriever")

    return builder.compile()


# Manual test function
def test_agent():
    """Run a manual test of the agent"""
    print("\n" + "=" * 50)
    print("Starting Agent Test")
    print("=" * 50)

    # Check environment variables
    if not os.getenv("HUGGINGFACEHUB_API_TOKEN"):
        print("\nError: HUGGINGFACEHUB_API_TOKEN not set")
        return
    if not os.getenv("GEMINI_API_KEY"):
        print("\nError: GEMINI_API_KEY not set")
        return
    if not os.getenv("TAVILY_API_KEY"):
        print("\nWarning: TAVILY_API_KEY not set - web search will be unavailable")

    if not os.getenv("SUPABASE_URL"):
        print("\nWarning: SUPABASE_URL not set - web search will be unavailable")

    print("\nInitializing agent...")
    try:
        graph = build_agent_graph(provider="groq")
        print("Agent initialized successfully")
    except Exception as e:
        print(f"Failed to initialize agent: {str(e)}")
        return

    # Test a single question
    question = "Examine the video at https://www.youtube.com/watch?v=1htKBjuUWec.\n\nWhat does Teal'c say in response to the question \"Isn't that hot?\""
    print("\nTesting question:", question)
    print("-" * 50)

    try:
        # Create messages state
        messages = [HumanMessage(content=question)]

        # Run agent
        print("\nWaiting for response...")
        result = graph.invoke({"messages": messages})

        # Get answer
        if result and "messages" in result and result["messages"]:

            answer = result["messages"][-1].content
            print("\nResponse received:")
            print("-" * 20)
            print(answer)
            print("-" * 20)
        else:
            print("\nError: No response from agent")

    except Exception as e:
        print(f"\nError processing question: {str(e)}")

    print("\n" + "=" * 50)
    print("Test Complete")
    print("=" * 50 + "\n")


# Run test if script is run directly
if __name__ == "__main__":
    test_agent()