agent.py

"""LangGraph Agent – versione senza Supabase"""

import os
from dotenv import load_dotenv
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition

# LLM providers
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import (
    ChatHuggingFace,
    HuggingFaceEndpoint,
)

# Tools & loaders
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_core.messages import SystemMessage, HumanMessage
from langchain_core.tools import tool

load_dotenv()  # carica eventuali variabili dal file .env

# --------------------------------------------------------------------------- #
#  TOOL: operazioni aritmetiche                                               #
# --------------------------------------------------------------------------- #
@tool
def multiply(a: int, b: int) -> int:
    """Multiply two integers and return the product."""
    return a * b


@tool
def add(a: int, b: int) -> int:
    """Add two integers and return the sum."""
    return a + b


@tool
def subtract(a: int, b: int) -> int:
    """Subtract the second integer from the first and return the difference."""
    return a - b


@tool
def divide(a: int, b: int) -> float:
    """Divide a by b and return the quotient (error if b == 0)."""
    if b == 0:
        raise ValueError("Cannot divide by zero.")
    return a / b


@tool
def modulus(a: int, b: int) -> int:
    """Return the remainder of the division of a by b."""
    return a % b


# --------------------------------------------------------------------------- #
#  TOOL: Wikipedia                                                            #
# --------------------------------------------------------------------------- #
@tool
def wiki_search(query: str) -> str:
    """Search Wikipedia (max 2 docs) and return formatted content."""
    docs = WikipediaLoader(query=query, load_max_docs=2).load()
    return "\n\n---\n\n".join(
        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
        f"{d.page_content}\n</Document>"
        for d in docs
    )


# --------------------------------------------------------------------------- #
#  TOOL: Tavily web search                                                    #
# --------------------------------------------------------------------------- #
@tool
def web_search(query: str) -> str:
    """Perform a web search with Tavily (max 3 docs) and return formatted content."""
    docs = TavilySearchResults(max_results=3).invoke(query=query)
    return "\n\n---\n\n".join(
        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
        f"{d.page_content}\n</Document>"
        for d in docs
    )


# --------------------------------------------------------------------------- #
#  TOOL: ArXiv                                                                #
# --------------------------------------------------------------------------- #
@tool
def arxiv_search(query: str) -> str:
    """Search ArXiv (max 3 docs) and return first 1000 characters per paper."""
    docs = ArxivLoader(query=query, load_max_docs=3).load()
    return "\n\n---\n\n".join(
        f'<Document source="{d.metadata["source"]}" page="{d.metadata.get("page","")}"/>\n'
        f"{d.page_content[:1000]}\n</Document>"
        for d in docs
    )


# --------------------------------------------------------------------------- #
#  System prompt                                                              #
# --------------------------------------------------------------------------- #
with open("system_prompt.txt", "r", encoding="utf-8") as f:
    system_prompt = f.read()
sys_msg = SystemMessage(content=system_prompt)

# --------------------------------------------------------------------------- #
#  Lista tool                                                                 #
# --------------------------------------------------------------------------- #
tools = [
    multiply,
    add,
    subtract,
    divide,
    modulus,
    wiki_search,
    web_search,
    arxiv_search,
]

# --------------------------------------------------------------------------- #
#  Build LangGraph                                                            #
# --------------------------------------------------------------------------- #
def build_graph(provider: str = "groq"):
    """Return a LangGraph graph without Supabase dependencies."""
    # ------------ LLM selection ------------------------------------------- #
    if provider == "google":
        llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", temperature=0)
    elif provider == "groq":
        llm = ChatGroq(model="qwen-qwq-32b", temperature=0)
    elif provider == "huggingface":
        llm = ChatHuggingFace(
            llm=HuggingFaceEndpoint(
                url="https://api-inference.huggingface.co/models/Meta-DeepLearning/llama-2-7b-chat-hf",
                temperature=0,
            )
        )
    else:
        raise ValueError("Invalid provider. Choose 'google', 'groq' or 'huggingface'.")

    llm_with_tools = llm.bind_tools(tools)

    # ------------------ Nodes -------------------------------------------- #
    def assistant(state: MessagesState):
        """Invoke LLM with system prompt prepended."""
        messages = [sys_msg] + state["messages"]
        return {"messages": [llm_with_tools.invoke(messages)]}

    # ------------------ Graph -------------------------------------------- #
    builder = StateGraph(MessagesState)
    builder.add_node("assistant", assistant)
    builder.add_node("tools", ToolNode(tools))

    builder.add_edge(START, "assistant")
    builder.add_conditional_edges("assistant", tools_condition)
    builder.add_edge("tools", "assistant")

    return builder.compile()


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