routers/ask.py

from dotenv import load_dotenv
from fastapi import APIRouter
from langchain.globals import set_verbose
from schemas.question import Question
from model.model_ai import ModelAI
from schemas.model_llm import ModelLLM
from tools.duckduckgo import search
from tools.search_db_vectorial import exists_database, list_databases, search_database
from tools.time import time
from os import getenv
import warnings

warnings.filterwarnings("ignore", category=DeprecationWarning)

load_dotenv()
set_verbose(bool(getenv("VERBOSE", False)))

LLM_API_NAME_MODEL = getenv("LLM_API_NAME_MODEL")
LLM_API_URL = getenv("LLM_API_URL")
LLM_API_KEY = getenv("LLM_API_KEY")

router = APIRouter(
    prefix="/ai",
    tags=["Ask Agent"],
    responses={404: {"description": "Not found"}},
)
model_llm = ModelLLM(
    name_model=LLM_API_NAME_MODEL, base_url=LLM_API_URL, api_key=LLM_API_KEY
)
model = ModelAI(model_llm)
tools = [time, search, list_databases, exists_database, search_database]
agent_executor = model.agent_executer(tools)


@router.get("/ask")
def ask_question():
    """Devuelve el formato de pregunta esperado."""
    return {"input": "Pregunta"}


@router.post("/ask")
async def ask_question(question: Question):
    """Recibe una pregunta y devuelve una respuesta del agente."""
    try:
        # Usamos ainvoke para realizar la llamada asíncrona
        respuesta = await agent_executor.ainvoke({"input": question.input})
        return {"respuesta": respuesta["output"]}
    except Exception as e:
        return {"error": str(e)}