from llm_setup import llm
from vector_db import get_local_content, check_local_knowledge
from web_scrapping import get_web_content

def generate_final_answer(context, query):
    """Generate final answer using LLM"""
    messages = [
        (
            "system",
            "You are a helpful assistant. Use the provided context to answer the query accurately.",
        ),
        ("system", f"Context: {context}"),
        ("human", query),
    ]
    response = llm.invoke(messages)
    return response.content

def process_query(query, vector_db, local_context):
    """Main function to process user query"""
    print(f"Processing query: {query}")
    
    # Etapa 1: Checar se é possível responder com conhecimento local
    can_answer_locally = check_local_knowledge(query, local_context)
    print(f"Can answer locally: {can_answer_locally}")
    
    # Etapa 2: Obter contexto do banco de dados local ou da web
    if can_answer_locally:
        context = get_local_content(vector_db, query)
        print("Retrieved context from local documents")
    else:
        context = get_web_content(query)
        print("Retrieved context from web scraping")
    
    # Etapa 3: Gerar resposta final
    answer = generate_final_answer(context, query)
    return answer