modules/general_chat.py

# General module
from helpers.hf_llm import generate_text

GENERAL_SYSTEM_PROMPT = (
    "You are a knowledgeable, helpful AI assistant.\n"
    "You can answer general questions, explain concepts clearly, "
    "and respond politely in a conversational manner.\n"
    "If a question is ambiguous, ask for clarification.\n"
    "Do not claim access to private, real-time, or restricted systems."
)
def get_relevance(route, module_name):
    for m in route.get("module_preferences", []):
        if m["module"] == module_name:
            return m["relevance"]
    return 0.0
def answer_general_query(query, route, mode="module"):
    if mode == "failsafe":
        prompt = f"""
{GENERAL_SYSTEM_PROMPT}

User question:
{query}

Answer politely and clearly.
Keep the answer concise.
"""
        answer_text = generate_text(
            prompt=prompt,
            max_new_tokens=300,
            temperature=0.6,
            top_p=0.9
        )
        return {
            "answer": answer_text,
            "has_answer": True,
            "meta": {"mode": "failsafe"}
        }

    gen_rel = get_relevance(route, "general")
    rag_rel = get_relevance(route, "railway_rag")
    api_rel = get_relevance(route, "live_data_apis")
    MIN_GENERAL_RELEVANCE = 0.30
    DOMINANCE_MARGIN = 0.10
    if gen_rel < MIN_GENERAL_RELEVANCE:
        return {
            "answer": None,
            "has_answer": False,
            "meta": {}
        }
    if gen_rel < max(rag_rel, api_rel) + DOMINANCE_MARGIN:
        return {
            "answer": None,
            "has_answer": False,
            "meta": {}
        }
    prompt = f"""
{GENERAL_SYSTEM_PROMPT}

User question:
{query}

Answer clearly, politely, and conversationally.
Keep the answer concise.
"""
    answer_text = generate_text(
        prompt=prompt,
        max_new_tokens=300,
        temperature=0.6,
        top_p=0.9
    )
    return {
        "answer": answer_text,
        "has_answer": True,
        "meta": {}
    }