AnukulChandra/Mini-AI_Assistant

1import logging2 3from fastapi import APIRouter, HTTPException4from pydantic import BaseModel5 6from services.llm import generate_response7from services.memory import add_to_memory, get_history8from services.prompt_builder import build_prompt9from services.retrieval import retrieve_context10from services.tools import detect_intent11 12logger = logging.getLogger(__name__)13 14router = APIRouter(prefix="/chat")15 16 17class QuestionRequest(BaseModel):18    question: str19 20 21def _build_history() -> str:22    questions, answers = get_history()23    if not questions:24        return ""25 26    lines = ["Previous conversation:"]27    for q, a in zip(questions, answers):28        lines.append(f"User: {q}")29        lines.append(f"Assistant: {a}")30 31    return "\n".join(lines)32 33 34@router.post("/ask")35async def ask_question(body: QuestionRequest):36    history = _build_history()37 38    intent, tool_result = None, None39    try:40        intent, tool_result = detect_intent(body.question)41    except Exception as e:42        logger.warning("Intent detection failed: %s", e)43 44    if intent == "order":45        logger.info("Detected intent: ORDER")46        status = tool_result.get("status", "unknown").capitalize()47        delivery = tool_result.get("estimated_delivery", "N/A")48        answer = f"Order {tool_result['order_id']} is {status}. Estimated delivery: {delivery}."49        add_to_memory(body.question, answer)50        return {51            "type": "order",52            "answer": answer,53            "data": tool_result,54        }55 56    if intent == "product":57        logger.info("Detected intent: PRODUCT")58        if isinstance(tool_result, list) and len(tool_result) > 0:59            product = tool_result[0]60            name = product.get("name", "Unknown")61            price = product.get("price", 0)62            stock = product.get("stock", 0)63            answer = f"{name} costs ${price} and {stock} units are in stock."64        else:65            answer = "No product found."66        add_to_memory(body.question, answer)67        return {68            "type": "product",69            "answer": answer,70            "data": tool_result if isinstance(tool_result, list) else [tool_result],71        }72 73    if history:74        logger.info("Detected intent: MEMORY")75 76    chunks = []77    try:78        chunks = retrieve_context(body.question)79        if chunks:80            logger.info("Detected intent: KNOWLEDGE")81    except ValueError as e:82        logger.info("No vector store available, proceeding without RAG context: %s", e)83 84    prompt = build_prompt(body.question, chunks, history)85 86    try:87        answer = generate_response(prompt)88    except ValueError as e:89        raise HTTPException(status_code=400, detail=str(e))90 91    add_to_memory(body.question, answer)92 93    return {94        "type": "knowledge",95        "answer": answer,96        "retrieved_chunks": chunks,97    }