main.py

from fastapi import FastAPI
from pydantic import BaseModel

import os
import requests

from rag import RAGEngine
from loader import smart_load
from llm import generate_answer

from langdetect import detect
from normalizer import normalize_text

from config import FALLBACK_ANSWER
from memory import ConversationMemory


app = FastAPI()


# memory per agent
agent_memories = {}

# RAG cache per agent  <<< NEW
agent_rags = {}



def get_agent_memory(agent_id):

    if agent_id not in agent_memories:

        agent_memories[agent_id] = ConversationMemory(
            max_messages=10
        )

    return agent_memories[agent_id]




# add 
# load rag one time 
def get_agent_rag(agent_id):

    if agent_id in agent_rags:
        print("Agent ID", agent_id)
        return agent_rags[agent_id]


    agent_folder = f"agents/{agent_id}"


    index_file = os.path.join(
        agent_folder,
        "index.faiss"
    )


    text_file = os.path.join(
        agent_folder,
        "texts.pkl"
    )


    if not os.path.exists(index_file) or not os.path.exists(text_file):

        print("Agent exists but not trained:", agent_id)

        return None



    print("Loading RAG first time:", agent_id)


    rag = RAGEngine()

    rag.load(agent_folder)


    agent_rags[agent_id] = rag


    return rag





class TrainRequest(BaseModel):
    agent_id: str
    file_url: str
    file_type: str
    file_name: str
    agent_type: str



class ChatRequest(BaseModel):
    agent_id: str
    message: str






@app.get("/")
def home():

    return {
        "service": "Customer Support AI",
        "status": "running"
    }






@app.post("/train")
async def train_agent(request: TrainRequest):

    try:

        agent_folder = f"agents/{request.agent_id}"

        os.makedirs(
            agent_folder,
            exist_ok=True
        )



        dataset_path = os.path.join(
            agent_folder,
            request.file_name
        )


        # download dataset
        response = requests.get(
            request.file_url,
            timeout=60
        )


        response.raise_for_status()



        with open(dataset_path,"wb") as f:

            f.write(
                response.content
            )




        # load dataset

        chunks = smart_load(dataset_path)



        if not chunks:

            return {
                "success":False,
                "message":"No valid data found"
            }





        rag = RAGEngine()


        rag.build_index(
            chunks
        )



        rag.save(
            agent_folder
        )



        # NEW
        # keep trained rag in memory
        agent_rags[request.agent_id] = rag



        return {

            "success":True,
            "agent_id":request.agent_id,
            "chunks":len(chunks)

        }



    except Exception as e:


        return {

            "success":False,
            "message":str(e)

        }









@app.post("/chat")
async def chat(request:ChatRequest):

    try:


        # NEW
        # get cached RAG
        rag = get_agent_rag(
            request.agent_id
        )



        if rag is None:

            return {

                "answer":"Agent not found",
                "sources":[]

            }






        question = request.message




        try:

            lang = detect(question)

        except:

            lang="en"




        normalized = normalize_text(
            question
        )




        # memory
        memory = get_agent_memory(
            request.agent_id
        )


        memory.add_user_message(
            question
        )






        # retrieval

        retrieved = rag.retrieve_multi_query(
            normalized,

            # changed for speed
            use_expansion=False
        )





        if not retrieved:


            retrieved, confidence = rag.retrieve_with_confidence(
                normalized,
                confidence_threshold=0.10
            )



            if not retrieved:


                return {

                    "answer":FALLBACK_ANSWER.get(
                        lang,
                        FALLBACK_ANSWER["en"]
                    ),

                    "sources":[]

                }






        context = "\n".join(
            retrieved
        )






        answer = generate_answer(

            context,

            question,

            lang,

            memory.get_memory()

        )





        memory.add_assistant_message(
            answer
        )






        return {

            "answer":answer,
            "sources":[]

        }





    except Exception as e:


        return {

            "answer":"Error",

            "error":str(e)

        }