main.py

from rag_pipeline import process_file, answer_query
from pydantic import BaseModel

class QueryRequest(BaseModel):
    file_id: str
    question: str
    page: int
    explainLike5: bool = False

from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from dotenv import load_dotenv
from fastapi import Body
import uuid
import os
from langchain_chroma import Chroma
from langchain_huggingface import HuggingFaceEmbeddings
import re

load_dotenv()

CHROMA_DIR = "./chroma_db"
embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")


app = FastAPI()
BASE_DIR = "files"
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_methods=["*"],
    allow_headers=["*"],
)
file_store = {}
@app.get("/test")
async def test():
    return {"message": "hello world!"}

@app.post("/upload")
async def upload(file: UploadFile = File(...)):
    content = await file.read()
    file_id = str(uuid.uuid4())
    safe_filename = file.filename.replace(" ", "_")
    full_filename = f"{file_id}_{safe_filename}"
    save_path = os.path.join(BASE_DIR, full_filename)
    os.makedirs(BASE_DIR, exist_ok=True)
    with open(save_path, "wb") as f:
        f.write(content)
    retriever = process_file(content, safe_filename, file_id)
    file_store[file_id] = retriever
    return {"message": "File processed", "file_id": file_id}

@app.post("/query")
async def query_endpoint(request = Body(...)):
    file_id = request.get("file_id")
    question = request.get("question")
    selected_text = request.get("selectedText")
    explain_like_5 = request.get("explainLike5", False)

    if not file_id or not question:
        return {"error": "Missing file_id or question"}

    retriever_path = f"{CHROMA_DIR}/{file_id}"
    # Load retriever from disk
    if not os.path.exists(retriever_path):
        return {"error": "Vectorstore for this file_id not found."}

    vectorstore = Chroma(
        embedding_function=embedding_model,
        persist_directory=retriever_path
    )
    retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 4})
    retrieved_docs = retriever.invoke(selected_text or question)
    retrieved_context = "\n\n".join(
        re.sub(r"\s+", " ", doc.page_content.strip()) for doc in retrieved_docs
    )
    
    combined_context = f"User selected this:\n\"{selected_text}\"\n\nRelated parts from the document:\n{retrieved_context}"
    print("Combined context", combined_context)
    answer = answer_query(question, combined_context, explain_like_5)
    return {"answer": answer}