app.py

from rag_pipeline import process_file, answer_query
from pydantic import BaseModel
from typing import List, Dict, Optional
from datetime import datetime

class ChatMessage(BaseModel):
    question: str
    answer: str
    timestamp: datetime

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

from fastapi import FastAPI, UploadFile, File, HTTPException
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 = {}

# Add chat memory store
chat_memory: Dict[str, List[ChatMessage]] = {}

@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:
        raise HTTPException(status_code=422, detail="Missing file_id or question")

    retriever_path = f"{CHROMA_DIR}/{file_id}"
    if not os.path.exists(retriever_path):
        raise HTTPException(status_code=404, detail="Vectorstore for this file_id not found.")

    try:
        # Initialize vectorstore with metadata filtering
        vectorstore = Chroma(
            embedding_function=embedding_model,
            persist_directory=retriever_path
        )
        
        # Configure retriever with MMR search
        retriever = vectorstore.as_retriever(
            search_type="mmr",
            search_kwargs={
                "k": 4,
                "fetch_k": 8,
                "lambda_mult": 0.7,
            }
        )

        # First, get context around selected text if it exists
        contexts = []
        if selected_text:
            selected_results = retriever.invoke(selected_text)
            contexts.extend([doc.page_content for doc in selected_results])
        
        # Then get context for the question
        question_results = retriever.invoke(question)
        contexts.extend([doc.page_content for doc in question_results])
        
        # Remove duplicates while preserving order
        contexts = list(dict.fromkeys(contexts))
        
        # Format the context with clear section separation
        formatted_context = ""
        if selected_text:
            formatted_context += f"Selected Text Context:\n{selected_text}\n\n"
        
        formatted_context += "Related Document Contexts:\n" + "\n---\n".join(
            re.sub(r"\s+", " ", context.strip())
            for context in contexts
        )

        # Add chat history to context if it exists
        if file_id in chat_memory and chat_memory[file_id]:
            chat_history = "\n\nPrevious Conversation:\n"
            for msg in chat_memory[file_id][-3:]:  # Include last 3 exchanges
                chat_history += f"Q: {msg.question}\nA: {msg.answer}\n\n"
            formatted_context = chat_history + formatted_context

        # Get the answer using the enhanced context
        answer = answer_query(question, formatted_context, explain_like_5)
        
        # Store the Q&A in chat memory
        if file_id not in chat_memory:
            chat_memory[file_id] = []
        chat_memory[file_id].append(ChatMessage(
            question=question,
            answer=answer,
            timestamp=datetime.now()
        ))
        
        return {
            "answer": answer,
            "context_used": formatted_context  # Optionally return context for debugging
        }
    
    except Exception as e:
        raise HTTPException(status_code=500, detail=f"Error processing query: {str(e)}")

@app.delete("/delete/{file_id}")
async def delete_file(file_id: str):
    try:
        # 1. Delete from Chroma DB
        chroma_path = f"{CHROMA_DIR}/{file_id}"
        if os.path.exists(chroma_path):
            try:
                # Load and delete the collection
                vectorstore = Chroma(
                    embedding_function=embedding_model,
                    persist_directory=chroma_path
                )
                vectorstore.delete_collection()
                # Delete the directory
                import shutil
                shutil.rmtree(chroma_path)
            except Exception as e:
                print(f"Error deleting Chroma DB: {str(e)}")

        # 2. Delete the actual file from disk
        file_pattern = f"{file_id}_*"
        matching_files = []
        for filename in os.listdir(BASE_DIR):
            if filename.startswith(file_id):
                file_path = os.path.join(BASE_DIR, filename)
                try:
                    os.remove(file_path)
                    matching_files.append(filename)
                except Exception as e:
                    print(f"Error deleting file {filename}: {str(e)}")

        # 3. Clear chat memory for this file
        if file_id in chat_memory:
            del chat_memory[file_id]

        if not matching_files and not os.path.exists(chroma_path):
            raise HTTPException(
                status_code=404,
                detail=f"No files found for file_id: {file_id}"
            )

        return {
            "message": "File, embeddings, and chat history deleted successfully",
            "deleted_files": matching_files,
            "embeddings_deleted": os.path.exists(chroma_path)
        }

    except Exception as e:
        if isinstance(e, HTTPException):
            raise e
        raise HTTPException(
            status_code=500,
            detail=f"Error during deletion: {str(e)}"
        )