📚 DocRAG — Document Q&A with RAG

A containerized Retrieval Augmented Generation system that lets you upload documents and ask questions about them. Built with FastAPI, Google Gemini, ChromaDB, and Streamlit.

Features

• Upload PDFs & Text files — documents are chunked and embedded locally
• Ask questions — answers are generated using your uploaded documents as context
• Streaming responses — answers appear in real-time
• Fully containerized — one command to run everything with Docker

Tech Stack

Component	Technology
LLM	Google Gemini (gemini-2.5-flash)
Embeddings	HuggingFace all-MiniLM-L6-v2 (runs locally)
Vector Store	ChromaDB
Backend	FastAPI
Frontend	Streamlit
Infra	Docker Compose

Quick Start

1. Get a Google API Key

Get a free API key from Google AI Studio.

2. Set up environment

cp .env.example .env
# Edit .env and add your Google API key

3. Run with Docker

docker compose up --build

4. Use

• Frontend: http://localhost:8501
• Backend API docs: http://localhost:8000/docs

Upload a document in the sidebar, then ask questions in the chat.

API Endpoints

Method	Endpoint	Description
GET	/api/health	Health check
POST	/api/upload	Upload PDF/TXT files
POST	/api/chat	Ask a question (streaming SSE)

Project Structure

doc_rag/
├── backend/
│   ├── app/
│   │   ├── api/endpoints.py      # API routes
│   │   ├── core/config.py        # Settings
│   │   └── services/
│   │       ├── ingestion.py      # Document processing
│   │       ├── rag_chain.py      # LLM + retrieval
│   │       └── vector_store.py   # ChromaDB
│   ├── tests/test_api.py
│   ├── Dockerfile
│   └── requirements.txt
├── frontend/
│   ├── app.py                    # Streamlit UI
│   ├── Dockerfile
│   └── requirements.txt
├── docker-compose.yml
├── .env.example
└── README.md

Running Without Docker

Backend:

cd backend
pip install -r requirements.txt
python -m app.main

Frontend:

cd frontend
pip install -r requirements.txt
streamlit run app.py

Set GOOGLE_API_KEY as an environment variable, or enter it in the frontend sidebar.