📄 RAG PDF Assistant

A Retrieval-Augmented Generation (RAG) document assistant built with Flask, Pinecone, Gemini Embeddings, Groq API, and Google Gemini. Upload PDFs, DOCX, TXT, or MD files and intuitively chat with them using modern AI models.

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🚀 Features

• 📂 Multi-format Uploads: Support for PDF, DOCX, TXT, and Markdown files.
• 💬 Interactive Chat: Query and chat with your documents using AI.
• 🔍 RAG-based Retrieval: Fast and accurate semantic search using Pinecone vector database.
• 🧠 Multiple LLM Support: Powered by Groq API (Llama 3) and Google Gemini.
• 🔐 Robust Authentication: Supports Google OAuth as well as standard Email/Password login.
• 🖼️ User Profiles: Custom profile picture uploads & Google profile pic sync.
• 👤 Data Isolation: Per-user namespaces in Pinecone for complete privacy.
• 🛡️ Admin Dashboard: Admin panel to monitor users and uploaded files.
• 🗑️ Data Management: Intuitive UI to delete files and clear vector stores.
• 📱 Responsive UI: Minimal and modern front-end for seamless user experience.
• ☁️ Lightweight & Cloud-Native: Zero local ML models — all embeddings and LLM calls are cloud-based API calls, requiring minimal server RAM.

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🛠️ Tech Stack

Layer	Technology
Backend	Flask (Python)
Authentication	Flask-Login + Flask-Dance (Google OAuth)
Embeddings	Google Gemini (gemini-embedding-001)
Vector Store	Pinecone (Serverless)
LLMs	Groq API (Llama 3.3 70B) & Google Gemini
User Database	MongoDB Atlas
Frontend	HTML, CSS, Vanilla JS

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📁 Project Structure

RAG_App/
├── app.py                  # Main Flask application & routes
├── models.py               # MongoDB user model & encrypted key storage
├── config.py               # Configuration & env variables
├── requirements.txt        # Python dependencies
├── render.yaml             # Render deployment blueprint
├── Dockerfile              # Docker containerization
├── .env.example            # Environment variable template
├── rag/
│   ├── chunker.py          # Document parsing & chunking logic
│   ├── embeddings.py       # Gemini embeddings + Pinecone upsert
│   ├── retriever.py        # Pinecone semantic search & retrieval
│   └── generator.py        # LLM integration for answer generation
├── templates/
│   ├── index.html          # File management & upload dashboard
│   ├── chat.html           # RAG chat interface
│   ├── login.html          # User login page
│   ├── register.html       # User registration page
│   ├── admin.html          # Admin dashboard
│   └── profile.html        # User profile & API key settings
├── static/                 # Static assets (CSS, JS, profile_pics)
├── uploads/                # User-uploaded files (isolated per user)
└── .github/workflows/
    ├── devsecops.yml       # Security scanning pipeline
    └── deploy.yml          # Docker build & GHCR push pipeline

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⚙️ Setup & Installation

1. Clone the Repository

git clone https://github.com/param20h/PDF-Assistant-RAG.git
cd PDF-Assistant-RAG

2. Create and Activate Virtual Environment

python -m venv .venv

# Windows
.venv\Scripts\activate

# Linux/Mac
source .venv/bin/activate

3. Install Dependencies

pip install -r requirements.txt

4. Configure Environment Variables

Create a .env file using the template:

cp .env.example .env

Fill in the required server-side variables:

SECRET_KEY=<your-secret-key>
ENCRYPTION_KEY=<your-fernet-key>
MONGO_URI=<your-mongodb-atlas-uri>
GOOGLE_CLIENT_ID=<your-google-client-id>
GOOGLE_CLIENT_SECRET=<your-google-client-secret>

Generate keys:

# SECRET_KEY
python -c "import secrets; print(secrets.token_hex(32))"
# ENCRYPTION_KEY
python -c "from cryptography.fernet import Fernet; print(Fernet.generate_key().decode())"

5. Run the Application

python app.py

6. Access in Browser

Visit http://localhost:5000 in your web browser.

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🔑 User Setup (Per-User API Keys)

After registering/logging in, each user must add their own API keys on the Profile page:

Service	Required?	Where to Get	Notes
Gemini API Key	✅ Required	aistudio.google.com	Free — used for embeddings & chat
Pinecone API Key	✅ Required	app.pinecone.io	Free tier available
Pinecone Index Name	✅ Required	Pinecone Dashboard	Create: dim 3072, metric cosine
Groq API Key	Optional	console.groq.com	For Llama 3 chat generation

🌲 Pinecone Index Setup

1. Create a free account at pinecone.io
2. Create a Serverless index with:
   • Dimension: 3072
   • Metric: cosine
3. Copy your API key and index name into the Profile page

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🌐 Google OAuth Setup

1. Go to Google Cloud Console → console.cloud.google.com
2. Create a new project and navigate to APIs & Services → Credentials
3. Click Create Credentials → OAuth Client ID
4. Set the Authorized redirect URI to: http://localhost:5000/login/google/authorized
5. Copy your Client ID and Client Secret into the .env file

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🔄 How It Works (The RAG Pipeline)

1. Upload: User uploads a document (PDF, DOCX, TXT, or MD).
2. Chunking: The document is parsed and split into manageable textual chunks.
3. Embedding: Chunks are converted to 3072-dimensional vectors using gemini-embedding-001.
4. Vector Storage: Vectors are stored in the user's Pinecone namespace.
5. Querying: The user submits a question.
6. Retrieval: Pinecone retrieves the most semantically relevant chunks.
7. Generation: The retrieved context is passed to the selected LLM (Groq or Gemini) to generate an accurate, grounded answer.

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🚀 Deployment

Deploy to Render (Recommended — Free)

1. Push your code to GitHub
2. Go to Render → New → Web Service
3. Connect your GitHub repository
4. Render auto-detects render.yaml and configures everything
5. Add environment variables: SECRET_KEY, ENCRYPTION_KEY, MONGO_URI, GOOGLE_CLIENT_ID, GOOGLE_CLIENT_SECRET
6. Update Google OAuth redirect URI to: https://your-app.onrender.com/login/google/authorized
7. Deploy!

Deploy with Docker

docker build -t rag-app .
docker run -p 5000:5000 --env-file .env rag-app

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🔐 DevSecOps Pipeline

Tool	Purpose
GitHub Actions	CI/CD Pipeline
Bandit	SAST — Python security vulnerability scanning
Gitleaks	Hardcoded secret and credential detection
Trivy	Container and dependency vulnerability checking
Snyk	Advanced dependency vulnerability scanning
OWASP ZAP	DAST — Dynamic web security scanning
SonarCloud	Overall code quality and security analysis
GHCR	Docker image hosting via GitHub Container Registry

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👨‍💻 Author

• Name: Paramjit Singh (param20h)

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📄 License

This project is licensed under the MIT License. Check the LICENSE file for more details.

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