🧠 RAG Knowledge Base

A production-ready Retrieval-Augmented Generation (RAG) system that allows users to upload PDFs and chat with them using AI. Built with 100% free and open-source technologies.

✨ Features

• 📄 PDF Upload: Upload and process multiple PDF documents
• 💬 AI Chat Interface: Ask questions and get intelligent answers
• 🔍 Vector Search: Semantic search using ChromaDB
• 📚 Source Citations: See which documents answers come from
• ⚡ Streaming Responses: Real-time AI responses with typewriter effect
• 🎨 Modern UI: Beautiful glassmorphism design with dark theme
• 🆓 100% Free: Uses free tiers and open-source models

🛠️ Tech Stack

• Frontend: Streamlit
• LLM: Google Gemini 1.5 Flash (Free Tier)
• Embeddings: Sentence Transformers (all-MiniLM-L6-v2)
• Vector DB: ChromaDB (Local)
• Framework: LangChain
• PDF Processing: PyPDF2

🚀 Quick Start

Prerequisites

• Python 3.9 or higher
• Google API Key (free from Google AI Studio)

Installation

1. Clone or download the project:

   cd rag-knowledge-base

2. Create a virtual environment:

   python -m venv venv
   
   # On Windows
   venv\Scripts\activate
   
   # On macOS/Linux
   source venv/bin/activate

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables:

   • Copy .env.example to .env
   • Add your Google API key:

     GOOGLE_API_KEY=your_api_key_here
     

5. Run the application:

   streamlit run app.py

6. Open your browser to http://localhost:8501

📖 Usage

1. Upload PDFs: Click the upload button and select your PDF files
2. Process Documents: Click "Process Documents" to analyze your PDFs
3. Start Chatting: Ask questions about your documents in the chat interface
4. View Sources: Expand source citations to see where answers came from
5. Manage Documents: Use the sidebar to view and delete documents

🏗️ Project Structure

rag-knowledge-base/
├── app.py                          # Main Streamlit application
├── requirements.txt                # Python dependencies
├── .env.example                    # Environment variables template
├── .streamlit/
│   └── config.toml                 # Streamlit configuration
├── src/
│   ├── components/
│   │   ├── chat_interface.py       # Chat UI component
│   │   ├── pdf_uploader.py         # PDF upload component
│   │   └── sidebar.py              # Sidebar component
│   ├── core/
│   │   ├── embeddings.py           # Embedding generation
│   │   ├── pdf_processor.py        # PDF processing
│   │   ├── rag_pipeline.py         # RAG logic
│   │   └── vector_store.py         # ChromaDB integration
│   └── utils/
│       ├── config.py               # Configuration
│       └── session_state.py        # State management
└── assets/
    └── styles.py                   # Custom CSS styles

🎨 UI Features

• Glassmorphism Effects: Modern semi-transparent UI elements
• Smooth Animations: Fade-ins, hover effects, and transitions
• Gradient Accents: Beautiful color gradients throughout
• Dark Theme: Professional dark mode with proper contrast
• Responsive Design: Works on desktop, tablet, and mobile

🚀 Deployment

Deploy to Streamlit Cloud (Free)

1. Push to GitHub:

   git init
   git add .
   git commit -m "Initial commit"
   git remote add origin your-repo-url
   git push -u origin main

2. Deploy on Streamlit Cloud:

   • Go to share.streamlit.io
   • Connect your GitHub repository
   • Select app.py as the main file
   • Add GOOGLE_API_KEY in Secrets Management
   • Click Deploy!

3. Share your URL with recruiters!

🔑 Getting API Keys

Google Gemini API (Free)

1. Visit Google AI Studio
2. Click "Create API Key"
3. Copy and paste into your .env file

Free Tier Limits:

• 15 requests per minute
• 1,500 requests per day
• Perfect for demos and personal projects!

💡 Tips for YC Recruiters

This project demonstrates:

• ✅ Modern AI Engineering: RAG, vector databases, LLM integration
• ✅ Production-Ready Code: Clean architecture, error handling, caching
• ✅ UX Excellence: Streaming responses, source citations, beautiful UI
• ✅ Full-Stack Skills: Frontend (Streamlit), backend (Python), AI/ML
• ✅ Deployment: Cloud-ready with free hosting

🤝 Contributing

Feel free to fork, improve, and submit pull requests!

📝 License

MIT License - feel free to use this project for your portfolio!

🌟 Showcase

Add screenshots or a demo video here after deployment!

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Built with ❤️ for YC Recruiters

Powered by Google Gemini, ChromaDB, and LangChain