SmartDocs RAG System

A production-ready Retrieval-Augmented Generation (RAG) system that allows users to ask questions about technical documentation using natural language. Built with FastAPI, React, and LangChain.

✨ Features

• 🤖 Dual LLM Support: Switch between Google Gemini (cloud) and Ollama (local)
• 📚 Document Ingestion: Automatically processes Markdown documentation
• 🔍 Semantic Search: Uses ChromaDB for fast vector similarity search
• 💬 Modern UI: Clean, responsive React interface
• 📊 Source Citations: Shows which documents were used to generate answers
• 🔒 Privacy-First: Option to run completely offline with local models

🏗️ Architecture

User Question → React Frontend → FastAPI Backend → ChromaDB (Vector Search)
                                        ↓
                                   LangChain RAG Pipeline
                                        ↓
                            Google Gemini / Ollama (LLM)
                                        ↓
                                Answer + Sources → User

🚀 Quick Start

Prerequisites

• Python 3.9+
• Node.js 18+
• Google AI API Key (free from Google AI Studio)
• (Optional) Ollama for local mode

Installation

1. Clone the repository

   git clone https://github.com/YOUR_USERNAME/smartdocs.git
   cd smartdocs

2. Backend Setup

   cd backend
   python3 -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate
   pip install -r requirements.txt

3. Configure Environment

   echo "GOOGLE_API_KEY=your_key_here" > .env

4. Ingest Documentation

   python ingest.py

5. Start Backend

   uvicorn main:app --reload --port 8000

6. Frontend Setup (in a new terminal)

   cd frontend
   npm install
   npm run dev

7. Access the App Open http://localhost:5173

📖 Documentation

• Development Guide: Complete step-by-step tutorial
• Features & Implementation: Deep dive into how features work
• Walkthrough: Testing and verification guide
• API Documentation: Auto-generated FastAPI docs (when server is running)

🛠️ Technology Stack

Backend

• FastAPI: Modern Python web framework
• LangChain: LLM application framework
• ChromaDB: Vector database for embeddings
• Sentence Transformers: Text embedding model
• Google Gemini API: Cloud LLM
• Ollama: Local LLM runtime

Frontend

• React: UI library
• Vite: Build tool and dev server

📝 Example Questions

Try asking SmartDocs:

• "What is OmegaCore?"
• "How do I install OmegaCore on Linux?"
• "What is error code 501?"
• "Does it work with Docker?"

🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Built with LangChain
• Powered by Google Gemini
• Vector search by ChromaDB
• Embeddings from Sentence Transformers

📧 Contact

Your Name - @yourtwitter

Project Link: https://github.com/YOUR_USERNAME/smartdocs