LLM Development Learning Repository

A personal collection of experiments, scripts, and applications exploring various aspects of Large Language Model (LLM) development. This repository serves as a companion on my learning journey.

🚧 Work in Progress

This repository is actively evolving as I learn and experiment with different LLM technologies. Code quality, documentation, and structure may vary across projects.

📚 Repository Structure

• 1 BASICS - Fundamental concepts and starter examples
• 2 RAG - Retrieval Augmented Generation implementations
• 3 AGENTS - LLM agent experiments using frameworks like SmolAgents and Langgraph
• 4 TEXT TO SQL - Natural language to SQL conversion using LangChain and LlamaIndex
• GRADIO - UI implementations using Gradio
• DATA - Sample datasets for testing and development

🛠️ Technologies

• LangChain
• LlamaIndex
• SmolAgents
• Langgraph
• Gradio
• Anthropic Claude
• Mistral AI
• HuggingFace

🚀 Getting Started

1. Clone this repository
2. Create a virtual environment: python -m venv .venv
3. Activate the environment:
   • Windows: .venv\Scripts\activate
   • Linux/Mac: source .venv/bin/activate
4. Install dependencies: pip install -r requirements.txt
5. Create a .env file with your API keys (see .env.example)

📝 Notes

• Each subdirectory contains its own README with specific information
• Scripts are organized by technology and use case
• This is a personal learning project, not production-ready code

📄 License

This project is for personal educational purposes. Feel free to explore and learn from it, but please use your own API keys for any services.