All RAG Techniques: A Simpler, Hands-On Approach ✨
This repository provides a clear, hands-on approach to Retrieval-Augmented Generation (RAG), breaking down advanced techniques into straightforward implementations. Instead of relying on complex frameworks, it utilizes familiar Python libraries such as OpenAI, NumPy, and Matplotlib.
Key Features:
- Comprehensive Collection: Contains Jupyter Notebooks for various RAG techniques, each with clear explanations and code examples.
- Educational Focus: Designed to be readable and modifiable, making it ideal for learning and experimentation.
- Hands-On Implementation: Step-by-step guides to implementing RAG techniques from scratch.
- Evaluation and Visualization: Includes evaluations and visualizations to demonstrate the effectiveness of each technique.
Benefits:
- Demystifies RAG: Helps users understand the fundamentals of RAG without the complexity of advanced frameworks.
- Accessible Learning: Suitable for both beginners and experienced developers looking to enhance their understanding of RAG.
- Community Contributions: Open for contributions, encouraging collaboration and improvement of the repository.
Highlights:
- Easy setup with clear instructions for cloning the repository and installing dependencies.
- Sample data and documents provided for testing and evaluation.
- Focus on core concepts like embeddings, vector stores, and retrieval processes.
