RAG Techniques is a collection of tutorial notebooks demonstrating advanced approaches to building Retrieval-Augmented Generation systems.
The repository addresses the challenge of implementing effective RAG pipelines by providing detailed notebook tutorials for each technique. Rather than offering a single framework or tool, it documents various methodologies that developers can study and adapt. The tutorials cover the full spectrum of RAG concerns, including embeddings, semantic search, vector database integration, and agentic approaches that extend beyond basic retrieval and generation.
Developers should use this repository as a learning resource when designing RAG systems. It suits teams evaluating different architectural approaches before committing to a specific implementation, or those seeking to understand how techniques like semantic search and vector databases integrate into larger language model workflows. The notebook format makes it accessible for experimentation and adaptation to specific use cases. The repository does not position itself as an alternative to existing frameworks; instead, it complements tools like LangChain and LlamaIndex by illustrating techniques that can be implemented with those libraries.
The project maintains a focused scope on tutorial content without attempting to be a production framework. Development activity shows consistent attention to the repository's core purpose of documenting RAG techniques through worked examples.
