microsoft/rag-time
📚 Tutorialmicrosoft
A comprehensive 5-week curriculum designed to help developers master Retrieval-Augmented Generation (RAG) systems.
RAG Time is a deep-dive educational resource that demystifies the complexities of building production-grade Retrieval-Augmented Generation (RAG) systems. The curriculum is meticulously organized into a 5-week progression, moving from foundational retrieval concepts to advanced optimization techniques.
Key technical areas covered include:
- Vector Search & Indexing: Implementation of HNSW and efficient search strategies.
- Advanced Quantization: Techniques like binary and scalar quantization to optimize memory and latency.
- Hybrid Search: Combining keyword and semantic search for improved retrieval accuracy.
- Representation Learning: Utilizing Matryoshka embeddings for flexible, multi-scale retrieval.
- Responsible AI: Integrating safety and ethical considerations into the RAG pipeline.
By leveraging Jupyter Notebooks, the project allows developers to experiment directly with code, providing a practical bridge between theoretical RAG concepts and real-world implementation. It is an essential resource for those looking to move beyond basic RAG implementations and master the nuances of high-performance information retrieval in generative AI.
💡Highlights
- ├─5-week structured RAG curriculum
- ├─Covers binary & scalar quantization
- └─Hands-on Jupyter Notebook labs
🎯For
- ├─AI Engineers
- ├─Software Developers
- └─Data Scientists