Summary

This repository provides Jupyter Notebooks designed for an advanced Retrieval Augmented Generation (RAG) course, focusing on tackling common enterprise RAG challenges. It is intended for engineers and researchers aiming to build more robust and accurate RAG systems, offering practical implementations and explanations of advanced techniques to improve retrieval performance and handle complex data scenarios.

How It Works

The project guides users through a series of Jupyter Notebooks, each dedicated to a specific RAG component or problem. Core concepts covered include fundamental RAG flows, the impact of embedding models, semantic chunking strategies, contextual retrieval, and advanced methods like Reverse Hyde, hybrid search, and multi-modal retrieval. The notebooks aim to address practical issues such as processing long documents, handling domain-specific jargon, and retrieving information from complex or non-textual document formats.

Quick Start & Requirements

Local setup can be achieved using uv (recommended for speed) or traditional pip, requiring Python 3.12.3. The process involves creating a virtual environment, compiling dependencies from requirements.in (if using uv), and installing from requirements.txt. Jupyter Lab and ipykernel are necessary for notebook execution. The repository also provides setup instructions for Google Colab and SageMaker Studio Lab, involving Git cloning and pip installation; the README refers to specific links for these hands-on labs but does not provide direct URLs.

Highlighted Details

• Explores advanced RAG techniques such as Semantic Chunking, Contextual Retrieval, Reverse Hyde, Hybrid Search (including temporal and multi-lingual support), and Multi-Modal Retrieval from images.
• Addresses key enterprise RAG challenges: long documents, mismatches between user queries and document formats, domain-specific jargon, and complex document structures (e.g., scanned documents).
• Offers practical solutions for improving retrieval accuracy, including various chunking options, Hypothetical Document Embeddings (HyDE), and reranking strategies.

Maintenance & Community

Information regarding maintainers, community channels (e.g., Discord/Slack), or roadmaps is not detailed.

Licensing & Compatibility

The license type and any compatibility notes for commercial use are not specified.

Limitations & Caveats

This repository serves as educational assets for learning RAG concepts and implementation through notebooks. It does not appear to be a production-ready framework, and specific details on enterprise deployment, performance benchmarks, or known bugs are not provided.