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Athina AI’s RAG Cookbooks is a GitHub repository that showcases advanced and agentic Retrieval-Augmented Generation techniques for building more accurate AI applications. It provides ready-to-use notebooks, implementations, and explanations that help developers move from basic RAG setups to more sophisticated workflows. Athina AI’s RAG Cookbooks covers the full RAG pipeline, including indexing, retrieval, augmentation, and generation, while also addressing evaluation to measure accuracy and relevance. It includes multiple approaches such as hybrid search, contextual compression, and agent-based retrieval strategies, allowing users to experiment and compare methods. It is designed to reduce development time by offering practical examples and references to research papers, making it useful for both learning and production use. Overall, it serves as a hands-on resource for improving LLM outputs using external data sources.
Features
- Collection of advanced and agentic RAG techniques with clear implementations
- End-to-end notebooks covering indexing, retrieval, and response generation
- Includes evaluation methods to measure accuracy and relevance of outputs
- Supports multiple approaches like hybrid, contextual, and corrective RAG
- Provides research references for deeper learning and experimentation
- Designed to speed up development with ready-to-use examples and workflows
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