RAGit is an open-source framework designed to simplify the creation and management of Retrieval Augmented Generation (RAG) solutions. By abstracting away the complexities of data management, model selection, and infrastructure, RAGit empowers developers to focus on application logic and customization.
The core principles behind RAGit can be summarized as follows:
RAGit can by applied to any set of data than can provided in a wide range of data types providing a flexible foundation for building custom RAG applications.
RAGit prioritizes ease of use by abstracting the underlying complexities of data management. Users can concentrate on refining document selection and optimizing results without being encumbered by low-level implementation details.
RAGit offers extensive customization options, enabling users to experiment with various hyperparameters. From chunk splitting strategies to vector distance algorithms and prompt engineering, users have granular control over the RAG pipeline.
Beyond model training and inference, RAGit provides tools for data ingestion, processing, and management.
RAGit is designed to be agnostic to specific underlying technologies, enabling easy switching between different components and services.
By adhering to these principles, RAGit aims to accelerate the development of effective and robust RAG solutions.