fed-rag and RAGHub
Fed-RAG is a specialized fine-tuning framework that could be integrated into or benchmarked against RAGHub's broader collection of RAG implementations and resources.
About fed-rag
VectorInstitute/fed-rag
A framework for fine-tuning retrieval-augmented generation (RAG) systems.
Supports federated learning architectures alongside centralized setups, enabling distributed RAG fine-tuning across multiple clients. Integrates seamlessly with HuggingFace, LlamaIndex, and LangChain, providing state-of-the-art fine-tuning methods through lightweight abstractions that maintain full flexibility and control.
About RAGHub
Andrew-Jang/RAGHub
A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem.
Organizes RAG tools across specialized categories—frameworks, evaluation/optimization systems, data preparation, and engines—with live activity tracking to distinguish actively maintained projects from outdated ones. Curated by the r/RAG community, it catalogs both established frameworks (LangChain, LlamaIndex, Haystack) and emerging tools like Korvus (database-native RAG) and Swiftide (Rust-based streaming), helping developers navigate rapid ecosystem fragmentation. Includes evaluation frameworks, model leaderboards, and resources to address the full RAG development lifecycle beyond basic framework selection.
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