RAGLight and rag-doctor
RAGLight provides the modular infrastructure to build RAG systems, while rag-doctor diagnoses failures in those systems—making them complements that work together in a RAG development workflow.
About RAGLight
Bessouat40/RAGLight
RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.
RAGLight helps you quickly build a chatbot that can answer questions using your own documents, like PDFs, Word files, or code. You feed it your collection of files, and it produces a chat interface where you can ask questions and get answers grounded in your specific information. This is ideal for anyone who needs to quickly create a custom AI assistant that understands their unique knowledge base.
About rag-doctor
balavenkatesh3322/rag-doctor
🩺 Agentic RAG pipeline failure diagnosis tool. Tells you why your RAG failed — chunk fragmentation, retrieval miss, position bias, hallucination, or query mismatch — with a root cause ID and concrete fix. CLI + Python SDK + Ollama support.
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