RAGLight and rag_blueprint
These are competitors offering overlapping modular RAG frameworks, though RAGLight emphasizes LLM/embedding/vector store flexibility with MCP integration while rag_blueprint focuses more heavily on built-in evaluation and monitoring capabilities.
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_blueprint
feld-m/rag_blueprint
A modular framework for building and deploying Retrieval-Augmented Generation (RAG) systems with built-in evaluation and monitoring.
This project helps engineering and product teams build robust AI chatbots and question-answering systems that provide accurate information from internal documents. It takes existing knowledge bases like Confluence, Notion, or PDF files, processes them, and delivers an interactive chat interface where users can ask questions and get answers. The ideal user is a developer or technical lead creating a reliable AI knowledge agent for their organization.
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