RAGLight and super-rag
RAGLight provides a modular foundation for building RAG systems with pluggable components, while Super RAG offers pre-built, specialized RAG pipelines (summarization, retrieval, reranking) that could be implemented as modules within RAGLight's framework—making them complementary rather than competitive.
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 super-rag
superagent-ai/super-rag
Super performant RAG pipelines for AI apps. Summarization, Retrieve/Rerank and Code Interpreters in one simple API.
This helps AI application developers create powerful question-answering systems by feeding various document types, like PDFs and web pages, into a backend that understands and retrieves relevant information. It takes your raw documents and a user's question, then outputs precise answers, even performing computations if needed. This is for developers building AI-powered chatbots, assistants, or knowledge bases who need robust information retrieval capabilities.
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