RAGLight and ragctl
RAGLight provides the modular framework for building RAG systems while ragctl offers command-line tooling to test and optimize those pipelines—making them complements that work together rather than alternatives.
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 ragctl
datallmhub/ragctl
A powerful CLI tool to manage, test, and optimize RAG pipelines. Streamline your Retrieval-Augmented Generation workflows from terminal.
This tool helps AI engineers and developers prepare various documents like PDFs, Word files, and images for use in Retrieval-Augmented Generation (RAG) applications. It takes raw documents, extracts text using advanced OCR, intelligently breaks them into meaningful chunks, and exports them in formats like JSON or directly into a vector store. This streamlines the crucial data preparation step for building robust RAG systems.
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