RAG-system and RAG-Simplified
Both tools are competitors, as they are independent RAG systems, each with their own implementations for document retrieval and LLM answering, targeting similar use cases of enhancing LLM outputs with retrieved context.
About RAG-system
xumozhu/RAG-system
Retrieval-Augmented Generation system: ask a question, retrieve relevant documents, and generate precise answers. RAG demo: document retrieval + LLM answering
This tool helps you get precise answers to questions based on your own PDF documents. You input your collection of PDFs and ask a question in plain language. The system retrieves relevant information from your documents and then generates a clear, concise answer. It's ideal for analysts, researchers, or anyone who needs to quickly extract specific facts from a set of business, research, or operational documents.
About RAG-Simplified
ShahMitul-GenAI/RAG-Simplified
Enhance GPT-3.5-Turbo output using Retrieval-Augmented Generation (RAG) with a user-friendly interface. Select between Wikipedia or integrate external documents to experience precise, context-aware responses.
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