RAG-system and LongRAG
These are ecosystem siblings where LongRAG represents a specialized research advancement (handling long-context QA at scale) built upon the foundational RAG paradigm that the basic RAG-system implements.
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 LongRAG
QingFei1/LongRAG
[EMNLP 2024] LongRAG: A Dual-perspective Retrieval-Augmented Generation Paradigm for Long-Context Question Answering
This project helps you answer complex questions by drawing information from very long documents or multiple sources. It takes your extensive text data and a question as input, then generates accurate, detailed answers. Researchers, analysts, or anyone who needs to extract precise information from large knowledge bases will find this valuable.
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