MiniRAG and MRAG
About MiniRAG
HKUDS/MiniRAG
"MiniRAG: Making RAG Simpler with Small and Open-Sourced Language Models"
This tool helps you quickly get accurate answers to complex questions from your own documents, even when using smaller, more efficient AI models. You provide your text data, and it processes it into a structured knowledge base, then uses that to generate precise responses. It's designed for anyone who needs to build an efficient question-answering system without relying on very large, expensive AI models.
About MRAG
spcl/MRAG
Official Implementation of "Multi-Head RAG: Solving Multi-Aspect Problems with LLMs"
This project helps developers working with large language models (LLMs) to improve information retrieval for complex queries. It takes queries that require diverse information and a collection of documents, then retrieves more relevant documents by understanding different facets of the query and documents. LLM developers, AI researchers, or data scientists building retrieval-augmented generation (RAG) systems would use this.
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