AutoRAG and Blended-RAG
AutoRAG provides a framework for systematically evaluating and optimizing RAG pipelines through automated experimentation, while Blended RAG offers a specific retrieval technique (hybrid semantic and query-based search) that could be integrated as a component within AutoRAG's evaluation and optimization workflows.
About AutoRAG
Marker-Inc-Korea/AutoRAG
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
This tool helps AI developers and researchers find the best Retrieval-Augmented Generation (RAG) pipeline for their specific data and use case. You provide your documents and an evaluation dataset (questions and their correct answers), and AutoRAG automatically tests various RAG components and configurations. The output is an optimized RAG pipeline that performs best for your application.
About Blended-RAG
ibm-self-serve-assets/Blended-RAG
Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers
This project helps you build better question-answering systems by more accurately finding the right information from your own large collection of documents. It takes your extensive document library and user questions, then uses advanced search techniques to retrieve the most relevant sections, producing highly accurate answers. This is ideal for knowledge managers, researchers, or anyone building an intelligent Q&A system from a vast internal knowledge base.
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