RAG-ARC and RAGLAB

RAG-ARC
50
Established
RAGLAB
43
Emerging
Maintenance 10/25
Adoption 7/25
Maturity 15/25
Community 18/25
Maintenance 0/25
Adoption 10/25
Maturity 16/25
Community 17/25
Stars: 38
Forks: 13
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
Stars: 310
Forks: 35
Downloads: —
Commits (30d): 0
Language: Python
License: MIT
No Package No Dependents
Stale 6m No Package No Dependents

About RAG-ARC

DataArcTech/RAG-ARC

A modular, high-performance Retrieval-Augmented Generation framework with multi-path retrieval, graph extraction, and fusion ranking

This project helps professionals working with large volumes of documents (like PDFs, PowerPoints, and Excel files) to extract precise answers and generate content. It takes your unstructured documents and questions, then processes them to provide accurate, context-rich responses or summarized information. Knowledge managers, researchers, and content creators who need to quickly retrieve and synthesize information from extensive knowledge bases would find this invaluable.

knowledge-management document-intelligence enterprise-search information-retrieval content-generation

About RAGLAB

fate-ubw/RAGLAB

[EMNLP 2024: Demo Oral] RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation

This project helps researchers and developers evaluate and compare different Retrieval-Augmented Generation (RAG) algorithms for large language models. It takes in various RAG algorithms and benchmark datasets, then outputs comprehensive evaluation results. It is ideal for AI researchers, NLP scientists, and machine learning engineers who need to understand, reproduce, and extend state-of-the-art RAG techniques.

AI research NLP development Generative AI Language model evaluation Information retrieval

Related comparisons

RAG-ARC and RAG_Techniques RAG-ARC and RAGHub RAG-ARC and HiRAG RAG-ARC and RAG_Techniques RAG-ARC and fed-rag RAG-ARC and FlexRAG

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