✨RAG-Anything: All-in-One RAG Framework

📝 Summary:
RAG-Anything is a unified framework extending RAG to all modalities, not just text. It integrates cross-modal relationships and semantic matching via dual-graph construction and hybrid retrieval. This significantly improves performance on complex multimodal benchmarks.

🔹 Publication Date: Published on Oct 14

🔹 Paper Links:
• arXiv Page: https://arxivexplained.com/papers/rag-anything-all-in-one-rag-framework
• PDF: https://arxiv.org/pdf/2510.12323
• Github: https://github.com/HKUDS/RAG-Anything

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#RAG #MultimodalAI #MachineLearning #InformationRetrieval #GraphAI

✨LightRAG: Simple and Fast Retrieval-Augmented Generation

📝 Summary:
LightRAG improves Retrieval-Augmented Generation by addressing limitations of flat data representations and inadequate contextual awareness. It integrates graph structures into text indexing and retrieval, enhancing accuracy, efficiency, and response times through a dual-level system.

🔹 Publication Date: Published on Oct 8, 2024

🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2410.05779
• PDF: https://arxiv.org/pdf/2410.05779
• Github: https://github.com/hkuds/lightrag

✨ Spaces citing this paper:
• https://huggingface.co/spaces/rm-lht/lightrag

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For more data science resources:
✓ https://t.iss.one/DataScienceT

#RAG #AI #NLP #GraphAI #InformationRetrieval