GraphRAG is a structured, hierarchical approach to Retrieval Augmented Generation (RAG), as opposed to naive semantic-search approaches using plain text snippets. The GraphRAG process involves extracting a knowledge graph out of raw text, building a community hierarchy, generating summaries for these communities, and then leveraging these structures when perform RAG-based tasks
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Microsoft Research
GraphRAG: A new approach for discovery using complex information
Microsoft is transforming retrieval-augmented generation with GraphRAG, using LLM-generated knowledge graphs to significantly improve Q&A when analyzing complex information and consistently outperforming baseline RAG. Get the details.
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A very nice video, must watch if you are into Machine Learning Algorithms.
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The moment we stopped understanding AI [AlexNet]
https://www.youtube.com/watch?v=UZDiGooFs54
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The moment we stopped understanding AI [AlexNet]
https://www.youtube.com/watch?v=UZDiGooFs54
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GitHub - black-forest-labs/flux: Official inference repo for FLUX.1 models
https://github.com/black-forest-labs/flux
https://github.com/black-forest-labs/flux
GitHub
GitHub - black-forest-labs/flux: Official inference repo for FLUX.1 models
Official inference repo for FLUX.1 models. Contribute to black-forest-labs/flux development by creating an account on GitHub.