Understanding RAG
RAG, or retrieval augmented generation, emerged from advancements in generative language models, particularly around 2019-2020. The combination of sequence-to-sequence models with dense embeddings laid the groundwork for modern retrieval systems. The rise of vector databases has significantly enhanced the scalability and efficiency of these retrieval methods, driving the growth of RAG applications since 2021.In this clip
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GraphRAG (beyond the hype)
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