Practical AI GraphRAG (beyond the hype)
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Sep 25, 2024 Prashanth Rao, an AI engineer at Kuzu known for his expertise in graph databases, joins the discussion to unravel the complexities of GraphRAG. He delves into the practical applications of graph databases, particularly in healthcare and finance. The conversation highlights how integrating graph and vector databases enhances information retrieval. Prashanth also shares insights on the evolution of retrieval augmented generation, focusing on how advanced techniques can improve the accuracy of AI models.
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RAG and its limitations
- RAG combines retrieval with LLM generation.
- Vector databases and hybrid search enhance retrieval, but struggle with explicit relationships.
Graph RAG improves accuracy
- Graph RAG adds graph retrieval to RAG, capturing explicit relationships missed by embeddings.
- This improves factual accuracy by providing richer context to the LLM.
Data in Graph RAG
- Documents are split into chunks and embedded for vector retrieval.
- Graph RAG combines this with graph retrieval, linking entities and relationships.