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GraphRAG Explained: AI Retrieval with Knowledge Graphs & Cypher



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🤖 Can AI turn text into structured knowledge? Discover how GraphRAG leverages knowledge graphs, graph databases, and Cypher queries to transform unstructured data into actionable insights. See how LLMs enable intelligent retrieval and automation, reshaping workflows across industries. 🚀

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21 thoughts on “GraphRAG Explained: AI Retrieval with Knowledge Graphs & Cypher
  1. I much prefer using a Graph store than a vector because you it skips the process of creating embeddings which are model specific, meaning that any language model can use data in a Graph, so you don't have to redo your embeddings everytime you change your model.

  2. how to automate and generate "allowed_nodes" and "allowed_relationships"? doing this manually kinda defeats its purpose imho …

  3. Won't using LLM to generate the graph from the input data involve significant pre-processing costs?
    What, if I have to, load some 800 pages of data into a graph??

  4. Why not just use old good SQL on relational DB instead of all this overhead? Before they prompted all those LLMs, I would already acoumplisged whole job with simple SQL

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