"Have you ever been working really hard on a piece of code and it's just stuck in your head but you cannot break through? You can't squash the bug or finish the algorithm or make the procedure run, and you try and try until you are exhausted and you go to bed. Then you sleep the night through, and the next morning, as you wake up, the answer is right there."
"The topic today is chatting with your knowledge graph about how to enable an LLM to chat directly with structured graph data. All of this is available both in a GitHub repo if you want to replicate it for yourself and in a published article on Medium. It's all here in great detail in a variety of formats if you need it later."
An experimental prototype enables an LLM to chat directly with structured knowledge graph data, allowing natural-language queries over graph databases. The prototype demonstrates techniques for modeling data, executing semantic search, and mapping LLM prompts to graph queries and results. The work emphasizes practical integration: connecting an LLM to graph storage, handling schema and relationships, and returning precise, data-grounded answers. The prototype serves as a learning tool for data modeling, graph databases, knowledge graphs, and semantic search. Anecdotal context notes how subconscious problem-solving can resemble an embedded LLM working during sleep. Generative AI functions are explored as an emergent extension.
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