▲ | polskibus 3 days ago | ||||||||||||||||
are you saying that a current gen LLM can answer such queries with EnrichMCP directly? or does it need guidance via prompts (for example tell it which tables to look at, etc. ) ? I did expose a db schema to LLM before, and it was ok-ish, however often times the devil was in the details (one join wrong, etc.), causing the whole thing to deliver junk answers. what is your experience with non trivial db schemas? | |||||||||||||||||
▲ | simba-k 3 days ago | parent [-] | ||||||||||||||||
So one big difference is that we aren't doing text2sql here, and the framework requires clear descriptions on all fields, entities, and relationships (it literally won't run otherwise). We also generate a few tools for the LLM specifically to explain the data model to it. It works quite well, even on complex schemas. The use case is more transactional than analytical, though we've seen it used for both. I recommend running the openai_chat_agent in examples/ (also supports ollama for local run) and connect it to the shop_api server and ask it a question like : "Find and explain fraud transactions" | |||||||||||||||||
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