My team is looking to perform AI queries on our databases. Would this look like storing our data with something like Xano, and periodically exporting to an AI tool to perform queries on it?
The silence is staggering! We are working with @cerum on trying to find ways to ingest Bubble data directly to an LLM. No progress thus far as we seem to be forced to create a file with the Bubble data and then ingest the file. Feel free to reach out to either of us to get an update.
To be clear, this doesn’t create an LLM with your data, but it can help you ingest and manage which parts of your data go into an AI model which is the next best thing at the moment, especially when using a RAG approach.
I’m looking to do the same. If there is a solution (or if I find one), I’ll post it here as well.
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What other method would theoretically solve for what you are trying to do?!?
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@sanastasi @bidlist @saviorabrams
I might be missing some context but there is definitely a way to query dynamic data from Bubble to LLM upon user query.
This is called “Functions”. Some functions are called when the LLM detects that the requested data from the conversation requires to get data from such functions.
At this point, nothing prevent you from feeding back the function results to the LLM which will then answer with the queried data.
I have implemented this kind of use-cases using some of the following plugins supporting functions calling along with demo.: