31–40 of 243 posts
The most success I had with AI+SQL was when I started feeding errors from the sql provider back to the LLM after each iteration. I also had a formatted error message wrapper that would strongly suggest querying system tables to discover schema information. These little tweaks made it scary good at finding queries, even ones requiring 4+ table joins. Even without any examples or fine tuning data.
Please turn this into a product. There's enormous demand for that.
Is the architecture they use in this diagram currently the best way to train LLMs in general on custom data sets? https://raw.githubusercontent.com/vanna-ai/vanna/main/img/va... That is, store your trained custom data in vector db and then use RAG to retrieve relevant content and inject that into the prompt of the LLM the user is querying with? As opposed to fine tuning or other methods?
The more domain specific knowledge needed for queries, the harder it has gotten in general. We've had good success `teaching` the model different concepts in relation to the dataset and giving it example questions and queries greatly improved performance.
Sorry, maybe I'm just too tired to see it, but how much control do you have over the SQL query that is generated by the AI? Is there a risk that it could access unwanted portions or, worse, delete parts of your data? (the AI equivalent of Bobby Tables, so to speak)
Sorry, maybe I'm just too tired to see it, but how much control do you have over the SQL query that is generated by the AI? Is there a risk that it could access unwanted portions or, worse, delete parts of your data? (the AI equivalent of Bobby Tables, so to speak)
I'm curious about how this performs with more complex queries, like joins across five tables. Also, does the training phase actually involve writing SELECT queries by hand? In the age of ORMs and so on, many people have probably forgotten how to write raw SQL queries.
I’ve heard this general sentiment repeated quite a lot - mostly by people that don’t use ORMs. In my experience pretty quickly you reach the limits of even the best ORMs and need to write some queries by hand. And these tend to be the relatively complicated queries. You need to know about all of the different join types, coalescing, having clauses, multiple joins to the same table with where filters, etc.
Not that this makes you a SQL expert but you can’t get too far if you don’t know SQL.
Is the architecture they use in this diagram currently the best way to train LLMs in general on custom data sets? https://raw.githubusercontent.com/vanna-ai/vanna/main/img/va... That is, store your trained custom data in vector db and then use RAG to retrieve relevant content and inject that into the prompt of the LLM the user is querying with? As opposed to fine tuning or other methods?
Sorry, maybe I'm just too tired to see it, but how much control do you have over the SQL query that is generated by the AI? Is there a risk that it could access unwanted portions or, worse, delete parts of your data? (the AI equivalent of Bobby Tables, so to speak)
Why not give it access to relevant parts of the database only? And read only access too?