One thing I dont understand.. does this rely on vector embeddings? Or how does the AI interact with the data? The example is a sqllite satabase with prices, and it shows claude being asked to give the average price and to suggest pricing optimizations.
So does the entire db get fed into the context? Or is there another layer in between. What if the database is huge, and you want to ask the AI for the most expensive or best selling items? With RAG that was only vaguely possible and didnt work very well.
Sorry I am a bit new but trying to learn more.
Vector embeddings are entirely unrelated to this.
This is about tool usage - the thing where an LLM can be told "if you want to run a SQL query, say <sql>select * from repos</sql> - the code harness well then spot that tag, run the query for you and return the results to you in a chat message so you can use them to help answer a question or continue generating text".
it doesnt feed the whole DB into the context, it gives Claude the option to QUERY it directly
It never accidentally deletes anything? Or I guess you give it read only access? It is querying it through this API and some adapter built for it, or the file gets sent through the API, they recognize it is sqllite and load it on their end?
It can absolutely accidentally delete things. You need to think carefully about what capabilities you enable for the model.