[ww520]

RAG is not just traditional search. It's any augmented data that can be fed to the LLM.

The most useful and verifiable RAG setup I've seen is hooking up a RDBMS and LLM, and asking querying questions in English to retrieve the table data. You can do it in several steps.

1. Extract the metadata of the tables, e.g. table names, columns of each table, related columns of the tables, indexed columns, etc. This is your RAG data.

2. Build the RAG context with the metadata, i.e. listing each table, its columns, relationships, etc.

3. Feed the RAG context and the user's querying questions to the LLM. Tell LLM to generate a SQL for the question given the RAG context.

4. Run the SQL query on the database.

It's uncannily good. And it can be easily verified given the SQL.

[Xenoamorphous]

Is that RAG though? Perhaps I’m missing something but I don’t see where the retrieval step is. Extracting the metadata and passing it to the LLM in the context sounds like a non-RAG LLM application. Or you’re saying that the DB schema is so big and/or the LLM context too small so not all the metadata can be passed in one go and there’s some search step to prune the number of tables?

[ww520]

RAG is augmenting the llm generation with external data. How the external data is retrieved is irrelevant. A search is not necessary.

Of course you can do a search on the related tables with regard to the question to narrow down the table list to help the llm to come up with the correct answer.