LlamaIndex relies heavily on RAG-style approaches, e.g., we're using items whose embedding vectors are close to the embedding vectors of the question (what you describe). RAG-style approaches work great if the answer depends only on a small part of the data, e.g., if the right answer can be extracted from a few top-N documents.

It's less applicable if the answer cannot be extracted from a small data subset. E.g., you want to count the number of pictures showing red cars in your database (rather than retrieving a few pictures of red cars). Or, let's say you want to tag beach holiday pictures with all the people who appear in them. That's another scenario where you cannot easily work with RAG. ThalamusDB supports such scenarios, e.g., you could use the query below in ThalamusDB:

SELECT H.pic FROM HolidayPictures H, ProfilePictures P as Tag WHERE NLFILTER(H.pic, 'this is a picture of the beach') AND NLJOIN(H.pic, P.pic, 'the same person appears in both pictures');

ThalamusDB handles scenarios where the LLM has to look at large data sets and uses a few techniques to make that more efficient. E.g., see here (https://arxiv.org/abs/2510.08489) for the implementation of the semantic join algorithm.

A few other things to consider:

1) ThalamusDB supports SQL with semantic operators. Lay users may prefer the natural language query interfaces offered by other frameworks. But people who are familiar with SQL might prefer writing SQL-style queries for maximum precision.

2) ThalamusDB offers various ways to restrict the per-query processing overheads, e.g., time and token limits. If the limit is reached, it actually returns a partial result (e.g., lower and upper bounds for query aggregates, subsets of result rows ...). Other frameworks do not return anything useful if query processing is interrupted before it's complete.