alt Hacker NewsMaybe I’m interpreting this differently but to me modern LLM+full text search means “agentic” - LLM gets to pick the search terms and iterate on them. The underlying LLM does know synonyms etc, better and more flexibly than an embedding model, and gets explainable feedback from failed searches.
That could work in a way, but it's very expensive as expressed and I do not know of prominent robust implementations.
On the other hand, your post may contain a good idea: L=instruct_LLM("provide a list of synonyms and periphrases of terms T within context C", T, C); then iter(`grep l in L`). One NN query and a `grep` collection. But again, if one wanted to order the results, it is either through a dumb crierion or through another LLM query - but this could make it extremely costly (requiring either a huge context or a quadratic number of ordering queries).
And, the above `grep` based procedure would remain keyword based and not semantic based, which means that the user must know that it will not be based on comprehension but on the possible results that keyword matching can yield.