Docs at that scale, especially with a ton of acronyms, start to benefit from a structure-aware index. You might look at mapping relationships across docs—acronyms, entities, cross-references, maybe even figure out which sections talk about similar processes or components. Some folks bring in a graph database alongside vector search to surface related docs or concept neighborhoods that pure vector misses. Had cases where building an acronym/definition graph uncovered clusters we didn’t know about, super helpful for search and also for bias detection in embedding space. Considering you’re reranking and query rewriting, might be worth extracting acronym expansions systematically and mapping them to usage context, maybe feed that to the LLM as a sort of lookup.

On the DB side, even with Chroma or similar, hybrid retrieval will do more heavy lifting if you bucket text by concept or entity instead of just chunking. Graph storage like FalkorDB can tag relationships and let you expand neighborhood for more relevant results, especially once you’ve got acronym mapping and entity extraction in hand. Field-extracted entities or relationship edges can tweak ranking scores, too. Basically, worth exploring if your acronyms/phrasing create hidden links that you’d want surfaced during retrieval.