[breadislove]

a good system (like openevidence) indexes every paper released and semantic search can incredible helpful since the the search api of all those providers are extremely limited in terms of quality.

now you get why those system are not cheap. keeping indexes fresh, maintaining high quality at large scale and being extremely precise is challenging. by having distributed indexes you are at the mercy of the api providers and i can tell you from previous experience that it won't be 'currently accurate'.

for transparency: i am building a search api, so i am biased. but i also build medical retrieval systems for some time.

[amber_raza]

Appreciate the transparency and the insight from a fellow builder.

You are spot on that maintaining a fresh, high-quality index at scale is the 'hard problem' (and why tools like OpenEvidence are expensive).

However, I found that for clinical queries, Vector/Semantic Search often suffers from 'Semantic Drift'—fuzzily matching concepts that sound similar but are medically distinct.

My architectural bet is on Hybrid RAG:

Trust the MeSH: I rely on PubMed's strict Boolean/MeSH search for the retrieval because for specific drug names or gene variants, exact keyword matching beats vector cosine similarity.

LLM as the Reranker: Since API search relevance can indeed be noisy, I fetch a wider net (top ~30-50 abstracts) and use the LLM's context window to 'rerank' and filter them before synthesis.

It's definitely a trade-off (latency vs. index freshness), but for a bootstrapped tool, leveraging the NLM's billions of dollars in indexing infrastructure feels like the right lever to pull vs. trying to out-index them.