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GPT-based search engines usually use some sort of a database to retrieve context for the LLM to summarize first. This is what people refer to as RAG these days: https://blogs.nvidia.com/blog/what-is-retrieval-augmented-ge....

Some of these GPT engines maintain their own vector DB to do semantic search, others are directly hooked into Bing / Google. So pubmedisearch.com would be one component of a GPT-based engine. We actually have a GPT-based engine here: https://medisearch.io/.


Lots of annoying edge cases as you can imagine, nothing particularly glamorous.


Done! Let me know if you have other feedback.


Thanks! Looks quite relevant


Training for multiple epochs is a bit like that :)


We use pinecone and it is not ideal, looking at https://turbopuffer.com/ now. They look quite promising :)


Did you compare pinecone against pgvector with Postgres? Self hosted of course


Isn't it funny how the best Choice somehow always comes back to Postgres in the end XD (for most)


Yes, that’s where I’m these days. I don’t even think of venturing outside of Postgres these days, except for say things like Redis etc. where there are mature and established options for specific use cases.


Will definitely check pgvector, thanks for the pointer.


What kinda dimensions did you keep it relatively low to keep costs down?


1. We cover all the articles on PMC. The exact cost is hard to estimate because we did a lot of iterations.

2. We do weight those ... it is a lot of trial and error and you have to have good & exhaustive benchmarks.


Yes


Glad you like it! I did this as a mini-project within our startup MediSearch (https://medisearch.io/) & the search pipeline is custom tuned for the problem.


Hi, it currently does not support search by PMID. But you can find the paper included in the results here (5th place):

https://pubmedisearch.com/share/Do%20some%20individuals%20wi...


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