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This is excellent. I have been running a very similar stack for 2 years, and you got all the tricks of the trade. Pgvector, HyDe, Web Search + document search. Good dashboard with logs and analytics.

I am leaving my position, and I recommended this to basically replace me with a junior dev who can just hit the API endpoints.




As someone without no experience with RAG in production, I'm curious how effective you've found HyDE to be in practice.


I can't answer for the kindly poster above (ty), but from our experience techniques like HyDE are great when you are getting a lot of comparative questions.

For instance, if a user asks "How does A compare to B" then the query expansion element of HyDE is incredibly useful. The actual value of translating queries into answers for embedding is a bit unclear, since most embedding models we are using have been ft'ed to map queries onto answers.


Not GP but Hyde is a crutch for having poor semantic indexing imho. Most people just take raw chunks and embed those. You really need a robust preprocessing pipeline.




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