Bit confused what the value add is over a framework like DSPy. This still requires you to create an eval dataset with ground truth, basically the only hard part of using DSPy. Easily getting the optimized prompt and having some metrics out of the box is not worth nearly $1k/mo IMO
Side note: I’ve had a lot of luck combining automatic prompt optimization with finetuning. There is definitely some synergy https://raw.sh/posts/chess_puzzles
Is it possible to use this for hybrid search in combination with pg_embedding? My understanding is that hybrid search currently requires syncing with Postgres
Nope, it’s a serious project; I mostly made it for personal use during my last semester of college. I rewrote it a few times and packaged it up because I think it’s genuinely useful. Langchain gets you 80% of the way there but you run into issues with it very quickly.
DankGPT is able to draw context from a library of documents (textbook, papers, class slides) to explain any topic and answer complicated reasoning problems.
It’s very similar to ChatPDF, but you can include multiple documents and it has much better context selection. This leads to better answers in practice (less “the source does not contain information on…” and hallucinations)