[riter]

I agree. I mentioned in a thread below that these frameworks are useful for discovering appropriate index-retrieval strategy that works best for you product.

On PGVector, I tried to use LangChains class (https://python.langchain.com/en/latest/modules/indexes/vecto...) but it was highly opinionated and it didn't make sense to subclass nor implement interfaces so in this particular project I did it myself.

As part of implementing with SQLModel I absolutely leaned on https://github.com/pgvector/pgvector :)

Thanks for the observation.

[lmeyerov]

FWIW, individual classes are generally tiny, so we found using langchain is fine and then for places we need to beef up (chunking, not calling 'eval', ...), we do our own class/subclass. That way we can align with community for broader pieces and patterns, and decrease technical risks from smaller fly-by-night repos.

At the same time, the underlying APIs are super simple, so just rolling your own entirely, with no framework, can make sense. We need to deal with businesses wanting to plug in their own APIs & models, so that happens to be less attractive to us.

That said, purpose built frameworks can be great. Our data agent has a headless tier and we are building it fine with langchain, and benefiting from the ecosystem there, but I can imagine someone with more specific needs enjoying rasa..