data_maan

Thanks for the insights.

I wonder if one needs even LlamaIndex?

From their site:

>Storing context in an easy-to-access format for prompt insertion.

>Dealing with prompt limitations (e.g. 4096 tokens for Davinci) when context is too big.

>Dealing with text splitting.

Not sure if it isn't easier to roll one's own for that...?

I know a thing or two about the math behind LLMs and all this software build around a few core ideas just seems to be a lot of overkill...

When mentioning about PGVevtor, did you refer to this repo or is there a class within LangChain that has the same name? https://github.com/pgvector/pgvector

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.