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Comment by lolpanda | Hacker News Reader

The compatibility layer can be also built in libraries. For example, Langchain has llm() which can work with multiple LLM backend. Which do you prefer?

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I'd prefer it in library but there are a number of issues with that currently, the larger of it being that the landscape moves too fast and library wrappers aren't keeping up. the other is, what if the world standardize on a terrible library like langchain we'd be stuck with it for a long time since maintenance cost of non uniform backend tend to kill possible runner ups. So for now the uniform api seems the choice of convenience.

but this means you need each library to support each llm, and I think this is the same issue what is with object storage where basically everyone support S3 compatible API

it's great to have some standard API even if that's isn't perfect, but having second API that allows you to use full potential (like B2 for backblaze) is also fine

so there isn't one model fits all, and if your model have different capabilities, then imo you should provide both options

This is hopefully much better than the s3 situation due to its simplicity. Many offerings that say “s3 compatible api” often mean “we support like 30% of api endpoints”. Granted often the most common stuff is supported and some stuff in the s3 api really only makes sense in AWS, but a good hunk of the s3 api is just hard or annoying to implement and a lot of vendors just don’t bother. Which ends up being rather annoying because you’ll pick some vendor and try to use an s3 client with it only to find out you can’t because of the 10% of the calls your client needs to make that are unsupported.

Before OpenAI released their app I was using langchain in a system that I built. It was a very simple SMS interface to LLMs. I preferred working with langchain's abstractions over directly interfacing with the GPT4 API.

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