Generally I don’t care much about the embedding and retrieval and connectors etc for playing with the LLMs, I imagined much more robust tools were available already indeed, my focus was more on the prompt development actually, connecting many prompts together for a better chain of thought kinda of thing, working out the memory and stateful parts of it and so on, and I think there might be a case for an “LLM framework” for that, and also a case for a small lib to solve it instead of an ETL cannon
However, I am indeed not experienced with ETLs, have to play more with the available tools to see if and how can I do the things I was building using them
I'd be happy to see some more examples of LLM application building on ETLs like the video you shared though
Their code seems all rushed, and seems it worked out for initial popularity, but with their current abstractions I personally don’t think it’s a good long term framework to learn and adopt
That’s why I built my own alternative to it, I call it LiteChain, where the chains are actual composable monads, the code is async streamed by default not ducktaped, it’s very bare bones yet but I’m really putting effort on building a solid foundation first, and having a final simple abstractions for users that don’t get in the way, check it out:
https://github.com/rogeriochaves/litechain