lololol. i think this opportunity gets bigger post $10m seed round. they'll likely double down and expand footprint vs the inverse.

check out llama-index. its purpose-built for document indexing and retrieval and less agents and "everything else"

What do you by post 10m seed round?

Do you mean if LlamaIndex starts collecting VC? I'm not sure, are they for-profit?...

I was referring to Langchain who raised $10mm from Benchmark

https://blog.langchain.dev/announcing-our-10m-seed-round-led...

I'm fairly Jerry Liu (LlamaIndex founder) already has angels or will see enough traction to warrant a seed.

That's pretty wild, I've been setting things up like this for about 5 years with just BERT or my own fine tuned encoder only systems. It should be done for free, not millions... Can I get millions for running `ls` too?

The use-cases and tooling around language models is very premature. So, any framework you build now will either look like bloatware or will remain close to just calling an API.

The dust around language models needs to settle a bit, for a useful framework to emerge from it.

For our own use-cases, I built a framework from scratch, and it was the best decision we made.

very much agreed re: dust settling.

it makes no sense deploying any of these libraries to prod. as-is. best to understand a configuration / workflow / tuning / etc. that fits your data best and write it from scratch in golang/rust/whatever.

Are these computationally expensive operations? If not, Elixir could fit.

They are not all computationally expensive. The rate limiting step here is the LLM call itself over the API. So, async is definitely needed. The other aspects would be loading the template from filesystem. I would assume this could be something that's needs to be optimized in the application.

> For our own use-cases, I built a framework from scratch, and it was the best decision we made.

My thinking precisely. So you just used the "raw" OpenAI (I presume?) API, and no other tech on top?

Exactly. The most important part was working with Jinja templating. So, openai + jinja2.

I sorta did this, feel free to check it out and let me know your thoughts!

On the main langchain post (In January) that got the traction on hackernews, i left this comment: https://news.ycombinator.com/item?id=34422917 . It still remains true, a "simpler langchain"

> To offer this code-style interface on top of LLMs, I made something similar to LangChain, but scoped what i made to only focus on the bare functional interface and the concept of a "prompt function", and leave the power of the "execution flow" up to the language interpreter itself (in this case python) so the user can make anything with it.

I made a really lightweight wrapper over requests and call it lambdaprompt https://github.com/approximatelabs/lambdaprompt It has served all of my personal use-cases since making it, including powering `sketch` (copilot for pandas) https://github.com/approximatelabs/sketch

Core things it does: Uses jinja templates, does sync and async, and most importantly treats LLM completion endpoints as "function calls", which you can compose and build structures around just with simple python. I also combined it with fastapi so you can just serve up any templates you want directly as rest endpoints. It also offers callback hooks so you can log & trace execution graphs.

All together its only ~600 lines of python.

I haven't had a chance to really push all the different examples out there, so I think it hasn't seen much adoption outside of those that give it a try.

I hope to get back to it sometime in the next week to introduce local-mode (eg. all the open source smaller models are now available, I want to make those first-class)

This recently via DataMachina substack:

https://blog.scottlogic.com/2023/05/04/langchain-mini.html

thanks for the share, will check out