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# LLM clients in Emacs, functionality and standardization
Andrew Hyatt (he/him) - <ahyatt@gmail.com> - <https://urbanists.social/@ahyatt> - <http://github.com/ahyatt>
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As an already powerful way to handle a variety of textual tasks, Emacs
seems unique well poised to take advantage of Large Language Models
(LLMs). We'll go over what LLMs are and are used for, followed by listing
the significant LLM client packages already for Emacs. That functionality
that these packages provide can be broken down into the basic features that
are provided through these packages. However, each package currently is
managing things in an uncoordinated way. Each might support different LLM
providers, or perhaps local LLMs. Those LLMs support different
functionality. Some packages directly connect to the LLM APIs, others use
popular non-Emacs packages for doing so. The LLMs themselves are evolving
rapidly. There is both a need to have some standardization so users don't
have to configure their API keys or other setup independently for each
package, but also a risk that any standardization will be premature. We
show what has been done in the area of standardization so far, and what
should happen in the future.
About the speaker:
Andrew Hyatt has contributed the Emacs websocket package, the triples
(making a triple-based DB library) and the ekg package (a tag-based
note-taking application). He has been using various other LLM
integrations, and ss part of extending ekg, he's been working on his own.
# Discussion
## Questions and answers
- Q: What is your use case for Embedding? Mainly for searching?
- A:
- I got you. It's kinda expand our memory capcity.
- Q: What do you think about "Embed Emacs manual" VS "GPTs Emacs
manual?
- A:
- yes GPTS actually how it's kind of embedding your document
into its memory and then using the logic that provided by
GPT-4 or other versions. I never tried that one but I'm
just wondering if you have ever tried the difference
- Q: When deferring commit messages to an LLM, what (if anything) do
you find you have lost?
- A:
- Q: Can you share your font settings in your emacs config? :) (Yeah,
those are some nice fonts for reading)
- A: I think it was Menlo, but I've sinced changed it (I'm
experimenting with Monaspace
- Q: In terms of standardisation, do you see a need for a
medium-to-large scale effort needed?
- A:
- I mean, as a user case, the interface is quite simple
because we're just providing an API to a server. I'm not
sure what standardization we are really looking at. I mean,
it's more like the how we use those callback from the llm.
- Q: What are your thoughts on the carbon footprint of LLM useage?
- A:
- Q: LLMs are slow in responding. Do you think Emacs should provide
more async primitives to keep it responsive? E.g. url-retrieve is
quite bad at building API clients with it.
- A:
- Gptel.el is async. And very good at tracking the point.
- Q: Speaking of which, anyone trained/fined-tuned/prompted a model
with their Org data yet and applied it to interesting use cases
(planning/scheduling, etc) and care to comment?
- A:
- I use GPTS doing weekly review. I'm not purely rely on it.
It's help me to find something I never thought about and I
just using as alternateive way to do the reviewing. I find
it's kind of interesting to do so.
### Notes and discussion
- gptel is another package doing a good job is flexible configuration and choice over LLM/API
- I came across this adapter to run multiple LLM's, apache 2.0 license too! https://github.com/predibase/lorax
- It will turn out the escape-hatch for AGI will be someone's integration of LLMs into their Emacs and enabling M-x control.
- i don't know what question to ask but i found presentation extremely useful thank you
- I think we are close to getting semantic search down for our own files
- yeah, khoj uses embeddings to search Org, I think
- I tried it a couple of times, latest about a month ago. The search was quite bad unfortunately
- did you try the GPT version or just the PyTorch version?
- just the local ones. For GPT I used a couple of other packages to embed in OpenAI APIs. But I am too shy to send all my notes :D
- Same for me. But I really suspect that GPT will be way better. They now also support LLama, which is hopeful
- I keep meaning to revisit the idea of the Remembrance Agent and see if it can be updated for these times (and maybe local HuggingFace embeddings)
- I think Andrew is right that Emacs is uniquely positioned, being a unified integrated interface with good universal abstractions (buffers, text manipulation, etc), and across all uses cases and notably one's Org data. Should be interesting...!
- Speaking of which, anyone trained/fined-tuned/prompted a model with their Org data yet and applied it to interesting use cases (planning/scheduling, etc) and care to comment?
- The ubiquitous integration of LLMs (multi-modal) for anything and everything in/across Emacs and Org is both 1) exciting, 2) scary.
- I could definitely use semantic search across all of my stored notes. Can't remember what words I used to capture things.
- Indeed. A "working group" / "birds of a feather" type of thing around the potential usages and integration of LLMs and other models into Emacs and Org-mode would be interesting, especially as this is what pulls people into other platforms these days.
- To that end, Andrew is right that we'll want to abstract it into the right abstractions and interfaces. And not just LLMs by vendor/models, but what comes after LLMs/GPTs in terms of approach.
- I lean toward thinking that LLMs may have some value but to me a potentially wrong result is worse than no result
- I think it would depend on the use case. A quasi-instant first approximation that can readily be fixed/tweaked can be quite useful in some contexts.
- not to mention the "summarization" use cases (for papers, and even across papers I've found, like a summarization across abstracts/contents of a multiplicity of papers and publications around a topic or in a field - weeks of grunt work saved, not to mention of procrastination avoided)
- IMHO summarization is exactly where LLMs can't be useful because they can't be trusted to be accurate
- <https://dindi.garjola.net/ai-assistants.html>; A friend wrote this <https://www.jordiinglada.net/sblog/llm.html>; < https://blogs.microsoft.com/on-the-issues/2023/09/07/copilot-copyright-commitment-ai-legal-concerns/>
- I have a feeling this is one of the 'em "if you can't beat them join them" scenario. I don't see that ending with a bit global rollback due to such issues anytime soon...
- (discussion about LLMs, copyright, privacy)
- I spent more time than I was hoping to setting up some custom Marginalia(s?) the other day, notably for cases where the "category" is dynamic, the annotation/affixation function varies, the candidates are an alist of key-value pairs and not just directly the value, and many little specificities like that. Idem for org-ql many moons back, org-agenda, etc. That sort of workflow always involves the same things: learning/reading, examples, trials, etc. I wonder if LLMs could be integrated at various points in that recurring exercise, to take just a sample case.
- that's yet another great use case for LLMs : externalizing one's thinking for its own sake, if only to hear back the echo of one's "voice", and do so with an infinitely patient quasi-omniscient second party.
- oooh, might be a good one for blog post writing: generate some follow-up questions people might have
- Yeah, a "rubber duck" LLM could be very handy
- I'm sure there would be great demand for such a thing, to dry-run one's presentations (video or text) and generate anticipate questions and so on. Great take.
- I've seen some journaling prompts along those lines. I think it'll get even more interesting as the text-to-speech and speech-to-text parts get better. Considering how much people bonded with Eliza, might be interesting to see what people can do with a Socratic assistant...
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