support chatml-function-calling via llama-cpp

[dnakov]

• support chatml-function-calling via llama-cpp

[trufae]

i would prefer to merge smaller changes rather than having so many items in this PR. also you wont need to be merging and rebasing that frequently.

I am working on the markdown fix because the current interpreter.py have some legacy code that is broken and its not really working well with some models right now. I hope to get this done hopefully today

[trufae]

See the EPIC ticket :)

[dnakov]

sounds good, i'll split them up!

[trufae]

So it's ok to merge now?

[dnakov]

not yet, i haven't done any regression testing yet, ill let you know soon

[trufae]

I want to merge this #6 as it's actually fixing the behaviour of code colorization and ive tested it with several models. the code is still not fully cleaned but its much simpler and at least it works as expected.

is it ok for you for me to merge it?

[dnakov]

Yeah go ahead, I'll deal with any merge conflicts when this is ready

[dnakov]

ok this works now, merged with your changes. Not getting any good results with mistral, but that's more of a model problem. Hopefully functionary will be a lot better

[trufae]

Ready to merge to start experimenting with it? I think enabling vectordb is important for the auto mode, as well as extending the doc/data stuff to give more contextual hints about how to achieve things and which commands use

[trufae]

uh "This branch cannot be rebased due to conflicts"

[trufae]

conflicts are too large to be resolved via github, please fix them and force push that.

i would recommend you for the next PR to use a separate branch (not named master) and use git rebase instead of git merge

[trufae]

Also, note that commit messages must be capitalized, and i would recommend you to squash the commits

[dnakov]

ok should be good now

[dnakov]

Ready to merge to start experimenting with it? I think enabling vectordb is important for the auto mode, as well as extending the doc/data stuff to give more contextual hints about how to achieve things and which commands use

yeah, to the extent of if you -m TheBloke/Mistral-7B-Instruct-v0.2-GGUF then prompt it with ' , it'll use that model and it will call some function, but chatml-function-calling and the models are not "smart" and reliable enough to know when to stop calling functions and send a message. This can probably be offset a bit with different prompting + RAG, but my hope is for functionary.

How do you envision structuring the RAG data? What's the best "use case -> commands" documentation/reference that we can shove into the vectordb? If we just put the r2 docs in there, i don't think we'd get the any good results for high level queries like "solve this crackme" or "what's the password"

[trufae]

We may probably update to the latest mistral models in the -M output (i filled a ticket for this). ive tested your code and it's not really working well. not sure if there's a way to debug what the model is doing internally to trace what's going on.. maybe via -e debug=true ?

About vectordb, what it does is to send the user prompt to the database and the database returns a list of sentences that can be prepended to the query for contextual information. this data can provide instructions to perform actions or information about the answer the user is looking for. so its transparent to the user and works across all models. i think integrating this in the auto mode will help a lot in the local results too.

[dnakov]

Yeah ive gotten it to work only like once by luck. I'll add some debugging.

About the vectors, yeah, I know how RAG works, I mean what text are you thinking of putting in there, do you have examples?