ChatALL VS Oobabooga_libs

First of all, I'd like it to be distributed for users to install as a hub running independently from any browser in the background - although with the possibility of accessing it's interface through a browser if necessary. If I'd have to give you some working examples of similar solutions, it would be something like connection of such apps like ChatAll with a service called SAMP that interconnects multiple apps/services provided by ESA CDPP and related to space weather and multiple satellite missions (http://3dview.cdpp.eu) - yup, that's yet another of those weird hobbies of mine :P

Source: ChatALL/wiki

For some reason, software developers and AI experts seem to be focused on achieving multimodality by extending capabilities of individual models and training them on different sets of data - while my plan is to make them work together. Maybe I should try contacting someone responsible for the 'HuggingFace LLM Arena' and propose to add a function that would allow the competing models to speak to each other - I think that it's a quite good way of evaluating model's 'strength'. There are multiple apps and sites that allow user to speak with multiple models simultaneously - ChatAll is a nice example - and I think that allowing all those models to speak with each other is the most obvious function that should be added to such apps...

--> https://github.com/sunner/ChatALL

I have had a lot of success with superbooga for document querying, it is pretty much plug and play for that. What I want is to be able to send urls and paths to superbooga via the oobabooga api so I can automatically update the knowledge of the model via some simple python code, and can then integrate the model into some other code. It uses chromadb and has pretty good performance as far as answering relevantly is concerned. [Here](https://github.com/MRobinBatman/Oobabooga_libs/blob/main/Superbooga_query.py) is an example of a python api call I made that takes advantage of superbooga and a pretty good general instruction to answer questions about documents and webpages.