One thing that I've been doing lately is creating a "synbiogpt", and from it, have come to realize the limitations of the custom GPTs. - Biological sequence da

One thing that I've been doing lately is creating a "synbiogpt", and from it, have come to realize the limitations of the custom GPTs. - Biological sequence data is usually quite long. This is fine if the biological data is in a file: however, if you need interact with an API for advanced function (like codon optimization), you have to send this across a wire. The API calling context window then gets filled up with sequence data, and fails. - I can't inject dependencies, many of which I've written myself specifically for biological engineering. Sometimes GPT will then try to code its own implementation, often which is incorrect. - The retrieval API often fails to open files if GPT-4 thinks it knows what it is talking about. When I'm talking about genetic parts, I often want to be very specific about the particular parts in my library, rather than the parts GPT-4 thinks is out there. I fixed most of this by just rolling my own lua-scripting environment (my biological functions are in golang, and I run gopher-lua to run the lua environment). I inject example lua for how to use the scripting functions, as well as my (right now, small) genetic part library, and then ask it to generate me lua to do certain operations on the files provided, without GPT-4 ever looking at the files. My internal golang app then executed the scripted lua. This works great, and is much faster than a custom GPT. The biggest problem I have right now is the frontend bits. I would love to have basically an open source ChatGPT looking-clone that I can just pull attachments out of + modify the initial user inputs (to add my lua examples and such). So far I haven't found a good option.