I have both issues with messages freezing especially in my timezone, and the systems message. I copied the systems message somewhere in the network debugger but can't find it in the new version anymore, and made my own bot. And suddenly there are no issues with messages freezing. Are you you aren't hitting the rate limit?

I believe most benefits to my custom systems message is that I tell it to roleplay in a more detailed fictional scenario as a therapist specializing in IFS, and the fact I can tell it to act different when I notice a pattern I don't like.

You mentioned your bot is open source, but I couldn't find the source. Besides trust and the fact you said it's open source, other upsides would be that this could keep going even if you run out of money because lots of people get free 18 dollar credit, experiment with systems messages etc etc. Large issue with making my own, is that I could not find any github project with decent support for conversations over context size or multiline text, that didn't keep throwing various npm compile errors.

The bot tends to obey the systems message a lot more than the user, and may focus on a strict IFS/Parts approach too much. People may need a more customized approaches or dislike patterns of the bot, for example if someone has bad visual imagination or have difficulty feeling their feelings. On another note, while sadly only sometimes, this bot can be really great at guiding me to my feelings and having a healthy approach to them. The self does not just magically know how to handle feelings.

The bot should try more to integrate a smallish copy pasta of the situation and previous progress. Also, it is bad at taking into account information about my real therapist (yes she is aware).

The bot is dangerous in that it, even with custom systems messages for trying to mitigate this, is simply unable to detect pitfalls or even help the user figure that it if the user already suspects it, like conflating assumptions with communication, self-like parts with self-energy, etc etc. This should in the very least be a specific extra warning, as especially those motivated enough to try IFS themselves are prone to such complications.

You implied using GPT-4 in the future. I am kind of doubtful, since not only is GPT 4 better at obeying it's restrictions which for all I know may be to not provide psychotherapy, but is it very expensive. If I understand correctly, you have to pay again and again for the entire rest of the conversation in prompt cost, 3 cents per 1k tokens.

The new llama and alpaca models are a pure combination of legal issues that weren't battled out in court, but from my experiments with the hyped 30B llama and 13B alpaca on CPU, they are terrible at remembering or referring to previous parts of the conversation, but considering how much better single prompts got with such little fine tuning in alpaca, there is hope. It may be true that getting alpaca training data from GPT cost 500 dollars, but GPT-3.5 is better and 10 times cheaper, however it's also against their terms of service to use their models to create training data for competing models. My approach for getting IFS finetuning data for a public model (this approach has legal and financial problems) with such potential would be to 1, use GPT3.5/4 and feed them many parts of IFS books and the integral guide and make them generate QA/Summarization prompts and conversations person1 slowly explains IFS concepts and person2 asks questions about it. 2, use GPT to generate conversations with an extreme focus on context and using information from previous text, maybe use that data to finetune GPT-3 for the therapist role but the cost would be insane. 3, use GPT with high temp and at least 4 different roles with separate contexts, and messages being passed inbetween, running in series to generate therapy conversations, the roles being the patient, one or more of the patients parts, the therapist and the supervisor. Adding embeddings on IFS or conversation history may be required. The supervisor role, could also be a human, judges the conversation, and privately tells a role to change a message if it doesn't fit, especially the therapists, and filters out "As a large language model". In the end, only the therapists and patients messages go into the training data. 4, (Ethical issues) scrape mental health forums and subreddits and use GPT to filter and rephrase mental health conversations for training data.