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PaperclipPerfector  10mo ago (text post)   2429 thread views

Wellness Wednesday for July 5, 2023

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I coded a langchain layer for ChatGPT-3.5 to turn it into a service dominant! It doms me to fulfill all of my self care needs on a daily basis now!

The mind commands itself, and the mind resists, but by externalizing and automating the reification loop, I can relax and give in completely to the psychic metamorphosis! Self modification has never been easier or more pleasant.

I was experimenting with something similar - an AI accountability buddy/nagbot

Is it mostly steered via the system prompt? How do you interact with it?

Ok so, right now I have a Vue GUI, with just the most basic functionality. One chat log saves to disk on the backend and I can't split the conversation at the moment-

It's powered by APIs, which, are really easy to throw together with gpt4 help.

now... as for the prompting... lets see... I'll probably release this publicly eventually so its not too secret...

len_ret_val = EFFECTIVE_TOKEN_LIMIT + 1

    while len_ret_val > EFFECTIVE_TOKEN_LIMIT:

        ret_val = json.dumps({

            "chat_history": self.chat_history[self.chat_history_start:],

            "END_CHAT_HISTORY":"Everything above here is chat history.",

            "unfinished_tasks": [t.dict() for t in tasks],

            "time_now": time_now.dict(),

            # "memories": [],

            "prompt_info": {k: dict(v) for k, v in prompts.items()},

        })

        len_ret_val = count_tokens(ret_val)

        if len_ret_val > EFFECTIVE_TOKEN_LIMIT:

            self.chat_history_start += 1

So, that's the code for configuring the system prompt, which is set every single chat cycle, so it changes.

Unfinished tasks is powered by a tasktimer class which is powered by uh... well it automatically checks whether any new tasks are scheduled once per minnute and updates a listing, and also triggers a chat cycle when one is added. prompts are... well right now theres assistant_prompt (the system's behavior), user_prompt (information about the user, good for letting it know the user consents to domming), master_goal, current_goal, emotion, and interaction_style... these last two are intended to be more hotswappable than the personality prompt but that isn't automated yet.

Speaking of automated...

We have two other major systems right now...

the alert system- we inject the highest priority alerts directly into the most recent user message. Otherwise gpt-3.5 sort of ignores the system prompt in favor of the user prompt. You can force it to focus on specific parts of the system prompt this way. Right now I have alerts that tell it what the subsystems did last step, and to either focus on the unfinished_tasks or the current_goal depending on whether there are any unfinished tasks.

messages = [{"role":"system", "content":f"{context_window}"},{"role":"user", "content":f"Priority Information : {json.dumps(alerts)}\nUser Comment : {input_text}"}]

Speaking of "what the subsystems did last step" before any information is sent to the main LLM circuit, I perform and execute the subsystem tasks:

right now this is just:

async def _run_analyses(self, user_comment_text):

    alerts = []

    tasks = await self.tasktimer.get_tasks()

    print(f"len tasks: {len(tasks)}")

    if len(tasks) > 0:

        task_ids = []

        try:

            task_ids = await self.task_analyser.analyse(user_comment_text)

            print(f"task Ids {task_ids}")

        except Exception as e:

            logger.info(e)

            alerts.append("task_analyser crashed while attempting to determine whether the user comment contained completed tasks.")

        for id in task_ids:

            try:

                await self.task_analyser.markcomplete(user_comment_text, id)

            except Exception as e:

                logger.info(e)

                alerts.append(f"task_analyser crashed while attempting to mark {id} as complete.")

So, what task_analyser does is it uses a completely different context window consisting of the list of tasks and the last user comment and prompting for a list of tasks the user is saying have been completed. Then if that list isn't empty I send a listing of complete information about how to submit task completions (I made marking tasks complete fully customizable using jsonschema so that you can force a format- in the interest of being able to graph this stuff later) as well as the user comment again, to a system empowered to submit the completion.

I didn't technically use langchain, but it's just custom langchain stuff.

Then of course, if things have been marked as complete or failed, that goes into the alerts.

I'm thinking the alerts are important enough that the user should see them too... so that will be an upcoming GUI feature.

Thanks, appreciate the write up. Interesting to see how you're doing things.

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