Hi there! It sounds like your ARC project is running into performance issues with the OpenAI ChatGPT robot skill. Let me explain a couple of things:

1. What the "44 msgs" Means

• The number "44 msgs" (messages) represents the conversation history that ARC is sending as part of the request to the OpenAI server. Essentially, each "msg" is one entry (or turn) in the conversation history.
• Over time, as you have more back-and-forth interactions without clearing the conversation, the total number of messages builds up. This history is used to maintain context for the AI's responses.

2. Possible Causes for Slow Response

When the conversation history grows, several factors can lead to slower responses:

• Increased Token Count:
  Each message contributes tokens (the pieces of text) that are sent to the OpenAI server. The more tokens sent with each request, the longer it might take to process the request. A history of 44 messages means the server is handling a significant amount of context data, which can slow down response times.

• Network Latency:
  If you’re experiencing network slowdowns or high latency between your EZ-B v4 and the OpenAI server, every request may take longer to complete.

• Timeout Settings:
  The 15-second timeout you’re encountering might be reached because of the combined processing time needed for a large conversation history plus any network delays.

• Processing Overhead in ARC:
  While ARC is designed to work with multiple skills simultaneously, if one of the skills (like OpenAI ChatGPT) is handling a lot of data or commands, it might cause some delays in overall responsiveness.

3. What You Can Do

Here are a few suggestions to help mitigate the slow response:

• Reset/Clear Conversation History:
  If your conversation history is not needed for context, consider using the ResetMemory controlCommand. For example, you can insert a step to clear the history:

  // Reset the conversation to clear previous messages
  ControlCommand(OpenAI ChatGPT, ResetMemory);
  

  This can reduce the total number of messages sent with each new request.

• Limit History Length:
  Consider modifying your setup so that only a limited number of recent messages are sent. This might involve adjusting settings in the OpenAI ChatGPT skill configuration (look for options related to "Chat History Count").

• Check Your Network:
  Ensure that your network connection is stable and has minimal latency. A WiFi connection, especially with the EZ-B v4, can sometimes be more prone to delays than a direct USB or Ethernet connection.

• Review Timeout Settings:
  If possible, look into whether the timeout settings in ARC or the OpenAI skill can be adjusted (although be cautious - extending the timeout too much might simply delay error handling rather than resolving the underlying problem).

I hope that helps clarify the "44 msgs" and gives you a direction to troubleshoot the slow response time. Do you have any more questions or need further clarification on any of these points?

Don’t forget to try different models. More complex models have a slower response during processing. It would be useful to know the details of your model and if camera image is attached to each request. If using images only the most recent image is added to the request. So that generally isn’t a problem. It’s the model I find matters most.

Hi there,

DJ_Sures raises some good points regarding model complexity. Here are a few considerations:

• Different Models:
  More advanced models (like GPT-4) inherently require more processing time given their larger size and complexity. If you're experiencing slower responses, your selected model might be contributing to this delay. You might try switching to a less complex model (e.g., GPT-3.5) to see if response times improve.

• Camera Image Attachment:
  As DJ_Sures mentioned, if you're using images, only the most recent image is attached to each request. This means that if you’re not actively capturing a camera image for each conversation input, the image data likely isn’t the problem. However, it's still a good idea to verify that your settings are as intended so that unnecessary image data isn't being attached.

• Conversation History:
  Even though it isn’t mentioned in this particular message, remember that a larger conversation history (like the 44 msgs noted earlier) can also add to processing time. If you don’t need the full context, consider clearing or limiting the history with something like:

  // Reset conversation history to help reduce processing time
  ControlCommand(OpenAI ChatGPT, ResetMemory);
  

• Testing and Adjustments:
  It might be useful to experiment by:

  • Trying different models to see which one best fits your balance between response quality and speed.
  • Disabling or simplifying features (like camera image attachment) when not needed.
  • Checking your network connection to rule out latency or connectivity issues.

Could you share which model you're currently using and whether your setup regularly attaches camera images? This will help narrow down the cause of the slow response in your specific case.

Feel free to ask more questions or give additional details if you need further assistance!

I am using GPT-5 nano. I have added a ChatGPT memory reset every time I connect to the robot. It can still take up to 40 secs to get a response to a simple question (via the mic). No image is attached the the conversion request. Image analysis, on the other hand, is fast. I can try different models but their doco suggests GPT-5 nano is the fastest.

Don’t forget that cloud processing means you’re at the mercy of the current demand load of the OpenAI service.

while it may be slow now, it might be quicker tomorrow or later in the day. This is due to the popularity usage of the model or service. Geolocation could also play a roll based on the processing capability of servers in your geographic location.

Hi there, thanks for the additional details. Given that you're using GPT5 nano and have already added a memory reset on connect, here are a few things to consider:

1. Model Complexity & Cloud Load:
   Although GPT5 nano is documented as the fastest, keep in mind that cloud processing means you're subject to external factors. High demand on OpenAI's service or peak usage times could lead to longer processing times. Geolocation can also affect the speed due to the capacity of servers in your region.

2. Network & Latency:
   Verify that your network connection is stable and that latency isn’t contributing to these delays. Even minor network issues can add up when waiting for the cloud response.

3. Testing Different Models:
   While GPT5 nano is intended to be fast, it's worth testing another model (even for a short period) to see if the response time improves. Sometimes the documented "faster" option might be affected by current server load or backend changes.

4. Cloud Service Variability:
   As Synthiam_Support mentioned, variability in response times can occur. When cloud demand is high, you might experience delays that could improve later in the day or on quieter days.

Next Steps / Suggestions:

• Continue monitoring your network performance to ensure there aren’t any local issues.
• Experiment with switching models temporarily (even if only for testing) to compare response times.
• Document specific times when the response time is slower to determine if there is a pattern related to peak load periods.

Does this help clarify the potential causes for the delay? Let me know if you have any other questions or if there are additional details you’d like to explore further!

I decided to use GPT-4.1nano. It seems to be the fastest option for me.

Nice - glad you found GPT-4.1nano faster. Below are targeted tips and step-by-step instructions to reduce latency, measure what's happening, and tune the OpenAI ChatGPT robot skill in ARC.

Quick summary

• "44 msgs" = 44 conversation entries that ARC is sending to OpenAI as part of the request (each turn adds tokens).
• Large history + model + network/cloud load = main causes of slow responses.
• You can dramatically reduce latency by lowering the conversation history, resetting or summarizing history, measuring latency, and verifying the network.

Where to change chat-history and other OpenAI ChatGPT settings

1. Open your ARC project.
2. On the OpenAI ChatGPT robot skill window, click the three-line hamburger menu (top-right of that skill window).
3. Select Configure Conversation History Tab.
4. Change Chat History Count to a low number (try 5-10 to start). Save.

Resetting memory (clear history)

• Use the kebab/hamburger menu Configure to add ResetMemory logic, or programmatically call ResetMemory whenever you want to clear context. Example (JavaScript):

// Reset conversation history
controlCommand(OpenAI ChatGPT, ResetMemory);
print("OpenAI memory reset");

Measure request latency

• Use the skill’s GetSecondsSinceLastRequest or a timing script to see where time is being spent.

Quick check with built-in command:

var seconds = controlCommand(OpenAI ChatGPT, GetSecondsSinceLastRequest);
print("Seconds since last request: " + seconds);

Full round-trip timing example (JS):

// start / send a request, then wait up to 30s for a response and report elapsed ms
setVar("$startTime", Date.now());
controlCommand(OpenAI ChatGPT, Send, "Hello, how fast are you?");
var waited = 0;
while (getVar("$OpenAIResponse","") == "" && waited < 30000) {
  sleep(250);
  waited += 250;
}
var elapsed = Date.now() - Number(getVar("$startTime"));
print("Elapsed ms: " + elapsed);
print("Response: " + getVar("$OpenAIResponse","(no response)"));

Notes:

• The $OpenAIResponse variable is populated when the skill responds.
• Use the Debug skill (you already have it) to capture detailed logs while running these tests.

Reduce tokens and history without losing context

• Lower Chat History Count (as above).
• Shorten the personality/system prompt - smaller system messages reduce tokens.
• Periodically summarize conversation history to one compact summary and use that summary as context instead of full message history. Example approach (pseudo-JS):

// Ask the AI to summarize recent conversation, store in $AI_Summary
controlCommand(OpenAI ChatGPT, Send, "Please summarize the last conversation in 2-3 short sentences and return only the summary.");
// Wait and then setVar("$AI_Summary", getVar("$OpenAIResponse"));
// For future requests send only $AI_Summary + latest user input

If you want, I can write a ready-to-use summarization script for your project.

Model & cloud considerations

• You already moved to GPT-4.1nano - good choice if it's fastest in your tests.
• However cloud load, region/geolocation, and OpenAI service demand can still introduce variability. If you see pattern-based slowness (certain times of day), that hints at service demand.
• If you host a local, compatible LLM endpoint, you can point the OpenAI skill’s Base Domain setting to that to avoid cloud variability. (Configure in OpenAI ChatGPT Settings API Settings Base Domain).

Network checks

• Use wired Ethernet if possible for a more stable link.
• Avoid VPNs or proxies during testing.
• Check DNS and latency to OpenAI endpoints (ping/traceroute from your network).

Debugging & logging

• Keep the Debug skill active and watch the logs when you send a request. Look for the "Sending Convo request X msgs" and timestamped responses.
• Note response times and whether the timeout (15s) is being triggered. If you continue to hit timeouts, increase the wait/timeout for testing (so you can collect data), then reduce again for normal operation.

Next steps I can help with

• Provide a step-by-step to set Chat History Count and add ResetMemory on connect (I can write the exact script to put in your Connect / Start events).
• Create a summarization script that automatically compresses conversation history into a single summary variable you pass with future requests.
• Make a small latency test script tailored to your project that logs times (for use in Debug skill).

Would you like the summarization script or a ready-to-paste Connect script that resets memory and times responses? Also, do you want me to walk through the Conversation History tab UI step-by-step with screenshots (links to the OpenAI ChatGPT skill manual)?

Helpful links

• OpenAI ChatGPT robot skill manual: https://synthiam.com/Support/Skills/Artificial-Intelligence/OpenAI-ChatGPT?id=21559
• ARC downloads & docs: https://synthiam.com/Products/ARC
• Debug skill manual: https://synthiam.com/Support/Skills/General/Debug?id=19885

Tell me which next step you want and I’ll provide the exact script/config to paste into your project.