You can “proxy” the roles as a developer. The only issue currently is there are only three roles.
However, for your app, you can call them whatever you want and just map them to the API keys before you submit your API call.
HTH
1 Like
Thanks for the fast reply! The issue is I want to share conversational context between two separate instances of openai.ChatCompletion (for lack of a better term, let’s call them characters). I want to treat them as if they’re in the same room so they should be able to answer questions about what I’ve said to the other. I’ve created a ConversationManager but when I pass the same instance of ConversationManager to the two separate instances of openai.ChatCompletion, it gets confused and the two characters become one. (Not sure I’ve explained that correctly)
This is amazing! Does anyone know when we can expect to be able to use the ChatGPT API with Zapier? It’s not showing up under models.
getting this same result for many codes usage: openai api [-h]
{engines.list,engines.get,engines.update,engines.generate,chat_completions.create,completions.create,deployments.list,deployments.get,deployments.delete,deployments.create,models.list,models.get,models.delete,files.create,files.get,files.delete,files.list,fine_tunes.list,fine_tunes.create,fine_tunes.get,fine_tunes.results,fine_tunes.events,fine_tunes.follow,fine_tunes.cancel,fine_tunes.delete,image.create,image.create_edit,image.create_variation,audio.transcribe,audio.translate}
…
positional arguments:
{engines.list,engines.get,engines.update,engines.generate,chat_completions.create,completions.create,deployments.list,deployments.get,deployments.delete,deployments.create,models.list,models.get,models.delete,files.create,files.get,files.delete,files.list,fine_tunes.list,fine_tunes.create,fine_tunes.get,fine_tunes.results,fine_tunes.events,fine_tunes.follow,fine_tunes.cancel,fine_tunes.delete,image.create,image.create_edit,image.create_variation,audio.transcribe,audio.translate}
All API subcommands
options:
-h, --help show this help message and exit
also for this command openai api fine_tunes.prepare_data -f C:\Games\Notepad++\prompteg.json
Nice! !!! !
@PaulBellow or @ruby_coder and everything, would you have an example of a simple view using “stream”:“true”? I use a version I created in python, but I couldn’t access delta content using the same code.
solved 
yupiiiIiiIi ii i
response = openai.ChatCompletion.create(
model="gpt-3.5-turbo",
temperature=0.9,
max_tokens=111,
top_p=1,
stream=True,
messages=[
{"role": "user", "content": "AHi he is my life"},
]
)
collected_events = []
completion_text = ''
for event in response:
collected_events.append(event)
for choices in event['choices']:
event_ntext = choices['delta']
event_ntext = str(event_ntext)
completion_text += event_ntext
print(f'Text received: {event_ntext} ')
2 Likes
Thanks for sharing the news about the new GPT-3.5 Turbo model! As someone interested in fine-tuning GPT models for specialized tasks, I’m curious about the cost implications of this approach with the new model. assuming this become available in the future (as it is not yet mentioned it would be possible)…
Fine-tuning the full GPT-3 model for specific tasks can be prohibitively expensive, even with the option to be billed only for the tokens used in requests. I’m hoping that the new GPT-3.5 Turbo model will offer more cost-effective options for fine-tuning, as well as improved performance in chat-based applications.
Overall, I’m excited to see how the GPT-3.5 Turbo model can improve chatbot technology and other specialized applications, while also being more cost-effective than the full GPT-3 model for fine-tuning. Thank you for sharing this news, and I look forward to learning more about the cost implications of fine-tuning with the GPT-3.5 Turbo model in the coming weeks and months.
Hello,
great news,
we are using davinci 3 from CURL, any doc ressource or details on what should be changed to use gpt3.5?
thanks
2 Likes
Will there be a playground for ChatGPT?
1 Like
Is this a way of addressing “memory” for a better lack of terminology? At the moment, I am still taking the previous response and passing it to the next. But I would love to have a better implementation and wondering if this is it?
Thank you!
That’s absolutely amazing news! 
As a senior full-stack dev, I’m pivoting into this field because of the huge potential to build products using OpenAI’s API. It provides great leverage! I’m offering my skills to implement OpenAI and improve your products, as well as create new products and services.
Feel free to reach out to me on this forum or on linkedin if you’re interested.
we upgraded from davinci 3 to gpt 3.5
FANTASTIC!
I guess now we can start thinking about moving to production
I’m not an expert, but be careful with embeddings. I suspect that you need to re-calculate all the embeddings you had stored if you change of model! Someone knows about this?
I have quite a few datasets on a very specific subject. I have implemented embeddings and semantic search following OpenAI documentation and QA works like a charm. I want to offer users a friendly (chatgpt) and inclusive (whisper) environment with the advantage of these APIs.
Is there somewhere I can read code and documentation for building solutions based on domain specific corpora?
Edit:
I am reading this now.
Perhaps changing the model from model=“text-davinci-003” to
model=“gpt-3.5-turbo” ?
From
response = openai.Completion.create()
To
response = openai.ChatCompletion.create()
and continue to modify code as needed.
No, you’re wrong. Take a look at the example here:
the tokens of the prompt are only 20, and the tokens of the response: 38.
There is one. Just change mode (to the right of playground UI).
Right now you have:
mode=complete
mode=chat
mode=insert
mode=edit
There are not new releases as regards embedding models. Just ChatGPT and Whisper. There’s no need to change anything regarding embeddings.
1 Like
There now seems to be a newly tuned variant, dated yesterday:
Also, following @PaulBellow contributions I decided to create and share a free app using retool (react/js) using embed/iframes using the new ChatGPT API for the Brazilian community =D
API do ChatGPT do OpenAI - Aprenda como Integrar ao WordPress com Retool em 5 minutos - YouTube
I have a software doing the same (use embeddings for semantic search and using completion davinci-003 for build a NL response once found the most relevant QA on db). I don’t use python, but PHP and i simply did a few curl lines to interact with API. So, it’s quite easy understand how does it work and what to change now.
In fact, i replaced the old endpoint by this one https://api.openai.com/v1/chat/completions and the model by this other gpt-3.5-turbo and also replaced the element prompt of the data send by this new messages array containing 2 arrays, one for role system with context and one for role user with user question/search string.
The response of this new endpoint chat also is different: only the element choices now contain a different array structure. But the difference is very minor.
Spoiler: there are a big DIFFERENCE in the responses. I’m still evaluating if it’s convenient for me. This new endpoint really SEEMS (talk) like the usual chatGPT. This means: their responses are easily 100% longer, but more comprehensive, i mean it give more details to the user… so, maybe is not so desirable for some uses… I’m talking even maintaining exactly equal the parameters (max_tokens, temperature, …).
When I call the OpenAI API, I encounter the following error message. How can I resolve it? Thank you.
$ curl https://api.openai.com/v1/chat/completions \
> -H "Authorization: Bearer {my apiKey}" \
> -H "Content-Type: application/json" \
> -d '{
> "model": "gpt-3.5-turbo",
> "messages": [{"role": "user", "content": "What is the OpenAI mission?"}]
> }'
curl: (28) SSL connection timeout
(base)