I’m working on a Python project that uses the OpenAI API to create a ChatGPT-like experience in the terminal. I’m trying to get the responses to stream smoothly, but I’m running into a problem.
Right now, my ask_stream function is printing each word on a new line. That’s not what I want. I’d like the text to flow naturally, like a normal conversation.
Here’s a snippet of my code:
async def ask_stream(prompt):
response = openai.ChatCompletion.create(
model='gpt-4',
messages=[{'role': 'user', 'content': prompt}],
max_tokens=8000,
temperature=0.4,
stream=True
)
for chunk in response:
content = chunk['choices'][0]['delta'].get('content', '')
console.print(Markdown(content), end='')
time.sleep(0.01)
I’m using the rich library for markup handling. Can anyone help me fix this so the text prints smoothly? I’m pretty new to Python, so any advice would be great. Thanks!
I’ve faced a similar issue when working with the OpenAI API for streaming responses. The problem likely stems from how you’re handling the chunks of content. Instead of printing each chunk immediately, try accumulating them into a buffer and only printing when you have a complete word or sentence.
Here’s a modification that might help:
async def ask_stream(prompt):
response = openai.ChatCompletion.create(
model='gpt-4',
messages=[{'role': 'user', 'content': prompt}],
max_tokens=8000,
temperature=0.4,
stream=True
)
buffer = ''
for chunk in response:
content = chunk['choices'][0]['delta'].get('content', '')
buffer += content
if ' ' in buffer:
words = buffer.split(' ')
console.print(Markdown(' '.join(words[:-1])), end=' ')
buffer = words[-1]
time.sleep(0.01)
if buffer:
console.print(Markdown(buffer))
This approach should give you a smoother, more natural flow of text in your terminal. It accumulates content until it has complete words, then prints them out. Hope this helps!
In my experience working with the OpenAI API, I found that handling streaming responses more smoothly is largely about buffering the incoming chunks. Instead of printing each fragment immediately, collecting the chunks and processing them when they form a coherent sentence is more effective. This approach involves detecting natural break points in the text, for example by using regular expressions to identify where sentences end. By implementing such a strategy, you can achieve a more natural flow in the terminal output while also managing any intermittent breaks in the data stream. Additionally, ensure that your code gracefully handles potential API errors and integrates proper rate limiting. This method has proven successful in similar projects and can provide a more seamless experience in your terminal application.