Streaming
Unlocking Real-Time Interactions
Why Choose Streaming?
Streaming is a great feature meant to enable real-time, dynamic interactions between developers and the OpenAI API. It provides an efficient way to receive responses from the OpenAI API incrementally, as the data becomes available. This real-time interaction offers enhanced flexibility and responsiveness, making it a valuable addition to your toolkit.
Developers often opt for streaming in scenarios where real-time responses are critical and standard request-response mechanisms may not suffice.
How to stream the response?
Simply add stream=True
to your request to receive the response in chunks as they're generated, instead of waiting for the complete response to be assembled.
# This code is for v1+ of the openai package https://pypi.org/project/openai/
from openai import OpenAI
client = OpenAI(
api_key= os.getenv("OPENAI_API_KEY"),
base_url = "https://turbo.gptboost.io/v1"
)
# Make a request to OpenAI API
stream = client.chat.completions.create(
model="gpt-4",
messages=[
{"role": "user", "content": "Tell me a story about a happy developer."}
],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "")
What are some use cases?
Here are a few use cases where streaming shines:
Conversational Interfaces: Streaming is perfect for building chatbots and conversational interfaces that require instant responses to user input. It allows for a more natural, interactive conversation flow.
Live Transcriptions: In applications like transcription services or live captioning, streaming provides immediate results as audio or speech input is processed. This is particularly useful in live event scenarios.
Interactive Gaming: For interactive games that demand dynamic and real-time AI-generated content, streaming ensures seamless integration of AI-driven elements.
On-the-Fly Content Generation: In applications where content is generated on the fly, such as dynamic content creation for websites or marketing materials, streaming ensures rapid content delivery.
Simultaneous Data Processing: For tasks requiring parallel processing of multiple inputs, like language translation or sentiment analysis for a stream of social media posts, streaming enhances efficiency.
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