Principle:Groq Groq python Streaming Request Execution
| Knowledge Sources | |
|---|---|
| Domains | NLP, Streaming |
| Last Updated | 2026-02-15 16:00 GMT |
Overview
A technique for receiving language model completions as a stream of incremental token chunks delivered via Server-Sent Events rather than waiting for the full response.
Description
Streaming Request Execution modifies the standard chat completion request by setting stream=True, which causes the API server to return tokens incrementally as they are generated rather than buffering the entire response. The response is delivered using the Server-Sent Events (SSE) protocol over HTTP, where each event contains a JSON chunk with a delta (partial update) of the completion.
Streaming provides:
- Lower perceived latency: Users see the first token immediately rather than waiting for full generation
- Real-time display: Enables typewriter-effect rendering in user interfaces
- Memory efficiency: Clients can process tokens incrementally without buffering the full response
- Early termination: Clients can close the stream if the response is not useful
Usage
Use streaming when building interactive applications (chatbots, coding assistants) where real-time token display improves user experience. Not recommended for batch processing or server-side workflows where the full response is needed before proceeding.
Theoretical Basis
Streaming follows the producer-consumer pattern with SSE as the transport:
# Abstract streaming algorithm
response_stream = http_post(
endpoint="/chat/completions",
body={**request, "stream": True},
stream=True
)
for event in parse_sse_events(response_stream):
if event.data == "[DONE]":
break
chunk = json.loads(event.data)
token = chunk["choices"][0]["delta"].get("content", "")
yield token # Process incrementally