Streaming Messages allow real-time content retrieval while the model generates responses, without waiting for the complete response to be generated. This approach can significantly improve user experience, especially when generating long text content, as users can immediately see output beginning to appear.
Features Streaming messages use an incremental generation mechanism, transmitting content in chunks in real-time during the generation process, rather than waiting for the complete response to be generated before returning it all at once. This mechanism allows developers to:
Real-time Response : No need to wait for complete response, content displays progressivelyImproved Experience : Reduce user waiting time, provide instant feedbackReduced Latency : Content is transmitted as it’s generated, reducing perceived latencyFlexible Processing : Real-time processing and display during reception
Core Parameter Description
stream=TrueTruemodelglm-4.6, glm-4.5, etc.
Streaming responses use Server-Sent Events (SSE) format, with each event containing:
choices[0].delta.content: Incremental text contentchoices[0].delta.reasoning_content: Incremental reasoning contentchoices[0].finish_reason: Completion reason (only appears in the last chunk)usage: Token usage statistics (only appears in the last chunk)
Code Examples curl --location 'https://api.z.ai/api/paas/v4/chat/completions' \ --header 'Authorization: Bearer YOUR_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "model": "glm-4.6", "messages": [ { "role": "user", "content": "Write a poem about spring" } ], "stream": true }' Install SDK # Install latest version pip install zai-sdk # Or specify version pip install zai-sdk== 0.1.0 Verify Installation import zai print (zai. __version__ ) Complete Example from zai import ZaiClient # Initialize client client = ZaiClient( api_key = 'Your API Key' ) # Create streaming message request response = client.chat.completions.create( model = "glm-4.6" , messages = [ { "role" : "user" , "content" : "Write a poem about spring" } ], stream = True # Enable streaming output ) # Process streaming response full_content = "" for chunk in response: if not chunk.choices: continue delta = chunk.choices[ 0 ].delta # Handle incremental content if hasattr (delta, 'content' ) and delta.content: full_content += delta.content print (delta.content, end = "" , flush = True ) # Check if completed if chunk.choices[ 0 ].finish_reason: print ( f " \n\n Completion reason: { chunk.choices[ 0 ].finish_reason } " ) if hasattr (chunk, 'usage' ) and chunk.usage: print ( f "Token usage: Input { chunk.usage.prompt_tokens } , Output { chunk.usage.completion_tokens } " ) print ( f " \n\n Complete content: \n { full_content } " ) Response Example The streaming response format is as follows:
data: {"id":"1","created":1677652288,"model":"glm-4.6","choices":[{"index":0,"delta":{"content":"Spring"},"finish_reason":null}]} data: {"id":"1","created":1677652288,"model":"glm-4.6","choices":[{"index":0,"delta":{"content":" comes"},"finish_reason":null}]} data: {"id":"1","created":1677652288,"model":"glm-4.6","choices":[{"index":0,"delta":{"content":" with"},"finish_reason":null}]} ... data: {"id":"1","created":1677652288,"model":"glm-4.6","choices":[{"index":0,"finish_reason":"stop","delta":{"role":"assistant","content":""}}],"usage":{"prompt_tokens":8,"completion_tokens":262,"total_tokens":270,"prompt_tokens_details":{"cached_tokens":0}}} data: [DONE] Application Scenarios
Chat Applications
Real-time conversation experience
Character-by-character reply display
Reduced waiting time
Content Generation
Article writing assistant
Code generation tools
Creative content creation
Educational Applications
Online Q&A systems
Learning assistance tools
Knowledge Q&A platforms
Customer Service Systems
Intelligent customer service bots
Real-time problem solving
User support systems
