Streaming Messages

Documentation Index

Fetch the complete documentation index at: https://docs.z.ai/llms.txt Use this file to discover all available pages before exploring further.

Streaming Messages

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 progressively
• Improved Experience: Reduce user waiting time, provide instant feedback
• Reduced Latency: Content is transmitted as it's generated, reducing perceived latency
• Flexible Processing: Real-time processing and display during reception

Core Parameter Description

• stream=True: Enable streaming output, must be set to True
• model: Models that support streaming output, such as glm-5, glm-4.7, glm-4.6, glm-4.5, etc.

Response Format Description

Streaming responses use Server-Sent Events (SSE) format, with each event containing:

• choices[0].delta.content: Incremental text content
• choices[0].delta.reasoning_content: Incremental reasoning content
• choices[0].finish_reason: Completion reason (only appears in the last chunk)
• usage: Token usage statistics (only appears in the last chunk)

Code Examples

```bash  theme={null}
# Install latest version
pip install zai-sdk

# Or specify version
pip install zai-sdk==0.1.0
```

**Verify Installation**

```python  theme={null}
import zai
print(zai.__version__)
```

**Complete Example**

```python  theme={null}
from zai import ZaiClient

# Initialize client
client = ZaiClient(api_key='Your API Key')

# Create streaming message request
response = client.chat.completions.create(
    model="glm-5",
    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\nCompletion 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\nComplete content:\n{full_content}")
```

Response Example

The streaming response format is as follows:

data: {"id":"1","created":1677652288,"model":"glm-5","choices":[{"index":0,"delta":{"content":"Spring"},"finish_reason":null}]}

data: {"id":"1","created":1677652288,"model":"glm-5","choices":[{"index":0,"delta":{"content":" comes"},"finish_reason":null}]}

data: {"id":"1","created":1677652288,"model":"glm-5","choices":[{"index":0,"delta":{"content":" with"},"finish_reason":null}]}

...

data: {"id":"1","created":1677652288,"model":"glm-5","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