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1. Using Stream Mode in Python

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1.1 Stream Mode with the OpenAI Library

In general scenarios, it is recommended to use the OpenAI library for Stream Mode.

from openai import OpenAI

client = OpenAI(
    base_url='https://api.ap.siliconflow.com/v1',
    api_key='your-api-key'
)

# Send a request with Stream Mode
response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V2.5",
    messages=[
        {"role": "user", "content": "What does the release of DeepSeek mean for the field of large AI models?"}
    ],
    stream=True  # Enable Stream Mode
)

# Receive and process the response incrementally
for chunk in response:
    chunk_message = chunk.choices[0].delta.content
    print(chunk_message, end='', flush=True)

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1.2 Stream Mode with the Requests Library

If you have non-OpenAI scenarios, such as using the SiliconCloud API with the Requests library, please note: In addition to setting stream in the payload, you also need to set stream=True in the request parameters to properly handle responses in streaming mode.

import requests
   
url = "https://api.ap.siliconflow.com/v1/chat/completions"
   
payload = {
        "model": "deepseek-ai/DeepSeek-V2.5",  # Replace with your model
        "messages": [
            {
                "role": "user",
                "content": "What does the release of DeepSeek mean for the field of large AI models?"
            }
        ],
        "stream": True  # This must be set to streaming mode
}

headers = {
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": "Bearer your-api-key"
    }
   
response = requests.post(url, json=payload, headers=headers, stream=True)  # Specify stream mode in the request

# Print the streaming response
if response.status_code == 200: 
    for chunk in response.iter_content(chunk_size=8192): 
        if chunk:
            decoded_chunk = chunk.decode('utf-8')
            print(decoded_chunk, end='')
else:
    print('Request failed with status code:', response.status_code)