​
1. Using Stream Mode in Python

​
1.1 Stream Mode with the OpenAI Library

It is recommended to use the OpenAI library for stream mode in most scenarios. 
from openai import OpenAI

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

# Send a request with streaming output
response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V2.5",
    messages=[
        {"role": "user", "content": "SiliconFlow public beta is online, giving each user 300 million tokens to unlock the innovative capabilities of open-source large models. What changes does this bring to the entire large model application field?"}
    ],
    stream=True  # Enable streaming output
)

# Gradually receive and process responses
for chunk in response:
    if not chunk.choices:
        continue
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
    if chunk.choices[0].delta.reasoning_content:
        print(chunk.choices[0].delta.reasoning_content, end="", flush=True)

​
1.2 Stream Mode with the Requests Library

If you are using the requests library for non-OpenAI scenarios, such as using the SiliconFlow API, you need to ensure that both the payload and the request parameters are set to stream mode. 
from openai import OpenAI
import requests
import json
   
url = "https://api.siliconflow.com/v1/chat/completions"
   
payload = {
        "model": "deepseek-ai/DeepSeek-V2.5", # Replace with your model
        "messages": [
            {
                "role": "user",
                "content": "SiliconFlow public beta is online, giving each user 300 million tokens to unlock the innovative capabilities of open-source large models. What changes does this bring to the entire large model application field?"
            }
        ],
        "stream": True # Set to stream mode here
}

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 here

# Print streaming return information
if response.status_code == 200:
    full_content = ""
    full_reasoning_content = ""

    for chunk in response.iter_lines():
        if chunk:
            chunk_str = chunk.decode('utf-8').replace('data: ', '')
            if chunk_str != "[DONE]":
                chunk_data = json.loads(chunk_str)
                delta = chunk_data['choices'][0].get('delta', {})
                content = delta.get('content', '')
                reasoning_content = delta.get('reasoning_content', '')
                if content:
                    print(content, end="", flush=True)
                    full_content += content
                if reasoning_content:
                    print(reasoning_content, end="", flush=True)
                    full_reasoning_content += reasoning_content
else:
    print(f"Request failed, status code: {response.status_code}")

​
2. Using Stream Mode in curl

By default, the processing mechanism of the curl command buffers the output stream, so even if the server sends data in chunks, you will only see the content after the buffer is filled or the connection is closed. Passing the -N (or --no-buffer) option disables this buffering, allowing chunks of data to be printed to the terminal immediately, thus achieving streaming output. 
curl -N -s \
  --request POST \
  --url https://api.siliconflow.com/v1/chat/completions \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "Qwen/Qwen2.5-72B-Instruct",
    "messages": [
      {"role":"user","content":"Tell me a story"}
    ],
    "stream": true
}'