Skip to main content

​
Overview

Streaming allows you to receive partial responses as they’re generated, providing a better user experience for chat applications.

​
Enable Streaming

Set stream: true in your request: 
from openai import OpenAI

client = OpenAI(
    api_key="sk-your-api-key",
    base_url="https://api.lemondata.cc/v1"
)

stream = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": "Write a short poem"}],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

​
Stream Response Format

Each chunk in the stream follows this format: 
data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":"Hello"},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{"content":" world"},"finish_reason":null}]}

data: {"id":"chatcmpl-xxx","object":"chat.completion.chunk","created":1234567890,"model":"gpt-4o","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]

​
Handling Stream End

The stream ends with:
  • finish_reason: "stop" - Normal completion
  • finish_reason: "length" - Hit max_tokens limit
  • finish_reason: "tool_calls" - Model wants to call a tool
  • data: [DONE] - Final message

​
Collecting Full Response

To collect the complete response while streaming: 
full_response = ""

for chunk in stream:
    if chunk.choices[0].delta.content:
        content = chunk.choices[0].delta.content
        full_response += content
        print(content, end="", flush=True)

print(f"\n\nFull response: {full_response}")

​
Async Streaming

For async applications: 
import asyncio
from openai import AsyncOpenAI

async def main():
    client = AsyncOpenAI(
        api_key="sk-your-api-key",
        base_url="https://api.lemondata.cc/v1"
    )

    stream = await client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Hello!"}],
        stream=True
    )

    async for chunk in stream:
        if chunk.choices[0].delta.content:
            print(chunk.choices[0].delta.content, end="")

asyncio.run(main())

​
Web Application Example

For a web chat interface: 
async function streamChat(message) {
  const response = await fetch('https://api.lemondata.cc/v1/chat/completions', {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer sk-your-api-key',
      'Content-Type': 'application/json'
    },
    body: JSON.stringify({
      model: 'gpt-4o',
      messages: [{ role: 'user', content: message }],
      stream: true
    })
  });

  const reader = response.body.getReader();
  const decoder = new TextDecoder();

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    const chunk = decoder.decode(value);
    const lines = chunk.split('\n').filter(line => line.startsWith('data: '));

    for (const line of lines) {
      const data = line.slice(6);
      if (data === '[DONE]') return;

      const parsed = JSON.parse(data);
      const content = parsed.choices[0]?.delta?.content;
      if (content) {
        // Append to your UI
        document.getElementById('output').textContent += content;
      }
    }
  }
}