Streaming Responses

Why Stream?

Streaming provides immediate feedback to users, making your application feel faster and more responsive.

Python Streaming

Basic Streaming

from openai import OpenAI

client = OpenAI(
    api_key="sk-savegate-xxxxxxxxxxxxx",
    base_url="https://api.savegate.ai/v1"
)

def stream_response(message, model="gpt-4"):
    stream = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": message}],
        stream=True
    )

    print("Response: ", end="")

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

    print()  # New line at end

# Usage
stream_response("Tell me a story about a robot")

Streaming with Callback

def stream_with_callback(message, callback, model="gpt-4"):
    """Stream response and call callback for each chunk"""
    stream = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": message}],
        stream=True
    )

    full_response = ""

    for chunk in stream:
        if chunk.choices[0].delta.content:
            content = chunk.choices[0].delta.content
            full_response += content
            callback(content)

    return full_response

# Usage with custom callback
def print_colored(text):
    print(f"\033[92m{text}\033[0m", end="", flush=True)

response = stream_with_callback("Write a poem", callback=print_colored)

Async Streaming

import asyncio
from openai import AsyncOpenAI

async_client = AsyncOpenAI(
    api_key="sk-savegate-xxxxxxxxxxxxx",
    base_url="https://api.savegate.ai/v1"
)

async def async_stream(message, model="gpt-4"):
    stream = await async_client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": message}],
        stream=True
    )

    full_response = ""

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

    return full_response

# Usage
asyncio.run(async_stream("Explain quantum computing"))

Node.js Streaming

Basic Streaming

import OpenAI from 'openai';

const client = new OpenAI({
  apiKey: 'sk-savegate-xxxxxxxxxxxxx',
  baseURL: 'https://api.savegate.ai/v1'
});

async function streamResponse(message, model = 'gpt-4') {
  const stream = await client.chat.completions.create({
    model: model,
    messages: [{ role: 'user', content: message }],
    stream: true
  });

  process.stdout.write('Response: ');

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content || '';
    process.stdout.write(content);
  }

  console.log();
}

// Usage
streamResponse('Tell me a story about a robot');

Streaming with Event Emitter

import { EventEmitter } from 'events';

async function streamWithEvents(message, model = 'gpt-4') {
  const emitter = new EventEmitter();

  const stream = await client.chat.completions.create({
    model: model,
    messages: [{ role: 'user', content: message }],
    stream: true
  });

  let fullResponse = '';

  (async () => {
    for await (const chunk of stream) {
      const content = chunk.choices[0]?.delta?.content || '';
      if (content) {
        fullResponse += content;
        emitter.emit('chunk', content);
      }
    }
    emitter.emit('done', fullResponse);
  })();

  return emitter;
}

// Usage
const stream = await streamWithEvents('Write a poem');

stream.on('chunk', (chunk) => {
  process.stdout.write(chunk);
});

stream.on('done', (fullResponse) => {
  console.log('\n\nFull response received!');
});

Web Streaming with Server-Sent Events

FastAPI Backend

from fastapi import FastAPI
from fastapi.responses import StreamingResponse
from openai import OpenAI
import json

app = FastAPI()
client = OpenAI(
    api_key="sk-savegate-xxxxxxxxxxxxx",
    base_url="https://api.savegate.ai/v1"
)

async def stream_generator(message: str):
    stream = client.chat.completions.create(
        model="gpt-4",
        messages=[{"role": "user", "content": message}],
        stream=True
    )

    for chunk in stream:
        if chunk.choices[0].delta.content:
            data = json.dumps({"content": chunk.choices[0].delta.content})
            yield f"data: {data}\n\n"

@app.post("/stream")
async def stream_chat(message: str):
    return StreamingResponse(
        stream_generator(message),
        media_type="text/event-stream"
    )

Express.js Backend

import express from 'express';
import OpenAI from 'openai';

const app = express();
const client = new OpenAI({
  apiKey: 'sk-savegate-xxxxxxxxxxxxx',
  baseURL: 'https://api.savegate.ai/v1'
});

app.use(express.json());

app.post('/stream', async (req, res) => {
  res.setHeader('Content-Type', 'text/event-stream');
  res.setHeader('Cache-Control', 'no-cache');
  res.setHeader('Connection', 'keep-alive');

  const stream = await client.chat.completions.create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: req.body.message }],
    stream: true
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content || '';
    if (content) {
      res.write(`data: ${JSON.stringify({ content })}\n\n`);
    }
  }

  res.write('data: [DONE]\n\n');
  res.end();
});

app.listen(3000, () => console.log('Server running on port 3000'));

Frontend (HTML + JavaScript)

<!DOCTYPE html>
<html>
<head>
    <title>Streaming Chat</title>
</head>
<body>
    <div id="chat-container">
        <div id="messages"></div>
        <input type="text" id="user-input" placeholder="Type a message...">
        <button onclick="sendMessage()">Send</button>
    </div>

    <script>
        async function sendMessage() {
            const input = document.getElementById('user-input');
            const message = input.value;
            input.value = '';

            // Add user message
            addMessage('User', message);

            // Create message div for assistant
            const assistantDiv = addMessage('Assistant', '');

            // Start streaming
            const response = await fetch('/stream', {
                method: 'POST',
                headers: { 'Content-Type': 'application/json' },
                body: JSON.stringify({ message })
            });

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

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

                const chunk = decoder.decode(value);
                const lines = chunk.split('\n');

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

                        try {
                            const parsed = JSON.parse(data);
                            assistantDiv.textContent += parsed.content;
                        } catch (e) {
                            console.error('Parse error:', e);
                        }
                    }
                }
            }
        }

        function addMessage(sender, content) {
            const messagesDiv = document.getElementById('messages');
            const messageDiv = document.createElement('div');
            messageDiv.innerHTML = `<strong>${sender}:</strong> <span>${content}</span>`;
            messagesDiv.appendChild(messageDiv);
            return messageDiv.querySelector('span');
        }
    </script>
</body>
</html>

Error Handling in Streams

def safe_stream(message, model="gpt-4"):
    try:
        stream = client.chat.completions.create(
            model=model,
            messages=[{"role": "user", "content": message}],
            stream=True
        )

        for chunk in stream:
            try:
                if chunk.choices[0].delta.content:
                    print(chunk.choices[0].delta.content, end="", flush=True)
            except (AttributeError, IndexError) as e:
                # Handle malformed chunks
                continue

    except Exception as e:
        print(f"\nError during streaming: {e}")
        return None

    print()  # New line at end

Next Steps

Learn how to use function calling for tool integration

Endpoints

Examples

Why Stream?

Python Streaming

Basic Streaming

Streaming with Callback

Async Streaming

Node.js Streaming

Basic Streaming

Streaming with Event Emitter

Web Streaming with Server-Sent Events

FastAPI Backend

Express.js Backend

Frontend (HTML + JavaScript)

Error Handling in Streams

Next Steps

Endpoints

Examples

​Why Stream?

​Python Streaming

​Basic Streaming

​Streaming with Callback

​Async Streaming

​Node.js Streaming

​Basic Streaming

​Streaming with Event Emitter

​Web Streaming with Server-Sent Events

​FastAPI Backend

​Express.js Backend

​Frontend (HTML + JavaScript)

​Error Handling in Streams

Next Steps

Why Stream?

Python Streaming

Basic Streaming

Streaming with Callback

Async Streaming

Node.js Streaming

Basic Streaming

Streaming with Event Emitter

Web Streaming with Server-Sent Events

FastAPI Backend

Express.js Backend

Frontend (HTML + JavaScript)

Error Handling in Streams