Streaming Response Implementation
Deliver LLM responses in real-time with streaming. Reduce perceived latency by 90% and improve user experience with progressive response rendering.
Server-Sent Events Implementation
// Client-side streaming handler
const streamCompletion = async (prompt) => {
const response = await fetch('https://api.parrotrouter.com/v1/chat/completions', {
method: 'POST',
headers: {
'Content-Type': 'application/json',
'Authorization': 'Bearer YOUR_API_KEY'
},
body: JSON.stringify({
model: 'gpt-4',
messages: [{ role: 'user', content: prompt }],
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');
for (const line of lines) {
if (line.startsWith('data: ')) {
const data = JSON.parse(line.slice(6));
if (data.choices[0].delta?.content) {
updateUI(data.choices[0].delta.content);
}
}
}
}
}First Token
50ms
Time to first response
Perceived Speed
90%
Faster perception
User Satisfaction
85%
Improvement score
References
- [1] arXiv. "Efficient LLM Inference" (2024)
- [2] Hugging Face. "GPU Inference Optimization" (2024)
- [3] NVIDIA. "LLM Inference Optimization" (2024)