Streaming

The Infron AI API allows streaming responses from any model. This is useful for building chat interfaces or other applications where the UI should update as the model generates the response.

To enable streaming, you can set the stream parameter to true in your request. The model will then stream the response to the client in chunks, rather than returning the entire response at once.

Examples

Here is an example of how to stream a response, and process it:

Python

import requests
import json

question = "How would you build the tallest building ever?"

url = completions"
headers = {
  "Authorization": f"Bearer {{API_KEY}}",
  "Content-Type": "application/json"
}

payload = {
  "model": "{{MODEL}}",
  "messages": [{"role": "user", "content": question}],
  "stream": True
}

buffer = ""
with requests.post(url, headers=headers, json=payload, stream=True) as r:
  for chunk in r.iter_content(chunk_size=1024, decode_unicode=True):
    buffer += chunk
    while True:
      try:
        # Find the next complete SSE line
        line_end = buffer.find('\n')
        if line_end == -1:
          break

        line = buffer[:line_end].strip()
        buffer = buffer[line_end + 1:]

        if line.startswith('data: '):
          data = line[6:]
          if data == '[DONE]':
            break

          try:
            data_obj = json.loads(data)
            content = data_obj["choices"][0]["delta"].get("content")
            if content:
              print(content, end="", flush=True)
          except json.JSONDecodeError:
            pass
      except Exception:
        break

Typescript

Additional Information

For SSE (Server-Sent Events) streams, Infron AI occasionally sends comments to prevent connection timeouts. These comments look like:

: Infron AI PROCESSING

Comment payload can be safely ignored per the SSE specs. However, you can leverage it to improve UX as needed, e.g. by showing a dynamic loading indicator.

Some SSE client implementations might not parse the payload according to spec, which leads to an uncaught error when you JSON.stringify the non-JSON payloads. We recommend the following clients:

• eventsource-parser

• OpenAI SDK

• Vercel AI SDK

Stream Cancellation

Streaming requests can be cancelled by aborting the connection. For supported providers, this immediately stops model processing and billing.

Provider Support

Supported

• OpenAI, Azure, Anthropic

• Fireworks, Mancer, Recursal

• AnyScale, Lepton, OctoAI

• Novita, DeepInfra, Together

• Cohere, Hyperbolic, Infermatic

• Avian, XAI, Cloudflare

• SFCompute, Nineteen, Liquid

• Friendli, Chutes, DeepSeek

Not Currently Supported

• AWS Bedrock, Groq, Modal

• Google, Google AI Studio, Minimax

• HuggingFace, Replicate, Perplexity

• Mistral, AI21, Featherless

• Lynn, Lambda, Reflection

• SambaNova, Inflection, ZeroOneAI

• AionLabs, Alibaba, Nebius

• Kluster, Targon, InferenceNet

To implement stream cancellation:

Python

import requests
from threading import Event, Thread

def stream_with_cancellation(prompt: str, cancel_event: Event):
    with requests.Session() as session:
        response = session.post(
            "https://llm.onerouter.pro/v1/chat/completions",
            headers={"Authorization": f"Bearer {{API_KEY}}"},
            json={"model": "{{MODEL}}", "messages": [{"role": "user", "content": prompt}], "stream": True},
            stream=True
        )

        try:
            for line in response.iter_lines():
                if cancel_event.is_set():
                    response.close()
                    return
                if line:
                    print(line.decode(), end="", flush=True)
        finally:
            response.close()

# Example usage:
cancel_event = Event()
stream_thread = Thread(target=lambda: stream_with_cancellation("Write a story", cancel_event))
stream_thread.start()

# To cancel the stream:
cancel_event.set()

Typescript

Cancellation only works for streaming requests with supported providers. For non-streaming requests or unsupported providers, the model will continue processing and you will be billed for the complete response.