Error handling & retries

Production workloads hit rate limits, network hiccups, and transient errors. This recipe shows how to wrap your Radium calls with robust retry logic and automatic fallback to a backup model.

The script

resilient_chat.py

import os
import time
import random
from openai import OpenAI, APIError, RateLimitError, APIConnectionError

client = OpenAI(
    api_key=os.environ["RADIUM_API_KEY"],
    base_url="https://api.radium.cloud/v1",
)


def chat_with_retry(
    messages,
    model="clarke-1.0",
    fallback_model="tycho-1.0",
    max_retries=4,
    base_delay=1.0,
    max_delay=60.0,
):
    """
    Send a chat completion with exponential-backoff retries.
    Falls back to `fallback_model` if the primary model keeps failing.
    """
    models_to_try = [model, fallback_model]

    for current_model in models_to_try:
        for attempt in range(max_retries):
            try:
                response = client.chat.completions.create(
                    model=current_model,
                    messages=messages,
                    temperature=0.7,
                    max_tokens=512,
                )
                return response.choices[0].message.content

            except RateLimitError:
                # 429 — back off and retry
                delay = min(base_delay * (2 ** attempt) + random.uniform(0, 1), max_delay)
                print(f"  Rate limited on {current_model}. Retrying in {delay:.1f}s...")
                time.sleep(delay)

            except APIConnectionError:
                # Network blip — short retry
                delay = min(base_delay * (2 ** attempt), max_delay)
                print(f"  Connection error on {current_model}. Retrying in {delay:.1f}s...")
                time.sleep(delay)

            except APIError as e:
                # 5xx or other API-level errors — retry a few times, then switch model
                if e.status_code and e.status_code >= 500:
                    delay = min(base_delay * (2 ** attempt), max_delay)
                    print(f"  API error {e.status_code} on {current_model}. Retrying in {delay:.1f}s...")
                    time.sleep(delay)
                else:
                    # 4xx client error — don't retry, just switch or raise
                    print(f"  Client error {e.status_code} on {current_model}. Switching model...")
                    break

        print(f"  Exhausted retries on {current_model}.")

    raise RuntimeError("All models failed. Check your API key or Radium status.")


# --- Run it ---
answer = chat_with_retry(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain recursion in one sentence."},
    ]
)
print(answer)

Run it

export RADIUM_API_KEY="YOUR_RADIUM_API_KEY"
python resilient_chat.py

What it does

Retries RateLimitError with exponential backoff + jitter — the standard recipe for 429 responses.
Retries APIConnectionError with shorter backoff for transient network issues.
Retries 5xx errors from the API itself, but gives up on unrecoverable 4xx client errors.
Falls back to tycho-1.0 if clarke-1.0 is overloaded or unavailable.
Fails loudly only after both models are exhausted — no silent swallowing.

Adding circuit-breaker logic

For production services, add a simple circuit breaker that stops hammering a struggling endpoint:

circuit_failures = 0
CIRCUIT_THRESHOLD = 5
CIRCUIT_TIMEOUT = 30  # seconds

def chat_with_circuit(messages):
    global circuit_failures
    if circuit_failures >= CIRCUIT_THRESHOLD:
        print("Circuit open — using fallback model only")
        return chat_with_retry(messages, model="tycho-1.0")

    try:
        result = chat_with_retry(messages)
        circuit_failures = 0
        return result
    except Exception:
        circuit_failures += 1
        raise

Tips

Start with max_retries=3 and tune based on your traffic patterns.
Monitor Retry-After headers from rate-limit responses — the example uses fixed backoff; production code should read the header.
Log every retry with model_name and attempt so you can spot recurring patterns in your observability tool.
Reserve your fallback model for a different tier (e.g. switch from clarke-1.0 to tycho-1.0) so you don’t hit the same capacity constraint.

Getting started

Recipes

Error handling & retries

Error handling & retries

The script

Run it

What it does

Adding circuit-breaker logic

Tips

Next steps

Switching with fallback

Tool calling agent

​Error handling & retries

​The script

​Run it

​What it does

​Adding circuit-breaker logic

​Tips

​Next steps

Switching with fallback

Tool calling agent

Error handling & retries

The script

Run it

What it does

Adding circuit-breaker logic

Tips

Next steps