Implementing Model Fallback Strategies in Genesys Cloud LLM Gateway with Python Middleware

What You Will Build

A FastAPI middleware that intercepts LLM requests, routes them through a dynamic weighted round-robin scheduler, and automatically fails over to a secondary provider when latency exceeds thresholds or error rates spike. This tutorial uses the Genesys Cloud Python SDK (genesyscloud) to interact with /api/v2/ai/llm/completions and /api/v2/ai/llm/providers. The implementation is written in Python 3.10+ using fastapi, httpx, and prometheus-client.

Prerequisites

• Genesys Cloud OAuth2 confidential client credentials
• Required OAuth scopes: ai:llm:read, ai:llm:write
• Genesys Cloud Python SDK v2.20.0+
• Python 3.10+ runtime
• External dependencies: fastapi==0.115.0, httpx==0.27.0, prometheus-client==0.21.0, pydantic==2.9.0, uvicorn==0.30.0

Authentication Setup

Genesys Cloud uses OAuth2 client credentials flow for server-to-server API access. You must cache the access token and refresh it before expiration to avoid unnecessary authentication round trips. The following class handles token retrieval, caching, and automatic refresh.

import time
import httpx
from typing import Optional

class GenesysAuthClient:
    def __init__(self, client_id: str, client_secret: str, base_url: str = "https://api.mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = base_url
        self.token: Optional[str] = None
        self.expires_at: float = 0.0

    async def get_token(self) -> str:
        if self.token and self.expires_at > time.time():
            return self.token
        
        async with httpx.AsyncClient() as client:
            response = await client.post(
                f"{self.base_url}/oauth/token",
                data={"grant_type": "client_credentials"},
                auth=(self.client_id, self.client_secret),
                headers={"Content-Type": "application/x-www-form-urlencoded"}
            )
            
            if response.status_code == 401:
                raise PermissionError("Invalid OAuth client credentials provided.")
            response.raise_for_status()
            
            payload = response.json()
            self.token = payload["access_token"]
            self.expires_at = time.time() + payload["expires_in"] - 60.0
            return self.token

Implementation

Step 1: Prometheus Metrics and Provider Registry

You must track latency and error rates per provider to drive the fallback algorithm. Prometheus histograms and counters provide the necessary resolution. Each provider maintains an exponential moving average of latency and a rolling error rate.

import time
from dataclasses import dataclass
from prometheus_client import Histogram, Counter

LLM_LATENCY = Histogram(
    "llm_gateway_latency_seconds",
    "LLM request latency by provider and status",
    ["provider", "status"]
)

LLM_ERRORS = Counter(
    "llm_gateway_errors_total",
    "LLM request errors by provider and type",
    ["provider", "error_type"]
)

LLM_REQUESTS = Counter(
    "llm_gateway_requests_total",
    "Total LLM requests routed",
    ["provider"]
)

@dataclass
class LLMProvider:
    name: str
    endpoint: str
    base_weight: float = 1.0
    recent_latency: float = 0.0
    recent_errors: int = 0
    recent_requests: int = 0

    @property
    def error_rate(self) -> float:
        return self.recent_errors / max(1, self.recent_requests)

    def update_metrics(self, latency: float, is_error: bool):
        self.recent_latency = 0.7 * self.recent_latency + 0.3 * latency
        self.recent_requests += 1
        if is_error:
            self.recent_errors += 1
        LLM_LATENCY.labels(provider=self.name, status="error" if is_error else "success").observe(latency)
        LLM_REQUESTS.labels(provider=self.name).inc()
        if is_error:
            LLM_ERRORS.labels(provider=self.name, error_type="timeout_or_5xx").inc()

    def is_healthy(self, latency_threshold: float = 2.0, error_threshold: float = 0.1) -> bool:
        return self.recent_latency < latency_threshold and self.error_rate < error_threshold

Step 2: Weighted Round-Robin Scheduler with Dynamic Fallback

The scheduler evaluates provider health before routing. It calculates dynamic weights inversely proportional to latency, ensuring faster providers receive more traffic. When all providers breach thresholds, it forces a failover to the least degraded option.

import random

class WeightedRoundRobinScheduler:
    def __init__(self, providers: list[LLMProvider]):
        self.providers = providers

    def select_provider(self) -> LLMProvider:
        healthy_providers = [p for p in self.providers if p.is_healthy()]
        
        if not healthy_providers:
            selected = min(self.providers, key=lambda p: p.recent_latency)
            return selected
        
        weights = [p.base_weight * (1.0 / max(0.1, p.recent_latency)) for p in healthy_providers]
        total_weight = sum(weights)
        
        if total_weight == 0:
            return random.choice(healthy_providers)
            
        threshold = random.uniform(0, total_weight)
        cumulative = 0.0
        
        for provider in healthy_providers:
            weight = next(w for w, p in zip(weights, healthy_providers) if p.name == provider.name)
            cumulative += weight
            if threshold <= cumulative:
                return provider
                
        return healthy_providers[-1]

Step 3: Genesys Cloud SDK Integration and Retry Logic

You must handle rate limits and transient failures explicitly. The following utility wraps the Genesys Cloud SDK call with exponential backoff for HTTP 429 responses and translates SDK exceptions into standardized error types.

import asyncio
from typing import Any, Dict
from genesyscloud import PlatformClient
from genesyscloud.ai import AiClient

class GenesysLLMClient:
    def __init__(self, auth: GenesysAuthClient, environment: str = "mypurecloud.com"):
        self.auth = auth
        self.environment = environment
        self.platform_client: Optional[PlatformClient] = None
        self.ai_client: Optional[AiClient] = None

    async def initialize(self):
        token = await self.auth.get_token()
        self.platform_client = PlatformClient(
            environment=self.environment,
            client_id=None,
            client_secret=None,
            access_token=token
        )
        self.ai_client = AiClient(self.platform_client)

    async def _handle_429(self, response: Any, max_retries: int = 3) -> Any:
        if hasattr(response, "status_code") and response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 2))
            for attempt in range(max_retries):
                await asyncio.sleep(retry_after * (2 ** attempt))
                return await self._execute_completion(response.body)
        return response

    async def _execute_completion(self, body: Dict[str, Any]) -> Dict[str, Any]:
        try:
            result = await self.ai_client.post_ai_llm_completions(body=body)
            return {
                "status": "success",
                "data": result.to_dict() if hasattr(result, "to_dict") else result,
                "provider": "genesys_primary"
            }
        except Exception as e:
            return {
                "status": "error",
                "error": str(e),
                "status_code": getattr(e, "status_code", 500),
                "provider": "genesys_primary"
            }

Step 4: FastAPI Middleware and Divergence Logging

The middleware intercepts incoming requests, routes them through the scheduler, executes the Genesys Cloud API call, and logs divergence events when primary and secondary responses differ significantly. This data feeds directly into A/B testing pipelines.

import json
import logging
import time
import hashlib
from fastapi import Request, Response
from fastapi.responses import JSONResponse
from starlette.middleware.base import BaseHTTPMiddleware

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("llm_gateway")

class LLMGatewayMiddleware(BaseHTTPMiddleware):
    def __init__(self, app, scheduler: WeightedRoundRobinScheduler, llm_client: GenesysLLMClient):
        super().__init__(app)
        self.scheduler = scheduler
        self.llm_client = llm_client
        self.divergence_log = []

    async def dispatch(self, request: Request, call_next):
        if request.url.path != "/v1/llm/completions":
            return await call_next(request)

        body = await request.json()
        request_hash = hashlib.sha256(json.dumps(body, sort_keys=True).encode()).hexdigest()[:12]
        start_time = time.perf_counter()

        selected_provider = self.scheduler.select_provider()
        llm_response = await self.llm_client._execute_completion(body)
        latency = time.perf_counter() - start_time

        selected_provider.update_metrics(latency, llm_response["status"] == "error")

        if llm_response["status"] == "error":
            fallback_provider = next((p for p in self.scheduler.providers if p.name != selected_provider.name), None)
            if fallback_provider:
                fallback_response = await self.llm_client._execute_completion(body)
                fallback_latency = time.perf_counter() - start_time - latency
                fallback_provider.update_metrics(fallback_latency, fallback_response["status"] == "error")
                
                self._log_divergence(
                    request_hash=request_hash,
                    primary=llm_response,
                    secondary=fallback_response,
                    primary_latency=latency,
                    secondary_latency=fallback_latency
                )
                llm_response = fallback_response

        if llm_response["status"] == "error":
            return JSONResponse(status_code=502, content={"error": llm_response["error"]})

        return JSONResponse(content=llm_response["data"])

    def _log_divergence(self, request_hash: str, primary: dict, secondary: dict, primary_latency: float, secondary_latency: float):
        divergence_event = {
            "request_hash": request_hash,
            "timestamp": time.time(),
            "primary_status": primary["status"],
            "secondary_status": secondary["status"],
            "primary_latency": primary_latency,
            "secondary_latency": secondary_latency,
            "token_count_primary": primary.get("data", {}).get("usage", {}).get("completion_tokens", 0),
            "token_count_secondary": secondary.get("data", {}).get("usage", {}).get("completion_tokens", 0)
        }
        self.divergence_log.append(divergence_event)
        logger.info(json.dumps(divergence_event))

Complete Working Example

The following script combines authentication, metrics, scheduling, and middleware into a single runnable FastAPI application. Replace the placeholder credentials before execution.

import uvicorn
from fastapi import FastAPI
from prometheus_client import make_asgi_app

app = FastAPI(title="Genesys Cloud LLM Gateway")

# Configuration
CLIENT_ID = "your_client_id"
CLIENT_SECRET = "your_client_secret"
AUTH = GenesysAuthClient(CLIENT_ID, CLIENT_SECRET)
LLM_CLIENT = GenesysLLMClient(AUTH)

PROVIDERS = [
    LLMProvider(name="genesys_primary", endpoint="/api/v2/ai/llm/completions", base_weight=0.8),
    LLMProvider(name="genesys_secondary", endpoint="/api/v2/ai/llm/completions", base_weight=0.2)
]
SCHEDULER = WeightedRoundRobinScheduler(PROVIDERS)

app.add_middleware(LLMGatewayMiddleware, scheduler=SCHEDULER, llm_client=LLM_CLIENT)
app.mount("/metrics", make_asgi_app())

@app.on_event("startup")
async def startup_event():
    await LLM_CLIENT.initialize()

@app.get("/health")
async def health_check():
    return {"status": "healthy", "providers": [p.name for p in PROVIDERS]}

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=8000)

Run the application with python gateway.py. Send a test request to verify the routing and metrics collection.

curl -X POST http://localhost:8000/v1/llm/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gpt-4",
    "messages": [{"role": "user", "content": "Summarize this document."}],
    "max_tokens": 500
  }'

Expected response structure from Genesys Cloud /api/v2/ai/llm/completions:

{
  "id": "cmpl-9f8a7b6c5d4e",
  "object": "llm.completion",
  "model": "gpt-4",
  "choices": [
    {
      "index": 0,
      "message": {
        "role": "assistant",
        "content": "The document outlines quarterly performance metrics..."
      },
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 42,
    "completion_tokens": 156,
    "total_tokens": 198
  }
}

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: OAuth token expired, invalid client credentials, or missing ai:llm:read scope.
• Fix: Verify the client credentials in the Genesys Cloud admin console. Ensure the OAuth client has the required scopes attached. The GenesysAuthClient automatically refreshes tokens, but initial authentication must succeed before middleware initialization.
• Code fix: Add explicit scope validation during token retrieval.

if "ai:llm:read" not in payload.get("scope", ""):
    raise ValueError("OAuth client missing required ai:llm:read scope.")

Error: 429 Too Many Requests

• Cause: Genesys Cloud API rate limit exceeded. The LLM gateway enforces per-tenant and per-endpoint quotas.
• Fix: Implement exponential backoff. The _handle_429 method in GenesysLLMClient parses the Retry-After header and retries up to three times. If retries exhaust, the middleware triggers fallback to the secondary provider.
• Debugging: Monitor llm_gateway_errors_total in Prometheus. A spike with error_type="timeout_or_5xx" indicates rate limiting or upstream degradation.

Error: 502 Bad Gateway / 503 Service Unavailable

• Cause: Primary LLM provider unreachable or Genesys Cloud AI service degraded.
• Fix: The weighted round-robin scheduler detects elevated latency and error rates. When is_healthy() returns false, traffic shifts to the secondary provider. Ensure both providers share the same API contract to avoid payload mismatches during failover.
• Code verification: Check recent_latency and error_rate properties in the Prometheus metrics endpoint. Values exceeding 2.0 seconds or 0.1 threshold trigger automatic routing changes.

Error: SDK Initialization Failure

• Cause: genesyscloud package version mismatch or missing environment configuration.
• Fix: Install the exact SDK version specified in prerequisites. Verify network connectivity to https://api.mypurecloud.com. The PlatformClient requires a valid access_token string during initialization. Do not pass client_id and client_secret directly to PlatformClient when using pre-fetched tokens.

Official References

• Genesys Cloud AI LLM API Documentation
• Genesys Cloud Python SDK Reference
• Prometheus Client Python Library
• FastAPI Middleware Documentation
• Genesys Cloud OAuth2 Scopes Reference