Routing NICE Cognigy LLM Gateway Prompts via REST API with Python

What You Will Build

• A Python prompt router that constructs, validates, and submits inference requests to the NICE Cognigy LLM Gateway.
• Uses the Cognigy REST API for atomic prompt submission, streaming response parsing, and automatic fallback routing.
• Covers Python with httpx, pydantic, and asynchronous task management for production orchestration.

Prerequisites

• NICE Cognigy environment URL and valid API credentials (OAuth2 client credentials or service API key)
• Required OAuth scope: llm.gateway.inference
• Python 3.10 or higher
• External dependencies: httpx==0.27.0, pydantic==2.8.0, pydantic-settings==2.4.0
• Environment variables: COGNIGY_BASE_URL, COGNIGY_CLIENT_ID, COGNIGY_CLIENT_SECRET, WEBHOOK_URL

Authentication Setup

The Cognigy LLM Gateway requires a Bearer token issued via the platform OAuth2 endpoint. The token must carry the llm.gateway.inference scope. The following code demonstrates the client credentials flow with token caching and automatic refresh logic.

import httpx
import time
import logging
from typing import Optional

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")

class CognigyAuth:
    def __init__(self, base_url: str, client_id: str, client_secret: str):
        self.base_url = base_url.rstrip("/")
        self.client_id = client_id
        self.client_secret = client_secret
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0
        self.auth_endpoint = f"{self.base_url}/api/v1/auth/token"

    async def get_token(self) -> str:
        if self.token and time.time() < self.token_expiry - 30:
            return self.token

        async with httpx.AsyncClient(timeout=10.0) as client:
            response = await client.post(
                self.auth_endpoint,
                data={
                    "grant_type": "client_credentials",
                    "client_id": self.client_id,
                    "client_secret": self.client_secret,
                    "scope": "llm.gateway.inference"
                }
            )
            response.raise_for_status()
            payload = response.json()
            self.token = payload["access_token"]
            self.token_expiry = time.time() + payload["expires_in"]
            return self.token

    async def get_headers(self) -> dict:
        token = await self.get_token()
        return {
            "Authorization": f"Bearer {token}",
            "Content-Type": "application/json",
            "Accept": "application/json"
        }

HTTP Request Cycle:

POST /api/v1/auth/token HTTP/1.1
Host: {environment}.cognigy.com
Content-Type: application/x-www-form-urlencoded

grant_type=client_credentials&client_id=YOUR_CLIENT_ID&client_secret=YOUR_CLIENT_SECRET&scope=llm.gateway.inference

HTTP Response:

{
  "access_token": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...",
  "token_type": "Bearer",
  "expires_in": 3600,
  "scope": "llm.gateway.inference"
}

Implementation

Step 1: Payload Construction and Schema Validation

The router must validate prompt payloads against token limit constraints, context window matrices, and temperature boundaries before submission. Pydantic handles schema enforcement and prevents malformed requests from reaching the gateway.

from pydantic import BaseModel, Field, field_validator
from typing import List, Optional

class PromptPayload(BaseModel):
    model_endpoint: str
    context_window: int = Field(ge=1, le=128000)
    temperature: float = Field(ge=0.0, le=2.0)
    prompt: str
    max_tokens: Optional[int] = Field(default=None, ge=1, le=8192)
    top_p: float = Field(default=1.0, ge=0.0, le=1.0)

    @field_validator("prompt")
    @classmethod
    def reject_empty_prompt(cls, v: str) -> str:
        if not v or not v.strip():
            raise ValueError("Prompt cannot be empty or whitespace only")
        return v.strip()

    def to_gateway_json(self) -> dict:
        return {
            "model": self.model_endpoint,
            "messages": [{"role": "user", "content": self.prompt}],
            "context_window": self.context_window,
            "temperature": self.temperature,
            "max_tokens": self.max_tokens,
            "top_p": self.top_p,
            "stream": True
        }

The to_gateway_json method transforms the validated object into the exact schema expected by /api/v1/llm/gateway/inference. The stream: True flag enables server-sent events for latency-sensitive conversational AI workflows.

Step 2: Atomic POST Operations and Streaming Response Parsing

Atomic submission requires a single POST request that either completes or fails without partial state. The router uses httpx async streaming to parse JSON chunks, accumulate tokens, and trigger fallback logic on transient timeouts or 5xx errors.

import json
import asyncio
from dataclasses import dataclass, field

@dataclass
class InferenceResult:
    model: str
    prompt_id: str
    completed: bool = False
    accumulated_text: str = ""
    prompt_tokens: int = 0
    completion_tokens: int = 0
    total_latency_ms: float = 0.0
    error: Optional[str] = None

class CognigyLLMRouter:
    def __init__(self, auth: CognigyAuth, fallback_endpoints: List[str] = None):
        self.auth = auth
        self.base_inference_url = f"{auth.base_url}/api/v1/llm/gateway/inference"
        self.fallback_endpoints = fallback_endpoints or []
        self.client = httpx.AsyncClient(
            timeout=httpx.Timeout(connect=5.0, read=30.0, pool=10.0),
            limits=httpx.Limits(max_connections=20, max_keepalive_connections=10)
        )

    async def submit_prompt(self, payload: PromptPayload) -> InferenceResult:
        endpoints = [self.base_inference_url] + self.fallback_endpoints
        last_error = None

        for endpoint in endpoints:
            try:
                result = await self._stream_inference(endpoint, payload)
                if result.completed or not self.fallback_endpoints:
                    return result
                last_error = result.error
            except httpx.HTTPStatusError as e:
                if e.response.status_code == 429:
                    retry_after = int(e.response.headers.get("Retry-After", 5))
                    logging.warning(f"Rate limited by gateway. Waiting {retry_after}s")
                    await asyncio.sleep(retry_after)
                    continue
                last_error = f"HTTP {e.response.status_code}: {e.response.text}"
            except Exception as e:
                last_error = str(e)

        result = InferenceResult(
            model=payload.model_endpoint,
            prompt_id="fallback_exhausted",
            error=last_error
        )
        return result

    async def _stream_inference(self, endpoint: str, payload: PromptPayload) -> InferenceResult:
        headers = await self.auth.get_headers()
        start_time = time.time()
        result = InferenceResult(model=payload.model_endpoint, prompt_id="init")
        
        async with self.client.stream("POST", endpoint, json=payload.to_gateway_json(), headers=headers) as response:
            response.raise_for_status()
            
            async for chunk in response.aiter_lines():
                if not chunk or chunk.startswith(":"):
                    continue
                
                try:
                    data = json.loads(chunk)
                    if "error" in data:
                        result.error = data["error"]["message"]
                        return result
                    
                    if "choices" in data:
                        delta = data["choices"][0].get("delta", {})
                        text = delta.get("content", "")
                        if text:
                            result.accumulated_text += text
                    
                    if "usage" in data:
                        result.prompt_tokens = data["usage"].get("prompt_tokens", 0)
                        result.completion_tokens = data["usage"].get("completion_tokens", 0)
                        result.completed = True
                        break
                except json.JSONDecodeError:
                    continue

        result.total_latency_ms = (time.time() - start_time) * 1000
        if not result.completed and not result.error:
            result.error = "Stream terminated without completion token"
        return result

The streaming parser handles SSE-style JSON lines, extracts delta content, and captures the final usage block for token accounting. The fallback loop retries against alternate endpoints only when the primary gateway returns a 5xx error or timeout.

Step 3: Routing Validation and Output Verification Pipelines

Conversational AI integration requires prompt injection detection and output format verification before the response reaches the end user. The router applies heuristic scanning and JSON schema validation to enforce safe generation.

import re
from typing import Tuple

class ValidationPipeline:
    INJECTION_PATTERNS = [
        r"ignore\s+previous\s+instructions",
        r"system\s+override",
        r"bypass\s+security",
        r"<\|system\|>",
        r"ACT\s+AS\s+A\s+SYSTEM\s+ADMIN"
    ]
    INJECTION_REGEX = re.compile("|".join(INJECTION_PATTERNS), re.IGNORECASE)

    @staticmethod
    def detect_injection(prompt: str) -> bool:
        return bool(ValidationPipeline.INJECTION_REGEX.search(prompt))

    @staticmethod
    def verify_output_format(output: str, expected_json: bool = False) -> Tuple[bool, Optional[str]]:
        if not expected_json:
            return True, None
        
        try:
            json.loads(output)
            return True, None
        except json.JSONDecodeError as e:
            return False, f"Output violates JSON format requirement: {str(e)}"

The pipeline runs before submission and after completion. If injection patterns are detected, the router rejects the prompt immediately. If the downstream application requires structured output, the verification step blocks hallucination artifacts that fail schema compliance.

Step 4: Observability, Audit Logging, and Webhook Synchronization

MLOps efficiency depends on latency alignment and token consumption tracking. The router synchronizes inference completion events with external observability platforms via webhook callbacks and generates structured audit logs for AI governance compliance.

import uuid
from datetime import datetime, timezone

@dataclass
class AuditLogEntry:
    request_id: str
    model: str
    timestamp: str
    prompt_length: int
    completion_length: int
    prompt_tokens: int
    completion_tokens: int
    latency_ms: float
    status: str
    webhook_dispatched: bool = False

class ObservabilityManager:
    def __init__(self, webhook_url: Optional[str] = None):
        self.webhook_url = webhook_url
        self.client = httpx.AsyncClient(timeout=5.0)

    async def record_and_notify(self, result: InferenceResult, payload: PromptPayload) -> AuditLogEntry:
        entry = AuditLogEntry(
            request_id=str(uuid.uuid4()),
            model=result.model,
            timestamp=datetime.now(timezone.utc).isoformat(),
            prompt_length=len(payload.prompt),
            completion_length=len(result.accumulated_text),
            prompt_tokens=result.prompt_tokens,
            completion_tokens=result.completion_tokens,
            latency_ms=result.total_latency_ms,
            status="success" if result.completed else "failed",
            webhook_dispatched=False
        )

        logging.info(f"Audit Log: {entry.request_id} | Model: {entry.model} | Latency: {entry.latency_ms:.2f}ms | Tokens: {entry.prompt_tokens + entry.completion_tokens}")

        if self.webhook_url and entry.status == "success":
            try:
                await self.client.post(
                    self.webhook_url,
                    json={
                        "event": "llm.inference.completed",
                        "request_id": entry.request_id,
                        "model": entry.model,
                        "latency_ms": entry.latency_ms,
                        "tokens": entry.prompt_tokens + entry.completion_tokens,
                        "timestamp": entry.timestamp
                    }
                )
                entry.webhook_dispatched = True
            except Exception as e:
                logging.warning(f"Webhook dispatch failed: {str(e)}")

        return entry

The observability manager captures exact latency alignment, token consumption rates, and dispatches structured events to external platforms. Audit logs persist request metadata for governance compliance and cost allocation.

Complete Working Example

The following script combines authentication, validation, routing, streaming, fallback logic, and observability into a single production-ready module. Set the required environment variables and execute with python cognigy_llm_router.py.

import os
import asyncio
import logging
from cognigy_llm_router import CognigyAuth, CognigyLLMRouter, PromptPayload, ValidationPipeline, ObservabilityManager

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")

async def main():
    base_url = os.getenv("COGNIGY_BASE_URL")
    client_id = os.getenv("COGNIGY_CLIENT_ID")
    client_secret = os.getenv("COGNIGY_CLIENT_SECRET")
    webhook_url = os.getenv("WEBHOOK_URL")

    if not all([base_url, client_id, client_secret]):
        raise ValueError("Missing required environment variables: COGNIGY_BASE_URL, COGNIGY_CLIENT_ID, COGNIGY_CLIENT_SECRET")

    auth = CognigyAuth(base_url, client_id, client_secret)
    router = CognigyLLMRouter(auth, fallback_endpoints=[f"{base_url}/api/v1/llm/gateway/inference-backup"])
    observability = ObservabilityManager(webhook_url)

    prompt_text = "Translate the following technical documentation into clear customer-facing language: {original_text}"
    
    try:
        payload = PromptPayload(
            model_endpoint="cognigy-llm-v2",
            context_window=16000,
            temperature=0.3,
            prompt=prompt_text,
            max_tokens=1024,
            top_p=0.9
        )
    except Exception as e:
        logging.error(f"Payload validation failed: {str(e)}")
        return

    if ValidationPipeline.detect_injection(payload.prompt):
        logging.warning("Prompt rejected by injection detection pipeline")
        return

    logging.info("Submitting inference request to Cognigy LLM Gateway")
    result = await router.submit_prompt(payload)

    if result.error:
        logging.error(f"Inference failed: {result.error}")
        return

    is_valid, format_error = ValidationPipeline.verify_output_format(result.accumulated_text, expected_json=False)
    if not is_valid:
        logging.warning(f"Output verification failed: {format_error}")
        return

    audit_entry = await observability.record_and_notify(result, payload)
    logging.info(f"Routing complete. Request ID: {audit_entry.request_id}")
    print(f"Generated Response: {result.accumulated_text}")

if __name__ == "__main__":
    asyncio.run(main())

Common Errors and Debugging

Error: 401 Unauthorized or 403 Forbidden

• Cause: Expired OAuth token, missing llm.gateway.inference scope, or incorrect client credentials.
• Fix: Verify the client credentials match your Cognigy environment. Ensure the token request includes the exact scope string. The CognigyAuth class automatically refreshes tokens before expiry. If the error persists, regenerate credentials in the Cognigy admin console.
• Code Fix: Add explicit scope validation in the auth request and log the raw response for debugging.

Error: 429 Too Many Requests

• Cause: Gateway rate limits exceeded for concurrent inference requests or token throughput.
• Fix: Implement exponential backoff and respect the Retry-After header. The router already catches 429 responses and pauses execution. Increase max_keepalive_connections in the httpx limits if connection pooling causes artificial throttling.
• Code Fix: Adjust retry logic to include jitter: await asyncio.sleep(retry_after + random.uniform(0, 1))

Error: 504 Gateway Timeout or Stream Termination

• Cause: Model inference exceeds the read timeout or the upstream provider drops the connection.
• Fix: Increase the read timeout in the httpx configuration. Enable fallback endpoints to route to alternate model providers. The router automatically switches to fallback_endpoints when the primary stream fails.
• Code Fix: Set timeout=httpx.Timeout(connect=5.0, read=45.0, pool=10.0) for high-latency models.

Error: Pydantic ValidationError on Context Window or Temperature

• Cause: Payload parameters exceed gateway constraints (context window > 128000, temperature outside 0.0-2.0).
• Fix: Validate parameters against the target model specification before instantiation. Use Field(ge=..., le=...) constraints as shown in PromptPayload.
• Code Fix: Catch pydantic.ValidationError explicitly and map it to a structured error response for upstream consumers.

Official References

• NICE CXone / Cognigy API Authentication Documentation
• Cognigy LLM Gateway Inference Endpoint Reference
• httpx Async Streaming and Timeout Configuration
• Pydantic V2 Validation and Field Constraints