Validating Cognigy.AI LLM Gateway Response Schemas with Python

What You Will Build

• A Python service that intercepts Cognigy.AI LLM gateway responses, validates them against a strict schema matrix, enforces token limits, and detects hallucinations or tone drift before returning results.
• This uses the Cognigy.AI REST API for AI generation and a Pydantic-based validation pipeline.
• The tutorial covers Python with FastAPI, httpx, pydantic, and jsonschema.

Prerequisites

• Cognigy.AI OAuth client credentials with scopes: cognigy:ai:generate, cognigy:audit:write
• Python 3.10 or higher
• External dependencies: pip install fastapi uvicorn httpx pydantic jsonschema pydantic-settings structlog
• A configured Cognigy.AI tenant with LLM gateway access enabled

Authentication Setup

Cognigy.AI uses OAuth2 client credentials flow for server-to-server API access. You must cache the access token and refresh it before expiration to avoid 401 interruptions during high-throughput validation cycles.

import httpx
import time
from dataclasses import dataclass
from typing import Optional

@dataclass
class TokenCache:
    access_token: str
    expires_at: float

class CognigyAuthManager:
    def __init__(self, tenant: str, client_id: str, client_secret: str):
        self.base_url = f"https://{tenant}.cognigy.ai/api/v1"
        self.client_id = client_id
        self.client_secret = client_secret
        self.token: Optional[TokenCache] = None
        self.http_client = httpx.AsyncClient(timeout=15.0)

    async def get_token(self) -> str:
        if self.token and time.time() < self.token.expires_at - 60:
            return self.token.access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "cognigy:ai:generate cognigy:audit:write"
        }

        response = await self.http_client.post(
            f"{self.base_url}/auth/oauth/token",
            data=payload
        )
        response.raise_for_status()
        data = response.json()

        self.token = TokenCache(
            access_token=data["access_token"],
            expires_at=time.time() + data["expires_in"]
        )
        return self.token.access_token

    async def close(self):
        await self.http_client.aclose()

The get_token method checks expiration with a sixty-second safety buffer. It requests the exact scopes required for LLM generation and audit logging. The method raises httpx.HTTPStatusError on failure, which the calling service must catch and handle.

Implementation

Step 1: Define Response Schema Matrix and Validation Rules

You must enforce a strict output structure before processing LLM responses. The schema matrix defines allowed fields, data types, and maximum token counts. Pydantic handles runtime validation while jsonschema validates raw payloads against external definitions.

import json
import jsonschema
from pydantic import BaseModel, Field, validator
from typing import List, Dict, Any

class LLMResponsePayload(BaseModel):
    text: str = Field(..., description="Generated response text")
    citations: List[Dict[str, str]] = Field(default_factory=list)
    metadata: Dict[str, Any] = Field(default_factory=dict)
    token_count: int = Field(..., gt=0)

    @validator("text")
    def enforce_max_tokens(cls, v: str, values: dict) -> str:
        max_tokens = values.get("metadata", {}).get("max_tokens", 1500)
        estimated_tokens = len(v.split()) * 1.3
        if estimated_tokens > max_tokens:
            raise ValueError(f"Response exceeds maximum token limit of {max_tokens}")
        return v

def validate_schema_matrix(payload: dict, schema: dict) -> bool:
    """Validates raw JSON against external schema matrix."""
    jsonschema.validate(instance=payload, schema=schema)
    return True

The enforce_max_tokens validator calculates an approximate token count based on word length. Production systems should integrate an exact tokenizer library, but this heuristic prevents runaway generation during validation. The validate_schema_matrix function runs before Pydantic parsing to catch structural drift.

Step 2: Implement LLM Generation Call with Retry Logic

Cognigy.AI rate-limits gateway endpoints at 429. You must implement exponential backoff with jitter. The generation call also handles automatic response truncation if the LLM exceeds safe boundaries.

import asyncio
import random
import structlog
from httpx import HTTPStatusError

logger = structlog.get_logger()

class CognigyLLMClient:
    def __init__(self, auth: CognigyAuthManager):
        self.auth = auth
        self.base_url = auth.base_url
        self.client = httpx.AsyncClient(timeout=20.0)

    async def generate_response(self, prompt: str, max_tokens: int) -> dict:
        token = await self.auth.get_token()
        headers = {"Authorization": f"Bearer {token}", "Content-Type": "application/json"}
        payload = {
            "prompt": prompt,
            "max_tokens": max_tokens,
            "temperature": 0.2,
            "model": "cognigy-llm-v2"
        }

        retries = 3
        for attempt in range(retries):
            try:
                start_time = time.perf_counter()
                response = await self.client.post(
                    f"{self.base_url}/ai/llm/generate",
                    headers=headers,
                    json=payload
                )
                latency_ms = (time.perf_counter() - start_time) * 1000
                response.raise_for_status()
                return self._parse_response(response.json(), latency_ms)
            except HTTPStatusError as e:
                if e.response.status_code == 429 and attempt < retries - 1:
                    wait_time = (2 ** attempt) + random.uniform(0.1, 0.5)
                    logger.warning("rate_limited", status_code=429, retry_in=wait_time)
                    await asyncio.sleep(wait_time)
                    continue
                raise
            except Exception as e:
                logger.error("generation_failed", error=str(e))
                raise

    def _parse_response(self, raw: dict, latency_ms: float) -> dict:
        text = raw.get("text", "")
        if len(text.split()) * 1.3 > raw.get("metadata", {}).get("max_tokens", 1500):
            text = text[:1200] + " [TRUNCATED]"
        return {
            "text": text,
            "citations": raw.get("citations", []),
            "metadata": raw.get("metadata", {}),
            "token_count": raw.get("token_count", len(text.split())),
            "latency_ms": latency_ms
        }

    async def close(self):
        await self.client.aclose()

The retry loop catches 429 responses and applies exponential backoff with jitter. The _parse_response method enforces truncation when token estimates exceed the configured limit. Latency tracking is captured at the HTTP level for audit reporting.

Step 3: Build Hallucination Detection, Citation Accuracy, and Tone Alignment

You must verify that generated claims match provided citations and maintain consistent tone. This pipeline runs after schema validation and before webhook sync.

import re
from typing import Tuple

class ValidationPipeline:
    def __init__(self, allowed_tone_keywords: List[str]):
        self.tone_keywords = allowed_tone_keywords

    def check_citation_accuracy(self, text: str, citations: List[Dict[str, str]]) -> Tuple[bool, str]:
        """Verifies that claims in text reference valid citation IDs."""
        cited_ids = {c.get("id") for c in citations}
        mentions = re.findall(r"\[ref:(\w+)\]", text)
        for mention in mentions:
            if mention not in cited_ids:
                return False, f"Unverified citation reference: {mention}"
        return True, "All citations verified"

    def check_tone_alignment(self, text: str) -> Tuple[bool, str]:
        """Ensures response matches approved tone profile."""
        lower_text = text.lower()
        match_count = sum(1 for kw in self.tone_keywords if kw in lower_text)
        if match_count < 2:
            return False, "Tone alignment threshold not met"
        return True, "Tone aligned"

    def detect_hallucination(self, text: str, known_facts: Dict[str, str]) -> Tuple[bool, str]:
        """Basic factual drift detection against known fact base."""
        for key, expected in known_facts.items():
            if key in text and expected.lower() not in text.lower():
                return False, f"Factual drift detected on: {key}"
        return True, "No hallucination detected"

The pipeline uses regex for citation ID extraction, keyword density for tone verification, and substring matching for factual drift. Production systems should replace the hallucination detector with an embedding similarity check or external fact-checking service.

Step 4: Handle Webhook Sync, Latency Tracking, and Audit Logs

You must synchronize validation events with external services and maintain structured audit trails for governance. The audit logger captures success rates, latency percentiles, and validation outcomes.

import json
from datetime import datetime, timezone

class AuditLogger:
    def __init__(self, webhook_url: str):
        self.webhook_url = webhook_url
        self.client = httpx.AsyncClient(timeout=10.0)
        self.metrics = {"success": 0, "failure": 0, "latency_sum": 0.0, "count": 0}

    async def log_validation(self, payload: dict, is_valid: bool, latency_ms: float, error_detail: str = ""):
        self.metrics["count"] += 1
        self.metrics["latency_sum"] += latency_ms
        if is_valid:
            self.metrics["success"] += 1
        else:
            self.metrics["failure"] += 1

        audit_record = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "validation_status": "PASS" if is_valid else "FAIL",
            "latency_ms": round(latency_ms, 2),
            "error_detail": error_detail,
            "response_hash": hash(json.dumps(payload, sort_keys=True)) % (2**32)
        }

        await self._sync_webhook(audit_record)
        await self._write_local_audit(audit_record)

    async def _sync_webhook(self, record: dict):
        try:
            await self.client.post(self.webhook_url, json=record)
        except Exception as e:
            logger.error("webhook_sync_failed", error=str(e))

    async def _write_local_audit(self, record: dict):
        with open("validation_audit.log", "a", encoding="utf-8") as f:
            f.write(json.dumps(record) + "\n")

    async def close(self):
        await self.client.aclose()

The audit logger maintains in-memory metrics for success rate calculation and persists structured JSON lines to a local file. The webhook sync runs asynchronously to avoid blocking the validation pipeline. All timestamps use UTC ISO 8601 format for governance compliance.

Complete Working Example

The following FastAPI application combines authentication, generation, validation, truncation, and audit logging into a single deployable service.

import uvicorn
import structlog
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel, Field
from typing import List, Dict, Any

structlog.configure(
    processors=[
        structlog.processors.TimeStamper(fmt="iso"),
        structlog.processors.JSONRenderer()
    ],
    wrapper_class=structlog.make_filtering_bound_logger("INFO"),
    context_class=dict,
    logger_factory=structlog.PrintLoggerFactory(),
    cache_logger_on_first_use=True,
)
logger = structlog.get_logger()

app = FastAPI(title="Cognigy LLM Response Validator")

# Configuration placeholders
TENANT = "your-tenant"
CLIENT_ID = "your-client-id"
CLIENT_SECRET = "your-client-secret"
WEBHOOK_URL = "https://your-fact-check-service.com/webhook"
ALLOWED_TONE = ["professional", "helpful", "concise", "empathetic"]
KNOWN_FACTS = {"api_version": "v2.1", "max_concurrency": "500"}

auth_manager = CognigyAuthManager(TENANT, CLIENT_ID, CLIENT_SECRET)
llm_client = CognigyLLMClient(auth_manager)
validation_pipeline = ValidationPipeline(ALLOWED_TONE)
audit_logger = AuditLogger(WEBHOOK_URL)

SCHEMA_MATRIX = {
    "type": "object",
    "required": ["text", "token_count", "metadata"],
    "properties": {
        "text": {"type": "string"},
        "token_count": {"type": "integer", "minimum": 1},
        "metadata": {"type": "object"},
        "citations": {"type": "array"}
    }
}

class ValidationRequest(BaseModel):
    prompt: str = Field(..., min_length=10)
    max_tokens: int = Field(default=1500, gt=0, le=4000)

class ValidationResponse(BaseModel):
    validated_text: str
    citations: List[Dict[str, Any]]
    latency_ms: float
    validation_checks: Dict[str, bool]
    audit_status: str

@app.post("/validate", response_model=ValidationResponse)
async def validate_llm_response(req: ValidationRequest):
    try:
        raw_response = await llm_client.generate_response(req.prompt, req.max_tokens)
        
        # Step 1: Schema matrix validation
        try:
            validate_schema_matrix(raw_response, SCHEMA_MATRIX)
        except jsonschema.ValidationError as e:
            await audit_logger.log_validation(raw_response, False, raw_response["latency_ms"], str(e))
            raise HTTPException(status_code=400, detail=f"Schema validation failed: {e.message}")

        # Step 2: Pydantic model validation
        try:
            validated_payload = LLMResponsePayload(**raw_response)
        except Exception as e:
            await audit_logger.log_validation(raw_response, False, raw_response["latency_ms"], str(e))
            raise HTTPException(status_code=400, detail=f"Payload validation failed: {str(e)}")

        # Step 3: Hallucination, citation, and tone checks
        hallucination_ok, hall_msg = validation_pipeline.detect_hallucination(
            validated_payload.text, KNOWN_FACTS
        )
        citation_ok, cit_msg = validation_pipeline.check_citation_accuracy(
            validated_payload.text, validated_payload.citations
        )
        tone_ok, tone_msg = validation_pipeline.check_tone_alignment(validated_payload.text)

        checks_passed = hallucination_ok and citation_ok and tone_ok
        error_detail = ""
        if not checks_passed:
            error_detail = f"Hallucination: {hall_msg} | Citation: {cit_msg} | Tone: {tone_msg}"

        await audit_logger.log_validation(
            raw_response, checks_passed, raw_response["latency_ms"], error_detail
        )

        if not checks_passed:
            raise HTTPException(status_code=422, detail=f"Validation checks failed: {error_detail}")

        return ValidationResponse(
            validated_text=validated_payload.text,
            citations=validated_payload.citations,
            latency_ms=raw_response["latency_ms"],
            validation_checks={
                "schema": True,
                "hallucination": hallucination_ok,
                "citation_accuracy": citation_ok,
                "tone_alignment": tone_ok
            },
            audit_status="logged"
        )

    except HTTPStatusError as e:
        raise HTTPException(status_code=e.response.status_code, detail=e.response.text)
    except Exception as e:
        logger.error("unhandled_validation_error", error=str(e))
        raise HTTPException(status_code=500, detail="Internal validation pipeline error")

@app.on_event("shutdown")
async def shutdown_event():
    await llm_client.close()
    await auth_manager.close()
    await audit_logger.close()

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

Run the service with python validator_service.py. The endpoint accepts a prompt and maximum token count, generates the response via Cognigy.AI, runs the full validation matrix, truncates if necessary, logs audit records, and returns a structured result. All external dependencies are handled with explicit timeout and retry boundaries.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired or missing OAuth token, incorrect client credentials, or missing cognigy:ai:generate scope.
• Fix: Verify the get_token method runs before each batch. Check the scope parameter in the token request. Add logging around token expiration to catch early refresh failures.
• Code fix: The CognigyAuthManager already implements a sixty-second safety buffer. If 401 persists, rotate the client secret and verify tenant URL formatting.

Error: 429 Too Many Requests

• Cause: Cognigy.AI gateway rate limit exceeded during high concurrency.
• Fix: The retry loop applies exponential backoff with jitter. Reduce concurrent requests or implement a token bucket limiter upstream.
• Code fix: Increase the retries variable in generate_response or adjust the base delay from 2 ** attempt to 3 ** attempt for stricter throttling environments.

Error: 400 Schema Validation Failed

• Cause: LLM response deviates from the expected JSON structure or missing required fields.
• Fix: Update the SCHEMA_MATRIX to match the actual Cognigy.AI model output. Enable strict mode in jsonschema.validate to catch unexpected keys.
• Code fix: Log the raw response payload before validation to inspect structural changes. Wrap validate_schema_matrix in a try-except block that captures the exact path violation.

Error: 422 Validation Checks Failed

• Cause: Hallucination detection, citation mismatch, or tone drift triggered pipeline rejection.
• Fix: Review the KNOWN_FACTS dictionary for outdated entries. Adjust tone keyword thresholds if legitimate responses are rejected. Verify citation IDs match the prompt context.
• Code fix: The ValidationPipeline returns explicit failure messages. Parse the error_detail field in the audit log to identify which sub-check triggered the rejection.

Official References

• Cognigy.AI REST API Documentation
• Cognigy.AI OAuth2 Authentication Guide
• Pydantic Validation Documentation
• jsonschema Library Reference
• FastAPI Application Structure
• httpx Async Client Documentation