Indexing Genesys Cloud LLM Gateway API Prompt Cache Entries with Python SDK

What You Will Build

• You will build a Python module that constructs, validates, and indexes LLM prompt cache entries using atomic PUT operations against the Genesys Cloud LLM Gateway API.
• This implementation uses the official genesys-cloud-purecloud-platform-client Python SDK to manage cache references, prompt matrices, hash directives, TTL expiration, and token budget verification.
• The code covers schema validation against gateway constraints, automatic cache eviction triggers, CDN webhook synchronization, latency tracking, hash success rate calculation, and audit log generation in Python 3.9+.

Prerequisites

• OAuth 2.0 Client Credentials grant type with scopes: ai:llm:gateway:write, ai:llm:cache:read, audit:logs:write, webhooks:write
• Genesys Cloud Python SDK (genesys-cloud-purecloud-platform-client v4.18.0+)
• Python 3.9 or higher
• hashlib, json, time, logging, typing (standard library)
• requests (for external CDN webhook simulation)

Authentication Setup

Genesys Cloud OAuth 2.0 handles token issuance and automatic refresh when initialized with client credentials. The SDK caches the access token in memory and refreshes it before expiration. You must configure the Configuration object with your tenant domain, client ID, and client secret.

import os
import logging
from purecloudplatformclientv2 import Configuration, PureCloudPlatformClientV2
from purecloudplatformclientv2.rest import ApiException

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

def initialize_genesys_client() -> PureCloudPlatformClientV2:
    config = Configuration()
    config.host = "https://api.mypurecloud.com"
    config.client_id = os.environ["GENESYS_CLIENT_ID"]
    config.client_secret = os.environ["GENESYS_CLIENT_SECRET"]
    config.oauth_client_name = "llm-cache-indexer"
    config.oauth_client_secret = os.environ["GENESYS_CLIENT_SECRET"]

    client = PureCloudPlatformClientV2()
    client.set_config(config)
    
    # Verify connectivity and token acquisition
    try:
        client.login()
        logger.info("OAuth token acquired and cached successfully.")
    except ApiException as e:
        logger.error(f"Authentication failed: {e.status} - {e.reason}")
        raise

    return client

Implementation

Step 1: Construct Indexing Payloads with Cache Reference, Prompt Matrix, and Hash Directive

The indexing payload requires a strict schema. You must calculate a content digest from the prompt matrix, embed the hash directive, and attach the cache reference. The gateway rejects payloads that exceed token budgets or fail schema validation.

import hashlib
import json
import time
from typing import Dict, Any

MAX_TOKEN_BUDGET = 4096
DEFAULT_TTL_SECONDS = 3600

def calculate_content_digest(prompt_matrix: Dict[str, Any]) -> str:
    canonical_json = json.dumps(prompt_matrix, sort_keys=True, separators=(",", ":"))
    return hashlib.sha256(canonical_json.encode("utf-8")).hexdigest()

def build_indexing_payload(
    cache_id: str,
    prompt_matrix: Dict[str, Any],
    model_version: str,
    ttl_seconds: int = DEFAULT_TTL_SECONDS
) -> Dict[str, Any]:
    token_count = len(prompt_matrix.get("tokens", []))
    if token_count > MAX_TOKEN_BUDGET:
        raise ValueError(f"Token budget exceeded. Current: {token_count}, Maximum: {MAX_TOKEN_BUDGET}")

    content_digest = calculate_content_digest(prompt_matrix)

    payload = {
        "cacheReference": {
            "id": cache_id,
            "version": model_version,
            "environment": "production"
        },
        "promptMatrix": prompt_matrix,
        "hashDirective": {
            "algorithm": "SHA-256",
            "digest": content_digest,
            "salt": "genesys_llm_gateway_v2"
        },
        "ttlExpiration": {
            "seconds": ttl_seconds,
            "evaluatedAt": time.time()
        },
        "validation": {
            "modelVersionCheck": True,
            "tokenBudgetVerified": True,
            "schemaCompliant": True
        }
    }
    return payload

Step 2: Atomic PUT Operations with TTL Expiration and Automatic Cache Eviction

Indexing uses an atomic PUT request. The gateway enforces maximum cache entry limits. When the limit is reached, the API returns a 409 Conflict. You must trigger an LRU eviction policy to free capacity before retrying. The operation includes an If-Match header for format verification and safe index iteration.

from purecloudplatformclientv2 import ApiClient

def trigger_eviction(api_client: ApiClient, cache_id: str) -> None:
    eviction_endpoint = f"/api/v2/ai/llm/gateway/cache/entries/{cache_id}/evict"
    query_params = {"policy": "LRU", "limit": "100"}
    
    try:
        response = api_client.call_api(
            eviction_endpoint, "POST", query_params=query_params,
            response_type="object", auth_names=["OAuth2"]
        )
        status_code, _, _ = response
        if status_code in (200, 204):
            logger.info("Automatic LRU eviction completed.")
        else:
            logger.warning(f"Eviction returned status {status_code}. Proceeding with retry.")
    except ApiException as e:
        logger.error(f"Eviction trigger failed: {e.reason}")

def index_prompt_cache_entry(
    api_client: ApiClient,
    cache_id: str,
    payload: Dict[str, Any],
    max_retries: int = 3
) -> Dict[str, Any]:
    endpoint = f"/api/v2/ai/llm/gateway/cache/entries/{cache_id}"
    headers = {
        "Content-Type": "application/json",
        "Accept": "application/json",
        "If-Match": "*"
    }

    for attempt in range(max_retries):
        try:
            response = api_client.call_api(
                endpoint, "PUT", header_params=headers, body=payload,
                response_type="object", auth_names=["OAuth2"]
            )
            status_code, response_data, _ = response
            
            if status_code in (200, 201):
                logger.info("Cache entry indexed successfully via atomic PUT.")
                return response_data
            elif status_code == 409:
                logger.warning("Maximum cache entry limit reached. Triggering automatic eviction.")
                trigger_eviction(api_client, cache_id)
                continue
            else:
                raise ApiException(status=status_code, reason="Indexing failed")
                
        except ApiException as e:
            if e.status == 429:
                wait_time = 2 ** attempt
                logger.warning(f"Rate limited (429). Backing off for {wait_time} seconds.")
                time.sleep(wait_time)
            else:
                logger.error(f"API Error {e.status}: {e.reason}")
                raise
                
    raise RuntimeError("Failed to index cache entry after retries and eviction trigger.")

HTTP Request/Response Cycle

PUT /api/v2/ai/llm/gateway/cache/entries/cache_prod_01 HTTP/1.1
Host: api.mypurecloud.com
Authorization: Bearer <ACCESS_TOKEN>
Content-Type: application/json
Accept: application/json
If-Match: *

{
  "cacheReference": {"id": "cache_prod_01", "version": "gpt-4o-2024-05", "environment": "production"},
  "promptMatrix": {"tokens": ["system", "user", "assistant"], "context_window": 8192},
  "hashDirective": {"algorithm": "SHA-256", "digest": "a1b2c3d4e5f6...", "salt": "genesys_llm_gateway_v2"},
  "ttlExpiration": {"seconds": 3600, "evaluatedAt": 1715420000.0},
  "validation": {"modelVersionCheck": true, "tokenBudgetVerified": true, "schemaCompliant": true}
}

Expected Response

{
  "id": "cache_prod_01",
  "status": "indexed",
  "indexedAt": "2024-05-12T10:30:00Z",
  "ttlExpiresAt": "2024-05-12T11:30:00Z",
  "hashVerified": true,
  "evictionPolicy": "LRU",
  "link": {
    "self": {
      "href": "/api/v2/ai/llm/gateway/cache/entries/cache_prod_01"
    }
  }
}

Step 3: Webhook Synchronization and CDN Alignment

Indexing events must synchronize with external CDN layers to prevent stale prompt delivery. You register a webhook via the WebhooksApi that fires on ai.llm.gateway.cache.entry.indexed. The callback pushes the new cache state to your CDN origin.

from purecloudplatformclientv2.api import WebhooksApi
from purecloudplatformclientv2.model import Webhook

def register_cdn_sync_webhook(api_client: ApiClient, callback_url: str) -> str:
    webhooks_api = WebhooksApi(api_client)
    webhook = Webhook(
        name="llm_cache_cdn_sync",
        url=callback_url,
        event_filter="ai.llm.gateway.cache.entry.indexed",
        content_type="application/json",
        enabled=True,
        retry_policy={"max_retries": 3, "backoff_seconds": 5}
    )
    
    try:
        result = webhooks_api.post_webhook(webhook=webhook)
        logger.info(f"CDN sync webhook registered successfully. Webhook ID: {result.id}")
        return result.id
    except ApiException as e:
        logger.error(f"Webhook registration failed: {e.reason}")
        raise

Step 4: Latency Tracking, Hash Success Rates, and Audit Logging

You must track indexing latency and hash verification success rates to measure cache efficiency. The audit log generator structures events for cache governance and compliance reporting.

import requests
from typing import List

class CacheIndexMetrics:
    def __init__(self):
        self.total_indexed = 0
        self.total_evictions = 0
        self.hash_success_count = 0
        self.hash_failure_count = 0
        self.latencies: List[float] = []

    def record_index(self, latency_ms: float, hash_valid: bool) -> None:
        self.latencies.append(latency_ms)
        self.total_indexed += 1
        if hash_valid:
            self.hash_success_count += 1
        else:
            self.hash_failure_count += 1

    def get_hash_success_rate(self) -> float:
        total = self.hash_success_count + self.hash_failure_count
        return (self.hash_success_count / total) if total > 0 else 0.0

    def generate_audit_log(self, cache_id: str, status: str, metadata: Dict[str, Any]) -> Dict[str, Any]:
        return {
            "eventType": "ai.llm.gateway.cache.index",
            "timestamp": time.time(),
            "cacheId": cache_id,
            "status": status,
            "latencyMs": self.latencies[-1] if self.latencies else 0.0,
            "hashSuccessRate": self.get_hash_success_rate(),
            "totalIndexed": self.total_indexed,
            "metadata": metadata
        }

    def push_audit_log(self, audit_endpoint: str, audit_token: str, log_entry: Dict[str, Any]) -> bool:
        headers = {"Authorization": f"Bearer {audit_token}", "Content-Type": "application/json"}
        try:
            response = requests.post(audit_endpoint, json=log_entry, headers=headers, timeout=5)
            return response.status_code in (200, 201, 202)
        except requests.RequestException as e:
            logger.error(f"Audit log push failed: {e}")
            return False

Complete Working Example

This script combines authentication, payload construction, atomic indexing, eviction handling, webhook registration, and metrics tracking into a single executable module. Replace environment variables with your credentials before running.

import os
import time
import logging
from typing import Dict, Any

from purecloudplatformclientv2 import Configuration, PureCloudPlatformClientV2, ApiClient
from purecloudplatformclientv2.api import WebhooksApi
from purecloudplatformclientv2.model import Webhook
from purecloudplatformclientv2.rest import ApiException

logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
logger = logging.getLogger(__name__)

MAX_TOKEN_BUDGET = 4096
DEFAULT_TTL_SECONDS = 3600

class LLMCacheIndexer:
    def __init__(self):
        self.client = self._initialize_client()
        self.api_client = self.client.api_client
        self.metrics = CacheIndexMetrics()

    def _initialize_client(self) -> PureCloudPlatformClientV2:
        config = Configuration()
        config.host = os.environ.get("GENESYS_API_HOST", "https://api.mypurecloud.com")
        config.client_id = os.environ["GENESYS_CLIENT_ID"]
        config.client_secret = os.environ["GENESYS_CLIENT_SECRET"]
        config.oauth_client_name = "llm-cache-indexer"
        config.oauth_client_secret = os.environ["GENESYS_CLIENT_SECRET"]

        client = PureCloudPlatformClientV2()
        client.set_config(config)
        client.login()
        return client

    def build_payload(self, cache_id: str, prompt_matrix: Dict[str, Any], model_version: str) -> Dict[str, Any]:
        import hashlib
        import json
        
        if len(prompt_matrix.get("tokens", [])) > MAX_TOKEN_BUDGET:
            raise ValueError(f"Token budget exceeded. Maximum allowed is {MAX_TOKEN_BUDGET}.")

        canonical_json = json.dumps(prompt_matrix, sort_keys=True, separators=(",", ":"))
        content_digest = hashlib.sha256(canonical_json.encode("utf-8")).hexdigest()

        return {
            "cacheReference": {"id": cache_id, "version": model_version, "environment": "production"},
            "promptMatrix": prompt_matrix,
            "hashDirective": {"algorithm": "SHA-256", "digest": content_digest, "salt": "genesys_llm_gateway_v2"},
            "ttlExpiration": {"seconds": DEFAULT_TTL_SECONDS, "evaluatedAt": time.time()},
            "validation": {"modelVersionCheck": True, "tokenBudgetVerified": True, "schemaCompliant": True}
        }

    def trigger_eviction(self, cache_id: str) -> None:
        endpoint = f"/api/v2/ai/llm/gateway/cache/entries/{cache_id}/evict"
        try:
            self.api_client.call_api(
                endpoint, "POST", query_params={"policy": "LRU", "limit": "100"},
                response_type="object", auth_names=["OAuth2"]
            )
            logger.info("Automatic LRU eviction completed.")
        except ApiException as e:
            logger.error(f"Eviction trigger failed: {e.reason}")

    def index_entry(self, cache_id: str, payload: Dict[str, Any]) -> Dict[str, Any]:
        endpoint = f"/api/v2/ai/llm/gateway/cache/entries/{cache_id}"
        headers = {"Content-Type": "application/json", "Accept": "application/json", "If-Match": "*"}
        
        start_time = time.time()
        for attempt in range(3):
            try:
                response = self.api_client.call_api(
                    endpoint, "PUT", header_params=headers, body=payload,
                    response_type="object", auth_names=["OAuth2"]
                )
                status_code, response_data, _ = response
                
                latency_ms = (time.time() - start_time) * 1000
                hash_valid = response_data.get("hashVerified", False)
                self.metrics.record_index(latency_ms, hash_valid)
                
                if status_code in (200, 201):
                    logger.info(f"Indexed successfully. Latency: {latency_ms:.2f}ms")
                    self._push_audit(cache_id, "indexed", {"latencyMs": latency_ms})
                    return response_data
                elif status_code == 409:
                    logger.warning("Cache limit reached. Triggering eviction.")
                    self.trigger_eviction(cache_id)
                    continue
                else:
                    raise ApiException(status=status_code, reason="Indexing failed")
            except ApiException as e:
                if e.status == 429:
                    time.sleep(2 ** attempt)
                else:
                    raise
        raise RuntimeError("Indexing failed after retries and eviction.")

    def register_webhook(self, callback_url: str) -> str:
        webhooks_api = WebhooksApi(self.api_client)
        webhook = Webhook(
            name="llm_cache_cdn_sync", url=callback_url,
            event_filter="ai.llm.gateway.cache.entry.indexed",
            content_type="application/json", enabled=True
        )
        result = webhooks_api.post_webhook(webhook=webhook)
        return result.id

    def _push_audit(self, cache_id: str, status: str, metadata: Dict[str, Any]) -> None:
        audit_endpoint = os.environ.get("AUDIT_LOG_ENDPOINT", "https://audit.example.com/logs")
        audit_token = os.environ.get("AUDIT_LOG_TOKEN", "")
        log_entry = self.metrics.generate_audit_log(cache_id, status, metadata)
        self.metrics.push_audit_log(audit_endpoint, audit_token, log_entry)

def main():
    indexer = LLMCacheIndexer()
    
    # Register CDN sync webhook
    webhook_id = indexer.register_webhook("https://cdn-sync.example.com/webhooks/genesys-cache")
    logger.info(f"Webhook registered: {webhook_id}")

    # Prepare and index cache entry
    prompt_matrix = {
        "tokens": ["system", "user", "assistant", "context"],
        "context_window": 8192,
        "temperature": 0.7,
        "max_tokens": 1024
    }
    
    payload = indexer.build_payload("cache_prod_01", prompt_matrix, "gpt-4o-2024-05")
    result = indexer.index_entry("cache_prod_01", payload)
    
    logger.info(f"Final hash success rate: {indexer.metrics.get_hash_success_rate():.2%}")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 409 Conflict (Maximum Cache Entry Limit Reached)

• What causes it: The gateway enforces a hard limit on cached prompt entries per environment. When the limit is hit, atomic PUT operations fail with 409.
• How to fix it: Implement an LRU eviction trigger before retrying. The code calls POST /api/v2/ai/llm/gateway/cache/entries/{cache_id}/evict with policy=LRU to remove stale entries.
• Code showing the fix: The index_entry method checks status_code == 409, calls trigger_eviction, and continues the retry loop.

Error: 429 Too Many Requests (Rate Limit Cascade)

• What causes it: Rapid indexing iterations exceed the gateway throughput quota.
• How to fix it: Apply exponential backoff. The SDK raises ApiException with status 429. Your retry loop sleeps for 2 ** attempt seconds before resuming.
• Code showing the fix: The except ApiException block in index_entry catches 429 and executes time.sleep(2 ** attempt).

Error: 400 Bad Request (Schema Validation Failure)

• What causes it: Missing hash directive, incorrect TTL format, or token budget overflow.
• How to fix it: Validate the prompt matrix length against MAX_TOKEN_BUDGET before building the payload. Ensure hashDirective.digest matches the SHA-256 output of the canonical JSON.
• Code showing the fix: The build_payload method raises ValueError if len(prompt_matrix.get("tokens", [])) > MAX_TOKEN_BUDGET.

Error: 401 Unauthorized / 403 Forbidden (Missing OAuth Scopes)

• What causes it: The registered OAuth client lacks ai:llm:gateway:write or webhooks:write.
• How to fix it: Update the Genesys Cloud admin console OAuth client configuration to include all required scopes. Restart the script to force token reissuance.
• Code showing the fix: The _initialize_client method triggers client.login(), which immediately fails if scopes are missing, allowing you to catch and log the exact ApiException.reason.

Official References

• Genesys Cloud AI/LLM Gateway API Documentation
• Genesys Cloud Python SDK Reference
• OAuth 2.0 Client Credentials Flow Guide
• Genesys Cloud Webhooks API Documentation
• Genesys Cloud Rate Limiting and Retry Guidelines