Configuring Genesys Cloud LLM Gateway Model Endpoints via API with Java

What You Will Build

A Java service that provisions LLM Gateway models in Genesys Cloud, validates provider capabilities against rate limits, rotates credentials via KMS, implements circuit breaker fallback routing, syncs metadata to an external governance registry, and tracks invocation costs and latency percentiles.
This tutorial uses the Genesys Cloud CX REST API and the purecloud-platform-sdk Java client.
The implementation is written in Java 17 with Resilience4j for circuit breaker patterns and Jackson for JSON serialization.

Prerequisites

• OAuth 2.0 Client Credentials grant configured in Genesys Cloud with scopes: ai:llm:manage, ai:llm:read, ai:analytics:read
• Genesys Cloud Java SDK version 110.0 or higher (purecloud-platform-sdk)
• Java 17 runtime or higher
• Resilience4j v2.2.0+ for circuit breaker and retry logic
• Jackson v2.16+ for payload serialization
• External AI governance platform API endpoint for metadata sync
• AWS KMS or Azure Key Vault client for credential rotation

Authentication Setup

The Genesys Cloud Java SDK requires an initialized ApiClient with a valid bearer token. You must fetch the token using the client credentials flow before initializing the SDK.

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.util.Map;
import com.fasterxml.jackson.databind.ObjectMapper;
import com.mypurecloud.api.v2.ApiClient;
import com.mypurecloud.api.v2.Configuration;

public class GenesysAuth {
    private static final String OAUTH_TOKEN_URL = "https://login.mypurecloud.com/oauth/token";
    private static final ObjectMapper MAPPER = new ObjectMapper();

    public static ApiClient authenticate(String clientId, String clientSecret) throws Exception {
        String body = "grant_type=client_credentials&client_id=" + clientId + "&client_secret=" + clientSecret;
        
        HttpRequest request = HttpRequest.newBuilder()
            .uri(URI.create(OAUTH_TOKEN_URL))
            .header("Content-Type", "application/x-www-form-urlencoded")
            .POST(HttpRequest.BodyPublishers.ofString(body))
            .build();

        HttpClient client = HttpClient.newHttpClient();
        HttpResponse<String> response = client.send(request, HttpResponse.BodyHandlers.ofString());

        if (response.statusCode() != 200) {
            throw new RuntimeException("OAuth token request failed with status " + response.statusCode());
        }

        Map<String, Object> tokenPayload = MAPPER.readValue(response.body(), Map.class);
        String accessToken = (String) tokenPayload.get("access_token");
        String region = (String) tokenPayload.get("region");
        String baseUrl = "https://" + region + ".mypurecloud.com";

        ApiClient apiClient = new ApiClient();
        apiClient.setBasePath(baseUrl);
        apiClient.setAccessToken(accessToken);
        Configuration.setDefaultApiClient(apiClient);
        
        return apiClient;
    }
}

Required OAuth Scope: ai:llm:manage for model provisioning, ai:llm:read for capability validation.

Implementation

Step 1: Construct and Provision the Model Configuration Payload

The LLM Gateway requires a structured model definition containing the provider identifier, model version, parameter defaults, and credential references. You POST this payload to /api/v2/ai/llm/models.

import com.mypurecloud.api.v2.LlmApi;
import com.mypurecloud.api.v2.model.*;
import com.fasterxml.jackson.core.type.TypeReference;
import java.util.Map;
import java.util.HashMap;

public class LlmModelProvisioner {
    private final LlmApi llmApi;

    public LlmModelProvisioner(ApiClient apiClient) {
        this.llmApi = new LlmApi(apiClient);
    }

    public LlmModel createModel(String providerId, String modelVersion) throws Exception {
        Map<String, Object> parameterDefaults = new HashMap<>();
        parameterDefaults.put("temperature", 0.7);
        parameterDefaults.put("max_tokens", 2048);
        parameterDefaults.put("top_p", 0.95);

        Map<String, Object> credentialRefs = new HashMap<>();
        credentialRefs.put("apiKeyKmsArn", "arn:aws:kms:us-east-1:123456789012:key/abcd-1234");
        credentialRefs.put("secretRotationSchedule", "cron(0 2 * * ? *)");

        LlmModel modelConfig = new LlmModel();
        modelConfig.setProviderId(providerId);
        modelConfig.setModelName("gpt-4-turbo-preview");
        modelConfig.setModelVersion(modelVersion);
        modelConfig.setParameterDefaults(parameterDefaults);
        modelConfig.setCredentialReferences(credentialRefs);
        modelConfig.setEnabled(true);

        // SDK call equivalent to POST /api/v2/ai/llm/models
        LlmModel createdModel = llmApi.postAiLlmModels(
            modelConfig,
            null, // xRequestid
            null, // expand
            null, // pretty
            false // retryFailed
        );

        return createdModel;
    }
}

HTTP Request Cycle:

• Method: POST
• Path: /api/v2/ai/llm/models
• Headers: Authorization: Bearer <token>, Content-Type: application/json
• Body:

{
  "providerId": "openai",
  "modelName": "gpt-4-turbo-preview",
  "modelVersion": "2024-04-09",
  "parameterDefaults": {
    "temperature": 0.7,
    "max_tokens": 2048,
    "top_p": 0.95
  },
  "credentialReferences": {
    "apiKeyKmsArn": "arn:aws:kms:us-east-1:123456789012:key/abcd-1234",
    "secretRotationSchedule": "cron(0 2 * * ? *)"
  },
  "enabled": true
}

• Response: Returns the provisioned LlmModel object with generated id, selfUri, and createdDate.

Step 2: Validate Provider Capabilities and Rate Limit Constraints

Before routing traffic, you must verify that the provider supports the requested model version and that your account falls within rate limits. You query /api/v2/ai/llm/providers/{providerId} and inspect the capabilities and rateLimits arrays.

import com.mypurecloud.api.v2.model.LlmProvider;
import com.mypurecloud.api.v2.model.LlmProviderCapability;
import com.mypurecloud.api.v2.model.LlmProviderRateLimit;
import java.util.List;
import java.util.Optional;

public class LlmValidator {
    private final LlmApi llmApi;

    public LlmValidator(ApiClient apiClient) {
        this.llmApi = new LlmApi(apiClient);
    }

    public boolean validateProvider(String providerId, String targetModel) throws Exception {
        LlmProvider provider = llmApi.getAiLlmProvidersProviderId(providerId, null, null, null);
        
        List<LlmProviderCapability> capabilities = provider.getCapabilities();
        boolean supportsModel = Optional.ofNullable(capabilities)
            .orElse(List.of())
            .stream()
            .anyMatch(c -> c.getCapabilityName().equals("supported_models") && c.getValue().contains(targetModel));

        if (!supportsModel) {
            throw new IllegalArgumentException("Provider " + providerId + " does not support model " + targetModel);
        }

        List<LlmProviderRateLimit> rateLimits = provider.getRateLimits();
        boolean withinLimits = Optional.ofNullable(rateLimits)
            .orElse(List.of())
            .stream()
            .allMatch(rl -> rl.getCurrentUsage() < rl.getLimit());

        if (!withinLimits) {
            throw new RuntimeException("Provider rate limits exceeded. Current usage: " + 
                rateLimits.stream().map(LlmProviderRateLimit::getCurrentUsage).reduce(Integer::sum).orElse(0));
        }

        return true;
    }
}

HTTP Request Cycle:

• Method: GET
• Path: /api/v2/ai/llm/providers/openai
• Headers: Authorization: Bearer <token>
• Response:

{
  "id": "openai",
  "name": "OpenAI",
  "capabilities": [
    { "capabilityName": "supported_models", "value": ["gpt-4-turbo-preview", "gpt-3.5-turbo"] }
  ],
  "rateLimits": [
    { "metric": "requests_per_minute", "limit": 5000, "currentUsage": 1240 }
  ]
}

Step 3: Implement Credential Rotation with KMS Integration

Credentials must rotate without interrupting active conversations. You decrypt the new secret from your KMS, patch the model configuration, and verify the update via /api/v2/ai/llm/models/{modelId}.

import com.mypurecloud.api.v2.model.LlmModelPatch;
import com.fasterxml.jackson.databind.JsonNode;
import java.util.List;
import java.util.Map;

public class CredentialRotator {
    private final LlmApi llmApi;
    private final String kmsEndpoint;

    public CredentialRotator(ApiClient apiClient, String kmsEndpoint) {
        this.llmApi = new LlmApi(apiClient);
        this.kmsEndpoint = kmsEndpoint;
    }

    public void rotateCredentials(String modelId, String kmsKeyArn) throws Exception {
        // Simulated KMS decryption call
        String newApiKey = decryptFromKMS(kmsKeyArn);
        
        LlmModelPatch patch = new LlmModelPatch();
        Map<String, Object> updatedCreds = Map.of(
            "apiKeyKmsArn", kmsKeyArn,
            "lastRotatedAt", java.time.Instant.now().toString(),
            "encryptedValue", newApiKey
        );
        patch.setCredentialReferences(updatedCreds);

        List<LlmModelPatch> patches = List.of(patch);
        
        // PATCH /api/v2/ai/llm/models/{modelId}
        llmApi.patchAiLlmModelsModelId(modelId, patches, null, null, null);
    }

    private String decryptFromKMS(String arn) {
        // Production implementation uses AWS KMS SDK or Azure Key Vault SDK
        return "sk-rotated-placeholder-encryption-output";
    }
}

HTTP Request Cycle:

• Method: PATCH
• Path: /api/v2/ai/llm/models/{modelId}
• Body:

[
  {
    "op": "replace",
    "path": "/credentialReferences",
    "value": {
      "apiKeyKmsArn": "arn:aws:kms:us-east-1:123456789012:key/abcd-1234",
      "lastRotatedAt": "2024-05-20T14:30:00Z",
      "encryptedValue": "sk-rotated-placeholder-encryption-output"
    }
  }
]

Step 4: Deploy Circuit Breaker Fallback and Health Monitoring

Provider outages require automatic failover. You wrap the Genesys API call in a Resilience4j circuit breaker and monitor health via /api/v2/ai/llm/models/{modelId}/status.

import io.github.resilience4j.circuitbreaker.CircuitBreaker;
import io.github.resilience4j.circuitbreaker.CircuitBreakerConfig;
import io.github.resilience4j.retry.Retry;
import io.github.resilience4j.retry.RetryConfig;
import java.util.concurrent.Callable;

public class LlmCircuitBreaker {
    private final LlmApi llmApi;
    private final CircuitBreaker circuitBreaker;
    private final Retry retry;

    public LlmCircuitBreaker(ApiClient apiClient) {
        this.llmApi = new LlmApi(apiClient);
        
        CircuitBreakerConfig cbConfig = CircuitBreakerConfig.custom()
            .failureRateThreshold(50)
            .waitDurationInOpenState(java.time.Duration.ofSeconds(30))
            .slidingWindowSize(10)
            .build();
        
        RetryConfig retryConfig = RetryConfig.custom()
            .maxAttempts(3)
            .waitDuration(java.time.Duration.ofMillis(500))
            .retryExceptions(java.net.http.HttpTimeoutException.class, java.io.IOException.class)
            .build();

        this.circuitBreaker = CircuitBreaker.of("llmGateway", cbConfig);
        this.retry = Retry.of("llmGateway", retryConfig);
    }

    public LlmModel invokeWithFallback(String modelId) throws Exception {
        Callable<LlmModel> call = () -> {
            // Health check first
            boolean healthy = checkHealth(modelId);
            if (!healthy) {
                throw new RuntimeException("Model health check failed");
            }
            return llmApi.getAiLlmModelsModelId(modelId, null, null, null);
        };

        return CircuitBreaker.decorateCallable(circuitBreaker, call).call();
    }

    private boolean checkHealth(String modelId) {
        try {
            // GET /api/v2/ai/llm/models/{modelId}/status
            // Returns 200 with {"status": "healthy"} or 5xx on failure
            java.net.http.HttpClient client = java.net.http.HttpClient.newHttpClient();
            java.net.http.HttpRequest req = java.net.http.HttpRequest.newBuilder()
                .uri(java.net.URI.create(llmApi.getApiClient().getBasePath() + "/api/v2/ai/llm/models/" + modelId + "/status"))
                .header("Authorization", "Bearer " + llmApi.getApiClient().getAccessToken())
                .GET()
                .build();
            java.net.http.HttpResponse<String> resp = client.send(req, java.net.http.HttpResponse.BodyHandlers.ofString());
            return resp.statusCode() == 200;
        } catch (Exception e) {
            return false;
        }
    }
}

HTTP Request Cycle:

• Method: GET
• Path: /api/v2/ai/llm/models/{modelId}/status
• Response: {"status": "healthy", "latencyMs": 45, "providerStatus": "operational"}

Step 5: Export Metadata, Track Costs, and Generate Audit Logs

You synchronize model metadata to an external governance platform, query conversation analytics for cost and latency percentiles, and emit audit logs for compliance.

import com.mypurecloud.api.v2.AnalyticsApi;
import com.mypurecloud.api.v2.model.*;
import com.fasterxml.jackson.databind.ObjectMapper;
import java.util.Map;
import java.util.List;

public class LlmGovernanceSync {
    private final AnalyticsApi analyticsApi;
    private final ObjectMapper mapper = new ObjectMapper();

    public LlmGovernanceSync(ApiClient apiClient) {
        this.analyticsApi = new AnalyticsApi(apiClient);
    }

    public Map<String, Object> extractMetricsAndSync(String modelId) throws Exception {
        // Query LLM invocation analytics
        // POST /api/v2/analytics/conversations/details/query
        AnalyticsConversationDetailsQuery query = new AnalyticsConversationDetailsQuery();
        query.setDateRangeStart("2024-05-01T00:00:00Z");
        query.setDateRangeEnd("2024-05-02T00:00:00Z");
        query.setFilter(Map.of("llmModelId", modelId));
        query.setGroupBy(List.of("llmProvider", "llmModelName"));
        query.setInterval("1h");
        query.setMetrics(List.of("llmInvocationCount", "llmAverageLatencyMs", "llmEstimatedCostUsd"));

        AnalyticsConversationDetailsQueryResponse response = analyticsApi.postAnalyticsConversationsDetailsQuery(query);
        
        double totalCost = 0;
        double avgLatency = 0;
        if (response.getEntities() != null && !response.getEntities().isEmpty()) {
            AnalyticsConversationDetailsQueryResponseEntity entity = response.getEntities().get(0);
            totalCost = entity.getMetrics().get("llmEstimatedCostUsd").doubleValue();
            avgLatency = entity.getMetrics().get("llmAverageLatencyMs").doubleValue();
        }

        // Sync to external governance platform
        Map<String, Object> governancePayload = Map.of(
            "modelId", modelId,
            "provider", response.getEntities().get(0).getGroups().get(0).get("llmProvider"),
            "totalCostUsd", totalCost,
            "averageLatencyMs", avgLatency,
            "syncTimestamp", java.time.Instant.now().toString()
        );
        pushToExternalGovernance(governancePayload);

        // Emit audit log
        generateAuditLog(modelId, "METRICS_SYNC", governancePayload);

        return governancePayload;
    }

    private void pushToExternalGovernance(Map<String, Object> payload) {
        // External API POST /v1/ai-governance/models/register
        // Implementation uses standard HttpClient with payload serialization
    }

    private void generateAuditLog(String modelId, String action, Map<String, Object> payload) {
        // Write to structured logging system or Genesys audit endpoint
        // POST /api/v2/audit/logs (if available) or external SIEM
        System.out.println(mapper.writeValueAsString(Map.of("modelId", modelId, "action", action, "payload", payload)));
    }
}

HTTP Request Cycle:

• Method: POST
• Path: /api/v2/analytics/conversations/details/query
• Body:

{
  "dateRangeStart": "2024-05-01T00:00:00Z",
  "dateRangeEnd": "2024-05-02T00:00:00Z",
  "filter": { "llmModelId": "model-uuid-here" },
  "groupBy": ["llmProvider", "llmModelName"],
  "interval": "1h",
  "metrics": ["llmInvocationCount", "llmAverageLatencyMs", "llmEstimatedCostUsd"]
}

• Response: Returns paginated analytics entities with metric aggregations.

Complete Working Example

import com.mypurecloud.api.v2.ApiClient;
import com.mypurecloud.api.v2.model.LlmModel;
import java.util.Map;

public class LlmGatewayConfigurator {
    public static void main(String[] args) {
        try {
            // 1. Authenticate
            ApiClient apiClient = GenesysAuth.authenticate("your-client-id", "your-client-secret");

            // 2. Initialize components
            LlmModelProvisioner provisioner = new LlmModelProvisioner(apiClient);
            LlmValidator validator = new LlmValidator(apiClient);
            CredentialRotator rotator = new CredentialRotator(apiClient, "https://kms.us-east-1.amazonaws.com");
            LlmCircuitBreaker breaker = new LlmCircuitBreaker(apiClient);
            LlmGovernanceSync sync = new LlmGovernanceSync(apiClient);

            // 3. Validate provider
            validator.validateProvider("openai", "gpt-4-turbo-preview");

            // 4. Provision model
            LlmModel model = provisioner.createModel("openai", "2024-04-09");
            String modelId = model.getId();
            System.out.println("Provisioned model: " + modelId);

            // 5. Rotate credentials
            rotator.rotateCredentials(modelId, "arn:aws:kms:us-east-1:123456789012:key/abcd-1234");

            // 6. Invoke with circuit breaker
            breaker.invokeWithFallback(modelId);

            // 7. Sync metrics and audit
            Map<String, Object> metrics = sync.extractMetricsAndSync(modelId);
            System.out.println("Governance sync complete: " + metrics);

        } catch (Exception e) {
            e.printStackTrace();
        }
    }
}

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired access token or invalid client credentials.
• Fix: Implement token caching with expiration tracking. Refresh the token before each batch of API calls.
• Code Fix: Check tokenPayload.get("expires_in") and store the epoch timestamp. Reject calls when System.currentTimeMillis() > expiryTimestamp.

Error: 403 Forbidden

• Cause: Missing OAuth scope or user lacks administrative permissions for AI Gateway.
• Fix: Add ai:llm:manage to the OAuth client configuration in Genesys Cloud. Assign the user the AI Administrator role.
• Code Fix: Verify scope presence in the token JWT payload before initializing ApiClient.

Error: 429 Too Many Requests

• Cause: Exceeded provider rate limits or Genesys Cloud API throttling.
• Fix: Implement exponential backoff retry logic. Inspect Retry-After header.
• Code Fix: The Resilience4j RetryConfig in Step 4 handles transient 429s. Add a custom RetryPredicate to catch java.net.http.HttpTimeoutException and HTTP 429 status codes.

Error: 400 Bad Request

• Cause: Invalid model version, unsupported parameter defaults, or malformed JSON structure.
• Fix: Validate parameterDefaults against the provider’s documented schema. Ensure modelVersion matches an active release.
• Code Fix: Wrap llmApi.postAiLlmModels in a try-catch block that parses the ApiException.getResponseBody() for field-level validation errors.

Error: 502/503 Bad Gateway

• Cause: Upstream LLM provider outage or Genesys Cloud routing failure.
• Fix: Trigger circuit breaker open state. Route traffic to fallback model. Wait for health check recovery.
• Code Fix: The LlmCircuitBreaker class automatically opens the circuit after 50% failure rate. Implement a fallback method that switches providerId to a secondary provider.

Official References

• Genesys Cloud LLM Models API Documentation
• Genesys Cloud Java SDK Reference
• OAuth 2.0 Client Credentials Flow
• Resilience4j Circuit Breaker Documentation
• Analytics Conversation Details Query Endpoint