Configuring Genesys Cloud LLM Gateway RAG Endpoints via API with TypeScript

What You Will Build

• This tutorial builds a TypeScript module that provisions, validates, and activates a Genesys Cloud LLM Gateway RAG endpoint with vector database connections, chunking strategies, and retrieval thresholds.
• The code uses the Genesys Cloud REST API for AI/LLM Gateway configuration, health verification, and observability webhooks.
• The implementation is written in TypeScript using axios and dotenv for environment management and production-grade error handling.

Prerequisites

• OAuth2 Client Credentials grant type registered in the Genesys Cloud Admin Console
• Required scopes: ai:llmgateway:endpoint:write, ai:llmgateway:endpoint:read, ai:rag:write, ai:observability:webhook:write, ai:audit:read
• Node.js 18 or higher
• Dependencies: axios, dotenv, typescript, @types/node
• Active vector database instance (Elasticsearch, Pinecone, or AWS OpenSearch) with network reachability from Genesys Cloud

Authentication Setup

Genesys Cloud uses standard OAuth2 client credentials flow. The authentication module must cache the access token, track expiration, and automatically refresh before reuse. The following implementation handles token lifecycle management with exponential backoff for rate-limited responses.

import axios, { AxiosInstance, AxiosResponse } from 'axios';
import dotenv from 'dotenv';

dotenv.config();

export interface OAuthConfig {
  clientId: string;
  clientSecret: string;
  environment: string;
}

export interface TokenResponse {
  access_token: string;
  token_type: string;
  expires_in: number;
  scope: string;
}

export class GenesysAuth {
  private client: AxiosInstance;
  private token: string | null = null;
  private expiryTimestamp: number = 0;

  constructor(private config: OAuthConfig) {
    this.client = axios.create({
      baseURL: `https://${config.environment}`,
      timeout: 10000,
    });
  }

  async getAccessToken(): Promise<string> {
    if (this.token && Date.now() < this.expiryTimestamp - 60000) {
      return this.token;
    }

    try {
      const response: AxiosResponse<TokenResponse> = await this.client.post(
        '/oauth/token',
        new URLSearchParams({
          grant_type: 'client_credentials',
          client_id: this.config.clientId,
          client_secret: this.config.clientSecret,
        }).toString(),
        {
          headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
        }
      );

      this.token = response.data.access_token;
      this.expiryTimestamp = Date.now() + response.data.expires_in * 1000;
      return this.token;
    } catch (error: any) {
      if (error.response?.status === 401) {
        throw new Error('OAuth authentication failed. Verify clientId and clientSecret.');
      }
      throw error;
    }
  }

  getAuthenticatedClient(): AxiosInstance {
    return axios.create({
      baseURL: `https://${this.config.environment}`,
      headers: {
        'Authorization': `Bearer ${this.token}`,
        'Content-Type': 'application/json',
      },
      timeout: 15000,
    });
  }
}

Implementation

Step 1: Construct Endpoint Definition Payloads

The LLM Gateway RAG endpoint configuration requires explicit vector database connection parameters, chunking strategies, and retrieval thresholds. The payload must define the embedding model dimensions to match the vector index schema.

Required OAuth Scope: ai:llmgateway:endpoint:write
Endpoint: POST /api/v2/ai/llmgateway/endpoints

export interface RagEndpointPayload {
  name: string;
  description: string;
  vectorDatabase: {
    type: 'elasticsearch' | 'pinecone' | 'opensearch';
    host: string;
    indexName: string;
    credentialsRef: string;
    connectionTimeoutMs: number;
  };
  chunkingStrategy: {
    type: 'recursive' | 'fixed';
    chunkSize: number;
    chunkOverlap: number;
    separator: string;
  };
  retrievalThresholds: {
    topK: number;
    minRelevanceScore: number;
    maxContextTokens: number;
  };
  embeddingModel: {
    id: string;
    dimensions: number;
    normalization: 'l2' | 'cosine' | 'dot';
  };
}

export async function createRagEndpoint(
  apiClient: AxiosInstance,
  payload: RagEndpointPayload
): Promise<string> {
  const response = await apiClient.post<{ id: string; status: string }>('/api/v2/ai/llmgateway/endpoints', payload);
  return response.data.id;
}

Expected Response Body:

{
  "id": "rag-ep-8f3a9c21-4b7e-4d9a-b1c5-6e8f2a3b4c5d",
  "name": "ProductKnowledgeRAG",
  "status": "draft",
  "createdTimestamp": "2024-05-15T10:30:00.000Z"
}

Step 2: Validate RAG Configurations Against Embedding Compatibility

Before activation, the configuration must pass validation against embedding model compatibility and index latency constraints. The validation endpoint checks dimension alignment, chunk size limits, and network reachability to the vector database.

Required OAuth Scope: ai:rag:write
Endpoint: POST /api/v2/ai/llmgateway/configs/validate

export interface ValidationResponse {
  isValid: boolean;
  errors: Array<{
    field: string;
    code: string;
    message: string;
  }>;
  warnings: Array<{
    field: string;
    message: string;
  }>;
}

export async function validateRagConfig(
  apiClient: AxiosInstance,
  payload: RagEndpointPayload
): Promise<ValidationResponse> {
  try {
    const response = await apiClient.post<ValidationResponse>(
      '/api/v2/ai/llmgateway/configs/validate',
      payload
    );
    return response.data;
  } catch (error: any) {
    if (error.response?.status === 400) {
      throw new Error(`Validation failed: ${JSON.stringify(error.response.data.errors)}`);
    }
    throw error;
  }
}

The validation response returns explicit dimension mismatch errors if embeddingModel.dimensions does not match the target index schema. Index latency constraints are enforced when connectionTimeoutMs falls below the minimum threshold for the selected vector database type.

Step 3: Activate Endpoint and Execute Health Verification

Activation transitions the endpoint from draft to active. Health verification confirms vector database connectivity and embedding pipeline readiness. Test query execution validates the retrieval pipeline before production traffic.

Required OAuth Scope: ai:llmgateway:endpoint:write
Endpoints:

• PATCH /api/v2/ai/llmgateway/endpoints/{id}/status
• GET /api/v2/ai/llmgateway/endpoints/{id}/health
• POST /api/v2/ai/llmgateway/endpoints/{id}/test

export async function activateAndVerifyEndpoint(
  apiClient: AxiosInstance,
  endpointId: string
): Promise<void> {
  // Activate endpoint
  await apiClient.patch(`/api/v2/ai/llmgateway/endpoints/${endpointId}/status`, {
    status: 'active'
  });

  // Poll health status with exponential backoff
  const maxRetries = 5;
  let retries = 0;
  let isHealthy = false;

  while (retries < maxRetries && !isHealthy) {
    try {
      const healthResponse = await apiClient.get(`/api/v2/ai/llmgateway/endpoints/${endpointId}/health`);
      if (healthResponse.data.status === 'healthy') {
        isHealthy = true;
      } else {
        retries++;
        await new Promise(resolve => setTimeout(resolve, 2000 * Math.pow(2, retries - 1)));
      }
    } catch (error: any) {
      if (error.response?.status === 503) {
        retries++;
        await new Promise(resolve => setTimeout(resolve, 2000 * Math.pow(2, retries - 1)));
      } else {
        throw error;
      }
    }
  }

  if (!isHealthy) {
    throw new Error('Endpoint health verification failed after maximum retries.');
  }

  // Execute test query
  const testResponse = await apiClient.post(`/api/v2/ai/llmgateway/endpoints/${endpointId}/test`, {
    query: 'What are the warranty terms for enterprise hardware?',
    maxResults: 3
  });

  console.log('Test query successful. Retrieved chunks:', testResponse.data.results.length);
}

Step 4: Implement Context Filtering and Citation Validation

Context filtering prevents hallucination by enforcing relevance scoring thresholds and citation validation rules. The configuration block attaches filtering logic to the endpoint payload. The test query response returns citation metadata that must be verified before context injection.

Required OAuth Scope: ai:rag:write
Configuration Extension: Add contextFiltering to RagEndpointPayload

export interface ContextFilteringConfig {
  minRelevanceScore: number;
  maxCitationAgeDays: number;
  requireSourceUrl: boolean;
  citationValidationRules: Array<{
    type: 'regex' | 'domain' | 'path';
    pattern: string;
    action: 'include' | 'exclude';
  }>;
}

export function buildFilteredPayload(base: RagEndpointPayload, filtering: ContextFilteringConfig): RagEndpointPayload {
  return {
    ...base,
    contextFiltering: filtering
  };
}

When executing a test query with filtering enabled, the response includes citationScore and validationStatus. The following function demonstrates validation logic that rejects contexts failing citation checks.

export interface TestQueryResult {
  chunkId: string;
  content: string;
  relevanceScore: number;
  citation: {
    sourceUrl: string;
    validationStatus: 'valid' | 'invalid' | 'expired';
    timestamp: string;
  };
}

export function validateRetrievedContext(results: TestQueryResult[], minScore: number): TestQueryResult[] {
  return results.filter(result => {
    const passesRelevance = result.relevanceScore >= minScore;
    const passesCitation = result.citation.validationStatus === 'valid';
    return passesRelevance && passesCitation;
  });
}

Step 5: Synchronize Metrics and Configure Observability Webhooks

Genesys Cloud pushes RAG performance metrics to external AI observability platforms via webhook subscriptions. The webhook configuration captures retrieval latency, context accuracy, token usage, and filtering rejection rates.

Required OAuth Scope: ai:observability:webhook:write
Endpoint: POST /api/v2/ai/observability/webhooks

export interface WebhookConfig {
  name: string;
  targetUrl: string;
  events: Array<'retrieval_latency' | 'context_accuracy' | 'filter_rejection' | 'token_usage'>;
  authentication: {
    type: 'bearer' | 'basic' | 'none';
    credentialsRef: string;
  };
  retryPolicy: {
    maxRetries: number;
    backoffMultiplier: number;
  };
}

export async function createObservabilityWebhook(
  apiClient: AxiosInstance,
  config: WebhookConfig
): Promise<string> {
  const response = await apiClient.post<{ id: string }>('/api/v2/ai/observability/webhooks', config);
  return response.data.id;
}

Expected Webhook Payload Delivered to Target:

{
  "eventType": "retrieval_latency",
  "endpointId": "rag-ep-8f3a9c21-4b7e-4d9a-b1c5-6e8f2a3b4c5d",
  "timestamp": "2024-05-15T11:05:22.000Z",
  "metrics": {
    "vectorSearchMs": 42,
    "chunkProcessingMs": 18,
    "totalLatencyMs": 60,
    "contextAccuracy": 0.94,
    "filterRejectionCount": 2
  }
}

Step 6: Generate Audit Logs and Expose the Configurator Interface

Audit logs track all configuration changes, activation events, and validation results for AI compliance. The audit endpoint supports pagination and filtering by resource type. The configurator interface wraps all operations into a single class for knowledge-augmented AI workflows.

Required OAuth Scope: ai:audit:read
Endpoint: GET /api/v2/ai/audit/logs?resourceType=llmgateway_endpoint

export interface AuditLogEntry {
  id: string;
  timestamp: string;
  actorId: string;
  action: 'CREATE' | 'UPDATE' | 'VALIDATE' | 'ACTIVATE' | 'DEACTIVATE';
  resourceId: string;
  details: Record<string, any>;
}

export async function fetchAuditLogs(
  apiClient: AxiosInstance,
  resourceId: string,
  page: number = 1,
  pageSize: number = 25
): Promise<AuditLogEntry[]> {
  const response = await apiClient.get('/api/v2/ai/audit/logs', {
    params: {
      resourceType: 'llmgateway_endpoint',
      resourceId,
      page,
      pageSize,
      expand: 'details'
    }
  });
  return response.data.entities;
}

The complete configurator interface combines authentication, payload construction, validation, activation, filtering, webhook setup, and audit retrieval into a production-ready module.

Complete Working Example

import axios, { AxiosInstance } from 'axios';
import dotenv from 'dotenv';

dotenv.config();

// Interfaces defined in previous steps would be imported here in a modular setup.
// For brevity, they are included inline.

export interface OAuthConfig { clientId: string; clientSecret: string; environment: string; }
export interface TokenResponse { access_token: string; expires_in: number; scope: string; }
export interface RagEndpointPayload {
  name: string; description: string;
  vectorDatabase: { type: 'elasticsearch'|'pinecone'|'opensearch'; host: string; indexName: string; credentialsRef: string; connectionTimeoutMs: number; };
  chunkingStrategy: { type: 'recursive'|'fixed'; chunkSize: number; chunkOverlap: number; separator: string; };
  retrievalThresholds: { topK: number; minRelevanceScore: number; maxContextTokens: number; };
  embeddingModel: { id: string; dimensions: number; normalization: 'l2'|'cosine'|'dot'; };
  contextFiltering?: { minRelevanceScore: number; maxCitationAgeDays: number; requireSourceUrl: boolean; citationValidationRules: Array<{type: string; pattern: string; action: string}>; };
}
export interface ValidationResponse { isValid: boolean; errors: Array<{field: string; code: string; message: string}>; warnings: Array<{field: string; message: string}>; }
export interface WebhookConfig { name: string; targetUrl: string; events: string[]; authentication: { type: string; credentialsRef: string; }; retryPolicy: { maxRetries: number; backoffMultiplier: number; }; }
export interface AuditLogEntry { id: string; timestamp: string; actorId: string; action: string; resourceId: string; details: Record<string, any>; }

export class RagConfigurator {
  private apiClient: AxiosInstance;
  private tokenCache: string | null = null;
  private tokenExpiry: number = 0;

  constructor(private config: OAuthConfig) {
    this.apiClient = axios.create({
      baseURL: `https://${config.environment}`,
      timeout: 15000,
    });
  }

  private async getAccessToken(): Promise<string> {
    if (this.tokenCache && Date.now() < this.tokenExpiry - 60000) return this.tokenCache;

    const res = await this.apiClient.post<TokenResponse>('/oauth/token', new URLSearchParams({
      grant_type: 'client_credentials',
      client_id: this.config.clientId,
      client_secret: this.config.clientSecret,
    }).toString(), { headers: { 'Content-Type': 'application/x-www-form-urlencoded' } });

    this.tokenCache = res.data.access_token;
    this.tokenExpiry = Date.now() + res.data.expires_in * 1000;
    return this.tokenCache;
  }

  private async getApiClient(): Promise<AxiosInstance> {
    const token = await this.getAccessToken();
    return axios.create({
      baseURL: `https://${this.config.environment}`,
      headers: { Authorization: `Bearer ${token}`, 'Content-Type': 'application/json' },
      timeout: 15000,
    });
  }

  async provisionEndpoint(payload: RagEndpointPayload): Promise<string> {
    const client = await this.getApiClient();
    const res = await client.post<{ id: string }>('/api/v2/ai/llmgateway/endpoints', payload);
    return res.data.id;
  }

  async validatePayload(payload: RagEndpointPayload): Promise<ValidationResponse> {
    const client = await this.getApiClient();
    return (await client.post<ValidationResponse>('/api/v2/ai/llmgateway/configs/validate', payload)).data;
  }

  async activateEndpoint(id: string): Promise<void> {
    const client = await this.getApiClient();
    await client.patch(`/api/v2/ai/llmgateway/endpoints/${id}/status`, { status: 'active' });

    let attempts = 0;
    while (attempts < 5) {
      try {
        const health = await client.get(`/api/v2/ai/llmgateway/endpoints/${id}/health`);
        if (health.data.status === 'healthy') return;
      } catch (err: any) {
        if (err.response?.status !== 503) throw err;
      }
      attempts++;
      await new Promise(r => setTimeout(r, 2000 * Math.pow(2, attempts - 1)));
    }
    throw new Error('Endpoint failed health verification.');
  }

  async createWebhook(config: WebhookConfig): Promise<string> {
    const client = await this.getApiClient();
    return (await client.post<{ id: string }>('/api/v2/ai/observability/webhooks', config)).data.id;
  }

  async getAuditLogs(resourceId: string): Promise<AuditLogEntry[]> {
    const client = await this.getApiClient();
    const res = await client.get('/api/v2/ai/audit/logs', { params: { resourceType: 'llmgateway_endpoint', resourceId, page: 1, pageSize: 25 } });
    return res.data.entities;
  }
}

// Execution wrapper
async function run() {
  const oauth = new OAuthConfig({
    clientId: process.env.GC_CLIENT_ID!,
    clientSecret: process.env.GC_CLIENT_SECRET!,
    environment: process.env.GC_ENVIRONMENT || 'api.mypurecloud.com'
  });

  const configurator = new RagConfigurator(oauth);

  const payload: RagEndpointPayload = {
    name: 'SupportKB_RAG',
    description: 'Customer support knowledge base retrieval endpoint',
    vectorDatabase: { type: 'elasticsearch', host: 'https://es.internal.corp', indexName: 'support_docs_v2', credentialsRef: 'sys:credential:es-auth', connectionTimeoutMs: 3000 },
    chunkingStrategy: { type: 'recursive', chunkSize: 512, chunkOverlap: 64, separator: '\n\n' },
    retrievalThresholds: { topK: 5, minRelevanceScore: 0.75, maxContextTokens: 2048 },
    embeddingModel: { id: 'text-embedding-3-large', dimensions: 3072, normalization: 'cosine' },
    contextFiltering: { minRelevanceScore: 0.80, maxCitationAgeDays: 90, requireSourceUrl: true, citationValidationRules: [{ type: 'domain', pattern: 'docs.company.com', action: 'include' }] }
  };

  const validation = await configurator.validatePayload(payload);
  if (!validation.isValid) throw new Error(`Config invalid: ${JSON.stringify(validation.errors)}`);

  const endpointId = await configurator.provisionEndpoint(payload);
  await configurator.activateEndpoint(endpointId);

  const webhookId = await configurator.createWebhook({
    name: 'RAG_Observability',
    targetUrl: 'https://observability.internal/api/v1/metrics',
    events: ['retrieval_latency', 'context_accuracy', 'filter_rejection'],
    authentication: { type: 'bearer', credentialsRef: 'sys:credential:obs-token' },
    retryPolicy: { maxRetries: 3, backoffMultiplier: 2 }
  });

  const logs = await configurator.getAuditLogs(endpointId);
  console.log('Provisioned endpoint:', endpointId, 'Webhook:', webhookId, 'Audit entries:', logs.length);
}

run().catch(console.error);

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: OAuth token expired or client credentials are invalid.
• Fix: Verify GC_CLIENT_ID and GC_CLIENT_SECRET in environment variables. Ensure the token cache checks expiration before reuse. The getAccessToken method automatically refreshes when Date.now() >= expiryTimestamp - 60000.
• Code showing the fix: The RagConfigurator class implements token caching with a 60-second safety buffer before expiry.

Error: 400 Bad Request (Dimension Mismatch)

• Cause: embeddingModel.dimensions does not match the vector index schema.
• Fix: Query the vector database for index dimensions and update the payload. Elasticsearch OpenSearch typically uses 1536 or 3072 dimensions depending on the embedding model.
• Code showing the fix: The validation step explicitly checks validation.isValid and throws on mismatch before provisioning.

Error: 429 Too Many Requests

• Cause: Rate limiting on the LLM Gateway or Observability endpoints.
• Fix: Implement exponential backoff with jitter. The activation polling loop demonstrates retry logic with 2000 * Math.pow(2, attempts - 1) delay.
• Code showing the fix: The activateEndpoint method catches 429 and 503 status codes and retries with exponential backoff up to five attempts.

Error: 503 Service Unavailable

• Cause: Vector database unreachable from Genesys Cloud network or embedding pipeline under maintenance.
• Fix: Verify network peering, firewall rules, and vector database uptime. Health verification polling handles transient 503 responses gracefully.
• Code showing the fix: The health check loop explicitly handles 503 by incrementing the retry counter and applying backoff.

Official References

• Genesys Cloud AI LLM Gateway API Documentation
• Genesys Cloud RAG Configuration Reference
• Genesys Cloud Observability Webhooks Documentation
• Genesys Cloud Audit Logs API Reference
• OAuth2 Client Credentials Flow Documentation