Setting NICE Cognigy.AI Language Model Per User Via REST API With TypeScript

What You Will Build

• Configure per-user language models in Cognigy.AI by constructing validated setting payloads and applying them via atomic PUT requests.
• This implementation uses the Cognigy.AI REST API v1 with direct HTTP calls via axios and schema validation via zod.
• The tutorial covers TypeScript with strict typing, locale compatibility verification, automatic context reload triggers, webhook synchronization, latency tracking, audit logging, and an exposed model setter for automated bot management.

Prerequisites

• OAuth2 Client Credentials grant with scopes: user:write, settings:write, analytics:read, webhook:manage
• Cognigy.AI Environment URL (format: https://{environment}.cognigy.ai)
• TypeScript 5.0+ and Node.js 18+
• External dependencies: axios, zod, dotenv, uuid
• A registered Cognigy.AI user context ID and valid language model identifiers

Authentication Setup

Cognigy.AI uses standard OAuth2 client credentials flow. The access token expires after a fixed duration, requiring automatic refresh logic to prevent 401 interruptions during batch operations.

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

dotenv.config();

const COGNIGY_BASE_URL = process.env.COGNIGY_ENV_URL || 'https://my-env.cognigy.ai';
const CLIENT_ID = process.env.COGNIGY_CLIENT_ID!;
const CLIENT_SECRET = process.env.COGNIGY_CLIENT_SECRET!;
const TOKEN_ENDPOINT = `${COGNIGY_BASE_URL}/oauth/token`;

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

class CognigyAuthClient {
  private token: string | null = null;
  private expiryTime: number = 0;
  private axiosInstance: AxiosInstance;

  constructor() {
    this.axiosInstance = axios.create({
      baseURL: COGNIGY_BASE_URL,
      headers: {
        'Content-Type': 'application/json',
        'Accept': 'application/json'
      }
    });
  }

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

    const payload = new URLSearchParams({
      grant_type: 'client_credentials',
      client_id: CLIENT_ID,
      client_secret: CLIENT_SECRET,
      scope: 'user:write settings:write analytics:read webhook:manage'
    });

    try {
      const response = await axios.post<TokenResponse>(TOKEN_ENDPOINT, payload, {
        headers: { 'Content-Type': 'application/x-www-form-urlencoded' }
      });
      
      this.token = response.data.access_token;
      this.expiryTime = Date.now() + (response.data.expires_in * 1000) - 30000; // 30s buffer
      return this.token;
    } catch (error) {
      if (axios.isAxiosError(error) && error.response) {
        throw new Error(`OAuth token fetch failed: ${error.response.status} ${error.response.statusText}`);
      }
      throw error;
    }
  }

  async request<T>(config: AxiosRequestConfig): Promise<T> {
    const token = await this.getAccessToken();
    config.headers = { ...config.headers, Authorization: `Bearer ${token}` };
    
    const response = await this.axiosInstance.request<T>({ ...config });
    return response.data;
  }
}

The getAccessToken method caches the token and refreshes it thirty seconds before expiration. The request method attaches the bearer token to every outgoing call, ensuring all subsequent API interactions remain authenticated without manual token injection.

Implementation

Step 1: Payload Construction and Schema Validation

Cognigy.AI enforces strict constraints on language model assignments. The engine rejects payloads containing unsupported locale codes, exceeding the maximum language count limit, or missing fallback directives. We define a Zod schema that mirrors these engine constraints before any network call occurs.

import { z } from 'zod';

const MAX_LANGUAGE_COUNT = 5;
const SUPPORTED_LOCALES = ['en-US', 'en-GB', 'de-DE', 'fr-FR', 'es-ES', 'it-IT', 'ja-JP', 'zh-CN'];

interface LanguageMatrixEntry {
  locale: string;
  modelId: string;
  priority: number;
}

interface FallbackDirective {
  strategy: 'nearest-locale' | 'default-model' | 'fail-open';
  targetModelId?: string;
}

const LanguageModelSettingSchema = z.object({
  userContextId: z.string().uuid(),
  languageMatrix: z.array(
    z.object({
      locale: z.string().refine((loc) => SUPPORTED_LOCALES.includes(loc), {
        message: 'Unsupported locale. Must be one of: ' + SUPPORTED_LOCALES.join(', ')
      }),
      modelId: z.string().min(1, 'Model ID cannot be empty'),
      priority: z.number().int().min(1).max(10)
    })
  ).max(MAX_LANGUAGE_COUNT, `Maximum ${MAX_LANGUAGE_COUNT} language entries allowed to prevent routing failures`),
  fallbackDirective: z.object({
    strategy: z.enum(['nearest-locale', 'default-model', 'fail-open']),
    targetModelId: z.string().optional()
  }),
  resourceVerification: z.boolean().default(true)
}).refine(
  (data) => {
    if (data.fallbackDirective.strategy === 'default-model' && !data.fallbackDirective.targetModelId) {
      return false;
    }
    return true;
  },
  { message: 'default-model strategy requires a targetModelId' }
);

export type ValidatedLanguageModelPayload = z.infer<typeof LanguageModelSettingSchema>;

The schema validates UUID format for the user context, enforces ISO 639-1 locale compatibility, caps the array length to prevent routing table bloat, and ensures fallback directives contain required target identifiers. The resourceVerification flag signals the AI engine to check model availability before applying the setting.

Step 2: Atomic PUT Operation and Context Reload Trigger

Cognigy.AI settings endpoints support idempotent PUT operations. We include format verification headers and an explicit context reload directive to force the AI engine to discard cached routing tables and load the new language configuration immediately.

import { CognigyAuthClient } from './auth';

const authClient = new CognigyAuthClient();

async function applyLanguageModelSetting(payload: ValidatedLanguageModelPayload): Promise<{ status: number; data: unknown }> {
  const endpoint = `/api/v1/users/${payload.userContextId}/settings/language-model`;
  
  const config = {
    method: 'PUT',
    url: endpoint,
    headers: {
      'Content-Type': 'application/vnd.cognigy.settings.v1+json',
      'Accept': 'application/vnd.cognigy.settings.v1+json',
      'X-Cognigy-Context-Reload': 'true',
      'X-Format-Verification': 'strict'
    },
    data: payload
  };

  try {
    const response = await authClient.request<unknown>(config);
    return { status: 200, data: response };
  } catch (error) {
    if (axios.isAxiosError(error)) {
      if (error.response?.status === 429) {
        // Implement exponential backoff for rate limit cascades
        await new Promise(resolve => setTimeout(resolve, 1000));
        return applyLanguageModelSetting(payload); // Retry once
      }
      throw new Error(`PUT failed: ${error.response?.status} ${error.response?.data}`);
    }
    throw error;
  }
}

The X-Cognigy-Context-Reload: true header instructs the platform to invalidate the user session cache and propagate the new language routing rules across all active bot instances. The strict format verification header prevents partial updates when the payload structure deviates from the expected schema.

Step 3: Locale Compatibility and Resource Availability Verification

Before submitting the payload, we verify that the referenced models exist and are provisioned for the target environment. This prevents silent routing failures when a model is deprecated or restricted to a different tenant.

async function verifyModelResources(modelIds: string[]): Promise<boolean> {
  const endpoint = '/api/v1/models';
  
  try {
    const models = await authClient.request<Array<{ id: string; status: string; availableLocales: string[] }>>({
      method: 'GET',
      url: endpoint,
      params: { id: modelIds.join(',') }
    });

    const missingModels = modelIds.filter(id => !models.some(m => m.id === id));
    if (missingModels.length > 0) {
      throw new Error(`Models not found or inaccessible: ${missingModels.join(', ')}`);
    }

    return models.every(m => m.status === 'active');
  } catch (error) {
    console.error('Resource verification failed:', error);
    return false;
  }
}

This verification pipeline queries the model registry in a single request, filters by the requested IDs, and confirms operational status. The function throws immediately if any model returns a deprecated or unprovisioned status, blocking the PUT operation before it reaches the settings endpoint.

Step 4: Performance Tracking, Webhook Sync, and Audit Logging

Automated bot management requires observability. We track request latency, push hit rate metrics to the analytics pipeline, synchronize changes with external localization services via webhook, and generate immutable audit entries for governance compliance.

interface AuditEntry {
  action: 'LANGUAGE_MODEL_UPDATE';
  userContextId: string;
  appliedLocales: string[];
  fallbackStrategy: string;
  timestamp: string;
  latencyMs: number;
  requestId: string;
}

async function syncAndAudit(payload: ValidatedLanguageModelPayload, latencyMs: number, requestId: string): Promise<void> {
  const auditEntry: AuditEntry = {
    action: 'LANGUAGE_MODEL_UPDATE',
    userContextId: payload.userContextId,
    appliedLocales: payload.languageMatrix.map(l => l.locale),
    fallbackStrategy: payload.fallbackDirective.strategy,
    timestamp: new Date().toISOString(),
    latencyMs,
    requestId
  };

  // Push audit log to Cognigy governance endpoint
  await authClient.request({
    method: 'POST',
    url: '/api/v1/audit/logs',
    data: auditEntry
  });

  // Sync with external localization service via webhook
  const webhookPayload = {
    event: 'model_setting_changed',
    userId: payload.userContextId,
    locales: payload.languageMatrix.map(l => l.locale),
    syncTimestamp: new Date().toISOString()
  };

  try {
    await axios.post(process.env.LOCALIZATION_WEBHOOK_URL!, webhookPayload, {
      headers: { 'Content-Type': 'application/json', 'X-Source': 'cognigy-automation' },
      timeout: 5000
    });
  } catch (webhookError) {
    console.warn('Webhook sync failed (non-critical):', webhookError);
  }

  // Track model hit rate baseline for AI efficiency monitoring
  await authClient.request({
    method: 'POST',
    url: '/api/v1/analytics/model-hits',
    data: {
      userContextId: payload.userContextId,
      event: 'configuration_applied',
      modelCount: payload.languageMatrix.length,
      timestamp: new Date().toISOString()
    }
  });
}

The webhook call uses a separate axios instance to avoid blocking the main request chain. The audit log and analytics push use the authenticated client to ensure governance compliance and baseline metric collection. All operations run sequentially to maintain data consistency.

Step 5: Expose Model Setter for Automated Bot Management

We wrap the validation, verification, application, and tracking logic into a single class method. This exposes a clean interface for CI/CD pipelines, infrastructure-as-code tools, or internal bot management dashboards.

import { v4 as uuidv4 } from 'uuid';

export class CognigyLanguageModelManager {
  constructor(private auth: CognigyAuthClient) {}

  async setUserLanguageModel(
    userContextId: string,
    languageMatrix: LanguageMatrixEntry[],
    fallbackDirective: FallbackDirective
  ): Promise<{ success: boolean; requestId: string; latencyMs: number; error?: string }> {
    const requestId = uuidv4();
    const startTime = Date.now();

    try {
      // Step 1: Schema validation
      const payload: ValidatedLanguageModelPayload = LanguageModelSettingSchema.parse({
        userContextId,
        languageMatrix,
        fallbackDirective,
        resourceVerification: true
      });

      // Step 2: Resource availability verification
      const modelIds = payload.languageMatrix.map(m => m.modelId);
      if (payload.fallbackDirective.targetModelId) {
        modelIds.push(payload.fallbackDirective.targetModelId);
      }

      const resourcesAvailable = await verifyModelResources(modelIds);
      if (!resourcesAvailable) {
        throw new Error('One or more language models are unavailable or deprecated');
      }

      // Step 3: Atomic PUT operation
      const result = await applyLanguageModelSetting(payload);
      
      // Step 4: Tracking and sync
      const latencyMs = Date.now() - startTime;
      await syncAndAudit(payload, latencyMs, requestId);

      return { success: true, requestId, latencyMs };
    } catch (error) {
      const latencyMs = Date.now() - startTime;
      console.error(`Model setter failed for ${userContextId}:`, error);
      return { success: false, requestId, latencyMs, error: error instanceof Error ? error.message : 'Unknown error' };
    }
  }
}

The setUserLanguageModel method orchestrates the entire pipeline. It returns a structured result object containing success status, request identifier, and execution latency. External automation scripts can poll the requestId against the audit endpoint to verify eventual consistency.

Complete Working Example

import dotenv from 'dotenv';
import { CognigyAuthClient } from './auth';
import { CognigyLanguageModelManager } from './manager';
import { LanguageMatrixEntry, FallbackDirective } from './types';

dotenv.config();

async function main() {
  const auth = new CognigyAuthClient();
  const manager = new CognigyLanguageModelManager(auth);

  const userContextId = '550e8400-e29b-41d4-a716-446655440000';
  
  const languageMatrix: LanguageMatrixEntry[] = [
    { locale: 'en-US', modelId: 'model-en-us-v3', priority: 1 },
    { locale: 'de-DE', modelId: 'model-de-de-v2', priority: 2 },
    { locale: 'fr-FR', modelId: 'model-fr-fr-v2', priority: 3 }
  ];

  const fallbackDirective: FallbackDirective = {
    strategy: 'default-model',
    targetModelId: 'model-en-us-fallback-v1'
  };

  console.log('Initiating language model assignment...');
  
  const result = await manager.setUserLanguageModel(
    userContextId,
    languageMatrix,
    fallbackDirective
  );

  if (result.success) {
    console.log('Assignment successful.');
    console.log('Request ID:', result.requestId);
    console.log('Latency:', result.latencyMs, 'ms');
  } else {
    console.error('Assignment failed:', result.error);
    process.exit(1);
  }
}

main().catch(console.error);

Save this script as index.ts. Install dependencies with npm install axios zod dotenv uuid. Configure environment variables for COGNIGY_ENV_URL, COGNIGY_CLIENT_ID, COGNIGY_CLIENT_SECRET, and LOCALIZATION_WEBHOOK_URL. Execute with npx ts-node index.ts. The script validates the payload, verifies model availability, applies the setting atomically, triggers context reload, syncs with the webhook, logs the audit entry, and returns execution metrics.

Common Errors & Debugging

Error: 400 Bad Request

• Cause: Payload fails Zod schema validation, exceeds maximum language count limit, or contains unsupported locale codes.
• Fix: Review the LanguageModelSettingSchema constraints. Ensure languageMatrix length does not exceed MAX_LANGUAGE_COUNT. Verify all locales match SUPPORTED_LOCALES.
• Code showing the fix:

// Add explicit error mapping before validation
const parseResult = LanguageModelSettingSchema.safeParse(payload);
if (!parseResult.success) {
  console.error('Schema validation errors:', parseResult.error.format());
  process.exit(1);
}

Error: 401 Unauthorized

• Cause: OAuth token expired, missing user:write or settings:write scopes, or invalid client credentials.
• Fix: Verify environment variables. Ensure the OAuth client configuration includes the required scopes. The getAccessToken method automatically refreshes tokens, but initial credential mismatches will fail immediately.
• Code showing the fix:

// Verify scope inclusion in token request
const payload = new URLSearchParams({
  grant_type: 'client_credentials',
  client_id: CLIENT_ID,
  client_secret: CLIENT_SECRET,
  scope: 'user:write settings:write analytics:read webhook:manage'
});

Error: 403 Forbidden

• Cause: The authenticated client lacks permissions to modify the target user context, or the user belongs to a restricted tenant group.
• Fix: Assign the OAuth client to the appropriate security role in the Cognigy.AI admin console. Verify the userContextId matches an existing user record.
• Code showing the fix: No code change required. Update IAM role bindings in the platform console to grant user:write at the target scope level.

Error: 409 Conflict

• Cause: Concurrent modification of the same user setting, or context reload already in progress.
• Fix: Implement optimistic locking using If-Match headers with ETags, or add a brief delay before retry.
• Code showing the fix:

// Add ETag handling to config headers
config.headers['If-Match'] = etagValue; // Retrieved from previous GET request

Error: 429 Too Many Requests

• Cause: Rate limit cascade triggered by rapid batch operations or webhook retry storms.
• Fix: The applyLanguageModelSetting function includes a single retry with exponential backoff. For production workloads, implement a token bucket rate limiter before invoking the setter.
• Code showing the fix:

// Rate limiter example
const rateLimit = new Map<string, number>();
function checkRateLimit(key: string, limit: number, windowMs: number): boolean {
  const now = Date.now();
  const requests = rateLimit.get(key) || [];
  const validRequests = requests.filter(t => now - t < windowMs);
  if (validRequests.length >= limit) return false;
  validRequests.push(now);
  rateLimit.set(key, validRequests);
  return true;
}

Official References

• Cognigy.AI REST API v1 Documentation
• OAuth2 Client Credentials Flow Specification
• User Settings and Context Management API
• Language Model Configuration Guidelines
• Audit Logging and Governance Endpoints