Updating NICE Cognigy.AI Knowledge Base Document Sets via REST API with TypeScript

What You Will Build

• A TypeScript module that updates Cognigy.AI knowledge base documents using structured payloads containing content streams, metadata matrices, and version directives.
• This uses the Cognigy.AI v1 REST API surface with direct axios HTTP calls and zod schema validation.
• The tutorial covers TypeScript/Node.js 18+ with production-grade error handling, async job orchestration, and MLOps tracking.

Prerequisites

• Cognigy.AI API credentials with Bearer token authentication
• Required OAuth scopes: knowledge-bases:write, documents:write, jobs:read, quota:read, indexing:write
• Node.js 18.0 or higher, TypeScript 5.0 or higher
• External dependencies: npm install axios zod uuid
• A target knowledge base ID and an external CMS webhook endpoint URL

Authentication Setup

Cognigy.AI uses JWT Bearer tokens issued by your identity provider or the Cognigy CLI. The following setup implements token caching and automatic refresh logic to prevent mid-execution 401 failures.

import axios, { AxiosInstance, AxiosResponse } from 'axios';
import { randomUUID } from 'uuid';

const API_BASE = 'https://your-instance.cognigy.ai/api/v1';
const TOKEN_STORAGE_KEY = 'cognigy_api_token';
const TOKEN_REFRESH_ENDPOINT = '/api/v1/auth/token/refresh';

let cachedToken: string | null = null;
let tokenExpiry: number = 0;

async function acquireToken(): Promise<string> {
  if (cachedToken && Date.now() < tokenExpiry - 60000) {
    return cachedToken;
  }

  const refreshToken = process.env.COGNIGY_REFRESH_TOKEN;
  if (!refreshToken) {
    throw new Error('COGNIGY_REFRESH_TOKEN environment variable is not set');
  }

  try {
    const response: AxiosResponse = await axios.post(
      `${API_BASE}${TOKEN_REFRESH_ENDPOINT}`,
      { refresh_token: refreshToken },
      { headers: { 'Content-Type': 'application/json' } }
    );

    cachedToken = response.data.access_token;
    tokenExpiry = Date.now() + (response.data.expires_in * 1000);
    return cachedToken;
  } catch (error) {
    if (axios.isAxiosError(error)) {
      throw new Error(`Token refresh failed: ${error.response?.status} ${error.response?.statusText}`);
    }
    throw error;
  }
}

export async function createApiClient(): Promise<AxiosInstance> {
  const client = axios.create({
    baseURL: API_BASE,
    headers: { 'Content-Type': 'application/json' }
  });

  client.interceptors.request.use(async (config) => {
    const token = await acquireToken();
    config.headers.Authorization = `Bearer ${token}`;
    return config;
  });

  return client;
}

Implementation

Step 1: Quota Validation and Schema Enforcement

Before constructing update payloads, you must verify that the target knowledge base has sufficient storage quota and embedding index capacity. Cognigy.AI enforces strict limits on vector dimensions and document storage. This step fetches the quota, validates the incoming payload against a Zod schema, and fails fast if constraints are violated.

import { z } from 'zod';
import { AxiosInstance } from 'axios';

const DocumentSchema = z.object({
  id: z.string().uuid(),
  content: z.string().min(1).max(100000),
  metadata: z.record(z.string(), z.union([z.string(), z.number(), z.boolean()])),
  versionDirective: z.enum(['overwrite', 'append', 'replace']).default('overwrite')
});

const BatchUpdateSchema = z.object({
  documents: z.array(DocumentSchema).max(50),
  preprocessing: z.object({
    trigger: z.boolean().default(true),
    chunkStrategy: z.enum(['sentence', 'paragraph', 'semantic']).default('semantic')
  }),
  indexing: z.object({
    strategy: z.enum(['semantic', 'hybrid']).default('semantic'),
    embeddingDimensions: z.number().min(128).max(1536)
  })
});

type BatchUpdatePayload = z.infer<typeof BatchUpdateSchema>;
type QuotaResponse = {
  storageUsedBytes: number;
  storageLimitBytes: number;
  embeddingIndexUsed: number;
  embeddingIndexLimit: number;
  maxChunkSize: number;
};

async function validateQuotaAndSchema(
  client: AxiosInstance,
  kbId: string,
  payload: BatchUpdatePayload
): Promise<void> {
  const quotaResponse = await client.get<QuotaResponse>(`/knowledge-bases/${kbId}/quota`);
  const quota = quotaResponse.data;

  const estimatedBytes = payload.documents.reduce(
    (acc, doc) => acc + new TextEncoder().encode(doc.content).length, 0
  );
  const estimatedVectors = payload.documents.reduce(
    (acc, doc) => acc + Math.ceil(doc.content.length / 512), 0
  );

  if (quota.storageUsedBytes + estimatedBytes > quota.storageLimitBytes) {
    throw new Error(
      `Storage quota exceeded. Used: ${quota.storageUsedBytes}, Limit: ${quota.storageLimitBytes}, Request: ${estimatedBytes}`
    );
  }

  if (quota.embeddingIndexUsed + estimatedVectors > quota.embeddingIndexLimit) {
    throw new Error(
      `Embedding index limit exceeded. Used: ${quota.embeddingIndexUsed}, Limit: ${quota.embeddingIndexLimit}, Request: ${estimatedVectors}`
    );
  }

  try {
    BatchUpdateSchema.parse(payload);
  } catch (error) {
    if (error instanceof z.ZodError) {
      throw new Error(`Schema validation failed: ${error.errors.map(e => e.message).join('; ')}`);
    }
    throw error;
  }
}

HTTP Request/Response Cycle:

GET /api/v1/knowledge-bases/kb_8f3a2c1d/quota HTTP/1.1
Host: your-instance.cognigy.ai
Authorization: Bearer eyJhbGciOiJSUzI1NiIs...
Content-Type: application/json

HTTP/1.1 200 OK
Content-Type: application/json

{
  "storageUsedBytes": 104857600,
  "storageLimitBytes": 1073741824,
  "embeddingIndexUsed": 45000,
  "embeddingIndexLimit": 500000,
  "maxChunkSize": 1500
}

Required Scope: quota:read

Step 2: Payload Construction and Async Job Orchestration

Cognigy.AI processes large document updates asynchronously to prevent timeout failures and manage vector indexing load. This step constructs the batch payload, applies retry logic for 429 rate limits, and submits the ingestion job.

import { AxiosInstance, AxiosResponse } from 'axios';
import { randomUUID } from 'uuid';

type JobResponse = {
  jobId: string;
  status: 'queued' | 'processing' | 'completed' | 'failed';
  estimatedCompletionMs: number;
};

async function submitDocumentUpdate(
  client: AxiosInstance,
  kbId: string,
  payload: BatchUpdatePayload
): Promise<JobResponse> {
  const maxRetries = 3;
  let retryCount = 0;
  let lastError: Error | null = null;

  while (retryCount <= maxRetries) {
    try {
      const response: AxiosResponse<JobResponse> = await client.post(
        `/knowledge-bases/${kbId}/documents/batch`,
        {
          requestId: randomUUID(),
          timestamp: new Date().toISOString(),
          ...payload
        }
      );

      return response.data;
    } catch (error) {
      if (axios.isAxiosError(error)) {
        if (error.response?.status === 429) {
          const retryAfter = parseInt(error.response.headers['retry-after'] || '5', 10);
          retryCount++;
          lastError = new Error(`Rate limited (429). Retry ${retryCount}/${maxRetries} after ${retryAfter}s`);
          await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
          continue;
        }
        if (error.response?.status === 401 || error.response?.status === 403) {
          throw new Error(`Authentication failure: ${error.response.status} ${error.response.statusText}`);
        }
        if (error.response?.status === 400) {
          throw new Error(`Bad request: ${JSON.stringify(error.response.data)}`);
        }
      }
      lastError = error instanceof Error ? error : new Error(String(error));
      throw lastError;
    }
  }

  throw lastError || new Error('Max retries exceeded for document update submission');
}

HTTP Request/Response Cycle:

POST /api/v1/knowledge-bases/kb_8f3a2c1d/documents/batch HTTP/1.1
Host: your-instance.cognigy.ai
Authorization: Bearer eyJhbGciOiJSUzI1NiIs...
Content-Type: application/json

{
  "requestId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "timestamp": "2024-01-15T10:30:00.000Z",
  "documents": [
    {
      "id": "doc_9f8e7d6c",
      "content": "The new product launch includes three core modules: analytics, routing, and orchestration.",
      "metadata": { "department": "engineering", "version": "2.1.0", "confidentiality": "internal" },
      "versionDirective": "overwrite"
    }
  ],
  "preprocessing": { "trigger": true, "chunkStrategy": "semantic" },
  "indexing": { "strategy": "semantic", "embeddingDimensions": 768 }
}

HTTP/1.1 202 Accepted
Content-Type: application/json

{
  "jobId": "job_7a6b5c4d",
  "status": "queued",
  "estimatedCompletionMs": 45000
}

Required Scope: knowledge-bases:write, documents:write

Step 3: Content Processing and Embedding Pipeline Integration

Before submission, content must be chunked to match Cognigy.AI vector search constraints. This function implements semantic boundary detection and triggers the server-side preprocessing pipeline.

function optimizeContentForVectorSearch(content: string, maxChunkSize: number): string[] {
  if (content.length <= maxChunkSize) return [content];

  const sentences = content.match(/[^.!?]+[.!?]+/g) || [content];
  const chunks: string[] = [];
  let currentChunk = '';

  for (const sentence of sentences) {
    if ((currentChunk + sentence).length > maxChunkSize && currentChunk.length > 0) {
      chunks.push(currentChunk.trim());
      currentChunk = sentence;
    } else {
      currentChunk += sentence;
    }
  }

  if (currentChunk.trim().length > 0) {
    chunks.push(currentChunk.trim());
  }

  return chunks;
}

async function verifyFormatAndTriggerPreprocessing(
  client: AxiosInstance,
  kbId: string,
  documentId: string
): Promise<{ status: string; supportedFormats: string[] }> {
  try {
    const response = await client.post(`/knowledge-bases/${kbId}/documents/${documentId}/preprocess`, {
      formatVerification: true,
      autoTrigger: true
    });
    return response.data;
  } catch (error) {
    if (axios.isAxiosError(error) && error.response?.status === 404) {
      throw new Error(`Document ${documentId} not found in knowledge base ${kbId}`);
    }
    throw error;
  }
}

Required Scope: documents:write, indexing:write

Step 4: Webhook Synchronization, MLOps Tracking, and Audit Logging

After job submission, you must poll the job status, track ingestion latency, calculate success rates, sync with external CMS platforms, and generate governance audit logs.

import { AxiosInstance } from 'axios';

type JobStatus = {
  jobId: string;
  status: 'queued' | 'processing' | 'completed' | 'failed';
  progress: number;
  errors?: string[];
};

type AuditLogEntry = {
  timestamp: string;
  action: string;
  knowledgeBaseId: string;
  documentCount: number;
  status: string;
  latencyMs: number;
  requestId: string;
};

async function pollJobStatus(
  client: AxiosInstance,
  jobId: string,
  pollIntervalMs: number = 5000,
  timeoutMs: number = 120000
): Promise<JobStatus> {
  const startTime = Date.now();
  const deadline = startTime + timeoutMs;

  while (Date.now() < deadline) {
    const response = await client.get<JobStatus>(`/jobs/${jobId}`);
    const status = response.data;

    if (status.status === 'completed' || status.status === 'failed') {
      return status;
    }

    await new Promise(resolve => setTimeout(resolve, pollIntervalMs));
  }

  throw new Error(`Job ${jobId} timed out after ${timeoutMs}ms`);
}

async function synchronizeAndLog(
  client: AxiosInstance,
  jobId: string,
  kbId: string,
  documentCount: number,
  requestId: string,
  webhookUrl: string
): Promise<AuditLogEntry> {
  const jobStatus = await pollJobStatus(client, jobId);
  const latencyMs = Date.now() - (jobStatus.status === 'completed' ? Date.now() - 5000 : Date.now());

  const auditEntry: AuditLogEntry = {
    timestamp: new Date().toISOString(),
    action: 'document_batch_update',
    knowledgeBaseId: kbId,
    documentCount,
    status: jobStatus.status,
    latencyMs,
    requestId
  };

  try {
    await axios.post(webhookUrl, {
      event: 'cognigy_kb_sync',
      payload: auditEntry,
      successRate: jobStatus.status === 'completed' ? 100 : 0,
      errors: jobStatus.errors || []
    });
  } catch (webhookError) {
    console.warn('Webhook synchronization failed:', webhookError);
  }

  console.log('Audit Log:', JSON.stringify(auditEntry, null, 2));
  return auditEntry;
}

Required Scope: jobs:read

Complete Working Example

import { createApiClient } from './auth';
import { validateQuotaAndSchema, BatchUpdatePayload } from './validation';
import { submitDocumentUpdate } from './orchestration';
import { optimizeContentForVectorSearch, verifyFormatAndTriggerPreprocessing } from './processing';
import { synchronizeAndLog } from './tracking';
import { randomUUID } from 'uuid';

async function runDocumentUpdater() {
  const kbId = 'kb_8f3a2c1d';
  const webhookUrl = 'https://your-cms.internal/api/v1/webhooks/cognigy-sync';
  const maxChunkSize = 1500;

  const client = await createApiClient();

  const rawDocuments = [
    {
      id: 'doc_9f8e7d6c',
      rawContent: 'The new product launch includes three core modules: analytics, routing, and orchestration. Each module integrates with existing enterprise systems.',
      metadata: { department: 'engineering', version: '2.1.0', confidentiality: 'internal' }
    }
  ];

  const payload: BatchUpdatePayload = {
    documents: rawDocuments.map(doc => ({
      id: doc.id,
      content: optimizeContentForVectorSearch(doc.rawContent, maxChunkSize).join(' '),
      metadata: doc.metadata,
      versionDirective: 'overwrite' as const
    })),
    preprocessing: { trigger: true, chunkStrategy: 'semantic' },
    indexing: { strategy: 'semantic', embeddingDimensions: 768 }
  };

  await validateQuotaAndSchema(client, kbId, payload);

  for (const doc of payload.documents) {
    await verifyFormatAndTriggerPreprocessing(client, kbId, doc.id);
  }

  const requestId = randomUUID();
  const jobResponse = await submitDocumentUpdate(client, kbId, payload);

  const auditLog = await synchronizeAndLog(
    client,
    jobResponse.jobId,
    kbId,
    payload.documents.length,
    requestId,
    webhookUrl
  );

  console.log('Update pipeline completed. Audit:', auditLog.status);
}

runDocumentUpdater().catch(console.error);

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired Bearer token or missing Authorization header.
• Fix: Ensure the token refresh interceptor is active. Verify COGNIGY_REFRESH_TOKEN is set in environment variables. Check that the token has not exceeded its expires_in window.
• Code Fix: The acquireToken() function automatically handles refresh. If it fails, verify your identity provider configuration.

Error: 403 Forbidden

• Cause: Missing OAuth scopes or insufficient role permissions in Cognigy.AI.
• Fix: Assign the knowledge-bases:write and documents:write scopes to your API client. Verify that the service account has Administrator or Knowledge Manager role access.
• Code Fix: Add missing scopes to your OAuth client registration and regenerate credentials.

Error: 429 Too Many Requests

• Cause: Exceeded API rate limits for batch submissions or job polling.
• Fix: Implement exponential backoff. The submitDocumentUpdate function includes a retry loop with Retry-After header parsing.
• Code Fix: Increase pollIntervalMs in pollJobStatus or reduce batch size to 20 documents per request.

Error: 400 Bad Request (Schema or Quota Violation)

• Cause: Payload exceeds storage quota, embedding index limits, or fails Zod validation.
• Fix: Review validateQuotaAndSchema output. Ensure content length stays under maxChunkSize and metadata values match allowed types.
• Code Fix: Adjust embeddingDimensions to match your model configuration (typically 768 or 1536). Split large documents into smaller batches.

Error: 500 Internal Server Error (Job Failure)

• Cause: Server-side preprocessing failure, unsupported file format, or vector model unavailability.
• Fix: Check the errors array in the job status response. Verify that the Cognigy.AI instance has the required embedding model deployed.
• Code Fix: Add format verification before submission using verifyFormatAndTriggerPreprocessing. Retry with sanitized content if special characters cause parsing failures.

Official References

• Cognigy.AI Knowledge Base API Documentation
• Cognigy.AI Document Management Endpoints
• Cognigy.AI Job Orchestration and Async Processing
• OAuth 2.0 Bearer Token Authentication for Cognigy
• Vector Indexing and Embedding Configuration