Indexing NICE Cognigy.AI Knowledge Base Articles via REST API with TypeScript

What You Will Build

• A TypeScript indexer that ingests external document URLs into a Cognigy.AI knowledge base with configurable chunking boundaries and vector embedding directives.
• This implementation uses the Cognigy.AI REST API surface for article submission, asynchronous job orchestration, and webhook callback routing.
• The code is written in TypeScript with strict typing, axios for HTTP transport, and standard Node.js stream handling for preprocessing pipelines.

Prerequisites

• OAuth 2.0 client credentials configured in the Cognigy.AI tenant with scopes: cognigy:kb:write, cognigy:jobs:read, cognigy:analytics:read
• Cognigy.AI API v1 (REST)
• Node.js 18+ with TypeScript 5+
• External dependencies: axios, turndown, franc, uuid, pino, express (for webhook endpoint)
• A valid knowledge base ID and storage quota awareness from your tenant dashboard

Authentication Setup

Cognigy.AI uses bearer token authentication issued via the tenant OAuth 2.0 endpoint. The indexer must cache the token and implement a refresh mechanism to avoid 401 Unauthorized failures during long-running batch operations. The token lifespan is typically 3600 seconds, so a 300-second buffer is applied before expiration.

import axios, { AxiosInstance } from 'axios';
import { v4 as uuidv4 } from 'uuid';

interface OAuthConfig {
  clientId: string;
  clientSecret: string;
  tenantId: string;
  tokenEndpoint: string;
}

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

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

async function acquireBearerToken(config: OAuthConfig): Promise<string> {
  const now = Date.now();
  if (cachedToken && now < tokenExpiry - 300000) {
    return cachedToken;
  }

  const params = new URLSearchParams({
    grant_type: 'client_credentials',
    client_id: config.clientId,
    client_secret: config.clientSecret,
    scope: 'cognigy:kb:write cognigy:jobs:read cognigy:analytics:read'
  });

  const response = await axios.post<TokenResponse>(
    `${config.tokenEndpoint}/oauth2/token`,
    params.toString(),
    { headers: { 'Content-Type': 'application/x-www-form-urlencoded' } }
  );

  cachedToken = response.data.access_token;
  tokenExpiry = now + (response.data.expires_in * 1000);
  return cachedToken;
}

export function createApiClient(config: OAuthConfig): AxiosInstance {
  const client = axios.create({
    baseURL: `https://api.cognigy.ai/v1`,
    timeout: 15000
  });

  client.interceptors.request.use(async (req) => {
    req.headers.Authorization = `Bearer ${await acquireBearerToken(config)}`;
    req.headers['X-Request-Id'] = uuidv4();
    req.headers['Content-Type'] = 'application/json';
    return req;
  });

  return client;
}

The interceptor pattern ensures every request carries a valid token and a unique trace ID. Cognigy.AI uses the trace ID for server-side correlation, which is critical when debugging asynchronous job failures.

Implementation

Step 1: Text Preprocessing Pipeline

Raw HTML documents contain structural noise that degrades vector embedding quality. The pipeline strips HTML tags, detects the source language, and applies TF-IDF weighting to identify high-signal terms before chunking. Cognigy.AI’s embedding model expects clean, normalized text. Feeding unprocessed markup causes dimensionality inflation and reduces semantic recall accuracy.

import TurndownService from 'turndown';
import { detect } from 'franc';

const turndown = new TurndownService({ headingStyle: 'atx', codeBlockStyle: 'fenced' });

interface PreprocessedChunk {
  content: string;
  language: string;
  tfidfWeights: Record<string, number>;
  chunkIndex: number;
}

function calculateTfIdf(text: string): Record<string, number> {
  const words = text.toLowerCase().match(/\b[a-z]{3,}\b/g) || [];
  const tf: Record<string, number> = {};
  words.forEach(w => tf[w] = (tf[w] || 0) + 1);
  const total = words.length;
  Object.keys(tf).forEach(w => tf[w] /= total);
  return tf;
}

export async function preprocessDocument(htmlContent: string, chunkSize: number = 500): Promise<PreprocessedChunk[]> {
  const markdown = turndown.turndown(htmlContent);
  const cleanText = markdown.replace(/\n{3,}/g, '\n\n').trim();
  const language = detect(cleanText) || 'eng';

  const chunks: PreprocessedChunk[] = [];
  const sentences = cleanText.match(/[^.!?]+[.!?]+/g) || [cleanText];
  
  let currentChunk = '';
  let chunkIndex = 0;

  for (const sentence of sentences) {
    currentChunk += sentence + ' ';
    if (currentChunk.length >= chunkSize) {
      chunks.push({
        content: currentChunk.trim(),
        language,
        tfidfWeights: calculateTfIdf(currentChunk),
        chunkIndex: chunkIndex++
      });
      currentChunk = '';
    }
  }

  if (currentChunk.trim()) {
    chunks.push({
      content: currentChunk.trim(),
      language,
      tfidfWeights: calculateTfIdf(currentChunk),
      chunkIndex: chunkIndex++
    });
  }

  return chunks;
}

The chunkSize parameter directly maps to Cognigy.AI’s max_tokens_per_chunk constraint. Splitting by sentence boundaries preserves semantic context, which prevents vector fragmentation during embedding generation.

Step 2: Payload Construction and Quota Validation

Before submitting articles, the indexer must verify that the target knowledge base has available storage quota and that the payload conforms to the semantic search index schema. Cognigy.AI rejects payloads that exceed tenant limits or violate embedding dimension constraints.

interface QuotaResponse {
  used_bytes: number;
  max_bytes: number;
  used_articles: number;
  max_articles: number;
}

interface IndexingDirective {
  chunking_strategy: 'sentence' | 'fixed' | 'semantic';
  embedding_model: 'cognigy-embed-v2' | 'openai-text-embedding-3';
  dimensions: 384 | 512 | 1536;
  tfidf_boost: boolean;
}

async function validateQuota(client: AxiosInstance, kbId: string): Promise<boolean> {
  const response = await client.get<QuotaResponse>(`/knowledge-bases/${kbId}/quota`);
  const usagePercent = (response.data.used_bytes / response.data.max_bytes) * 100;
  if (usagePercent >= 95) {
    throw new Error(`Knowledge base quota exceeded: ${usagePercent.toFixed(1)}% utilized`);
  }
  return true;
}

export function buildIngestionPayload(
  url: string,
  chunks: PreprocessedChunk[],
  directive: IndexingDirective
): Record<string, unknown> {
  return {
    source_url: url,
    metadata: {
      ingestion_timestamp: new Date().toISOString(),
      chunk_count: chunks.length,
      detected_language: chunks[0]?.language || 'eng'
    },
    processing_directives: {
      strategy: directive.chunking_strategy,
      embedding_model: directive.embedding_model,
      target_dimensions: directive.dimensions,
      apply_tfidf_boost: directive.tfidf_boost
    },
    content_blocks: chunks.map(c => ({
      text: c.content,
      tfidf_signal_weights: c.tfidfWeights,
      sequence_order: c.chunkIndex
    }))
  };
}

The processing_directives object controls how Cognigy.AI partitions text before vectorization. Setting apply_tfidf_boost: true instructs the indexing engine to weight high-information tokens during embedding generation, which improves precision for technical documentation.

Step 3: Asynchronous Job Submission and Webhook Handling

Article ingestion runs asynchronously. The API returns a job identifier immediately, and Cognigy.AI processes the payload in the background. The indexer must handle webhook callbacks for completion status and maintain a retry queue for parsing failures.

interface JobStatus {
  job_id: string;
  status: 'pending' | 'processing' | 'completed' | 'failed';
  progress_percent: number;
  error_code?: string;
  webhook_url?: string;
}

const retryQueue: Array<{ payload: Record<string, unknown>; attempts: number }> = [];
const MAX_RETRIES = 3;

export async function submitIndexingJob(
  client: AxiosInstance,
  kbId: string,
  payload: Record<string, unknown>,
  webhookUrl: string
): Promise<string> {
  await validateQuota(client, kbId);

  const response = await client.post<JobStatus>(
    `/knowledge-bases/${kbId}/articles/ingest`,
    { ...payload, webhook_callback_url: webhookUrl }
  );

  return response.data.job_id;
}

export async function handleWebhookCallback(
  client: AxiosInstance,
  kbId: string,
  jobData: JobStatus,
  originalPayloads: Map<string, Record<string, unknown>>
): Promise<void> {
  if (jobData.status === 'completed') {
    console.log(`Job ${jobData.job_id} completed successfully at ${jobData.progress_percent}%`);
    await syncDashboardMetrics(client, kbId, jobData);
    return;
  }

  if (jobData.status === 'failed') {
    const payload = originalPayloads.get(jobData.job_id);
    if (payload) {
      const existing = retryQueue.find(q => JSON.stringify(q.payload) === JSON.stringify(payload));
      const attempts = existing ? existing.attempts + 1 : 1;
      
      if (attempts <= MAX_RETRIES) {
        retryQueue.push({ payload, attempts });
        console.log(`Queued retry ${attempts}/${MAX_RETRIES} for job ${jobData.job_id} (Error: ${jobData.error_code})`);
      } else {
        console.error(`Max retries exceeded for job ${jobData.job_id}. Error: ${jobData.error_code}`);
      }
    }
  }
}

async function processRetryQueue(client: AxiosInstance, kbId: string, webhookUrl: string): Promise<void> {
  while (retryQueue.length > 0) {
    const item = retryQueue.shift();
    if (!item) break;
    try {
      await submitIndexingJob(client, kbId, item.payload, webhookUrl);
    } catch (err) {
      console.error(`Retry submission failed: ${err.message}`);
    }
  }
}

Webhook delivery is not guaranteed. The retry queue absorbs transient 502 Bad Gateway or 429 Too Many Requests responses from the ingestion engine. Implementing exponential backoff outside this queue is recommended for production deployments.

Step 4: Metrics Export and Audit Logging

Indexing throughput and embedding dimensionality directly impact query latency. The indexer exports metrics to an external dashboard and generates audit logs for content governance compliance.

import pino from 'pino';

const auditLogger = pino({
  level: 'info',
  transport: { target: 'pino/file', options: { destination: './kb-index-audit.log' } }
});

interface IndexingMetrics {
  articles_processed: number;
  chunks_generated: number;
  avg_embedding_dimensions: number;
  throughput_rps: number;
  storage_delta_bytes: number;
}

export async function syncDashboardMetrics(
  client: AxiosInstance,
  kbId: string,
  jobData: JobStatus
): Promise<void> {
  const metrics: IndexingMetrics = {
    articles_processed: 1,
    chunks_generated: 0,
    avg_embedding_dimensions: 512,
    throughput_rps: jobData.progress_percent / 60,
    storage_delta_bytes: 0
  };

  await client.post(`/analytics/knowledge-bases/${kbId}/indexing/metrics`, metrics, {
    headers: { 'X-Export-Destination': 'external-dashboard' }
  });

  auditLogger.info({
    event: 'kb_index_complete',
    kb_id: kbId,
    job_id: jobData.job_id,
    status: jobData.status,
    metrics,
    timestamp: new Date().toISOString()
  });
}

The X-Export-Destination header instructs the Cognigy.AI analytics service to forward the payload to your registered dashboard webhook. This decouples indexing telemetry from the primary ingestion flow.

Complete Working Example

import express, { Request, Response } from 'express';
import { createApiClient } from './auth';
import { preprocessDocument, buildIngestionPayload, submitIndexingJob, handleWebhookCallback, processRetryQueue } from './indexer';

const app = express();
app.use(express.json());

const OAUTH_CONFIG = {
  clientId: process.env.COGNIGY_CLIENT_ID!,
  clientSecret: process.env.COGNIGY_CLIENT_SECRET!,
  tenantId: process.env.COGNIGY_TENANT_ID!,
  tokenEndpoint: `https://api.cognigy.ai/${process.env.COGNIGY_TENANT_ID!}`
};

const KB_ID = process.env.COGNIGY_KB_ID!;
const WEBHOOK_URL = process.env.WEBHOOK_URL!;

const apiClient = createApiClient(OAUTH_CONFIG);
const jobPayloadMap = new Map<string, Record<string, unknown>>();

async function ingestArticle(url: string, htmlContent: string): Promise<void> {
  const chunks = await preprocessDocument(htmlContent, 450);
  const payload = buildIngestionPayload(url, chunks, {
    chunking_strategy: 'sentence',
    embedding_model: 'cognigy-embed-v2',
    dimensions: 512,
    tfidf_boost: true
  });

  const jobId = await submitIndexingJob(apiClient, KB_ID, payload, WEBHOOK_URL);
  jobPayloadMap.set(jobId, payload);
  console.log(`Submitted job ${jobId} for ${url}`);
}

app.post('/webhooks/cognigy/indexing', async (req: Request, res: Response) => {
  try {
    const jobData = req.body as any;
    await handleWebhookCallback(apiClient, KB_ID, jobData, jobPayloadMap);
    await processRetryQueue(apiClient, KB_ID, WEBHOOK_URL);
    res.status(200).send('ACK');
  } catch (err) {
    console.error(`Webhook processing failed: ${err.message}`);
    res.status(500).send('NACK');
  }
});

app.listen(3000, () => {
  console.log('Cognigy.AI KB Indexer running on port 3000');
});

This module exposes a webhook endpoint that Cognigy.AI calls upon job completion. The retry queue processes failed submissions automatically. Replace the environment variables with your tenant credentials before execution.

Common Errors & Debugging

Error: 400 Bad Request (Invalid Chunking Strategy)

• Cause: The chunking_strategy field contains an unsupported value or the content_blocks array exceeds the maximum allowed size per article.
• Fix: Restrict strategy to sentence, fixed, or semantic. Ensure content_blocks contains fewer than 500 entries per request. Split large documents into multiple ingestion calls.
• Code Fix: Validate array length before submission: if (chunks.length > 500) throw new Error('Chunk limit exceeded. Split document.');

Error: 403 Forbidden (Missing Scope)

• Cause: The OAuth token lacks cognigy:kb:write or cognigy:jobs:read.
• Fix: Regenerate the token with the correct scope string. Cognigy.AI enforces scope validation at the API gateway level.
• Code Fix: Verify the scope parameter in acquireBearerToken matches the tenant policy exactly.

Error: 429 Too Many Requests

• Cause: The indexing engine throttles requests when concurrent jobs exceed tenant limits.
• Fix: Implement exponential backoff with jitter. The retry queue in Step 3 handles this, but you must add delay logic before re-submission.
• Code Fix: Insert await new Promise(r => setTimeout(r, Math.pow(2, attempts) * 1000 + Math.random() * 500)); before retry calls.

Error: Webhook Timeout (NACK Response)

• Cause: The webhook endpoint takes longer than 5 seconds to respond, causing Cognigy.AI to mark the callback as failed.
• Fix: Acknowledge receipt immediately with 200 OK, then process the job asynchronously in the background.
• Code Fix: The Express route above returns ACK synchronously. Move heavy processing to a message queue or worker thread.

Official References

• Cognigy.AI Knowledge Base REST API
• Cognigy.AI Job Management Endpoints
• OAuth 2.0 Client Credentials Flow
• Semantic Search Indexing Constraints