Annotating Cognigy.AI Conversation Logs via REST APIs with Node.js

What You Will Build

• A Node.js service that programmatically annotates Cognigy.AI conversation logs with structured tags, entity highlights, and sentiment labels.
• The service uses the Cognigy.AI v1 REST API to perform atomic PUT operations, validate payload sizes, enforce privacy checks, and trigger review queues.
• The implementation runs in Node.js 18+ using axios, joi, and standard async/await patterns.

Prerequisites

• Cognigy.AI OAuth2 client credentials with scopes: annotation:write, logs:read, webhooks:write
• Cognigy.AI API v1
• Node.js 18.0 or higher
• External dependencies: npm install axios joi dotenv

Authentication Setup

Cognigy.AI uses standard OAuth2 client credentials flow. The service must cache the access token and refresh it before expiration to avoid 401 interrupts during batch annotation.

const axios = require('axios');
const dotenv = require('dotenv');

dotenv.config();

const COGNIGY_BASE_URL = process.env.COGNIGY_BASE_URL;
const CLIENT_ID = process.env.COGNIGY_CLIENT_ID;
const CLIENT_SECRET = process.env.COGNIGY_CLIENT_SECRET;

let accessToken = null;
let tokenExpiry = 0;

async function getAccessToken() {
  if (accessToken && Date.now() < tokenExpiry - 60000) {
    return accessToken;
  }

  const authResponse = await axios.post(`${COGNIGY_BASE_URL}/oauth/token`, {
    grant_type: 'client_credentials',
    client_id: CLIENT_ID,
    client_secret: CLIENT_SECRET,
    scope: 'annotation:write logs:read webhooks:write'
  }, {
    headers: { 'Content-Type': 'application/x-www-form-urlencoded' },
    params: new URLSearchParams({ grant_type: 'client_credentials', client_id: CLIENT_ID, client_secret: CLIENT_SECRET, scope: 'annotation:write logs:read webhooks:write' })
  });

  accessToken = authResponse.data.access_token;
  tokenExpiry = Date.now() + (authResponse.data.expires_in * 1000);
  return accessToken;
}

HTTP Request Cycle

POST /oauth/token HTTP/1.1
Host: api.cognigy.ai
Content-Type: application/x-www-form-urlencoded

grant_type=client_credentials&client_id=YOUR_CLIENT_ID&client_secret=YOUR_CLIENT_SECRET&scope=annotation:write+logs:read+webhooks:write

Expected Response

{
  "access_token": "eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...",
  "token_type": "Bearer",
  "expires_in": 3600,
  "scope": "annotation:write logs:read webhooks:write"
}

Implementation

Step 1: Payload Construction and Schema Validation

Cognigy.AI enforces strict storage constraints. The annotation payload must include a log reference, a tag matrix, and a label directive. The service validates the schema and measures the byte size before transmission. The maximum annotation payload is 8192 bytes.

const Joi = require('joi');

const MAX_ANNOTATION_BYTES = 8192;

const annotationSchema = Joi.object({
  logId: Joi.string().hex().required(),
  tags: Joi.object({
    sentiment: Joi.string().valid('positive', 'neutral', 'negative').required(),
    intent: Joi.string().min(2).max(50).required(),
    priority: Joi.number().integer().min(1).max(5).default(3)
  }).required(),
  labels: Joi.array().items(Joi.string()).min(1).max(5).required(),
  entities: Joi.array().items(Joi.object({
    type: Joi.string().required(),
    start: Joi.number().integer().min(0).required(),
    end: Joi.number().integer().required(),
    text: Joi.string().required()
  })).default([]),
  reviewRequired: Joi.boolean().default(false)
}).unknown(false);

function validateAnnotationPayload(payload) {
  const { error, value } = annotationSchema.validate(payload, { abortEarly: false });
  if (error) {
    throw new Error(`Schema validation failed: ${error.message}`);
  }

  const payloadBytes = Buffer.byteLength(JSON.stringify(value), 'utf8');
  if (payloadBytes > MAX_ANNOTATION_BYTES) {
    throw new Error(`Annotation payload exceeds storage limit: ${payloadBytes} bytes (max ${MAX_ANNOTATION_BYTES})`);
  }

  return value;
}

Step 2: Atomic PUT Operations for Entity and Sentiment Tagging

The service uses atomic PUT requests to update annotations. This ensures idempotency and prevents race conditions when multiple annotators modify the same log. The request includes format verification headers and triggers the internal review queue when reviewRequired is true.

async function annotateConversation(logId, annotationData) {
  const validatedPayload = validateAnnotationPayload({ logId, ...annotationData });
  const token = await getAccessToken();

  const requestConfig = {
    method: 'put',
    url: `${COGNIGY_BASE_URL}/api/v1/logs/${logId}/annotations`,
    headers: {
      'Authorization': `Bearer ${token}`,
      'Content-Type': 'application/json',
      'Accept': 'application/json',
      'X-Idempotency-Key': `annot-${logId}-${Date.now()}`
    },
    data: validatedPayload,
    maxRedirects: 0
  };

  let response;
  try {
    response = await axios(requestConfig);
  } catch (error) {
    if (error.response?.status === 429) {
      const retryAfter = parseInt(error.response.headers['retry-after'] || '5', 10);
      console.warn(`Rate limited. Retrying after ${retryAfter} seconds...`);
      await new Promise(resolve => setTimeout(resolve, retryAfter * 1000));
      response = await axios(requestConfig);
    } else {
      throw error;
    }
  }

  return response.data;
}

HTTP Request Cycle

PUT /api/v1/logs/a1b2c3d4e5f6/annotations HTTP/1.1
Host: api.cognigy.ai
Authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...
Content-Type: application/json
X-Idempotency-Key: annot-a1b2c3d4e5f6-1715420000000

{
  "logId": "a1b2c3d4e5f6",
  "tags": { "sentiment": "positive", "intent": "billing", "priority": 2 },
  "labels": ["ml-training", "review-pending"],
  "entities": [{ "type": "AMOUNT", "start": 45, "end": 50, "text": "$150" }],
  "reviewRequired": true
}

Expected Response

{
  "id": "annot_9x8y7z",
  "logId": "a1b2c3d4e5f6",
  "tags": { "sentiment": "positive", "intent": "billing", "priority": 2 },
  "labels": ["ml-training", "review-pending"],
  "entities": [{ "type": "AMOUNT", "start": 45, "end": 50, "text": "$150" }],
  "reviewRequired": true,
  "updatedAt": "2024-05-10T14:32:00Z",
  "status": "accepted"
}

Step 3: Role Verification and Privacy Pipeline

Before annotation submission, the service verifies the annotator role and scans the log text for sensitive information. The privacy pipeline blocks annotation if unredacted PII is detected, preventing data leakage during scaling.

const PII_PATTERNS = [
  { name: 'SSN', regex: /\b\d{3}-\d{2}-\d{4}\b/ },
  { name: 'CREDIT_CARD', regex: /\b(?:\d[ -]*?){13,16}\b/ },
  { name: 'EMAIL', regex: /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b/ }
];

async function verifyAnnotatorRole(role) {
  const allowedRoles = ['annotator_admin', 'annotator_lead', 'ml_engineer'];
  if (!allowedRoles.includes(role)) {
    throw new Error(`Role ${role} is not authorized for annotation operations`);
  }
  return true;
}

function scanForPII(text) {
  const violations = [];
  for (const pattern of PII_PATTERNS) {
    if (pattern.regex.test(text)) {
      violations.push(pattern.name);
    }
  }
  if (violations.length > 0) {
    throw new Error(`Privacy violation detected: ${violations.join(', ')}`);
  }
  return false;
}

Step 4: Webhook Synchronization and Metrics Tracking

After successful annotation, the service triggers a webhook to external ML training pipelines, tracks latency and success rates, and writes an immutable audit log for data governance.

const METRICS = {
  totalAttempts: 0,
  successfulAnnotations: 0,
  totalLatencyMs: 0
};

const AUDIT_LOG = [];

async function syncToMLPipeline(annotationId, logId) {
  const webhookPayload = {
    event: 'annotation.completed',
    timestamp: new Date().toISOString(),
    data: { annotationId, logId, pipeline: 'external-ml-training' }
  };

  await axios.post(`${COGNIGY_BASE_URL}/api/v1/webhooks/trigger`, webhookPayload, {
    headers: { 'Authorization': `Bearer ${await getAccessToken()}` }
  });
}

function recordMetrics(latencyMs, success) {
  METRICS.totalAttempts++;
  if (success) {
    METRICS.successfulAnnotations++;
    METRICS.totalLatencyMs += latencyMs;
  }
  console.log(`Metrics: Success Rate ${((METRICS.successfulAnnotations / METRICS.totalAttempts) * 100).toFixed(2)}%, Avg Latency ${Math.round(METRICS.totalLatencyMs / METRICS.successfulAnnotations)}ms`);
}

function writeAuditLog(action, logId, annotationId, status, details) {
  AUDIT_LOG.push({
    timestamp: new Date().toISOString(),
    action,
    logId,
    annotationId,
    status,
    details,
    governanceId: `gov-${Date.now()}-${Math.random().toString(36).slice(2, 8)}`
  });
  console.log(`Audit: [${action}] ${status} | Log: ${logId} | Annotation: ${annotationId}`);
}

Complete Working Example

const axios = require('axios');
const Joi = require('joi');
const dotenv = require('dotenv');

dotenv.config();

const COGNIGY_BASE_URL = process.env.COGNIGY_BASE_URL;
const CLIENT_ID = process.env.COGNIGY_CLIENT_ID;
const CLIENT_SECRET = process.env.COGNIGY_CLIENT_SECRET;
const ANNOTATOR_ROLE = process.env.ANNOTATOR_ROLE || 'annotator_lead';

let accessToken = null;
let tokenExpiry = 0;

const MAX_ANNOTATION_BYTES = 8192;
const PII_PATTERNS = [
  { name: 'SSN', regex: /\b\d{3}-\d{2}-\d{4}\b/ },
  { name: 'CREDIT_CARD', regex: /\b(?:\d[ -]*?){13,16}\b/ },
  { name: 'EMAIL', regex: /\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b/ }
];

const METRICS = { totalAttempts: 0, successfulAnnotations: 0, totalLatencyMs: 0 };
const AUDIT_LOG = [];

const annotationSchema = Joi.object({
  logId: Joi.string().hex().required(),
  tags: Joi.object({
    sentiment: Joi.string().valid('positive', 'neutral', 'negative').required(),
    intent: Joi.string().min(2).max(50).required(),
    priority: Joi.number().integer().min(1).max(5).default(3)
  }).required(),
  labels: Joi.array().items(Joi.string()).min(1).max(5).required(),
  entities: Joi.array().items(Joi.object({
    type: Joi.string().required(),
    start: Joi.number().integer().min(0).required(),
    end: Joi.number().integer().required(),
    text: Joi.string().required()
  })).default([]),
  reviewRequired: Joi.boolean().default(false)
}).unknown(false);

async function getAccessToken() {
  if (accessToken && Date.now() < tokenExpiry - 60000) return accessToken;
  const authResponse = await axios.post(`${COGNIGY_BASE_URL}/oauth/token`, new URLSearchParams({
    grant_type: 'client_credentials',
    client_id: CLIENT_ID,
    client_secret: CLIENT_SECRET,
    scope: 'annotation:write logs:read webhooks:write'
  }), { headers: { 'Content-Type': 'application/x-www-form-urlencoded' } });
  accessToken = authResponse.data.access_token;
  tokenExpiry = Date.now() + (authResponse.data.expires_in * 1000);
  return accessToken;
}

async function verifyAnnotatorRole(role) {
  if (!['annotator_admin', 'annotator_lead', 'ml_engineer'].includes(role)) {
    throw new Error(`Role ${role} is not authorized for annotation operations`);
  }
  return true;
}

function scanForPII(text) {
  const violations = [];
  for (const pattern of PII_PATTERNS) {
    if (pattern.regex.test(text)) violations.push(pattern.name);
  }
  if (violations.length > 0) throw new Error(`Privacy violation detected: ${violations.join(', ')}`);
  return false;
}

function validateAnnotationPayload(payload) {
  const { error, value } = annotationSchema.validate(payload, { abortEarly: false });
  if (error) throw new Error(`Schema validation failed: ${error.message}`);
  const payloadBytes = Buffer.byteLength(JSON.stringify(value), 'utf8');
  if (payloadBytes > MAX_ANNOTATION_BYTES) throw new Error(`Payload exceeds limit: ${payloadBytes} bytes`);
  return value;
}

async function annotateConversation(logId, logText, annotationData) {
  const startTime = Date.now();
  METRICS.totalAttempts++;
  
  await verifyAnnotatorRole(ANNOTATOR_ROLE);
  scanForPII(logText);
  
  const validatedPayload = validateAnnotationPayload({ logId, ...annotationData });
  const token = await getAccessToken();

  try {
    const response = await axios.put(`${COGNIGY_BASE_URL}/api/v1/logs/${logId}/annotations`, validatedPayload, {
      headers: {
        'Authorization': `Bearer ${token}`,
        'Content-Type': 'application/json',
        'X-Idempotency-Key': `annot-${logId}-${startTime}`
      }
    });

    const latencyMs = Date.now() - startTime;
    METRICS.successfulAnnotations++;
    METRICS.totalLatencyMs += latencyMs;

    if (validatedPayload.reviewRequired) {
      console.log(`Review queue triggered for log ${logId}`);
    }

    await syncToMLPipeline(response.data.id, logId);
    recordMetrics(latencyMs, true);
    writeAuditLog('ANNOTATE', logId, response.data.id, 'SUCCESS', `Latency: ${latencyMs}ms`);
    return response.data;
  } catch (error) {
    const latencyMs = Date.now() - startTime;
    recordMetrics(latencyMs, false);
    writeAuditLog('ANNOTATE', logId, null, 'FAILURE', error.message);
    if (error.response?.status === 429) {
      console.warn(`Rate limited. Retry logic triggered.`);
    }
    throw error;
  }
}

async function syncToMLPipeline(annotationId, logId) {
  await axios.post(`${COGNIGY_BASE_URL}/api/v1/webhooks/trigger`, {
    event: 'annotation.completed',
    timestamp: new Date().toISOString(),
    data: { annotationId, logId, pipeline: 'external-ml-training' }
  }, { headers: { 'Authorization': `Bearer ${await getAccessToken()}` } });
}

function recordMetrics(latencyMs, success) {
  if (success) {
    console.log(`Metrics: Success Rate ${((METRICS.successfulAnnotations / METRICS.totalAttempts) * 100).toFixed(2)}%, Avg Latency ${Math.round(METRICS.totalLatencyMs / METRICS.successfulAnnotations)}ms`);
  }
}

function writeAuditLog(action, logId, annotationId, status, details) {
  AUDIT_LOG.push({ timestamp: new Date().toISOString(), action, logId, annotationId, status, details });
}

module.exports = { annotateConversation, METRICS, AUDIT_LOG };

Common Errors & Debugging

Error: 400 Bad Request

• Cause: Payload schema mismatch, invalid hex log ID, or entity start/end indices exceed log text length.
• Fix: Verify annotationSchema validation passes. Ensure entities[].start and entities[].end fall within the actual transcript bounds. Check that labels array contains exactly 1 to 5 strings.
• Code Fix: Add pre-flight index validation before PUT:

  if (annotationData.entities) {
    for (const ent of annotationData.entities) {
      if (ent.start < 0 || ent.end > logText.length || ent.start > ent.end) {
        throw new Error(`Invalid entity boundaries for log ${logId}`);
      }
    }
  }
  

Error: 401 Unauthorized

• Cause: Expired access token, missing annotation:write scope, or invalid client credentials.
• Fix: Ensure getAccessToken() refreshes before expiration. Verify the OAuth client has the exact scope string annotation:write logs:read webhooks:write. Check that the Authorization header uses the Bearer prefix.

Error: 403 Forbidden

• Cause: Annotator role lacks permission, or the log belongs to a restricted workspace.
• Fix: Run verifyAnnotatorRole() before submission. Confirm the OAuth client is assigned to the correct Cognigy.AI workspace in the admin console.

Error: 413 Payload Too Large

• Cause: Annotation JSON exceeds the 8192 byte storage constraint.
• Fix: The validateAnnotationPayload() function catches this automatically. Reduce tag verbosity, truncate entity text, or split multi-label directives into separate requests.

Error: 429 Too Many Requests

• Cause: Exceeded Cognigy.AI rate limits (typically 100 requests per minute per client).
• Fix: The implementation includes automatic retry with Retry-After header parsing. For batch operations, implement exponential backoff and queue throttling.

Official References

• Cognigy.AI REST API v1 Documentation
• Cognigy.AI OAuth2 Authentication Guide
• NICE CXone Conversation Data Integration
• Joi Validation Library
• Axios HTTP Client