Deduplicating Cognigy.AI Entity Extraction Logs with Python

What You Will Build

• A Python module that retrieves Cognigy.AI entity extraction logs, identifies duplicates using an overlap matrix and confidence verification, and consolidates records via atomic batch deletion.
• The script uses the Cognigy.AI REST API directly through requests with Pydantic schema validation and structured audit logging.
• The implementation covers Python 3.9+ with production-grade error handling, rate-limit retry logic, and external webhook synchronization.

Prerequisites

• Cognigy.AI tenant URL and OAuth2 client credentials
• Required OAuth scopes: logs:read, logs:write, entities:read, webhooks:notify
• Python 3.9 or higher
• External dependencies: pip install requests pydantic cryptography
• Access to a data lake endpoint that accepts JSON webhook payloads

Authentication Setup

Cognigy.AI uses OAuth2 client credentials flow for server-to-server API access. Token caching prevents unnecessary authentication requests and reduces latency during batch operations.

import requests
import time
import logging
from typing import Optional, Dict

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")
logger = logging.getLogger(__name__)

class CognigyAuthManager:
    def __init__(self, base_url: str, client_id: str, client_secret: str):
        self.base_url = base_url.rstrip("/")
        self.client_id = client_id
        self.client_secret = client_secret
        self.token: Optional[str] = None
        self.token_expiry: float = 0.0

    def _fetch_token(self) -> str:
        url = f"{self.base_url}/api/v1/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        response = requests.post(url, data=payload, timeout=15)
        response.raise_for_status()
        data = response.json()
        self.token = data["access_token"]
        self.token_expiry = time.time() + data["expires_in"] - 30
        return self.token

    def get_token(self) -> str:
        if self.token and time.time() < self.token_expiry:
            return self.token
        return self._fetch_token()

HTTP Request/Response Cycle

• Method: POST
• Path: /api/v1/oauth/token
• Headers: Content-Type: application/x-www-form-urlencoded
• Request Body: grant_type=client_credentials&client_id=YOUR_ID&client_secret=YOUR_SECRET
• Response: {"access_token": "eyJ...", "expires_in": 3600, "token_type": "Bearer"}
• Required Scope: Implicit in client credentials configuration; API calls must include Authorization: Bearer <token>.

Implementation

Step 1: Fetch Extraction Logs with Pagination

Cognigy.AI returns extraction logs in paginated batches. The endpoint supports cursor-based pagination via page and pageSize. Rate limits trigger HTTP 429 responses, which require exponential backoff.

from typing import List, Any

class CognigyLogFetcher:
    def __init__(self, auth: CognigyAuthManager):
        self.auth = auth
        self.base_url = auth.base_url

    def _request_with_retry(self, method: str, path: str, params: Optional[Dict] = None, json: Optional[Dict] = None) -> requests.Response:
        url = f"{self.base_url}{path}"
        headers = {"Authorization": f"Bearer {self.auth.get_token()}", "Content-Type": "application/json"}
        backoff = 1.0
        while True:
            response = requests.request(method, url, headers=headers, params=params, json=json, timeout=30)
            if response.status_code == 429:
                retry_after = float(response.headers.get("Retry-After", backoff))
                logger.warning("Rate limited. Waiting %.2f seconds", retry_after)
                time.sleep(retry_after)
                backoff = min(backoff * 2, 60)
                continue
            if response.status_code == 401:
                self.auth.token = None
                headers["Authorization"] = f"Bearer {self.auth.get_token()}"
                response = requests.request(method, url, headers=headers, params=params, json=json, timeout=30)
            response.raise_for_status()
            return response

    def fetch_extraction_logs(self, page_size: int = 500, max_pages: int = 10) -> List[Dict[str, Any]]:
        logs = []
        for page in range(1, max_pages + 1):
            params = {"page": page, "pageSize": page_size, "type": "extraction"}
            resp = self._request_with_retry("GET", "/api/v1/logs", params=params)
            data = resp.json()
            batch = data.get("items", [])
            if not batch:
                break
            logs.extend(batch)
            if len(batch) < page_size:
                break
        logger.info("Fetched %d extraction logs", len(logs))
        return logs

Expected Response Structure

{
  "items": [
    {
      "id": "log_8f3a2c1b",
      "timestamp": "2024-05-12T14:23:11.402Z",
      "entityType": "product_name",
      "extractedValue": "Enterprise Suite",
      "confidence": 0.94,
      "sessionId": "sess_9921",
      "channel": "webchat"
    }
  ],
  "total": 1240,
  "page": 1,
  "pageSize": 500
}

Step 2: Construct Deduplication Payload with Overlap Matrix

Duplicates in extraction logs typically share identical entity values, similar timestamps, and overlapping confidence scores. The overlap matrix groups logs by normalized entity signature and identifies primary versus secondary records. Hash collisions trigger a fallback to timestamp proximity and confidence verification.

import hashlib
import json
from collections import defaultdict
from datetime import datetime, timezone

class DeduplicationEngine:
    def __init__(self, timestamp_threshold_seconds: float = 5.0, confidence_threshold: float = 0.85):
        self.timestamp_threshold = timestamp_threshold_seconds
        self.confidence_threshold = confidence_threshold
        self.groups: Dict[str, List[Dict]] = defaultdict(list)

    def _normalize_signature(self, log: Dict) -> str:
        sig = f"{log['entityType']}|{log['extractedValue'].lower().strip()}|{log['channel']}"
        return hashlib.sha256(sig.encode()).hexdigest()

    def _parse_timestamp(self, ts_str: str) -> datetime:
        return datetime.fromisoformat(ts_str.replace("Z", "+00:00"))

    def build_overlap_matrix(self, logs: List[Dict]) -> Dict[str, List[Dict]]:
        for log in logs:
            sig = self._normalize_signature(log)
            self.groups[sig].append(log)
        return dict(self.groups)

    def construct_merge_directives(self) -> List[Dict]:
        directives = []
        for sig, group in self.groups.items():
            if len(group) < 2:
                continue
            group.sort(key=lambda x: (x["confidence"], x["timestamp"]), reverse=True)
            primary = group[0]
            secondaries = group[1:]
            
            for sec in secondaries:
                primary_ts = self._parse_timestamp(primary["timestamp"])
                sec_ts = self._parse_timestamp(sec["timestamp"])
                delta = abs((primary_ts - sec_ts).total_seconds())
                
                if delta > self.timestamp_threshold:
                    continue
                if sec["confidence"] < self.confidence_threshold:
                    continue
                    
                directives.append({
                    "primaryId": primary["id"],
                    "secondaryId": sec["id"],
                    "overlapScore": min(primary["confidence"], sec["confidence"]),
                    "timestampDelta": delta,
                    "action": "merge"
                })
        return directives

Overlap Matrix Validation

• Each group shares a SHA256 signature of entityType|extractedValue|channel.
• The engine verifies timestamp proximity and confidence thresholds before marking records for consolidation.
• Hash collisions (different entities producing identical signatures) are resolved by enforcing strict timestamp deltas and confidence floors.

Step 3: Validate Constraints and Execute Atomic Consolidation

Cognigy.AI enforces maximum log retention limits and batch operation sizes. The consolidation step validates the payload against engine constraints, executes an atomic DELETE operation, and handles format verification.

from pydantic import BaseModel, Field
from typing import List

class MergeDirective(BaseModel):
    primaryId: str
    secondaryId: str
    overlapScore: float
    timestampDelta: float
    action: str = "merge"

class BatchDeletePayload(BaseModel):
    directives: List[MergeDirective] = Field(..., max_items=500)
    retentionCheck: bool = True
    formatVersion: str = "2024.1"

class CognigyConsolidator:
    def __init__(self, auth: CognigyAuthManager, max_batch_size: int = 500):
        self.auth = auth
        self.base_url = auth.base_url
        self.max_batch_size = max_batch_size

    def validate_and_consolidate(self, directives: List[Dict]) -> Dict:
        payload = BatchDeletePayload(directives=directives)
        
        if len(payload.directives) > self.max_batch_size:
            raise ValueError(f"Batch size {len(payload.directives)} exceeds engine limit {self.max_batch_size}")
            
        url = f"{self.base_url}/api/v1/logs/batch-operation"
        headers = {
            "Authorization": f"Bearer {self.auth.get_token()}",
            "Content-Type": "application/json",
            "X-Consolidation-Mode": "atomic"
        }
        
        response = requests.post(url, headers=headers, json=payload.model_dump(), timeout=30)
        response.raise_for_status()
        return response.json()

HTTP Request/Response Cycle

• Method: POST
• Path: /api/v1/logs/batch-operation
• Headers: Authorization: Bearer <token>, Content-Type: application/json, X-Consolidation-Mode: atomic
• Request Body: {"directives": [{"primaryId": "log_8f3a2c1b", "secondaryId": "log_7d2e1a9f", "overlapScore": 0.91, "timestampDelta": 2.3, "action": "merge"}], "retentionCheck": true, "formatVersion": "2024.1"}
• Response: {"processed": 1, "deleted": 1, "skipped": 0, "auditId": "aud_44921", "status": "completed"}
• Required Scope: logs:write

Step 4: Synchronize Events and Generate Audit Trails

After consolidation, the system posts deduplication events to an external data lake, tracks latency, calculates reduction success rates, and writes structured audit logs for AI governance.

import time
import uuid

class DeduplicationOrchestrator:
    def __init__(self, auth: CognigyAuthManager, webhook_url: str):
        self.auth = auth
        self.fetcher = CognigyLogFetcher(auth)
        self.engine = DeduplicationEngine()
        self.consolidator = CognigyConsolidator(auth)
        self.webhook_url = webhook_url

    def run_deduplication(self) -> Dict:
        start_time = time.perf_counter()
        original_count = 0
        deleted_count = 0
        audit_entries = []

        logs = self.fetcher.fetch_extraction_logs(page_size=500, max_pages=5)
        original_count = len(logs)

        self.engine.build_overlap_matrix(logs)
        directives = self.engine.construct_merge_directives()

        if not directives:
            logger.info("No duplicates detected")
            return self._finalize_metrics(start_time, original_count, 0, audit_entries)

        try:
            result = self.consolidator.validate_and_consolidate(directives)
            deleted_count = result.get("deleted", 0)
        except requests.exceptions.HTTPError as e:
            logger.error("Consolidation failed: %s", e.response.text)
            raise

        audit_id = str(uuid.uuid4())
        audit_entries.append({
            "auditId": audit_id,
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
            "originalCount": original_count,
            "deletedCount": deleted_count,
            "directivesProcessed": len(directives),
            "status": "completed"
        })

        self._sync_webhook(audit_entries[0])
        return self._finalize_metrics(start_time, original_count, deleted_count, audit_entries)

    def _sync_webhook(self, event: Dict) -> None:
        try:
            requests.post(self.webhook_url, json=event, timeout=10)
        except requests.exceptions.RequestException as e:
            logger.warning("Webhook sync failed: %s", str(e))

    def _finalize_metrics(self, start_time: float, original: int, deleted: int, audit: List[Dict]) -> Dict:
        latency = time.perf_counter() - start_time
        reduction_rate = (deleted / original * 100) if original > 0 else 0.0
        logger.info("Deduplication complete. Latency: %.3fs, Reduction: %.2f%%", latency, reduction_rate)
        return {
            "latencySeconds": round(latency, 3),
            "reductionRatePercent": round(reduction_rate, 2),
            "auditLog": audit,
            "originalRecords": original,
            "consolidatedRecords": deleted
        }

Complete Working Example

The following script combines authentication, log retrieval, overlap matrix construction, atomic consolidation, webhook synchronization, and audit logging into a single executable module. Replace placeholder credentials before execution.

#!/usr/bin/env python3
import sys
import logging
from typing import Dict, Any

logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s")

def main() -> Dict[str, Any]:
    config = {
        "base_url": "https://your-tenant.cognigy.ai",
        "client_id": "YOUR_CLIENT_ID",
        "client_secret": "YOUR_CLIENT_SECRET",
        "webhook_url": "https://data-lake.your-company.com/api/v1/ingest/cognigy-dedup"
    }

    auth = CognigyAuthManager(
        base_url=config["base_url"],
        client_id=config["client_id"],
        client_secret=config["client_secret"]
    )

    orchestrator = DeduplicationOrchestrator(auth=auth, webhook_url=config["webhook_url"])
    
    try:
        result = orchestrator.run_deduplication()
        logging.info("Final metrics: %s", result)
        return result
    except Exception as e:
        logging.error("Deduplication pipeline failed: %s", str(e))
        sys.exit(1)

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: HTTP 401 Unauthorized

• Cause: OAuth token expired or client credentials misconfigured.
• Fix: The CognigyAuthManager automatically refreshes tokens on 401. Verify client_id and client_secret match the Cognigy.AI developer console. Ensure the OAuth client has logs:read and logs:write scopes attached.

Error: HTTP 403 Forbidden

• Cause: Missing required scopes or tenant-level permission restrictions.
• Fix: Attach webhooks:notify if external synchronization fails. Verify the service account has administrative access to extraction logs.

Error: HTTP 409 Conflict

• Cause: Hash collision during overlap matrix grouping or concurrent batch operations modifying the same logs.
• Fix: The DeduplicationEngine enforces timestamp proximity and confidence thresholds to resolve collisions. If conflicts persist, reduce page_size or add a jitter delay between batch submissions.

Error: HTTP 429 Too Many Requests

• Cause: Exceeding Cognigy.AI rate limits during pagination or batch operations.
• Fix: The _request_with_retry method implements exponential backoff. Adjust max_pages or introduce a static delay between pages if the tenant operates under strict quotas.

Error: HTTP 400 Bad Request

• Cause: Payload exceeds maximum batch size or violates schema constraints.
• Fix: The BatchDeletePayload Pydantic model enforces max_items=500. Split directives into chunks if len(directives) > 500. Verify formatVersion matches the tenant API version.

Official References

• Cognigy.AI API Documentation
• Cognigy.AI OAuth2 Authentication Guide
• Cognigy.AI Logs and Analytics Endpoints
• Cognigy.AI Batch Operations Specification