Filter Cognigy.AI Conversation Logs with Python: Schema Validation, Pagination, and Metrics

What You Will Build

• A Python module that retrieves Cognigy.AI conversation logs using structured filter payloads, validates queries against API constraints, handles pagination, and tracks execution metrics.
• This implementation uses the Cognigy.AI REST API endpoint /api/v1/logs and a production-grade Python client built with httpx and pydantic.
• The code is written in Python 3.10+ with type hints, strict schema validation, structured audit logging, and webhook synchronization.

Prerequisites

• Cognigy.AI API token with logs:read permission
• Cognigy.AI API v1 base URL (format: https://<your-instance>.cognigy.com)
• Python 3.10 or newer
• External dependencies: httpx, pydantic, pydantic-core, python-dotenv, structlog
• Install dependencies: pip install httpx pydantic python-dotenv structlog

Authentication Setup

Cognigy.AI uses bearer token authentication. The token carries an expiration timestamp and requires refresh logic to maintain continuous access. The following client manages token caching, expiration validation, and automatic refresh via the /api/v1/auth endpoint.

import httpx
import time
import os
from typing import Optional
from dotenv import load_dotenv

load_dotenv()

class CognigyAuthClient:
    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.access_token: Optional[str] = None
        self.token_expiry: float = 0.0
        self.http = httpx.Client(timeout=30.0, follow_redirects=True)

    def _refresh_token(self) -> str:
        auth_url = f"{self.base_url}/api/v1/auth"
        payload = {
            "clientId": self.client_id,
            "clientSecret": self.client_secret
        }
        response = self.http.post(auth_url, json=payload)
        response.raise_for_status()
        data = response.json()
        if "accessToken" not in data:
            raise ValueError("Authentication response missing accessToken")
        self.access_token = data["accessToken"]
        self.token_expiry = time.time() + float(data.get("expiresIn", 3600))
        return self.access_token

    def get_bearer_token(self) -> str:
        if self.access_token and time.time() < self.token_expiry - 300:
            return self.access_token
        return self._refresh_token()

The get_bearer_token method ensures the token remains valid. It refreshes automatically when the remaining validity drops below five minutes. The required OAuth scope for log retrieval is logs:read.

Implementation

Step 1: Construct Filtering Payloads and Validate Against Constraints

The Cognigy.AI logs API accepts query parameters for filtering. You must construct a filter matrix that references log identifiers, applies query directives, and validates against known constraints. The API enforces a maximum page size of 100, a default date range of ninety days, and strict query parameter length limits. The following Pydantic model enforces these constraints before transmission.

from pydantic import BaseModel, field_validator, ConfigDict
from datetime import datetime, timedelta
import re

class LogFilterPayload(BaseModel):
    model_config = ConfigDict(str_strip_whitespace=True)
    
    bot_id: Optional[str] = None
    user_id: Optional[str] = None
    log_ref: Optional[str] = None  # logId reference
    query_directive: Optional[str] = None  # free-text search
    from_timestamp: Optional[str] = None
    to_timestamp: Optional[str] = None
    page: int = 1
    page_size: int = 50
    content_regex: Optional[str] = None

    @field_validator("page_size")
    @classmethod
    def validate_page_size(cls, v: int) -> int:
        if v < 1 or v > 100:
            raise ValueError("page_size must be between 1 and 100 per cognigy-constraints")
        return v

    @field_validator("from_timestamp", "to_timestamp")
    @classmethod
    def validate_date_range(cls, v: Optional[str], info) -> Optional[str]:
        if v is None:
            return v
        try:
            parsed = datetime.fromisoformat(v)
        except ValueError:
            raise ValueError("Timestamps must be ISO 8601 format")
        return v

    @field_validator("content_regex")
    @classmethod
    def validate_regex_complexity(cls, v: Optional[str]) -> Optional[str]:
        if v is None:
            return v
        try:
            re.compile(v)
        except re.error as e:
            raise ValueError(f"Invalid regex pattern: {e}")
        if len(v) > 500:
            raise ValueError("Exceeds maximum-query-complexity limit of 500 characters")
        return v

This model prevents filtering failures by enforcing schema rules before the HTTP request. The log_ref field maps to the logId parameter. The query_directive maps to the query parameter. The content_regex field is processed locally to avoid server-side regex compilation overhead.

Step 2: Handle Regex Optimization and Index-Scanning Logic

The Cognigy.AI backend uses indexed fields (botId, userId, timestamp) for fast retrieval. Queries that lack indexed field references trigger full-table-scan behavior, which degrades performance and risks database locks during scaling events. The following builder validates index usage, pre-compiles regex patterns, and constructs atomic HTTP GET operations.

import logging
from urllib.parse import urlencode

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

class LogQueryBuilder:
    def __init__(self, payload: LogFilterPayload):
        self.payload = payload
        self.compiled_regex: Optional[re.Pattern] = None
        self._validate_index_usage()
        if self.payload.content_regex:
            self.compiled_regex = re.compile(self.payload.content_regex, re.IGNORECASE)

    def _validate_index_usage(self) -> None:
        has_indexed_field = (
            self.payload.bot_id is not None or
            self.payload.user_id is not None or
            (self.payload.from_timestamp is not None and self.payload.to_timestamp is not None)
        )
        if not has_indexed_field:
            logger.warning("Query lacks indexed fields. Full-table-scan risk detected. Adding default date window.")
            now = datetime.utcnow()
            self.payload.from_timestamp = (now - timedelta(days=7)).isoformat()
            self.payload.to_timestamp = now.isoformat()

    def build_query_string(self) -> str:
        params: dict = {}
        if self.payload.bot_id:
            params["botId"] = self.payload.bot_id
        if self.payload.user_id:
            params["userId"] = self.payload.user_id
        if self.payload.log_ref:
            params["logId"] = self.payload.log_ref
        if self.payload.query_directive:
            params["query"] = self.payload.query_directive
        if self.payload.from_timestamp:
            params["from"] = self.payload.from_timestamp
        if self.payload.to_timestamp:
            params["to"] = self.payload.to_timestamp
        params["page"] = self.payload.page
        params["pageSize"] = self.payload.page_size
        return urlencode(params, safe=":/")

    def validate_injection_prevention(self) -> bool:
        dangerous_patterns = ["../", "<script", "union select", "--", "/*"]
        check_fields = [
            self.payload.bot_id, self.payload.user_id, 
            self.payload.log_ref, self.payload.query_directive
        ]
        for field in check_fields:
            if field and any(pat in field.lower() for pat in dangerous_patterns):
                logger.error("Injection-prevention pipeline blocked malicious input")
                return False
        return True

The builder enforces index-scanning evaluation logic by requiring at least one indexed field. It applies injection-prevention verification by scanning for common attack patterns. The build_query_string method returns a URL-encoded string ready for atomic GET operations.

Step 3: Pagination, Automatic Fetch Triggers, and Webhook Sync

The logs API returns paginated results. You must implement automatic fetch triggers to iterate through pages safely. The following client handles pagination, tracks latency and success rates, synchronizes results with external analytics dashboards via webhooks, and generates audit logs for governance.

from dataclasses import dataclass, field
from typing import List, Dict, Any
import json

@dataclass
class FilterMetrics:
    total_requests: int = 0
    successful_requests: int = 0
    failed_requests: int = 0
    total_latency_ms: float = 0.0
    pages_fetched: int = 0
    records_retrieved: int = 0

class CognigyLogClient:
    def __init__(self, base_url: str, auth_client: CognigyAuthClient, webhook_url: Optional[str] = None):
        self.base_url = base_url.rstrip("/")
        self.auth_client = auth_client
        self.webhook_url = webhook_url
        self.http = httpx.Client(timeout=30.0, follow_redirects=True)
        self.metrics = FilterMetrics()
        self.audit_log: List[Dict[str, Any]] = []

    def _record_audit(self, event_type: str, details: Dict[str, Any]) -> None:
        entry = {
            "timestamp": datetime.utcnow().isoformat(),
            "event": event_type,
            "details": details,
            "metrics_snapshot": {
                "requests": self.metrics.total_requests,
                "success_rate": self.metrics.successful_requests / max(self.metrics.total_requests, 1)
            }
        }
        self.audit_log.append(entry)
        logger.info(json.dumps(entry))

    def fetch_logs(self, builder: LogQueryBuilder) -> List[Dict[str, Any]]:
        if not builder.validate_injection_prevention():
            raise ValueError("Query rejected by injection-prevention pipeline")

        all_logs: List[Dict[str, Any]] = []
        query_str = builder.build_query_string()
        endpoint = f"{self.base_url}/api/v1/logs?{query_str}"

        while True:
            start_time = time.time()
            self.metrics.total_requests += 1
            headers = {"Authorization": f"Bearer {self.auth_client.get_bearer_token()}"}
            
            try:
                response = self.http.get(endpoint, headers=headers)
                response.raise_for_status()
                latency_ms = (time.time() - start_time) * 1000
                self.metrics.total_latency_ms += latency_ms
                self.metrics.successful_requests += 1
            except httpx.HTTPStatusError as e:
                latency_ms = (time.time() - start_time) * 1000
                self.metrics.total_latency_ms += latency_ms
                self.metrics.failed_requests += 1
                logger.error(f"HTTP Error {e.response.status_code}: {e.response.text}")
                self._record_audit("filter_failure", {"status": e.response.status_code, "latency_ms": latency_ms})
                break
            except Exception as e:
                self.metrics.failed_requests += 1
                logger.error(f"Request failed: {e}")
                break

            data = response.json()
            logs = data.get("data", [])
            if not logs:
                break

            # Local regex filtering optimization
            if builder.compiled_regex:
                logs = [log for log in logs if builder.compiled_regex.search(log.get("text", ""))]

            all_logs.extend(logs)
            self.metrics.pages_fetched += 1
            self.metrics.records_retrieved += len(logs)

            self._record_audit("page_fetched", {
                "page": builder.payload.page,
                "records_in_page": len(logs),
                "latency_ms": latency_ms
            })

            # Automatic fetch trigger for next page
            next_page = data.get("nextPage")
            if not next_page:
                break
            builder.payload.page = next_page
            query_str = builder.build_query_string()
            endpoint = f"{self.base_url}/api/v1/logs?{query_str}"

        self._sync_webhook(all_logs)
        self._record_audit("filter_complete", {
            "total_records": len(all_logs),
            "success_rate": self.metrics.successful_requests / max(self.metrics.total_requests, 1),
            "avg_latency_ms": self.metrics.total_latency_ms / max(self.metrics.total_requests, 1)
        })
        return all_logs

    def _sync_webhook(self, logs: List[Dict[str, Any]]) -> None:
        if not self.webhook_url:
            return
        try:
            payload = {
                "event": "log_fetched",
                "timestamp": datetime.utcnow().isoformat(),
                "recordCount": len(logs),
                "metrics": {
                    "successRate": self.metrics.successful_requests / max(self.metrics.total_requests, 1),
                    "avgLatencyMs": self.metrics.total_latency_ms / max(self.metrics.total_requests, 1)
                }
            }
            resp = self.http.post(self.webhook_url, json=payload)
            resp.raise_for_status()
            logger.info("Webhook sync successful")
        except Exception as e:
            logger.error(f"Webhook sync failed: {e}")

The client performs atomic HTTP GET operations per page. It calculates regex matches locally to reduce backend load. It tracks filtering latency and query success rates in the FilterMetrics dataclass. It pushes synchronization events to an external analytics dashboard via POST webhooks. It maintains a structured audit log for Cognigy governance compliance.

Complete Working Example

The following script combines all components into a runnable module. Replace the environment variables with your Cognigy.AI credentials before execution.

import os
from dotenv import load_dotenv

load_dotenv()

def main():
    base_url = os.getenv("COGNIGY_BASE_URL", "https://your-instance.cognigy.com")
    client_id = os.getenv("COGNIGY_CLIENT_ID")
    client_secret = os.getenv("COGNIGY_CLIENT_SECRET")
    webhook_url = os.getenv("WEBHOOK_URL")

    if not client_id or not client_secret:
        raise ValueError("Missing COGNIGY_CLIENT_ID or COGNIGY_CLIENT_SECRET")

    auth = CognigyAuthClient(base_url, client_id, client_secret)
    client = CognigyLogClient(base_url, auth, webhook_url)

    payload = LogFilterPayload(
        bot_id="bot_12345",
        from_timestamp="2024-01-01T00:00:00Z",
        to_timestamp="2024-01-31T23:59:59Z",
        page_size=50,
        content_regex=r"order|refund|shipping"
    )

    builder = LogQueryBuilder(payload)
    logs = client.fetch_logs(builder)

    print(f"Retrieved {len(logs)} conversation logs")
    print(f"Success Rate: {client.metrics.successful_requests / max(client.metrics.total_requests, 1):.2%}")
    print(f"Average Latency: {client.metrics.total_latency_ms / max(client.metrics.total_requests, 1):.2f} ms")
    print(f"Audit Log Entries: {len(client.audit_log)}")

if __name__ == "__main__":
    main()

This script constructs the filter payload, validates constraints, executes paginated requests, applies regex optimization, synchronizes with webhooks, and outputs metrics. It requires zero UI interaction and operates entirely through the REST API.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The bearer token expired or the API token lacks the logs:read permission.
• Fix: Verify the token in your Cognigy.AI console. Ensure the CognigyAuthClient refresh logic triggers before expiration. Check the token payload for the correct scope.
• Code Fix: The get_bearer_token method already handles expiration. Add explicit scope validation during initialization if your environment requires it.

Error: 403 Forbidden

• Cause: The API token is valid but restricted from accessing log data, or the instance enforces IP allowlisting.
• Fix: Confirm the token role includes logs:read. Verify your client IP is whitelisted in the Cognigy.AI security settings.
• Code Fix: Log the response headers to identify the specific policy violation.

Error: 400 Bad Request

• Cause: The filter payload violates cognigy-constraints, such as page size exceeding 100, invalid ISO timestamps, or malformed regex.
• Fix: Review the Pydantic validation errors. Adjust page_size to 100 or less. Ensure timestamps use UTC ISO 8601 format.
• Code Fix: The LogFilterPayload validators catch these errors before the HTTP call. Check the console for the exact validation failure message.

Error: 429 Too Many Requests

• Cause: You exceeded the Cognigy.AI rate limit for log queries.
• Fix: Implement exponential backoff between pages. Reduce page_size to lower request frequency.
• Code Fix: Insert a retry loop with time.sleep(min(2 ** attempt, 10)) inside the pagination loop when catching httpx.HTTPStatusError with status 429.

Error: 5xx Server Error

• Cause: Backend scaling events or transient database locks.
• Fix: Retry the request after a short delay. Ensure indexed fields are present to avoid full-table-scan degradation.
• Code Fix: The builder automatically injects a date window if no indexed fields exist. Add a retry counter in the fetch_logs method to handle transient 5xx responses.

Official References

• Cognigy.AI API v1 Logs Endpoint
• Cognigy.AI Authentication Guide
• httpx Python HTTP Client
• Pydantic Data Validation