Querying Conversation Analytics with Pagination in Genesys Cloud

What You Will Build

• A Python script that retrieves detailed conversation analytics data from Genesys Cloud using the PureCloudPlatformClientV2 SDK.
• The script demonstrates how to handle pagination for the /api/v2/analytics/conversations/details/query endpoint, which uses cursor-based pagination rather than traditional page numbers.
• The implementation covers authentication, request construction, iterative fetching, and robust error handling for rate limits and timeouts.

Prerequisites

• OAuth Client Type: Confidential Client (Client Credentials Grant) or Public Client (PKCE). For server-side scripts, a Confidential Client is standard.
• Required Scopes: analytics:conversation:view is mandatory. If you need to filter by specific user attributes or custom segments, additional scopes like user:read may be required depending on the filter complexity.
• SDK Version: Genesys Cloud Python SDK version 3.0.0 or higher.
• Language/Runtime: Python 3.8+.
• External Dependencies:

  pip install purecloudplatformclientv2
  

Authentication Setup

Genesys Cloud APIs require OAuth 2.0 authentication. The Python SDK handles the token exchange and refresh automatically once initialized. You must provide your client ID, client secret, and environment (e.g., mypurecloud.com, euw2.pure.cloud, or au02.pure.cloud).

import os
from purecloudplatformclientv2 import ApiClient, Configuration, OAuth2Client

def get_auth_client() -> ApiClient:
    """
    Initializes the API client with OAuth2 credentials.
    Uses environment variables for security.
    """
    client_id = os.environ.get("GENESYS_CLIENT_ID")
    client_secret = os.environ.get("GENESYS_CLIENT_SECRET")
    environment = os.environ.get("GENESYS_ENVIRONMENT", "mypurecloud.com")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set.")

    # Configure the API client
    config = Configuration()
    config.host = f"https://{environment}"
    
    # Initialize the OAuth2 client
    oauth_client = OAuth2Client(client_id, client_secret)
    
    # Create the API client instance
    api_client = ApiClient(configuration=config, oauth_client=oauth_client)
    
    return api_client

Implementation

Step 1: Constructing the Analytics Query Body

The /api/v2/analytics/conversations/details/query endpoint accepts a complex JSON body to define the query. Unlike simple GET endpoints, this is a POST request that allows for sophisticated filtering, grouping, and date range specification.

Key parameters in the body:

• dateFrom / dateTo: ISO 8601 timestamps defining the analysis window.
• interval: The time bucket for aggregation (e.g., PT1H for hourly).
• view: The analytics view to use (e.g., default).
• groupBy: Dimensions to group results by (e.g., mediaType, queueId).
• filter: Criteria to include/exclude specific conversations.

from purecloudplatformclientv2 import AnalyticsQueryDefinition

def build_query_body(start_date: str, end_date: str) -> dict:
    """
    Constructs the request body for the conversation details query.
    
    Args:
        start_date: ISO 8601 string (e.g., '2023-10-01T00:00:00.000Z')
        end_date: ISO 8601 string (e.g., '2023-10-08T00:00:00.000Z')
    
    Returns:
        Dictionary representing the AnalyticsQueryDefinition
    """
    query_body = {
        "dateFrom": start_date,
        "dateTo": end_date,
        "interval": "PT1H",  # 1-hour intervals
        "view": "default",
        "groupBy": ["mediaType"],
        "filter": {
            "and": [
                {
                    "dimension": "queueId",
                    "operator": "eq",
                    "value": "your-queue-id-here"  # Replace with actual queue ID
                }
            ]
        },
        "select": [
            "wrapUpCode",
            "mediaType",
            "totalHandleTime",
            "talkTime",
            "holdTime",
            "holdCount"
        ]
    }
    return query_body

Step 2: Handling Cursor-Based Pagination

The Genesys Cloud Analytics API uses cursor-based pagination. This is distinct from offset-based pagination (page 1, page 2).

Why Cursor-Based?

1. Consistency: New data inserted during the query does not cause duplicate or missing rows.
2. Performance: The server uses the cursor as an index pointer, avoiding expensive OFFSET calculations on large datasets.
3. Statelessness: The client only needs to pass the cursor string returned from the previous response to get the next batch.

The Loop Logic:

1. Send the initial query without a cursor.
2. Process the entities (conversations) in the response.
3. Check the nextPageUri or the cursor field in the response metadata.
4. If a cursor exists, append it to the request body and repeat.
5. Stop when the response indicates no more pages (usually nextPageUri is null or empty).

from purecloudplatformclientv2 import AnalyticsApi
from purecloudplatformclientv2.rest import ApiException
import time
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def fetch_conversation_details(api_client: ApiClient, query_body: dict, max_pages: int = 100):
    """
    Iterates through paginated results using cursor-based pagination.
    
    Args:
        api_client: Initialized PureCloudPlatformClientV2 ApiClient
        query_body: The AnalyticsQueryDefinition dictionary
        max_pages: Safety limit to prevent infinite loops
    
    Returns:
        List of conversation detail objects
    """
    analytics_api = AnalyticsApi(api_client)
    all_conversations = []
    page_count = 0
    cursor = None

    while page_count < max_pages:
        page_count += 1
        logger.info(f"Fetching page {page_count}, cursor: {cursor}")

        try:
            # The SDK method for this endpoint is post_analytics_conversations_details_query
            # We pass the query body. The SDK handles the POST request.
            # Note: The SDK does not automatically paginate. We must manage the cursor.
            
            # If we have a cursor from the previous page, we must add it to the body
            if cursor:
                query_body["cursor"] = cursor

            response = analytics_api.post_analytics_conversations_details_query(
                body=query_body
            )

            # Extract the entities (conversations)
            entities = response.entities if response.entities else []
            all_conversations.extend(entities)
            
            logger.info(f"Retrieved {len(entities)} conversations on page {page_count}")

            # Check for next page cursor
            # In the Genesys Cloud Analytics response, the cursor is typically in the 'nextPageUri' 
            # or explicitly in a 'cursor' field if the SDK model exposes it.
            # For this specific endpoint, the response object usually contains 'nextPageUri'.
            # However, the raw JSON response often has a 'cursor' field.
            # The Python SDK model 'AnalyticsConversationDetailsQueryResponse' has a 'next_page_uri' attribute.
            
            next_page_uri = response.next_page_uri
            
            if next_page_uri:
                # Extract cursor from the URI if necessary, or use the SDK's helper if available.
                # The Genesys Cloud API usually returns the full URI for the next page.
                # We need to parse the cursor parameter from the URI.
                # Example URI: .../query?cursor=abc123xyz
                if "cursor=" in next_page_uri:
                    cursor = next_page_uri.split("cursor=")[1].split("&")[0]
                else:
                    # Fallback: if the API returns a direct cursor field in the body (less common in SDK models)
                    # Check if the raw response has it. For robustness, we rely on nextPageUri parsing.
                    logger.warning("Next page URI found but no cursor parameter detected. Stopping.")
                    break
            else:
                # No more pages
                logger.info("No more pages found. Pagination complete.")
                break

            # Optional: Small delay to respect rate limits and avoid 429s
            time.sleep(1)

        except ApiException as e:
            logger.error(f"API Exception: {e}")
            if e.status == 429:
                logger.warning("Rate limited. Waiting 10 seconds before retrying.")
                time.sleep(10)
                page_count -= 1  # Retry the same page
                continue
            elif e.status == 401 or e.status == 403:
                logger.error("Authentication or Authorization failed. Check scopes and token.")
                break
            else:
                raise e

    return all_conversations

Step 3: Processing and Exporting Results

Once the data is fetched, it is typically useful to export it to CSV or JSON for further analysis. The entities list contains detailed objects for each conversation.

import csv
import json
from datetime import datetime

def export_to_csv(conversations: list, filename: str = "conversation_details.csv"):
    """
    Exports the list of conversation detail objects to a CSV file.
    """
    if not conversations:
        logger.warning("No conversations to export.")
        return

    # Define headers based on common fields
    headers = [
        "conversationId", "mediaType", "wrapUpCode", "totalHandleTime", 
        "talkTime", "holdTime", "holdCount", "startTime", "endTime"
    ]

    with open(filename, 'w', newline='', encoding='utf-8') as f:
        writer = csv.DictWriter(f, fieldnames=headers, extrasaction='ignore')
        writer.writeheader()
        
        for conv in conversations:
            # Flatten the object for CSV
            row = {
                "conversationId": conv.conversation_id,
                "mediaType": conv.media_type,
                "wrapUpCode": conv.wrap_up_code if hasattr(conv, 'wrap_up_code') else "",
                "totalHandleTime": conv.total_handle_time if hasattr(conv, 'total_handle_time') else "",
                "talkTime": conv.talk_time if hasattr(conv, 'talk_time') else "",
                "holdTime": conv.hold_time if hasattr(conv, 'hold_time') else "",
                "holdCount": conv.hold_count if hasattr(conv, 'hold_count') else "",
                "startTime": conv.start_time if hasattr(conv, 'start_time') else "",
                "endTime": conv.end_time if hasattr(conv, 'end_time') else ""
            }
            writer.writerow(row)

    logger.info(f"Exported {len(conversations)} conversations to {filename}")

Complete Working Example

This script combines authentication, query construction, pagination logic, and export functionality into a single runnable module.

import os
import time
import logging
import csv
from purecloudplatformclientv2 import ApiClient, Configuration, OAuth2Client, AnalyticsApi
from purecloudplatformclientv2.rest import ApiException

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)

def get_auth_client() -> ApiClient:
    """Initializes the API client with OAuth2 credentials."""
    client_id = os.environ.get("GENESYS_CLIENT_ID")
    client_secret = os.environ.get("GENESYS_CLIENT_SECRET")
    environment = os.environ.get("GENESYS_ENVIRONMENT", "mypurecloud.com")

    if not client_id or not client_secret:
        raise ValueError("GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET must be set.")

    config = Configuration()
    config.host = f"https://{environment}"
    oauth_client = OAuth2Client(client_id, client_secret)
    api_client = ApiClient(configuration=config, oauth_client=oauth_client)
    
    return api_client

def build_query_body(start_date: str, end_date: str, queue_id: str) -> dict:
    """Constructs the AnalyticsQueryDefinition body."""
    return {
        "dateFrom": start_date,
        "dateTo": end_date,
        "interval": "PT1H",
        "view": "default",
        "groupBy": ["mediaType"],
        "filter": {
            "and": [
                {
                    "dimension": "queueId",
                    "operator": "eq",
                    "value": queue_id
                }
            ]
        },
        "select": [
            "conversationId",
            "mediaType",
            "wrapUpCode",
            "totalHandleTime",
            "talkTime",
            "holdTime",
            "holdCount",
            "startTime",
            "endTime"
        ]
    }

def fetch_all_conversations(api_client: ApiClient, query_body: dict, max_pages: int = 50) -> list:
    """
    Fetches all conversations using cursor-based pagination.
    """
    analytics_api = AnalyticsApi(api_client)
    all_conversations = []
    page_count = 0
    cursor = None

    while page_count < max_pages:
        page_count += 1
        logger.info(f"Page {page_count}: Fetching with cursor '{cursor}'")

        try:
            # Add cursor to body if it exists
            if cursor:
                query_body["cursor"] = cursor

            # Execute the query
            response = analytics_api.post_analytics_conversations_details_query(
                body=query_body
            )

            # Collect entities
            entities = response.entities if response.entities else []
            all_conversations.extend(entities)
            logger.info(f"Page {page_count}: Retrieved {len(entities)} records.")

            # Determine next cursor
            next_page_uri = response.next_page_uri
            
            if next_page_uri and "cursor=" in next_page_uri:
                cursor = next_page_uri.split("cursor=")[1].split("&")[0]
            else:
                logger.info("End of data reached.")
                break

            # Respect rate limits
            time.sleep(1)

        except ApiException as e:
            logger.error(f"API Error on page {page_count}: Status {e.status}, Message {e.body}")
            if e.status == 429:
                logger.warning("Rate limit hit. Waiting 10s...")
                time.sleep(10)
                page_count -= 1  # Retry same page
                continue
            elif e.status in [401, 403]:
                logger.error("Auth failed. Exiting.")
                break
            else:
                raise e

    return all_conversations

def export_conversations(conversations: list, filename: str = "gen_conversations.csv"):
    """Exports conversations to CSV."""
    if not conversations:
        logger.warning("No data to export.")
        return

    headers = [
        "conversationId", "mediaType", "wrapUpCode", "totalHandleTime", 
        "talkTime", "holdTime", "holdCount", "startTime", "endTime"
    ]

    with open(filename, 'w', newline='', encoding='utf-8') as f:
        writer = csv.DictWriter(f, fieldnames=headers, extrasaction='ignore')
        writer.writeheader()
        
        for conv in conversations:
            row = {
                "conversationId": getattr(conv, 'conversation_id', None),
                "mediaType": getattr(conv, 'media_type', None),
                "wrapUpCode": getattr(conv, 'wrap_up_code', None),
                "totalHandleTime": getattr(conv, 'total_handle_time', None),
                "talkTime": getattr(conv, 'talk_time', None),
                "holdTime": getattr(conv, 'hold_time', None),
                "holdCount": getattr(conv, 'hold_count', None),
                "startTime": getattr(conv, 'start_time', None),
                "endTime": getattr(conv, 'end_time', None)
            }
            writer.writerow(row)
    
    logger.info(f"Successfully exported {len(conversations)} rows to {filename}")

def main():
    # Configuration
    QUEUE_ID = os.environ.get("GENESYS_QUEUE_ID", "default-queue-id")
    START_DATE = "2023-10-01T00:00:00.000Z"
    END_DATE = "2023-10-08T00:00:00.000Z"
    
    try:
        # 1. Authenticate
        logger.info("Initializing API Client...")
        api_client = get_auth_client()
        
        # 2. Build Query
        logger.info("Building query body...")
        query_body = build_query_body(START_DATE, END_DATE, QUEUE_ID)
        
        # 3. Fetch Data
        logger.info("Starting pagination fetch...")
        conversations = fetch_all_conversations(api_client, query_body)
        
        # 4. Export
        logger.info("Exporting data...")
        export_conversations(conversations)
        
        logger.info("Process completed successfully.")

    except Exception as e:
        logger.error(f"Fatal error: {e}")
        raise

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token has expired, or the client ID/secret is incorrect.
• Fix: Ensure GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET are correct. The SDK handles token refresh automatically, but if the initial grant fails, check credentials. Verify the client has the analytics:conversation:view scope assigned in the Admin Console under Organization > Security > OAuth 2.0.

Error: 403 Forbidden

• Cause: The OAuth client lacks the necessary permissions.
• Fix: Confirm that the OAuth client is assigned the analytics:conversation:view scope. Also, ensure the user associated with the service account (if using user-based auth) has access to the analytics views.

Error: 429 Too Many Requests

• Cause: You have exceeded the API rate limit for the analytics endpoint.
• Fix: Implement exponential backoff. The example above includes a time.sleep(1) between pages. For high-volume queries, consider reducing the date range (dateFrom to dateTo) to fewer days per request and running the script multiple times.

Error: 500 Internal Server Error

• Cause: The query body is malformed or the date range is too large for the engine to process in one go.
• Fix: Validate the ISO 8601 format of dateFrom and dateTo. Ensure interval is valid (e.g., PT1H, PT1D). If the date range is larger than 6 months, split it into smaller chunks.

Official References

• Genesys Cloud Analytics API Documentation
• Python SDK Reference
• OAuth 2.0 Configuration Guide