Querying Genesys Cloud Speech Analytics Insights via API with Python

What You Will Build

A Python client that constructs and executes speech analytics queries, validates model and data constraints, handles offset pagination, correlates insights with CSAT scores, registers webhooks for external coaching sync, tracks execution latency, and generates audit logs for governance.
This tutorial uses the Genesys Cloud Analytics and Speech Analytics APIs.
The implementation is written in Python 3.9+ using the official genesys-cloud-sdk and httpx.

Prerequisites

• Genesys Cloud OAuth client (Confidential type) with these scopes: analytics:query, speechanalytics:read, webhooks:readwrite, reports:read
• Genesys Cloud SDK version 2.100.0 or higher
• Python 3.9 runtime
• External dependencies: httpx, pydantic, python-dotenv, genesys-cloud-sdk

Authentication Setup

Genesys Cloud uses OAuth 2.0 for API authentication. The client credentials flow is required for server-to-server analytics queries. The following code fetches an access token, caches it, and handles expiration before each API call.

import httpx
import time
import os
from typing import Optional

class GenesysAuthClient:
    def __init__(self, client_id: str, client_secret: str, base_url: str = "https://api.mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.base_url = base_url
        self.token_url = f"{base_url}/oauth/token"
        self._access_token: Optional[str] = None
        self._token_expiry: float = 0.0

    def get_access_token(self) -> str:
        if self._access_token and time.time() < self._token_expiry - 30:
            return self._access_token
        
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        
        with httpx.Client(timeout=15.0) as client:
            response = client.post(self.token_url, data=payload)
            response.raise_for_status()
            
            token_data = response.json()
            self._access_token = token_data["access_token"]
            self._token_expiry = time.time() + token_data["expires_in"]
            
        return self._access_token

    def get_headers(self) -> dict:
        return {
            "Authorization": f"Bearer {self.get_access_token()}",
            "Content-Type": "application/json"
        }

Implementation

Step 1: Validate Model Version and Data Availability Window

Speech analytics queries fail silently or return empty datasets when targeting deprecated models or exceeding data retention windows. You must verify the target model status and ensure the query window falls within the tenant’s availability policy before submitting the payload.

from genesyscloud.platform_client_v2 import PureCloudPlatformClientV2
from genesyscloud.speechanalytics.api import SpeechAnalyticsApi

def validate_speech_model_and_window(auth: GenesysAuthClient, model_id: str, date_from: str, date_to: str) -> bool:
    client = PureCloudPlatformClientV2()
    client.set_access_token(auth.get_access_token())
    api = SpeechAnalyticsApi(client)
    
    try:
        model_response = api.get_speechanalytics_model(model_id=model_id)
        if model_response.body.status != "deployed":
            raise ValueError(f"Model {model_id} is not deployed. Current status: {model_response.body.status}")
            
        # Genesys Cloud analytics data availability is typically T+1 for batch, real-time for live.
        # We validate against a 730-day retention window.
        from datetime import datetime, timedelta
        start_dt = datetime.fromisoformat(date_from.replace("Z", "+00:00"))
        end_dt = datetime.fromisoformat(date_to.replace("Z", "+00:00"))
        max_window = timedelta(days=730)
        
        if (end_dt - start_dt) > max_window:
            raise ValueError(f"Query window exceeds 730-day retention policy. Reduce date range.")
            
        return True
    except Exception as e:
        print(f"Validation failed: {e}")
        return False

Required OAuth Scope: speechanalytics:read
HTTP Request: GET /api/v2/speechanalytics/v1/models/{modelId}
Expected Response:

{
  "id": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "name": "Customer Sentiment Model v2",
  "status": "deployed",
  "type": "sentiment",
  "lastUpdated": "2023-11-15T08:30:00.000Z"
}

Step 2: Construct Query Payload with Interaction IDs, Topics, and Sentiment

The Analytics API requires a structured JSON payload. You must specify the conversation type, metric groups, filter groups for interaction IDs, topic models, and sentiment thresholds. The groupings field determines how results are aggregated.

def build_analytics_query_payload(
    interaction_ids: list[str],
    topic_names: list[str],
    sentiment_threshold: float,
    date_from: str,
    date_to: str,
    model_id: str
) -> dict:
    return {
        "dateFrom": date_from,
        "dateTo": date_to,
        "interval": "PT1H",
        "conversationType": "speech",
        "filterGroups": [
            {
                "filters": [
                    {
                        "field": "id",
                        "op": "in",
                        "values": interaction_ids
                    }
                ]
            },
            {
                "filters": [
                    {
                        "field": "speechAnalytics.topics.name",
                        "op": "in",
                        "values": topic_names
                    }
                ]
            },
            {
                "filters": [
                    {
                        "field": "sentiment.score",
                        "op": "gte",
                        "values": [sentiment_threshold]
                    }
                ]
            }
        ],
        "metrics": ["speechAnalytics", "sentiment"],
        "groupings": ["interactionId", "agentId"],
        "size": 100,
        "offset": 0,
        "speechAnalyticsModelId": model_id
    }

Required OAuth Scope: analytics:query
HTTP Request: POST /api/v2/analytics/conversations/summary/query
Expected Response:

{
  "byGroup": [
    {
      "groupId": "int-987654321",
      "groupName": "int-987654321",
      "byMetric": {
        "speechAnalytics": {"count": 12, "sum": 0},
        "sentiment": {"count": 8, "avg": 0.72}
      }
    }
  ],
  "totalCount": 1245,
  "pageSize": 100,
  "offset": 0
}

Step 3: Execute Query with Offset Pagination and CSAT Correlation

Offset-based pagination requires incrementing the offset parameter until totalCount is reached. After retrieving speech insights, you must correlate them with customer satisfaction scores using time-series alignment. The following code fetches CSAT data and merges it by interaction ID.

from genesyscloud.analytics.api import AnalyticsApi
import time

def fetch_paginated_insights_and_correlate(
    auth: GenesysAuthClient,
    query_payload: dict,
    csat_query_payload: dict
) -> list[dict]:
    client = PureCloudPlatformClientV2()
    client.set_access_token(auth.get_access_token())
    analytics_api = AnalyticsApi(client)
    
    all_insights = []
    offset = 0
    page_size = query_payload["size"]
    
    while True:
        query_payload["offset"] = offset
        response = analytics_api.post_analytics_conversations_summary_query(body=query_payload)
        
        if not response.body.byGroup:
            break
            
        all_insights.extend(response.body.byGroup)
        
        if offset + page_size >= response.body.totalCount:
            break
            
        offset += page_size
        time.sleep(0.2)  # Prevent 429 rate limit cascades
        
    # Correlate with CSAT using time-series alignment
    csat_response = analytics_api.post_analytics_surveys_summary_query(body=csat_query_payload)
    csat_map = {item.groupId: item.byMetric.get("csat", {}).get("avg", 0) 
                for item in (csat_response.body.byGroup or [])}
    
    correlated_results = []
    for insight in all_insights:
        interaction_id = insight.groupId
        csat_score = csat_map.get(interaction_id, None)
        correlated_results.append({
            "interactionId": interaction_id,
            "speechAnalytics": insight.byMetric.get("speechAnalytics"),
            "sentiment": insight.byMetric.get("sentiment"),
            "csat": csat_score,
            "timestamp": insight.get("groupBy", {}).get("time", None)
        })
        
    return correlated_results

Step 4: Register Webhook for External Coaching Synchronization

Webhooks enable asynchronous synchronization with external coaching platforms. You register a webhook that triggers on query completion or insight updates, pushing payloads to your coaching platform endpoint.

from genesyscloud.webhooks.api import WebhooksApi

def register_coaching_webhook(auth: GenesysAuthClient, target_url: str, model_id: str) -> dict:
    client = PureCloudPlatformClientV2()
    client.set_access_token(auth.get_access_token())
    webhooks_api = WebhooksApi(client)
    
    webhook_body = {
        "name": "SpeechInsightsToCoachingSync",
        "description": "Syncs speech analytics insights to external coaching platform",
        "url": target_url,
        "type": "rest",
        "enabled": True,
        "events": [
            "analytics:query:completed",
            "speechanalytics:insight:updated"
        ],
        "headers": {
            "X-Webhook-Source": "GenesysCloud",
            "Content-Type": "application/json"
        },
        "filter": {
            "field": "speechAnalyticsModelId",
            "op": "eq",
            "value": model_id
        }
    }
    
    response = webhooks_api.post_webhooks(body=webhook_body)
    return response.body

Required OAuth Scope: webhooks:readwrite
HTTP Request: POST /api/v2/webhooks
Expected Response:

{
  "id": "webhook-12345678-abcd-efgh-ijkl-9876543210ab",
  "name": "SpeechInsightsToCoachingSync",
  "url": "https://coaching-platform.example.com/api/v1/insights",
  "type": "rest",
  "enabled": true,
  "events": ["analytics:query:completed", "speechanalytics:insight:updated"]
}

Step 5: Track Latency, Accuracy, and Generate Audit Logs

Quality assurance requires tracking query execution latency and insight accuracy. Audit logs must capture query parameters, timestamps, and result counts for data governance compliance.

import json
import logging
from datetime import datetime, timezone

def setup_audit_logger() -> logging.Logger:
    logger = logging.getLogger("speech_insights_audit")
    logger.setLevel(logging.INFO)
    handler = logging.FileHandler("speech_insights_audit.json")
    handler.setFormatter(logging.Formatter("%(message)s"))
    logger.addHandler(handler)
    return logger

def track_execution_metrics(
    start_time: float,
    query_payload: dict,
    result_count: int,
    accuracy_score: float,
    logger: logging.Logger
) -> dict:
    end_time = time.perf_counter()
    latency_ms = (end_time - start_time) * 1000
    
    audit_entry = {
        "timestamp": datetime.now(timezone.utc).isoformat(),
        "queryParams": {
            "dateFrom": query_payload["dateFrom"],
            "dateTo": query_payload["dateTo"],
            "modelId": query_payload.get("speechAnalyticsModelId"),
            "interactionCount": len(query_payload["filterGroups"][0]["filters"][0]["values"])
        },
        "executionLatencyMs": round(latency_ms, 2),
        "resultCount": result_count,
        "accuracyScore": round(accuracy_score, 4),
        "status": "completed"
    }
    
    logger.info(json.dumps(audit_entry))
    return audit_entry

Complete Working Example

import os
import time
import httpx
from genesyscloud.platform_client_v2 import PureCloudPlatformClientV2
from genesyscloud.analytics.api import AnalyticsApi
from genesyscloud.speechanalytics.api import SpeechAnalyticsApi
from genesyscloud.webhooks.api import WebhooksApi
import logging
import json
from datetime import datetime, timezone, timedelta

class SpeechInsightClient:
    def __init__(self, client_id: str, client_secret: str):
        self.auth = GenesysAuthClient(client_id, client_secret)
        self.analytics_api = AnalyticsApi(PureCloudPlatformClientV2())
        self.speech_api = SpeechAnalyticsApi(PureCloudPlatformClientV2())
        self.webhooks_api = WebhooksApi(PureCloudPlatformClientV2())
        self.logger = setup_audit_logger()

    def run_full_pipeline(
        self,
        model_id: str,
        interaction_ids: list[str],
        topic_names: list[str],
        sentiment_threshold: float,
        date_from: str,
        date_to: str,
        coaching_url: str
    ) -> list[dict]:
        client = PureCloudPlatformClientV2()
        client.set_access_token(self.auth.get_access_token())
        self.analytics_api.set_access_token(self.auth.get_access_token())
        self.speech_api.set_access_token(self.auth.get_access_token())
        self.webhooks_api.set_access_token(self.auth.get_access_token())

        # Step 1: Validate
        if not validate_speech_model_and_window(self.auth, model_id, date_from, date_to):
            raise RuntimeError("Model or data window validation failed.")

        # Step 2: Build Query
        query_payload = build_analytics_query_payload(
            interaction_ids, topic_names, sentiment_threshold, date_from, date_to, model_id
        )
        
        csat_payload = {
            "dateFrom": date_from,
            "dateTo": date_to,
            "interval": "PT1H",
            "filterGroups": [{"filters": [{"field": "id", "op": "in", "values": interaction_ids}]}],
            "metrics": ["csat"],
            "groupings": ["interactionId"],
            "size": 100,
            "offset": 0
        }

        # Step 3: Execute & Correlate
        start_time = time.perf_counter()
        correlated = fetch_paginated_insights_and_correlate(self.auth, query_payload, csat_payload)
        
        # Calculate accuracy (sentiment vs CSAT alignment rate)
        aligned_count = sum(1 for r in correlated if r["csat"] is not None and r["sentiment"]["avg"] > 0.6)
        accuracy = aligned_count / len(correlated) if correlated else 0.0

        # Step 4: Webhook
        register_coaching_webhook(self.auth, coaching_url, model_id)

        # Step 5: Audit & Metrics
        track_execution_metrics(
            start_time, query_payload, len(correlated), accuracy, self.logger
        )

        return correlated

# Usage
if __name__ == "__main__":
    client_id = os.getenv("GENESYS_CLIENT_ID")
    client_secret = os.getenv("GENESYS_CLIENT_SECRET")
    
    client = SpeechInsightClient(client_id, client_secret)
    results = client.run_full_pipeline(
        model_id="a1b2c3d4-e5f6-7890-abcd-ef1234567890",
        interaction_ids=["int-111", "int-222", "int-333"],
        topic_names=["billing_inquiry", "product_return"],
        sentiment_threshold=0.5,
        date_from="2023-10-01T00:00:00.000Z",
        date_to="2023-10-02T00:00:00.000Z",
        coaching_url="https://coaching-platform.example.com/api/v1/insights"
    )
    print(json.dumps(results, indent=2, default=str))

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token or invalid client credentials.
• Fix: Ensure the GenesysAuthClient refreshes tokens before each request. Verify the OAuth client type is Confidential and the secret matches the Genesys Cloud administration console.
• Code Fix: The get_access_token() method checks time.time() < self._token_expiry - 30 to preemptively refresh tokens.

Error: 403 Forbidden

• Cause: Missing OAuth scopes or insufficient user permissions.
• Fix: Add analytics:query, speechanalytics:read, and webhooks:readwrite to the OAuth client scope configuration in Genesys Cloud. Verify the service account has the Analytics Viewer or Speech Analytics Admin role.

Error: 429 Too Many Requests

• Cause: Exceeding the tenant’s API rate limit during pagination or bulk queries.
• Fix: Implement exponential backoff. The fetch_paginated_insights_and_correlate function includes a time.sleep(0.2) delay. For production, wrap API calls in a retry decorator that parses the Retry-After header.
• Code Fix:

import httpx

def safe_api_call(func, *args, max_retries=3, **kwargs):
    for attempt in range(max_retries):
        try:
            return func(*args, **kwargs)
        except httpx.HTTPStatusError as e:
            if e.response.status_code == 429 and attempt < max_retries - 1:
                retry_after = int(e.response.headers.get("Retry-After", 2 ** attempt))
                time.sleep(retry_after)
            else:
                raise

Error: 400 Bad Request (Invalid Query Constraints)

• Cause: Mismatched date formats, invalid metric names, or unsupported filter operators.
• Fix: Validate ISO 8601 timestamps with Z suffix. Ensure metrics array contains valid Genesys Cloud metric keys. Verify filterGroups structure matches the QueryConversationSummaryRequest schema.

Official References

• Genesys Cloud Analytics API Documentation
• Speech Analytics Model Management
• Webhooks API Reference
• Python SDK Repository