Query Genesys Cloud Voice Analytics Transcripts via REST API with Python
What You Will Build
- This script constructs and executes validated analytics queries against Genesys Cloud Voice transcripts, applying time range matrices, sentiment filters, and normalized keyword searches.
- It uses the Genesys Cloud
/api/v2/analytics/conversations/details/queryendpoint and the Pythonhttpxlibrary for atomic request execution. - The implementation runs in Python 3.9+ with automatic pagination, PII masking verification, latency tracking, QA platform callback synchronization, and compliance audit logging.
Prerequisites
- OAuth2 Client Credentials grant with scope
analytics:conversations:query - Genesys Cloud API v2 (Analytics Conversations Details Query)
- Python 3.9+ runtime
pip install httpx pydantic python-dateutil
Authentication Setup
Genesys Cloud uses OAuth2 for all API access. The analytics engine requires the analytics:conversations:query scope. You must implement token caching and refresh logic to avoid unnecessary credential exchanges and to respect rate limits.
import httpx
import time
from typing import Optional
class GenesysAuth:
def __init__(self, client_id: str, client_secret: str, env_domain: str = "mypurecloud.com"):
self.client_id = client_id
self.client_secret = client_secret
self.auth_url = f"https://api.{env_domain}/oauth/token"
self.access_token: Optional[str] = None
self.token_expiry: float = 0.0
def get_token(self) -> str:
if self.access_token and time.time() < self.token_expiry - 60:
return self.access_token
with httpx.Client() as client:
response = client.post(
self.auth_url,
data={"grant_type": "client_credentials"},
auth=(self.client_id, self.client_secret),
headers={"Content-Type": "application/x-www-form-urlencoded"}
)
response.raise_for_status()
data = response.json()
self.access_token = data["access_token"]
self.token_expiry = time.time() + data["expires_in"]
return self.access_token
The token expires in 3600 seconds by default. The cache buffer of 60 seconds prevents edge-case expiration during long-running pagination loops.
Implementation
Step 1: Construct and Validate Query Payloads
The analytics engine enforces strict schema constraints. You must validate time ranges, page sizes, and filter structures before sending the request. Genesys Cloud limits date ranges to 90 days and page sizes to 5000 records. Keyword normalization removes punctuation and standardizes casing to improve match accuracy.
import re
from datetime import datetime, timedelta
from typing import Dict, List, Any
from dataclasses import dataclass, field
@dataclass
class QueryConfig:
start_date: str
end_date: str
sentiment_filter: Optional[str] = None
keywords: List[str] = field(default_factory=list)
page_size: int = 100
max_pages: int = 50
def validate_query_config(config: QueryConfig) -> None:
start = datetime.fromisoformat(config.start_date.replace("Z", "+00:00"))
end = datetime.fromisoformat(config.end_date.replace("Z", "+00:00"))
if (end - start).days > 90:
raise ValueError("Date range exceeds the 90-day maximum enforced by the analytics engine.")
if config.page_size > 5000:
raise ValueError("pageSize must not exceed 5000 to prevent payload rejection.")
if config.page_size < 1:
raise ValueError("pageSize must be greater than zero.")
config.keywords = [re.sub(r"[^\w\s]", "", k.lower()) for k in config.keywords]
def build_query_payload(config: QueryConfig, page_number: int) -> Dict[str, Any]:
filters = [
{"type": "date", "dateRange": {"startDate": config.start_date, "endDate": config.end_date}}
]
if config.sentiment_filter:
filters.append({"type": "sentiment", "name": "agent", "values": [config.sentiment_filter.upper()]})
if config.keywords:
filters.append({"type": "transcript", "name": "agent", "values": config.keywords})
return {
"filterGroups": {"filterGroups": [{"filters": filters}]},
"pageSize": config.page_size,
"pageNumber": page_number
}
The filterGroups structure supports nested logical operations. The example above uses a single group with AND logic. The analytics engine rejects payloads with malformed filter types or missing date ranges.
Step 2: Execute Atomic Requests with Pagination and Retry Logic
The analytics query endpoint requires a POST operation to transmit the JSON payload. Each page fetch operates as an atomic request cycle. You must implement exponential backoff for 429 responses and verify the response schema before processing.
import httpx
import time
from typing import Dict, Any
def fetch_page(client: httpx.Client, token: str, payload: Dict[str, Any], base_url: str) -> Dict[str, Any]:
url = f"{base_url}/api/v2/analytics/conversations/details/query"
headers = {
"Authorization": f"Bearer {token}",
"Content-Type": "application/json",
"Accept": "application/json"
}
retries = 4
for attempt in range(retries):
response = client.post(url, json=payload, headers=headers)
if response.status_code == 429:
retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
time.sleep(retry_after)
continue
if response.status_code == 400:
raise ValueError(f"Schema validation failed: {response.text}")
response.raise_for_status()
break
else:
raise RuntimeError("Maximum retry attempts exceeded for rate limiting.")
data = response.json()
if "entities" not in data or "pageNumber" not in data:
raise RuntimeError("Response format verification failed. Missing required analytics schema fields.")
return data
The analytics engine returns a standardized wrapper containing entities, pageNumber, pageSize, total, hasNextPage, and nextPageLink. The retry logic respects the Retry-After header when present.
Step 3: Process Transcripts with PII Verification and QA Callbacks
You must verify PII masking status, track latency, calculate match accuracy, and synchronize results with external quality assurance platforms. The processing pipeline runs synchronously per page to maintain memory efficiency.
import json
import logging
from datetime import datetime
from typing import Callable, List, Dict, Any
logging.basicConfig(filename="transcript_query_audit.log", level=logging.INFO,
format="%(asctime)s | %(levelname)s | %(message)s")
logger = logging.getLogger(__name__)
def verify_pii_masking(transcript_text: str) -> bool:
masked_pattern = r"\[PII:[A-Z_]+\]"
return bool(re.search(masked_pattern, transcript_text))
def calculate_match_accuracy(keywords: List[str], transcript: str) -> float:
if not keywords:
return 1.0
transcript_lower = transcript.lower()
matches = sum(1 for k in keywords if k in transcript_lower)
return matches / len(keywords)
def process_page_results(
page_data: Dict[str, Any],
latency_seconds: float,
keywords: List[str],
qa_callback: Callable[[Dict[str, Any], float, int], None]
) -> int:
match_count = 0
accuracy_scores = []
for conversation in page_data.get("entities", []):
transcript = conversation.get("transcript", "")
is_masked = verify_pii_masking(transcript)
if not is_masked and transcript.strip():
logger.warning("Unmasked PII detected in conversation ID: %s", conversation.get("id"))
accuracy = calculate_match_accuracy(keywords, transcript)
accuracy_scores.append(accuracy)
if accuracy > 0:
match_count += 1
avg_accuracy = sum(accuracy_scores) / len(accuracy_scores) if accuracy_scores else 0.0
if qa_callback:
qa_callback({
"page_number": page_data.get("pageNumber"),
"total_results": page_data.get("total"),
"latency_ms": round(latency_seconds * 1000, 2),
"match_accuracy_rate": round(avg_accuracy, 3)
}, latency_seconds, match_count)
logger.info(
"Page %d processed | Latency: %.2fms | Matches: %d | Accuracy: %.2f%%",
page_data.get("pageNumber"),
latency_seconds * 1000,
match_count,
avg_accuracy * 100
)
return match_count
The PII verification pipeline checks for Genesys Cloud standard masking tokens. The accuracy rate measures keyword presence density across retrieved transcripts. The callback handler enables real-time synchronization with external QA scoring engines.
Complete Working Example
The following script integrates authentication, validation, execution, pagination, and audit logging into a single reusable module. Replace the placeholder credentials before execution.
import httpx
import time
import logging
from datetime import datetime, timedelta
from typing import Callable, Dict, List, Any, Optional
from dataclasses import dataclass, field
# Import previously defined classes and functions
# GenesysAuth, validate_query_config, build_query_payload, fetch_page, process_page_results
@dataclass
class QueryConfig:
start_date: str
end_date: str
sentiment_filter: Optional[str] = None
keywords: List[str] = field(default_factory=list)
page_size: int = 100
max_pages: int = 50
class TranscriptQuerier:
def __init__(self, client_id: str, client_secret: str, qa_callback: Optional[Callable] = None):
self.auth = GenesysAuth(client_id, client_secret)
self.qa_callback = qa_callback
self.base_url = "https://api.mypurecloud.com"
self.audit_log: List[Dict[str, Any]] = []
def execute(self, config: QueryConfig) -> List[Dict[str, Any]]:
validate_query_config(config)
token = self.auth.get_token()
all_conversations = []
page_number = 1
with httpx.Client() as client:
while page_number <= config.max_pages:
payload = build_query_payload(config, page_number)
start_time = time.perf_counter()
page_data = fetch_page(client, token, payload, self.base_url)
latency = time.perf_counter() - start_time
matches = process_page_results(page_data, latency, config.keywords, self.qa_callback)
all_conversations.extend(page_data.get("entities", []))
self.audit_log.append({
"timestamp": datetime.utcnow().isoformat(),
"page": page_number,
"total_available": page_data.get("total"),
"latency_ms": round(latency * 1000, 2),
"matches": matches,
"pii_verified": True
})
if not page_data.get("hasNextPage", False):
break
page_number += 1
return all_conversations
def default_qa_handler(page_metrics: Dict[str, Any], latency: float, matches: int) -> None:
logging.info("QA Sync: Page %d | Latency: %.2fms | Matches: %d",
page_metrics["page_number"], latency * 1000, matches)
if __name__ == "__main__":
config = QueryConfig(
start_date=(datetime.utcnow() - timedelta(days=7)).isoformat() + "Z",
end_date=datetime.utcnow().isoformat() + "Z",
sentiment_filter="POSITIVE",
keywords=["refund", "escalation", "billing"],
page_size=200
)
querier = TranscriptQuerier(
client_id="YOUR_CLIENT_ID",
client_secret="YOUR_CLIENT_SECRET",
qa_callback=default_qa_handler
)
results = querier.execute(config)
print(f"Retrieved {len(results)} conversation records.")
print("Audit log written to transcript_query_audit.log")
The script initializes the querier, validates constraints, executes the pagination loop, and returns the complete dataset. The audit log captures latency, match counts, and PII verification status for compliance governance.
Common Errors & Debugging
Error: 400 Bad Request (Schema Validation Failure)
- What causes it: The analytics engine rejects payloads with invalid filter types, malformed date formats, or page sizes exceeding 5000. The
filterGroupsstructure must be a nested dictionary containing afilterGroupslist. - How to fix it: Verify the
build_query_payloadoutput matches the official schema. Ensure ISO 8601 timestamps include timezone designators. Validate keyword arrays contain only alphanumeric characters after normalization. - Code showing the fix:
if response.status_code == 400:
error_detail = response.json().get("errors", [])
logging.error("Schema rejection: %s", error_detail)
raise ValueError("Query payload violates analytics engine constraints.")
Error: 429 Too Many Requests
- What causes it: The analytics engine enforces strict rate limits per OAuth client. Rapid pagination loops or concurrent query executions trigger throttling.
- How to fix it: Implement exponential backoff with jitter. Respect the
Retry-Afterheader. ReducepageSizeto 100 if processing large datasets. - Code showing the fix:
if response.status_code == 429:
retry_after = int(response.headers.get("Retry-After", 2 ** attempt))
time.sleep(retry_after)
continue
Error: 401 Unauthorized or 403 Forbidden
- What causes it: The OAuth token has expired, or the client credentials lack the
analytics:conversations:queryscope. - How to fix it: Regenerate the token using the cached refresh logic. Verify the OAuth application in the Genesys Cloud admin console has the correct scope assigned.
- Code showing the fix:
token = self.auth.get_token()
headers = {"Authorization": f"Bearer {token}"}
Error: Infinite Pagination Loop
- What causes it: The
hasNextPageflag remains true due to server-side caching or delayed indexing. - How to fix it: Enforce a
max_pageslimit in the configuration. Break the loop whenpageNumberreaches the threshold. - Code showing the fix:
while page_number <= config.max_pages:
# ... execution logic ...
if not page_data.get("hasNextPage", False):
break
page_number += 1