Annotating Genesys Cloud Real-Time Transcripts via Agent Assist API with Python

How-To-Articles
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Annotating Genesys Cloud Real-Time Transcripts via Agent Assist API with Python

What You Will Build

  • A Python service that consumes real-time transcript streams, validates annotation payloads against assist constraints, and submits structured annotations to the Genesys Cloud Agent Assist API.
  • This tutorial uses the Genesys Cloud Agent Assist REST API, Integration Webhook API, and the real-time Agent Assist WebSocket stream.
  • The implementation is written in Python 3.10+ using the official genesys-cloud-purecloud-platform-client SDK, requests, websocket-client, and standard library modules.

Prerequisites

  • OAuth 2.0 Client Credentials flow configured in Genesys Cloud with the following scopes: agentassist:annotations:write, agentassist:stream:read, integrations:webhooks:write, conversation:read
  • Genesys Cloud Python SDK version 2.14.0 or higher
  • Python 3.10+ runtime environment
  • External dependencies: genesys-cloud-purecloud-platform-client, requests, websocket-client, textblob, uuid, json, time, logging, threading

Authentication Setup

Genesys Cloud requires a Bearer token for all REST and WebSocket connections. The Client Credentials flow provides a long-lived token suitable for server-side annotation services. Implement token caching and automatic refresh to prevent mid-stream authentication failures.

import requests
import time
import threading
import logging

logger = logging.getLogger(__name__)

class OAuthTokenManager:
    def __init__(self, client_id: str, client_secret: str, environment: str = "mypurecloud.com"):
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://login.{environment}/v2/oauth/token"
        self.access_token = None
        self.expires_at = 0.0
        self.lock = threading.Lock()

    def _fetch_token(self) -> str:
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        response = requests.post(self.token_url, data=payload, timeout=10)
        response.raise_for_status()
        token_data = response.json()
        self.access_token = token_data["access_token"]
        self.expires_at = time.time() + token_data["expires_in"] - 30
        return self.access_token

    def get_token(self) -> str:
        with self.lock:
            if not self.access_token or time.time() >= self.expires_at:
                try:
                    return self._fetch_token()
                except requests.exceptions.HTTPError as e:
                    if e.response.status_code == 401:
                        raise RuntimeError("OAuth client credentials are invalid or expired.") from e
                    raise
            return self.access_token

Implementation

Step 1: Initialize SDK and WebSocket Stream

Connect to the real-time transcript stream using the WebSocket API. The stream delivers transcript chunks with conversation metadata, speaker identifiers, and timestamp boundaries. The SDK handles REST calls, while websocket-client manages the persistent connection.

import websocket
import json
import uuid
from genesyscloud.platform.client import PureCloudPlatformClientV2
from genesyscloud.agent_assist.api import AgentAssistApi

class TranscriptStreamManager:
    def __init__(self, environment: str, token_manager: OAuthTokenManager):
        self.environment = environment
        self.token_manager = token_manager
        self.ws_url = f"wss://api.{environment}/api/v2/agent-assist/stream"
        self.sdk_client = PureCloudPlatformClientV2()
        self.agent_assist_api = AgentAssistApi(self.sdk_client)

    def connect(self):
        token = self.token_manager.get_token()
        headers = {"Authorization": f"Bearer {token}"}
        ws = websocket.WebSocketApp(
            self.ws_url,
            header=headers,
            on_message=self._on_message,
            on_error=self._on_error,
            on_close=self._on_close
        )
        ws.run_forever(ping_interval=30, ping_timeout=10)

    def _on_message(self, ws, message: str):
        try:
            payload = json.loads(message)
            if payload.get("eventType") == "TRANSCRIPT_UPDATED":
                self._process_transcript_chunk(payload)
        except json.JSONDecodeError as e:
            logger.error("Malformed WebSocket message: %s", e)

    def _on_error(self, ws, error):
        logger.error("WebSocket connection error: %s", error)

    def _on_close(self, ws, close_status_code, close_msg):
        logger.info("WebSocket closed: %s %s", close_status_code, close_msg)

    def _process_transcript_chunk(self, chunk: dict):
        # Placeholder for Step 2 and Step 3 logic
        pass

Step 2: Construct and Validate Annotation Payloads

Every annotation must reference a valid transcript, align with an active assist matrix, and respect maximum depth limits. Genesys Cloud rejects annotations that exceed matrix rule constraints or violate schema requirements. Implement a validation pipeline before submission.

from dataclasses import dataclass
from typing import Optional
from datetime import datetime

@dataclass
class AnnotationPayload:
    transcript_id: str
    matrix_id: str
    rule_id: str
    tag: str
    start_timestamp: str
    end_timestamp: str
    confidence: float
    metadata: dict

MAX_ANNOTATION_DEPTH = 50
ALLOWED_TAGS = {"sentiment_negative", "sentiment_positive", "keyword_pricing", "keyword_complaint", "escalation_risk"}

class AnnotationValidator:
    def __init__(self, agent_assist_api: AgentAssistApi):
        self.api = agent_assist_api

    def validate_matrix_constraints(self, matrix_id: str, tag: str) -> bool:
        try:
            response = self.api.agent_assist_matrices_get(matrix_id)
            rules = response.entity.get("rules", [])
            allowed_tags = {r.get("tag") for r in rules if r.get("enabled", False)}
            return tag in allowed_tags
        except Exception as e:
            logger.error("Matrix validation failed for %s: %s", matrix_id, e)
            return False

    def validate_payload(self, payload: AnnotationPayload, existing_count: int) -> bool:
        if payload.tag not in ALLOWED_TAGS:
            logger.warning("Tag %s is not in allowed directive set.", payload.tag)
            return False
        if existing_count >= MAX_ANNOTATION_DEPTH:
            logger.warning("Maximum annotation depth of %s reached for transcript %s.", MAX_ANNOTATION_DEPTH, payload.transcript_id)
            return False
        if not self.validate_matrix_constraints(payload.matrix_id, payload.tag):
            logger.error("Tag %s does not match active rules in matrix %s.", payload.tag, payload.matrix_id)
            return False
        try:
            datetime.fromisoformat(payload.start_timestamp.replace("Z", "+00:00"))
            datetime.fromisoformat(payload.end_timestamp.replace("Z", "+00:00"))
        except ValueError:
            logger.error("Invalid timestamp format in annotation payload.")
            return False
        return True

Step 3: Process Sentiment, Keywords, Privacy, and Submit Annotations

Real-time transcript processing requires atomic evaluation of sentiment polarity, keyword extraction, and privacy checks. The pipeline verifies context relevance, redacts sensitive data, and triggers automatic suggestion overlays. Annotations are submitted with exponential backoff for rate limits.

import time
import logging
from textblob import TextBlob
from typing import Dict, List

logger = logging.getLogger(__name__)

class AnnotationPipeline:
    def __init__(self, validator: AnnotationValidator, agent_assist_api: AgentAssistApi):
        self.validator = validator
        self.api = agent_assist_api
        self.transcript_annotation_counts: Dict[str, int] = {}
        self.latency_tracker: List[float] = []
        self.audit_log: List[Dict] = []

    def _check_privacy_and_relevance(self, text: str) -> bool:
        # Simulated PII check and context relevance scoring
        sensitive_patterns = ["ssn", "credit card", "password", "dob"]
        lower_text = text.lower()
        has_sensitive = any(pattern in lower_text for pattern in sensitive_patterns)
        if has_sensitive:
            logger.warning("Privacy check failed. Skipping annotation for sensitive content.")
            return False
        relevance_score = 0.0
        if any(word in lower_text for word in ["price", "cost", "billing"]):
            relevance_score += 0.4
        if any(word in lower_text for word in ["complaint", "issue", "problem"]):
            relevance_score += 0.4
        return relevance_score >= 0.5

    def _extract_keywords_and_sentiment(self, text: str) -> Dict:
        blob = TextBlob(text)
        polarity = blob.sentiment.polarity
        keywords = [word for word in blob.words if word.lower() not in {"the", "a", "an", "is", "it"}]
        return {"polarity": polarity, "keywords": keywords[:5]}

    def _submit_with_retry(self, payload: AnnotationPayload, max_retries: int = 3) -> bool:
        for attempt in range(max_retries):
            start_time = time.time()
            try:
                body = {
                    "transcriptId": payload.transcript_id,
                    "matrixId": payload.matrix_id,
                    "ruleId": payload.rule_id,
                    "tag": payload.tag,
                    "startTimestamp": payload.start_timestamp,
                    "endTimestamp": payload.end_timestamp,
                    "confidence": payload.confidence,
                    "metadata": payload.metadata
                }
                self.api.agent_assist_annotations_post(body=body)
                elapsed = time.time() - start_time
                self.latency_tracker.append(elapsed)
                logger.info("Annotation submitted successfully in %.2fs", elapsed)
                self._log_audit(payload, "SUCCESS", elapsed)
                return True
            except Exception as e:
                status_code = getattr(e, "status_code", None)
                if status_code == 429:
                    wait_time = 2 ** attempt
                    logger.warning("Rate limited (429). Retrying in %s seconds.", wait_time)
                    time.sleep(wait_time)
                elif status_code in (401, 403):
                    logger.error("Authentication/Authorization failed (401/403). Aborting.")
                    self._log_audit(payload, "AUTH_FAILURE", 0.0)
                    return False
                else:
                    logger.error("Submission failed after %s attempts: %s", attempt + 1, e)
                    self._log_audit(payload, "FAILURE", 0.0)
                    return False
        return False

    def _log_audit(self, payload: AnnotationPayload, status: str, latency: float):
        self.audit_log.append({
            "timestamp": datetime.utcnow().isoformat() + "Z",
            "transcript_id": payload.transcript_id,
            "tag": payload.tag,
            "status": status,
            "latency_ms": round(latency * 1000, 2),
            "audit_id": str(uuid.uuid4())
        })

    def process_chunk(self, chunk: dict):
        text = chunk.get("text", "")
        if not self._check_privacy_and_relevance(text):
            return

        analysis = self._extract_keywords_and_sentiment(text)
        polarity = analysis["polarity"]
        tag = "sentiment_negative" if polarity < -0.3 else ("sentiment_positive" if polarity > 0.3 else "keyword_pricing")

        transcript_id = chunk.get("transcriptId", "")
        self.transcript_annotation_counts[transcript_id] = self.transcript_annotation_counts.get(transcript_id, 0) + 1

        payload = AnnotationPayload(
            transcript_id=transcript_id,
            matrix_id=chunk.get("matrixId", "default-matrix-id"),
            rule_id=chunk.get("ruleId", "default-rule-id"),
            tag=tag,
            start_timestamp=chunk.get("startTimestamp", datetime.utcnow().isoformat() + "Z"),
            end_timestamp=chunk.get("endTimestamp", datetime.utcnow().isoformat() + "Z"),
            confidence=abs(polarity),
            metadata=analysis
        )

        if self.validator.validate_payload(payload, self.transcript_annotation_counts.get(transcript_id, 0) - 1):
            self._submit_with_retry(payload)

Step 4: Synchronize with External QM Systems via Webhooks

Register a webhook to push annotation events to external quality management systems. The webhook triggers on agentassist.annotation.created and includes transcript references, tag directives, and confidence scores.

from genesyscloud.integration.api import IntegrationApi

class WebhookSyncManager:
    def __init__(self, sdk_client: PureCloudPlatformClientV2, target_url: str):
        self.api = IntegrationApi(sdk_client)
        self.target_url = target_url

    def register_annotation_webhook(self, webhook_name: str, webhook_id: str) -> str:
        webhook_body = {
            "name": webhook_name,
            "description": "Syncs Genesys Cloud Agent Assist annotations to external QM system",
            "enabled": True,
            "type": "webhook",
            "events": ["agentassist.annotation.created"],
            "configuration": {
                "url": self.target_url,
                "method": "POST",
                "headers": {
                    "Content-Type": "application/json",
                    "X-Webhook-Source": "genesys-agent-assist-annotator"
                },
                "payloadFormat": "json",
                "retryPolicy": {
                    "maxRetries": 3,
                    "retryIntervalSeconds": 30
                }
            },
            "filter": {
                "type": "advanced",
                "predicate": "event.transcriptId != null && event.confidence > 0.7"
            }
        }
        try:
            response = self.api.integration_webhooks_post(body=webhook_body)
            logger.info("Webhook registered successfully: %s", response.entity.get("id"))
            return response.entity.get("id", webhook_id)
        except Exception as e:
            logger.error("Failed to register webhook: %s", e)
            raise

Complete Working Example

The following script integrates all components into a single executable module. Configure environment variables for credentials and webhook endpoints before execution.

import os
import logging
import threading
from datetime import datetime

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

def main():
    # Configuration
    CLIENT_ID = os.getenv("GENESYS_CLIENT_ID")
    CLIENT_SECRET = os.getenv("GENESYS_CLIENT_SECRET")
    ENVIRONMENT = os.getenv("GENESYS_ENV", "mypurecloud.com")
    WEBHOOK_URL = os.getenv("QM_WEBHOOK_URL", "https://qm.example.com/api/v1/annotations")

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

    # Initialize components
    token_manager = OAuthTokenManager(CLIENT_ID, CLIENT_SECRET, ENVIRONMENT)
    sdk_client = PureCloudPlatformClientV2()
    agent_assist_api = AgentAssistApi(sdk_client)
    
    # Validate initial token and set SDK auth
    token = token_manager.get_token()
    sdk_client.set_access_token(token)

    validator = AnnotationValidator(agent_assist_api)
    pipeline = AnnotationPipeline(validator, agent_assist_api)
    stream_manager = TranscriptStreamManager(ENVIRONMENT, token_manager)
    
    # Override stream processor to use pipeline
    stream_manager._process_transcript_chunk = lambda chunk: pipeline.process_chunk(chunk)

    # Register webhook for QM sync
    webhook_manager = WebhookSyncManager(sdk_client, WEBHOOK_URL)
    try:
        webhook_manager.register_annotation_webhook("Agent Assist QM Sync", "qm-sync-hook")
    except Exception as e:
        logger.error("Webhook registration failed. Continuing without QM sync: %s", e)

    # Start WebSocket stream in a background thread
    ws_thread = threading.Thread(target=stream_manager.connect, daemon=True)
    ws_thread.start()
    logger.info("Real-time transcript annotation service started. Press Ctrl+C to exit.")

    try:
        while True:
            time.sleep(1)
    except KeyboardInterrupt:
        logger.info("Shutting down annotation service.")
        # Export audit log before exit
        audit_output = {
            "export_timestamp": datetime.utcnow().isoformat() + "Z",
            "total_annotations_processed": len(pipeline.audit_log),
            "average_latency_ms": round(sum(pipeline.latency_tracker) / max(len(pipeline.latency_tracker), 1) * 1000, 2),
            "audit_records": pipeline.audit_log
        }
        logger.info("Audit log exported: %s records", len(audit_output["audit_records"]))

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

  • What causes it: The OAuth token has expired, the client credentials are incorrect, or the SDK did not receive the Bearer header.
  • How to fix it: Verify that OAuthTokenManager refreshes the token before expiration. Ensure the SDK client calls set_access_token() after each refresh. Check that the client credentials grant the agentassist:annotations:write scope.
  • Code showing the fix: The OAuthTokenManager class implements automatic refresh with a 30-second safety buffer. The main() function updates the SDK token on initialization.

Error: 403 Forbidden

  • What causes it: The OAuth client lacks the required scopes, or the annotation targets a matrix or transcript outside the client permissions.
  • How to fix it: Grant agentassist:stream:read and agentassist:annotations:write in the Genesys Cloud Admin Console under Applications. Verify that the matrix ID exists and is enabled.
  • Code showing the fix: The AnnotationValidator.validate_matrix_constraints method checks rule alignment before submission. The WebhookSyncManager uses integrations:webhooks:write scope.

Error: 429 Too Many Requests

  • What causes it: The annotation submission rate exceeds Genesys Cloud rate limits for the tenant or API endpoint.
  • How to fix it: Implement exponential backoff with jitter. Batch annotations when possible. Monitor the Retry-After header if present.
  • Code showing the fix: The AnnotationPipeline._submit_with_retry method implements a 3-attempt retry loop with 2 ** attempt second delays.

Error: 400 Bad Request (Validation Failure)

  • What causes it: The payload violates schema constraints, exceeds maximum annotation depth, or contains invalid timestamp formats.
  • How to fix it: Validate timestamps against ISO 8601 with UTC timezone. Ensure the tag matches an enabled rule in the assist matrix. Enforce the MAX_ANNOTATION_DEPTH limit.
  • Code showing the fix: The AnnotationValidator class checks tag allowlists, matrix rule alignment, timestamp parsing, and depth counters before allowing submission.

Error: WebSocket Connection Drops

  • What causes it: Network instability, token expiration during the stream, or server-side stream rotation.
  • How to fix it: Implement automatic reconnection with token refresh. Use ping_interval and ping_timeout in the WebSocket client.
  • Code showing the fix: The TranscriptStreamManager uses websocket.WebSocketApp with ping configuration. The daemon thread allows graceful shutdown and manual restart if needed.

Official References