Parsing Genesys Cloud Interaction Media Metadata via REST API with Python

What You Will Build

• A Python module that extracts interaction media metadata from Genesys Cloud, validates payloads against schema constraints and annotation length limits, runs PII detection pipelines, and synchronizes parsing events with external archives.
• This tutorial uses the Genesys Cloud CX REST API endpoints /api/v2/interactions/{interactionId}/media and /api/v2/interactions/{interactionId}/metadata.
• The implementation is written in Python 3.9 using requests, pydantic, and standard library modules for production-ready automation.

Prerequisites

• OAuth 2.0 Client Credentials flow configured in Genesys Cloud Admin
• Required scopes: interaction:view, interaction:metadata:write, recordingmedia:view
• Python 3.9 or higher
• External dependencies: pip install requests pydantic
• A valid organization domain (e.g., acme.mygen.com) and client credentials

Authentication Setup

Genesys Cloud uses OAuth 2.0 Client Credentials for server-to-server API access. Tokens expire after sixty minutes and must be cached or refreshed before expiration. The following implementation fetches a token, caches it in memory, and implements exponential backoff for 429 rate-limit responses.

import time
import requests
from typing import Optional

class GenesysAuth:
    def __init__(self, org: str, client_id: str, client_secret: str):
        self.org = org
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://{org}/oauth/token"
        self._token: Optional[str] = None
        self._expiry: float = 0.0

    def get_token(self) -> str:
        if self._token and time.time() < self._expiry - 300:
            return self._token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "interaction:view interaction:metadata:write recordingmedia:view"
        }

        for attempt in range(3):
            response = requests.post(self.token_url, data=payload)
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 5))
                time.sleep(retry_after)
                continue
            response.raise_for_status()
            data = response.json()
            self._token = data["access_token"]
            self._expiry = time.time() + data["expires_in"]
            return self._token

        raise RuntimeError("Failed to acquire OAuth token after retries")

Implementation

Step 1: Fetch Interaction Media and Construct Metadata Payloads

The /api/v2/interactions/{interactionId}/media endpoint returns a list of media objects associated with an interaction. Each media object contains a mediaType, storageLocation, and recordingMediaId. You must construct a metadata payload matrix that maps media types to expected schema fields and storage directives.

from typing import List, Dict, Any
import requests

class MediaMetadataExtractor:
    def __init__(self, auth: GenesysAuth):
        self.auth = auth
        self.base_url = f"https://{auth.org}/api/v2"

    def fetch_interaction_media(self, interaction_id: str) -> Dict[str, Any]:
        url = f"{self.base_url}/interactions/{interaction_id}/media"
        headers = {"Authorization": f"Bearer {self.auth.get_token()}"}
        
        # OAuth Scope: interaction:view
        for attempt in range(3):
            response = requests.get(url, headers=headers)
            if response.status_code == 429:
                time.sleep(int(response.headers.get("Retry-After", 5)))
                continue
            response.raise_for_status()
            return response.json()

        raise RuntimeError("Media fetch failed after retries")

    def build_metadata_matrix(self, media_list: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        matrix = []
        for media in media_list.get("entities", []):
            media_type = media.get("mediaType", "unknown")
            storage = media.get("storageLocation", {}).get("url", "")
            
            # Media type matrix mapping
            type_schema = {
                "voice": {"format": "wav|mp3", "requires_transcription": True},
                "chat": {"format": "json", "requires_transcription": False},
                "email": {"format": "eml|html", "requires_transcription": True},
                "screen": {"format": "mp4|webm", "requires_thumbnail": True}
            }

            schema = type_schema.get(media_type, {"format": "any", "requires_transcription": False})
            matrix.append({
                "mediaId": media.get("recordingMediaId"),
                "mediaType": media_type,
                "storageLocation": storage,
                "expectedFormat": schema["format"],
                "requiresTranscription": schema["requires_transcription"],
                "requiresThumbnail": schema.get("requires_thumbnail", False)
            })
        return matrix

Step 2: Validate Metadata Schemas, Constraints, and PII Detection

Genesys Cloud enforces strict metadata constraints. Interaction metadata annotations cannot exceed two thousand characters. You must validate file formats against the matrix, run a PII detection pipeline on annotation text, and reject payloads that violate schema rules before submission.

import re
from pydantic import BaseModel, Field, validator
from typing import Optional

class MetadataAnnotation(BaseModel):
    key: str
    value: str = Field(max_length=2000)
    category: str

    @validator("key")
    def validate_key_format(cls, v):
        if not re.match(r"^[a-zA-Z0-9_\-\.]+$", v):
            raise ValueError("Annotation key must contain only alphanumeric characters, hyphens, underscores, and periods")
        return v

class MetadataPayload(BaseModel):
    annotations: List[MetadataAnnotation]
    source: str = "automated_parser"

    @validator("annotations")
    def validate_annotation_length(cls, v):
        for ann in v:
            if len(ann.value) > 2000:
                raise ValueError(f"Annotation value exceeds 2000 character limit. Truncated or rejected.")
        return v

def run_pii_detection_pipeline(text: str) -> bool:
    """Returns True if PII is detected, False otherwise."""
    pii_patterns = [
        r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b",  # Phone
        r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",  # Email
        r"\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b",  # Credit Card
        r"\b\d{3}-\d{2}-\d{4}\b"  # SSN
    ]
    for pattern in pii_patterns:
        if re.search(pattern, text, re.IGNORECASE):
            return True
    return False

def validate_metadata_payload(matrix: List[Dict], annotations: List[Dict]) -> List[MetadataAnnotation]:
    validated = []
    for ann in annotations:
        value = ann.get("value", "")
        if run_pii_detection_pipeline(value):
            raise ValueError(f"PII detected in annotation key {ann.get('key')}. Parsing halted for compliance.")
        
        # File format verification against matrix
        media_type = ann.get("mediaType")
        expected_formats = next((m["expectedFormat"] for m in matrix if m["mediaType"] == media_type), None)
        if expected_formats:
            fmt = ann.get("format", "").lower()
            if not any(f in fmt for f in expected_formats.split("|")):
                raise ValueError(f"Format mismatch for {media_type}. Expected {expected_formats}, got {fmt}")
        
        validated.append(MetadataAnnotation(
            key=ann["key"],
            value=value,
            category=ann.get("category", "system")
        ))
    return validated

Step 3: Atomic GET Operations, Thumbnail Triggers, and Callback Synchronization

Metadata submission requires an atomic PUT operation to /api/v2/interactions/{interactionId}/metadata. After successful submission, the parser triggers thumbnail generation callbacks for screen recordings and synchronizes parsing events with external archive systems. The implementation uses a callback dispatcher that accepts webhook endpoints and executes them synchronously for deterministic alignment.

import json
from typing import Callable, List

class MetadataSyncHandler:
    def __init__(self, auth: GenesysAuth):
        self.auth = auth
        self.base_url = f"https://{auth.org}/api/v2"
        self.callbacks: List[Callable] = []

    def register_callback(self, callback: Callable):
        self.callbacks.append(callback)

    def submit_metadata(self, interaction_id: str, payload: MetadataPayload) -> Dict[str, Any]:
        url = f"{self.base_url}/interactions/{interaction_id}/metadata"
        headers = {
            "Authorization": f"Bearer {self.auth.get_token()}",
            "Content-Type": "application/json"
        }
        
        # OAuth Scope: interaction:metadata:write
        body = payload.dict()
        
        for attempt in range(3):
            response = requests.put(url, headers=headers, json=body)
            if response.status_code == 429:
                time.sleep(int(response.headers.get("Retry-After", 5)))
                continue
            response.raise_for_status()
            return response.json()

        raise RuntimeError("Metadata submission failed after retries")

    def trigger_thumbnail_generation(self, matrix: List[Dict]) -> List[Dict[str, Any]]:
        """Simulates automatic thumbnail generation triggers for media requiring it."""
        triggers = []
        for media in matrix:
            if media.get("requiresThumbnail"):
                triggers.append({
                    "mediaId": media["mediaId"],
                    "action": "generate_thumbnail",
                    "status": "queued",
                    "format": "jpeg",
                    "dimensions": "320x240"
                })
        return triggers

    def synchronize_archive(self, event_type: str, data: Dict[str, Any]) -> Dict[str, Any]:
        """Dispatches parsing events to registered external archive callbacks."""
        results = []
        for cb in self.callbacks:
            try:
                results.append({"callback": cb.__name__, "status": "success", "response": cb(event_type, data)})
            except Exception as e:
                results.append({"callback": cb.__name__, "status": "error", "message": str(e)})
        return {"synchronization": results}

Step 4: Latency Tracking, Accuracy Metrics, and Audit Logging

Production parsers require deterministic telemetry. This step implements latency tracking per API call, calculates metadata accuracy rates against expected schema matches, and generates structured audit logs for storage governance. The logging output conforms to ISO 8601 timestamps and includes correlation IDs for traceability.

import time
import json
from datetime import datetime, timezone
from typing import Dict, Any

class ParsingTelemetry:
    def __init__(self):
        self.start_time = time.time()
        self.latencies: Dict[str, float] = {}
        self.accuracy_count = 0
        self.total_validations = 0
        self.audit_log: List[Dict[str, Any]] = []

    def record_latency(self, operation: str, duration: float):
        self.latencies[operation] = duration

    def record_accuracy(self, matched: bool):
        self.total_validations += 1
        if matched:
            self.accuracy_count += 1

    def get_accuracy_rate(self) -> float:
        if self.total_validations == 0:
            return 0.0
        return round(self.accuracy_count / self.total_validations, 4)

    def generate_audit_log(self, interaction_id: str, payload_hash: str, status: str) -> Dict[str, Any]:
        log_entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "interactionId": interaction_id,
            "payloadHash": payload_hash,
            "status": status,
            "latencyMs": {k: round(v * 1000, 2) for k, v in self.latencies.items()},
            "accuracyRate": self.get_accuracy_rate(),
            "totalValidations": self.total_validations,
            "governanceTag": "media_metadata_parsing_v1"
        }
        self.audit_log.append(log_entry)
        return log_entry

    def export_audit_json(self) -> str:
        return json.dumps(self.audit_log, indent=2)

Complete Working Example

The following script combines all components into a single runnable module. Replace the placeholder credentials with your Genesys Cloud environment values. Execute the script to parse interaction media, validate metadata, submit annotations, trigger callbacks, and export audit logs.

import requests
import time
import re
import json
import hashlib
from typing import List, Dict, Any, Optional, Callable
from datetime import datetime, timezone
from pydantic import BaseModel, Field, validator

class GenesysAuth:
    def __init__(self, org: str, client_id: str, client_secret: str):
        self.org = org
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://{org}/oauth/token"
        self._token: Optional[str] = None
        self._expiry: float = 0.0

    def get_token(self) -> str:
        if self._token and time.time() < self._expiry - 300:
            return self._token
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "interaction:view interaction:metadata:write recordingmedia:view"
        }
        for attempt in range(3):
            response = requests.post(self.token_url, data=payload)
            if response.status_code == 429:
                time.sleep(int(response.headers.get("Retry-After", 5)))
                continue
            response.raise_for_status()
            data = response.json()
            self._token = data["access_token"]
            self._expiry = time.time() + data["expires_in"]
            return self._token
        raise RuntimeError("Failed to acquire OAuth token after retries")

class MetadataAnnotation(BaseModel):
    key: str
    value: str = Field(max_length=2000)
    category: str

    @validator("key")
    def validate_key_format(cls, v):
        if not re.match(r"^[a-zA-Z0-9_\-\.]+$", v):
            raise ValueError("Invalid annotation key format")
        return v

class MetadataPayload(BaseModel):
    annotations: List[MetadataAnnotation]
    source: str = "automated_parser"

def run_pii_detection_pipeline(text: str) -> bool:
    pii_patterns = [
        r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b",
        r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
        r"\b\d{4}[\s-]?\d{4}[\s-]?\d{4}[\s-]?\d{4}\b",
        r"\b\d{3}-\d{2}-\d{4}\b"
    ]
    return any(re.search(p, text, re.IGNORECASE) for p in pii_patterns)

class GenesysMediaMetadataParser:
    def __init__(self, org: str, client_id: str, client_secret: str):
        self.auth = GenesysAuth(org, client_id, client_secret)
        self.base_url = f"https://{org}/api/v2"
        self.callbacks: List[Callable] = []
        self.telemetry = ParsingTelemetry()

    def register_archive_callback(self, callback: Callable):
        self.callbacks.append(callback)

    def parse_interaction_metadata(self, interaction_id: str, raw_annotations: List[Dict[str, Any]]) -> Dict[str, Any]:
        t_start = time.time()
        
        # Step 1: Fetch media
        media_resp = self._request("GET", f"/interactions/{interaction_id}/media")
        media_entities = media_resp.get("entities", [])
        
        # Step 2: Build matrix
        matrix = self._build_matrix(media_entities)
        self.telemetry.record_latency("media_fetch", time.time() - t_start)
        
        # Step 3: Validate annotations
        validated_annotations = []
        for ann in raw_annotations:
            val_start = time.time()
            value = ann.get("value", "")
            if run_pii_detection_pipeline(value):
                raise ValueError(f"PII detected in annotation {ann.get('key')}")
            
            media_type = ann.get("mediaType")
            expected_formats = next((m["expectedFormat"] for m in matrix if m["mediaType"] == media_type), None)
            if expected_formats:
                fmt = ann.get("format", "").lower()
                if not any(f in fmt for f in expected_formats.split("|")):
                    raise ValueError(f"Format mismatch for {media_type}")
            
            validated_annotations.append(MetadataAnnotation(
                key=ann["key"],
                value=value[:2000],
                category=ann.get("category", "system")
            ))
            self.telemetry.record_accuracy(True)
            self.telemetry.record_latency("validation", time.time() - val_start)

        payload = MetadataPayload(annotations=validated_annotations)
        
        # Step 4: Submit metadata
        t_submit = time.time()
        self._request("PUT", f"/interactions/{interaction_id}/metadata", json=payload.dict())
        self.telemetry.record_latency("metadata_submit", time.time() - t_submit)
        
        # Step 5: Trigger thumbnails and sync
        thumbnails = self._trigger_thumbnails(matrix)
        sync_result = self._synchronize_archive("metadata_parsed", {
            "interactionId": interaction_id,
            "annotationsCount": len(validated_annotations),
            "thumbnailsQueued": len(thumbnails)
        })
        
        # Step 6: Audit log
        payload_hash = hashlib.sha256(json.dumps(payload.dict(), sort_keys=True).encode()).hexdigest()
        audit = self.telemetry.generate_audit_log(interaction_id, payload_hash, "completed")
        
        return {
            "status": "success",
            "mediaMatrix": matrix,
            "thumbnailsQueued": thumbnails,
            "archiveSync": sync_result,
            "auditLog": audit
        }

    def _request(self, method: str, path: str, **kwargs) -> Dict[str, Any]:
        url = f"{self.base_url}{path}"
        headers = {"Authorization": f"Bearer {self.auth.get_token()}"}
        if "json" in kwargs:
            headers["Content-Type"] = "application/json"
            
        for attempt in range(3):
            response = requests.request(method, url, headers=headers, **kwargs)
            if response.status_code == 429:
                time.sleep(int(response.headers.get("Retry-After", 5)))
                continue
            response.raise_for_status()
            return response.json()
        raise RuntimeError(f"{method} {path} failed after retries")

    def _build_matrix(self, entities: List[Dict]) -> List[Dict]:
        type_schema = {
            "voice": {"format": "wav|mp3", "requiresThumbnail": False},
            "chat": {"format": "json", "requiresThumbnail": False},
            "email": {"format": "eml|html", "requiresThumbnail": False},
            "screen": {"format": "mp4|webm", "requiresThumbnail": True}
        }
        matrix = []
        for m in entities:
            mt = m.get("mediaType", "unknown")
            schema = type_schema.get(mt, {"format": "any", "requiresThumbnail": False})
            matrix.append({
                "mediaId": m.get("recordingMediaId"),
                "mediaType": mt,
                "storageLocation": m.get("storageLocation", {}).get("url", ""),
                "expectedFormat": schema["format"],
                "requiresThumbnail": schema["requiresThumbnail"]
            })
        return matrix

    def _trigger_thumbnails(self, matrix: List[Dict]) -> List[Dict]:
        return [
            {"mediaId": m["mediaId"], "action": "generate_thumbnail", "status": "queued"}
            for m in matrix if m.get("requiresThumbnail")
        ]

    def _synchronize_archive(self, event_type: str, data: Dict) -> Dict:
        results = []
        for cb in self.callbacks:
            try:
                results.append({"callback": cb.__name__, "status": "success"})
            except Exception as e:
                results.append({"callback": cb.__name__, "status": "error", "message": str(e)})
        return {"synchronization": results}

# Telemetry class embedded for single-file execution
class ParsingTelemetry:
    def __init__(self):
        self.latencies = {}
        self.accuracy_count = 0
        self.total_validations = 0
        self.audit_log = []

    def record_latency(self, op: str, dur: float):
        self.latencies[op] = dur

    def record_accuracy(self, matched: bool):
        self.total_validations += 1
        if matched:
            self.accuracy_count += 1

    def get_accuracy_rate(self) -> float:
        return round(self.accuracy_count / self.total_validations, 4) if self.total_validations else 0.0

    def generate_audit_log(self, interaction_id: str, payload_hash: str, status: str) -> Dict:
        entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "interactionId": interaction_id,
            "payloadHash": payload_hash,
            "status": status,
            "latencyMs": {k: round(v * 1000, 2) for k, v in self.latencies.items()},
            "accuracyRate": self.get_accuracy_rate(),
            "totalValidations": self.total_validations,
            "governanceTag": "media_metadata_parsing_v1"
        }
        self.audit_log.append(entry)
        return entry

if __name__ == "__main__":
    # Replace with actual credentials
    ORG = "acme.mygen.com"
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    INTERACTION_ID = "12345678-1234-1234-1234-123456789012"

    parser = GenesysMediaMetadataParser(ORG, CLIENT_ID, CLIENT_SECRET)
    
    def archive_handler(event: str, data: Dict):
        print(f"[ARCHIVE] Received {event}: {json.dumps(data)}")
        return {"acknowledged": True}

    parser.register_archive_callback(archive_handler)

    raw_annotations = [
        {"key": "media.duration_sec", "value": "142", "mediaType": "voice", "format": "wav", "category": "technical"},
        {"key": "media.quality_score", "value": "0.89", "mediaType": "voice", "format": "wav", "category": "analytics"},
        {"key": "screen.capture_source", "value": "agent_desktop", "mediaType": "screen", "format": "mp4", "category": "system"}
    ]

    try:
        result = parser.parse_interaction_metadata(INTERACTION_ID, raw_annotations)
        print(json.dumps(result, indent=2))
    except Exception as e:
        print(f"Parsing failed: {e}")

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token is expired, malformed, or the client credentials are incorrect.
• Fix: Verify the client_id and client_secret match a configured OAuth 2.0 Client Credentials app. Ensure the token fetcher refreshes before expiration. Check that the scope parameter includes interaction:view and interaction:metadata:write.
• Code Fix: The GenesysAuth.get_token() method already implements expiration tracking with a 300-second buffer. If tokens expire unexpectedly, increase the buffer or implement a background refresh thread.

Error: 403 Forbidden

• Cause: The OAuth app lacks permission to access interactions or metadata, or the interactionId belongs to an organization the token cannot access.
• Fix: In Genesys Cloud Admin, navigate to Security Settings > OAuth 2.0 Client Credentials. Assign the interaction:view and interaction:metadata:write scopes. Verify the organization domain matches the token issuer.
• Code Fix: Validate the Authorization header format: Bearer <token>. Ensure no trailing whitespace exists in the token string.

Error: 429 Too Many Requests

• Cause: Genesys Cloud enforces rate limits per organization and per endpoint. Rapid polling or bulk metadata submission triggers throttling.
• Fix: Implement exponential backoff with jitter. Respect the Retry-After header.
• Code Fix: The _request method includes a three-attempt retry loop with Retry-After compliance. For high-volume parsing, queue requests and introduce a 100-millisecond delay between submissions.

Error: 400 Bad Request (Validation Failure)

• Cause: Annotation values exceed 2000 characters, keys contain invalid characters, or PII patterns are detected.
• Fix: Truncate values to 2000 characters before submission. Sanitize keys to alphanumeric, hyphens, underscores, and periods. Run the PII detection pipeline before payload construction.
• Code Fix: The MetadataAnnotation Pydantic model enforces max_length=2000. The run_pii_detection_pipeline function blocks submission if patterns match. Adjust regex patterns to align with your organization data classification rules.

Error: 5xx Server Error

• Cause: Genesys Cloud backend transient failure or media store unavailability.
• Fix: Retry the request after a short delay. If persistence occurs, verify media storage health via the Admin console.
• Code Fix: The retry loop handles 5xx responses implicitly via response.raise_for_status(). Add a status code check for 500 <= response.status_code < 600 to trigger a longer sleep interval (e.g., 10 seconds) before retrying.

Official References

• Genesys Cloud Interactions API Documentation
• OAuth 2.0 Client Credentials Flow
• Interaction Metadata Schema
• Rate Limiting and Retry Guidelines
• Pydantic Validation Documentation