Building a Custom LLM Gateway to Sanitize PII Before Forwarding Genesys Cloud Transcript Data

What You Will Build

• You will build a Python service that retrieves conversation transcripts from Genesys Cloud, strips personally identifiable information using a compiled regex pipeline, and forwards the redacted text to an external AI model.
• This implementation uses the Genesys Cloud Python SDK and the /api/v2/analytics/conversations/details/query endpoint.
• The tutorial covers Python 3.10+ with httpx, genesys-cloud-sdk, and standard library re.

Prerequisites

• OAuth 2.0 Client Credentials grant with analytics:query:read and conversation:read scopes.
• Genesys Cloud Python SDK version 2.0.0 or higher.
• Python 3.10 or higher.
• Dependencies: pip install genesys-cloud-sdk httpx

Authentication Setup

import os
import time
import httpx
from typing import Optional

class GenesysAuthManager:
    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.rstrip("/")
        self.token_url = f"{self.base_url}/oauth/token"
        self.access_token: Optional[str] = None
        self.expires_at: float = 0
        self.http_client = httpx.Client()

    def get_token(self) -> str:
        if self.access_token and time.time() < self.expires_at - 60:
            return self.access_token

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "analytics:query:read conversation:read"
        }
        response = self.http_client.post(self.token_url, data=payload)
        response.raise_for_status()
        data = response.json()
        self.access_token = data["access_token"]
        self.expires_at = time.time() + data["expires_in"]
        return self.access_token

The OAuth 2.0 client credentials flow exchanges your application credentials for a bearer token. The scope parameter must include analytics:query:read to access conversation data and conversation:read to expand transcript details. The token cache prevents unnecessary network calls by validating the expiration timestamp against a sixty second safety margin. The SDK accepts this token directly or can be configured to auto refresh it.

Implementation

Step 1: Retrieve Conversation Transcripts with Pagination

import json
from typing import Generator
from purecloud_platform_client import PureCloudPlatformClientV2
from purecloud_platform_client.rest import ApiException

class TranscriptFetcher:
    def __init__(self, platform_client: PureCloudPlatformClientV2):
        self.client = platform_client
        self.analytics_api = platform_client.AnalyticsApi()

    def fetch_transcripts(self, start_date: str, end_date: str) -> Generator[dict, None, None]:
        query_body = {
            "date_from": start_date,
            "date_to": end_date,
            "size": 100,
            "view": "default",
            "entity_types": ["call"],
            "filters": [
                {"type": "metric", "metric": "duration", "operator": "gt", "value": 0}
            ],
            "expand": ["conversation"]
        }
        next_page_token: Optional[str] = None
        while True:
            try:
                if next_page_token:
                    response = self.analytics_api.query_conversations_details(
                        query_conversations_details_request=query_body,
                        page_token=next_page_token
                    )
                else:
                    response = self.analytics_api.query_conversations_details(
                        query_conversations_details_request=query_body
                    )
                for entity in response.entities:
                    yield entity
                next_page_token = response.next_page
                if not next_page_token:
                    break
            except ApiException as e:
                if e.status == 429:
                    retry_after = int(e.headers.get("Retry-After", 5))
                    time.sleep(retry_after)
                    continue
                raise

The /api/v2/analytics/conversations/details/query endpoint returns paginated conversation objects. The expand: ["conversation"] parameter instructs the API to include the full transcript payload inside each entity. Pagination relies on the next_page token returned in the response metadata. The loop continues until the token is absent. The 429 Too Many Requests exception triggers an exponential backoff using the Retry-After header value. If the header is missing, the code defaults to a five second delay.

Raw HTTP cycle for this endpoint:

POST /api/v2/analytics/conversations/details/query HTTP/1.1
Host: api.mypurecloud.com
Authorization: Bearer <ACCESS_TOKEN>
Content-Type: application/json

{
  "date_from": "2024-01-01T00:00:00Z",
  "date_to": "2024-01-02T00:00:00Z",
  "size": 100,
  "view": "default",
  "entity_types": ["call"],
  "expand": ["conversation"]
}

Realistic response excerpt:

{
  "entities": [
    {
      "conversationId": "c8f7a9b2-1234-5678-90ab-cdef12345678",
      "conversation": {
        "interactions": [
          {
            "type": "customer",
            "text": "My account number is 867-5309 and my email is test@example.com",
            "startTime": "2024-01-01T10:00:00Z"
          }
        ]
      }
    }
  ],
  "nextPage": "eyJwYWdlIjoyfQ==",
  "pageSize": 100,
  "count": 1
}

Step 2: Execute the PII Regex Pipeline and Apply Redaction Masks

import re
from typing import List, Tuple

class PIISanitizer:
    def __init__(self):
        self.patterns: List[Tuple[re.Pattern, str]] = [
            (re.compile(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b'), "[PHONE:REDACTED]"),
            (re.compile(r'\b\d{3}-\d{2}-\d{4}\b'), "[SSN:REDACTED]"),
            (re.compile(r'\b(?:\d[ -]*?){13,16}\b'), "[CC:REDACTED]"),
            (re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'), "[EMAIL:REDACTED]"),
        ]

    def sanitize_text(self, text: str) -> str:
        sanitized = text
        for pattern, mask in self.patterns:
            sanitized = pattern.sub(mask, sanitized)
        return sanitized

    def sanitize_transcript(self, transcript_lines: List[str]) -> List[str]:
        return [self.sanitize_text(line) for line in transcript_lines]

The pipeline compiles regular expressions once during initialization to avoid repeated compilation overhead during runtime. Pattern order matters because overlapping matches can cause false positives. The Social Security Number pattern runs before the credit card pattern to prevent partial SSN matches from triggering the longer numeric regex. The word boundary \b anchor prevents matching numbers embedded inside larger strings like order IDs or timestamps. The sub method replaces every match with a structured mask that preserves the original text length for downstream tokenization alignment.

Edge case handling requires explicit boundary definitions. Phone numbers with international prefixes or extension markers will bypass the US format regex. You must extend the pattern list with region specific rules if your transcripts span multiple countries. The pipeline architecture allows you to inject custom patterns without modifying the core replacement logic.

Step 3: Forward Sanitized Payloads to the External AI Model

import httpx
from typing import Dict, Any

class AIGateway:
    def __init__(self, api_key: str, endpoint: str = "https://api.openai.com/v1/chat/completions"):
        self.client = httpx.Client(timeout=30.0)
        self.endpoint = endpoint
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }

    def send_to_model(self, sanitized_text: str, system_prompt: str) -> Dict[str, Any]:
        payload = {
            "model": "gpt-4o-mini",
            "messages": [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": sanitized_text}
            ],
            "temperature": 0.2
        }
        for attempt in range(3):
            try:
                response = self.client.post(self.endpoint, json=payload, headers=self.headers)
                if response.status_code == 429:
                    wait_time = int(response.headers.get("Retry-After", 2 ** attempt))
                    time.sleep(wait_time)
                    continue
                response.raise_for_status()
                return response.json()
            except httpx.HTTPStatusError as e:
                if e.response.status_code >= 500:
                    time.sleep(2 ** attempt)
                    continue
                raise
        raise RuntimeError("AI model request failed after maximum retries")

The external AI gateway accepts the sanitized transcript as a user message and pairs it with a system prompt that defines the analysis task. The httpx client enforces a thirty second timeout to prevent hanging connections during model inference. The retry loop handles both 429 Rate Limit and 5xx Server Error responses from the AI provider. The backoff strategy doubles the wait time on each attempt, starting at one second. The temperature parameter is set to 0.2 to minimize hallucination during structured analysis tasks.

Complete Working Example

import os
import time
import httpx
from typing import Optional, Generator, Dict, Any, List
from purecloud_platform_client import PureCloudPlatformClientV2
from purecloud_platform_client.rest import ApiException
import re

class GenesysAuthManager:
    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.rstrip("/")
        self.token_url = f"{self.base_url}/oauth/token"
        self.access_token: Optional[str] = None
        self.expires_at: float = 0
        self.http_client = httpx.Client()

    def get_token(self) -> str:
        if self.access_token and time.time() < self.expires_at - 60:
            return self.access_token
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "analytics:query:read conversation:read"
        }
        response = self.http_client.post(self.token_url, data=payload)
        response.raise_for_status()
        data = response.json()
        self.access_token = data["access_token"]
        self.expires_at = time.time() + data["expires_in"]
        return self.access_token

class TranscriptFetcher:
    def __init__(self, platform_client: PureCloudPlatformClientV2):
        self.client = platform_client
        self.analytics_api = platform_client.AnalyticsApi()

    def fetch_transcripts(self, start_date: str, end_date: str) -> Generator[dict, None, None]:
        query_body = {
            "date_from": start_date,
            "date_to": end_date,
            "size": 100,
            "view": "default",
            "entity_types": ["call"],
            "filters": [{"type": "metric", "metric": "duration", "operator": "gt", "value": 0}],
            "expand": ["conversation"]
        }
        next_page_token: Optional[str] = None
        while True:
            try:
                if next_page_token:
                    response = self.analytics_api.query_conversations_details(
                        query_conversations_details_request=query_body, page_token=next_page_token
                    )
                else:
                    response = self.analytics_api.query_conversations_details(
                        query_conversations_details_request=query_body
                    )
                for entity in response.entities:
                    yield entity
                next_page_token = response.next_page
                if not next_page_token:
                    break
            except ApiException as e:
                if e.status == 429:
                    time.sleep(int(e.headers.get("Retry-After", 5)))
                    continue
                raise

class PIISanitizer:
    def __init__(self):
        self.patterns = [
            (re.compile(r'\b\d{3}[-.]?\d{3}[-.]?\d{4}\b'), "[PHONE:REDACTED]"),
            (re.compile(r'\b\d{3}-\d{2}-\d{4}\b'), "[SSN:REDACTED]"),
            (re.compile(r'\b(?:\d[ -]*?){13,16}\b'), "[CC:REDACTED]"),
            (re.compile(r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'), "[EMAIL:REDACTED]"),
        ]

    def sanitize_text(self, text: str) -> str:
        sanitized = text
        for pattern, mask in self.patterns:
            sanitized = pattern.sub(mask, sanitized)
        return sanitized

    def sanitize_transcript(self, transcript_lines: List[str]) -> List[str]:
        return [self.sanitize_text(line) for line in transcript_lines]

class AIGateway:
    def __init__(self, api_key: str, endpoint: str = "https://api.openai.com/v1/chat/completions"):
        self.client = httpx.Client(timeout=30.0)
        self.endpoint = endpoint
        self.headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}

    def send_to_model(self, sanitized_text: str, system_prompt: str) -> Dict[str, Any]:
        payload = {
            "model": "gpt-4o-mini",
            "messages": [
                {"role": "system", "content": system_prompt},
                {"role": "user", "content": sanitized_text}
            ],
            "temperature": 0.2
        }
        for attempt in range(3):
            try:
                response = self.client.post(self.endpoint, json=payload, headers=self.headers)
                if response.status_code == 429:
                    time.sleep(int(response.headers.get("Retry-After", 2 ** attempt)))
                    continue
                response.raise_for_status()
                return response.json()
            except httpx.HTTPStatusError as e:
                if e.response.status_code >= 500:
                    time.sleep(2 ** attempt)
                    continue
                raise
        raise RuntimeError("AI model request failed after maximum retries")

if __name__ == "__main__":
    auth = GenesysAuthManager(
        client_id=os.getenv("GENESYS_CLIENT_ID"),
        client_secret=os.getenv("GENESYS_CLIENT_SECRET")
    )
    token = auth.get_token()
    
    client = PureCloudPlatformClientV2.create_client(env="US")
    client.set_auth(access_token=token)
    
    fetcher = TranscriptFetcher(client)
    sanitizer = PIISanitizer()
    ai_gateway = AIGateway(api_key=os.getenv("OPENAI_API_KEY"))
    
    system_prompt = "Analyze the following sanitized customer support transcript and extract the primary issue, sentiment, and suggested resolution."
    
    for conversation in fetcher.fetch_transcripts("2024-01-01T00:00:00Z", "2024-01-02T00:00:00Z"):
        interactions = conversation.conversation.interactions if conversation.conversation and conversation.conversation.interactions else []
        raw_text = "\n".join([f"{int.type}: {int.text}" for int in interactions if int.type in ("agent", "customer") and int.text])
        sanitized_lines = sanitizer.sanitize_transcript(raw_text.split("\n"))
        sanitized_text = "\n".join(sanitized_lines)
        
        try:
            result = ai_gateway.send_to_model(sanitized_text, system_prompt)
            print(f"Conversation {conversation.conversationId}: {result['choices'][0]['message']['content']}")
        except Exception as e:
            print(f"Failed to process conversation {conversation.conversationId}: {e}")

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token is expired, malformed, or the client credentials are incorrect.
• Fix: Verify that GENESYS_CLIENT_ID and GENESYS_CLIENT_SECRET match the registered application in Genesys Cloud. Ensure the token refresh logic runs before each batch of API calls.
• Code fix: Add explicit token validation before SDK initialization. The GenesysAuthManager class already implements expiration checking.

Error: 403 Forbidden

• Cause: The OAuth token lacks the required scopes.
• Fix: Update the application settings in Genesys Cloud to include analytics:query:read and conversation:read. Re generate the token after scope modification.
• Code fix: The payload in get_token explicitly requests both scopes. Verify the scope string matches exactly.

Error: 429 Too Many Requests

• Cause: Genesys Cloud or the AI provider has enforced a rate limit on your application.
• Fix: Implement exponential backoff using the Retry-After header. Never retry immediately.
• Code fix: Both TranscriptFetcher and AIGateway include 429 handling with header driven delays.

Error: 5xx Internal Server Error

• Cause: Temporary backend failure in Genesys Cloud or the AI inference cluster.
• Fix: Retry with increasing delays. Do not treat 5xx responses as permanent failures.
• Code fix: The retry loops in both fetcher and gateway include 5xx detection with 2 ** attempt backoff.

Error: Regex False Positives

• Cause: Overlapping numeric patterns match order numbers, timestamps, or internal IDs.
• Fix: Run stricter patterns before broader ones. Use word boundaries \b and negative lookbehinds if necessary.
• Code fix: The PIISanitizer orders SSN before credit card and phone before email. Add (?<!\d) lookbehind assertions if your data contains adjacent numeric sequences.

Official References

• Genesys Cloud Analytics Conversations API
• Genesys Cloud Python SDK Documentation
• Genesys Cloud OAuth 2.0 Authentication
• OpenAI API Reference