Implementing Token-Aware Context Window Truncation for Genesys Cloud Transcript Data Using a Python Sliding Window Algorithm

What You Will Build

• This tutorial delivers a production-ready Python script that extracts conversation transcripts from Genesys Cloud, applies a token-aware sliding window algorithm to enforce strict LLM context limits, and outputs a properly formatted message array ready for API ingestion.
• This implementation uses the Genesys Cloud Interactions API (/api/v2/interactions/conversations/details/query) and the tiktoken library for precise token counting.
• This tutorial covers Python 3.9+ with requests for HTTP communication and tiktoken for encoding management.

Prerequisites

• OAuth 2.0 Client Credentials flow configured in Genesys Cloud Admin with the interaction:read scope
• Genesys Cloud API version v2
• Python 3.9 or higher
• External dependencies: pip install requests tiktoken

Authentication Setup

Genesys Cloud uses OAuth 2.0 for API authentication. The following implementation caches the access token and automatically refreshes it when the expiry window approaches. The SDK handles this internally, but manual management provides explicit control over token lifecycle and retry boundaries.

import time
import requests
from typing import Optional

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, org_id: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.org_id = org_id
        self.token_url = f"https://{org_id}.mygen.com/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0

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

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        
        response = requests.post(self.token_url, data=payload)
        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

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

Implementation

Step 1: Fetch Transcript Data with Pagination and Retry Logic

The Interactions API returns conversation details in paginated batches. You must handle nextPageUri for complete data retrieval and implement exponential backoff for HTTP 429 rate limit responses. The following function queries a specific conversation ID and aggregates all transcript turns.

import json
import time
from typing import List, Dict, Any

def fetch_conversation_transcript(
    auth: GenesysAuth,
    conversation_id: str,
    max_pages: int = 50
) -> List[Dict[str, Any]]:
    base_url = f"https://{auth.org_id}.mygen.com/api/v2/interactions/conversations/details/query"
    all_turns = []
    page = 1
    
    while page <= max_pages:
        query_payload = {
            "query": f"conversationId:{conversation_id}",
            "pageSize": 250
        }
        
        headers = auth.get_headers()
        
        # Retry logic for 429 Too Many Requests
        retries = 0
        max_retries = 3
        while retries < max_retries:
            response = requests.post(base_url, headers=headers, json=query_payload)
            
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 2 ** retries))
                time.sleep(retry_after)
                retries += 1
                continue
            elif response.status_code == 401:
                raise PermissionError("Invalid or expired OAuth token. Verify client credentials.")
            elif response.status_code == 403:
                raise PermissionError("Missing interaction:read scope. Update OAuth client permissions.")
            else:
                response.raise_for_status()
                break
        
        data = response.json()
        entities = data.get("entities", [])
        
        for entity in entities:
            transcript = entity.get("interactions", {}).get("transcript", [])
            all_turns.extend(transcript)
        
        if not data.get("nextPageUri"):
            break
        page += 1
        
    return all_turns

Expected HTTP Request:

POST /api/v2/interactions/conversations/details/query HTTP/1.1
Host: your-org-id.mygen.com
Authorization: Bearer eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCJ9...
Content-Type: application/json

{
  "query": "conversationId:a1b2c3d4-e5f6-7890-abcd-ef1234567890",
  "pageSize": 250
}

Expected HTTP Response:

{
  "entities": [
    {
      "conversationId": "a1b2c3d4-e5f6-7890-abcd-ef1234567890",
      "interactions": {
        "transcript": [
          {
            "from": {
              "participantId": "cust-98765",
              "participantType": "customer"
            },
            "text": "My shipment is delayed. Can you check status?",
            "timestamp": "2024-05-12T14:30:00.000Z"
          },
          {
            "from": {
              "participantId": "agent-11223",
              "participantType": "agent"
            },
            "text": "I can look into that immediately. Please provide your tracking number.",
            "timestamp": "2024-05-12T14:30:05.000Z"
          }
        ]
      }
    }
  ],
  "nextPageUri": null,
  "pageSize": 250,
  "page": 1,
  "firstPageUri": "/api/v2/interactions/conversations/details/query?page=1",
  "lastPageUri": "/api/v2/interactions/conversations/details/query?page=1"
}

Step 2: Flatten and Normalize Transcript Turns

Genesys Cloud returns transcript turns with nested participant objects. You must map these to a flat structure that separates role, content, and metadata. This normalization step prepares the data for deterministic token counting.

from typing import List, Dict, Any

def normalize_turns(raw_turns: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    normalized = []
    for turn in raw_turns:
        participant_type = turn.get("from", {}).get("participantType", "unknown")
        role = "assistant" if participant_type == "agent" else "user"
        
        normalized.append({
            "role": role,
            "content": turn.get("text", ""),
            "timestamp": turn.get("timestamp", ""),
            "participant_id": turn.get("from", {}).get("participantId", "")
        })
    return normalized

Step 3: Execute Token-Aware Sliding Window Truncation

Large transcripts frequently exceed LLM context windows (typically 4096 or 8192 tokens). A sliding window algorithm preserves the most recent conversational context while discarding older turns. The following implementation tracks token counts dynamically and shifts the window forward when the limit is breached.

import tiktoken
from typing import List, Dict, Any

def apply_sliding_window(
    turns: List[Dict[str, Any]],
    max_tokens: int = 4096,
    encoding_name: str = "cl100k_base"
) -> List[Dict[str, Any]]:
    if not turns:
        return []
        
    encoding = tiktoken.get_encoding(encoding_name)
    window: List[Dict[str, Any]] = []
    current_tokens = 0
    
    for turn in turns:
        # Calculate token cost for the turn content plus role metadata
        content_tokens = len(encoding.encode(turn["content"]))
        role_metadata = f"{turn['role']}: "
        metadata_tokens = len(encoding.encode(role_metadata))
        turn_total = content_tokens + metadata_tokens
        
        # If adding this turn exceeds the limit, slide the window forward
        if current_tokens + turn_total > max_tokens:
            while current_tokens + turn_total > max_tokens and window:
                removed = window.pop(0)
                removed_metadata = f"{removed['role']}: "
                current_tokens -= len(encoding.encode(removed["content"])) + len(encoding.encode(removed_metadata))
        
        # Edge case: single turn exceeds max_tokens
        if current_tokens + turn_total <= max_tokens:
            window.append(turn)
            current_tokens += turn_total
            
    return window

Step 4: Format for LLM Ingestion

LLM providers expect a specific message array structure. The following function wraps the truncated window with a system prompt and validates the final token count against your target model limits.

import tiktoken
from typing import List, Dict, Any

def format_for_llm(
    window_turns: List[Dict[str, Any]],
    system_prompt: str = "You are a helpful customer support assistant analyzing conversation history.",
    encoding_name: str = "cl100k_base"
) -> Dict[str, Any]:
    encoding = tiktoken.get_encoding(encoding_name)
    
    messages = [
        {"role": "system", "content": system_prompt}
    ]
    
    total_tokens = len(encoding.encode(system_prompt))
    
    for turn in window_turns:
        messages.append({
            "role": turn["role"],
            "content": turn["content"]
        })
        total_tokens += len(encoding.encode(turn["content"])) + 2  # +2 for message structure overhead
        
    return {
        "messages": messages,
        "token_count": total_tokens
    }

Complete Working Example

The following script combines all components into a single executable module. Replace the placeholder credentials with your Genesys Cloud service account values before execution.

import time
import requests
import tiktoken
from typing import List, Dict, Any, Optional

class GenesysAuth:
    def __init__(self, client_id: str, client_secret: str, org_id: str):
        self.client_id = client_id
        self.client_secret = client_secret
        self.org_id = org_id
        self.token_url = f"https://{org_id}.mygen.com/oauth/token"
        self.access_token: Optional[str] = None
        self.token_expiry: float = 0

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

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret
        }
        
        response = requests.post(self.token_url, data=payload)
        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

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

def fetch_conversation_transcript(auth: GenesysAuth, conversation_id: str, max_pages: int = 50) -> List[Dict[str, Any]]:
    base_url = f"https://{auth.org_id}.mygen.com/api/v2/interactions/conversations/details/query"
    all_turns = []
    page = 1
    
    while page <= max_pages:
        query_payload = {
            "query": f"conversationId:{conversation_id}",
            "pageSize": 250
        }
        
        headers = auth.get_headers()
        retries = 0
        max_retries = 3
        while retries < max_retries:
            response = requests.post(base_url, headers=headers, json=query_payload)
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 2 ** retries))
                time.sleep(retry_after)
                retries += 1
                continue
            elif response.status_code == 401:
                raise PermissionError("Invalid or expired OAuth token.")
            elif response.status_code == 403:
                raise PermissionError("Missing interaction:read scope.")
            else:
                response.raise_for_status()
                break
        
        data = response.json()
        for entity in data.get("entities", []):
            transcript = entity.get("interactions", {}).get("transcript", [])
            all_turns.extend(transcript)
        
        if not data.get("nextPageUri"):
            break
        page += 1
    return all_turns

def normalize_turns(raw_turns: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
    normalized = []
    for turn in raw_turns:
        participant_type = turn.get("from", {}).get("participantType", "unknown")
        role = "assistant" if participant_type == "agent" else "user"
        normalized.append({
            "role": role,
            "content": turn.get("text", ""),
            "timestamp": turn.get("timestamp", ""),
            "participant_id": turn.get("from", {}).get("participantId", "")
        })
    return normalized

def apply_sliding_window(turns: List[Dict[str, Any]], max_tokens: int = 4096) -> List[Dict[str, Any]]:
    if not turns:
        return []
    encoding = tiktoken.get_encoding("cl100k_base")
    window = []
    current_tokens = 0
    
    for turn in turns:
        content_tokens = len(encoding.encode(turn["content"]))
        metadata_tokens = len(encoding.encode(f"{turn['role']}: "))
        turn_total = content_tokens + metadata_tokens
        
        if current_tokens + turn_total > max_tokens:
            while current_tokens + turn_total > max_tokens and window:
                removed = window.pop(0)
                current_tokens -= len(encoding.encode(removed["content"])) + len(encoding.encode(f"{removed['role']}: "))
        
        if current_tokens + turn_total <= max_tokens:
            window.append(turn)
            current_tokens += turn_total
    return window

def format_for_llm(window_turns: List[Dict[str, Any]], system_prompt: str) -> Dict[str, Any]:
    encoding = tiktoken.get_encoding("cl100k_base")
    messages = [{"role": "system", "content": system_prompt}]
    total_tokens = len(encoding.encode(system_prompt))
    
    for turn in window_turns:
        messages.append({"role": turn["role"], "content": turn["content"]})
        total_tokens += len(encoding.encode(turn["content"])) + 2
        
    return {"messages": messages, "token_count": total_tokens}

if __name__ == "__main__":
    CLIENT_ID = "your_client_id"
    CLIENT_SECRET = "your_client_secret"
    ORG_ID = "your_org_id"
    CONVERSATION_ID = "a1b2c3d4-e5f6-7890-abcd-ef1234567890"
    MAX_CONTEXT_TOKENS = 4096
    
    auth = GenesysAuth(CLIENT_ID, CLIENT_SECRET, ORG_ID)
    raw_turns = fetch_conversation_transcript(auth, CONVERSATION_ID)
    normalized = normalize_turns(raw_turns)
    truncated_window = apply_sliding_window(normalized, MAX_CONTEXT_TOKENS)
    llm_payload = format_for_llm(truncated_window, "Analyze customer sentiment and extract key issues.")
    
    print(f"Original turns: {len(normalized)}")
    print(f"Window turns: {len(truncated_window)}")
    print(f"Final token count: {llm_payload['token_count']}")
    print("LLM Payload ready for ingestion.")

Common Errors & Debugging

Error: HTTP 401 Unauthorized

• What causes it: The OAuth token has expired, the client credentials are incorrect, or the token endpoint URL is malformed.
• How to fix it: Verify the client_id and client_secret match a configured service account in Genesys Admin. Ensure the org_id exactly matches your Genesys Cloud environment identifier. The authentication class automatically refreshes tokens, but initial handshake failures require credential verification.
• Code showing the fix: The GenesysAuth.get_token() method raises a clear PermissionError on 401 responses. Wrap API calls in a try-except block to catch and log credential failures before retrying.

Error: HTTP 403 Forbidden

• What causes it: The OAuth client lacks the required interaction:read scope.
• How to fix it: Navigate to Admin > Security > OAuth Clients. Select your service account and add interaction:read to the granted scopes. Save the configuration and regenerate the client secret if the scope was added after initial creation.
• Code showing the fix: The fetch function explicitly checks for 403 and raises a descriptive exception. Add scope validation logic at startup if you manage multiple API clients.

Error: HTTP 429 Too Many Requests

• What causes it: You have exceeded the Genesys Cloud API rate limits for your organization tier.
• How to fix it: Implement exponential backoff with jitter. The provided fetch_conversation_transcript function reads the Retry-After header and sleeps accordingly. For high-throughput pipelines, distribute queries across multiple service accounts or implement a local queue with rate limiting.
• Code showing the fix: The retry loop inside fetch_conversation_transcript handles 429 responses automatically. Increase max_retries to 5 for production environments with bursty traffic patterns.

Error: tiktoken Encoding Mismatch

• What causes it: Using cl100k_base for models that require p50k_base or r50k_base.
• How to fix it: Match the encoding to your target LLM provider. OpenAI models (GPT-3.5, GPT-4) use cl100k_base. Legacy models use p50k_base. Parameterize the encoding name in apply_sliding_window and format_for_llm to support multi-model deployments.
• Code showing the fix: Pass encoding_name="p50k_base" to both windowing and formatting functions when targeting older model versions.

Official References

• Genesys Cloud Interactions API Documentation
• OAuth 2.0 Client Credentials Flow Guide
• tiktoken Python Package Reference
• Genesys Cloud Rate Limiting Policy