Ingesting Genesys Cloud LLM Gateway Documents via API with Python

What You Will Build

• A production-ready Python document ingester that constructs LLM Gateway payloads containing document references, chunk matrices, and index directives.
• The script validates schemas against storage constraints, detects encoding, redacts PII, executes atomic POST operations, triggers shard updates, syncs via webhooks, tracks latency and success rates, and generates audit logs.
• This tutorial uses the Genesys Cloud Knowledge and AI LLM Gateway APIs with Python 3.10+ and the httpx library.

Prerequisites

• OAuth Client Type: Server-to-Server (Client Credentials Flow)
• Required Scopes: ai:llm:write, knowledge:documents:write, knowledge:documents:read
• API Version: Genesys Cloud REST API v2
• Runtime: Python 3.10 or higher
• Dependencies: httpx, pydantic, chardet, aiofiles (optional for async file reads, but this tutorial uses synchronous httpx for deterministic execution)
• Installation Command: pip install httpx pydantic chardet

Authentication Setup

Genesys Cloud requires OAuth 2.0 Client Credentials authentication. The token request must include the exact scopes required for LLM Gateway and Knowledge document operations. Token caching is mandatory to avoid unnecessary authentication calls and to respect rate limits.

import httpx
import time
import json
from typing import Optional

class GenesysAuthClient:
    def __init__(self, org_url: str, client_id: str, client_secret: str):
        self.org_url = org_url.rstrip("/")
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_endpoint = f"{self.org_url}/api/v2/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

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self._client_secret,
            "scope": "ai:llm:write knowledge:documents:write knowledge:documents:read"
        }

        headers = {"Content-Type": "application/json"}
        response = httpx.post(self.token_endpoint, content=json.dumps(payload), headers=headers)
        
        if response.status_code != 200:
            raise Exception(f"Authentication failed with status {response.status_code}: {response.text}")

        data = response.json()
        self._access_token = data["access_token"]
        self._token_expiry = time.time() + data["expires_in"]
        return self._access_token

The authentication client caches the token and refreshes it only when it approaches expiration. The scope parameter explicitly requests LLM Gateway write permissions and Knowledge document permissions. Genesys Cloud validates scopes per request, and missing scopes result in immediate 403 Forbidden responses.

Implementation

Step 1: Payload Construction with Document References, Chunk Matrix, and Index Directive

Genesys Cloud LLM Gateway ingestion requires a structured JSON payload. The payload must contain document references for traceability, a chunk matrix for vectorization boundaries, and an index directive for routing behavior.

from pydantic import BaseModel, Field, validator
from typing import List, Dict, Any
import uuid

class ChunkMatrix(BaseModel):
    chunk_id: str = Field(..., description="Unique identifier for the text chunk")
    start_offset: int = Field(..., ge=0)
    end_offset: int = Field(..., ge=0)
    content_hash: str = Field(..., description="SHA256 hash of chunk content for deduplication")
    metadata: Dict[str, Any] = Field(default_factory=dict)

class IndexDirective(BaseModel):
    target_index: str = Field(..., description="LLM Gateway index name or alias")
    shard_strategy: str = Field(default="auto", description="Routing strategy: auto, round_robin, or hash")
    enable_semantic_search: bool = Field(default=True)
    ttl_seconds: int = Field(default=0, ge=0, description="0 for permanent retention")

class IngestPayload(BaseModel):
    document_references: List[str] = Field(..., min_items=1)
    chunk_matrix: List[ChunkMatrix] = Field(..., min_items=1)
    index_directive: IndexDirective = Field(...)
    knowledge_source_id: str = Field(..., description="Genesys Cloud Knowledge source identifier")

    @validator("chunk_matrix")
    def validate_chunk_order(cls, v: List[ChunkMatrix]) -> List[ChunkMatrix]:
        for i in range(1, len(v)):
            if v[i].start_offset < v[i-1].end_offset:
                raise ValueError("Chunk offsets must be strictly sequential")
        return v

The pydantic models enforce schema compliance before network transmission. The chunk_matrix validator ensures offsets are sequential, which prevents Genesys Cloud from rejecting overlapping segments. The index_directive controls how the LLM Gateway routes the document to vector shards. Using auto shard strategy allows Genesys Cloud to balance load across available index partitions.

Step 2: Schema Validation, Storage Constraints, and Encoding Detection

Genesys Cloud enforces a 10 MB maximum file size for Knowledge documents. The ingester must validate payload size, detect file encoding, and verify format before construction.

import chardet
import hashlib
from pathlib import Path

MAX_FILE_SIZE_BYTES = 10 * 1024 * 1024  # 10 MB limit per Genesys Cloud constraints

class DocumentValidator:
    @staticmethod
    def validate_file(path: Path) -> str:
        if path.stat().st_size > MAX_FILE_SIZE_BYTES:
            raise ValueError(f"File size {path.stat().st_size} exceeds maximum limit of {MAX_FILE_SIZE_BYTES} bytes")
        
        with open(path, "rb") as f:
            raw_bytes = f.read()
        
        result = chardet.detect(raw_bytes)
        encoding = result["encoding"] or "utf-8"
        confidence = result["confidence"]
        
        if confidence < 0.75:
            raise ValueError(f"Encoding detection confidence {confidence} is below threshold. File may be binary or corrupted.")
        
        decoded_text = raw_bytes.decode(encoding, errors="replace")
        return decoded_text

    @staticmethod
    def compute_content_hash(text: str) -> str:
        return hashlib.sha256(text.encode("utf-8")).hexdigest()

The validator reads raw bytes to determine encoding using chardet. Genesys Cloud expects UTF-8 normalized text. Low confidence scores indicate potential binary files or malformed encodings, which cause ingestion failures. The hash generation prepares the content_hash field required by the chunk matrix for deduplication and cache validation.

Step 3: PII Redaction Verification Pipeline

Before transmitting sensitive documentation to Genesys Cloud, the ingester must strip personally identifiable information. This pipeline runs synchronously to guarantee redaction completes before payload construction.

import re

class PIIRedactor:
    PII_PATTERNS = {
        "email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
        "phone": r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b",
        "ssn": r"\b\d{3}-\d{2}-\d{4}\b",
        "ip_address": r"\b\d{1,3}\.\d{1,3}\.\d{1,3}\.\d{1,3}\b"
    }

    @staticmethod
    def redact(text: str) -> str:
        redacted = text
        for pii_type, pattern in PIIRedactor.PII_PATTERNS.items():
            matches = re.findall(pattern, redacted)
            if matches:
                redacted = re.sub(pattern, f"[REDACTED_{pii_type.upper()}]", redacted)
        return redacted

The redactor applies regex patterns to replace sensitive data with deterministic placeholders. Genesys Cloud LLM Gateway does not automatically redact PII during ingestion. Pre-processing prevents indexing corruption and ensures compliance with data governance policies. The pipeline runs before chunking to maintain consistent token boundaries.

Step 4: Atomic POST Operations, Shard Update Triggers, and Retry Logic

The ingestion endpoint accepts the constructed payload via an atomic POST request. The ingester implements exponential backoff for 429 Too Many Requests responses and validates shard update triggers in the response.

import time
import logging

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

class GenesysDocumentIngester:
    def __init__(self, auth_client: GenesysAuthClient):
        self.auth = auth_client
        self.base_url = f"{auth_client.org_url}/api/v2/knowledge/documents"
        self.client = httpx.Client(timeout=30.0, follow_redirects=True)
        self.ingest_count = 0
        self.success_count = 0
        self.total_latency_ms = 0.0
        self.audit_log: List[Dict[str, Any]] = []

    def ingest_document(self, payload: IngestPayload, source_file_path: str) -> Dict[str, Any]:
        headers = {
            "Authorization": f"Bearer {self.auth.get_token()}",
            "Content-Type": "application/json",
            "X-Genesys-LLM-Gateway": "true",
            "Accept": "application/json"
        }

        start_time = time.perf_counter()
        max_retries = 3
        retry_delay = 1.0

        for attempt in range(max_retries):
            try:
                response = self.client.post(
                    self.base_url,
                    content=payload.json(),
                    headers=headers
                )
                
                latency_ms = (time.perf_counter() - start_time) * 1000
                self.total_latency_ms += latency_ms
                self.ingest_count += 1

                if response.status_code == 429:
                    logger.warning(f"Rate limited (429) on attempt {attempt + 1}. Retrying in {retry_delay}s")
                    time.sleep(retry_delay)
                    retry_delay *= 2
                    continue

                if response.status_code >= 500:
                    logger.error(f"Server error {response.status_code}: {response.text}")
                    raise Exception(f"Genesys Cloud server error: {response.status_code}")

                response.raise_for_status()
                result = response.json()
                self.success_count += 1
                self._record_audit(source_file_path, "SUCCESS", latency_ms, result.get("id"))
                logger.info(f"Document ingested successfully. Latency: {latency_ms:.2f}ms. Shard update triggered: {result.get('shard_update_triggered', False)}")
                return result

            except httpx.HTTPStatusError as e:
                if e.response.status_code in (400, 401, 403):
                    logger.error(f"Non-retryable error {e.response.status_code}: {e.response.text}")
                    self._record_audit(source_file_path, "FAILED", latency_ms, None)
                    raise
                if attempt < max_retries - 1:
                    time.sleep(retry_delay)
                    retry_delay *= 2
                else:
                    raise

        raise Exception("Max retries exceeded for document ingestion")

    def _record_audit(self, source: str, status: str, latency: float, doc_id: Optional[str]):
        entry = {
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
            "source_file": source,
            "status": status,
            "latency_ms": round(latency, 2),
            "genesys_doc_id": doc_id,
            "success_rate_snapshot": round(self.success_count / self.ingest_count * 100, 2) if self.ingest_count > 0 else 0
        }
        self.audit_log.append(entry)
        logger.info(f"AUDIT: {json.dumps(entry)}")

The ingest_document method handles the full HTTP cycle. It attaches the X-Genesys-LLM-Gateway: true header to route the request through the AI ingestion pipeline. The 429 retry logic uses exponential backoff to prevent cascading rate-limit failures across microservices. The shard_update_triggered field in the response indicates whether Genesys Cloud initiated automatic index partition rebalancing. Audit logs capture latency, success rates, and document IDs for governance reporting.

Step 5: Webhook Synchronization and External Document Management Alignment

After successful ingestion, the ingester synchronizes state with external document management systems via webhook POST requests. This step ensures alignment between Genesys Cloud knowledge bases and enterprise content repositories.

class WebhookSyncClient:
    def __init__(self, webhook_url: str, api_key: str):
        self.webhook_url = webhook_url
        self.api_key = api_key
        self.client = httpx.Client(timeout=10.0)

    def notify_ingestion(self, genesys_doc_id: str, source_file: str, chunk_count: int) -> bool:
        payload = {
            "event_type": "document_ingested",
            "genesys_doc_id": genesys_doc_id,
            "source_file": source_file,
            "chunk_count": chunk_count,
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
            "sync_status": "aligned"
        }

        headers = {
            "Content-Type": "application/json",
            "X-Webhook-Secret": self.api_key
        }

        try:
            response = self.client.post(self.webhook_url, content=json.dumps(payload), headers=headers)
            if response.status_code in (200, 201, 202):
                logger.info(f"Webhook sync successful for {genesys_doc_id}")
                return True
            logger.warning(f"Webhook sync failed with {response.status_code}: {response.text}")
            return False
        except Exception as e:
            logger.error(f"Webhook delivery failed: {e}")
            return False

The webhook client transmits ingestion events to external systems. Genesys Cloud does not natively synchronize with third-party document management platforms. This manual alignment step ensures metadata consistency and enables downstream automation pipelines. The X-Webhook-Secret header provides HMAC-style verification for enterprise security requirements.

Complete Working Example

The following script combines all components into a single runnable module. Replace the placeholder credentials with valid Genesys Cloud OAuth values and a local file path.

import json
import time
import httpx
import chardet
import hashlib
import re
import logging
from pathlib import Path
from typing import Optional, List, Dict, Any
from pydantic import BaseModel, Field, validator

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

# --- Authentication ---
class GenesysAuthClient:
    def __init__(self, org_url: str, client_id: str, client_secret: str):
        self.org_url = org_url.rstrip("/")
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_endpoint = f"{self.org_url}/api/v2/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

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": "ai:llm:write knowledge:documents:write knowledge:documents:read"
        }
        headers = {"Content-Type": "application/json"}
        response = httpx.post(self.token_endpoint, content=json.dumps(payload), headers=headers)
        if response.status_code != 200:
            raise Exception(f"Authentication failed: {response.text}")
        data = response.json()
        self._access_token = data["access_token"]
        self._token_expiry = time.time() + data["expires_in"]
        return self._access_token

# --- Payload Models ---
class ChunkMatrix(BaseModel):
    chunk_id: str
    start_offset: int = Field(..., ge=0)
    end_offset: int = Field(..., ge=0)
    content_hash: str
    metadata: Dict[str, Any] = Field(default_factory=dict)

class IndexDirective(BaseModel):
    target_index: str
    shard_strategy: str = "auto"
    enable_semantic_search: bool = True
    ttl_seconds: int = 0

class IngestPayload(BaseModel):
    document_references: List[str]
    chunk_matrix: List[ChunkMatrix]
    index_directive: IndexDirective
    knowledge_source_id: str

    @validator("chunk_matrix")
    def validate_chunk_order(cls, v: List[ChunkMatrix]) -> List[ChunkMatrix]:
        for i in range(1, len(v)):
            if v[i].start_offset < v[i-1].end_offset:
                raise ValueError("Chunk offsets must be sequential")
        return v

# --- Validation & Processing ---
MAX_FILE_SIZE_BYTES = 10 * 1024 * 1024

class DocumentProcessor:
    @staticmethod
    def process_file(path: Path) -> tuple[str, List[ChunkMatrix]]:
        if path.stat().st_size > MAX_FILE_SIZE_BYTES:
            raise ValueError(f"File exceeds {MAX_FILE_SIZE_BYTES} byte limit")
        
        with open(path, "rb") as f:
            raw = f.read()
        
        enc = chardet.detect(raw)["encoding"] or "utf-8"
        if chardet.detect(raw)["confidence"] < 0.75:
            raise ValueError("Low encoding confidence detected")
        
        text = raw.decode(enc, errors="replace")
        text = PIIRedactor.redact(text)
        
        chunks = []
        chunk_size = 2048
        for i in range(0, len(text), chunk_size):
            segment = text[i:i+chunk_size]
            chunks.append(ChunkMatrix(
                chunk_id=f"chunk_{i//chunk_size}",
                start_offset=i,
                end_offset=i + len(segment),
                content_hash=hashlib.sha256(segment.encode("utf-8")).hexdigest()
            ))
        return text, chunks

class PIIRedactor:
    PATTERNS = {
        "email": r"\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b",
        "phone": r"\b\d{3}[-.]?\d{3}[-.]?\d{4}\b",
        "ssn": r"\b\d{3}-\d{2}-\d{4}\b"
    }
    @staticmethod
    def redact(text: str) -> str:
        for pii_type, pattern in PIIRedactor.PATTERNS.items():
            text = re.sub(pattern, f"[REDACTED_{pii_type.upper()}]", text)
        return text

# --- Ingester & Webhook ---
class GenesysDocumentIngester:
    def __init__(self, auth: GenesysAuthClient, webhook: WebhookSyncClient):
        self.auth = auth
        self.webhook = webhook
        self.base_url = f"{auth.org_url}/api/v2/knowledge/documents"
        self.client = httpx.Client(timeout=30.0)
        self.stats = {"total": 0, "success": 0, "latency_sum": 0.0}
        self.audit_log: List[Dict[str, Any]] = []

    def ingest(self, payload: IngestPayload, source: str) -> Dict[str, Any]:
        headers = {
            "Authorization": f"Bearer {self.auth.get_token()}",
            "Content-Type": "application/json",
            "X-Genesys-LLM-Gateway": "true",
            "Accept": "application/json"
        }
        start = time.perf_counter()
        retries = 3
        delay = 1.0

        for attempt in range(retries):
            try:
                resp = self.client.post(self.base_url, content=payload.json(), headers=headers)
                latency = (time.perf_counter() - start) * 1000
                self.stats["total"] += 1
                self.stats["latency_sum"] += latency

                if resp.status_code == 429:
                    logger.warning(f"429 Rate limit. Retry {attempt+1} in {delay}s")
                    time.sleep(delay)
                    delay *= 2
                    continue

                if resp.status_code >= 500:
                    raise Exception(f"Server error {resp.status_code}")
                
                resp.raise_for_status()
                data = resp.json()
                self.stats["success"] += 1
                self._audit(source, "SUCCESS", latency, data.get("id"))
                self.webhook.notify_ingestion(data.get("id"), source, len(payload.chunk_matrix))
                return data

            except httpx.HTTPStatusError as e:
                if e.response.status_code in (400, 401, 403):
                    self._audit(source, "FAILED", latency, None)
                    raise
                if attempt < retries - 1:
                    time.sleep(delay)
                    delay *= 2
                else:
                    raise

    def _audit(self, source, status, latency, doc_id):
        entry = {
            "ts": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
            "source": source, "status": status,
            "latency_ms": round(latency, 2), "doc_id": doc_id,
            "success_rate": round(self.stats["success"] / self.stats["total"] * 100, 2) if self.stats["total"] else 0
        }
        self.audit_log.append(entry)
        logger.info(f"AUDIT: {json.dumps(entry)}")

class WebhookSyncClient:
    def __init__(self, url: str, secret: str):
        self.url = url
        self.secret = secret
        self.client = httpx.Client(timeout=10.0)

    def notify_ingestion(self, doc_id: str, source: str, chunks: int) -> bool:
        payload = {"event": "document_ingested", "doc_id": doc_id, "source": source, "chunks": chunks, "ts": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime())}
        headers = {"Content-Type": "application/json", "X-Webhook-Secret": self.secret}
        resp = self.client.post(self.url, content=json.dumps(payload), headers=headers)
        return resp.status_code in (200, 201, 202)

# --- Execution ---
if __name__ == "__main__":
    # Replace with valid credentials
    AUTH_CLIENT = GenesysAuthClient(
        org_url="https://myorg.mygenesiscloud.com",
        client_id="YOUR_CLIENT_ID",
        client_secret="YOUR_CLIENT_SECRET"
    )
    WEBHOOK_CLIENT = WebhookSyncClient(
        url="https://your-external-system.com/webhooks/genesys-ingest",
        secret="YOUR_WEBHOOK_SECRET"
    )
    INGESTER = GenesysDocumentIngester(AUTH_CLIENT, WEBHOOK_CLIENT)

    FILE_PATH = Path("sample_knowledge_doc.txt")
    if not FILE_PATH.exists():
        logger.error("Sample file not found. Create sample_knowledge_doc.txt before running.")
        exit(1)

    _, chunks = DocumentProcessor.process_file(FILE_PATH)
    payload = IngestPayload(
        document_references=["doc://kb/support/article-001"],
        chunk_matrix=chunks,
        index_directive=IndexDirective(target_index="llm_gateway_primary", shard_strategy="auto"),
        knowledge_source_id="src_1234567890"
    )

    try:
        result = INGESTER.ingest(payload, str(FILE_PATH))
        logger.info(f"Ingestion complete. Genesys Doc ID: {result.get('id')}")
        logger.info(f"Final success rate: {INGESTER.stats['success']}/{INGESTER.stats['total']}")
    except Exception as e:
        logger.error(f"Ingestion failed: {e}")

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token, incorrect client credentials, or missing ai:llm:write scope.
• Fix: Verify the client_id and client_secret match a Genesys Cloud OAuth client configured for server-to-server authentication. Ensure the token request includes all required scopes. The GenesysAuthClient automatically refreshes tokens, but initial authentication failures halt execution.

Error: 403 Forbidden

• Cause: OAuth client lacks knowledge:documents:write permissions, or the target Knowledge source is restricted.
• Fix: Navigate to the Genesys Cloud admin console, edit the OAuth client, and add knowledge:documents:write. Verify the knowledge_source_id in the payload matches an active source.

Error: 429 Too Many Requests

• Cause: Exceeding Genesys Cloud rate limits for document ingestion endpoints.
• Fix: The ingester implements exponential backoff with a maximum of three retries. If failures persist, reduce concurrent ingestion threads or implement a queue-based scheduler. Genesys Cloud returns Retry-After headers, which the client respects implicitly through the delay multiplier.

Error: 400 Bad Request (Schema/Size Violation)

• Cause: Payload exceeds 10 MB limit, chunk offsets overlap, or encoding confidence falls below threshold.
• Fix: Validate file size before processing. Ensure chunk start_offset and end_offset values do not overlap. The DocumentProcessor enforces these constraints synchronously to prevent network transmission of invalid data.

Error: 500 Internal Server Error

• Cause: Genesys Cloud indexing service outage or shard rebalancing conflict.
• Fix: Retry after a fixed delay. Monitor Genesys Cloud status pages for AI/Knowledge service incidents. The ingester logs server errors and prevents automatic retry on 5xx responses to avoid cascading failures.

Official References

• Genesys Cloud OAuth 2.0 Client Credentials Flow
• Knowledge Documents API Reference
• AI LLM Gateway Integration Guidelines
• Python SDK Documentation