Indexing External PDF Documents to Genesys Cloud Knowledge via Metadata Extraction and the Articles API

What You Will Build

• A Python script that reads a local PDF file, extracts raw text and document metadata, transforms that metadata into structured search facets, and publishes the content as a searchable article in Genesys Cloud Knowledge.
• This implementation uses the Genesys Cloud Knowledge Articles API (/api/v2/knowledge/articles) for document ingestion.
• The tutorial covers Python 3.9+ using requests for HTTP transport and PyPDF2 for document parsing.

Prerequisites

• Genesys Cloud OAuth 2.0 Client Credentials grant configured with knowledge:article:write and knowledge:article:read scopes.
• Genesys Cloud API v2 runtime environment.
• Python 3.9 or higher installed on your development machine.
• External dependencies installed via pip: requests, PyPDF2, typing-extensions.
• A target Knowledge Space ID if you intend to bind the article to a specific space. The API allows spaceless articles, but space binding is recommended for access control and routing.

Authentication Setup

Genesys Cloud uses standard OAuth 2.0 Client Credentials flow for machine-to-machine API access. The authentication endpoint returns a bearer token that expires after a fixed duration. Production integrations must cache tokens and refresh them before expiration to avoid unnecessary network overhead and potential authentication failures during batch operations.

The following function handles token retrieval, TTL caching, and scope validation. It uses requests to post credentials to the /oauth/token endpoint.

import requests
import time
from typing import Optional, Dict

class GenesysAuth:
    def __init__(self, org_domain: str, client_id: str, client_secret: str, scopes: list[str]):
        self.org_domain = org_domain
        self.client_id = client_id
        self.client_secret = client_secret
        self.scopes = scopes
        self.token_url = f"https://{org_domain}.mypurecloud.com/oauth/token"
        self._token: Optional[str] = None
        self._expires_at: float = 0.0

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

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self._client_secret,
            "scope": " ".join(self.scopes)
        }

        response = requests.post(self.token_url, data=payload)
        response.raise_for_status()

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

    @property
    def _client_secret(self) -> str:
        return self.client_secret

The scope parameter must explicitly include knowledge:article:write. Genesys Cloud enforces scope binding at the token level. If you request only read scopes, the Knowledge API will return a 403 Forbidden response on POST requests. The TTL buffer of 60 seconds prevents edge-case expiration during long-running PDF parsing operations.

Implementation

Step 1: Extract PDF Metadata and Text Content

PDF documents store metadata in the document information dictionary. PyPDF2 exposes this dictionary through the reader.metadata object. The Knowledge API requires a title, body content, and language. We extract the title from metadata, fallback to a generated title if missing, and concatenate all page text into a single string.

Genesys Cloud Knowledge stores article bodies as HTML. We convert plain text newlines to HTML line breaks to preserve document structure. This design choice exists because Knowledge agents and end-users view articles through a WYSIWYG editor that expects HTML markup.

from PyPDF2 import PdfReader
from typing import Dict, Any

def extract_pdf_content(file_path: str) -> Dict[str, Any]:
    reader = PdfReader(file_path)
    
    # Extract metadata with safe fallbacks
    meta = reader.metadata
    title = meta.title or "Untitled Document"
    author = meta.author or "Unknown Author"
    keywords_raw = meta.keywords or ""
    
    # Extract and concatenate page text
    page_texts = []
    for page in reader.pages:
        text = page.extract_text()
        if text:
            page_texts.append(text.strip())
            
    raw_content = "\n".join(page_texts)
    
    # Convert to HTML for Knowledge API compatibility
    html_body = raw_content.replace("\n", "<br>")
    
    return {
        "title": title,
        "author": author,
        "keywords": keywords_raw,
        "body": html_body,
        "page_count": len(reader.pages)
    }

The extract_text() method may return empty strings for scanned PDFs or documents with embedded fonts that lack mapping tables. Production pipelines should include a fallback OCR step or validation check if raw_content length falls below a threshold.

Step 2: Map Metadata to Knowledge Facets

Genesys Cloud Knowledge uses facets for filtering, routing, and access control. Facets are key-value pairs where the value is always an array of strings. The API design enforces arrays because a single article can match multiple facet values (for example, a document can belong to multiple product lines).

We transform the extracted keywords into a structured facet object. The script also adds a document_type facet to categorize the ingestion source.

from typing import Dict, List

def build_knowledge_facets(metadata: Dict[str, Any]) -> Dict[str, List[str]]:
    # Split comma-separated keywords into individual facet values
    keywords = [k.strip() for k in metadata["keywords"].split(",") if k.strip()]
    
    facets: Dict[str, List[str]] = {
        "document_type": ["external_pdf"],
        "author": [metadata["author"]],
        "keywords": keywords if keywords else ["uncategorized"]
    }
    
    # Remove empty arrays to prevent API validation errors
    return {k: v for k, v in facets.items() if v}

The Knowledge API validates facet keys against the tenant configuration. If your organization enforces strict facet schemas, you must predefine these keys in the Genesys Cloud Admin Console under Knowledge Center > Facets. If the tenant allows ad-hoc facets, the API will create them automatically on first use.

Step 3: Publish the Article via the Knowledge API

The final step posts the structured payload to /api/v2/knowledge/articles. The endpoint returns a 201 Created response with the article ID and revision history. We implement exponential backoff retry logic for 429 Too Many Requests responses. Genesys Cloud enforces rate limits per OAuth client, and batch PDF ingestion can easily trigger throttling.

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

def publish_article(auth: GenesysAuth, payload: Dict[str, Any], space_id: Optional[str] = None) -> Dict[str, Any]:
    base_url = f"https://{auth.org_domain}.mypurecloud.com"
    endpoint = "/api/v2/knowledge/articles"
    
    headers = {
        "Authorization": f"Bearer {auth.get_token()}",
        "Content-Type": "application/json"
    }
    
    # Attach space ID if provided
    if space_id:
        payload["spaceId"] = space_id
        
    max_retries = 3
    retry_count = 0
    
    while retry_count <= max_retries:
        response = requests.post(f"{base_url}{endpoint}", json=payload, headers=headers)
        
        if response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 2 ** retry_count))
            print(f"Rate limited. Retrying in {retry_after} seconds...")
            time.sleep(retry_after)
            retry_count += 1
            continue
            
        response.raise_for_status()
        return response.json()
        
    raise RuntimeError("Max retries exceeded for article publication.")

The status field in the payload defaults to draft if omitted. We explicitly set status to published to make the article immediately searchable. The API requires language to match a locale supported by your Genesys Cloud tenant. Mismatched language codes return a 400 Bad Request response.

Complete Working Example

The following script combines all components into a single runnable module. Replace the placeholder credentials and file path before execution.

import os
import sys
import requests
import time
from typing import Dict, Any, List, Optional
from PyPDF2 import PdfReader

class GenesysAuth:
    def __init__(self, org_domain: str, client_id: str, client_secret: str, scopes: list[str]):
        self.org_domain = org_domain
        self.client_id = client_id
        self.client_secret = client_secret
        self.scopes = scopes
        self.token_url = f"https://{org_domain}.mypurecloud.com/oauth/token"
        self._token: Optional[str] = None
        self._expires_at: float = 0.0

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

        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": " ".join(self.scopes)
        }

        response = requests.post(self.token_url, data=payload)
        response.raise_for_status()

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

def extract_pdf_content(file_path: str) -> Dict[str, Any]:
    reader = PdfReader(file_path)
    meta = reader.metadata
    title = meta.title or "Untitled Document"
    author = meta.author or "Unknown Author"
    keywords_raw = meta.keywords or ""
    
    page_texts = []
    for page in reader.pages:
        text = page.extract_text()
        if text:
            page_texts.append(text.strip())
            
    raw_content = "\n".join(page_texts)
    html_body = raw_content.replace("\n", "<br>")
    
    return {
        "title": title,
        "author": author,
        "keywords": keywords_raw,
        "body": html_body,
        "page_count": len(reader.pages)
    }

def build_knowledge_facets(metadata: Dict[str, Any]) -> Dict[str, List[str]]:
    keywords = [k.strip() for k in metadata["keywords"].split(",") if k.strip()]
    facets: Dict[str, List[str]] = {
        "document_type": ["external_pdf"],
        "author": [metadata["author"]],
        "keywords": keywords if keywords else ["uncategorized"]
    }
    return {k: v for k, v in facets.items() if v}

def publish_article(auth: GenesysAuth, payload: Dict[str, Any], space_id: Optional[str] = None) -> Dict[str, Any]:
    base_url = f"https://{auth.org_domain}.mypurecloud.com"
    endpoint = "/api/v2/knowledge/articles"
    
    headers = {
        "Authorization": f"Bearer {auth.get_token()}",
        "Content-Type": "application/json"
    }
    
    if space_id:
        payload["spaceId"] = space_id
        
    max_retries = 3
    retry_count = 0
    
    while retry_count <= max_retries:
        response = requests.post(f"{base_url}{endpoint}", json=payload, headers=headers)
        
        if response.status_code == 429:
            retry_after = int(response.headers.get("Retry-After", 2 ** retry_count))
            print(f"Rate limited. Retrying in {retry_after} seconds...")
            time.sleep(retry_after)
            retry_count += 1
            continue
            
        response.raise_for_status()
        return response.json()
        
    raise RuntimeError("Max retries exceeded for article publication.")

def main():
    # Configuration
    ORG_DOMAIN = "your-org-domain"
    CLIENT_ID = "your-client-id"
    CLIENT_SECRET = "your-client-secret"
    PDF_PATH = "sample_document.pdf"
    SPACE_ID = None  # Optional: "your-space-id"
    
    auth = GenesysAuth(ORG_DOMAIN, CLIENT_ID, CLIENT_SECRET, ["knowledge:article:write"])
    
    if not os.path.exists(PDF_PATH):
        print(f"Error: PDF file not found at {PDF_PATH}")
        sys.exit(1)
        
    print("Extracting PDF metadata and content...")
    pdf_data = extract_pdf_content(PDF_PATH)
    
    print("Mapping metadata to Knowledge facets...")
    facets = build_knowledge_facets(pdf_data)
    
    article_payload = {
        "title": pdf_data["title"],
        "body": pdf_data["body"],
        "language": "en",
        "status": "published",
        "facets": facets
    }
    
    print("Publishing to Genesys Cloud Knowledge...")
    try:
        result = publish_article(auth, article_payload, space_id)
        print(f"Success. Article ID: {result['id']}")
        print(f"Revision: {result['version']}")
    except requests.exceptions.HTTPError as e:
        print(f"API Error: {e.response.status_code} - {e.response.text}")
    except Exception as e:
        print(f"Unexpected error: {e}")

if __name__ == "__main__":
    main()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The OAuth token is expired, malformed, or the client credentials are incorrect.
• Fix: Verify that client_id and client_secret match an active application in Genesys Cloud. Ensure the token caching logic does not serve expired tokens. Check that the Authorization header uses the Bearer prefix.
• Code Fix: The GenesysAuth class includes a 60-second TTL buffer. If you still receive 401 responses, force a token refresh by setting self._expires_at = 0.0 before calling get_token().

Error: 403 Forbidden

• Cause: The OAuth token lacks the knowledge:article:write scope, or the application is restricted by IP allowlisting.
• Fix: Navigate to Genesys Cloud Admin > Platform > Applications > OAuth 2.0 Applications. Verify that the client credentials grant includes the correct scope. Check that your server IP is added to the application allowlist if enabled.
• Code Fix: Update the scopes list in GenesysAuth initialization to explicitly include knowledge:article:write.

Error: 400 Bad Request

• Cause: Invalid facet structure, missing required fields, or unsupported language code.
• Fix: The Knowledge API requires title, body, language, and status. Facet values must be arrays of strings. Language codes must match ISO 639-1 standards supported by your tenant.
• Code Fix: Validate article_payload before posting. Ensure facets values are lists. Use print(json.dumps(article_payload, indent=2)) to inspect the exact JSON sent to the API.

Error: 429 Too Many Requests

• Cause: The OAuth client exceeded the rate limit for the Knowledge API. Batch PDF ingestion often triggers this.
• Fix: Implement exponential backoff. The publish_article function includes a retry loop that reads the Retry-After header or falls back to 2 ** retry_count seconds.
• Code Fix: Increase max_retries or add a fixed delay between batch iterations. Genesys Cloud resets rate limit windows per OAuth client, not per endpoint.

Error: PyPDF2.errors.PdfReadError or Empty Content

• Cause: The PDF is password-protected, encrypted, or uses unsupported compression.
• Fix: PyPDF2 cannot parse encrypted documents without the password. Scanned images lack text layers.
• Code Fix: Wrap PdfReader(file_path) in a try-except block. If len(page_texts) == 0, route the file to an OCR service before ingestion.

Official References

• Genesys Cloud Knowledge Articles API Documentation
• Genesys Cloud OAuth 2.0 Client Credentials Flow
• PyPDF2 Documentation
• Genesys Cloud Knowledge Center Facets Guide