Migrate NICE Cognigy Knowledge Base Articles via REST API with Python

What You Will Build

A Python migration pipeline that constructs, validates, and pushes knowledge base articles to NICE Cognigy using atomic PUT operations while enforcing reference depth limits and markdown integrity. This tutorial uses the NICE Cognigy REST API v1. The implementation covers Python 3.9+ with httpx, pydantic, and markdown.

Prerequisites

• OAuth2 client credentials with cognigy:knowledge:read and cognigy:knowledge:write scopes
• Cognigy API v1 tenant endpoint (https://{tenant}.cognigy.com/api/v1)
• Python 3.9+ runtime
• External dependencies: httpx, pydantic, markdown, python-dotenv, rich
• Install dependencies via: pip install httpx pydantic markdown python-dotenv rich

Authentication Setup

Cognigy requires a Bearer token for all API requests. The token is obtained via the /api/v1/auth/login endpoint or an external OAuth2 provider. You must cache the token and implement automatic refresh logic to avoid 401 interruptions during batch migrations.

import httpx
import time
from typing import Optional
from dotenv import load_dotenv
import os

load_dotenv()

COGNIGY_TENANT = os.getenv("COGNIGY_TENANT")
API_BASE = f"https://{COGNIGY_TENANT}.cognigy.com/api/v1"
CLIENT_ID = os.getenv("COGNIGY_CLIENT_ID")
CLIENT_SECRET = os.getenv("COGNIGY_CLIENT_SECRET")

class CognigyAuthClient:
    def __init__(self) -> None:
        self.base_url = API_BASE
        self.token: Optional[str] = None
        self.token_expiry: float = 0
        self.http = httpx.Client(timeout=30.0)

    def _refresh_token(self) -> str:
        response = self.http.post(
            f"{self.base_url}/auth/login",
            json={
                "clientId": CLIENT_ID,
                "clientSecret": CLIENT_SECRET
            },
            headers={"Content-Type": "application/json"}
        )
        response.raise_for_status()
        data = response.json()
        self.token = data["accessToken"]
        self.token_expiry = time.time() + data["expiresIn"]
        return self.token

    def get_authenticated_client(self) -> httpx.Client:
        if not self.token or time.time() >= self.token_expiry:
            self._refresh_token()
        return httpx.Client(
            headers={"Authorization": f"Bearer {self.token}"},
            timeout=30.0,
            base_url=self.base_url
        )

OAuth Scopes Required: cognigy:knowledge:read, cognigy:knowledge:write

Implementation

Step 1: Construct Migrate Payloads with Article ID References, Taxonomy Matrix, and Version Directive

You must structure the payload to match Cognigy knowledge engine expectations. The payload contains the article content, a taxonomy array for categorization, a version directive for optimistic concurrency, and a list of internal article ID references.

from pydantic import BaseModel, Field
from typing import List, Dict

class ArticleMigratePayload(BaseModel):
    title: str
    content: str
    taxonomy_ids: List[str] = Field(default_factory=list, description="Knowledge taxonomy node IDs")
    references: List[str] = Field(default_factory=list, description="Related Cognigy article IDs")
    version: int = Field(default=1, ge=1, description="Optimistic concurrency version")
    metadata: Dict[str, str] = Field(default_factory=dict, description="Custom key-value pairs")

    def to_cognigy_json(self) -> Dict:
        return {
            "title": self.title,
            "content": self.content,
            "taxonomy": self.taxonomy_ids,
            "references": self.references,
            "version": self.version,
            "metadata": self.metadata
        }

Expected Response Structure: Cognigy returns 200 OK with the updated article object on successful PUT. The response includes the resolved _id, lastModified, and version fields.

Step 2: Validate Migrate Schemas Against Knowledge Engine Constraints and Maximum Reference Depth Limits

Cognigy enforces strict reference graph constraints to prevent circular dependencies and deep traversal loops. You must validate the reference chain against a maximum depth limit before submission. This example implements a breadth-first depth check.

from collections import deque

MAX_REFERENCE_DEPTH = 3

def validate_reference_depth(
    article_id: str,
    references: List[str],
    known_graph: Dict[str, List[str]],
    depth: int = 0
) -> bool:
    if depth >= MAX_REFERENCE_DEPTH:
        return False
    
    visited = set()
    queue = deque(references)
    
    while queue:
        ref_id = queue.popleft()
        if ref_id in visited:
            continue
        visited.add(ref_id)
        
        next_refs = known_graph.get(ref_id, [])
        if not validate_reference_depth(ref_id, next_refs, known_graph, depth + 1):
            return False
            
    return True

Error Handling: If validate_reference_depth returns False, the pipeline raises a ValueError before any HTTP request occurs. This prevents 400 Bad Request responses from the knowledge engine.

Step 3: Handle Content Transformation via Atomic PUT Operations with Format Verification and Automatic Link Resolution Triggers

Content must pass markdown syntax verification before transformation. You must also replace external CMS URLs with Cognigy internal anchor patterns to trigger automatic link resolution. Atomic PUT operations ensure partial failures do not corrupt existing articles.

from markdown import markdown as md_parse
import re
import logging

logger = logging.getLogger(__name__)

def verify_markdown_syntax(content: str) -> bool:
    try:
        md_parse(content, output_format="html")
        return True
    except Exception as e:
        logger.error(f"Markdown syntax error: {e}")
        return False

def resolve_internal_links(content: str, tenant: str) -> str:
    pattern = re.compile(r"\[([^\]]+)\]\((https?://[^)]+)\)")
    def replacer(match):
        text = match.group(1)
        url = match.group(2)
        if tenant in url or "internal" in url.lower():
            return f"[{text}](cognigy://internal/{text})"
        return match.group(0)
    return pattern.sub(replacer, content)

def atomic_put_article(client: httpx.Client, article_id: str, payload: Dict) -> Dict:
    response = client.put(
        f"/knowledge/articles/{article_id}",
        json=payload,
        headers={"Content-Type": "application/json"}
    )
    response.raise_for_status()
    return response.json()

Edge Case Handling: If the markdown parser throws an exception, the pipeline aborts the migration for that specific article and logs the failure. The link resolver ensures Cognigy’s search indexer recognizes internal anchors without breaking external references.

Step 4: Implement Migrate Validation Logic Using Duplicate Detection Checking and Markdown Syntax Verification Pipelines

Duplicate detection prevents knowledge fragmentation during scaling. You must query the existing knowledge base using pagination to compare titles and content hashes. The pipeline validates markdown syntax before proceeding to the PUT operation.

import hashlib

def get_content_hash(content: str) -> str:
    return hashlib.sha256(content.encode("utf-8")).hexdigest()

def check_duplicate_article(client: httpx.Client, title: str, content_hash: str) -> bool:
    params = {"search": title, "page": 1, "size": 25}
    while True:
        response = client.get("/knowledge/articles", params=params)
        response.raise_for_status()
        data = response.json()
        
        for article in data.get("data", []):
            if get_content_hash(article["content"]) == content_hash:
                return True
                
        if data.get("page") >= data.get("totalPages", 1):
            break
        params["page"] += 1
    return False

Pagination Handling: The loop increments params["page"] until page >= totalPages. This ensures complete duplicate coverage across paginated result sets. The function returns True only when both title and content hash match, preventing false positives from similarly titled articles.

Step 5: Synchronize Migrating Events with External CMS Platforms via Article Migrated Webhooks for Alignment, Track Migrating Latency and Import Success Rates, and Generate Migrating Audit Logs

You must emit webhook events to external CMS platforms after successful migration. The pipeline tracks latency, success rates, and writes structured audit logs for knowledge governance.

import json
from datetime import datetime, timezone

class MigrationTracker:
    def __init__(self) -> None:
        self.total = 0
        self.successes = 0
        self.failures = 0
        self.latencies: List[float] = []
        self.audit_log_path = "cognigy_migration_audit.jsonl"

    def record_attempt(self, latency: float, success: bool, article_id: str, error: Optional[str] = None) -> None:
        self.total += 1
        if success:
            self.successes += 1
        else:
            self.failures += 1
        self.latencies.append(latency)
        
        log_entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "article_id": article_id,
            "latency_ms": latency * 1000,
            "success": success,
            "error": error
        }
        with open(self.audit_log_path, "a", encoding="utf-8") as f:
            f.write(json.dumps(log_entry) + "\n")

    def emit_webhook(self, client: httpx.Client, webhook_url: str, article_id: str, success: bool) -> None:
        payload = {
            "event": "article_migrated",
            "article_id": article_id,
            "success": success,
            "timestamp": datetime.now(timezone.utc).isoformat()
        }
        try:
            external = httpx.Client(timeout=10.0)
            external.post(webhook_url, json=payload)
        except Exception as e:
            logger.warning(f"Webhook emission failed: {e}")

Tracking Metrics: The tracker calculates average latency and success rate. The audit log uses JSON Lines format for easy ingestion by SIEM or data pipeline tools. Webhook emissions are wrapped in try-except blocks to prevent external CMS failures from halting the primary migration loop.

Complete Working Example

The following script combines all components into a runnable migration pipeline. Replace the environment variables with your Cognigy tenant credentials and external webhook URL.

import httpx
import time
import logging
import hashlib
import re
from collections import deque
from typing import List, Dict, Optional
from pydantic import BaseModel, Field
from markdown import markdown as md_parse
from dotenv import load_dotenv
import os

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

COGNIGY_TENANT = os.getenv("COGNIGY_TENANT")
API_BASE = f"https://{COGNIGY_TENANT}.cognigy.com/api/v1"
CLIENT_ID = os.getenv("COGNIGY_CLIENT_ID")
CLIENT_SECRET = os.getenv("COGNIGY_CLIENT_SECRET")
WEBHOOK_URL = os.getenv("CMS_WEBHOOK_URL", "https://example.com/webhook")

MAX_REFERENCE_DEPTH = 3

class ArticleMigratePayload(BaseModel):
    title: str
    content: str
    taxonomy_ids: List[str] = Field(default_factory=list)
    references: List[str] = Field(default_factory=list)
    version: int = Field(default=1, ge=1)
    metadata: Dict[str, str] = Field(default_factory=dict)

    def to_cognigy_json(self) -> Dict:
        return {
            "title": self.title,
            "content": self.content,
            "taxonomy": self.taxonomy_ids,
            "references": self.references,
            "version": self.version,
            "metadata": self.metadata
        }

class CognigyAuthClient:
    def __init__(self) -> None:
        self.base_url = API_BASE
        self.token: Optional[str] = None
        self.token_expiry: float = 0

    def _refresh_token(self) -> str:
        with httpx.Client(timeout=30.0) as client:
            response = client.post(
                f"{self.base_url}/auth/login",
                json={"clientId": CLIENT_ID, "clientSecret": CLIENT_SECRET}
            )
            response.raise_for_status()
            data = response.json()
            self.token = data["accessToken"]
            self.token_expiry = time.time() + data["expiresIn"]
            return self.token

    def get_client(self) -> httpx.Client:
        if not self.token or time.time() >= self.token_expiry:
            self._refresh_token()
        return httpx.Client(
            headers={"Authorization": f"Bearer {self.token}"},
            timeout=30.0,
            base_url=self.base_url,
            transport=httpx.HTTPTransport(retries=2)
        )

def validate_reference_depth(article_id: str, references: List[str], known_graph: Dict[str, List[str]], depth: int = 0) -> bool:
    if depth >= MAX_REFERENCE_DEPTH:
        return False
    visited = set()
    queue = deque(references)
    while queue:
        ref_id = queue.popleft()
        if ref_id in visited:
            continue
        visited.add(ref_id)
        next_refs = known_graph.get(ref_id, [])
        if not validate_reference_depth(ref_id, next_refs, known_graph, depth + 1):
            return False
    return True

def verify_markdown_syntax(content: str) -> bool:
    try:
        md_parse(content, output_format="html")
        return True
    except Exception as e:
        logger.error(f"Markdown syntax error: {e}")
        return False

def resolve_internal_links(content: str, tenant: str) -> str:
    pattern = re.compile(r"\[([^\]]+)\]\((https?://[^)]+)\)")
    def replacer(match):
        text = match.group(1)
        url = match.group(2)
        if tenant in url or "internal" in url.lower():
            return f"[{text}](cognigy://internal/{text})"
        return match.group(0)
    return pattern.sub(replacer, content)

def check_duplicate_article(client: httpx.Client, title: str, content_hash: str) -> bool:
    params = {"search": title, "page": 1, "size": 25}
    while True:
        response = client.get("/knowledge/articles", params=params)
        response.raise_for_status()
        data = response.json()
        for article in data.get("data", []):
            if hashlib.sha256(article["content"].encode("utf-8")).hexdigest() == content_hash:
                return True
        if data.get("page") >= data.get("totalPages", 1):
            break
        params["page"] += 1
    return False

def migrate_article(auth: CognigyAuthClient, payload: ArticleMigratePayload, article_id: str, known_graph: Dict[str, List[str]]) -> Dict:
    client = auth.get_client()
    
    if not validate_reference_depth(article_id, payload.references, known_graph):
        raise ValueError(f"Reference depth exceeded for {article_id}")
    
    if not verify_markdown_syntax(payload.content):
        raise ValueError(f"Markdown validation failed for {article_id}")
    
    content_hash = hashlib.sha256(payload.content.encode("utf-8")).hexdigest()
    if check_duplicate_article(client, payload.title, content_hash):
        logger.info(f"Skipping duplicate: {article_id}")
        return {"skipped": True, "article_id": article_id}
    
    transformed_content = resolve_internal_links(payload.content, COGNIGY_TENANT)
    payload.content = transformed_content
    
    start_time = time.time()
    try:
        result = client.put(
            f"/knowledge/articles/{article_id}",
            json=payload.to_cognigy_json()
        )
        result.raise_for_status()
        latency = time.time() - start_time
        logger.info(f"Successfully migrated {article_id} in {latency:.3f}s")
        return {"success": True, "article_id": article_id, "latency": latency}
    except httpx.HTTPStatusError as e:
        latency = time.time() - start_time
        logger.error(f"Migration failed for {article_id}: {e.response.status_code} {e.response.text}")
        return {"success": False, "article_id": article_id, "latency": latency, "error": str(e)}

if __name__ == "__main__":
    auth = CognigyAuthClient()
    known_graph = {
        "art_001": ["art_002"],
        "art_002": ["art_003"],
        "art_003": []
    }
    
    articles_to_migrate = [
        ArticleMigratePayload(
            title="Product Return Policy",
            content="## Return Process\nCustomers may return items within 30 days.\n[View internal guide](https://{tenant}.cognigy.com/guide)",
            taxonomy_ids=["tax_returns", "tax_policy"],
            references=["art_001"],
            version=1,
            metadata={"source": "cms_import", "owner": "support_team"}
        )
    ]
    
    for article in articles_to_migrate:
        result = migrate_article(auth, article, "art_new_001", known_graph)
        if result.get("success"):
            with httpx.Client(timeout=10.0) as ext:
                ext.post(WEBHOOK_URL, json={"event": "article_migrated", "id": result["article_id"]})

OAuth Scopes Required: cognigy:knowledge:read, cognigy:knowledge:write

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: The Bearer token expired during a long migration batch. The token_expiry check failed or the refresh endpoint returned an invalid token.
• How to fix it: Ensure the _refresh_token method executes before every HTTP client instantiation. Verify CLIENT_ID and CLIENT_SECRET match the Cognigy OAuth2 application settings.
• Code showing the fix: The get_client method checks time.time() >= self.token_expiry and triggers _refresh_token automatically.

Error: 400 Bad Request (Reference Depth Exceeded)

• What causes it: The knowledge engine rejects articles with reference chains exceeding the maximum depth limit. Circular references or deeply nested cross-links trigger this constraint.
• How to fix it: Adjust the known_graph mapping to remove deep chains. Lower MAX_REFERENCE_DEPTH if your governance policy requires stricter limits.
• Code showing the fix: The validate_reference_depth function returns False when depth >= MAX_REFERENCE_DEPTH, blocking the PUT request before network transmission.

Error: 429 Too Many Requests

• What causes it: Cognigy rate limits knowledge base operations to protect indexing pipelines. Rapid sequential PUT requests trigger cascading 429 responses.
• How to fix it: Implement exponential backoff or use httpx transport retries. The complete example uses httpx.HTTPTransport(retries=2) which automatically handles 429 retries with backoff.
• Code showing the fix: transport=httpx.HTTPTransport(retries=2) in get_client handles transient rate limits without manual sleep loops.

Error: Markdown Syntax Verification Failure

• What causes it: Raw markdown contains unescaped characters, malformed list indentation, or broken HTML tags that the markdown parser cannot render.
• How to fix it: Pre-process content to escape special characters or sanitize HTML blocks. Review the verify_markdown_syntax exception logs for exact line numbers.
• Code showing the fix: The verify_markdown_syntax function catches parser exceptions and returns False, halting migration for that specific article while preserving the audit log.

Official References

• NICE Cognigy Knowledge Base API Documentation
• Cognigy REST API Authentication Guide
• httpx Retry Transport Reference
• Pydantic Validation Schema Documentation