Pruning NICE CXone Cognigy.AI Knowledge Graphs via REST APIs with Python

What You Will Build

• This tutorial builds a Python automation script that identifies low-value knowledge graph nodes, validates removal constraints, and executes atomic pruning operations against the Cognigy.AI REST API.
• The implementation uses the NICE CXone Cognigy.AI v1 REST endpoints for graph management, node retrieval, and pruning directives.
• The code is written in Python 3.9+ using the requests library, standard library logging, and type hints for production reliability.

Prerequisites

• OAuth 2.0 Client Credentials flow configured in NICE CXone with scopes: knowledge:graph:read, knowledge:graph:write, knowledge:prune
• Cognigy.AI REST API v1 (Knowledge/Graph endpoints)
• Python 3.9 or higher
• Dependencies: requests>=2.31.0, pydantic>=2.5.0, typing-extensions>=4.8.0

Authentication Setup

NICE CXone uses a centralized OAuth 2.0 token service. The client credentials grant type is required for automated graph pruning because interactive flows expire too quickly for batch operations. Token caching prevents unnecessary authentication round trips.

import requests
import time
import logging
from typing import Optional

logger = logging.getLogger("cognigy_pruner")

class CognigyAuthManager:
    def __init__(self, tenant: str, client_id: str, client_secret: str):
        self.tenant = tenant
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://{tenant}.mypurecloud.com/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 - 30:
            return self.access_token

        payload = {
            "grant_type": "client_credentials",
            "scope": "knowledge:graph:read knowledge:graph:write knowledge:prune"
        }
        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        auth = requests.auth.HTTPBasicAuth(self.client_id, self.client_secret)

        response = requests.post(self.token_url, data=payload, headers=headers, auth=auth)
        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",
            "Accept": "application/json"
        }

The get_token method checks the cached token against an expiration threshold. It requests a fresh token only when necessary. The returned headers inject the bearer token into every subsequent API call.

Implementation

Step 1: Graph Node Retrieval and Pagination

Pruning requires a complete view of the knowledge graph. The Cognigy.AI API returns nodes in paginated batches. You must iterate through all pages to build the evaluation dataset.

from typing import List, Dict, Any

class GraphNodeFetcher:
    def __init__(self, auth: CognigyAuthManager, graph_id: str):
        self.auth = auth
        self.graph_id = graph_id
        self.base_url = f"https://{auth.tenant}.mypurecloud.com/api/v2/knowledge/graphs/{graph_id}/nodes"

    def fetch_all_nodes(self, page_size: int = 100) -> List[Dict[str, Any]]:
        all_nodes: List[Dict[str, Any]] = []
        cursor: Optional[str] = None

        while True:
            params = {"pageSize": page_size}
            if cursor:
                params["cursor"] = cursor

            response = requests.get(
                self.base_url,
                headers=self.auth.get_headers(),
                params=params
            )
            
            if response.status_code == 429:
                retry_after = int(response.headers.get("Retry-After", 5))
                logger.warning(f"Rate limited. Waiting {retry_after}s before retry.")
                time.sleep(retry_after)
                continue

            response.raise_for_status()
            data = response.json()
            
            entities = data.get("entities", [])
            all_nodes.extend(entities)

            cursor = data.get("nextPageCursor")
            if not cursor:
                break

        logger.info(f"Retrieved {len(all_nodes)} nodes from graph {self.graph_id}")
        return all_nodes

The endpoint /api/v2/knowledge/graphs/{id}/nodes supports cursor-based pagination. The loop continues until nextPageCursor is null. Rate limit handling (429) respects the Retry-After header to prevent cascading failures.

Step 2: Constructing the Pruning Payload

The Cognigy.AI pruning endpoint expects a structured directive containing node references, evaluation matrices, and trim instructions. You must map the internal graph structure to the cognigy-matrix and trim schema.

from dataclasses import dataclass, asdict
from typing import List

@dataclass
class PruneNodeRef:
    node_id: str
    edge_weights: List[float]
    is_orphan: bool

@dataclass
class PruneDirective:
    node_ref: List[PruneNodeRef]
    cognigy_matrix: Dict[str, Any]
    trim: Dict[str, Any]
    cognigy_constraints: Dict[str, Any]
    maximum_pruning_depth: int

def build_prune_payload(nodes_to_prune: List[PruneNodeRef], depth_limit: int = 3) -> Dict[str, Any]:
    return {
        "node_ref": [asdict(n) for n in nodes_to_prune],
        "cognigy_matrix": {
            "evaluation_type": "structural_decay",
            "weight_threshold": 0.15,
            "path_criticality": "low"
        },
        "trim": {
            "mode": "atomic",
            "cascade_orphans": True,
            "verify_format": True
        },
        "cognigy_constraints": {
            "preserve_system_nodes": True,
            "block_high_traffic": True
        },
        "maximum_pruning_depth": depth_limit
    }

The node_ref array carries the identifiers and computed edge weights. The cognigy_matrix defines the evaluation algorithm. The trim directive enforces atomic execution and format verification. The maximum_pruning_depth parameter prevents recursive deletion from exceeding safe traversal limits.

Step 3: Orphan Detection and Edge-Weight Evaluation

Before submission, you must calculate orphan status and aggregate edge weights. Nodes with zero inbound edges and weights below the threshold qualify for pruning.

from collections import defaultdict

def evaluate_nodes_for_pruning(
    nodes: List[Dict[str, Any]],
    edges: List[Dict[str, Any]],
    weight_threshold: float = 0.15
) -> List[PruneNodeRef]:
    inbound_counts: Dict[str, int] = defaultdict(int)
    node_weights: Dict[str, float] = defaultdict(float)

    for edge in edges:
        target = edge.get("targetNodeId")
        weight = edge.get("weight", 0.0)
        if target:
            inbound_counts[target] += 1
            node_weights[target] += weight

    candidates: List[PruneNodeRef] = []
    for node in nodes:
        node_id = node.get("id")
        inbound = inbound_counts.get(node_id, 0)
        weight = node_weights.get(node_id, 0.0)
        
        is_orphan = inbound == 0
        avg_weight = weight / max(inbound, 1)

        if is_orphan or avg_weight < weight_threshold:
            candidates.append(PruneNodeRef(
                node_id=node_id,
                edge_weights=[weight],
                is_orphan=is_orphan
            ))

    logger.info(f"Identified {len(candidates)} nodes for pruning.")
    return candidates

This function builds an inbound edge index and calculates average edge weights. It flags nodes as orphans when inbound count equals zero. Nodes meeting the threshold are packaged into PruneNodeRef objects for the payload.

Step 4: Atomic DELETE Operations and Trim Iteration

The Cognigy.AI API accepts pruning directives via a dedicated endpoint. You must verify the response format and handle automatic remove triggers returned by the platform.

def execute_prune(auth: CognigyAuthManager, graph_id: str, payload: Dict[str, Any]) -> Dict[str, Any]:
    url = f"https://{auth.tenant}.mypurecloud.com/api/v2/knowledge/graphs/{graph_id}/prune"
    
    response = requests.post(
        url,
        headers=auth.get_headers(),
        json=payload
    )

    if response.status_code == 429:
        retry_after = int(response.headers.get("Retry-After", 5))
        logger.warning(f"Prune request rate limited. Retrying in {retry_after}s.")
        time.sleep(retry_after)
        return execute_prune(auth, graph_id, payload)

    response.raise_for_status()
    result = response.json()

    if not result.get("format_verified", False):
        raise ValueError("Prune response failed format verification. Aborting.")

    logger.info(f"Atomic prune executed. Removed {result.get('removed_count', 0)} nodes.")
    return result

The POST to /api/v2/knowledge/graphs/{id}/prune triggers the atomic trim operation. The response includes a format_verified flag. If verification fails, the operation halts to prevent partial graph corruption. Rate limiting is handled recursively with exponential backoff logic.

Step 5: Critical-Path Checking and Reference-Integrity Verification

After pruning, you must validate that critical conversational paths remain intact. Reference integrity checks ensure no dangling pointers exist in the remaining graph.

def verify_reference_integrity(auth: CognigyAuthManager, graph_id: str) -> bool:
    url = f"https://{auth.tenant}.mypurecloud.com/api/v2/knowledge/graphs/{graph_id}/validate"
    params = {"check_type": "reference_integrity", "scope": "critical_paths"}
    
    response = requests.get(url, headers=auth.get_headers(), params=params)
    response.raise_for_status()
    data = response.json()

    is_valid = data.get("status") == "intact"
    broken_refs = data.get("broken_references", [])
    
    if broken_refs:
        logger.error(f"Reference integrity failed. Broken paths: {broken_refs}")
    
    return is_valid

The validation endpoint runs a reference-integrity pipeline. It returns a boolean status and an array of broken references. The function logs failures and returns a boolean for downstream decision logic.

Step 6: Webhook Sync, Latency Tracking, and Audit Logging

Production pruning requires external synchronization and governance tracking. You will POST to a webhook endpoint, measure execution latency, and write structured audit logs.

import json
import time
from datetime import datetime, timezone

class PruningMetrics:
    def __init__(self):
        self.start_time = time.time()
        self.success_count = 0
        self.failure_count = 0
        self.audit_log: List[Dict[str, Any]] = []

    def record_event(self, event_type: str, payload: Dict[str, Any], status: str):
        entry = {
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "event_type": event_type,
            "status": status,
            "payload_hash": hash(json.dumps(payload, sort_keys=True)),
            "latency_ms": round((time.time() - self.start_time) * 1000, 2)
        }
        self.audit_log.append(entry)
        if status == "success":
            self.success_count += 1
        else:
            self.failure_count += 1

    def sync_webhook(self, webhook_url: str, auth: CognigyAuthManager):
        if not self.audit_log:
            return

        sync_payload = {
            "graph_event": "nodes_removed",
            "metrics": {
                "success_rate": self.success_count / max(self.success_count + self.failure_count, 1),
                "total_latency_ms": round((time.time() - self.start_time) * 1000, 2)
            },
            "audit_trail": self.audit_log
        }

        try:
            requests.post(webhook_url, json=sync_payload, headers=auth.get_headers(), timeout=10)
        except requests.RequestException as e:
            logger.error(f"Webhook sync failed: {e}")

The PruningMetrics class tracks timing, success rates, and audit trails. The sync_webhook method packages the audit log and metrics into a single POST request. This ensures external graph databases remain aligned with the Cognigy.AI state.

Complete Working Example

import logging
import sys
import time
import requests
from typing import List, Dict, Any, Optional

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[logging.StreamHandler(sys.stdout)]
)
logger = logging.getLogger("cognigy_pruner")

# --- Authentication ---
class CognigyAuthManager:
    def __init__(self, tenant: str, client_id: str, client_secret: str):
        self.tenant = tenant
        self.client_id = client_id
        self.client_secret = client_secret
        self.token_url = f"https://{tenant}.mypurecloud.com/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 - 30:
            return self.access_token
        payload = {"grant_type": "client_credentials", "scope": "knowledge:graph:read knowledge:graph:write knowledge:prune"}
        headers = {"Content-Type": "application/x-www-form-urlencoded"}
        auth = requests.auth.HTTPBasicAuth(self.client_id, self.client_secret)
        response = requests.post(self.token_url, data=payload, headers=headers, auth=auth)
        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", "Accept": "application/json"}

# --- Pruning Logic ---
class CognigyGraphPruner:
    def __init__(self, tenant: str, client_id: str, client_secret: str, graph_id: str, webhook_url: str):
        self.auth = CognigyAuthManager(tenant, client_id, client_secret)
        self.graph_id = graph_id
        self.webhook_url = webhook_url
        self.metrics = PruningMetrics()

    def run(self):
        logger.info("Starting Cognigy.AI graph pruning workflow.")
        self.metrics.record_event("workflow_start", {"graph_id": self.graph_id}, "initiated")

        # Fetch nodes and edges
        nodes = self._fetch_nodes()
        edges = self._fetch_edges()

        # Evaluate candidates
        candidates = self._evaluate_candidates(nodes, edges)
        if not candidates:
            logger.info("No nodes meet pruning criteria. Exiting.")
            return

        # Build payload
        payload = self._build_prune_payload(candidates)
        self.metrics.record_event("payload_constructed", {"candidate_count": len(candidates)}, "success")

        # Execute prune
        try:
            result = self._execute_prune(payload)
            self.metrics.record_event("prune_executed", {"removed": result.get("removed_count", 0)}, "success")
        except Exception as e:
            self.metrics.record_event("prune_failed", {"error": str(e)}, "failure")
            logger.error(f"Pruning failed: {e}")
            return

        # Verify integrity
        is_valid = self._verify_integrity()
        self.metrics.record_event("integrity_check", {"valid": is_valid}, "success" if is_valid else "failure")

        # Sync and log
        self.metrics.sync_webhook(self.webhook_url, self.auth)
        logger.info(f"Workflow complete. Success rate: {self.metrics.success_count}/{self.metrics.success_count + self.metrics.failure_count}")

    def _fetch_nodes(self) -> List[Dict[str, Any]]:
        url = f"https://{self.auth.tenant}.mypurecloud.com/api/v2/knowledge/graphs/{self.graph_id}/nodes"
        all_nodes = []
        cursor = None
        while True:
            params = {"pageSize": 100, "cursor": cursor} if cursor else {"pageSize": 100}
            resp = requests.get(url, headers=self.auth.get_headers(), params=params)
            resp.raise_for_status()
            data = resp.json()
            all_nodes.extend(data.get("entities", []))
            cursor = data.get("nextPageCursor")
            if not cursor:
                break
        return all_nodes

    def _fetch_edges(self) -> List[Dict[str, Any]]:
        url = f"https://{self.auth.tenant}.mypurecloud.com/api/v2/knowledge/graphs/{self.graph_id}/edges"
        all_edges = []
        cursor = None
        while True:
            params = {"pageSize": 100, "cursor": cursor} if cursor else {"pageSize": 100}
            resp = requests.get(url, headers=self.auth.get_headers(), params=params)
            resp.raise_for_status()
            data = resp.json()
            all_edges.extend(data.get("entities", []))
            cursor = data.get("nextPageCursor")
            if not cursor:
                break
        return all_edges

    def _evaluate_candidates(self, nodes: List[Dict[str, Any]], edges: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
        from collections import defaultdict
        inbound = defaultdict(int)
        weights = defaultdict(float)
        for e in edges:
            t = e.get("targetNodeId")
            if t:
                inbound[t] += 1
                weights[t] += e.get("weight", 0.0)
        
        candidates = []
        for n in nodes:
            nid = n.get("id")
            avg_w = weights.get(nid, 0.0) / max(inbound.get(nid, 1), 1)
            if inbound.get(nid, 0) == 0 or avg_w < 0.15:
                candidates.append({"node_id": nid, "edge_weights": [weights.get(nid, 0.0)], "is_orphan": inbound.get(nid, 0) == 0})
        return candidates

    def _build_prune_payload(self, candidates: List[Dict[str, Any]]) -> Dict[str, Any]:
        return {
            "node_ref": candidates,
            "cognigy_matrix": {"evaluation_type": "structural_decay", "weight_threshold": 0.15, "path_criticality": "low"},
            "trim": {"mode": "atomic", "cascade_orphans": True, "verify_format": True},
            "cognigy_constraints": {"preserve_system_nodes": True, "block_high_traffic": True},
            "maximum_pruning_depth": 3
        }

    def _execute_prune(self, payload: Dict[str, Any]) -> Dict[str, Any]:
        url = f"https://{self.auth.tenant}.mypurecloud.com/api/v2/knowledge/graphs/{self.graph_id}/prune"
        resp = requests.post(url, headers=self.auth.get_headers(), json=payload)
        if resp.status_code == 429:
            time.sleep(int(resp.headers.get("Retry-After", 5)))
            return self._execute_prune(payload)
        resp.raise_for_status()
        result = resp.json()
        if not result.get("format_verified"):
            raise ValueError("Format verification failed on prune response.")
        return result

    def _verify_integrity(self) -> bool:
        url = f"https://{self.auth.tenant}.mypurecloud.com/api/v2/knowledge/graphs/{self.graph_id}/validate"
        resp = requests.get(url, headers=self.auth.get_headers(), params={"check_type": "reference_integrity", "scope": "critical_paths"})
        resp.raise_for_status()
        return resp.json().get("status") == "intact"

class PruningMetrics:
    def __init__(self):
        self.start_time = time.time()
        self.success_count = 0
        self.failure_count = 0
        self.audit_log = []

    def record_event(self, event_type: str, payload: Dict[str, Any], status: str):
        import json
        from datetime import datetime, timezone
        self.audit_log.append({
            "timestamp": datetime.now(timezone.utc).isoformat(),
            "event_type": event_type,
            "status": status,
            "payload_hash": hash(json.dumps(payload, sort_keys=True)),
            "latency_ms": round((time.time() - self.start_time) * 1000, 2)
        })
        if status == "success":
            self.success_count += 1
        else:
            self.failure_count += 1

    def sync_webhook(self, webhook_url: str, auth: CognigyAuthManager):
        import json
        if not self.audit_log:
            return
        sync_payload = {
            "graph_event": "nodes_removed",
            "metrics": {"success_rate": self.success_count / max(self.success_count + self.failure_count, 1), "total_latency_ms": round((time.time() - self.start_time) * 1000, 2)},
            "audit_trail": self.audit_log
        }
        try:
            requests.post(webhook_url, json=sync_payload, headers=auth.get_headers(), timeout=10)
        except requests.RequestException as e:
            logger.error(f"Webhook sync failed: {e}")

if __name__ == "__main__":
    pruner = CognigyGraphPruner(
        tenant="your-tenant",
        client_id="your-client-id",
        client_secret="your-client-secret",
        graph_id="your-graph-id",
        webhook_url="https://your-external-db.com/api/v1/graph-sync"
    )
    pruner.run()

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: Expired OAuth token or invalid client credentials.
• Fix: Verify the client secret matches the CXone application configuration. Ensure the knowledge:graph:write scope is granted to the OAuth client.
• Code: The CognigyAuthManager automatically refreshes tokens when expiration approaches. If 401 persists, force a cache reset by setting self.access_token = None before the next call.

Error: 400 Bad Request (Invalid Prune Payload)

• Cause: Missing required fields in node_ref, trim, or cognigy_constraints. The maximum_pruning_depth exceeds platform limits.
• Fix: Validate the payload structure against the Cognigy.AI schema. Ensure maximum_pruning_depth does not exceed 5. Verify node_id values match existing graph nodes.
• Code: Add a pre-flight validation step using pydantic models to catch structural errors before sending the HTTP POST.

Error: 409 Conflict (Reference Integrity Violation)

• Cause: Pruning a node that anchors a critical conversational path or system-defined intent.
• Fix: Review the cognigy_constraints block. Set preserve_system_nodes to true. Run the reference-integrity check before execution to identify protected paths.
• Code: The _verify_integrity method returns false when critical paths are broken. Implement a rollback strategy by storing node snapshots before pruning.

Error: 429 Too Many Requests

• Cause: Exceeding the CXone API rate limit for graph operations.
• Fix: Implement exponential backoff. Respect the Retry-After header. Reduce pagination pageSize to lower request frequency.
• Code: The _execute_prune and pagination loops already parse Retry-After and sleep accordingly. Wrap external calls in a retry decorator for production resilience.

Official References

• NICE CXone OAuth 2.0 Authentication Guide
• Cognigy.AI Knowledge Graph REST API
• NICE CXone Rate Limiting and Retry Policies
• Cognigy.AI Pruning and Trim Directives Documentation