Pruning Redundant Knowledge Graph Nodes in Cognigy.AI via REST API with Python
What You Will Build
This tutorial builds a Python utility that scans a Cognigy.AI knowledge graph, constructs prune payloads using node IDs and edge weight matrices, executes atomic deletion operations, validates graph connectivity, synchronizes with external vector stores via webhooks, and generates governance audit logs. The code uses the Cognigy.AI REST API v1 and Python 3.9+. The implementation covers authentication, payload construction, constraint validation, atomic deletion, webhook synchronization, and performance tracking.
Prerequisites
- OAuth2 Client Credentials grant with scopes:
graph:read,graph:write,webhook:manage,audit:write - Cognigy.AI API v1 (REST)
- Python 3.9+
- Dependencies:
httpx>=0.25.0,pydantic>=2.0,tenacity>=8.2.0,pyjwt>=2.8.0 - Install dependencies:
pip install httpx pydantic tenacity pyjwt
Authentication Setup
Cognigy.AI uses standard OAuth2 client credentials flow. You must request a JWT token before invoking graph operations. The token expires after thirty minutes, so you must implement caching and automatic refresh logic.
import httpx
import time
from typing import Optional
import logging
logger = logging.getLogger(__name__)
class CognigyAuthClient:
def __init__(self, tenant: str, client_id: str, client_secret: str, token_url: str):
self.tenant = tenant
self.client_id = client_id
self.client_secret = client_secret
self.token_url = token_url
self.token_cache: Optional[str] = None
self.token_expiry: float = 0.0
async def get_token(self) -> str:
if self.token_cache and time.time() < self.token_expiry - 60:
return self.token_cache
async with httpx.AsyncClient(timeout=15.0) as client:
response = await client.post(
self.token_url,
data={
"grant_type": "client_credentials",
"client_id": self.client_id,
"client_secret": self.client_secret,
"scope": "graph:read graph:write webhook:manage audit:write"
}
)
if response.status_code not in (200, 201):
raise httpx.HTTPStatusError(
f"OAuth token request failed with status {response.status_code}",
request=response.request,
response=response
)
payload = response.json()
self.token_cache = payload["access_token"]
self.token_expiry = time.time() + payload["expires_in"]
logger.info("OAuth token refreshed successfully.")
return self.token_cache
The client returns a bearer token that you attach to every subsequent request via the Authorization: Bearer {token} header. The cache prevents redundant token requests during batch operations.
Implementation
Step 1: Construct Prune Payloads with Node ID References and Edge Weight Matrix
You must retrieve nodes and edges, calculate edge weights, and build a threshold directive. Cognigy.AI returns paginated results, so you must iterate through all pages.
import asyncio
from typing import List, Dict, Any
class GraphPayloadBuilder:
def __init__(self, http_client: httpx.AsyncClient, base_url: str):
self.client = http_client
self.base_url = base_url
async def fetch_all_nodes(self, auth_token: str, limit: int = 500) -> List[Dict[str, Any]]:
nodes = []
cursor = None
while True:
params = {"limit": limit}
if cursor:
params["cursor"] = cursor
response = await self.client.get(
f"{self.base_url}/api/v1/knowledge-graph/nodes",
headers={"Authorization": f"Bearer {auth_token}"},
params=params
)
if response.status_code == 429:
retry_after = int(response.headers.get("Retry-After", 2))
logger.warning("Rate limited on node fetch. Waiting %d seconds.", retry_after)
await asyncio.sleep(retry_after)
continue
response.raise_for_status()
data = response.json()
nodes.extend(data.get("items", []))
cursor = data.get("next_cursor")
if not cursor:
break
return nodes
async def fetch_all_edges(self, auth_token: str, limit: int = 500) -> List[Dict[str, Any]]:
edges = []
cursor = None
while True:
params = {"limit": limit}
if cursor:
params["cursor"] = cursor
response = await self.client.get(
f"{self.base_url}/api/v1/knowledge-graph/edges",
headers={"Authorization": f"Bearer {auth_token}"},
params=params
)
if response.status_code == 429:
retry_after = int(response.headers.get("Retry-After", 2))
await asyncio.sleep(retry_after)
continue
response.raise_for_status()
data = response.json()
edges.extend(data.get("items", []))
cursor = data.get("next_cursor")
if not cursor:
break
return edges
def build_prune_payload(
self,
nodes: List[Dict[str, Any]],
edges: List[Dict[str, Any]],
weight_threshold: float = 0.15,
max_traversal_depth: int = 3
) -> Dict[str, Any]:
# Construct edge weight matrix
edge_weights: Dict[str, Dict[str, float]] = {}
for edge in edges:
src = edge["source_node_id"]
tgt = edge["target_node_id"]
weight = edge.get("weight", 1.0)
if src not in edge_weights:
edge_weights[src] = {}
edge_weights[src][tgt] = weight
# Identify nodes below threshold
low_weight_nodes = []
for node in nodes:
node_id = node["id"]
outgoing_weights = edge_weights.get(node_id, {})
avg_weight = sum(outgoing_weights.values()) / len(outgoing_weights) if outgoing_weights else 0.0
if avg_weight < weight_threshold:
low_weight_nodes.append(node_id)
return {
"node_ids": low_weight_nodes,
"edge_weight_matrix": edge_weights,
"threshold": weight_threshold,
"max_traversal_depth": max_traversal_depth
}
The payload contains the exact node identifiers, the weight matrix for reference, and the directive parameters. Cognigy.AI validates the max_traversal_depth against engine constraints. Values above five cause a 400 Bad Request.
Step 2: Validate Prune Schema Against Graph Engine Constraints
You must verify the payload structure and simulate connectivity checks before submission. The graph engine rejects payloads that disconnect critical root nodes or exceed traversal limits.
from pydantic import BaseModel, field_validator
from typing import Set
import networkx as nx # pip install networkx
class PrunePayloadSchema(BaseModel):
node_ids: List[str]
edge_weight_matrix: Dict[str, Dict[str, float]]
threshold: float
max_traversal_depth: int
@field_validator("threshold")
@classmethod
def validate_threshold(cls, v: float) -> float:
if not (0.0 < v <= 1.0):
raise ValueError("Threshold must be between 0.0 and 1.0 exclusive of zero.")
return v
@field_validator("max_traversal_depth")
@classmethod
def validate_depth(cls, v: int) -> int:
if not (1 <= v <= 5):
raise ValueError("Graph engine constraint: max_traversal_depth must be between 1 and 5.")
return v
def validate_connectivity_and_relevance(
payload: Dict[str, Any],
nodes: List[Dict[str, Any]],
edges: List[Dict[str, Any]]
) -> tuple[bool, str]:
try:
PrunePayloadSchema.model_validate(payload)
except Exception as e:
return False, f"Schema validation failed: {str(e)}"
# Build directed graph for connectivity check
G = nx.DiGraph()
node_ids = {n["id"] for n in nodes}
for edge in edges:
G.add_edge(edge["source_node_id"], edge["target_node_id"])
prune_ids = set(payload["node_ids"])
if not prune_ids.issubset(node_ids):
return False, "Payload references non-existent node IDs."
# Remove candidate nodes temporarily to test connectivity
test_graph = G.copy()
test_graph.remove_nodes_from(prune_ids)
# Check if root nodes remain connected to at least 70% of remaining nodes
roots = [n["id"] for n in nodes if n.get("type") == "root"]
if roots:
reachable_count = sum(1 for r in roots if len(nx.descendants(test_graph, r)) > 0)
retention_rate = reachable_count / len(roots)
if retention_rate < 0.7:
return False, f"Connectivity check failed. Retention rate {retention_rate:.2f} is below 0.70 threshold."
# Semantic relevance verification pipeline (simulated scoring)
relevance_scores = []
for nid in prune_ids:
# In production, this calls an embedding similarity service
relevance_scores.append(0.12) # Placeholder for low semantic relevance
if any(score > 0.8 for score in relevance_scores):
return False, "Semantic relevance verification failed. High-relevance nodes cannot be pruned."
return True, "Validation passed. Payload conforms to graph engine constraints."
The function returns a boolean and a descriptive message. You must abort the prune cycle if connectivity drops below acceptable limits or if high-relevance nodes are targeted.
Step 3: Handle Graph Reduction via Atomic DELETE Operations
Cognigy.AI processes bulk deletions atomically. You must enable automatic orphan removal and verify the response format. The API returns a transaction ID that you track for auditing.
from tenacity import retry, wait_exponential, stop_after_attempt, retry_if_exception_type
import json
@retry(
retry=retry_if_exception_type(httpx.HTTPStatusError),
wait=wait_exponential(multiplier=2, min=4, max=30),
stop=stop_after_attempt(3)
)
async def execute_atomic_prune(
http_client: httpx.AsyncClient,
base_url: str,
auth_token: str,
payload: Dict[str, Any],
auto_remove_orphans: bool = True
) -> Dict[str, Any]:
request_body = {
"prune_payload": payload,
"auto_remove_orphans": auto_remove_orphans,
"format_verification": True
}
response = await http_client.post(
f"{base_url}/api/v1/knowledge-graph/bulk-delete",
headers={
"Authorization": f"Bearer {auth_token}",
"Content-Type": "application/json"
},
content=json.dumps(request_body)
)
if response.status_code == 409:
error_detail = response.json().get("detail", "Conflict")
raise httpx.HTTPStatusError(
f"Conflict during atomic prune: {error_detail}",
request=response.request,
response=response
)
response.raise_for_status()
result = response.json()
# Format verification
required_keys = {"transaction_id", "nodes_deleted", "orphans_removed", "status"}
if not required_keys.issubset(result.keys()):
raise ValueError(f"Response format verification failed. Missing keys: {required_keys - result.keys()}")
return result
The retry decorator handles transient 429 and 503 responses. The format_verification flag triggers the engine to validate internal state before committing. Orphan removal executes automatically when auto_remove_orphans is true.
Step 4: Synchronize Pruning Events via Webhooks and Track Metrics
You must register a webhook to notify external vector stores of node removal. You also track latency and retention success rates for operational visibility.
import time
from datetime import datetime, timezone
async def register_prune_webhook(
http_client: httpx.AsyncClient,
base_url: str,
auth_token: str,
target_url: str,
webhook_secret: str
) -> Dict[str, Any]:
payload = {
"event": "node.pruned",
"target_url": target_url,
"secret": webhook_secret,
"active": True
}
response = await http_client.post(
f"{base_url}/api/v1/webhooks",
headers={
"Authorization": f"Bearer {auth_token}",
"Content-Type": "application/json"
},
content=json.dumps(payload)
)
response.raise_for_status()
return response.json()
def calculate_prune_metrics(
start_time: float,
total_nodes: int,
deleted_nodes: int,
orphans_removed: int
) -> Dict[str, Any]:
latency_seconds = time.time() - start_time
retention_rate = (total_nodes - deleted_nodes) / total_nodes if total_nodes > 0 else 1.0
return {
"prune_latency_seconds": round(latency_seconds, 3),
"total_nodes_scanned": total_nodes,
"nodes_deleted": deleted_nodes,
"orphans_removed": orphans_removed,
"retention_success_rate": round(retention_rate, 4),
"timestamp": datetime.now(timezone.utc).isoformat()
}
The webhook payload uses the node.pruned event type. Cognigy.AI signs the webhook request with the provided secret. Your vector store endpoint must verify the signature before updating embeddings.
Step 5: Generate Pruning Audit Logs and Expose Automated Pruner
You must log every prune cycle for AI governance. The final class exposes a single method that orchestrates the entire workflow.
class CognigyGraphPruner:
def __init__(self, auth_client: CognigyAuthClient, base_url: str):
self.auth = auth_client
self.base_url = base_url
self.payload_builder = GraphPayloadBuilder(httpx.AsyncClient(timeout=30.0), base_url)
async def run_prune_cycle(
self,
weight_threshold: float = 0.15,
max_depth: int = 3,
webhook_url: str = "https://vector-store.example.com/sync",
webhook_secret: str = "governance-secret-123"
) -> Dict[str, Any]:
start_time = time.time()
token = await self.auth.get_token()
# Step 1: Fetch graph data
nodes = await self.payload_builder.fetch_all_nodes(token)
edges = await self.payload_builder.fetch_all_edges(token)
# Step 2: Build payload
payload = self.payload_builder.build_prune_payload(nodes, edges, weight_threshold, max_depth)
# Step 3: Validate
is_valid, validation_msg = validate_connectivity_and_relevance(payload, nodes, edges)
if not is_valid:
logger.error("Prune aborted. Validation failed: %s", validation_msg)
return {"status": "aborted", "reason": validation_msg}
# Step 4: Register webhook
await register_prune_webhook(
httpx.AsyncClient(timeout=15.0), self.base_url, token, webhook_url, webhook_secret
)
# Step 5: Execute atomic prune
prune_result = await execute_atomic_prune(
httpx.AsyncClient(timeout=30.0), self.base_url, token, payload, auto_remove_orphans=True
)
# Step 6: Calculate metrics
metrics = calculate_prune_metrics(
start_time, len(nodes), prune_result["nodes_deleted"], prune_result["orphans_removed"]
)
# Step 7: Generate audit log
audit_log = {
"event": "graph_prune_cycle",
"transaction_id": prune_result["transaction_id"],
"metrics": metrics,
"validation_result": "passed",
"governance_flag": "compliant"
}
await self._write_audit_log(token, audit_log)
logger.info("Prune cycle completed. Transaction: %s", prune_result["transaction_id"])
return {"status": "completed", "result": prune_result, "metrics": metrics, "audit": audit_log}
async def _write_audit_log(self, token: str, log_entry: Dict[str, Any]) -> None:
async with httpx.AsyncClient(timeout=15.0) as client:
await client.post(
f"{self.base_url}/api/v1/audit/logs",
headers={
"Authorization": f"Bearer {token}",
"Content-Type": "application/json"
},
content=json.dumps(log_entry)
)
The run_prune_cycle method handles the complete lifecycle. It returns structured metrics and audit data. You can schedule this method via cron or CI/CD pipelines for automated graph maintenance.
Complete Working Example
The following script combines all components into a runnable module. Replace the credential placeholders before execution.
import asyncio
import logging
import httpx
logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
async def main():
tenant = "your-tenant-name"
client_id = "your-client-id"
client_secret = "your-client-secret"
token_url = f"https://{tenant}.cognigy.ai/api/v1/oauth/token"
base_url = f"https://{tenant}.cognigy.ai"
auth = CognigyAuthClient(tenant, client_id, client_secret, token_url)
pruner = CognigyGraphPruner(auth, base_url)
try:
result = await pruner.run_prune_cycle(
weight_threshold=0.15,
max_depth=3,
webhook_url="https://your-vector-store.example.com/webhook",
webhook_secret="your-webhook-secret"
)
print(json.dumps(result, indent=2))
except httpx.HTTPStatusError as e:
logger.error("HTTP Error: %d - %s", e.response.status_code, e.response.text)
except Exception as e:
logger.error("Unexpected error during prune cycle: %s", str(e))
if __name__ == "__main__":
asyncio.run(main())
Execute the script with python graph_pruner.py. The output contains the transaction ID, deletion counts, latency metrics, and audit confirmation.
Common Errors & Debugging
Error: 400 Bad Request
- Cause: The
max_traversal_depthexceeds five, or thethresholdfalls outside the valid range. The graph engine also returns 400 when the payload references nodes that do not exist in the current tenant. - Fix: Validate the payload using the Pydantic schema before submission. Ensure
max_traversal_depthis between one and five. Verify node IDs against the fetched node list. - Code showing the fix: The
PrunePayloadSchemavalidators and thevalidate_connectivity_and_relevancefunction catch these constraints before the API call.
Error: 409 Conflict
- Cause: A targeted node is currently bound to an active intent, entity mapping, or dialogue flow. Cognigy.AI prevents deletion of nodes with active references to maintain conversational integrity.
- Fix: Query the node dependency graph before pruning. Remove or reassign bindings to placeholder nodes, then retry the prune cycle.
- Code showing the fix: The
execute_atomic_prunefunction catches 409 responses and raises a descriptive exception. You must implement a dependency resolution step in your orchestration layer before invoking the pruner.
Error: 429 Too Many Requests
- Cause: Bulk operations trigger rate limits when processing large graphs. Cognigy.AI enforces per-tenant request quotas on the
/api/v1/knowledge-graphnamespace. - Fix: Implement exponential backoff with jitter. The
@retrydecorator in the implementation handles automatic retries with configurable wait intervals. - Code showing the fix: The
retry_if_exception_typeandwait_exponentialparameters inexecute_atomic_pruneautomatically pause and retry on rate limit responses.
Error: 500 Internal Server Error
- Cause: Graph engine constraint violation during atomic commit, or orphan removal triggers a circular dependency resolution failure.
- Fix: Reduce the batch size by splitting the
node_idslist into chunks of fifty. Disableauto_remove_orphanstemporarily to isolate the failing node. Review the Cognigy.AI system logs for engine-level stack traces. - Code showing the fix: Modify the
run_prune_cyclemethod to slicepayload["node_ids"]into batches and loop throughexecute_atomic_prunewith chunked payloads.