Exporting NICE Cognigy.AI Dialogue Graph Topology via REST API with Python

What You Will Build

• The script extracts the complete dialogue graph topology from a Cognigy.AI bot, validates structural constraints, and exports a serialized JSON payload.
• This uses the Cognigy.AI v2 REST API for topology extraction and export operations.
• The implementation uses Python 3.9+ with the requests library, networkx for graph analysis, and pydantic for schema validation.

Prerequisites

• OAuth2 Client Credentials flow with bot:read scope
• Cognigy.AI API v2 base URL (e.g., https://api.cognigy.ai/v2)
• Python 3.9+ runtime
• Dependencies: requests>=2.28.0, networkx>=3.0, pydantic>=2.0, urllib3>=2.0.0

Authentication Setup

Cognigy.AI uses standard OAuth2 token endpoints. The authentication client caches tokens and automatically refreshes before expiration. Every API call requires the bot:read scope for topology extraction.

import requests
import time
from dataclasses import dataclass
from typing import Optional

@dataclass
class OAuthToken:
    access_token: str
    expires_in: int
    issued_at: float

class CognigyAuth:
    def __init__(self, base_url: str, client_id: str, client_secret: str, scopes: str = "bot:read"):
        self.base_url = base_url.rstrip("/")
        self.client_id = client_id
        self.client_secret = client_secret
        self.scopes = scopes
        self.token: Optional[OAuthToken] = None

    def get_token(self) -> str:
        if self.token and time.time() < (self.token.issued_at + self.token.expires_in - 30):
            return self.token.access_token

        url = f"{self.base_url}/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": self.scopes
        }

        response = requests.post(url, data=payload, timeout=10)
        response.raise_for_status()

        data = response.json()
        self.token = OAuthToken(
            access_token=data["access_token"],
            expires_in=data["expires_in"],
            issued_at=time.time()
        )
        return self.token.access_token

Implementation

Step 1: Atomic Topology Extraction with Pagination and Retry Logic

Topology extraction requires atomic GET operations against the nodes and edges endpoints. Cognigy.AI v2 returns paginated results. The client implements exponential backoff for 429 rate-limit responses and verifies JSON format on every response.

from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
from typing import List, Dict, Any
import json

class CognigyAPIClient:
    def __init__(self, auth: CognigyAuth):
        self.auth = auth
        self.session = requests.Session()
        retry_strategy = Retry(
            total=3,
            backoff_factor=1,
            status_forcelist=[429, 500, 502, 503, 504],
            allowed_methods=["GET"]
        )
        self.session.mount("https://", HTTPAdapter(max_retries=retry_strategy))

    def _fetch_paginated(self, endpoint: str, bot_id: str) -> List[Dict[str, Any]]:
        results = []
        offset = 0
        limit = 100
        token = self.auth.get_token()

        while True:
            url = f"{self.auth.base_url}{endpoint}"
            params = {"botId": bot_id, "limit": limit, "offset": offset}
            headers = {"Authorization": f"Bearer {token}", "Accept": "application/json"}

            response = self.session.get(url, params=params, headers=headers, timeout=15)
            response.raise_for_status()

            data = response.json()
            if not isinstance(data, dict) or "data" not in data:
                raise ValueError("Invalid API response format. Expected JSON object with 'data' key.")

            results.extend(data["data"])
            if offset + limit >= data["pagination"]["total"]:
                break
            offset += limit
        return results

Step 2: Graph Validation Pipeline with Orphan and Cycle Detection

The extraction pipeline feeds into a validation engine. The engine constructs a directed graph, checks for orphan nodes, verifies circular paths, and enforces maximum graph complexity limits defined by the dialogue manager constraints.

import networkx as nx
from pydantic import BaseModel, Field, validator
from typing import Set, Tuple

class GraphConstraints(BaseModel):
    max_nodes: int = Field(500, gt=0)
    max_edges: int = Field(1500, gt=0)
    max_depth: int = Field(20, gt=0)

class TopologyValidator:
    def __init__(self, constraints: GraphConstraints):
        self.constraints = constraints

    def validate(self, nodes: List[Dict], edges: List[Dict]) -> Tuple[bool, Dict[str, Any]]:
        if len(nodes) > self.constraints.max_nodes:
            return False, {"error": f"Graph exceeds maximum node limit: {len(nodes)} > {self.constraints.max_nodes}"}
        if len(edges) > self.constraints.max_edges:
            return False, {"error": f"Graph exceeds maximum edge limit: {len(edges)} > {self.constraints.max_edges}"}

        graph = nx.DiGraph()
        start_nodes = {n["id"] for n in nodes if n.get("type") == "start"}
        end_nodes = {n["id"] for n in nodes if n.get("type") == "end"}

        for node in nodes:
            graph.add_node(node["id"], type=node.get("type", "action"))

        for edge in edges:
            graph.add_edge(edge["source"], edge["target"])

        # Orphan node detection
        orphans = set()
        for node_id in graph.nodes():
            if node_id not in start_nodes and node_id not in end_nodes:
                if graph.in_degree(node_id) == 0 and graph.out_degree(node_id) == 0:
                    orphans.add(node_id)

        # Circular path verification
        cycles = []
        try:
            cycles = list(nx.simple_cycles(graph))
        except nx.NetworkXUnbounded:
            cycles = ["Infinite cycles detected due to unbounded graph"]

        # Depth verification via BFS
        max_depth = 0
        for start in start_nodes:
            try:
                shortest_paths = nx.single_source_shortest_path_length(graph, start)
                max_depth = max(max_depth, max(shortest_paths.values()) if shortest_paths else 0)
            except nx.NetworkXNoPath:
                continue

        if max_depth > self.constraints.max_depth:
            return False, {"error": f"Graph depth {max_depth} exceeds maximum allowed depth {self.constraints.max_depth}"}

        return True, {
            "orphans": list(orphans),
            "cycles": cycles,
            "max_depth": max_depth,
            "fidelity_rate": (len(nodes) - len(orphans)) / len(nodes) if nodes else 1.0
        }

Step 3: Export Payload Construction, Serialization, and Callback Synchronization

The validated topology converts into a structured export payload containing bot ID references, node traversal matrices, and edge dependency directives. The exporter triggers automatic serialization, tracks latency, and forwards the payload to external versioning systems via callback handlers.

import time
import logging
from typing import Callable, Optional

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

class CognigyGraphExporter:
    def __init__(self, auth: CognigyAuth, constraints: GraphConstraints = GraphConstraints()):
        self.client = CognigyAPIClient(auth)
        self.validator = TopologyValidator(constraints)
        self.callback: Optional[Callable[[Dict[str, Any]], None]] = None

    def register_callback(self, handler: Callable[[Dict[str, Any]], None]) -> None:
        self.callback = handler

    def export_bot_topology(self, bot_id: str) -> Dict[str, Any]:
        start_time = time.perf_counter()
        logger.info("Initiating topology extraction for bot: %s", bot_id)

        # Atomic GET operations
        nodes = self.client._fetch_paginated("/v2/bots/{botId}/nodes", bot_id)
        edges = self.client._fetch_paginated("/v2/bots/{botId}/edges", bot_id)

        # Validation pipeline
        is_valid, validation_result = self.validator.validate(nodes, edges)
        if not is_valid:
            raise ValueError(f"Topology validation failed: {validation_result['error']}")

        # Construct traversal matrix
        adjacency = {node["id"]: [] for node in nodes}
        for edge in edges:
            adjacency[edge["source"]].append(edge["target"])

        # Construct edge dependency directives
        dependency_directives = []
        for edge in edges:
            dependency_directives.append({
                "source": edge["source"],
                "target": edge["target"],
                "condition": edge.get("condition", "default"),
                "priority": edge.get("priority", 0)
            })

        latency = time.perf_counter() - start_time

        export_payload = {
            "botId": bot_id,
            "exportVersion": "2.0",
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
            "topology": {
                "nodes": nodes,
                "edges": edges,
                "traversalMatrix": adjacency,
                "edgeDependencyDirectives": dependency_directives
            },
            "validation": validation_result,
            "metadata": {
                "exportLatencyMs": round(latency * 1000, 2),
                "graphFidelityRate": validation_result["fidelity_rate"],
                "nodeCount": len(nodes),
                "edgeCount": len(edges)
            }
        }

        # Audit log generation
        audit_entry = {
            "event": "GRAPH_EXPORT",
            "botId": bot_id,
            "status": "SUCCESS",
            "latencyMs": export_payload["metadata"]["exportLatencyMs"],
            "fidelityRate": export_payload["metadata"]["graphFidelityRate"],
            "timestamp": export_payload["timestamp"]
        }
        logger.info("AUDIT_LOG: %s", json.dumps(audit_entry))

        # Callback synchronization
        if self.callback:
            try:
                self.callback(export_payload)
                logger.info("Callback synchronization completed successfully.")
            except Exception as e:
                logger.warning("Callback synchronization failed: %s", str(e))

        return export_payload

Complete Working Example

The following script combines all components into a runnable module. Replace the placeholder credentials and base URL with your Cognigy.AI tenant values.

import sys
import json
import requests
import time
import networkx as nx
from pydantic import BaseModel, Field
from typing import List, Dict, Any, Tuple, Optional, Callable
from requests.adapters import HTTPAdapter
from urllib3.util.retry import Retry
from dataclasses import dataclass

@dataclass
class OAuthToken:
    access_token: str
    expires_in: int
    issued_at: float

class CognigyAuth:
    def __init__(self, base_url: str, client_id: str, client_secret: str, scopes: str = "bot:read"):
        self.base_url = base_url.rstrip("/")
        self.client_id = client_id
        self.client_secret = client_secret
        self.scopes = scopes
        self.token: Optional[OAuthToken] = None

    def get_token(self) -> str:
        if self.token and time.time() < (self.token.issued_at + self.token.expires_in - 30):
            return self.token.access_token
        url = f"{self.base_url}/oauth/token"
        payload = {
            "grant_type": "client_credentials",
            "client_id": self.client_id,
            "client_secret": self.client_secret,
            "scope": self.scopes
        }
        response = requests.post(url, data=payload, timeout=10)
        response.raise_for_status()
        data = response.json()
        self.token = OAuthToken(
            access_token=data["access_token"],
            expires_in=data["expires_in"],
            issued_at=time.time()
        )
        return self.token.access_token

class CognigyAPIClient:
    def __init__(self, auth: CognigyAuth):
        self.auth = auth
        self.session = requests.Session()
        retry_strategy = Retry(
            total=3,
            backoff_factor=1,
            status_forcelist=[429, 500, 502, 503, 504],
            allowed_methods=["GET"]
        )
        self.session.mount("https://", HTTPAdapter(max_retries=retry_strategy))

    def _fetch_paginated(self, endpoint: str, bot_id: str) -> List[Dict[str, Any]]:
        results = []
        offset = 0
        limit = 100
        token = self.auth.get_token()
        while True:
            url = f"{self.auth.base_url}{endpoint}"
            params = {"botId": bot_id, "limit": limit, "offset": offset}
            headers = {"Authorization": f"Bearer {token}", "Accept": "application/json"}
            response = self.session.get(url, params=params, headers=headers, timeout=15)
            response.raise_for_status()
            data = response.json()
            if not isinstance(data, dict) or "data" not in data:
                raise ValueError("Invalid API response format.")
            results.extend(data["data"])
            if offset + limit >= data["pagination"]["total"]:
                break
            offset += limit
        return results

class GraphConstraints(BaseModel):
    max_nodes: int = Field(500, gt=0)
    max_edges: int = Field(1500, gt=0)
    max_depth: int = Field(20, gt=0)

class TopologyValidator:
    def __init__(self, constraints: GraphConstraints):
        self.constraints = constraints

    def validate(self, nodes: List[Dict], edges: List[Dict]) -> Tuple[bool, Dict[str, Any]]:
        if len(nodes) > self.constraints.max_nodes:
            return False, {"error": f"Graph exceeds maximum node limit: {len(nodes)} > {self.constraints.max_nodes}"}
        if len(edges) > self.constraints.max_edges:
            return False, {"error": f"Graph exceeds maximum edge limit: {len(edges)} > {self.constraints.max_edges}"}
        graph = nx.DiGraph()
        start_nodes = {n["id"] for n in nodes if n.get("type") == "start"}
        end_nodes = {n["id"] for n in nodes if n.get("type") == "end"}
        for node in nodes:
            graph.add_node(node["id"], type=node.get("type", "action"))
        for edge in edges:
            graph.add_edge(edge["source"], edge["target"])
        orphans = set()
        for node_id in graph.nodes():
            if node_id not in start_nodes and node_id not in end_nodes:
                if graph.in_degree(node_id) == 0 and graph.out_degree(node_id) == 0:
                    orphans.add(node_id)
        cycles = []
        try:
            cycles = list(nx.simple_cycles(graph))
        except nx.NetworkXUnbounded:
            cycles = ["Infinite cycles detected"]
        max_depth = 0
        for start in start_nodes:
            try:
                shortest_paths = nx.single_source_shortest_path_length(graph, start)
                max_depth = max(max_depth, max(shortest_paths.values()) if shortest_paths else 0)
            except nx.NetworkXNoPath:
                continue
        if max_depth > self.constraints.max_depth:
            return False, {"error": f"Graph depth {max_depth} exceeds maximum allowed depth {self.constraints.max_depth}"}
        return True, {
            "orphans": list(orphans),
            "cycles": cycles,
            "max_depth": max_depth,
            "fidelity_rate": (len(nodes) - len(orphans)) / len(nodes) if nodes else 1.0
        }

class CognigyGraphExporter:
    def __init__(self, auth: CognigyAuth, constraints: GraphConstraints = GraphConstraints()):
        self.client = CognigyAPIClient(auth)
        self.validator = TopologyValidator(constraints)
        self.callback: Optional[Callable[[Dict[str, Any]], None]] = None

    def register_callback(self, handler: Callable[[Dict[str, Any]], None]) -> None:
        self.callback = handler

    def export_bot_topology(self, bot_id: str) -> Dict[str, Any]:
        start_time = time.perf_counter()
        nodes = self.client._fetch_paginated("/v2/bots/{botId}/nodes", bot_id)
        edges = self.client._fetch_paginated("/v2/bots/{botId}/edges", bot_id)
        is_valid, validation_result = self.validator.validate(nodes, edges)
        if not is_valid:
            raise ValueError(f"Topology validation failed: {validation_result['error']}")
        adjacency = {node["id"]: [] for node in nodes}
        for edge in edges:
            adjacency[edge["source"]].append(edge["target"])
        dependency_directives = []
        for edge in edges:
            dependency_directives.append({
                "source": edge["source"],
                "target": edge["target"],
                "condition": edge.get("condition", "default"),
                "priority": edge.get("priority", 0)
            })
        latency = time.perf_counter() - start_time
        export_payload = {
            "botId": bot_id,
            "exportVersion": "2.0",
            "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()),
            "topology": {
                "nodes": nodes,
                "edges": edges,
                "traversalMatrix": adjacency,
                "edgeDependencyDirectives": dependency_directives
            },
            "validation": validation_result,
            "metadata": {
                "exportLatencyMs": round(latency * 1000, 2),
                "graphFidelityRate": validation_result["fidelity_rate"],
                "nodeCount": len(nodes),
                "edgeCount": len(edges)
            }
        }
        audit_entry = {
            "event": "GRAPH_EXPORT",
            "botId": bot_id,
            "status": "SUCCESS",
            "latencyMs": export_payload["metadata"]["exportLatencyMs"],
            "fidelityRate": export_payload["metadata"]["graphFidelityRate"],
            "timestamp": export_payload["timestamp"]
        }
        print(f"AUDIT_LOG: {json.dumps(audit_entry)}")
        if self.callback:
            try:
                self.callback(export_payload)
            except Exception as e:
                print(f"Callback synchronization failed: {e}")
        return export_payload

def versioning_callback(payload: Dict[str, Any]) -> None:
    print(f"Syncing version {payload['botId']} to external registry. Fidelity: {payload['metadata']['graphFidelityRate']:.2f}")

if __name__ == "__main__":
    AUTH_CONFIG = {
        "base_url": "https://api.cognigy.ai/v2",
        "client_id": "YOUR_CLIENT_ID",
        "client_secret": "YOUR_CLIENT_SECRET"
    }
    BOT_ID = "YOUR_BOT_ID"

    auth = CognigyAuth(**AUTH_CONFIG)
    exporter = CognigyGraphExporter(auth)
    exporter.register_callback(versioning_callback)

    try:
        result = exporter.export_bot_topology(BOT_ID)
        print(json.dumps(result, indent=2))
    except requests.exceptions.HTTPError as e:
        print(f"HTTP Error: {e.response.status_code} - {e.response.text}")
        sys.exit(1)
    except ValueError as e:
        print(f"Validation Error: {e}")
        sys.exit(1)

Common Errors & Debugging

Error: 401 Unauthorized

• What causes it: The OAuth token has expired or the client credentials are invalid.
• How to fix it: Verify the client_id and client_secret match a registered Cognigy.AI integration. Ensure the bot:read scope is included in the token request. The CognigyAuth class automatically refreshes tokens 30 seconds before expiration.
• Code showing the fix: The get_token method checks time.time() < (self.token.issued_at + self.token.expires_in - 30) and re-fetches when the threshold is crossed.

Error: 403 Forbidden

• What causes it: The OAuth client lacks permission to access the specified bot ID or the tenant restricts programmatic exports.
• How to fix it: Assign the Bot Developer or Bot Administrator role to the service account in the Cognigy.AI admin console. Confirm the bot ID belongs to the authenticated tenant.
• Code showing the fix: No code change is required. Verify IAM roles in the Cognigy.AI dashboard under Integrations > Users & Roles.

Error: 429 Too Many Requests

• What causes it: The API rate limit is exceeded during paginated node or edge extraction.
• How to fix it: The CognigyAPIClient implements urllib3.util.Retry with exponential backoff for 429 responses. If failures persist, increase the backoff_factor or reduce concurrent export jobs.
• Code showing the fix: The Retry configuration in CognigyAPIClient.__init__ handles automatic retries with status_forcelist=[429, 500, 502, 503, 504].

Error: Topology Validation Failed

• What causes it: The graph exceeds max_nodes, max_edges, or max_depth constraints, or contains unreachable orphan nodes.
• How to fix it: Review the validation_result dictionary returned by TopologyValidator. Remove orphan nodes in the Cognigy.AI editor or adjust GraphConstraints thresholds if the complexity is intentional.
• Code showing the fix: The validate method raises a ValueError with the exact constraint violation. Catch it in the main block and adjust constraints or bot design accordingly.

Official References

• Cognigy.AI v2 REST API Documentation
• Cognigy.AI Bot Export & Import Guide
• NetworkX Graph Analysis Documentation
• Pydantic Schema Validation Reference