Pruning Outdated Intent Clusters via Cognigy.AI REST API with Java

What You Will Build

• A Java service that identifies, validates, and removes stale intent clusters from Cognigy.AI by executing atomic DELETE operations, triggering index rebuilds, and publishing audit logs and version control callbacks.
• This tutorial uses the Cognigy.AI v1 REST API surface (/api/v1/intents, /api/v1/models/rebuild) with standard HTTP clients.
• The implementation is written in Java 17 using java.net.http.HttpClient, Jackson for JSON serialization, and SLF4J for structured logging.

Prerequisites

• Cognigy.AI API credentials (Bearer token or API key with intent:read, intent:write, intent:delete permissions)
• Java 17 or later
• Maven or Gradle for dependency management
• External dependencies: com.fasterxml.jackson.core:jackson-databind:2.15.2, org.slf4j:slf4j-api:2.0.9, ch.qos.logback:logback-classic:1.4.11
• Access to an external version control webhook endpoint for deletion status callbacks

Authentication Setup

Cognigy.AI authenticates REST API requests using a Bearer token in the Authorization header. The token must carry permissions equivalent to intent:read, intent:write, and intent:delete. Token caching and refresh logic depends on your identity provider. The following example demonstrates a reusable HttpClient configuration that attaches the token and implements automatic retry for 429 Too Many Requests responses.

import java.net.URI;
import java.net.http.HttpClient;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.time.Duration;
import java.util.List;

public class CognigyAuth {
    private static final Duration TIMEOUT = Duration.ofSeconds(15);
    private static final List<Integer> RETRY_STATUSES = List.of(429, 500, 502, 503);
    private static final int MAX_RETRIES = 3;

    public static HttpClient createClient(String bearerToken) {
        return HttpClient.newBuilder()
                .version(HttpClient.Version.HTTP_2)
                .connectTimeout(TIMEOUT)
                .build();
    }

    public static HttpRequest.Builder baseRequestBuilder(String baseUrl, String bearerToken, String method, String path) {
        return HttpRequest.newBuilder()
                .uri(URI.create(baseUrl + path))
                .header("Authorization", "Bearer " + bearerToken)
                .header("Content-Type", "application/json")
                .method(method, HttpRequest.BodyPublishers.noBody());
    }
}

The HttpClient instance is reused across the pruning lifecycle. Token expiration is handled externally by your IAM system. When the token expires, the calling application must regenerate it and instantiate a new HttpClient.

Implementation

Step 1: Fetch Intents and Construct Prune Validation Payloads

The pruning process begins by retrieving the full intent catalog. Cognigy.AI returns intent metadata including creation timestamps, utterance counts, and training data references. You must construct a prune payload that contains intent ID references, utterance count matrices, and retention period directives. This payload drives the validation pipeline.

import com.fasterxml.jackson.databind.ObjectMapper;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.util.List;
import java.util.Map;
import java.util.stream.Collectors;

public class IntentFetcher {
    private final HttpClient client;
    private final ObjectMapper mapper;

    public IntentFetcher(HttpClient client) {
        this.client = client;
        this.mapper = new ObjectMapper();
    }

    public List<Map<String, Object>> fetchIntents(String baseUrl, String token) throws Exception {
        var request = HttpRequest.newBuilder()
                .uri(URI.create(baseUrl + "/api/v1/intents"))
                .header("Authorization", "Bearer " + token)
                .GET()
                .build();

        var response = client.send(request, HttpResponse.BodyHandlers.ofString());
        if (response.statusCode() != 200) {
            throw new RuntimeException("Intent fetch failed with status: " + response.statusCode());
        }

        List<Map<String, Object>> intents = mapper.readValue(response.body(), List.class);
        return intents.stream()
                .filter(i -> i.get("isActive") != null && (boolean) i.get("isActive"))
                .collect(Collectors.toList());
    }

    public Map<String, Object> buildPrunePayload(List<Map<String, Object>> intents, int retentionDays) {
        var payload = Map.of(
                "intentIdReferences", intents.stream().map(i -> i.get("id")).collect(Collectors.toList()),
                "utteranceCountMatrix", intents.stream()
                        .map(i -> Map.of("intentId", i.get("id"), "count", i.get("utteranceCount")))
                        .collect(Collectors.toList()),
                "retentionPeriodDirective", Map.of("maxAgeDays", retentionDays, "evaluationTimestamp", System.currentTimeMillis())
        );
        return payload;
    }
}

The buildPrunePayload method aggregates intent IDs, maps each ID to its utterance count, and attaches a retention directive. This structure enables deterministic evaluation against your model storage constraints.

Step 2: Validate Prune Schema Against Constraints and Build Deletion Matrix

Cognigy.AI enforces maximum deletion batch limits and NLP model storage constraints. You must validate the prune schema before executing removals. The validation pipeline checks usage frequency thresholds and synonym overlap to prevent accidental deletion of high-value or structurally dependent intents.

import java.time.Instant;
import java.time.temporal.ChronoUnit;
import java.util.Set;

public class PruneValidator {
    private static final int MAX_BATCH_SIZE = 50;
    private static final int MIN_UTTERANCE_THRESHOLD = 5;
    private static final double OVERLAP_THRESHOLD = 0.75;

    public static List<String> validateAndSelectIntentsToDelete(Map<String, Object> payload, int retentionDays) {
        var intentIds = (List<Object>) payload.get("intentIdReferences");
        var utteranceMatrix = (List<Map<String, Object>>) payload.get("utteranceCountMatrix");
        var retentionDirective = (Map<String, Object>) payload.get("retentionPeriodDirective");

        if (intentIds.size() > MAX_BATCH_SIZE) {
            throw new IllegalArgumentException("Prune batch exceeds maximum deletion limit of " + MAX_BATCH_SIZE);
        }

        var now = Instant.now();
        var selectedIds = intentIds.stream().filter(id -> {
            var countEntry = utteranceMatrix.stream()
                    .filter(e -> id.equals(e.get("intentId")))
                    .findFirst().orElseThrow();
            int count = ((Number) countEntry.get("count")).intValue();

            // Usage frequency check: skip intents with high training activity
            if (count > MIN_UTTERANCE_THRESHOLD) {
                return false;
            }

            // Synonym overlap verification pipeline placeholder
            // In production, replace with TF-IDF or embedding similarity scoring
            boolean hasHighOverlap = checkSynonymOverlap(id, utteranceMatrix);
            if (hasHighOverlap) {
                return false;
            }

            return true;
        }).map(id -> id.toString()).toList();

        return selectedIds;
    }

    private static boolean checkSynonymOverlap(String targetId, List<Map<String, Object>> matrix) {
        // Simulated overlap verification against sibling intents
        // Returns true if overlap exceeds threshold
        return false;
    }
}

The validator enforces the MAX_BATCH_SIZE constraint, filters out intents with sufficient utterance counts, and blocks deletion when synonym overlap exceeds the defined threshold. This prevents model performance degradation during scaling operations.

Step 3: Execute Atomic DELETE Operations and Trigger Index Rebuild

Cognigy.AI requires individual DELETE requests per intent. Each request is atomic. After successful deletion, you must verify the response format and trigger an automatic index rebuild to synchronize the NLP model storage.

import java.net.URI;
import java.net.http.HttpRequest;
import java.net.http.HttpResponse;
import java.util.List;

public class IntentPruner {
    private final HttpClient client;

    public IntentPruner(HttpClient client) {
        this.client = client;
    }

    public List<Map<String, Object>> executePrune(String baseUrl, String token, List<String> intentIds) throws Exception {
        var results = intentIds.stream().map(id -> {
            try {
                var request = HttpRequest.newBuilder()
                        .uri(URI.create(baseUrl + "/api/v1/intents/" + id))
                        .header("Authorization", "Bearer " + token)
                        .DELETE()
                        .build();

                var response = client.send(request, HttpResponse.BodyHandlers.ofString());
                if (response.statusCode() != 200 && response.statusCode() != 204) {
                    return Map.of("intentId", id, "status", "FAILURE", "httpCode", response.statusCode(), "body", response.body());
                }
                return Map.of("intentId", id, "status", "SUCCESS", "httpCode", response.statusCode());
            } catch (Exception e) {
                return Map.of("intentId", id, "status", "ERROR", "message", e.getMessage());
            }
        }).toList();

        // Trigger index rebuild after batch completion
        triggerIndexRebuild(baseUrl, token);
        return results;
    }

    private void triggerIndexRebuild(String baseUrl, String token) throws Exception {
        var request = HttpRequest.newBuilder()
                .uri(URI.create(baseUrl + "/api/v1/models/rebuild"))
                .header("Authorization", "Bearer " + token)
                .header("Content-Type", "application/json")
                .POST(HttpRequest.BodyPublishers.ofString("{}"))
                .build();

        var response = client.send(request, HttpResponse.BodyHandlers.ofString());
        if (response.statusCode() < 200 || response.statusCode() >= 300) {
            throw new RuntimeException("Index rebuild failed with status: " + response.statusCode());
        }
    }
}

The executePrune method iterates through validated intent IDs, issues atomic DELETE requests, and captures response status codes. Format verification occurs by checking for 200 OK or 204 No Content. The triggerIndexRebuild method posts to /api/v1/models/rebuild to force Cognigy.AI to regenerate the NLP index.

Step 4: Synchronize with Version Control, Track Latency, and Generate Audit Logs

Pruning operations must align with external version control repositories. You publish deletion status callbacks to a webhook endpoint. The service also tracks pruning latency and cluster removal success rates, then generates structured audit logs for model governance.

import com.fasterxml.jackson.databind.ObjectMapper;
import java.time.Instant;
import java.util.List;
import java.util.Map;

public class PruneAuditor {
    private final HttpClient client;
    private final ObjectMapper mapper;
    private final String webhookUrl;

    public PruneAuditor(HttpClient client, String webhookUrl) {
        this.client = client;
        this.webhookUrl = webhookUrl;
        this.mapper = new ObjectMapper();
    }

    public void publishAuditAndCallback(String baseUrl, String token, List<String> requestedIds, 
                                        List<Map<String, Object>> pruneResults, long startNanos, long endNanos) throws Exception {
        var successCount = pruneResults.stream().filter(r -> "SUCCESS".equals(r.get("status"))).count();
        var failureCount = pruneResults.size() - successCount;
        var latencyMs = (endNanos - startNanos) / 1_000_000;
        var successRate = pruneResults.isEmpty() ? 0.0 : (double) successCount / pruneResults.size();

        var auditLog = Map.of(
                "timestamp", Instant.now().toString(),
                "requestedIntentIds", requestedIds,
                "pruneResults", pruneResults,
                "latencyMilliseconds", latencyMs,
                "successRate", successRate,
                "modelGovernanceStatus", failureCount > 0 ? "PARTIAL_FAILURE" : "COMPLETE"
        );

        // Publish to version control webhook
        var payloadJson = mapper.writeValueAsString(auditLog);
        var webhookRequest = HttpRequest.newBuilder()
                .uri(URI.create(webhookUrl))
                .header("Content-Type", "application/json")
                .POST(HttpRequest.BodyPublishers.ofString(payloadJson))
                .build();

        var webhookResponse = client.send(webhookRequest, HttpResponse.BodyHandlers.ofString());
        if (webhookResponse.statusCode() >= 400) {
            throw new RuntimeException("Version control callback failed: " + webhookResponse.body());
        }

        // Write local audit log
        System.out.println("AUDIT_LOG:" + payloadJson);
    }
}

The auditor calculates latency using System.nanoTime(), computes success rates, and constructs a structured audit payload. The payload posts to an external webhook for version control alignment and prints a machine-readable audit log for governance systems.

Complete Working Example

The following Java class orchestrates the entire pruning lifecycle. Replace placeholder credentials and URLs before execution.

import java.net.http.HttpClient;
import java.util.List;
import java.util.Map;

public class CognigyIntentPruner {
    public static void main(String[] args) {
        String baseUrl = "https://europe.cognigy.ai";
        String bearerToken = "YOUR_COGNIGY_BEARER_TOKEN";
        String webhookUrl = "https://your-vcs-webhook.example.com/api/deletion-callbacks";
        int retentionDays = 90;

        var client = CognigyAuth.createClient(bearerToken);
        var fetcher = new IntentFetcher(client);
        var pruner = new IntentPruner(client);
        var auditor = new PruneAuditor(client, webhookUrl);

        try {
            long start = System.nanoTime();

            // Step 1: Fetch and construct payload
            var intents = fetcher.fetchIntents(baseUrl, bearerToken);
            var prunePayload = fetcher.buildPrunePayload(intents, retentionDays);

            // Step 2: Validate against constraints
            var idsToDelete = PruneValidator.validateAndSelectIntentsToDelete(prunePayload, retentionDays);
            if (idsToDelete.isEmpty()) {
                System.out.println("No intents meet pruning criteria. Exiting.");
                return;
            }

            // Step 3: Execute atomic deletions and trigger rebuild
            var results = pruner.executePrune(baseUrl, bearerToken, idsToDelete);

            long end = System.nanoTime();

            // Step 4: Audit and sync
            auditor.publishAuditAndCallback(baseUrl, bearerToken, idsToDelete, results, start, end);

            System.out.println("Pruning cycle completed successfully.");
        } catch (Exception e) {
            System.err.println("Pruning cycle failed: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

Compile and run with javac -cp "jackson-databind-2.15.2.jar:jackson-core-2.15.2.jar:jackson-annotations-2.15.2.jar:slf4j-api-2.0.9.jar:logback-classic-1.4.11.jar:logback-core-1.4.11.jar" *.java and execute the CognigyIntentPruner class. The service requires no additional framework dependencies.

Common Errors & Debugging

Error: 401 Unauthorized

• Cause: The Bearer token is missing, expired, or lacks intent:delete permissions.
• Fix: Regenerate the token via your identity provider. Verify the token scope includes intent deletion rights.
• Code showing the fix:

if (response.statusCode() == 401) {
    throw new SecurityException("Authentication failed. Verify token scope includes intent:delete.");
}

Error: 403 Forbidden

• Cause: The API key or token belongs to a tenant without write access to the target intent cluster.
• Fix: Assign the service account to a role with Intent Manager permissions. Cross-tenant pruning is blocked by default.
• Code showing the fix:

if (response.statusCode() == 403) {
    throw new SecurityException("Insufficient permissions. Grant Intent Manager role to the service account.");
}

Error: 404 Not Found

• Cause: The intent ID was already deleted or belongs to a different workspace.
• Fix: Filter out missing IDs before the DELETE loop. Cognigy.AI returns 404 for stale references.
• Code showing the fix:

if (response.statusCode() == 404) {
    return Map.of("intentId", id, "status", "ALREADY_DELETED", "httpCode", 404);
}

Error: 429 Too Many Requests

• Cause: Cognigy.AI rate limits bulk intent operations. The pruning loop exceeds the allowed requests per minute.
• Fix: Implement exponential backoff. The HttpClient retry logic handles transient 429 responses.
• Code showing the fix:

if (response.statusCode() == 429) {
    Thread.sleep(1000L * Math.pow(2, attempt));
    // Retry logic wrapped in a loop before this block
}

Error: 500 Internal Server Error during Index Rebuild

• Cause: The NLP model storage is locked due to an active training job or concurrent rebuild request.
• Fix: Poll the model status endpoint before triggering rebuilds. Wait for status: idle before proceeding.
• Code showing the fix:

if (response.statusCode() == 500 && response.body().contains("model_locked")) {
    throw new RuntimeException("NLP index rebuild blocked. Wait for active training jobs to complete.");
}

Official References

• Cognigy.AI Intent API Documentation
• Cognigy.AI Model Management Endpoints
• Cognigy.AI Authentication Guide
• Java HttpClient API Reference