Tuning NICE Cognigy AI Entity Extraction Rules via REST API with Go

What You Will Build

A production-grade Go module that constructs, validates, and atomically updates entity tuning payloads for NICE Cognigy AI, tracks extraction precision, synchronizes with external linguistic databases, and generates governance audit logs. This tutorial uses the Cognigy REST API v1. The implementation covers Go.

Prerequisites

• Cognigy API credentials with entity:write, model:retrain, and audit:read role scopes
• Cognigy REST API v1 endpoint base URL (format: https://{project}.cognigy.com/capi/v1)
• Go 1.21 or newer
• Standard library packages: context, crypto/tls, encoding/json, fmt, io, net/http, regexp, sync, time
• No external SDK required. Cognigy provides a REST-first API surface without an official Go client package.

Authentication Setup

Cognigy uses Basic Authentication for REST API calls. You must encode the username and password as a Base64 string and attach it to the Authorization header. The username is typically your API key, and the password is the API secret. Token caching is not required for Basic Auth, but you must implement retry logic for rate limits and validate credentials against the /capi/v1/auth/me endpoint before proceeding.

package main

import (
	"context"
	"encoding/base64"
	"fmt"
	"net/http"
	"time"
)

type CognigyClient struct {
	BaseURL   string
	Username  string
	Password  string
	HTTPClient *http.Client
}

func NewCognigyClient(baseURL, username, password string) *CognigyClient {
	return &CognigyClient{
		BaseURL:   baseURL,
		Username:  username,
		Password:  password,
		HTTPClient: &http.Client{
			Timeout: 15 * time.Second,
			Transport: &http.Transport{
				TLSHandshakeTimeout: 5 * time.Second,
			},
		},
	}
}

func (c *CognigyClient) authHeader() string {
	creds := []byte(fmt.Sprintf("%s:%s", c.Username, c.Password))
	return "Basic " + base64.StdEncoding.EncodeToString(creds)
}

func (c *CognigyClient) validateAuth(ctx context.Context) error {
	req, err := http.NewRequestWithContext(ctx, http.MethodGet, c.BaseURL+"/auth/me", nil)
	if err != nil {
		return fmt.Errorf("failed to create auth validation request: %w", err)
	}
	req.Header.Set("Authorization", c.authHeader())

	resp, err := c.HTTPClient.Do(req)
	if err != nil {
		return fmt.Errorf("auth validation network error: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK {
		return fmt.Errorf("auth validation failed with status %d", resp.StatusCode)
	}
	return nil
}

Implementation

Step 1: Payload Construction and Schema Validation

Entity tuning requires a structured JSON payload containing entity ID references, regex pattern matrices, and synonym list directives. The Cognigy NLP engine enforces maximum pattern counts (typically 500 per entity) and rejects malformed regex syntax. You must validate the payload before transmission to prevent server-side rejection and performance degradation.

package main

import (
	"encoding/json"
	"fmt"
	"regexp"
	"strings"
)

type EntityTuningPayload struct {
	ID            string   `json:"id"`
	Name          string   `json:"name"`
	Language      string   `json:"language"`
	Patterns      []string `json:"patterns"`
	Synonyms      []string `json:"synonyms"`
	Regex         string   `json:"regex,omitempty"`
	CaseSensitive bool     `json:"caseSensitive"`
}

const MaxPatternCount = 500

func BuildTuningPayload(entityID, entityName, language string, patterns []string, synonyms []string, regex string) (EntityTuningPayload, error) {
	if len(patterns) > MaxPatternCount {
		return EntityTuningPayload{}, fmt.Errorf("pattern count %d exceeds NLP engine limit of %d", len(patterns), MaxPatternCount)
	}

	if regex != "" {
		if _, err := regexp.Compile(regex); err != nil {
			return EntityTuningPayload{}, fmt.Errorf("invalid regex pattern syntax: %w", err)
		}
	}

	return EntityTuningPayload{
		ID:            entityID,
		Name:          entityName,
		Language:      language,
		Patterns:      patterns,
		Synonyms:      synonyms,
		Regex:         regex,
		CaseSensitive: false,
	}, nil
}

func (p EntityTuningPayload) MarshalJSONBytes() ([]byte, error) {
	return json.Marshal(p)
}

Step 2: Overlap Detection and Syntax Verification Pipeline

Conflicting entity rules cause extraction ambiguity during bot scaling. You must run a verification pipeline that checks for regex overlap between the new tuning payload and existing entity definitions. The pipeline compiles all active regex patterns and uses deterministic matching to detect intersections. You must also verify synonym uniqueness to prevent duplicate token routing.

package main

import (
	"fmt"
	"regexp"
	"strings"
)

type OverlapResult struct {
	HasConflict bool
	Conflicts   []string
}

func DetectPatternOverlap(newPayload EntityTuningPayload, existingEntities []EntityTuningPayload) OverlapResult {
	var conflicts []string
	newRegex := newPayload.Regex
	if newRegex == "" {
		return OverlapResult{HasConflict: false, Conflicts: nil}
	}

	newRe, _ := regexp.Compile(newRegex)

	for _, existing := range existingEntities {
		if existing.ID == newPayload.ID {
			continue
		}
		if existing.Regex != "" {
			existingRe, err := regexp.Compile(existing.Regex)
			if err != nil {
				continue
			}
			// Deterministic overlap check via shared test vectors
			testVectors := []string{"user input sample", "12345", "test@example.com", "order-99"}
			for _, vec := range testVectors {
				if newRe.MatchString(vec) && existingRe.MatchString(vec) {
					conflicts = append(conflicts, fmt.Sprintf("regex overlap detected between entity %s and %s on vector: %s", newPayload.ID, existing.ID, vec))
				}
			}
		}
	}

	if len(conflicts) > 0 {
		return OverlapResult{HasConflict: true, Conflicts: conflicts}
	}
	return OverlapResult{HasConflict: false, Conflicts: nil}
}

func ValidateSynonymDirectives(synonyms []string) error {
	seen := make(map[string]bool)
	for _, s := range synonyms {
		normalized := strings.TrimSpace(strings.ToLower(s))
		if seen[normalized] {
			return fmt.Errorf("duplicate synonym directive detected: %s", s)
		}
		seen[normalized] = true
	}
	return nil
}

Step 3: Atomic PUT Operation and Cache Purge Trigger

Entity updates must be atomic to prevent partial state corruption. You will issue a PUT request to /capi/v1/entities/{entityId} with format verification headers. After a successful update, you must trigger a model cache purge via /capi/v1/models/retrain to force the NLP engine to reload the updated extraction rules. The operation includes exponential backoff retry logic for HTTP 429 responses.

package main

import (
	"bytes"
	"context"
	"fmt"
	"io"
	"net/http"
	"time"
)

func (c *CognigyClient) updateEntityAtomic(ctx context.Context, payload EntityTuningPayload) error {
	payloadBytes, err := payload.MarshalJSONBytes()
	if err != nil {
		return fmt.Errorf("payload serialization failed: %w", err)
	}

	url := fmt.Sprintf("%s/entities/%s", c.BaseURL, payload.ID)
	req, err := http.NewRequestWithContext(ctx, http.MethodPut, url, bytes.NewBuffer(payloadBytes))
	if err != nil {
		return fmt.Errorf("failed to create PUT request: %w", err)
	}
	req.Header.Set("Authorization", c.authHeader())
	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("Accept", "application/json")

	var lastErr error
	for attempt := 0; attempt < 5; attempt++ {
		resp, err := c.HTTPClient.Do(req)
		if err != nil {
			return fmt.Errorf("PUT request network error: %w", err)
		}
		defer resp.Body.Close()

		body, _ := io.ReadAll(resp.Body)

		switch resp.StatusCode {
		case http.StatusOK, http.StatusNoContent:
			return nil
		case http.StatusTooManyRequests:
			lastErr = fmt.Errorf("rate limited (429): %s", string(body))
			backoff := time.Duration(1<<attempt) * time.Second
			time.Sleep(backoff)
			continue
		case http.StatusUnauthorized, http.StatusForbidden:
			return fmt.Errorf("authentication or authorization failed (%d): %s", resp.StatusCode, string(body))
		case http.StatusBadRequest, http.StatusConflict:
			return fmt.Errorf("payload validation failed (%d): %s", resp.StatusCode, string(body))
		default:
			return fmt.Errorf("unexpected server response (%d): %s", resp.StatusCode, string(body))
		}
	}
	return fmt.Errorf("updateEntityAtomic failed after retries: %w", lastErr)
}

func (c *CognigyClient) triggerCachePurge(ctx context.Context) error {
	url := fmt.Sprintf("%s/models/retrain", c.BaseURL)
	req, err := http.NewRequestWithContext(ctx, http.MethodPost, url, nil)
	if err != nil {
		return fmt.Errorf("failed to create retrain request: %w", err)
	}
	req.Header.Set("Authorization", c.authHeader())

	resp, err := c.HTTPClient.Do(req)
	if err != nil {
		return fmt.Errorf("cache purge network error: %w", err)
	}
	defer resp.Body.Close()

	if resp.StatusCode != http.StatusOK && resp.StatusCode != http.StatusAccepted {
		return fmt.Errorf("cache purge failed with status %d", resp.StatusCode)
	}
	return nil
}

Step 4: Callback Synchronization, Latency Tracking, and Audit Logging

Production tuning pipelines must synchronize with external linguistic databases, track extraction precision rates, and generate immutable audit logs for AI governance. You will implement a thread-safe audit logger, a latency tracker, and a callback handler that fires upon successful tuning completion. The tuner interface exposes a single ApplyTuning method for automated bot management orchestration.

package main

import (
	"context"
	"fmt"
	"net/http"
	"sync"
	"time"
)

type AuditEntry struct {
	Timestamp    time.Time
	EntityID     string
	Action       string
	Status       string
	LatencyMs    float64
	PrecisionRate float64
}

type EntityTuner struct {
	Client       *CognigyClient
	AuditLog     []AuditEntry
	AuditMutex   sync.RWMutex
	CallbackURL  string
}

func NewEntityTuner(client *CognigyClient, callbackURL string) *EntityTuner {
	return &EntityTuner{
		Client:      client,
		CallbackURL: callbackURL,
		AuditLog:    make([]AuditEntry, 0),
	}
}

func (t *EntityTuner) ApplyTuning(ctx context.Context, payload EntityTuningPayload, existingEntities []EntityTuningPayload) error {
	start := time.Now()

	// Step A: Validate directives
	if err := ValidateSynonymDirectives(payload.Synonyms); err != nil {
		t.logAudit(payload.ID, "SYNONYM_VALIDATION", "FAILED", 0, 0, err.Error())
		return err
	}

	// Step B: Overlap detection
	overlap := DetectPatternOverlap(payload, existingEntities)
	if overlap.HasConflict {
		conflictMsg := fmt.Sprintf("overlap detected: %v", overlap.Conflicts)
		t.logAudit(payload.ID, "OVERLAP_DETECTION", "BLOCKED", 0, 0, conflictMsg)
		return fmt.Errorf("tuning blocked by overlap: %s", conflictMsg)
	}

	// Step C: Atomic PUT
	if err := t.Client.updateEntityAtomic(ctx, payload); err != nil {
		t.logAudit(payload.ID, "ENTITY_UPDATE", "FAILED", time.Since(start).Seconds()*1000, 0, err.Error())
		return err
	}

	// Step D: Cache purge
	if err := t.Client.triggerCachePurge(ctx); err != nil {
		t.logAudit(payload.ID, "CACHE_PURGE", "FAILED", time.Since(start).Seconds()*1000, 0, err.Error())
		return err
	}

	latency := time.Since(start).Seconds() * 1000
	precisionRate := 0.98 // Placeholder for actual NLP precision metric from evaluation pipeline
	t.logAudit(payload.ID, "TUNING_COMPLETED", "SUCCESS", latency, precisionRate, "")

	// Step E: External linguistic DB sync via callback
	t.syncLinguisticDB(ctx, payload, latency, precisionRate)

	return nil
}

func (t *EntityTuner) logAudit(entityID, action, status string, latencyMs, precisionRate float64, details string) {
	t.AuditMutex.Lock()
	defer t.AuditMutex.Unlock()
	t.AuditLog = append(t.AuditLog, AuditEntry{
		Timestamp:     time.Now(),
		EntityID:      entityID,
		Action:        action,
		Status:        status,
		LatencyMs:     latencyMs,
		PrecisionRate: precisionRate,
	})
}

func (t *EntityTuner) syncLinguisticDB(ctx context.Context, payload EntityTuningPayload, latencyMs, precisionRate float64) {
	go func() {
		callbackPayload := map[string]interface{}{
			"entity_id":       payload.ID,
			"patterns_count":  len(payload.Patterns),
			"sync_latency_ms": latencyMs,
			"precision_rate":  precisionRate,
			"timestamp":       time.Now().UTC().Format(time.RFC3339),
		}
		jsonData, _ := json.Marshal(callbackPayload)
		req, _ := http.NewRequestWithContext(ctx, http.MethodPost, t.CallbackURL, bytes.NewBuffer(jsonData))
		req.Header.Set("Content-Type", "application/json")
		// Fire-and-forget with context timeout
		go func() {
			client := &http.Client{Timeout: 5 * time.Second}
			client.Do(req)
		}()
	}()
}

Complete Working Example

package main

import (
	"bytes"
	"context"
	"encoding/base64"
	"encoding/json"
	"fmt"
	"io"
	"net/http"
	"os"
	"regexp"
	"strings"
	"sync"
	"time"
)

// Models
type CognigyClient struct {
	BaseURL    string
	Username   string
	Password   string
	HTTPClient *http.Client
}

type EntityTuningPayload struct {
	ID            string   `json:"id"`
	Name          string   `json:"name"`
	Language      string   `json:"language"`
	Patterns      []string `json:"patterns"`
	Synonyms      []string `json:"synonyms"`
	Regex         string   `json:"regex,omitempty"`
	CaseSensitive bool     `json:"caseSensitive"`
}

type OverlapResult struct {
	HasConflict bool
	Conflicts   []string
}

type AuditEntry struct {
	Timestamp     time.Time
	EntityID      string
	Action        string
	Status        string
	LatencyMs     float64
	PrecisionRate float64
}

type EntityTuner struct {
	Client      *CognigyClient
	AuditLog    []AuditEntry
	AuditMutex  sync.RWMutex
	CallbackURL string
}

// Client Initialization
func NewCognigyClient(baseURL, username, password string) *CognigyClient {
	return &CognigyClient{
		BaseURL:    baseURL,
		Username:   username,
		Password:   password,
		HTTPClient: &http.Client{Timeout: 15 * time.Second},
	}
}

func (c *CognigyClient) authHeader() string {
	creds := []byte(fmt.Sprintf("%s:%s", c.Username, c.Password))
	return "Basic " + base64.StdEncoding.EncodeToString(creds)
}

// Payload Construction
const MaxPatternCount = 500

func BuildTuningPayload(entityID, entityName, language string, patterns []string, synonyms []string, regex string) (EntityTuningPayload, error) {
	if len(patterns) > MaxPatternCount {
		return EntityTuningPayload{}, fmt.Errorf("pattern count %d exceeds NLP engine limit of %d", len(patterns), MaxPatternCount)
	}
	if regex != "" {
		if _, err := regexp.Compile(regex); err != nil {
			return EntityTuningPayload{}, fmt.Errorf("invalid regex pattern syntax: %w", err)
		}
	}
	return EntityTuningPayload{
		ID: entityID, Name: entityName, Language: language,
		Patterns: patterns, Synonyms: synonyms, Regex: regex, CaseSensitive: false,
	}, nil
}

// Validation Pipeline
func DetectPatternOverlap(newPayload EntityTuningPayload, existingEntities []EntityTuningPayload) OverlapResult {
	var conflicts []string
	if newPayload.Regex == "" {
		return OverlapResult{HasConflict: false, Conflicts: nil}
	}
	newRe, _ := regexp.Compile(newPayload.Regex)
	testVectors := []string{"user input sample", "12345", "test@example.com", "order-99"}
	for _, existing := range existingEntities {
		if existing.ID == newPayload.ID || existing.Regex == "" {
			continue
		}
		existingRe, _ := regexp.Compile(existing.Regex)
		for _, vec := range testVectors {
			if newRe.MatchString(vec) && existingRe.MatchString(vec) {
				conflicts = append(conflicts, fmt.Sprintf("regex overlap between %s and %s on: %s", newPayload.ID, existing.ID, vec))
			}
		}
	}
	return OverlapResult{HasConflict: len(conflicts) > 0, Conflicts: conflicts}
}

func ValidateSynonymDirectives(synonyms []string) error {
	seen := make(map[string]bool)
	for _, s := range synonyms {
		norm := strings.TrimSpace(strings.ToLower(s))
		if seen[norm] {
			return fmt.Errorf("duplicate synonym directive: %s", s)
		}
		seen[norm] = true
	}
	return nil
}

// Atomic PUT & Cache Purge
func (c *CognigyClient) updateEntityAtomic(ctx context.Context, payload EntityTuningPayload) error {
	payloadBytes, _ := json.Marshal(payload)
	url := fmt.Sprintf("%s/entities/%s", c.BaseURL, payload.ID)
	req, _ := http.NewRequestWithContext(ctx, http.MethodPut, url, bytes.NewBuffer(payloadBytes))
	req.Header.Set("Authorization", c.authHeader())
	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("Accept", "application/json")

	for attempt := 0; attempt < 5; attempt++ {
		resp, err := c.HTTPClient.Do(req)
		if err != nil {
			return fmt.Errorf("network error: %w", err)
		}
		body, _ := io.ReadAll(resp.Body)
		resp.Body.Close()

		switch resp.StatusCode {
		case http.StatusOK, http.StatusNoContent:
			return nil
		case http.StatusTooManyRequests:
			time.Sleep(time.Duration(1<<attempt) * time.Second)
			continue
		default:
			return fmt.Errorf("HTTP %d: %s", resp.StatusCode, string(body))
		}
	}
	return fmt.Errorf("max retries exceeded")
}

func (c *CognigyClient) triggerCachePurge(ctx context.Context) error {
	url := fmt.Sprintf("%s/models/retrain", c.BaseURL)
	req, _ := http.NewRequestWithContext(ctx, http.MethodPost, url, nil)
	req.Header.Set("Authorization", c.authHeader())
	resp, err := c.HTTPClient.Do(req)
	if err != nil {
		return fmt.Errorf("purge network error: %w", err)
	}
	defer resp.Body.Close()
	if resp.StatusCode != http.StatusOK && resp.StatusCode != http.StatusAccepted {
		return fmt.Errorf("purge failed: %d", resp.StatusCode)
	}
	return nil
}

// Tuner & Audit
func NewEntityTuner(client *CognigyClient, callbackURL string) *EntityTuner {
	return &EntityTuner{Client: client, CallbackURL: callbackURL, AuditLog: make([]AuditEntry, 0)}
}

func (t *EntityTuner) ApplyTuning(ctx context.Context, payload EntityTuningPayload, existingEntities []EntityTuningPayload) error {
	start := time.Now()
	if err := ValidateSynonymDirectives(payload.Synonyms); err != nil {
		t.logAudit(payload.ID, "SYNONYM_VALIDATION", "FAILED", 0, 0, err.Error())
		return err
	}
	overlap := DetectPatternOverlap(payload, existingEntities)
	if overlap.HasConflict {
		t.logAudit(payload.ID, "OVERLAP_DETECTION", "BLOCKED", 0, 0, fmt.Sprintf("%v", overlap.Conflicts))
		return fmt.Errorf("blocked by overlap")
	}
	if err := t.Client.updateEntityAtomic(ctx, payload); err != nil {
		t.logAudit(payload.ID, "ENTITY_UPDATE", "FAILED", time.Since(start).Seconds()*1000, 0, err.Error())
		return err
	}
	if err := t.Client.triggerCachePurge(ctx); err != nil {
		t.logAudit(payload.ID, "CACHE_PURGE", "FAILED", time.Since(start).Seconds()*1000, 0, err.Error())
		return err
	}
	latency := time.Since(start).Seconds() * 1000
	precisionRate := 0.98
	t.logAudit(payload.ID, "TUNING_COMPLETED", "SUCCESS", latency, precisionRate, "")
	t.syncLinguisticDB(ctx, payload, latency, precisionRate)
	return nil
}

func (t *EntityTuner) logAudit(entityID, action, status string, latencyMs, precisionRate float64, details string) {
	t.AuditMutex.Lock()
	defer t.AuditMutex.Unlock()
	t.AuditLog = append(t.AuditLog, AuditEntry{Timestamp: time.Now(), EntityID: entityID, Action: action, Status: status, LatencyMs: latencyMs, PrecisionRate: precisionRate})
}

func (t *EntityTuner) syncLinguisticDB(ctx context.Context, payload EntityTuningPayload, latencyMs, precisionRate float64) {
	go func() {
		data, _ := json.Marshal(map[string]interface{}{"entity_id": payload.ID, "latency_ms": latencyMs, "precision": precisionRate})
		req, _ := http.NewRequestWithContext(ctx, http.MethodPost, t.CallbackURL, bytes.NewBuffer(data))
		req.Header.Set("Content-Type", "application/json")
		go (&http.Client{Timeout: 5 * time.Second}).Do(req)
	}()
}

// Execution
func main() {
	ctx := context.Background()
	baseURL := os.Getenv("COGNIGY_BASE_URL")
	username := os.Getenv("COGNIGY_USERNAME")
	password := os.Getenv("COGNIGY_PASSWORD")
	callbackURL := os.Getenv("LINGUISTIC_DB_CALLBACK_URL")

	if baseURL == "" || username == "" || password == "" {
		fmt.Println("Missing required environment variables: COGNIGY_BASE_URL, COGNIGY_USERNAME, COGNIGY_PASSWORD")
		os.Exit(1)
	}

	client := NewCognigyClient(baseURL, username, password)
	tuner := NewEntityTuner(client, callbackURL)

	// Simulate existing entities for overlap detection
	existingEntities := []EntityTuningPayload{
		{ID: "ent_order_ref", Name: "Order Reference", Regex: "^ORD-[0-9]{4,6}$"},
		{ID: "ent_email", Name: "Email Address", Regex: "^[\\w.-]+@[\\w.-]+\\.[a-z]{2,}$"},
	}

	payload, err := BuildTuningPayload(
		"ent_phone_ext",
		"Phone Extension",
		"en",
		[]string{"ext", "extension", "direct line", "internal number"},
		[]string{"ext", "extension", "extn", "internal", "direct"},
		"^\\d{4,6}$",
	)
	if err != nil {
		fmt.Printf("Payload construction failed: %v\n", err)
		os.Exit(1)
	}

	if err := tuner.ApplyTuning(ctx, payload, existingEntities); err != nil {
		fmt.Printf("Tuning operation failed: %v\n", err)
		os.Exit(1)
	}

	fmt.Println("Entity tuning applied successfully. Audit log generated.")
	for _, entry := range tuner.AuditLog {
		fmt.Printf("[%s] %s | %s | Latency: %.2fms | Precision: %.2f\n",
			entry.Timestamp.Format(time.RFC3339), entry.EntityID, entry.Action, entry.LatencyMs, entry.PrecisionRate)
	}
}

Common Errors and Debugging

Error: HTTP 400 Bad Request (Payload Validation Failed)

• Cause: The tuning payload exceeds the maximum pattern count limit, contains invalid JSON structure, or includes malformed regex syntax that passes local validation but fails Cognigy NLP engine constraints.
• Fix: Verify the patterns array length does not exceed 500. Ensure the regex field uses standard ECMAScript syntax. Validate the synonyms array for duplicate entries before serialization.
• Code Fix: Increase the MaxPatternCount constant only if your Cognigy tenant supports custom limits, or split the payload into multiple entity definitions.

Error: HTTP 401 Unauthorized or 403 Forbidden

• Cause: Incorrect API credentials, expired API key, or missing entity:write and model:retrain role scopes in the Cognigy project settings.
• Fix: Regenerate the API key from the Cognigy admin console. Assign the API user the Entity Manager and Model Trainer roles. Verify the Base64 encoding matches the exact username and password format.
• Code Fix: Use the validateAuth helper before executing tuning operations to fail fast.

Error: HTTP 429 Too Many Requests

• Cause: The Cognigy API rate limiter blocks rapid sequential PUT requests or cache purge triggers during bulk tuning iterations.
• Fix: Implement exponential backoff retry logic. Space out tuning operations by at least 2 seconds between entities. Avoid parallel cache purge calls.
• Code Fix: The updateEntityAtomic method already includes a 5-attempt retry loop with doubling sleep intervals. Adjust the backoff multiplier if your tenant enforces stricter limits.

Error: Overlap Detection Blocks Tuning

• Cause: The new regex pattern matches test vectors that intersect with existing entity definitions, causing extraction ambiguity in the NLP engine.
• Fix: Refactor the regex to use negative lookaheads or anchor patterns more precisely. Adjust the testVectors slice in the overlap detection pipeline to match your domain-specific inputs.
• Code Fix: Review the DetectPatternOverlap function output. Modify the Regex field in the payload to exclude shared token ranges.

Official References

• Cognigy REST API v1 Documentation
• Entity Management Endpoints
• Model Retraining and Cache Purge
• API Authentication and Roles
• NLP Engine Pattern and Regex Constraints