Executing NICE Cognigy.AI Semantic Search Queries via REST API with Go

What You Will Build

A production-ready Go module that executes semantic search queries against Cognigy.AI knowledge bases, normalizes relevance scores, extracts contextual snippets, applies synonym expansion and fuzzy matching, tracks latency and accuracy metrics, exports analytics, generates audit logs, and exposes a reusable executor interface. The implementation uses the Cognigy.AI REST API directly with net/http and standard library packages. The tutorial covers Go 1.21+ with context-aware HTTP clients, exponential backoff retry logic, and structured audit logging.

Prerequisites

• Cognigy.AI OAuth client credentials (client ID and client secret)
• Required OAuth scopes: knowledge:read, search:execute
• Go 1.21 or later
• Standard library packages: net/http, encoding/json, context, time, log/slog, strings, math, sync, io
• No external dependencies required for this implementation

Authentication Setup

Cognigy.AI uses OAuth 2.0 client credentials flow for server-to-server API access. The following code implements token acquisition, caching, and automatic refresh when the token expires.

package cognigy

import (
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"net/http"
	"sync"
	"time"
)

type OAuthConfig struct {
	BaseURL      string
	ClientID     string
	ClientSecret string
}

type TokenResponse struct {
	AccessToken string `json:"access_token"`
	TokenType   string `json:"token_type"`
	ExpiresIn   int64  `json:"expires_in"`
}

type TokenCache struct {
	mu        sync.RWMutex
	token     string
	expiresAt time.Time
}

func (c *TokenCache) IsExpired() bool {
	c.mu.RLock()
	defer c.mu.RUnlock()
	return time.Now().After(c.expiresAt)
}

func (c *TokenCache) Set(token string, expiresIn int64) {
	c.mu.Lock()
	defer c.mu.Unlock()
	c.token = token
	c.expiresAt = time.Now().Add(time.Duration(expiresIn) * time.Second)
}

func (c *TokenCache) Get() string {
	c.mu.RLock()
	defer c.mu.RUnlock()
	return c.token
}

func FetchOAuthToken(ctx context.Context, cfg OAuthConfig) (string, error) {
	payload := map[string]string{
		"grant_type":    "client_credentials",
		"client_id":     cfg.ClientID,
		"client_secret": cfg.ClientSecret,
		"scope":         "knowledge:read search:execute",
	}
	
	jsonPayload, err := json.Marshal(payload)
	if err != nil {
		return "", fmt.Errorf("failed to marshal OAuth payload: %w", err)
	}

	req, err := http.NewRequestWithContext(ctx, http.MethodPost, cfg.BaseURL+"/api/v1/oauth/token", bytes.NewBuffer(jsonPayload))
	if err != nil {
		return "", fmt.Errorf("failed to create OAuth request: %w", err)
	}
	req.Header.Set("Content-Type", "application/json")

	client := &http.Client{Timeout: 10 * time.Second}
	resp, err := client.Do(req)
	if err != nil {
		return "", fmt.Errorf("OAuth request failed: %w", err)
	}
	defer resp.Body.Close()

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

	var tokenResp TokenResponse
	if err := json.NewDecoder(resp.Body).Decode(&tokenResp); err != nil {
		return "", fmt.Errorf("failed to decode OAuth response: %w", err)
	}

	return tokenResp.AccessToken, nil
}

Implementation

Step 1: Query Payload Construction and Schema Validation

Cognigy.AI requires explicit knowledge base identification and vector index constraints for semantic search. The payload must validate limits, filter structures, and KB availability before dispatch.

type SearchRequest struct {
	Text          string            `json:"text"`
	KnowledgeBaseID string          `json:"knowledgeBaseId"`
	Limit         int               `json:"limit"`
	Offset        int               `json:"offset"`
	Filters       map[string]string `json:"filters,omitempty"`
	Semantic      bool              `json:"semantic"`
}

type SearchResponse struct {
	Results   []SearchResult `json:"results"`
	Total     int            `json:"total"`
	NextToken string         `json:"nextToken,omitempty"`
}

type SearchResult struct {
	ID        string   `json:"id"`
	Title     string   `json:"title"`
	Content   string   `json:"content"`
	Score     float64  `json:"score"`
	Snippets  []string `json:"snippets"`
	Tags      []string `json:"tags"`
}

func ValidateSearchRequest(req SearchRequest) error {
	if req.Text == "" {
		return fmt.Errorf("search text cannot be empty")
	}
	if req.KnowledgeBaseID == "" {
		return fmt.Errorf("knowledgeBaseId is required")
	}
	if req.Limit < 1 || req.Limit > 100 {
		return fmt.Errorf("limit must be between 1 and 100")
	}
	if req.Offset < 0 {
		return fmt.Errorf("offset cannot be negative")
	}
	if !req.Semantic {
		return fmt.Errorf("semantic flag must be enabled for vector index queries")
	}
	return nil
}

Step 2: Core Search Execution with Retry Logic

The Cognigy.AI search endpoint enforces strict rate limits. This implementation includes exponential backoff for HTTP 429 responses and context-aware cancellation.

HTTP Request/Response Cycle Example:

Method: POST
Path: /api/v1/knowledge/search
Headers: 
  Authorization: Bearer <access_token>
  Content-Type: application/json
  X-Request-ID: <uuid>

Request Body:
{
  "text": "how do I reset my password",
  "knowledgeBaseId": "kb_prod_support",
  "limit": 5,
  "offset": 0,
  "filters": {"category": "account-management"},
  "semantic": true
}

Response Body (200 OK):
{
  "results": [
    {
      "id": "doc_8f3a9c",
      "title": "Password Reset Workflow",
      "content": "Users can reset passwords via the self-service portal or by contacting support...",
      "score": 0.87,
      "snippets": ["Users can reset passwords via the self-service portal", "Support agents can trigger resets via admin console"],
      "tags": ["account", "security"]
    }
  ],
  "total": 12,
  "nextToken": "eyJvZmZzZXQiOjV9"
}

func ExecuteSearch(ctx context.Context, client *http.Client, baseURL, token string, req SearchRequest) (*SearchResponse, error) {
	jsonBody, err := json.Marshal(req)
	if err != nil {
		return nil, fmt.Errorf("failed to marshal search request: %w", err)
	}

	endpoint := baseURL + "/api/v1/knowledge/search"
	var lastErr error
	retryDelay := 500 * time.Millisecond
	maxRetries := 3

	for attempt := 0; attempt <= maxRetries; attempt++ {
		httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, endpoint, bytes.NewBuffer(jsonBody))
		if err != nil {
			return nil, fmt.Errorf("failed to create search request: %w", err)
		}
		httpReq.Header.Set("Authorization", "Bearer "+token)
		httpReq.Header.Set("Content-Type", "application/json")
		httpReq.Header.Set("Accept", "application/json")

		resp, err := client.Do(httpReq)
		if err != nil {
			lastErr = fmt.Errorf("search request failed: %w", err)
			time.Sleep(retryDelay)
			retryDelay *= 2
			continue
		}
		defer resp.Body.Close()

		if resp.StatusCode == http.StatusTooManyRequests {
			lastErr = fmt.Errorf("rate limit exceeded (429)")
			time.Sleep(retryDelay)
			retryDelay *= 2
			continue
		}

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

		var searchResp SearchResponse
		if err := json.NewDecoder(resp.Body).Decode(&searchResp); err != nil {
			return nil, fmt.Errorf("failed to decode search response: %w", err)
		}

		return &searchResp, nil
	}

	return nil, fmt.Errorf("search exhausted retries: %w", lastErr)
}

Step 3: Response Parsing, Score Normalization, and Snippet Extraction

Raw vector scores vary across knowledge base index sizes. Min-max normalization maps scores to a consistent 0.0-1.0 range. Snippet extraction trims content to a readable context window.

func NormalizeScores(results []SearchResult) []SearchResult {
	if len(results) == 0 {
		return results
	}

	maxScore := results[0].Score
	minScore := results[0].Score
	for _, r := range results {
		if r.Score > maxScore {
			maxScore = r.Score
		}
		if r.Score < minScore {
			minScore = r.Score
		}
	}

	rangeDiff := maxScore - minScore
	normalized := make([]SearchResult, len(results))
	for i, r := range results {
		norm := r
		if rangeDiff > 0 {
			norm.Score = (r.Score - minScore) / rangeDiff
		} else {
			norm.Score = 1.0
		}
		normalized[i] = norm
	}
	return normalized
}

func ExtractTopSnippet(snippets []string, maxLength int) string {
	if len(snippets) == 0 {
		return ""
	}
	s := snippets[0]
	if len(s) > maxLength {
		return s[:maxLength] + "..."
	}
	return s
}

Step 4: Query Optimization with Synonym Expansion and Fuzzy Matching

Variant user inputs reduce recall rates. This step expands the query using a synonym dictionary and applies Levenshtein distance filtering to match known knowledge base terms.

var synonymMap = map[string][]string{
	"password": {"passcode", "credentials", "login secret"},
	"reset":    {"restart", "clear", "refresh"},
	"account":  {"profile", "user", "login"},
}

func ExpandSynonyms(query string) string {
	words := strings.Fields(strings.ToLower(query))
	expanded := make([]string, 0, len(words))
	for _, w := range words {
		expanded = append(expanded, w)
		if syns, ok := synonymMap[w]; ok {
			expanded = append(expanded, syns...)
		}
	}
	return strings.Join(expanded, " ")
}

func LevenshteinDistance(a, b string) int {
	if len(a) == 0 {
		return len(b)
	}
	if len(b) == 0 {
		return len(a)
	}
	m, n := len(a), len(b)
	d := make([][]int, m+1)
	for i := range d {
		d[i] = make([]int, n+1)
		d[i][0] = i
	}
	for j := 0; j <= n; j++ {
		d[0][j] = j
	}
	for i := 1; i <= m; i++ {
		for j := 1; j <= n; j++ {
			cost := 0
			if a[i-1] != b[j-1] {
				cost = 1
			}
			d[i][j] = min(d[i-1][j]+1, min(d[i][j-1]+1, d[i-1][j-1]+cost))
		}
	}
	return d[m][n]
}

func min(a, b int) int {
	if a < b {
		return a
	}
	return b
}

func ApplyFuzzyMatch(query string, threshold int) string {
	words := strings.Fields(strings.ToLower(query))
	matched := make([]string, 0)
	for _, w := range words {
		if len(w) < 3 {
			matched = append(matched, w)
			continue
		}
		bestMatch := w
		bestDist := len(w)
		for candidate := range synonymMap {
			dist := LevenshteinDistance(w, candidate)
			if dist < bestDist && dist <= threshold {
				bestMatch = candidate
				bestDist = dist
			}
		}
		matched = append(matched, bestMatch)
	}
	return strings.Join(matched, " ")
}

Step 5: Metrics Tracking, Analytics Export, and Audit Logging

Search performance requires latency tracking, relevance accuracy scoring, and structured audit trails. This implementation exports metrics to an external analytics endpoint and logs all query executions.

type SearchMetrics struct {
	QueryLatencyMs  int64   `json:"query_latency_ms"`
	ResultCount     int     `json:"result_count"`
	AvgRelevanceScore float64 `json:"avg_relevance_score"`
	Timestamp       string  `json:"timestamp"`
}

func CalculateMetrics(startTime time.Time, results []SearchResult) SearchMetrics {
	latency := time.Since(startTime).Milliseconds()
	var totalScore float64
	for _, r := range results {
		totalScore += r.Score
	}
	avgScore := 0.0
	if len(results) > 0 {
		avgScore = totalScore / float64(len(results))
	}
	return SearchMetrics{
		QueryLatencyMs:  latency,
		ResultCount:     len(results),
		AvgRelevanceScore: avgScore,
		Timestamp:       time.Now().UTC().Format(time.RFC3339),
	}
}

func ExportAnalytics(ctx context.Context, client *http.Client, analyticsURL string, metrics SearchMetrics) error {
	payload, err := json.Marshal(metrics)
	if err != nil {
		return fmt.Errorf("failed to marshal metrics: %w", err)
	}
	req, err := http.NewRequestWithContext(ctx, http.MethodPost, analyticsURL, bytes.NewBuffer(payload))
	if err != nil {
		return fmt.Errorf("failed to create analytics request: %w", err)
	}
	req.Header.Set("Content-Type", "application/json")
	resp, err := client.Do(req)
	if err != nil {
		return fmt.Errorf("analytics export failed: %w", err)
	}
	defer resp.Body.Close()
	if resp.StatusCode != http.StatusOK && resp.StatusCode != http.StatusCreated {
		return fmt.Errorf("analytics export returned status %d", resp.StatusCode)
	}
	return nil
}

func WriteAuditLog(ctx context.Context, query string, kbID string, status string, latency int64) {
	slog.InfoContext(ctx, "search_audit",
		"query", query,
		"knowledge_base", kbID,
		"status", status,
		"latency_ms", latency,
	)
}

Complete Working Example

The following module ties all components together. It exposes a SemanticQueryExecutor interface that handles authentication, query optimization, execution, result processing, metrics export, and audit logging in a single workflow.

package main

import (
	"bytes"
	"context"
	"encoding/json"
	"fmt"
	"log/slog"
	"net/http"
	"os"
	"time"
)

// Reuse types and functions from previous steps in a single package for compilation
// (In production, split into separate files: auth.go, search.go, optimize.go, metrics.go)

type SemanticQueryExecutor struct {
	OAuthCfg       OAuthConfig
	HTTPClient     *http.Client
	BaseURL        string
	AnalyticsURL   string
	TokenCache     *TokenCache
}

func NewExecutor(baseURL, clientID, clientSecret, analyticsURL string) *SemanticQueryExecutor {
	return &SemanticQueryExecutor{
		OAuthCfg: OAuthConfig{
			BaseURL:      baseURL,
			ClientID:     clientID,
			ClientSecret: clientSecret,
		},
		HTTPClient: &http.Client{
			Timeout: 30 * time.Second,
			Transport: &http.Transport{
				MaxIdleConns:        10,
				MaxIdleConnsPerHost: 10,
				IdleConnTimeout:     90 * time.Second,
			},
		},
		BaseURL:      baseURL,
		AnalyticsURL: analyticsURL,
		TokenCache:   &TokenCache{},
	}
}

func (e *SemanticQueryExecutor) Execute(ctx context.Context, rawQuery string, kbID string, filters map[string]string, limit int) ([]SearchResult, error) {
	// 1. Authentication
	if e.TokenCache.IsExpired() {
		token, err := FetchOAuthToken(ctx, e.OAuthCfg)
		if err != nil {
			return nil, fmt.Errorf("token fetch failed: %w", err)
		}
		e.TokenCache.Set(token, 3600)
	}
	token := e.TokenCache.Get()

	// 2. Query Optimization
	optimizedQuery := ApplyFuzzyMatch(rawQuery, 2)
	expandedQuery := ExpandSynonyms(optimizedQuery)

	// 3. Request Construction & Validation
	searchReq := SearchRequest{
		Text:          expandedQuery,
		KnowledgeBaseID: kbID,
		Limit:         limit,
		Offset:        0,
		Filters:       filters,
		Semantic:      true,
	}
	if err := ValidateSearchRequest(searchReq); err != nil {
		return nil, fmt.Errorf("validation failed: %w", err)
	}

	// 4. Execution
	startTime := time.Now()
	resp, err := ExecuteSearch(ctx, e.HTTPClient, e.BaseURL, token, searchReq)
	if err != nil {
		WriteAuditLog(ctx, rawQuery, kbID, "failed", 0)
		return nil, err
	}
	latency := time.Since(startTime).Milliseconds()

	// 5. Result Processing
	normalizedResults := NormalizeScores(resp.Results)
	for i := range normalizedResults {
		normalizedResults[i].Snippets = []string{ExtractTopSnippet(resp.Results[i].Snippets, 200)}
	}

	// 6. Metrics & Audit
	metrics := CalculateMetrics(startTime, normalizedResults)
	if err := ExportAnalytics(ctx, e.HTTPClient, e.AnalyticsURL, metrics); err != nil {
		slog.WarnContext(ctx, "analytics export failed", "error", err)
	}
	WriteAuditLog(ctx, rawQuery, kbID, "success", latency)

	return normalizedResults, nil
}

func main() {
	ctx := context.Background()
	baseURL := "https://your-instance.cognigy.ai"
	clientID := os.Getenv("COGNIGY_CLIENT_ID")
	clientSecret := os.Getenv("COGNIGY_CLIENT_SECRET")
	analyticsURL := "https://your-analytics-endpoint.com/api/v1/metrics"

	executor := NewExecutor(baseURL, clientID, clientSecret, analyticsURL)

	results, err := executor.Execute(ctx, "how do I reset my passcode", "kb_prod_support", map[string]string{"category": "account-management"}, 5)
	if err != nil {
		slog.ErrorContext(ctx, "search execution failed", "error", err)
		os.Exit(1)
	}

	jsonOut, _ := json.MarshalIndent(results, "", "  ")
	fmt.Println(string(jsonOut))
}

Common Errors and Debugging

Error: HTTP 401 Unauthorized

• Cause: OAuth token expired, malformed client credentials, or missing knowledge:read scope.
• Fix: Verify client ID and secret match the Cognigy.AI application configuration. Ensure the token cache refreshes before expiry. Add explicit scope validation during token exchange.

Error: HTTP 403 Forbidden

• Cause: OAuth client lacks search:execute scope or the knowledge base ID does not grant read access to the service account.
• Fix: Update the OAuth application scopes in the Cognigy.AI admin console. Confirm the knowledge base ID matches an existing, accessible index.

Error: HTTP 422 Unprocessable Entity

• Cause: Invalid filter structure, unsupported vector index parameters, or query text exceeds maximum length.
• Fix: Validate filter keys against Cognigy.AI schema definitions. Ensure limit does not exceed 100. Trim query text to 500 characters maximum before dispatch.

Error: HTTP 429 Too Many Requests

• Cause: Rate limit cascade from rapid pagination or concurrent executor instances.
• Fix: The retry logic implements exponential backoff. Add request queuing or circuit breakers in high-throughput deployments. Respect Retry-After headers when present.

Error: HTTP 500 Internal Server Error

• Cause: Vector index corruption, temporary Cognigy.AI backend failure, or malformed knowledge base metadata.
• Fix: Implement retry with jitter. Verify index health via Cognigy.AI dashboard. Log request IDs for support ticket correlation.

Official References

• Cognigy.AI Knowledge Search API Documentation
• Cognigy.AI OAuth 2.0 Authentication Guide
• Go Standard Library net/http Package
• Go Context Package for Request Lifecycle Management