Managing Genesys Cloud LLM Gateway Vector Store Indexes via REST API with Go

What You Will Build

A production-ready Go module that constructs, validates, and deploys vector store index configurations to the Genesys Cloud LLM Gateway using atomic PUT operations. This tutorial uses the Genesys Cloud REST API and the official Go SDK. The code is written in Go 1.21+.

Prerequisites

• Genesys Cloud service account or OAuth2 client credentials with ai:vectorstore:read and ai:vectorstore:write scopes
• Genesys Cloud Go SDK v2.0.0+ (github.com/MyPureCloud/platform-client-v2-go)
• Go 1.21+ runtime
• Environment variables: GENESYS_CLOUD_REGION, GENESYS_CLOUD_CLIENT_ID, GENESYS_CLOUD_CLIENT_SECRET, GENESYS_CLOUD_VECTOR_STORE_ID

Authentication Setup

The Genesys Cloud LLM Gateway requires a valid OAuth2 bearer token. The following code implements a client credentials grant flow with token caching and automatic refresh logic.

package main

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

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

func Authenticate(ctx context.Context) (string, error) {
	clientID := os.Getenv("GENESYS_CLOUD_CLIENT_ID")
	clientSecret := os.Getenv("GENESYS_CLOUD_CLIENT_SECRET")
	region := os.Getenv("GENESYS_CLOUD_REGION")

	if clientID == "" || clientSecret == "" || region == "" {
		return "", fmt.Errorf("missing required environment variables")
	}

	authURL := fmt.Sprintf("https://%s.my.genesyscloud.com/oauth/token", region)
	payload := map[string]string{
		"grant_type": "client_credentials",
		"client_id":  clientID,
		"client_secret": clientSecret,
		"scope": "ai:vectorstore:read ai:vectorstore:write",
	}

	body, err := json.Marshal(payload)
	if err != nil {
		return "", fmt.Errorf("failed to marshal auth payload: %w", err)
	}

	req, err := http.NewRequestWithContext(ctx, http.MethodPost, authURL, nil)
	if err != nil {
		return "", fmt.Errorf("failed to create auth request: %w", err)
	}
	req.Header.Set("Content-Type", "application/x-www-form-urlencoded")
	req.SetBasicAuth(clientID, clientSecret)

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

	if resp.StatusCode != http.StatusOK {
		return "", fmt.Errorf("auth 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 token response: %w", err)
	}

	return tokenResp.AccessToken, nil
}

Implementation

Step 1: Initialize SDK & Configure HTTP Client

The Go SDK provides base URL resolution and configuration management. You will initialize the SDK configuration, then wrap it in a custom HTTP client that handles retry logic for 429 rate limits.

package main

import (
	"context"
	"net/http"
	"time"

	"github.com/MyPureCloud/platform-client-v2-go"
)

type IndexManager struct {
	sdkConfig    *genesyscloud.Configuration
	httpClient   *http.Client
	vectorStoreID string
	indexID      string
}

func NewIndexManager(ctx context.Context, vectorStoreID, indexID string) (*IndexManager, error) {
	token, err := Authenticate(ctx)
	if err != nil {
		return nil, fmt.Errorf("authentication failed: %w", err)
	}

	cfg := genesyscloud.NewConfiguration()
	cfg.AccessToken = token
	cfg.Region = os.Getenv("GENESYS_CLOUD_REGION")

	// Custom HTTP client with retry logic for 429
	retryClient := &http.Client{
		Timeout: 30 * time.Second,
		Transport: &RetryTransport{
			Base:      http.DefaultTransport.(*http.Transport),
			MaxRetries: 3,
			Backoff:    1 * time.Second,
		},
	}

	return &IndexManager{
		sdkConfig:    cfg,
		httpClient:   retryClient,
		vectorStoreID: vectorStoreID,
		indexID:      indexID,
	}, nil
}

type RetryTransport struct {
	Base       http.RoundTripper
	MaxRetries int
	Backoff    time.Duration
}

func (rt *RetryTransport) RoundTrip(req *http.Request) (*http.Response, error) {
	var resp *http.Response
	var err error

	for i := 0; i <= rt.MaxRetries; i++ {
		resp, err = rt.Base.RoundTrip(req)
		if err != nil {
			return resp, err
		}

		if resp.StatusCode != http.StatusTooManyRequests {
			return resp, err
		}

		if i < rt.MaxRetries {
			time.Sleep(rt.Backoff)
			rt.Backoff *= 2
		}
	}

	return resp, err
}

Step 2: Construct Index Payload & Validate Schema

You will build the index configuration payload with dimension matrices, similarity metrics, and ingestion triggers. The validation pipeline enforces Genesys Cloud vector database constraints before any network call occurs.

package main

import (
	"fmt"
	"net/http"
	"time"

	"github.com/google/uuid"
)

type IndexConfig struct {
	IndexID              string   `json:"indexId"`
	Dimensions           int      `json:"dimensions"`
	SimilarityMetric     string   `json:"similarityMetric"`
	EmbeddingFormat      string   `json:"embeddingFormat"`
	AutoIngestTrigger    bool     `json:"autoIngestTrigger"`
	WebhookSyncURL       string   `json:"webhookSyncUrl"`
	MaxDimensionLimit    int      `json:"-"`
	ValidMetrics         []string `json:"-"`
	ValidFormats         []string `json:"-"`
}

type ValidationResult struct {
	Valid   bool
	Errors  []string
	Latency time.Duration
}

func (im *IndexManager) ValidateIndexSchema(config *IndexConfig) ValidationResult {
	start := time.Now()
	var errors []string

	if config.Dimensions <= 0 || config.Dimensions > config.MaxDimensionLimit {
		errors = append(errors, fmt.Sprintf("dimensions %d exceed maximum limit %d", config.Dimensions, config.MaxDimensionLimit))
	}

	validMetric := false
	for _, m := range config.ValidMetrics {
		if m == config.SimilarityMetric {
			validMetric = true
			break
		}
	}
	if !validMetric {
		errors = append(errors, fmt.Sprintf("similarity metric %q is not compatible with vector database constraints", config.SimilarityMetric))
	}

	validFormat := false
	for _, f := range config.ValidFormats {
		if f == config.EmbeddingFormat {
			validFormat = true
			break
		}
	}
	if !validFormat {
		errors = append(errors, fmt.Sprintf("embedding format %q failed format verification", config.EmbeddingFormat))
	}

	return ValidationResult{
		Valid:   len(errors) == 0,
		Errors:  errors,
		Latency: time.Since(start),
	}
}

Step 3: Execute Atomic PUT & Handle Webhook Sync

Index updates require atomic PUT operations with format verification. You will include an If-Match header for concurrency control and trigger automatic vector ingestion upon successful deployment.

package main

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

type IndexUpdateResponse struct {
	ID              string    `json:"id"`
	Status          string    `json:"status"`
	UpdatedAt       time.Time `json:"updatedAt"`
	SyncEventID     string    `json:"syncEventId"`
	RetrievalPrecision float64 `json:"retrievalPrecision"`
}

func (im *IndexManager) UpdateIndex(ctx context.Context, config *IndexConfig, etag string) (*IndexUpdateResponse, error) {
	payload, err := json.Marshal(config)
	if err != nil {
		return nil, fmt.Errorf("failed to marshal index payload: %w", err)
	}

	endpoint := fmt.Sprintf("https://%s.my.genesyscloud.com/api/v2/ai/vectorstores/%s/indexes/%s",
		im.sdkConfig.Region, im.vectorStoreID, im.indexID)

	req, err := http.NewRequestWithContext(ctx, http.MethodPut, endpoint, bytes.NewReader(payload))
	if err != nil {
		return nil, fmt.Errorf("failed to create update request: %w", err)
	}

	req.Header.Set("Authorization", "Bearer "+im.sdkConfig.AccessToken)
	req.Header.Set("Content-Type", "application/json")
	req.Header.Set("Accept", "application/json")
	if etag != "" {
		req.Header.Set("If-Match", etag)
	}

	start := time.Now()
	resp, err := im.httpClient.Do(req)
	if err != nil {
		return nil, fmt.Errorf("update request failed: %w", err)
	}
	defer resp.Body.Close()

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

	switch resp.StatusCode {
	case http.StatusOK, http.StatusCreated:
		var updateResp IndexUpdateResponse
		if err := json.Unmarshal(body, &updateResp); err != nil {
			return nil, fmt.Errorf("failed to decode update response: %w", err)
		}

		// Track latency and precision
		latency := time.Since(start)
		precision := updateResp.RetrievalPrecision
		im.LogAudit("index_update", map[string]interface{}{
			"indexId":            config.IndexID,
			"latency_ms":         latency.Milliseconds(),
			"retrieval_precision": precision,
			"dimensions":         config.Dimensions,
			"similarity_metric":  config.SimilarityMetric,
		})

		return &updateResp, nil
	case http.StatusConflict:
		return nil, fmt.Errorf("index update conflict: index is currently being processed or etag mismatch")
	case http.StatusUnprocessableEntity:
		return nil, fmt.Errorf("schema validation failed: %s", string(body))
	default:
		return nil, fmt.Errorf("update failed with status %d: %s", resp.StatusCode, string(body))
	}
}

Step 4: Implement Audit Logging & MLOps Metrics Tracking

Governance compliance requires structured audit logs. The following method generates JSON audit entries and exposes metrics for MLOps dashboards.

package main

import (
	"encoding/json"
	"fmt"
	"log"
	"os"
	"time"
)

type AuditLog struct {
	Timestamp time.Time                 `json:"timestamp"`
	Event     string                    `json:"event"`
	Details   map[string]interface{}    `json:"details"`
	CorrelationID string               `json:"correlationId"`
}

func (im *IndexManager) LogAudit(event string, details map[string]interface{}) {
	correlationID := uuid.New().String()
	auditEntry := AuditLog{
		Timestamp:     time.Now().UTC(),
		Event:         event,
		Details:       details,
		CorrelationID: correlationID,
	}

	logBytes, _ := json.Marshal(auditEntry)
	log.Printf("AUDIT: %s", string(logBytes))

	// Write to file for governance compliance
	f, err := os.OpenFile("index_audit.log", os.O_APPEND|os.O_CREATE|os.O_WRONLY, 0644)
	if err == nil {
		defer f.Close()
		f.WriteString(string(logBytes) + "\n")
	}
}

func (im *IndexManager) GetMLOpsMetrics() map[string]interface{} {
	return map[string]interface{}{
		"vector_store_id": im.vectorStoreID,
		"index_id":        im.indexID,
		"last_update":     time.Now().UTC().Format(time.RFC3339),
		"status":          "active",
	}
}

Complete Working Example

The following script combines all components into a runnable module. Set the required environment variables before execution.

package main

import (
	"context"
	"fmt"
	"log"
	"os"
)

func main() {
	ctx := context.Background()

	vectorStoreID := os.Getenv("GENESYS_CLOUD_VECTOR_STORE_ID")
	if vectorStoreID == "" {
		log.Fatal("GENESYS_CLOUD_VECTOR_STORE_ID is required")
	}

	im, err := NewIndexManager(ctx, vectorStoreID, "prod-embedding-index-v2")
	if err != nil {
		log.Fatalf("Failed to initialize index manager: %v", err)
	}

	config := &IndexConfig{
		IndexID:           "prod-embedding-index-v2",
		Dimensions:        1536,
		SimilarityMetric:  "cosine",
		EmbeddingFormat:   "float32",
		AutoIngestTrigger: true,
		WebhookSyncURL:    "https://mlops.internal/webhooks/genesys-vector-sync",
		MaxDimensionLimit: 4096,
		ValidMetrics:      []string{"cosine", "euclidean", "dot_product"},
		ValidFormats:      []string{"float32", "int8", "binary"},
	}

	fmt.Println("Validating index schema against vector database constraints...")
	result := im.ValidateIndexSchema(config)
	if !result.Valid {
		log.Fatalf("Schema validation failed: %v", result.Errors)
	}
	fmt.Printf("Validation passed in %v\n", result.Latency)

	fmt.Println("Executing atomic PUT operation...")
	updateResp, err := im.UpdateIndex(ctx, config, "")
	if err != nil {
		log.Fatalf("Index update failed: %v", err)
	}

	fmt.Printf("Index updated successfully. Status: %s, Precision: %.4f\n", updateResp.Status, updateResp.RetrievalPrecision)

	metrics := im.GetMLOpsMetrics()
	fmt.Printf("MLOps Metrics: %+v\n", metrics)
}

Common Errors & Debugging

Error: 400 Bad Request (Dimension Mismatch)

Cause: The dimensions field exceeds the maximum allowed limit for the selected vector database backend, or the embedding format does not support the requested dimension count.
Fix: Verify the MaxDimensionLimit constraint in your payload. Genesys Cloud LLM Gateway typically caps dimensions at 4096 for standard indexes and 2048 for high-precision cosine indexes.
Code Fix:

if config.Dimensions > 4096 {
    config.Dimensions = 4096 // Fallback to maximum supported
}

Error: 409 Conflict (ETag Mismatch)

Cause: Another process modified the index configuration between your GET and PUT operations. The If-Match header enforces optimistic concurrency control.
Fix: Fetch the latest index state, extract the ETag from the response headers, and include it in the subsequent PUT request.
Code Fix:

// Before PUT, fetch current state
getReq, _ := http.NewRequestWithContext(ctx, http.MethodGet, endpoint, nil)
getReq.Header.Set("Authorization", "Bearer "+im.sdkConfig.AccessToken)
getResp, _ := im.httpClient.Do(getReq)
etag := getResp.Header.Get("ETag")
// Pass etag to UpdateIndex(ctx, config, etag)

Error: 429 Too Many Requests

Cause: Rate limiting triggered by rapid index configuration updates or concurrent webhook sync events.
Fix: The RetryTransport in Step 1 implements exponential backoff. If failures persist, implement a token bucket rate limiter or batch index updates.
Code Fix: The retry logic is already embedded in RetryTransport.RoundTrip. Increase MaxRetries or adjust Backoff duration if your workload requires higher throughput.

Error: 503 Service Unavailable (AI Gateway Unavailable)

Cause: The Genesys Cloud LLM Gateway vector store service is undergoing maintenance or experiencing temporary scaling events.
Fix: Implement circuit breaker logic. Return a cached index state if available, and queue updates for retry when the service recovers.

Official References

• Genesys Cloud AI Vector Store API Documentation
• Genesys Cloud Go SDK Reference
• OAuth 2.0 Client Credentials Flow
• LLM Gateway Similarity Metrics & Embedding Formats