diff --git a/ai/select-algorithm-go/.env.example b/ai/select-algorithm-go/.env.example
new file mode 100644
index 0000000..2f283ab
--- /dev/null
+++ b/ai/select-algorithm-go/.env.example
@@ -0,0 +1,26 @@
+# Azure DocumentDB cluster name (find in Azure Portal > DocumentDB > Overview)
+MONGO_CLUSTER_NAME=your-cluster-name
+
+# Azure OpenAI embedding endpoint (find in Azure Portal > Azure OpenAI > Keys and Endpoint)
+AZURE_OPENAI_EMBEDDING_ENDPOINT=https://your-resource.openai.azure.com
+
+# Azure OpenAI embedding model deployment name
+AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small
+
+# Database name (default: Hotels)
+AZURE_DOCUMENTDB_DATABASENAME=Hotels
+
+# Path to pre-computed vectors JSON file (default: ../data/Hotels_Vector.json)
+DATA_FILE_WITH_VECTORS=../data/Hotels_Vector.json
+
+# Embedding dimensions (default: 1536)
+EMBEDDING_DIMENSIONS=1536
+
+# Field name containing embeddings in the data file
+EMBEDDED_FIELD=contentVector
+
+# Algorithm to test: all, diskann, hnsw, ivf (default: all)
+ALGORITHM=all
+
+# Similarity to test: COS, L2, IP (default: COS)
+SIMILARITY=COS
diff --git a/ai/select-algorithm-go/.gitignore b/ai/select-algorithm-go/.gitignore
new file mode 100644
index 0000000..594492e
--- /dev/null
+++ b/ai/select-algorithm-go/.gitignore
@@ -0,0 +1,19 @@
+# Build output
+build/
+*.exe
+*.exe~
+*.dll
+*.so
+*.dylib
+
+# Test binary
+*.test
+
+# Output
+*.out
+
+# Vendor
+vendor/
+
+# Environment
+.env
diff --git a/ai/select-algorithm-go/README.md b/ai/select-algorithm-go/README.md
new file mode 100644
index 0000000..9a67bda
--- /dev/null
+++ b/ai/select-algorithm-go/README.md
@@ -0,0 +1,158 @@
+# Select algorithm - Go
+
+Compare DiskANN, HNSW, and IVF vector index algorithms across COS, L2, and IP similarity metrics using Azure DocumentDB.
+
+## Prerequisites
+
+- Go 1.22+
+- Azure DocumentDB cluster
+- Azure OpenAI resource with `text-embedding-3-small` deployment
+
+## Setup
+
+1. Copy `.env.example` to `.env` in this directory and fill in your values.
+2. Source the `.env` file into your shell (the app reads `os.Getenv` directly):
+
+```bash
+export $(grep -v '^#' .env | xargs)   # Linux / macOS
+```
+
+```powershell
+Get-Content .env | ForEach-Object { if ($_ -match '^\s*([^#][^=]+)=(.*)') { [System.Environment]::SetEnvironmentVariable($Matches[1].Trim(), $Matches[2].Trim()) } }
+```
+
+3. Install dependencies:
+
+```bash
+go mod download
+```
+
+## Usage
+
+### Compare all algorithms (default: COS similarity)
+
+```bash
+go run src/main.go
+```
+
+Set `ALGORITHM` and `SIMILARITY` env vars in `.env` to control which collections are queried:
+
+| ALGORITHM | SIMILARITY | Collections queried |
+|-----------|------------|---------------------|
+| `all`     | `COS`      | 3 (one per algorithm, COS) |
+| `all`     | `all`      | 9 (all combinations) |
+| `diskann` | `COS`      | 1 (hotels_diskann_cos) |
+| `diskann` | `all`      | 3 (diskann × all similarities) |
+
+## Architecture
+
+Creates collections per algorithm and similarity combination (3 algorithms × 3 distance metrics):
+
+| Algorithm | COS | L2 | IP |
+|-----------|-----|----|----|
+| DiskANN   | `hotels_diskann_cos` | `hotels_diskann_l2` | `hotels_diskann_ip` |
+| HNSW      | `hotels_hnsw_cos` | `hotels_hnsw_l2` | `hotels_hnsw_ip` |
+| IVF       | `hotels_ivf_cos` | `hotels_ivf_l2` | `hotels_ivf_ip` |
+
+Each collection gets its own vector index created via `db.RunCommand()` and data inserted via `InsertMany()`. The main script runs `$search` aggregation queries and prints a comparison table.
+
+## Expected output
+
+When you run the sample with `ALGORITHM=all` and `SIMILARITY=COS`, the console prints a comparison table similar to the following (exact timings and scores vary per run):
+
+```
+Vector Algorithm Comparison
+   Database: hotels
+   Algorithms: all
+   Similarity: COS
+   Collections to query: hotels_diskann_cos, hotels_hnsw_cos, hotels_ivf_cos
+   Search query: "luxury hotel with ocean view"
+
+Initializing MongoDB and Azure OpenAI clients...
+Loading data from data/hotels.json...
+Loaded 10 documents
+Generating query embedding...
+
+Processing collection: hotels_diskann_cos
+  Creating collection...
+  Creating vector index (diskann / COS)...
+  Inserting 10 documents...
+  Running vector search...
+[OK] 3 results, 42ms
+
+Processing collection: hotels_hnsw_cos
+  Creating collection...
+  Creating vector index (hnsw / COS)...
+  Inserting 10 documents...
+  Running vector search...
+[OK] 3 results, 38ms
+
+Processing collection: hotels_ivf_cos
+  Creating collection...
+  Creating vector index (ivf / COS)...
+  Inserting 10 documents...
+  Running vector search...
+[OK] 3 results, 35ms
+
++-------------------+-----------+--------+----------------------------------+
+| Collection        | Algorithm | Latency| Top Result                       |
++-------------------+-----------+--------+----------------------------------+
+| hotels_diskann_cos| diskann   |  42 ms | Oceanfront Resort (score: 0.87)  |
+| hotels_hnsw_cos   | hnsw      |  38 ms | Oceanfront Resort (score: 0.87)  |
+| hotels_ivf_cos    | ivf       |  35 ms | Oceanfront Resort (score: 0.86)  |
++-------------------+-----------+--------+----------------------------------+
+
+Done.
+```
+
+> Results vary based on cluster size, region latency, and data. The table format and column names are stable; the values are not.
+
+## Notes
+
+- Uses passwordless authentication with `DefaultAzureCredential` for both Azure OpenAI and DocumentDB
+- Environment variables are read directly via `os.Getenv`; source your `.env` file before running
+- Algorithm-specific index parameters:
+  - DiskANN: maxDegree=32, lBuild=50
+  - HNSW: m=16, efConstruction=64
+  - IVF: numLists=1
+- Algorithm-specific search parameters:
+  - DiskANN: lSearch=100
+  - HNSW: efSearch=80
+  - IVF: nProbes=1
+
+## Troubleshooting
+
+### Authentication with DefaultAzureCredential
+
+This sample uses `DefaultAzureCredential` for passwordless authentication to both Azure OpenAI and DocumentDB. Before running, sign in with the Azure CLI:
+
+```bash
+az login
+```
+
+`DefaultAzureCredential` tries multiple credential sources in order (environment variables, managed identity, Azure CLI, and others). For local development, the Azure CLI credential is typically used.
+
+### RBAC role requirements
+
+Your Azure identity needs the following roles:
+
+- **Cognitive Services OpenAI User** on the Azure OpenAI resource (for embedding generation).
+- A DocumentDB/Cosmos DB data-plane role that permits read and write operations on the target database. Consult your cluster's access control settings.
+
+### MongoDB OIDC connection errors
+
+If you see `MONGODB-OIDC` authentication failures:
+
+- Confirm the `MONGO_CLUSTER_NAME` environment variable is set correctly (cluster name only, not the full URI).
+- Verify your Azure identity has been granted access to the DocumentDB cluster.
+- Check that the token resource (`https://ossrdbms-aad.database.windows.net`) is correct for your cluster type.
+- Ensure network connectivity to the cluster (firewall rules, VNet configuration).
+
+### Common error codes
+
+| Error | Cause | Fix |
+|-------|-------|-----|
+| `failed to create Azure credential` | No valid Azure credential found | Run `az login` or configure a service principal |
+| `failed to connect to MongoDB` | Network or auth issue | Check cluster name, firewall rules, and RBAC |
+| `failed to generate embedding` | Azure OpenAI call failed | Verify endpoint URL, deployment name, and RBAC role |
+| `invalid ALGORITHM` / `invalid SIMILARITY` | Bad env var value | Use one of: `all`, `diskann`, `hnsw`, `ivf` / `all`, `COS`, `L2`, `IP` |
diff --git a/ai/select-algorithm-go/go.mod b/ai/select-algorithm-go/go.mod
new file mode 100644
index 0000000..3888faa
--- /dev/null
+++ b/ai/select-algorithm-go/go.mod
@@ -0,0 +1,35 @@
+module documentdb-vector-samples
+
+go 1.22
+
+require (
+	github.com/Azure/azure-sdk-for-go/sdk/azcore v1.20.0
+	github.com/Azure/azure-sdk-for-go/sdk/azidentity v1.13.1
+	github.com/openai/openai-go/v3 v3.12.0
+	go.mongodb.org/mongo-driver v1.17.6
+)
+
+require (
+	github.com/Azure/azure-sdk-for-go/sdk/internal v1.11.2 // indirect
+	github.com/AzureAD/microsoft-authentication-library-for-go v1.6.0 // indirect
+	github.com/golang-jwt/jwt/v5 v5.3.0 // indirect
+	github.com/golang/snappy v1.0.0 // indirect
+	github.com/google/uuid v1.6.0 // indirect
+	github.com/klauspost/compress v1.18.2 // indirect
+	github.com/kylelemons/godebug v1.1.0 // indirect
+	github.com/montanaflynn/stats v0.7.1 // indirect
+	github.com/pkg/browser v0.0.0-20240102092130-5ac0b6a4141c // indirect
+	github.com/tidwall/gjson v1.18.0 // indirect
+	github.com/tidwall/match v1.2.0 // indirect
+	github.com/tidwall/pretty v1.2.1 // indirect
+	github.com/tidwall/sjson v1.2.5 // indirect
+	github.com/xdg-go/pbkdf2 v1.0.0 // indirect
+	github.com/xdg-go/scram v1.2.0 // indirect
+	github.com/xdg-go/stringprep v1.0.4 // indirect
+	github.com/youmark/pkcs8 v0.0.0-20240726163527-a2c0da244d78 // indirect
+	golang.org/x/crypto v0.46.0 // indirect
+	golang.org/x/net v0.48.0 // indirect
+	golang.org/x/sync v0.19.0 // indirect
+	golang.org/x/sys v0.39.0 // indirect
+	golang.org/x/text v0.32.0 // indirect
+)
diff --git a/ai/select-algorithm-go/go.sum b/ai/select-algorithm-go/go.sum
new file mode 100644
index 0000000..c301141
--- /dev/null
+++ b/ai/select-algorithm-go/go.sum
@@ -0,0 +1,96 @@
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diff --git a/ai/select-algorithm-go/src/main.go b/ai/select-algorithm-go/src/main.go
new file mode 100644
index 0000000..e2f6a28
--- /dev/null
+++ b/ai/select-algorithm-go/src/main.go
@@ -0,0 +1,543 @@
+package main
+
+import (
+	"context"
+	"encoding/json"
+	"errors"
+	"fmt"
+	"log"
+	"os"
+	"strconv"
+	"strings"
+	"time"
+
+	"github.com/Azure/azure-sdk-for-go/sdk/azcore/policy"
+	"github.com/Azure/azure-sdk-for-go/sdk/azidentity"
+	"github.com/openai/openai-go/v3"
+	"github.com/openai/openai-go/v3/azure"
+	"github.com/openai/openai-go/v3/option"
+	"go.mongodb.org/mongo-driver/bson"
+	"go.mongodb.org/mongo-driver/mongo"
+	"go.mongodb.org/mongo-driver/mongo/options"
+)
+
+type Algorithm string
+type Similarity string
+
+const (
+	DiskANN Algorithm = "diskann"
+	HNSW    Algorithm = "hnsw"
+	IVF     Algorithm = "ivf"
+)
+
+const (
+	COS Similarity = "COS"
+	L2  Similarity = "L2"
+	IP  Similarity = "IP"
+)
+
+var (
+	AllAlgorithms   = []Algorithm{DiskANN, HNSW, IVF}
+	AllSimilarities = []Similarity{COS, L2, IP}
+)
+
+var AlgorithmLabels = map[Algorithm]string{
+	DiskANN: "DiskANN",
+	HNSW:    "HNSW",
+	IVF:     "IVF",
+}
+
+type CollectionTarget struct {
+	CollectionName string
+	Algorithm      Algorithm
+	Similarity     Similarity
+}
+
+type SearchResult struct {
+	Document interface{} `bson:"document"`
+	Score    float64     `bson:"score"`
+}
+
+type ComparisonResult struct {
+	CollectionName string
+	Algorithm      string
+	Similarity     string
+	SearchResults  []SearchResult
+	LatencyMs      int64
+}
+
+func getEnvOrDefault(key, defaultValue string) string {
+	if value := os.Getenv(key); value != "" {
+		return value
+	}
+	return defaultValue
+}
+
+func getTargetCollections(algorithmEnv, similarityEnv string) ([]CollectionTarget, error) {
+	algorithmEnv = strings.ToLower(strings.TrimSpace(algorithmEnv))
+	similarityEnv = strings.ToUpper(strings.TrimSpace(similarityEnv))
+
+	algorithms := []Algorithm{}
+	if algorithmEnv == "all" {
+		algorithms = AllAlgorithms
+	} else {
+		algorithms = []Algorithm{Algorithm(algorithmEnv)}
+	}
+
+	similarities := []Similarity{}
+	if similarityEnv == "all" {
+		similarities = AllSimilarities
+	} else {
+		similarities = []Similarity{Similarity(similarityEnv)}
+	}
+
+	targets := []CollectionTarget{}
+	for _, alg := range algorithms {
+		validAlg := false
+		for _, validAlgorithm := range AllAlgorithms {
+			if alg == validAlgorithm {
+				validAlg = true
+				break
+			}
+		}
+		if !validAlg {
+			return nil, fmt.Errorf("invalid ALGORITHM '%s'. Must be one of: all, diskann, hnsw, ivf", alg)
+		}
+
+		for _, sim := range similarities {
+			validSim := false
+			for _, validSimilarity := range AllSimilarities {
+				if sim == validSimilarity {
+					validSim = true
+					break
+				}
+			}
+			if !validSim {
+				return nil, fmt.Errorf("invalid SIMILARITY '%s'. Must be one of: all, COS, L2, IP", sim)
+			}
+
+			targets = append(targets, CollectionTarget{
+				CollectionName: fmt.Sprintf("hotels_%s_%s", alg, strings.ToLower(string(sim))),
+				Algorithm:      alg,
+				Similarity:     sim,
+			})
+		}
+	}
+
+	return targets, nil
+}
+
+func getIndexOptions(collectionName, indexName, embeddedField string, dimensions int, algorithm Algorithm, similarity Similarity) bson.D {
+	cosmosSearchOptions := bson.D{
+		{"dimensions", dimensions},
+		{"similarity", string(similarity)},
+	}
+
+	switch algorithm {
+	case DiskANN:
+		cosmosSearchOptions = append(bson.D{{"kind", "vector-diskann"}}, cosmosSearchOptions...)
+		cosmosSearchOptions = append(cosmosSearchOptions, bson.E{"maxDegree", 32})
+		cosmosSearchOptions = append(cosmosSearchOptions, bson.E{"lBuild", 50})
+	case HNSW:
+		cosmosSearchOptions = append(bson.D{{"kind", "vector-hnsw"}}, cosmosSearchOptions...)
+		cosmosSearchOptions = append(cosmosSearchOptions, bson.E{"m", 16})
+		cosmosSearchOptions = append(cosmosSearchOptions, bson.E{"efConstruction", 64})
+	case IVF:
+		cosmosSearchOptions = append(bson.D{{"kind", "vector-ivf"}}, cosmosSearchOptions...)
+		cosmosSearchOptions = append(cosmosSearchOptions, bson.E{"numLists", 1})
+	}
+
+	return bson.D{
+		{"createIndexes", collectionName},
+		{"indexes", []bson.D{
+			{
+				{"name", indexName},
+				{"key", bson.D{{embeddedField, "cosmosSearch"}}},
+				{"cosmosSearchOptions", cosmosSearchOptions},
+			},
+		}},
+	}
+}
+
+func getSearchPipeline(queryEmbedding []float64, embeddedField string, k int, algorithm Algorithm) []bson.M {
+	cosmosSearch := bson.M{
+		"vector": queryEmbedding,
+		"path":   embeddedField,
+		"k":      k,
+	}
+
+	switch algorithm {
+	case DiskANN:
+		cosmosSearch["lSearch"] = 100
+	case HNSW:
+		cosmosSearch["efSearch"] = 80
+	case IVF:
+		cosmosSearch["nProbes"] = 1
+	}
+
+	return []bson.M{
+		{"$search": bson.M{"cosmosSearch": cosmosSearch}},
+		{"$project": bson.M{
+			"score":    bson.M{"$meta": "searchScore"},
+			"document": "$$ROOT",
+		}},
+	}
+}
+
+func getClientsPasswordless() (*mongo.Client, openai.Client, error) {
+	ctx := context.Background()
+
+	clusterName := os.Getenv("MONGO_CLUSTER_NAME")
+	if clusterName == "" {
+		return nil, openai.Client{}, fmt.Errorf("MONGO_CLUSTER_NAME environment variable is required")
+	}
+
+	credential, err := azidentity.NewDefaultAzureCredential(nil)
+	if err != nil {
+		return nil, openai.Client{}, fmt.Errorf("failed to create Azure credential: %w", err)
+	}
+
+	mongoURI := fmt.Sprintf("mongodb+srv://%s.mongocluster.cosmos.azure.com/", clusterName)
+
+	oidcCallback := func(ctx context.Context, args *options.OIDCArgs) (*options.OIDCCredential, error) {
+		scope := "https://ossrdbms-aad.database.windows.net/.default"
+		token, err := credential.GetToken(ctx, policy.TokenRequestOptions{
+			Scopes: []string{scope},
+		})
+		if err != nil {
+			return nil, fmt.Errorf("failed to get token with scope %s: %w", scope, err)
+		}
+
+		return &options.OIDCCredential{
+			AccessToken: token.Token,
+		}, nil
+	}
+
+	clientOptions := options.Client().
+		ApplyURI(mongoURI).
+		SetConnectTimeout(120 * time.Second).
+		SetServerSelectionTimeout(120 * time.Second).
+		SetRetryWrites(false).
+		SetAuth(options.Credential{
+			AuthMechanism: "MONGODB-OIDC",
+			AuthMechanismProperties: map[string]string{
+				"TOKEN_RESOURCE": "https://ossrdbms-aad.database.windows.net",
+			},
+			OIDCMachineCallback: oidcCallback,
+		})
+
+	mongoClient, err := mongo.Connect(ctx, clientOptions)
+	if err != nil {
+		return nil, openai.Client{}, fmt.Errorf("failed to connect to MongoDB: %w", err)
+	}
+
+	azureOpenAIEndpoint := os.Getenv("AZURE_OPENAI_EMBEDDING_ENDPOINT")
+	if azureOpenAIEndpoint == "" {
+		return nil, openai.Client{}, fmt.Errorf("AZURE_OPENAI_EMBEDDING_ENDPOINT environment variable is required")
+	}
+
+	openAIClient := openai.NewClient(
+		option.WithBaseURL(fmt.Sprintf("%s/openai/v1", azureOpenAIEndpoint)),
+		azure.WithTokenCredential(credential))
+
+	return mongoClient, openAIClient, nil
+}
+
+func readFileReturnJSON(filePath string) ([]map[string]interface{}, error) {
+	file, err := os.ReadFile(filePath)
+	if err != nil {
+		return nil, fmt.Errorf("error reading file '%s': %w", filePath, err)
+	}
+
+	var data []map[string]interface{}
+	err = json.Unmarshal(file, &data)
+	if err != nil {
+		return nil, fmt.Errorf("error parsing JSON in file '%s': %w", filePath, err)
+	}
+
+	return data, nil
+}
+
+func insertData(ctx context.Context, collection *mongo.Collection, data []map[string]interface{}, batchSize int) (int, int, error) {
+	totalDocuments := len(data)
+	insertedCount := 0
+	failedCount := 0
+
+	for i := 0; i < totalDocuments; i += batchSize {
+		end := i + batchSize
+		if end > totalDocuments {
+			end = totalDocuments
+		}
+
+		batch := data[i:end]
+
+		documents := make([]interface{}, len(batch))
+		for j, doc := range batch {
+			documents[j] = doc
+		}
+
+		result, err := collection.InsertMany(ctx, documents, options.InsertMany().SetOrdered(false))
+		if err != nil {
+			var bulkErr mongo.BulkWriteException
+			if errors.As(err, &bulkErr) {
+				inserted := len(batch) - len(bulkErr.WriteErrors)
+				insertedCount += inserted
+				failedCount += len(bulkErr.WriteErrors)
+			} else {
+				failedCount += len(batch)
+			}
+		} else {
+			insertedCount += len(result.InsertedIDs)
+		}
+
+		if i+batchSize < totalDocuments {
+			time.Sleep(100 * time.Millisecond)
+		}
+	}
+
+	indexColumns := []string{"HotelId", "Category", "Description", "Description_fr"}
+	for _, col := range indexColumns {
+		indexModel := mongo.IndexModel{
+			Keys: bson.D{{Key: col, Value: 1}},
+		}
+		_, err := collection.Indexes().CreateOne(ctx, indexModel)
+		if err != nil {
+			fmt.Printf("Warning: Could not create index on %s: %v\n", col, err)
+		}
+	}
+
+	return insertedCount, failedCount, nil
+}
+
+func generateEmbedding(ctx context.Context, client openai.Client, text, modelName string) ([]float64, error) {
+	resp, err := client.Embeddings.New(ctx, openai.EmbeddingNewParams{
+		Input: openai.EmbeddingNewParamsInputUnion{
+			OfString: openai.String(text),
+		},
+		Model: modelName,
+	})
+	if err != nil {
+		return nil, fmt.Errorf("failed to generate embedding: %w", err)
+	}
+
+	if len(resp.Data) == 0 {
+		return nil, fmt.Errorf("no embedding data received")
+	}
+
+	embedding := make([]float64, len(resp.Data[0].Embedding))
+	for i, v := range resp.Data[0].Embedding {
+		embedding[i] = float64(v)
+	}
+
+	return embedding, nil
+}
+
+func printComparisonTable(results []ComparisonResult) {
+	fmt.Println("\n╔══════════════════════════════════════════════════════════════════════════════════╗")
+	fmt.Println("║                     Vector Algorithm Comparison Results                         ║")
+	fmt.Println("╠══════════════════════════════════════════════════════════════════════════════════╣")
+
+	fmt.Printf("║ %-12s%-14s%-24s%-12s%-14s║\n", "Algorithm", "Similarity", "Top Result", "Score", "Latency(ms)")
+	fmt.Println("╠══════════════════════════════════════════════════════════════════════════════════╣")
+
+	for _, r := range results {
+		topName := "N/A"
+		topScore := "N/A"
+
+		if len(r.SearchResults) > 0 {
+			topResult := r.SearchResults[0]
+			doc := topResult.Document.(bson.D)
+			for _, elem := range doc {
+				if elem.Key == "HotelName" {
+					hotelName := fmt.Sprintf("%v", elem.Value)
+					if len(hotelName) > 22 {
+						hotelName = hotelName[:22]
+					}
+					topName = hotelName
+					break
+				}
+			}
+			topScore = fmt.Sprintf("%.4f", topResult.Score)
+		}
+
+		fmt.Printf("║ %-12s%-14s%-24s%-12s%-14s║\n",
+			r.Algorithm,
+			r.Similarity,
+			topName,
+			topScore,
+			fmt.Sprintf("%d", r.LatencyMs))
+	}
+
+	fmt.Println("╚══════════════════════════════════════════════════════════════════════════════════╝")
+
+	for _, r := range results {
+		fmt.Printf("\n--- %s / %s (%s) ---\n", r.Algorithm, r.Similarity, r.CollectionName)
+		if len(r.SearchResults) == 0 {
+			fmt.Println("  No results.")
+			continue
+		}
+		for i, item := range r.SearchResults {
+			doc := item.Document.(bson.D)
+			var hotelName string
+			for _, elem := range doc {
+				if elem.Key == "HotelName" {
+					hotelName = fmt.Sprintf("%v", elem.Value)
+					break
+				}
+			}
+			fmt.Printf("  %d. %s, Score: %.4f\n", i+1, hotelName, item.Score)
+		}
+		fmt.Printf("  Latency: %dms\n", r.LatencyMs)
+	}
+}
+
+func main() {
+	// Set environment variables before running, or source your .env file manually:
+	//   export $(grep -v '^#' .env | xargs)  # Linux/macOS
+	//   Get-Content .env | ForEach-Object { if ($_ -match '^\s*([^#][^=]+)=(.*)') { [System.Environment]::SetEnvironmentVariable($Matches[1].Trim(), $Matches[2].Trim()) } }  # PowerShell
+
+	ctx := context.Background()
+
+	dbName := getEnvOrDefault("AZURE_DOCUMENTDB_DATABASENAME", "Hotels")
+	embeddedField := getEnvOrDefault("EMBEDDED_FIELD", "DescriptionVector")
+	embeddingDimensions, err := strconv.Atoi(getEnvOrDefault("EMBEDDING_DIMENSIONS", "1536"))
+	if err != nil {
+		log.Fatalf("Invalid value for EMBEDDING_DIMENSIONS: %v", err)
+	}
+	dataFile := getEnvOrDefault("DATA_FILE_WITH_VECTORS", "../data/Hotels_Vector.json")
+	deployment := os.Getenv("AZURE_OPENAI_EMBEDDING_MODEL")
+	if deployment == "" {
+		log.Fatal("AZURE_OPENAI_EMBEDDING_MODEL environment variable is required")
+	}
+	batchSize, err := strconv.Atoi(getEnvOrDefault("LOAD_SIZE_BATCH", "100"))
+	if err != nil {
+		log.Fatalf("Invalid value for LOAD_SIZE_BATCH: %v", err)
+	}
+	algorithmEnv := getEnvOrDefault("ALGORITHM", "all")
+	similarityEnv := getEnvOrDefault("SIMILARITY", "COS")
+	searchQuery := "quintessential lodging near running trails, eateries, retail"
+
+	targets, err := getTargetCollections(algorithmEnv, similarityEnv)
+	if err != nil {
+		log.Fatal(err)
+	}
+
+	collectionNames := []string{}
+	for _, t := range targets {
+		collectionNames = append(collectionNames, t.CollectionName)
+	}
+
+	fmt.Println("\nVector Algorithm Comparison")
+	fmt.Printf("   Database: %s\n", dbName)
+	fmt.Printf("   Algorithms: %s\n", algorithmEnv)
+	fmt.Printf("   Similarity: %s\n", similarityEnv)
+	fmt.Printf("   Collections to query: %s\n", strings.Join(collectionNames, ", "))
+	fmt.Printf("   Search query: \"%s\"\n\n", searchQuery)
+
+	fmt.Println("Initializing MongoDB and Azure OpenAI clients...")
+	mongoClient, azureOpenAIClient, err := getClientsPasswordless()
+	if err != nil {
+		log.Fatalf("Failed to initialize clients: %v", err)
+	}
+	defer mongoClient.Disconnect(context.Background())
+
+	db := mongoClient.Database(dbName)
+
+	fmt.Printf("Loading data from %s...\n", dataFile)
+	data, err := readFileReturnJSON(dataFile)
+	if err != nil {
+		log.Fatalf("Failed to load data: %v", err)
+	}
+	fmt.Printf("Loaded %d documents\n", len(data))
+
+	fmt.Println("Generating query embedding...")
+	queryEmbedding, err := generateEmbedding(ctx, azureOpenAIClient, searchQuery, deployment)
+	if err != nil {
+		log.Fatalf("Failed to generate embedding: %v", err)
+	}
+	if len(queryEmbedding) != embeddingDimensions {
+		log.Fatalf("Embedding dimension mismatch: expected %d, got %d. Verify EMBEDDING_DIMENSIONS matches your model.", embeddingDimensions, len(queryEmbedding))
+	}
+	fmt.Printf("Query embedding: %d dimensions\n\n", len(queryEmbedding))
+
+	comparisonResults := []ComparisonResult{}
+
+	for _, target := range targets {
+		fmt.Printf("\n━━━ %s / %s ━━━\n", AlgorithmLabels[target.Algorithm], target.Similarity)
+		fmt.Printf("Collection: %s\n", target.CollectionName)
+
+		if err := db.Collection(target.CollectionName).Drop(ctx); err != nil {
+			log.Printf("Warning: failed to drop collection %s: %v (may not exist)", target.CollectionName, err)
+		}
+
+		collection := db.Collection(target.CollectionName)
+		fmt.Printf("Created collection: %s\n", target.CollectionName)
+
+		inserted, failed, err := insertData(ctx, collection, data, batchSize)
+		if err != nil {
+			fmt.Printf("Error inserting data: %v\n", err)
+			continue
+		}
+		fmt.Printf("Inserted: %d/%d\n", inserted, len(data))
+		if failed > 0 {
+			fmt.Printf("Failed: %d\n", failed)
+		}
+
+		indexName := fmt.Sprintf("vectorIndex_%s_%s", target.Algorithm, strings.ToLower(string(target.Similarity)))
+		indexOptions := getIndexOptions(
+			target.CollectionName,
+			indexName,
+			embeddedField,
+			embeddingDimensions,
+			target.Algorithm,
+			target.Similarity,
+		)
+
+		var result bson.M
+		err = db.RunCommand(ctx, indexOptions).Decode(&result)
+		if err != nil {
+			fmt.Printf("Error creating vector index: %v\n", err)
+			continue
+		}
+		fmt.Printf("Created vector index: %s\n", indexName)
+
+		fmt.Println("Executing vector search...")
+		startTime := time.Now()
+
+		pipeline := getSearchPipeline(queryEmbedding, embeddedField, 5, target.Algorithm)
+		cursor, err := collection.Aggregate(ctx, pipeline)
+		if err != nil {
+			fmt.Printf("Error performing vector search: %v\n", err)
+			continue
+		}
+
+		var searchResults []SearchResult
+		for cursor.Next(ctx) {
+			var result SearchResult
+			if err := cursor.Decode(&result); err != nil {
+				fmt.Printf("Warning: Could not decode result: %v\n", err)
+				continue
+			}
+			searchResults = append(searchResults, result)
+		}
+		cursor.Close(ctx)
+
+		latencyMs := time.Since(startTime).Milliseconds()
+
+		comparisonResults = append(comparisonResults, ComparisonResult{
+			CollectionName: target.CollectionName,
+			Algorithm:      AlgorithmLabels[target.Algorithm],
+			Similarity:     string(target.Similarity),
+			SearchResults:  searchResults,
+			LatencyMs:      latencyMs,
+		})
+
+		fmt.Printf("[OK] %d results, %dms\n", len(searchResults), latencyMs)
+	}
+
+	if len(comparisonResults) > 0 {
+		printComparisonTable(comparisonResults)
+	}
+
+	fmt.Println("\nDone.")
+}
diff --git a/ai/vector-search-go/src/create_embeddings.go b/ai/vector-search-go/src/create_embeddings.go
index 4550a01..8f4700a 100644
--- a/ai/vector-search-go/src/create_embeddings.go
+++ b/ai/vector-search-go/src/create_embeddings.go
@@ -41,7 +41,7 @@ func CreateEmbeddings(ctx context.Context, texts []string, openAIClient openai.C
 	})
 
 	if err != nil {
-		return nil, fmt.Errorf("error generating embeddings: %v", err)
+		return nil, fmt.Errorf("error generating embeddings: %w", err)
 	}
 
 	// Extract embedding vectors from the API response
@@ -87,7 +87,7 @@ func ProcessEmbeddingBatch(ctx context.Context, dataBatch []map[string]interface
 	if len(textsToEmbed) > 0 {
 		embeddings, err := CreateEmbeddings(ctx, textsToEmbed, openAIClient, modelName)
 		if err != nil {
-			return fmt.Errorf("failed to create embeddings: %v", err)
+			return fmt.Errorf("failed to create embeddings: %w", err)
 		}
 
 		// Add embeddings back to the original documents
@@ -118,7 +118,7 @@ func LoadEmbeddingConfig() *EmbeddingConfig {
 	// Load environment variables from .env file
 	err := godotenv.Load()
 	if err != nil {
-		log.Printf("Warning: Error loading .env file: %v", err)
+		log.Printf("Warning: Error loading .env file: %w", err)
 	}
 
 	batchSize, _ := strconv.Atoi(getEnvOrDefault("EMBEDDING_SIZE_BATCH", "16"))
@@ -141,7 +141,8 @@ func LoadEmbeddingConfig() *EmbeddingConfig {
 // 3. Processes data in batches to generate embeddings
 // 4. Saves the enhanced data with embeddings
 func main() {
-	ctx := context.Background()
+	ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute)
+	defer cancel()
 
 	fmt.Println("Starting embedding creation process...")
 
@@ -158,9 +159,9 @@ func main() {
 
 	// Initialize clients for MongoDB and Azure OpenAI
 	fmt.Println("\nInitializing Azure OpenAI client...")
-	mongoClient, azureOpenAIClient, err := GetClientsPasswordless()
+	mongoClient, azureOpenAIClient, err := GetClientsPasswordless(ctx)
 	if err != nil {
-		log.Fatalf("Failed to initialize clients: %v", err)
+		log.Fatalf("Failed to initialize clients: %w", err)
 	}
 	defer func() {
 		if mongoClient != nil {
@@ -172,7 +173,7 @@ func main() {
 	fmt.Printf("\nReading input data from %s...\n", config.DataWithoutVectors)
 	data, err := ReadFileReturnJSON(config.DataWithoutVectors)
 	if err != nil {
-		log.Fatalf("Failed to read input file: %v", err)
+		log.Fatalf("Failed to read input file: %w", err)
 	}
 	fmt.Printf("Loaded %d documents\n", len(data))
 
@@ -215,7 +216,7 @@ func main() {
 	fmt.Printf("\nSaving enhanced data to %s...\n", config.DataWithVectors)
 	err = WriteFileJSON(data, config.DataWithVectors)
 	if err != nil {
-		log.Fatalf("Failed to save output file: %v", err)
+		log.Fatalf("Failed to save output file: %w", err)
 	}
 
 	fmt.Println("\nEmbedding creation completed successfully!")
diff --git a/ai/vector-search-go/src/diskann.go b/ai/vector-search-go/src/diskann.go
index 8991f58..85bdccd 100644
--- a/ai/vector-search-go/src/diskann.go
+++ b/ai/vector-search-go/src/diskann.go
@@ -67,7 +67,7 @@ func CreateDiskANNVectorIndex(ctx context.Context, collection *mongo.Collection,
 			fmt.Println("  • Use HNSW instead: go run src/hnsw.go")
 			fmt.Println("  • Use IVF instead: go run src/ivf.go")
 		}
-		return fmt.Errorf("error creating DiskANN vector index: %v", err)
+		return fmt.Errorf("error creating DiskANN vector index: %w", err)
 	}
 
 	fmt.Println("DiskANN vector index created successfully")
@@ -81,7 +81,7 @@ func PerformDiskANNVectorSearch(ctx context.Context, collection *mongo.Collectio
 	// Generate embedding for the query text
 	queryEmbedding, err := GenerateEmbedding(ctx, openAIClient, queryText, modelName)
 	if err != nil {
-		return nil, fmt.Errorf("error generating embedding: %v", err)
+		return nil, fmt.Errorf("error generating embedding: %w", err)
 	}
 
 	// Construct the aggregation pipeline for vector search
@@ -115,7 +115,7 @@ func PerformDiskANNVectorSearch(ctx context.Context, collection *mongo.Collectio
 	// Execute the aggregation pipeline
 	cursor, err := collection.Aggregate(ctx, pipeline)
 	if err != nil {
-		return nil, fmt.Errorf("error performing DiskANN vector search: %v", err)
+		return nil, fmt.Errorf("error performing DiskANN vector search: %w", err)
 	}
 	defer cursor.Close(ctx)
 
@@ -130,7 +130,7 @@ func PerformDiskANNVectorSearch(ctx context.Context, collection *mongo.Collectio
 	}
 
 	if err := cursor.Err(); err != nil {
-		return nil, fmt.Errorf("cursor error: %v", err)
+		return nil, fmt.Errorf("cursor error: %w", err)
 	}
 
 	return results, nil
@@ -138,15 +138,16 @@ func PerformDiskANNVectorSearch(ctx context.Context, collection *mongo.Collectio
 
 // main function demonstrates DiskANN vector search functionality
 func main() {
-	ctx := context.Background()
+	ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute)
+	defer cancel()
 
 	// Load configuration from environment variables
 	config := LoadConfig()
 
 	fmt.Println("\nInitializing MongoDB and Azure OpenAI clients...")
-	mongoClient, azureOpenAIClient, err := GetClientsPasswordless()
+	mongoClient, azureOpenAIClient, err := GetClientsPasswordless(ctx)
 	if err != nil {
-		log.Fatalf("Failed to initialize clients: %v", err)
+		log.Fatalf("Failed to initialize clients: %w", err)
 	}
 	defer mongoClient.Disconnect(ctx)
 
@@ -158,7 +159,7 @@ func main() {
 	fmt.Printf("\nLoading data from %s...\n", config.DataFile)
 	data, err := ReadFileReturnJSON(config.DataFile)
 	if err != nil {
-		log.Fatalf("Failed to load data: %v", err)
+		log.Fatalf("Failed to load data: %w", err)
 	}
 	fmt.Printf("Loaded %d documents\n", len(data))
 
@@ -180,7 +181,7 @@ func main() {
 	// Clear existing data to ensure clean state
 	deleteResult, err := collection.DeleteMany(ctx, bson.M{})
 	if err != nil {
-		log.Fatalf("Failed to clear existing data: %v", err)
+		log.Fatalf("Failed to clear existing data: %w", err)
 	}
 	if deleteResult.DeletedCount > 0 {
 		fmt.Printf("Cleared %d existing documents from collection\n", deleteResult.DeletedCount)
@@ -189,7 +190,7 @@ func main() {
 	// Insert the hotel data
 	stats, err := InsertData(ctx, collection, documentsWithEmbeddings, config.BatchSize, nil)
 	if err != nil {
-		log.Fatalf("Failed to insert data: %v", err)
+		log.Fatalf("Failed to insert data: %w", err)
 	}
 
 	if stats.Inserted == 0 {
@@ -201,7 +202,7 @@ func main() {
 	// Create DiskANN vector index
 	err = CreateDiskANNVectorIndex(ctx, collection, config.VectorField, config.Dimensions)
 	if err != nil {
-		log.Fatalf("Failed to create DiskANN vector index: %v", err)
+		log.Fatalf("Failed to create DiskANN vector index: %w", err)
 	}
 
 	// Wait briefly for index to be ready
@@ -221,7 +222,7 @@ func main() {
 		5,
 	)
 	if err != nil {
-		log.Fatalf("Failed to perform vector search: %v", err)
+		log.Fatalf("Failed to perform vector search: %w", err)
 	}
 
 	// Display results
diff --git a/ai/vector-search-go/src/hnsw.go b/ai/vector-search-go/src/hnsw.go
index ab6977c..c7d57e7 100644
--- a/ai/vector-search-go/src/hnsw.go
+++ b/ai/vector-search-go/src/hnsw.go
@@ -66,7 +66,7 @@ func CreateHNSWVectorIndex(ctx context.Context, collection *mongo.Collection, ve
 			fmt.Println("  • Use IVF instead: go run src/ivf.go")
 			fmt.Println("  • Use DiskANN instead: go run src/diskann.go")
 		}
-		return fmt.Errorf("error creating HNSW vector index: %v", err)
+		return fmt.Errorf("error creating HNSW vector index: %w", err)
 	}
 
 	fmt.Println("HNSW vector index created successfully")
@@ -80,7 +80,7 @@ func PerformHNSWVectorSearch(ctx context.Context, collection *mongo.Collection,
 	// Convert query text to embedding vector
 	queryEmbedding, err := GenerateEmbedding(ctx, openAIClient, queryText, modelName)
 	if err != nil {
-		return nil, fmt.Errorf("error generating embedding: %v", err)
+		return nil, fmt.Errorf("error generating embedding: %w", err)
 	}
 
 	// Build aggregation pipeline for HNSW vector search
@@ -114,7 +114,7 @@ func PerformHNSWVectorSearch(ctx context.Context, collection *mongo.Collection,
 	// Execute the search pipeline
 	cursor, err := collection.Aggregate(ctx, pipeline)
 	if err != nil {
-		return nil, fmt.Errorf("error performing HNSW vector search: %v", err)
+		return nil, fmt.Errorf("error performing HNSW vector search: %w", err)
 	}
 	defer cursor.Close(ctx)
 
@@ -129,7 +129,7 @@ func PerformHNSWVectorSearch(ctx context.Context, collection *mongo.Collection,
 	}
 
 	if err := cursor.Err(); err != nil {
-		return nil, fmt.Errorf("cursor error: %v", err)
+		return nil, fmt.Errorf("cursor error: %w", err)
 	}
 
 	return results, nil
@@ -139,15 +139,16 @@ func PerformHNSWVectorSearch(ctx context.Context, collection *mongo.Collection,
 func main() {
 	fmt.Println("Starting HNSW vector search demonstration...")
 
-	ctx := context.Background()
+	ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute)
+	defer cancel()
 
 	// Load configuration from environment variables
 	config := LoadConfig()
 
 	fmt.Println("\nInitializing MongoDB and Azure OpenAI clients...")
-	mongoClient, azureOpenAIClient, err := GetClientsPasswordless()
+	mongoClient, azureOpenAIClient, err := GetClientsPasswordless(ctx)
 	if err != nil {
-		log.Fatalf("Failed to initialize clients: %v", err)
+		log.Fatalf("Failed to initialize clients: %w", err)
 	}
 	defer mongoClient.Disconnect(ctx)
 
@@ -159,7 +160,7 @@ func main() {
 	fmt.Printf("\nLoading data from %s...\n", config.DataFile)
 	data, err := ReadFileReturnJSON(config.DataFile)
 	if err != nil {
-		log.Fatalf("Failed to load data: %v", err)
+		log.Fatalf("Failed to load data: %w", err)
 	}
 	fmt.Printf("Loaded %d documents\n", len(data))
 
@@ -181,7 +182,7 @@ func main() {
 	// Clear any existing data to start fresh
 	deleteResult, err := collection.DeleteMany(ctx, bson.M{})
 	if err != nil {
-		log.Fatalf("Failed to clear existing data: %v", err)
+		log.Fatalf("Failed to clear existing data: %w", err)
 	}
 	if deleteResult.DeletedCount > 0 {
 		fmt.Printf("Cleared %d existing documents from collection\n", deleteResult.DeletedCount)
@@ -190,7 +191,7 @@ func main() {
 	// Insert hotel data with embeddings
 	stats, err := InsertData(ctx, collection, documentsWithEmbeddings, config.BatchSize, nil)
 	if err != nil {
-		log.Fatalf("Failed to insert data: %v", err)
+		log.Fatalf("Failed to insert data: %w", err)
 	}
 
 	if stats.Inserted == 0 {
@@ -203,7 +204,7 @@ func main() {
 	fmt.Println("\nCreating HNSW vector index...")
 	err = CreateHNSWVectorIndex(ctx, collection, config.VectorField, config.Dimensions)
 	if err != nil {
-		log.Fatalf("Failed to create HNSW vector index: %v", err)
+		log.Fatalf("Failed to create HNSW vector index: %w", err)
 	}
 
 	// Allow time for index to become ready
@@ -224,7 +225,7 @@ func main() {
 		16, // efSearch (not used directly in DocumentDB but kept for API consistency)
 	)
 	if err != nil {
-		log.Fatalf("Failed to perform HNSW vector search: %v", err)
+		log.Fatalf("Failed to perform HNSW vector search: %w", err)
 	}
 
 	// Display the search results
diff --git a/ai/vector-search-go/src/ivf.go b/ai/vector-search-go/src/ivf.go
index 2aeddd8..306c28c 100644
--- a/ai/vector-search-go/src/ivf.go
+++ b/ai/vector-search-go/src/ivf.go
@@ -63,7 +63,7 @@ func CreateIVFVectorIndex(ctx context.Context, collection *mongo.Collection, vec
 			fmt.Println("  • Use HNSW instead: go run src/hnsw.go")
 			fmt.Println("  • Use DiskANN instead: go run src/diskann.go")
 		}
-		return fmt.Errorf("error creating IVF vector index: %v", err)
+		return fmt.Errorf("error creating IVF vector index: %w", err)
 	}
 
 	fmt.Println("IVF vector index created successfully")
@@ -77,7 +77,7 @@ func PerformIVFVectorSearch(ctx context.Context, collection *mongo.Collection, o
 	// Generate embedding vector for the search query
 	queryEmbedding, err := GenerateEmbedding(ctx, openaAIClient, queryText, modelName)
 	if err != nil {
-		return nil, fmt.Errorf("error generating embedding: %v", err)
+		return nil, fmt.Errorf("error generating embedding: %w", err)
 	}
 
 	// Construct aggregation pipeline for IVF vector search
@@ -111,7 +111,7 @@ func PerformIVFVectorSearch(ctx context.Context, collection *mongo.Collection, o
 	// Execute the aggregation pipeline
 	cursor, err := collection.Aggregate(ctx, pipeline)
 	if err != nil {
-		return nil, fmt.Errorf("error performing IVF vector search: %v", err)
+		return nil, fmt.Errorf("error performing IVF vector search: %w", err)
 	}
 	defer cursor.Close(ctx)
 
@@ -126,7 +126,7 @@ func PerformIVFVectorSearch(ctx context.Context, collection *mongo.Collection, o
 	}
 
 	if err := cursor.Err(); err != nil {
-		return nil, fmt.Errorf("cursor error: %v", err)
+		return nil, fmt.Errorf("cursor error: %w", err)
 	}
 
 	return results, nil
@@ -136,15 +136,16 @@ func PerformIVFVectorSearch(ctx context.Context, collection *mongo.Collection, o
 func main() {
 	fmt.Println("Starting IVF vector search demonstration...")
 
-	ctx := context.Background()
+	ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute)
+	defer cancel()
 
 	// Load configuration from environment variables
 	config := LoadConfig()
 
 	fmt.Println("\nInitializing MongoDB and Azure OpenAI clients...")
-	mongoClient, azureOpenAIClient, err := GetClientsPasswordless()
+	mongoClient, azureOpenAIClient, err := GetClientsPasswordless(ctx)
 	if err != nil {
-		log.Fatalf("Failed to initialize clients: %v", err)
+		log.Fatalf("Failed to initialize clients: %w", err)
 	}
 	defer mongoClient.Disconnect(ctx)
 
@@ -156,7 +157,7 @@ func main() {
 	fmt.Printf("\nLoading data from %s...\n", config.DataFile)
 	data, err := ReadFileReturnJSON(config.DataFile)
 	if err != nil {
-		log.Fatalf("Failed to load data: %v", err)
+		log.Fatalf("Failed to load data: %w", err)
 	}
 	fmt.Printf("Loaded %d documents\n", len(data))
 
@@ -178,7 +179,7 @@ func main() {
 	// Remove any existing data for clean state
 	deleteResult, err := collection.DeleteMany(ctx, bson.M{})
 	if err != nil {
-		log.Fatalf("Failed to clear existing data: %v", err)
+		log.Fatalf("Failed to clear existing data: %w", err)
 	}
 	if deleteResult.DeletedCount > 0 {
 		fmt.Printf("Cleared %d existing documents from collection\n", deleteResult.DeletedCount)
@@ -187,7 +188,7 @@ func main() {
 	// Insert hotel data with embeddings
 	stats, err := InsertData(ctx, collection, documentsWithEmbeddings, config.BatchSize, nil)
 	if err != nil {
-		log.Fatalf("Failed to insert data: %v", err)
+		log.Fatalf("Failed to insert data: %w", err)
 	}
 
 	if stats.Inserted == 0 {
@@ -200,7 +201,7 @@ func main() {
 	fmt.Println("\nCreating IVF vector index...")
 	err = CreateIVFVectorIndex(ctx, collection, config.VectorField, config.Dimensions)
 	if err != nil {
-		log.Fatalf("Failed to create IVF vector index: %v", err)
+		log.Fatalf("Failed to create IVF vector index: %w", err)
 	}
 
 	// Wait for index to be built and ready
@@ -221,7 +222,7 @@ func main() {
 		1, // numProbes (not used in DocumentDB but kept for API consistency)
 	)
 	if err != nil {
-		log.Fatalf("Failed to perform IVF vector search: %v", err)
+		log.Fatalf("Failed to perform IVF vector search: %w", err)
 	}
 
 	// Display the search results
diff --git a/ai/vector-search-go/src/show_indexes.go b/ai/vector-search-go/src/show_indexes.go
index 00e758e..9c33d69 100644
--- a/ai/vector-search-go/src/show_indexes.go
+++ b/ai/vector-search-go/src/show_indexes.go
@@ -5,6 +5,7 @@ import (
 	"fmt"
 	"log"
 	"strings"
+	"time"
 
 	"go.mongodb.org/mongo-driver/bson"
 	"go.mongodb.org/mongo-driver/mongo"
@@ -138,7 +139,7 @@ func showCollectionIndexes(ctx context.Context, collection *mongo.Collection, co
 
 	var indexes []IndexInfo
 	if err := cursor.All(ctx, &indexes); err != nil {
-		return fmt.Errorf("error decoding indexes: %v", err)
+		return fmt.Errorf("error decoding indexes: %w", err)
 	}
 
 	if len(indexes) == 0 {
@@ -172,7 +173,7 @@ func showDatabaseCollectionsAndIndexes(ctx context.Context, database *mongo.Data
 	// Get list of all collections in the database
 	collectionNames, err := database.ListCollectionNames(ctx, bson.M{})
 	if err != nil {
-		return fmt.Errorf("error accessing database '%s': %v", databaseName, err)
+		return fmt.Errorf("error accessing database '%s': %w", databaseName, err)
 	}
 
 	if len(collectionNames) == 0 {
@@ -208,7 +209,8 @@ func showDatabaseCollectionsAndIndexes(ctx context.Context, database *mongo.Data
 
 // main function displays vector indexes and collection information
 func main() {
-	ctx := context.Background()
+	ctx, cancel := context.WithTimeout(context.Background(), 10*time.Minute)
+	defer cancel()
 
 	fmt.Println("Vector Index Information Display")
 	fmt.Printf("%s\n", strings.Repeat("=", 50))
@@ -221,9 +223,9 @@ func main() {
 
 	// Initialize MongoDB client
 	fmt.Println("\nConnecting to MongoDB...")
-	mongoClient, _, err := GetClientsPasswordless()
+	mongoClient, _, err := GetClientsPasswordless(ctx)
 	if err != nil {
-		log.Fatalf("Failed to initialize MongoDB client: %v", err)
+		log.Fatalf("Failed to initialize MongoDB client: %w", err)
 	}
 	defer mongoClient.Disconnect(ctx)
 
diff --git a/ai/vector-search-go/src/utils.go b/ai/vector-search-go/src/utils.go
index cd02a5b..ab9948a 100644
--- a/ai/vector-search-go/src/utils.go
+++ b/ai/vector-search-go/src/utils.go
@@ -61,7 +61,7 @@ func LoadConfig() *Config {
 	// services instead of .env files. For development/demo purposes only.
 	err := godotenv.Load()
 	if err != nil {
-		log.Printf("Warning: Error loading .env file: %v", err)
+		log.Printf("Warning: Error loading .env file: %w", err)
 	}
 
 	dimensions, _ := strconv.Atoi(getEnvOrDefault("EMBEDDING_DIMENSIONS", "1536"))
@@ -88,8 +88,7 @@ func getEnvOrDefault(key, defaultValue string) string {
 }
 
 // GetClients creates MongoDB and Azure OpenAI clients with connection string authentication
-func GetClients() (*mongo.Client, openai.Client, error) {
-	ctx := context.Background()
+func GetClients(ctx context.Context) (*mongo.Client, openai.Client, error) {
 
 	// Get MongoDB connection string
 	mongoConnectionString := os.Getenv("MONGO_CONNECTION_STRING")
@@ -109,13 +108,13 @@ func GetClients() (*mongo.Client, openai.Client, error) {
 
 	mongoClient, err := mongo.Connect(ctx, clientOptions)
 	if err != nil {
-		return nil, openai.Client{}, fmt.Errorf("failed to connect to MongoDB: %v", err)
+		return nil, openai.Client{}, fmt.Errorf("failed to connect to MongoDB: %w", err)
 	}
 
 	// Test the connection
 	err = mongoClient.Ping(ctx, nil)
 	if err != nil {
-		return nil, openai.Client{}, fmt.Errorf("failed to ping MongoDB: %v", err)
+		return nil, openai.Client{}, fmt.Errorf("failed to ping MongoDB: %w", err)
 	}
 
 	// Get Azure OpenAI configuration
@@ -135,8 +134,7 @@ func GetClients() (*mongo.Client, openai.Client, error) {
 }
 
 // GetClientsPasswordless creates MongoDB and Azure OpenAI clients with passwordless authentication
-func GetClientsPasswordless() (*mongo.Client, openai.Client, error) {
-	ctx := context.Background()
+func GetClientsPasswordless(ctx context.Context) (*mongo.Client, openai.Client, error) {
 
 	// Get MongoDB cluster name
 	clusterName := os.Getenv("MONGO_CLUSTER_NAME")
@@ -147,7 +145,7 @@ func GetClientsPasswordless() (*mongo.Client, openai.Client, error) {
 	// Create Azure credential
 	credential, err := azidentity.NewDefaultAzureCredential(nil)
 	if err != nil {
-		return nil, openai.Client{}, fmt.Errorf("failed to create Azure credential: %v", err)
+		return nil, openai.Client{}, fmt.Errorf("failed to create Azure credential: %w", err)
 	}
 
 	// Attempt OIDC authentication
@@ -156,7 +154,7 @@ func GetClientsPasswordless() (*mongo.Client, openai.Client, error) {
 	fmt.Println("Attempting OIDC authentication...")
 	mongoClient, err := connectWithOIDC(ctx, mongoURI, credential)
 	if err != nil {
-		return nil, openai.Client{}, fmt.Errorf("OIDC authentication failed: %v", err)
+		return nil, openai.Client{}, fmt.Errorf("OIDC authentication failed: %w", err)
 	}
 	fmt.Println("OIDC authentication successful!")
 
@@ -184,7 +182,7 @@ func connectWithOIDC(ctx context.Context, mongoURI string, credential *azidentit
 			Scopes: []string{scope},
 		})
 		if err != nil {
-			return nil, fmt.Errorf("failed to get token with scope %s: %v", scope, err)
+			return nil, fmt.Errorf("failed to get token with scope %s: %w", scope, err)
 		}
 
 		fmt.Printf("Successfully obtained token")
@@ -238,13 +236,13 @@ func connectWithConnectionString(ctx context.Context, connectionString string) (
 func ReadFileReturnJSON(filePath string) ([]map[string]interface{}, error) {
 	file, err := os.ReadFile(filePath)
 	if err != nil {
-		return nil, fmt.Errorf("error reading file '%s': %v", filePath, err)
+		return nil, fmt.Errorf("error reading file '%s': %w", filePath, err)
 	}
 
 	var data []map[string]interface{}
 	err = json.Unmarshal(file, &data)
 	if err != nil {
-		return nil, fmt.Errorf("error parsing JSON in file '%s': %v", filePath, err)
+		return nil, fmt.Errorf("error parsing JSON in file '%s': %w", filePath, err)
 	}
 
 	return data, nil
@@ -254,12 +252,12 @@ func ReadFileReturnJSON(filePath string) ([]map[string]interface{}, error) {
 func WriteFileJSON(data []map[string]interface{}, filePath string) error {
 	jsonData, err := json.MarshalIndent(data, "", "  ")
 	if err != nil {
-		return fmt.Errorf("error marshalling data to JSON: %v", err)
+		return fmt.Errorf("error marshalling data to JSON: %w", err)
 	}
 
 	err = os.WriteFile(filePath, jsonData, 0644)
 	if err != nil {
-		return fmt.Errorf("error writing to file '%s': %v", filePath, err)
+		return fmt.Errorf("error writing to file '%s': %w", filePath, err)
 	}
 
 	fmt.Printf("Data successfully written to '%s'\n", filePath)
@@ -346,7 +344,7 @@ func DropVectorIndexes(ctx context.Context, collection *mongo.Collection, vector
 	// Get all indexes for the collection
 	cursor, err := collection.Indexes().List(ctx)
 	if err != nil {
-		return fmt.Errorf("could not list indexes: %v", err)
+		return fmt.Errorf("could not list indexes: %w", err)
 	}
 	defer cursor.Close(ctx)
 
@@ -432,7 +430,7 @@ func GenerateEmbedding(ctx context.Context, client openai.Client, text, modelNam
 		Model: modelName,
 	})
 	if err != nil {
-		return nil, fmt.Errorf("failed to generate embedding: %v", err)
+		return nil, fmt.Errorf("failed to generate embedding: %w", err)
 	}
 
 	if len(resp.Data) == 0 {