diff --git a/ai/select-algorithm-dotnet/CompareAll.cs b/ai/select-algorithm-dotnet/CompareAll.cs
index 9eb9c75..562d8c1 100644
--- a/ai/select-algorithm-dotnet/CompareAll.cs
+++ b/ai/select-algorithm-dotnet/CompareAll.cs
@@ -30,11 +30,11 @@ public static void Run(AppConfiguration appConfig)
         Console.WriteLine(new string('=', 60));
 
         // Use config values with env var overrides for compare-specific settings
-        var databaseName = appConfig.MongoDB.DatabaseName;
+        var databaseName = appConfig.DocumentDB.DatabaseName;
         var dataFile = appConfig.DataFiles.WithVectors;
         var vectorField = appConfig.Embedding.EmbeddedField;
         var dimensions = appConfig.Embedding.Dimensions;
-        var batchSize = appConfig.MongoDB.LoadBatchSize;
+        var batchSize = appConfig.DocumentDB.LoadBatchSize;
         var queryText = Environment.GetEnvironmentVariable("QUERY_TEXT") ?? "luxury hotel near the beach";
         var topK = int.Parse(Environment.GetEnvironmentVariable("TOP_K") ?? "5");
 
@@ -65,7 +65,7 @@ public static void Run(AppConfiguration appConfig)
             Console.WriteLine("Embedding generated (reused for all searches)\n");
 
             // Define 9 index configurations
-            var configs = BuildIndexConfigs(dimensions);
+            var configs = BuildIndexConfigs();
 
             // Run each config sequentially: drop→create→wait→search
             // DocumentDB doesn't allow multiple vector indexes of the same kind on the same field
@@ -77,7 +77,7 @@ public static void Run(AppConfiguration appConfig)
                 DropVectorIndexes(collection, vectorField);
 
                 // 2. Create this specific index
-                CreateIndex(collection, vectorField, config);
+                CreateIndex(collection, vectorField, dimensions, config);
                 Console.WriteLine($"  ✓ {config.Name} created");
 
                 // 3. Search with retries while the index becomes available
@@ -139,7 +139,7 @@ public static void Run(AppConfiguration appConfig)
         }
     }
 
-    private static List<IndexConfig> BuildIndexConfigs(int dimensions)
+    private static List<IndexConfig> BuildIndexConfigs()
     {
         string[] metrics = ["COS", "L2", "IP"];
         var configs = new List<IndexConfig>();
@@ -177,7 +177,7 @@ private static void DropVectorIndexes(IMongoCollection<BsonDocument> collection,
         catch { }
     }
 
-    private static void CreateIndex(IMongoCollection<BsonDocument> collection, string vectorField, IndexConfig config)
+    private static void CreateIndex(IMongoCollection<BsonDocument> collection, string vectorField, int dimensions, IndexConfig config)
     {
         // Drop existing index with same name if present
         try
@@ -192,7 +192,7 @@ private static void CreateIndex(IMongoCollection<BsonDocument> collection, strin
         var cosmosSearchOptions = new BsonDocument
         {
             { "kind", config.Kind },
-            { "dimensions", int.Parse(Environment.GetEnvironmentVariable("EMBEDDING_DIMENSIONS") ?? "1536") },
+            { "dimensions", dimensions },
             { "similarity", config.Similarity }
         };
 
diff --git a/ai/select-algorithm-dotnet/Models/Configuration.cs b/ai/select-algorithm-dotnet/Models/Configuration.cs
index cbca25b..3b4cb09 100644
--- a/ai/select-algorithm-dotnet/Models/Configuration.cs
+++ b/ai/select-algorithm-dotnet/Models/Configuration.cs
@@ -3,7 +3,7 @@ namespace SelectAlgorithm.Models;
 public class AppConfiguration
 {
     public AzureOpenAIConfiguration AzureOpenAI { get; set; } = new();
-    public MongoDBConfiguration MongoDB { get; set; } = new();
+    public DocumentDBConfiguration DocumentDB { get; set; } = new();
     public EmbeddingConfiguration Embedding { get; set; } = new();
     public VectorSearchConfiguration VectorSearch { get; set; } = new();
     public DataFilesConfiguration DataFiles { get; set; } = new();
@@ -15,7 +15,7 @@ public class AzureOpenAIConfiguration
     public string EmbeddingModel { get; set; } = "text-embedding-3-small";
 }
 
-public class MongoDBConfiguration
+public class DocumentDBConfiguration
 {
     public string ClusterName { get; set; } = string.Empty;
     public string DatabaseName { get; set; } = "Hotels";
diff --git a/ai/select-algorithm-dotnet/README.md b/ai/select-algorithm-dotnet/README.md
index 2621f77..b750f20 100644
--- a/ai/select-algorithm-dotnet/README.md
+++ b/ai/select-algorithm-dotnet/README.md
@@ -19,13 +19,13 @@ Demonstrates three vector index algorithms available in Azure DocumentDB:
 
 1. **Configure environment:**
 
-   The .NET sample uses `appsettings.json` for configuration. After deploying with `azd up`, you can export values:
+   The .NET sample uses `appsettings.json` for configuration. You can set values directly in `appsettings.json`, or if you deployed with `azd up`, export your provisioned values first:
 
    ```bash
    azd env get-values
    ```
 
-   Then update `appsettings.json` with your Azure resource values.
+   > **Note:** `azd` is optional. You can skip it and edit `appsettings.json` manually with your Azure resource values.
 
 2. Edit `appsettings.json` with your configuration:
 
@@ -35,29 +35,43 @@ Demonstrates three vector index algorithms available in Azure DocumentDB:
        "EmbeddingModel": "text-embedding-3-small",
        "Endpoint": "https://<your-resource>.openai.azure.com"
      },
-     "MongoDB": {
+     "DocumentDB": {
        "ClusterName": "<your-cluster-name>",
        "DatabaseName": "Hotels",
        "LoadBatchSize": 100
      },
      "Embedding": {
        "EmbeddedField": "DescriptionVector",
-       "Dimensions": 1536,
-       "EmbeddingSizeBatch": 16
+       "Dimensions": 1536
      },
      "DataFiles": {
-       "WithVectors": "../data/Hotels_Vector.json"
+       "WithVectors": "data/Hotels_Vector.json"
      }
    }
    ```
 
-3. Copy the shared data file:
+   > **Note:** .NET configuration also supports environment variable overrides. Use double-underscore (`__`) as the hierarchy separator:
+   > ```bash
+   > export DocumentDB__ClusterName=your-cluster-name
+   > export DocumentDB__DatabaseName=Hotels
+   > export AzureOpenAI__Endpoint=https://your-resource.openai.azure.com
+   > export AzureOpenAI__EmbeddingModel=text-embedding-3-small
+   > ```
+   > Environment variables take precedence over `appsettings.json` values.
+
+3. Copy the shared data file into the local `data/` directory:
+
+   ```bash
+   mkdir -p data && cp ../data/Hotels_Vector.json data/
+   ```
+
+4. Sign in to Azure for passwordless authentication:
 
    ```bash
-   cp ../data/Hotels_Vector.json .
+   az login
    ```
 
-4. Restore packages:
+5. Restore packages:
 
    ```bash
    dotnet restore
@@ -75,15 +89,14 @@ dotnet run
 
 | Setting (appsettings.json) | Default | Description |
 |---------------------------|---------|-------------|
-| `MongoDB:ClusterName` | (required) | DocumentDB cluster name |
+| `DocumentDB:ClusterName` | (required) | DocumentDB cluster name |
 | `AzureOpenAI:Endpoint` | (required) | Azure OpenAI endpoint |
-| `AzureOpenAI:EmbeddingModel` | (required) | Embedding model deployment name |
-| `DataFiles:WithVectors` | `../data/Hotels_Vector.json` | Path to vectors JSON file |
+| `AzureOpenAI:EmbeddingModel` | `text-embedding-3-small` | Embedding model deployment name |
+| `DataFiles:WithVectors` | `data/Hotels_Vector.json` | Path to vectors JSON file |
 | `Embedding:EmbeddedField` | `DescriptionVector` | Field name containing embeddings |
 | `Embedding:Dimensions` | `1536` | Vector dimensions |
-| `MongoDB:DatabaseName` | `Hotels` | Target database name |
-| `MongoDB:LoadBatchSize` | `100` | Batch size for data loading |
-| `Embedding:EmbeddingSizeBatch` | `16` | Batch size for embedding requests |
+| `DocumentDB:DatabaseName` | `Hotels` | Target database name |
+| `DocumentDB:LoadBatchSize` | `100` | Batch size for data loading |
 
 **Additional environment variables for compare mode:**
 
@@ -91,7 +104,6 @@ dotnet run
 |----------|---------|-------------|
 | `QUERY_TEXT` | `luxury hotel near the beach` | Search query text |
 | `TOP_K` | `5` | Number of results per search |
-| `VERBOSE` | `false` | Show detailed per-result output |
 
 ## How It Works
 
diff --git a/ai/select-algorithm-dotnet/Utils.cs b/ai/select-algorithm-dotnet/Utils.cs
index 62590ad..01d97a2 100644
--- a/ai/select-algorithm-dotnet/Utils.cs
+++ b/ai/select-algorithm-dotnet/Utils.cs
@@ -34,9 +34,9 @@ public static class Utils
 {
     public static IMongoClient GetMongoClientPasswordless(AppConfiguration config)
     {
-        var clusterName = config.MongoDB.ClusterName;
+        var clusterName = config.DocumentDB.ClusterName;
         if (string.IsNullOrEmpty(clusterName))
-            throw new InvalidOperationException("MongoDB:ClusterName is required in appsettings.json");
+            throw new InvalidOperationException("DocumentDB:ClusterName is required in appsettings.json");
 
         var credential = new DefaultAzureCredential();
 
diff --git a/ai/select-algorithm-dotnet/appsettings.json b/ai/select-algorithm-dotnet/appsettings.json
index 68ee696..b6634fb 100644
--- a/ai/select-algorithm-dotnet/appsettings.json
+++ b/ai/select-algorithm-dotnet/appsettings.json
@@ -1,8 +1,8 @@
 {
-  "MongoDB": {
+  "DocumentDB": {
     "DatabaseName": "Hotels",
     "ClusterName": "<your-cluster-name>",
-    "LoadBatchSize": 50
+    "LoadBatchSize": 100
   },
   "VectorSearch": {
     "Similarity": "",
@@ -14,11 +14,10 @@
     "EmbeddingModel": "text-embedding-3-small"
   },
   "DataFiles": {
-    "WithVectors": "../data/Hotels_Vector.json"
+    "WithVectors": "data/Hotels_Vector.json"
   },
   "Embedding": {
     "EmbeddedField": "DescriptionVector",
-    "Dimensions": 1536,
-    "EmbeddingSizeBatch": 16
+    "Dimensions": 1536
   }
 }
diff --git a/ai/select-algorithm-dotnet/quickstart.md b/ai/select-algorithm-dotnet/quickstart.md
index e94c2da..140a83d 100644
--- a/ai/select-algorithm-dotnet/quickstart.md
+++ b/ai/select-algorithm-dotnet/quickstart.md
@@ -149,7 +149,7 @@ Find the [sample code](https://github.com/Azure-Samples/documentdb-samples/tree/
    ```bash
    export AzureOpenAI__Endpoint="https://<your-resource>.openai.azure.com"
    export AzureOpenAI__EmbeddingModel="text-embedding-3-small"
-   export MongoDB__ClusterName="<your-cluster-name>"
+   export DocumentDB__ClusterName="<your-cluster-name>"
    export DataFiles__WithVectors="data/Hotels_Vector.json"
    export AZURE_TENANT_ID="<your-tenant-id>"
    ```
@@ -159,7 +159,7 @@ Find the [sample code](https://github.com/Azure-Samples/documentdb-samples/tree/
    ```powershell
    $env:AzureOpenAI__Endpoint="https://<your-resource>.openai.azure.com"
    $env:AzureOpenAI__EmbeddingModel="text-embedding-3-small"
-   $env:MongoDB__ClusterName="<your-cluster-name>"
+   $env:DocumentDB__ClusterName="<your-cluster-name>"
    $env:DataFiles__WithVectors="data/Hotels_Vector.json"
    $env:AZURE_TENANT_ID="<your-tenant-id>"
    ```
@@ -171,7 +171,7 @@ Find the [sample code](https://github.com/Azure-Samples/documentdb-samples/tree/
    - `<your-cluster-name>`: Your Azure DocumentDB cluster name
    - `<your-tenant-id>`: Your Microsoft Entra tenant ID
 
-   These environment variables override the matching values in `appsettings.json`. For example, `MongoDB__ClusterName` overrides `MongoDB:ClusterName` and `AzureOpenAI__Endpoint` overrides `AzureOpenAI:Endpoint`.
+   These environment variables override the matching values in `appsettings.json`. For example, `DocumentDB__ClusterName` overrides `DocumentDB:ClusterName` and `AzureOpenAI__Endpoint` overrides `AzureOpenAI:Endpoint`.
 
    You should always prefer passwordless authentication. For more information on setting up managed identity and the full range of your authentication options, see [Authenticate .NET apps to Azure services by using the Azure SDK for .NET](/dotnet/azure/sdk/authentication).
 
@@ -205,7 +205,7 @@ Find the [sample code](https://github.com/Azure-Samples/documentdb-samples/tree/
        "Endpoint": "https://<your-resource>.openai.azure.com",
        "EmbeddingModel": "text-embedding-3-small"
      },
-     "MongoDB": {
+     "DocumentDB": {
        "ClusterName": "<your-cluster-name>",
        "DatabaseName": "Hotels",
        "LoadBatchSize": 100
@@ -226,7 +226,7 @@ Find the [sample code](https://github.com/Azure-Samples/documentdb-samples/tree/
    }
    ```
 
-   You can keep placeholder values in `appsettings.json` and override them at runtime with environment variables such as `AzureOpenAI__Endpoint` and `MongoDB__ClusterName`.
+   You can keep placeholder values in `appsettings.json` and override them at runtime with environment variables such as `AzureOpenAI__Endpoint` and `DocumentDB__ClusterName`.
 
 ## Create code files
 
diff --git a/ai/select-algorithm-go/README.md b/ai/select-algorithm-go/README.md
index f03828e..18d6f86 100644
--- a/ai/select-algorithm-go/README.md
+++ b/ai/select-algorithm-go/README.md
@@ -20,36 +20,25 @@ This sample demonstrates how to compare different vector search algorithms (IVF,
 
 2. **Configure environment variables:**
 
-   After deploying with `azd up`, create a `.env` file with your provisioned resource values:
+   After deploying with `azd up`, use the provisioned output values to set these required environment variables in your shell:
 
    ```bash
-   azd env get-values > .env
+   export DOCUMENTDB_CLUSTER_NAME=your-cluster-name
+   export AZURE_OPENAI_EMBEDDING_ENDPOINT=https://your-resource.openai.azure.com
+   export AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small
+   export AZURE_DOCUMENTDB_DATABASENAME=Hotels
+   export DATA_FILE_WITH_VECTORS=data/Hotels_Vector.json
+   export EMBEDDED_FIELD=DescriptionVector
+   export EMBEDDING_DIMENSIONS=1536
    ```
 
-   Alternatively, copy the example and fill in values manually:
+3. **Copy the shared data file** into the local `data/` directory:
 
    ```bash
-   cp .env.example .env
+   mkdir -p data && cp ../data/Hotels_Vector.json data/
    ```
 
-   Required variables:
-   ```env
-   DOCUMENTDB_CLUSTER_NAME=your-cluster-name
-   AZURE_OPENAI_EMBEDDING_ENDPOINT=https://your-resource.openai.azure.com
-   AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small
-   AZURE_DOCUMENTDB_DATABASENAME=Hotels
-   DATA_FILE_WITH_VECTORS=../data/Hotels_Vector.json
-   EMBEDDED_FIELD=DescriptionVector
-   EMBEDDING_DIMENSIONS=1536
-   ```
-
-3. **Copy the shared data file** into this directory:
-
-   ```bash
-   cp ../data/Hotels_Vector.json .
-   ```
-
-   The `DATA_FILE_WITH_VECTORS` env var defaults to `../data/Hotels_Vector.json`.
+   The `DATA_FILE_WITH_VECTORS` env var defaults to `data/Hotels_Vector.json`.
 
 4. **Install dependencies**:
 
@@ -116,7 +105,7 @@ go run ./src/...
 | `AZURE_OPENAI_EMBEDDING_ENDPOINT` | *(required)* | Azure OpenAI endpoint |
 | `AZURE_OPENAI_EMBEDDING_MODEL` | `text-embedding-3-small` | Embedding model name |
 | `AZURE_DOCUMENTDB_DATABASENAME` | `Hotels` | Database name |
-| `DATA_FILE_WITH_VECTORS` | `../data/Hotels_Vector.json` | Path to data file |
+| `DATA_FILE_WITH_VECTORS` | `data/Hotels_Vector.json` | Path to data file |
 | `EMBEDDED_FIELD` | `DescriptionVector` | Field containing embeddings |
 | `EMBEDDING_DIMENSIONS` | `1536` | Embedding vector dimensions |
 | `LOAD_SIZE_BATCH` | `100` | Batch size for data insertion |
@@ -147,7 +136,6 @@ go run ./src/...
 
 ```
 select-algorithm-go/
-├── .env.example          # Environment variable template
 ├── go.mod                # Go module dependencies
 ├── go.sum                # Go module checksums
 ├── output/               # Sample output files
diff --git a/ai/select-algorithm-java/README.md b/ai/select-algorithm-java/README.md
index 2449f40..ca2648c 100644
--- a/ai/select-algorithm-java/README.md
+++ b/ai/select-algorithm-java/README.md
@@ -14,30 +14,22 @@ This sample demonstrates how to compare all three vector search index algorithms
 
 1. ### Configure environment variables
 
-   After deploying with `azd up`, create a `.env` file with your provisioned resource values:
+   After deploying with `azd up`, use the provisioned output values to set the required environment variables in your terminal:
 
    ```bash
-   azd env get-values > .env
+   export DOCUMENTDB_CLUSTER_NAME=your-cluster-name
+   export AZURE_OPENAI_EMBEDDING_ENDPOINT=https://your-resource.openai.azure.com
+   export AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small
+   export AZURE_DOCUMENTDB_DATABASENAME=Hotels
+   export DATA_FILE_WITH_VECTORS=data/Hotels_Vector.json
+   export EMBEDDED_FIELD=DescriptionVector
+   export EMBEDDING_DIMENSIONS=1536
    ```
 
-   This creates a `.env` file at the repository root with the connection strings and endpoints needed to run the sample.
-
-   Alternatively, copy the example and fill in values manually:
-
-   ```bash
-   cp .env.example .env
-   ```
-
-2. Update `.env` with your Azure resource details (if not using `azd`):
-   - `DOCUMENTDB_CLUSTER_NAME` — your DocumentDB cluster name
-   - `AZURE_OPENAI_EMBEDDING_ENDPOINT` — your Azure OpenAI endpoint
-   - `AZURE_OPENAI_EMBEDDING_MODEL` — deployment name (e.g., `text-embedding-3-small`)
-   - `DATA_FILE_WITH_VECTORS` — path to the pre-computed vectors JSON file
-
-3. Copy the shared data file:
+2. Copy the shared data file into the local `data/` directory:
 
    ```bash
-   cp ../data/Hotels_Vector.json .
+   mkdir -p data && cp ../data/Hotels_Vector.json data/
    ```
 
 ## Build
@@ -54,16 +46,10 @@ Compare all 9 algorithm × similarity combinations:
 mvn exec:java -Pcompare
 ```
 
-Or via the `ALGORITHM` environment variable:
-
-```bash
-ALGORITHM=compare mvn exec:java
-```
-
 On Windows (PowerShell):
 
 ```powershell
-$env:ALGORITHM="compare"; mvn exec:java
+mvn exec:java -Pcompare
 ```
 
 ## Algorithms
@@ -80,18 +66,15 @@ $env:ALGORITHM="compare"; mvn exec:java
 |----------|---------|-------------|
 | `DOCUMENTDB_CLUSTER_NAME` | (required) | DocumentDB cluster name |
 | `AZURE_OPENAI_EMBEDDING_ENDPOINT` | (required) | Azure OpenAI endpoint |
-| `AZURE_OPENAI_EMBEDDING_MODEL` | (required) | Embedding model deployment name |
-| `DATA_FILE_WITH_VECTORS` | `../data/Hotels_Vector.json` | Path to vectors JSON file |
+| `AZURE_OPENAI_EMBEDDING_MODEL` | `text-embedding-3-small` | Embedding model deployment name |
+| `DATA_FILE_WITH_VECTORS` | `data/Hotels_Vector.json` | Path to vectors JSON file |
 | `EMBEDDED_FIELD` | `DescriptionVector` | Field name containing embeddings |
 | `EMBEDDING_DIMENSIONS` | `1536` | Vector dimensions |
 | `AZURE_DOCUMENTDB_DATABASENAME` | `Hotels` | Target database name |
-| `LOAD_SIZE_BATCH` | `100` | Batch size for data loading |
-| `EMBEDDING_SIZE_BATCH` | `16` | Batch size for embedding requests |
-| `ALGORITHM` | (empty = all) | Which algorithm to run |
-| `SIMILARITY` | (empty = all) | Similarity metric: `COS`, `L2`, `IP` |
 | `QUERY_TEXT` | `luxury hotel near the beach` | Search query text |
 | `TOP_K` | `5` | Number of results per search |
-| `VERBOSE` | `false` | Print detailed per-index results |
+
+`CompareAll.java` always runs all 9 algorithm/metric combinations. It does not read `ALGORITHM` or `SIMILARITY` environment variables.
 
 ## Authentication
 
diff --git a/ai/select-algorithm-java/src/main/java/com/azure/documentdb/selectalgorithm/CompareAll.java b/ai/select-algorithm-java/src/main/java/com/azure/documentdb/selectalgorithm/CompareAll.java
index 66281ed..ee8733b 100644
--- a/ai/select-algorithm-java/src/main/java/com/azure/documentdb/selectalgorithm/CompareAll.java
+++ b/ai/select-algorithm-java/src/main/java/com/azure/documentdb/selectalgorithm/CompareAll.java
@@ -55,7 +55,8 @@ public static void run() {
 
             collection.drop();
             System.out.println("  Collection reset.");
-            Utils.insertData(collection, data, 100);
+            int batchSize = Integer.parseInt(System.getenv().getOrDefault("LOAD_SIZE_BATCH", "100"));
+            Utils.insertData(collection, data, batchSize);
 
             // Generate ONE embedding for the query (reused for all 9 searches)
             OpenAIClient aiClient = Utils.getOpenAIClient();
@@ -113,12 +114,11 @@ public static void run() {
 
             int successCount = (int) results.stream().filter(r -> !r.top1Name().equals("(failed)")).count();
             if (successCount == 0) {
-                System.out.println("\n❌ All 9 comparisons failed — no algorithm returned results.");
-                System.exit(1);
-            } else {
-                System.out.printf("%nSummary: %d succeeded, %d failed%n", successCount, 9 - successCount);
+                throw new IllegalStateException("All 9 comparisons failed — no algorithm returned results.");
             }
 
+            System.out.printf("%nSummary: %d succeeded, %d failed%n", successCount, 9 - successCount);
+
             // Cleanup: drop the comparison collection
             System.out.println("\n  Cleanup: dropping comparison collection...");
             collection.drop();
diff --git a/ai/select-algorithm-java/src/main/java/com/azure/documentdb/selectalgorithm/Utils.java b/ai/select-algorithm-java/src/main/java/com/azure/documentdb/selectalgorithm/Utils.java
index b8b761e..641dcb5 100644
--- a/ai/select-algorithm-java/src/main/java/com/azure/documentdb/selectalgorithm/Utils.java
+++ b/ai/select-algorithm-java/src/main/java/com/azure/documentdb/selectalgorithm/Utils.java
@@ -7,7 +7,6 @@
 import com.azure.core.credential.AccessToken;
 import com.azure.identity.DefaultAzureCredential;
 import com.azure.identity.DefaultAzureCredentialBuilder;
-import com.mongodb.ConnectionString;
 import com.mongodb.MongoClientSettings;
 import com.mongodb.MongoCredential;
 import com.mongodb.client.MongoClient;
@@ -39,8 +38,7 @@ public static MongoClient getMongoClient() {
             throw new IllegalStateException("DOCUMENTDB_CLUSTER_NAME environment variable is required");
         }
 
-        String connectionUri = String.format(
-                "mongodb+srv://%s.global.mongocluster.cosmos.azure.com/", clusterName);
+        String clusterHost = String.format("%s.global.mongocluster.cosmos.azure.com", clusterName);
 
         // Use custom OIDC callback with DefaultAzureCredential
         // This chains through CLI, managed identity, etc.
@@ -56,7 +54,8 @@ public static MongoClient getMongoClient() {
                 });
 
         MongoClientSettings settings = MongoClientSettings.builder()
-                .applyConnectionString(new ConnectionString(connectionUri))
+                .applyToClusterSettings(builder -> builder.srvHost(clusterHost))
+                .applyToSslSettings(builder -> builder.enabled(true))
                 .credential(mongoCredential)
                 .retryWrites(false)
                 .build();
diff --git a/ai/select-algorithm-python/README.md b/ai/select-algorithm-python/README.md
index 1fe7746..b89a800 100644
--- a/ai/select-algorithm-python/README.md
+++ b/ai/select-algorithm-python/README.md
@@ -34,30 +34,27 @@ Compare IVF, HNSW, and DiskANN vector index algorithms in Azure DocumentDB. Each
 
 1. ### Configure environment variables
 
-   After deploying with `azd up`, create a `.env` file with your provisioned resource values:
+   After deploying with `azd up`, use the provisioned output values to set the required environment variables in your terminal:
 
    ```bash
-   azd env get-values > .env
-   ```
-
-   This creates a `.env` file at the repository root with the connection strings and endpoints needed to run the sample.
-
-   Alternatively, copy the example and fill in values manually:
-
-   ```bash
-   cp .env.example .env
+   export DOCUMENTDB_CLUSTER_NAME=your-cluster-name
+   export AZURE_OPENAI_EMBEDDING_ENDPOINT=https://your-resource.openai.azure.com
+   export AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small
+   export AZURE_DOCUMENTDB_DATABASENAME=Hotels
+   export DATA_FILE_WITH_VECTORS=data/Hotels_Vector.json
+   export EMBEDDED_FIELD=DescriptionVector
+   export EMBEDDING_DIMENSIONS=1536
    ```
 
 2. Install dependencies:
    ```bash
-   cd src
-   pip install -r ../requirements.txt
+   pip install -r requirements.txt
    ```
 
-3. Copy the shared data file:
+3. Copy the shared data file into the local `data/` directory:
 
    ```bash
-   cp ../data/Hotels_Vector.json .
+   mkdir -p data && cp ../data/Hotels_Vector.json data/
    ```
 
 4. Ensure you're logged in to Azure:
@@ -70,8 +67,7 @@ Compare IVF, HNSW, and DiskANN vector index algorithms in Azure DocumentDB. Each
 Compare all 9 combinations (3 algorithms × 3 similarity metrics) in a single invocation:
 
 ```bash
-cd src
-python compare_all.py
+python src/compare_all.py
 ```
 
 The script creates a single `hotels` collection, loads data once, then for each of the 9 algorithm/metric combinations: creates the index → searches → drops the index. DocumentDB only allows one vector index per kind per field, so indexes are created sequentially.
@@ -82,15 +78,14 @@ The script creates a single `hotels` collection, loads data once, then for each
 |----------|---------|-------------|
 | `DOCUMENTDB_CLUSTER_NAME` | (required) | DocumentDB cluster name |
 | `AZURE_OPENAI_EMBEDDING_ENDPOINT` | (required) | Azure OpenAI endpoint |
-| `AZURE_OPENAI_EMBEDDING_MODEL` | (required) | Embedding model deployment name |
-| `DATA_FILE_WITH_VECTORS` | `../data/Hotels_Vector.json` | Path to vectors JSON file |
+| `AZURE_OPENAI_EMBEDDING_MODEL` | `text-embedding-3-small` | Embedding model deployment name |
+| `AZURE_OPENAI_EMBEDDING_API_VERSION` | `2023-05-15` | Azure OpenAI API version |
+| `DATA_FILE_WITH_VECTORS` | `data/Hotels_Vector.json` | Path to vectors JSON file |
 | `EMBEDDED_FIELD` | `DescriptionVector` | Field name containing embeddings |
 | `EMBEDDING_DIMENSIONS` | `1536` | Vector dimensions |
 | `AZURE_DOCUMENTDB_DATABASENAME` | `Hotels` | Target database name |
 | `LOAD_SIZE_BATCH` | `100` | Batch size for data loading |
-| `EMBEDDING_SIZE_BATCH` | `16` | Batch size for embedding requests |
-| `ALGORITHM` | (empty = all) | Which algorithm to run |
-| `SIMILARITY` | (empty = all) | Similarity metric: `COS`, `L2`, `IP` |
 | `QUERY_TEXT` | `luxury hotel near the beach` | Search query text |
 | `TOP_K` | `5` | Number of results per search |
-| `VERBOSE` | `false` | Show all k results per combo |
+
+`compare_all.py` always runs all 9 algorithm/metric combinations. It does not read `ALGORITHM` or `SIMILARITY` environment variables.
diff --git a/ai/select-algorithm-typescript/README.md b/ai/select-algorithm-typescript/README.md
index 73414a8..302df29 100644
--- a/ai/select-algorithm-typescript/README.md
+++ b/ai/select-algorithm-typescript/README.md
@@ -25,18 +25,18 @@ Compare IVF, HNSW, and DiskANN vector index algorithms in Azure DocumentDB using
 
 3. **Configure environment variables:**
 
-    After deploying with `azd up`, create a `.env` file with your provisioned resource values:
+    This sample reads configuration from environment variables. Export them in your shell before running:
 
     ```bash
-    azd env get-values > .env
-    ```
-
-    This creates a `.env` file in the project folder with the connection strings and endpoints needed to run the sample.
-
-    Alternatively, copy the example and fill in values manually:
-
-    ```bash
-    cp .env.example .env
+    export DOCUMENTDB_CLUSTER_NAME=your-cluster
+    export AZURE_OPENAI_EMBEDDING_ENDPOINT=https://your-resource.openai.azure.com
+    export AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small
+    export AZURE_OPENAI_EMBEDDING_API_VERSION=2023-05-15
+    export AZURE_DOCUMENTDB_DATABASENAME=Hotels
+    export DATA_FILE_WITH_VECTORS=data/Hotels_Vector.json
+    export EMBEDDED_FIELD=DescriptionVector
+    export EMBEDDING_DIMENSIONS=1536
+    export LOAD_SIZE_BATCH=100
     ```
 
     | Variable | Description |
@@ -50,17 +50,19 @@ Compare IVF, HNSW, and DiskANN vector index algorithms in Azure DocumentDB using
     | `EMBEDDED_FIELD` | Field name containing the vector (default: `DescriptionVector`) |
     | `EMBEDDING_DIMENSIONS` | Vector dimensions (default: `1536`) |
     | `LOAD_SIZE_BATCH` | Batch size for data insertion |
-    | `SIMILARITY` | Similarity metric: `COS`, `L2`, or `IP` |
 
-5. **Copy the shared data file** into this directory:
+    `compare-all.ts` always runs all 9 algorithm/metric combinations. It does not read `ALGORITHM` or `SIMILARITY` environment variables.
+
+    4. **Create a `data/` directory and copy the data file:**
 
     ```bash
-    cp ../data/Hotels_Vector.json .
+    mkdir -p data
+    cp ../data/Hotels_Vector.json data/
     ```
 
-    The `DATA_FILE_WITH_VECTORS` env var defaults to `../data/Hotels_Vector.json`.
+    The `DATA_FILE_WITH_VECTORS` env var defaults to `data/Hotels_Vector.json`.
 
-6. **Build the project:**
+5. **Build the project:**
 
     ```bash
     npm run build
diff --git a/ai/select-algorithm-typescript/package.json b/ai/select-algorithm-typescript/package.json
index 5c1f24a..8a9d155 100644
--- a/ai/select-algorithm-typescript/package.json
+++ b/ai/select-algorithm-typescript/package.json
@@ -4,7 +4,6 @@
   "description": "Compare IVF, HNSW, and DiskANN vector index algorithms in Azure DocumentDB",
   "type": "module",
   "scripts": {
-    "env:init": "azd env get-values > .env",
     "build": "tsc",
     "start": "node dist/compare-all.js"
   },
diff --git a/ai/select-algorithm-typescript/src/utils.ts b/ai/select-algorithm-typescript/src/utils.ts
index 09ec3dd..5f99337 100644
--- a/ai/select-algorithm-typescript/src/utils.ts
+++ b/ai/select-algorithm-typescript/src/utils.ts
@@ -20,9 +20,14 @@ export function getConfig() {
 
 export const AzureIdentityTokenCallback = async (params: OIDCCallbackParams, credential: TokenCredential): Promise<OIDCResponse> => {
     const tokenResponse: AccessToken | null = await credential.getToken(['https://ossrdbms-aad.database.windows.net/.default']);
+
+    if (!tokenResponse || !tokenResponse.token) {
+        throw new Error('Failed to acquire token');
+    }
+
     return {
-        accessToken: tokenResponse?.token || '',
-        expiresInSeconds: Math.floor(((tokenResponse?.expiresOnTimestamp || 0) - Date.now()) / 1000)
+        accessToken: tokenResponse.token,
+        expiresInSeconds: Math.floor((tokenResponse.expiresOnTimestamp - Date.now()) / 1000)
     };
 };