fix: Address code review issues in select-algorithm samples

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 }
         };
 

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";

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
@@ -75,23 +89,21 @@ 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:**
 
 | Variable | Default | Description |
 |----------|---------|-------------|
 | `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
 

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();
 

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
   }
 }

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
 

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