Correct branding from Cosmos DB to DocumentDB across all projects

Co-authored-by: diberry <41597107+diberry@users.noreply.github.com>

Author: Copilot

ai/vector-search-dotnet/.devcontainer/devcontainer.json

@@ -1,5 +1,5 @@
 {
-  "name": "Cosmos DB Vector Search - .NET 8",
+  "name": "Azure DocumentDB Vector Search - .NET 8",
   "image": "mcr.microsoft.com/devcontainers/dotnet:1-8.0-bookworm",
 
   "features": {

ai/vector-search-dotnet/Program.cs

@@ -38,7 +38,7 @@ static async Task Main(string[] args)
             string? command;
             while (true)
             {
-                Console.WriteLine("\n=== Cosmos DB Vector Samples Menu ===\nPlease enter your choice (0-5):\n1. Create embeddings for data\n2. Show all database indexes\n3. Run IVF vector search\n4. Run HNSW vector search\n5. Run DiskANN vector search\n0. Exit\n");
+                Console.WriteLine("\n=== DocumentDB Vector Samples Menu ===\nPlease enter your choice (0-5):\n1. Create embeddings for data\n2. Show all database indexes\n3. Run IVF vector search\n4. Run HNSW vector search\n5. Run DiskANN vector search\n0. Exit\n");
 
                 var input = Console.ReadLine();
                 command = input switch

ai/vector-search-dotnet/README.md

@@ -1,27 +1,27 @@
-# Cosmos DB Vector Search Sample
+# DocumentDB Vector Search Sample
 
-This .NET 8.0 sample application demonstrates vector similarity searches using Cosmos DB with different vector search algorithms (IVF, HNSW, and DiskANN). The application uses hotel data and text embeddings generated by Azure OpenAI.
+This .NET 8.0 sample application demonstrates vector similarity searches using Azure DocumentDB with different vector search algorithms (IVF, HNSW, and DiskANN). The application uses hotel data and text embeddings generated by Azure OpenAI.
 
 ## Features
 
 - Generate vector embeddings for hotel descriptions using Azure OpenAI
 - Compare performance across IVF, HNSW, and DiskANN vector search algorithms
-- Seamless integration with Cosmos DB and Azure OpenAI services
+- Seamless integration with DocumentDB and Azure OpenAI services
 - Interactive menu-driven application
 
 ## Prerequisites
 
 ### Azure Services
 
-- Cosmos DB account
+- Azure DocumentDB account
 - Azure OpenAI Service with text-embedding-ada-002 model deployed
 
 ### Development Environment
 
 - .NET 8.0 SDK
 - Visual Studio Code or Visual Studio 2022
 - Azure CLI
-- [Cosmos DB for VS Code](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-documentdb)
+- [Azure DocumentDB for VS Code](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-documentdb)
 
 ## Setup
 
@@ -105,17 +105,17 @@ Update the following in `appsettings.json`:
 
 - `AzureOpenAI.Endpoint`: Your Azure OpenAI service endpoint
 - `MongoDB.TenantId`: Your Azure tenant ID
-- `MongoDB.ClusterName`: Your Cosmos DB cluster name
+- `MongoDB.ClusterName`: Your DocumentDB cluster name
 
 ## Troubleshooting
 
 - **Authentication errors**: Run `az login` to authenticate with Azure
-- **Connection issues**: Verify Cosmos DB cluster is running and accessible
+- **Connection issues**: Verify DocumentDB cluster is running and accessible
 - **Embedding errors**: Ensure Azure OpenAI service is deployed with text-embedding-ada-002 model
 - **Index failures**: Check vector dimensions match (1536) and sufficient storage is available
 
 ## Additional Resources
 
-- [Cosmos DB Documentation](https://docs.microsoft.com/azure/cosmos-db/mongodb/vcore/)
+- [Azure DocumentDB Documentation](https://docs.microsoft.com/azure/documentdb/)
 - [Azure OpenAI Service Documentation](https://docs.microsoft.com/azure/cognitive-services/openai/)
 - [.NET MongoDB Driver Documentation](https://mongodb.github.io/mongo-csharp-driver/)

ai/vector-search-dotnet/Services/MongoDbService.cs

@@ -10,7 +10,7 @@ namespace CosmosDbVectorSamples.Services;
 
 /// <summary>
 /// Service for MongoDB operations including data insertion, index management, and vector index creation.
-/// Supports Azure Cosmos DB for MongoDB with passwordless authentication.
+/// Supports Azure DocumentDB with passwordless authentication.
 /// </summary>
 public class MongoDbService
 {
@@ -28,7 +28,7 @@ public MongoDbService(ILogger<MongoDbService> logger, IConfiguration configurati
         if (string.IsNullOrEmpty(_config.MongoDB.ClusterName))
             throw new InvalidOperationException("MongoDB connection not configured. Please provide ConnectionString or ClusterName.");
 
-        // Configure MongoDB connection for Azure Cosmos DB with OIDC authentication
+        // Configure MongoDB connection for Azure DocumentDB with OIDC authentication
         var connectionString = $"mongodb+srv://{_config.MongoDB.ClusterName}.global.mongocluster.cosmos.azure.com/?tls=true&authMechanism=MONGODB-OIDC&retrywrites=false&maxIdleTimeMS=120000";
         var settings = MongoClientSettings.FromUrl(MongoUrl.Create(connectionString));
         settings.UseTls = true;
@@ -49,7 +49,7 @@ public IMongoCollection<T> GetCollection<T>(string databaseName, string collecti
         _client.GetDatabase(databaseName).GetCollection<T>(collectionName);
 
     /// <summary>
-    /// Creates a vector search index for Cosmos DB MongoDB, with support for IVF, HNSW, and DiskANN algorithms
+    /// Creates a vector search index for DocumentDB, with support for IVF, HNSW, and DiskANN algorithms
     /// </summary>
     public async Task<BsonDocument> CreateVectorIndexAsync(string databaseName, string collectionName, string indexName, string embeddedField, BsonDocument cosmosSearchOptions)
     {

ai/vector-search-go/.env.example

@@ -45,12 +45,12 @@ EMBEDDING_DIMENSIONS=1536
 # Number of records to load per batch during data insertion
 LOAD_SIZE_BATCH=100
 
-# Cosmos DB Configuration
+# DocumentDB Configuration
 
-# Cosmos DB connection string
+# DocumentDB connection string
 MONGO_CONNECTION_STRING=mongodb+srv://username:password@cluster.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000
 
-# Name of the Cosmos DB cluster
+# Name of the DocumentDB cluster
 MONGO_CLUSTER_NAME=vectorSearch
 
 # Example connection string format:

ai/vector-search-go/README.md

@@ -1,13 +1,13 @@
-# Cosmos DB Vector Samples (Go)
+# Azure DocumentDB Vector Samples (Go)
 
-This project demonstrates vector search capabilities using Cosmos DB with Go. It includes implementations of three different vector index types: DiskANN, HNSW, and IVF, along with utilities for embedding generation and data management.
+This project demonstrates vector search capabilities using Azure DocumentDB with Go. It includes implementations of three different vector index types: DiskANN, HNSW, and IVF, along with utilities for embedding generation and data management.
 
 ## Overview
 
 Vector search enables semantic similarity searching by converting text into high-dimensional vector representations (embeddings) and finding the most similar vectors in the database. This project shows how to:
 
 - Generate embeddings using Azure OpenAI
-- Store vectors in Cosmos DB
+- Store vectors in Azure DocumentDB
 - Create and use different types of vector indexes
 - Perform similarity searches with various algorithms
 - Handle authentication using Azure Active Directory (passwordless) or connection strings
@@ -19,7 +19,7 @@ Before running this project, you need:
 ### Azure Resources
 1. **Azure subscription** with appropriate permissions
 2. **Azure OpenAI resource** with embedding model deployment
-3. **Cosmos DB resource**
+3. **Azure DocumentDB resource**
 4. **Azure CLI** installed and configured
 
 ### Development Environment
@@ -70,9 +70,9 @@ az cognitiveservices account create \
 4. Choose **text-embedding-ada-002** model
 5. Note the deployment name for configuration
 
-#### Create Cosmos DB Resource
+#### Create Azure DocumentDB Resource
 
-Create a Cosmos DB cluster by using the [Azure portal](https://learn.microsoft.com/azure/documentdb/quickstart-portal), [Bicep](https://learn.microsoft.com/azure/documentdb/quickstart-bicep), or [Terraform](https://learn.microsoft.com/azure/documentdb/quickstart-terraform).
+Create a Azure DocumentDB cluster by using the [Azure portal](https://learn.microsoft.com/azure/documentdb/quickstart-portal), [Bicep](https://learn.microsoft.com/azure/documentdb/quickstart-bicep), or [Terraform](https://learn.microsoft.com/azure/documentdb/quickstart-terraform).
 
 ### Step 3: Get Your Connection Information
 
@@ -91,9 +91,9 @@ az cognitiveservices account keys list \
     --query "key1" --output tsv
 ```
 
-#### Cosmos DB Connection String
+#### DocumentDB Connection String
 ```bash
-# Get Cosmos DB connection string
+# Get DocumentDB connection string
 az resource show \
     --resource-group "<resource-group>" \
     --name "<cluster-name>" \
@@ -119,7 +119,7 @@ AZURE_OPENAI_EMBEDDING_ENDPOINT=https://your-openai-resource.openai.azure.com/
 AZURE_OPENAI_EMBEDDING_KEY=your-azure-openai-api-key
 AZURE_OPENAI_EMBEDDING_API_VERSION=2024-02-01
 
-# Cosmos DB Configuration
+# DocumentDB Configuration
 MONGO_CONNECTION_STRING=mongodb+srv://username:password@your-cluster.mongocluster.cosmos.azure.com/?tls=true&authMechanism=SCRAM-SHA-256&retrywrites=false&maxIdleTimeMS=120000
 MONGO_CLUSTER_NAME=vectorSearch
 
@@ -136,10 +136,10 @@ LOAD_SIZE_BATCH=100
 ### Step 5: Configure passwordless authentication (optional)
 To use passwordless authentication with Microsoft Entra ID, follow these steps:
 
-1. In your Cosmos DB resource, enable **Native Cosmos DB** and **Microsoft Entra ID** authentication methods.
-2. Assign your Microsoft Entra ID user the following roles on the Cosmos DB resource:
-   - **Cosmos DB Account Reader Role**
-   - **Cosmos DB Account Contributor**
+1. In your Azure DocumentDB resource, enable **Native DocumentDB** and **Microsoft Entra ID** authentication methods.
+2. Assign your Microsoft Entra ID user the following roles on the DocumentDB resource:
+   - **DocumentDB Account Reader Role**
+   - **DocumentDB Account Contributor**
 
 ## Usage
 
@@ -211,7 +211,7 @@ This utility shows:
 ## Important Notes
 
 ### Vector Index Limitations
-**One Index Per Field**: Cosmos DB allows only one vector index per field. Each script automatically handles this by:
+**One Index Per Field**: Azure DocumentDB allows only one vector index per field. Each script automatically handles this by:
 
 1. **Dropping existing indexes**: Before creating a new vector index, the script removes any existing vector indexes on the same field
 2. **Safe switching**: You can run different vector index scripts in any order - each will clean up previous indexes first
@@ -239,7 +239,7 @@ Different vector index types require different cluster tiers:
 If you encounter "not enabled for this cluster tier" errors:
 1. Try a different index type (IVF is most widely supported)
 2. Consider upgrading your cluster tier
-3. Check the [Cosmos DB pricing page](https://azure.microsoft.com/pricing/details/cosmos-db/) for tier features
+3. Check the [Azure DocumentDB pricing page](https://azure.microsoft.com/pricing/details/document-db/) for tier features
 
 ## Authentication Options
 
@@ -268,8 +268,8 @@ defer mongoClient.Disconnect(ctx)
 **Setup for passwordless authentication:**
 
 1. Ensure you're logged in with `az login`
-2. Enable **Native Cosmos DB and Microsoft Entra ID authentication** methods for your Cosmos DB resource.
-3. Grant your identity appropriate RBAC permissions on your Cosmos DB instance. You need **Cosmos DB Account Reader Role** and **Cosmos DB Account Contributor** roles assigned to your user.
+2. Enable **Native DocumentDB and Microsoft Entra ID authentication** methods for your Azure DocumentDB resource.
+3. Grant your identity appropriate RBAC permissions on your Azure DocumentDB instance. You need **DocumentDB Account Reader Role** and **DocumentDB Account Contributor** roles assigned to your user.
 4. Set `MONGO_CLUSTER_NAME` instead of `MONGO_CONNECTION_STRING` in `.env`
 
 ### Method 2: Connection String Authentication
@@ -332,8 +332,8 @@ mongo-vcore-vector-search-go/
 
 1. **Authentication Errors**
    - Verify Azure OpenAI endpoint and key
-   - Check Cosmos DB connection string
-   - Ensure proper RBAC permissions for passwordless authentication. You need **Cosmos DB Account Reader Role** and **Cosmos DB Account Contributor** roles assigned to your user. Roles may take some time to propagate.
+   - Check Azure DocumentDB connection string
+   - Ensure proper RBAC permissions for passwordless authentication. You need **DocumentDB Account Reader Role** and **DocumentDB Account Contributor** roles assigned to your user. Roles may take some time to propagate.
 
 2. **Embedding Generation Fails**
    - Check Azure OpenAI model deployment name
@@ -375,9 +375,9 @@ DEBUG=true
 
 ## Further Resources
 
-- [Cosmos DB Documentation](https://learn.microsoft.com/azure/documentdb/)
+- [Azure DocumentDB Documentation](https://learn.microsoft.com/azure/documentdb/)
 - [Azure OpenAI Service Documentation](https://learn.microsoft.com/azure/ai-services/openai/)
-- [Vector Search in Cosmos DB](https://learn.microsoft.com/azure/documentdb/vector-search)
+- [Vector Search in DocumentDB](https://learn.microsoft.com/azure/documentdb/vector-search)
 - [Go MongoDB Driver Documentation](https://pkg.go.dev/go.mongodb.org/mongo-driver/mongo)
 - [Azure SDK for Go Documentation](https://pkg.go.dev/github.com/Azure/azure-sdk-for-go)
 

ai/vector-search-go/src/diskann.go

@@ -23,15 +23,15 @@ func CreateDiskANNVectorIndex(ctx context.Context, collection *mongo.Collection,
 		fmt.Printf("Warning: Could not drop existing indexes: %v\n", err)
 	}
 
-	// Use the native MongoDB command for Cosmos DB vector indexes
+	// Use the native MongoDB command for DocumentDB vector indexes
 	// Note: Must use bson.D for commands to preserve order and avoid "multi-key map" errors
 	indexCommand := bson.D{
 		{"createIndexes", collection.Name()},
 		{"indexes", []bson.D{
 			{
 				{"name", fmt.Sprintf("diskann_index_%s", vectorField)},
 				{"key", bson.D{
-					{vectorField, "cosmosSearch"}, // Cosmos DB vector search index type
+					{vectorField, "cosmosSearch"}, // DocumentDB vector search index type
 				}},
 				{"cosmosSearchOptions", bson.D{
 					// DiskANN algorithm configuration
@@ -63,7 +63,7 @@ func CreateDiskANNVectorIndex(ctx context.Context, collection *mongo.Collection,
 		if strings.Contains(err.Error(), "not enabled for this cluster tier") {
 			fmt.Println("\nDiskANN indexes require a higher cluster tier.")
 			fmt.Println("Try one of these alternatives:")
-			fmt.Println("  • Upgrade your Cosmos DB cluster to a higher tier")
+			fmt.Println("  • Upgrade your DocumentDB cluster to a higher tier")
 			fmt.Println("  • Use HNSW instead: go run src/hnsw.go")
 			fmt.Println("  • Use IVF instead: go run src/ivf.go")
 		}
@@ -85,11 +85,11 @@ func PerformDiskANNVectorSearch(ctx context.Context, collection *mongo.Collectio
 	}
 
 	// Construct the aggregation pipeline for vector search
-	// Cosmos DB uses $search with cosmosSearch
+	// DocumentDB uses $search with cosmosSearch
 	pipeline := []bson.M{
 		{
 			"$search": bson.M{
-				// Use cosmosSearch for vector operations in Cosmos DB
+				// Use cosmosSearch for vector operations in DocumentDB
 				"cosmosSearch": bson.M{
 					// The query vector to search for
 					"vector": queryEmbedding,

ai/vector-search-go/src/hnsw.go

@@ -22,15 +22,15 @@ func CreateHNSWVectorIndex(ctx context.Context, collection *mongo.Collection, ve
 		fmt.Printf("Warning: Could not drop existing indexes: %v\n", err)
 	}
 
-	// Use the native MongoDB command for Cosmos DB vector indexes
+	// Use the native MongoDB command for DocumentDB vector indexes
 	// Note: Must use bson.D for commands to preserve order and avoid "multi-key map" errors
 	indexCommand := bson.D{
 		{"createIndexes", collection.Name()},
 		{"indexes", []bson.D{
 			{
 				{"name", fmt.Sprintf("hnsw_index_%s", vectorField)},
 				{"key", bson.D{
-					{vectorField, "cosmosSearch"}, // Cosmos DB vector search index type
+					{vectorField, "cosmosSearch"}, // DocumentDB vector search index type
 				}},
 				{"cosmosSearchOptions", bson.D{
 					// HNSW algorithm configuration
@@ -62,7 +62,7 @@ func CreateHNSWVectorIndex(ctx context.Context, collection *mongo.Collection, ve
 		if strings.Contains(err.Error(), "not enabled for this cluster tier") {
 			fmt.Println("\nHNSW indexes require a higher cluster tier.")
 			fmt.Println("Try one of these alternatives:")
-			fmt.Println("  • Upgrade your Cosmos DB cluster to a higher tier")
+			fmt.Println("  • Upgrade your DocumentDB cluster to a higher tier")
 			fmt.Println("  • Use IVF instead: go run src/ivf.go")
 			fmt.Println("  • Use DiskANN instead: go run src/diskann.go")
 		}
@@ -84,11 +84,11 @@ func PerformHNSWVectorSearch(ctx context.Context, collection *mongo.Collection,
 	}
 
 	// Build aggregation pipeline for HNSW vector search
-	// Cosmos DB uses $search with cosmosSearch
+	// DocumentDB uses $search with cosmosSearch
 	pipeline := []bson.M{
 		{
 			"$search": bson.M{
-				// Use cosmosSearch for vector operations in Cosmos DB
+				// Use cosmosSearch for vector operations in DocumentDB
 				"cosmosSearch": bson.M{
 					// Query vector to find similar documents for
 					"vector": queryEmbedding,

ai/vector-search-go/src/ivf.go

@@ -22,15 +22,15 @@ func CreateIVFVectorIndex(ctx context.Context, collection *mongo.Collection, vec
 		fmt.Printf("Warning: Could not drop existing indexes: %v\n", err)
 	}
 
-	// Use the native MongoDB command for Cosmos DB vector indexes
+	// Use the native MongoDB command for DocumentDB vector indexes
 	// Note: Must use bson.D for commands to preserve order and avoid "multi-key map" errors
 	indexCommand := bson.D{
 		{"createIndexes", collection.Name()},
 		{"indexes", []bson.D{
 			{
 				{"name", fmt.Sprintf("ivf_index_%s", vectorField)},
 				{"key", bson.D{
-					{vectorField, "cosmosSearch"}, // Cosmos DB vector search index type
+					{vectorField, "cosmosSearch"}, // DocumentDB vector search index type
 				}},
 				{"cosmosSearchOptions", bson.D{
 					// IVF algorithm configuration
@@ -59,7 +59,7 @@ func CreateIVFVectorIndex(ctx context.Context, collection *mongo.Collection, vec
 		if strings.Contains(err.Error(), "not enabled for this cluster tier") {
 			fmt.Println("\nIVF indexes require a higher cluster tier.")
 			fmt.Println("Try one of these alternatives:")
-			fmt.Println("  • Upgrade your Cosmos DB cluster to a higher tier")
+			fmt.Println("  • Upgrade your DocumentDB cluster to a higher tier")
 			fmt.Println("  • Use HNSW instead: go run src/hnsw.go")
 			fmt.Println("  • Use DiskANN instead: go run src/diskann.go")
 		}
@@ -81,11 +81,11 @@ func PerformIVFVectorSearch(ctx context.Context, collection *mongo.Collection, o
 	}
 
 	// Construct aggregation pipeline for IVF vector search
-	// Cosmos DB uses $search with cosmosSearch
+	// DocumentDB uses $search with cosmosSearch
 	pipeline := []bson.M{
 		{
 			"$search": bson.M{
-				// Use cosmosSearch for vector operations in Cosmos DB
+				// Use cosmosSearch for vector operations in DocumentDB
 				"cosmosSearch": bson.M{
 					// Query vector to find similar documents
 					"vector": queryEmbedding,

ai/vector-search-go/src/show_indexes.go

@@ -32,9 +32,9 @@ func formatIndexInfo(indexInfo IndexInfo) string {
 	}
 	lines = append(lines, fmt.Sprintf("Index Name: %s", name))
 
-	// Check if this is a vector index by looking for Cosmos DB vector search configuration
+	// Check if this is a vector index by looking for DocumentDB vector search configuration
 	if indexInfo.CosmosSearchOptions != nil && len(indexInfo.CosmosSearchOptions) > 0 {
-		lines = append(lines, "Type: Cosmos DB Vector Search Index")
+		lines = append(lines, "Type: DocumentDB Vector Search Index")
 
 		// Vector search specific details
 		if similarity, ok := indexInfo.CosmosSearchOptions["similarity"]; ok {