Merge pull request #8 from Azure-Samples/copilot/update-packages-branding

Standardize branding to "DocumentDB" across all vector search projects

Author: diberry

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

@@ -1,19 +1,19 @@
-# Azure Cosmos DB MongoDB vCore - Vector Search Sample
+# DocumentDB Vector Search Sample
 
-This .NET 8.0 sample application demonstrates vector similarity searches using Azure Cosmos DB for MongoDB vCore 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 Azure Cosmos DB and Azure OpenAI services
+- Seamless integration with DocumentDB and Azure OpenAI services
 - Interactive menu-driven application
 
 ## Prerequisites
 
 ### Azure Services
 
-- Azure Cosmos DB for MongoDB vCore account
+- Azure DocumentDB account
 - Azure OpenAI Service with text-embedding-ada-002 model deployed
 
 ### Development Environment
@@ -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
 
-- [Azure Cosmos DB for MongoDB vCore 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/README.md

@@ -137,8 +137,8 @@ LOAD_SIZE_BATCH=100
 To use passwordless authentication with Microsoft Entra ID, follow these steps:
 
 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 Cosmos DB resource:
-   - **Cosmos DB Account Reader Role**
+2. Assign your Microsoft Entra ID user the following roles on the DocumentDB resource:
+   - **DocumentDB Account Reader Role**
    - **DocumentDB Account Contributor**
 
 ## Usage
@@ -269,7 +269,7 @@ defer mongoClient.Disconnect(ctx)
 
 1. Ensure you're logged in with `az login`
 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 **Cosmos DB Account Reader Role** and **DocumentDB Account Contributor** roles assigned to your user.
+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
@@ -333,7 +333,7 @@ mongo-vcore-vector-search-go/
 1. **Authentication Errors**
    - Verify Azure OpenAI endpoint and key
    - Check Azure DocumentDB connection string
-   - Ensure proper RBAC permissions for passwordless authentication. You need **Cosmos DB Account Reader Role** and **DocumentDB Account Contributor** roles assigned to your user. Roles may take some time to propagate.
+   - 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
@@ -377,7 +377,7 @@ DEBUG=true
 
 - [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-python/README.md

@@ -1,13 +1,13 @@
-# Cosmos DB Vector Samples (Python)
+# DocumentDB Vector Samples (Python)
 
-This project demonstrates vector search capabilities using Azure Cosmos DB for MongoDB (vCore) with Python. 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 Python. 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 for MongoDB (vCore)
+- Store vectors in DocumentDB
 - Create and use different types of vector indexes
 - Perform similarity searches with various algorithms
 
@@ -18,7 +18,7 @@ Before running this project, you need:
 ### Azure Resources
 1. **Azure subscription** with appropriate permissions
 2. **Azure OpenAI resource** with embedding model deployment
-3. **Azure Cosmos DB for MongoDB (vCore) resource**
+3. **Azure DocumentDB resource**
 4. **Azure CLI** installed and configured
 
 ### Development Environment
@@ -75,9 +75,9 @@ az cognitiveservices account create \
 4. Choose **text-embedding-ada-002** model
 5. Note the deployment name for configuration
 
-#### Create Cosmos DB for MongoDB (vCore)
+#### Create DocumentDB
 
-Learn how to create a Cosmos DB for MongoDB (vCore) account in the [official documentation](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/quickstart-portal).
+Learn how to create an Azure DocumentDB account in the [official documentation](https://learn.microsoft.com/azure/documentdb/).
 
 ### Step 3: Configure Environment Variables
 
@@ -95,7 +95,7 @@ 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
 
-# MongoDB/Cosmos DB Configuration
+# MongoDB/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
 
@@ -126,14 +126,15 @@ az cognitiveservices account keys list \
     --query "key1" --output tsv
 ```
 
-#### Cosmos DB Connection String
+#### DocumentDB Connection String
 ```bash
-# Get Cosmos DB connection string
-az cosmosdb keys list \
-    --name myCosmosAccount \
+# Get DocumentDB connection string
+az resource show \
     --resource-group myResourceGroup \
-    --type connection-strings \
-    --query "connectionStrings[0].connectionString" --output tsv
+    --name myDocumentDBCluster \
+    --resource-type "Microsoft.DocumentDB/mongoClusters" \
+    --query "properties.connectionString" \
+    --output tsv
 ```
 
 ## Usage
@@ -206,7 +207,7 @@ This utility shows:
 ## Important Notes
 
 ### Vector Index Limitations
-**One Index Per Field**: Cosmos DB for MongoDB (vCore) allows only one vector index per field. Each script automatically handles this by:
+**One Index Per Field**: 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
@@ -234,7 +235,7 @@ 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 [DocumentDB pricing page](https://azure.microsoft.com/pricing/details/documentdb/) for tier features
 
 ## Authentication Options
 
@@ -257,7 +258,7 @@ mongo_client, openai_client = get_clients_passwordless()
 
 **Setup for passwordless authentication:**
 1. Ensure you're logged in with `az login`
-2. Grant your identity appropriate RBAC permissions on Cosmos DB
+2. Grant your identity appropriate RBAC permissions on DocumentDB
 3. Set `MONGO_CLUSTER_NAME` instead of `MONGO_CONNECTION_STRING` in `.env`
 
 ### Method 2: Connection String Authentication 
@@ -314,7 +315,7 @@ cosmos-db-vector-samples/
 
 1. **Authentication Errors**
    - Verify Azure OpenAI endpoint and key
-   - Check Cosmos DB connection string
+   - Check DocumentDB connection string
    - Ensure proper RBAC permissions for passwordless auth
 
 2. **Embedding Generation Fails**
@@ -368,14 +369,14 @@ except Exception as e:
 
 ### Cost Optimization
 - Use appropriate Azure OpenAI pricing tier
-- Consider Cosmos DB serverless vs provisioned throughput
+- Consider DocumentDB serverless vs provisioned throughput
 - Monitor API usage and optimize batch processing
 
 ## Further Resources
 
-- [Azure Cosmos DB for MongoDB (vCore) Documentation](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/)
+- [Azure DocumentDB Documentation](https://learn.microsoft.com/azure/documentdb/)
 - [Azure OpenAI Service Documentation](https://learn.microsoft.com/en-us/azure/ai-services/openai/)
-- [Vector Search in Cosmos DB](https://learn.microsoft.com/en-us/azure/cosmos-db/mongodb/vcore/vector-search)
+- [Vector Search in DocumentDB](https://learn.microsoft.com/azure/documentdb/vector-search)
 - [Python MongoDB Driver Documentation](https://pymongo.readthedocs.io/)
 
 ## Support

ai/vector-search-python/src/diskann.py

@@ -14,14 +14,14 @@ def create_diskann_vector_index(collection, vector_field: str, dimensions: int)
     # Drop any existing vector indexes on this field first
     drop_vector_indexes(collection, vector_field)
 
-    # Use the native MongoDB command for Cosmos DB vector indexes
+    # Use the native MongoDB command for DocumentDB vector indexes
     index_command = {
         "createIndexes": collection.name,
         "indexes": [
             {
                 "name": f"diskann_index_{vector_field}",
                 "key": {
-                    vector_field: "cosmosSearch"  # Cosmos DB vector search index type
+                    vector_field: "cosmosSearch"  # DocumentDB vector search index type
                 },
                 "cosmosSearchOptions": {
                     # DiskANN algorithm configuration
@@ -57,7 +57,7 @@ def create_diskann_vector_index(collection, vector_field: str, dimensions: int)
         if "not enabled for this cluster tier" in str(e):
             print("\nDiskANN indexes require a higher cluster tier.")
             print("Try one of these alternatives:")
-            print("  • Upgrade your Cosmos DB cluster to a higher tier")
+            print("  • Upgrade your DocumentDB cluster to a higher tier")
             print("  • Use HNSW instead: python src/hnsw.py")
             print("  • Use IVF instead: python src/ivf.py")
         raise
@@ -82,11 +82,11 @@ def perform_diskann_vector_search(collection,
         query_embedding = embedding_response.data[0].embedding
 
         # Construct the aggregation pipeline for vector search
-        # Cosmos DB for MongoDB vCore uses $search with cosmosSearch
+        # DocumentDB uses $search with cosmosSearch
         pipeline = [
             {
                 "$search": {
-                    # Use cosmosSearch for vector operations in Cosmos DB
+                    # Use cosmosSearch for vector operations in DocumentDB
                     "cosmosSearch": {
                         # The query vector to search for
                         "vector": query_embedding,

ai/vector-search-python/src/hnsw.py

@@ -14,14 +14,14 @@ def create_hnsw_vector_index(collection, vector_field: str, dimensions: int) ->
     # Drop any existing vector indexes on this field first
     drop_vector_indexes(collection, vector_field)
 
-    # Use the native MongoDB command for Cosmos DB vector indexes
+    # Use the native MongoDB command for DocumentDB vector indexes
     index_command = {
         "createIndexes": collection.name,
         "indexes": [
             {
                 "name": f"hnsw_index_{vector_field}",
                 "key": {
-                    vector_field: "cosmosSearch"  # Cosmos DB vector search index type
+                    vector_field: "cosmosSearch"  # DocumentDB vector search index type
                 },
                 "cosmosSearchOptions": {
                     # HNSW algorithm configuration
@@ -78,7 +78,7 @@ def perform_hnsw_vector_search(collection,
         pipeline = [
             {
                 "$search": {
-                    # Use cosmosSearch for vector operations in Cosmos DB
+                    # Use cosmosSearch for vector operations in DocumentDB
                     "cosmosSearch": {
                         # Query vector to find similar documents for
                         "vector": query_embedding,

ai/vector-search-python/src/ivf.py

@@ -14,14 +14,14 @@ def create_ivf_vector_index(collection, vector_field: str, dimensions: int) -> N
     # Drop any existing vector indexes on this field first
     drop_vector_indexes(collection, vector_field)
 
-    # Use the native MongoDB command for Cosmos DB vector indexes
+    # Use the native MongoDB command for DocumentDB vector indexes
     index_command = {
         "createIndexes": collection.name,
         "indexes": [
             {
                 "name": f"ivf_index_{vector_field}",
                 "key": {
-                    vector_field: "cosmosSearch"  # Cosmos DB vector search index type
+                    vector_field: "cosmosSearch"  # DocumentDB vector search index type
                 },
                 "cosmosSearchOptions": {
                     # IVF algorithm configuration
@@ -75,7 +75,7 @@ def perform_ivf_vector_search(collection,
         pipeline = [
             {
                 "$search": {
-                    # Use cosmosSearch for vector operations in Cosmos DB
+                    # Use cosmosSearch for vector operations in DocumentDB
                     "cosmosSearch": {
                         # Query vector to find similar documents
                         "vector": query_embedding,

ai/vector-search-python/src/show_indexes.py

@@ -1,5 +1,5 @@
 """
-Display vector indexes for Cosmos DB collections.
+Display vector indexes for DocumentDB collections.
 
 This utility script shows all vector indexes in a collection, including
 their configuration details, status, and performance characteristics.

ai/vector-search-python/src/utils.py

@@ -24,12 +24,12 @@ def fetch(self, context: OIDCCallbackContext) -> OIDCCallbackResult:
 
 def get_clients() -> Tuple[MongoClient, AzureOpenAI]:
 
-    # Get MongoDB connection string - required for Cosmos DB access
+    # Get MongoDB connection string - required for DocumentDB access
     mongo_connection_string = os.getenv("MONGO_CONNECTION_STRING")
     if not mongo_connection_string:
         raise ValueError("MONGO_CONNECTION_STRING environment variable is required")
 
-    # Create MongoDB client with optimized settings for Cosmos DB
+    # Create MongoDB client with optimized settings for DocumentDB
     mongo_client = MongoClient(
         mongo_connection_string,
         maxPoolSize=50,  # Allow up to 50 connections for better performance
@@ -95,7 +95,7 @@ def get_clients_passwordless() -> Tuple[MongoClient, AzureOpenAI]:
 
 def azure_identity_token_callback(credential: DefaultAzureCredential) -> str:
 
-    # Cosmos DB for MongoDB requires this specific scope
+    # DocumentDB requires this specific scope
     token_scope = "https://cosmos.azure.com/.default"
 
     # Get token from Azure AD