Merge branch 'MicrosoftDocs:main' into high-availability

Author: gaurikasar

.openpublishing.publish.config.json

@@ -24,6 +24,7 @@
       "build_output_subfolder": "postgresql-citus",
       "locale": "en-us",
       "monikers": [],
+      "moniker_ranges": [],
       "open_to_public_contributors": false,
       "type_mapping": {
         "Conceptual": "Content"

articles/documentdb/TOC.yml

@@ -24,6 +24,8 @@
         href: quickstart-dotnet-vector-search.md
       - name: Create AI vector search app with Go
         href: quickstart-go-vector-search.md
+      - name: Create AI vector search app with Java
+        href: quickstart-java-vector-search.md
       - name: Create AI agent with vector search in Go
         href: quickstart-agent-go.md
     - name: Develop a Python console application

articles/documentdb/azure-mongo-first-party.md

@@ -3,8 +3,7 @@ title: Why Mission-Critical MongoDB Workloads Run on Azure First-Party Services
 description: Learn why mission-critical workloads rely on first-party database services.
 author: gahl-levy
 ms.author: gahllevy
-ms.service: azure-cosmos-db
-ms.subservice: mongodb-vcore
+ms.service: azure-documentdb
 ms.topic: product-comparison
 ms.date: 06/03/2025
 ---

articles/documentdb/multi-cloud.md

@@ -3,8 +3,7 @@ title: Azure DocumentDB is your multi-cloud solution
 description: Azure DocumentDB offers a flexible, multi-cloud database service, using the MongoDB wire protocol for seamless migration and integration across environments.
 author: gahl-levy
 ms.author: gahllevy
-ms.service: azure-cosmos-db
-ms.subservice: mongodb-vcore
+ms.service: azure-documentdb
 ms.topic: overview
 ms.date: 02/12/2024
 ---

articles/documentdb/oss.md

@@ -3,8 +3,7 @@ title: DocumentDBis the Open-Source Engine Powering Azure DocumentDB
 description: Learn about DocumentDB, the open-source document database platform that powers Azure DocumentDB, its features, architecture, and how to get started.
 author: patty-chow
 ms.author: pattychow
-ms.service: azure-cosmos-db
-ms.subservice: mongodb-vcore
+ms.service: azure-documentdb
 ms.topic: overview
 ms.date: 02/07/2025
 ---

articles/documentdb/quickstart-dotnet-vector-search.md

@@ -7,7 +7,7 @@ ms.author: alexwolf
 ms.reviewer: khelanmodi
 ms.devlang: csharp
 ms.topic: quickstart-sdk
-ms.date: 10/23/2025
+ms.date: 02/12/2026
 ms.custom:
   - devx-track-dotnet
   - devx-track-dotnet-ai
@@ -19,7 +19,7 @@ ms.custom:
 
 Learn to use vector search in Azure DocumentDB with the .NET MongoDB driver to store and query vector data efficiently.
 
-This quickstart provides a guided tour of key vector search techniques using a [.NET sample app](https://github.com/Azure-Samples/cosmos-db-vector-samples/tree/main/mongo-vcore-vector-search-dotnet) on GitHub.
+This quickstart provides a guided tour of key vector search techniques using a [.NET sample app](https://github.com/Azure-Samples/documentdb-samples/tree/main/ai/vector-search-dotnet) on GitHub.
 
 The app uses a sample hotel dataset in a JSON file with pre-calculated vectors from the `text-embedding-ada-002` model, though you can also generate the vectors yourself. The hotel data includes hotel names, locations, descriptions, and vector embeddings.
 
@@ -49,7 +49,7 @@ Complete the following steps to configure the app with your own values and run s
 
 Update the `appsettings.json` placeholder values with your own:
 
-:::code language="json" source="~/cosmos-db-vector-samples/mongo-vcore-vector-search-dotnet/appsettings.json" :::
+:::code language="json" source="~/documentdb-samples/ai/vector-search-dotnet/appsettings.json" :::
 
 ### Authenticate to Azure
 
@@ -93,7 +93,7 @@ The sample app populates vectorized sample data in a MongoDB collection and lets
     The app prints a menu for you to select database and search options:
 
     ```output
-    === Cosmos DB Vector Samples Menu ===
+    === DocumentDB Vector Samples Menu ===
     Please enter your choice (0-5):
     1. Create embeddings for data
     2. Show all database indexes
@@ -124,11 +124,11 @@ The sample app populates vectorized sample data in a MongoDB collection and lets
     Executing DiskANN vector search for top 5 results
     
     Search Results (5 found using DiskANN):
-    1. Roach Motel (Similarity: 0.8399)
-    2. Royal Cottage Resort (Similarity: 0.8385)
-    3. Economy Universe Motel (Similarity: 0.8360)
-    4. Foot Happy Suites (Similarity: 0.8354)
-    5. Country Comfort Inn (Similarity: 0.8346)
+    1. Royal Cottage Resort (Similarity: 0.4991)
+    2. Country Comfort Inn (Similarity: 0.4786)
+    3. Nordick's Valley Motel (Similarity: 0.4635)
+    4. Economy Universe Motel (Similarity: 0.4461)
+    5. Roach Motel (Similarity: 0.4388)
     ```
 
 #### [IVF](#tab/tab-ivf)
@@ -142,7 +142,7 @@ The sample app populates vectorized sample data in a MongoDB collection and lets
     The app prints a menu for you to select database and search options:
     
     ```output
-    === Cosmos DB Vector Samples Menu ===
+    === DocumentDB Vector Samples Menu ===
     Please enter your choice (0-5):
     1. Create embeddings for data
     2. Show all database indexes
@@ -173,11 +173,11 @@ The sample app populates vectorized sample data in a MongoDB collection and lets
     Executing IVF vector search for top 5 results
     
     Search Results (5 found using IVF):
-    1. Roach Motel (Similarity: 0.8399)
-    2. Royal Cottage Resort (Similarity: 0.8385)
-    3. Economy Universe Motel (Similarity: 0.8360)
-    4. Foot Happy Suites (Similarity: 0.8354)
-    5. Country Comfort Inn (Similarity: 0.8346)
+    1. Royal Cottage Resort (Similarity: 0.4991)
+    2. Country Comfort Inn (Similarity: 0.4786)
+    3. Nordick's Valley Motel (Similarity: 0.4635)
+    4. Economy Universe Motel (Similarity: 0.4461)
+    5. Roach Motel (Similarity: 0.4388)
     ```
 
 #### [HNSW](#tab/tab-hnsw)
@@ -191,7 +191,7 @@ The sample app populates vectorized sample data in a MongoDB collection and lets
     The app prints a menu for you to select database and search options:
 
     ```output
-    === Cosmos DB Vector Samples Menu ===
+    === DocumentDB Vector Samples Menu ===
     Please enter your choice (0-5):
     1. Create embeddings for data
     2. Show all database indexes
@@ -222,24 +222,24 @@ The sample app populates vectorized sample data in a MongoDB collection and lets
     Executing HNSW vector search for top 5 results
     
     Search Results (5 found using HNSW):
-    1. Roach Motel (Similarity: 0.8399)
-    2. Royal Cottage Resort (Similarity: 0.8385)
-    3. Economy Universe Motel (Similarity: 0.8360)
-    4. Foot Happy Suites (Similarity: 0.8354)
-    5. Country Comfort Inn (Similarity: 0.8346)
+    1. Royal Cottage Resort (Similarity: 0.4991)
+    2. Country Comfort Inn (Similarity: 0.4786)
+    3. Nordick's Valley Motel (Similarity: 0.4635)
+    4. Economy Universe Motel (Similarity: 0.4461)
+    5. Roach Motel (Similarity: 0.4388)
     ```
 
-----
+---
 
 ## Explore the app code
 
-The following sections provide details about the most important services and code in the sample app. [Visit the GitHub repo](https://github.com/Azure-Samples/cosmos-db-vector-samples/tree/main/mongo-vcore-vector-search-dotnet) to explore the full app code.
+The following sections provide details about the most important services and code in the sample app. [Visit the GitHub repo](https://github.com/Azure-Samples/documentdb-samples/tree/main/ai/vector-search-dotnet) to explore the full app code.
 
 ### Explore the search service
 
 The `VectorSearchService` orchestrates an end‑to‑end vector similarity search using IVF, HNSW, and DiskANN search techniques with Azure OpenAI embeddings.
 
-:::code language="csharp" source="~/cosmos-db-vector-samples/mongo-vcore-vector-search-dotnet/services/vectorsearchservice.cs" :::
+:::code language="csharp" source="~/documentdb-samples/ai/vector-search-dotnet/services/vectorsearchservice.cs" :::
 
 In the preceding code, the `VectorSearchService` performs the following tasks:
 
@@ -254,7 +254,7 @@ In the preceding code, the `VectorSearchService` performs the following tasks:
 
 The `MongoDbService` manages interactions with Azure DocumentDB to handle tasks like loading data, vector index creation, index listing, and bulk inserts for hotel vector search.
 
-:::code language="csharp" source="~/cosmos-db-vector-samples/mongo-vcore-vector-search-dotnet/services/MongoDbService.cs" :::
+:::code language="csharp" source="~/documentdb-samples/ai/vector-search-dotnet/services/MongoDbService.cs" :::
 
 In the preceding code, the `MongoDbService` performs the following tasks:
 

articles/documentdb/quickstart-java-vector-search.md

@@ -0,0 +1,217 @@
+---
+title: Quickstart - Vector Search with Java
+description: Learn how to use vector search in Azure DocumentDB with Java. Store and query vector data efficiently in your applications.
+author: diberry
+ms.author: diberry
+ms.reviewer: khelanmodi
+ms.devlang: java
+ms.topic: quickstart-sdk
+ms.date: 02/12/2026
+ai-usage: ai-assisted
+ms.custom:
+  - devx-track-java
+  - devx-track-java-ai
+  - devx-track-data-ai
+# CustomerIntent: As a developer, I want to learn how to use vector search in Java applications with Azure DocumentDB.
+---
+
+# Quickstart: Vector search with Java in Azure DocumentDB
+
+Learn to use vector search in Azure DocumentDB with the Java MongoDB driver to store and query vector data efficiently.
+
+This quickstart provides a guided tour of key vector search techniques using a [Java sample app](https://github.com/Azure-Samples/documentdb-samples/tree/main/ai/vector-search-java) on GitHub.
+
+The app uses a sample hotel dataset in a JSON file with pre-calculated vectors from the `text-embedding-3-small` model, though you can also generate the vectors yourself. The hotel data includes hotel names, locations, descriptions, and vector embeddings.
+
+## Prerequisites
+
+[!INCLUDE[Prerequisites - Vector Search Quickstart](includes/prerequisite-quickstart-vector-search-model.md)]
+
+- [Java 21](/java/openjdk/download) or later
+
+- [Maven 3.6](https://maven.apache.org/download.cgi) or later
+
+
+## Create data file with vectors
+
+1. Create a new data directory for the hotels data file:
+
+    ```bash
+    mkdir data
+    ```
+
+1. Copy the `Hotels_Vector.json` [raw data file with vectors](https://raw.githubusercontent.com/Azure-Samples/documentdb-samples/refs/heads/main/ai/data/Hotels_Vector.json) to your `data` directory.
+
+## Create a Java project
+
+1. Create a new sibling directory for your project, at the same level as the data directory, and open it in Visual Studio Code:
+
+    ```bash
+    mkdir vector-search-quickstart
+    mkdir vector-search-quickstart/src
+    code vector-search-quickstart
+    ```
+
+1. Create a `pom.xml` file in the project root with the following content:
+
+    :::code language="xml" source="~/documentdb-samples/ai/vector-search-java/pom.xml" :::
+
+    The app uses the following Maven dependencies specified in the `pom.xml`:
+    
+    - [`mongodb-driver-sync`](https://mvnrepository.com/artifact/org.mongodb/mongodb-driver-sync): Official MongoDB Java driver for database connectivity and operations
+    - [`azure-identity`](https://mvnrepository.com/artifact/com.azure/azure-identity): Azure Identity library for passwordless authentication with Microsoft Entra ID
+    - [`azure-ai-openai`](https://mvnrepository.com/artifact/com.azure/azure-ai-openai): Azure OpenAI client library to communicate with AI models and create vector embeddings
+    - [`jackson-databind`](https://mvnrepository.com/artifact/com.fasterxml.jackson.core/jackson-databind): JSON serialization and deserialization library
+    - [`slf4j-nop`](https://mvnrepository.com/artifact/org.slf4j/slf4j-nop): No-operation SLF4J binding to suppress logging output from the MongoDB driver
+    
+
+1. Create a `.env` file in your project root for environment variables:
+
+    ```ini
+    # Azure OpenAI Embedding Settings
+    AZURE_OPENAI_EMBEDDING_MODEL=text-embedding-3-small
+    AZURE_OPENAI_EMBEDDING_API_VERSION=2023-05-15
+    AZURE_OPENAI_EMBEDDING_ENDPOINT=
+    EMBEDDING_SIZE_BATCH=16
+
+    # Azure DocumentDB configuration
+    MONGO_CLUSTER_NAME=
+
+    # Data file
+    DATA_FILE_WITH_VECTORS=../data/Hotels_Vector.json
+    EMBEDDED_FIELD=DescriptionVector
+    EMBEDDING_DIMENSIONS=1536
+    LOAD_SIZE_BATCH=50
+    ```
+
+    Replace the placeholder values in the `.env` file with your own information:
+    - `AZURE_OPENAI_EMBEDDING_ENDPOINT`: Your Azure OpenAI resource endpoint URL.
+    - `MONGO_CLUSTER_NAME`: Your Azure DocumentDB resource name.
+
+1. Load the environment variables:
+
+    ```bash
+    set -a && source .env && set +a
+    ```
+
+1. The project structure should look like this:
+
+    ```plaintext
+    data
+    └── Hotels_Vector.json
+    vector-search-quickstart
+    ├── .env
+    ├── pom.xml
+    └── src
+    ```
+
+## Add code for vector search
+
+#### [DiskANN](#tab/tab-diskann)
+
+Create a `DiskAnn.java` file in the `src` directory and paste in the following code:
+
+:::code language="java" source="~/documentdb-samples/ai/vector-search-java/src/main/java/com/azure/documentdb/samples/DiskAnn.java" :::
+
+#### [IVF](#tab/tab-ivf)
+
+Create an `IVF.java` file in the `src` directory and paste in the following code:
+
+:::code language="java" source="~/documentdb-samples/ai/vector-search-java/src/main/java/com/azure/documentdb/samples/IVF.java" :::
+
+#### [HNSW](#tab/tab-hnsw)
+
+Create an `HNSW.java` file in the `src` directory and paste in the following code:
+
+:::code language="java" source="~/documentdb-samples/ai/vector-search-java/src/main/java/com/azure/documentdb/samples/HNSW.java" :::
+
+---
+
+This code performs the following tasks:
+
+- Creates a passwordless connection to Azure DocumentDB using `DefaultAzureCredential` and the MongoDB OIDC mechanism
+- Creates an Azure OpenAI client for generating embeddings
+- Drops and recreates the collection, then loads hotel data from the JSON file in batches
+- Creates standard indexes and a vector index with algorithm-specific options
+- Generates an embedding for a sample query and runs an aggregation search pipeline
+- Prints the top five matching hotels with similarity scores
+
+## Authenticate to Azure
+
+Sign in to Azure before you run the application so it can access Azure resources securely.
+
+> [!NOTE]
+> Ensure you're signed-in identity has the required data plane roles on both the Azure DocumentDB account and the Azure OpenAI resource.
+
+```bash
+az login
+```
+
+## Build the application
+
+Compile the application:
+
+```bash
+mvn clean compile
+```
+
+#### [DiskANN](#tab/tab-diskann)
+
+Run DiskANN (Disk-based Approximate Nearest Neighbor) search:
+
+```bash
+mvn exec:java -Dexec.mainClass="com.azure.documentdb.samples.DiskAnn"
+```
+
+DiskANN is optimized for large datasets that don't fit in memory, efficient disk-based storage, and a good balance of speed and accuracy.
+
+Example output:
+
+:::code language="output" source="~/documentdb-samples/ai/vector-search-java/output/diskann.txt" :::
+
+#### [IVF](#tab/tab-ivf)
+
+Run IVF (Inverted File) search:
+
+```bash
+mvn exec:java -Dexec.mainClass="com.azure.documentdb.samples.IVF"
+```
+
+IVF clusters vectors by similarity and provides fast search through cluster centroids. It offers configurable accuracy vs speed trade-offs for large vector datasets.
+
+Example output:
+
+:::code language="output" source="~/documentdb-samples/ai/vector-search-java/output/ivf.txt" :::
+
+#### [HNSW](#tab/tab-hnsw)
+
+Run HNSW (Hierarchical Navigable Small World) search:
+
+```bash
+mvn exec:java -Dexec.mainClass="com.azure.documentdb.samples.HNSW"
+```
+
+HNSW provides excellent search performance with high recall rates using a hierarchical graph structure, making it suitable for real-time applications.
+
+Example output:
+
+:::code language="output" source="~/documentdb-samples/ai/vector-search-java/output/hnsw.txt" :::
+
+---
+
+## View and manage data in Visual Studio Code
+
+1. Install the [DocumentDB extension](https://marketplace.visualstudio.com/items?itemName=ms-azuretools.vscode-documentdb) and [Extension Pack for Java](https://marketplace.visualstudio.com/items?itemName=vscjava.vscode-java-pack) in Visual Studio Code.
+1. Connect to your Azure DocumentDB account using the DocumentDB extension.
+1. View the data and indexes in the Hotels database.
+
+    :::image type="content" source="./media/quickstart-nodejs-vector-search/visual-studio-code-documentdb.png" lightbox="./media/quickstart-nodejs-vector-search/visual-studio-code-documentdb.png" alt-text="Screenshot of DocumentDB extension showing the DocumentDB collection.":::
+
+## Clean up resources
+
+Delete the resource group, Azure DocumentDB cluster, and Azure OpenAI resource when you no longer need them to avoid unnecessary costs.
+
+## Related content
+
+- [Vector store in Azure DocumentDB](vector-search.md)
+- [Support for geospatial queries](geospatial-support.md)