Java vector search sample

.devcontainer/java/devcontainer.json

@@ -1,24 +1,20 @@
 // For format details, see https://aka.ms/devcontainer.json. For config options, see the
-// README at: https://github.com/devcontainers/templates/tree/main/src/java
+// README at: https://github.com/devcontainers/templates/tree/main/src/universal
 {
 	"name": "Default Java",
 	// Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
 	"image": "mcr.microsoft.com/devcontainers/java:latest",
 	"features": {
-		"ghcr.io/devcontainers/features/azure-cli:1": {},
-		"ghcr.io/devcontainers/features/docker-in-docker:2": {},
-		"ghcr.io/azure/azure-dev/azd:0": {}
+		"ghcr.io/devcontainers/features/java:1": {
+			"version": "none",
+			"installMaven": "true"
+		},
+		"ghcr.io/devcontainers/features/azure-cli:1": {}
 	},
 	"customizations": {
 		"vscode": {
 			"extensions": [
-				"ms-azuretools.vscode-cosmosdb",
-				"buildwithlayer.mongodb-integration-expert-qS6DB",
-				"mongodb.mongodb-vscode",
-				"ms-azuretools.vscode-documentdb",
-				"redhat.java",
-				"vscjava.vscode-maven",
-				"vscjava.vscode-gradle"
+				"ms-azuretools.vscode-documentdb"
 			]
 		}
 	}

.gitignore

@@ -485,3 +485,8 @@ dist/
 *.user
 *.suo
 *.sln.docstates
+
+# Java
+*.class
+*.jar
+target/

ai/vector-search-java/README.md

@@ -0,0 +1,177 @@
+# DocumentDB Vector Samples (Java)
+
+This project demonstrates vector search capabilities using Azure DocumentDB with Java. It includes implementations of three different vector index types: DiskANN, HNSW, and IVF.
+
+## 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 DocumentDB
+- Create and use different types of vector indexes
+- Perform similarity searches with various algorithms
+
+## Prerequisites
+
+Before running this project, you need:
+
+### Azure Resources
+1. **Azure subscription** with appropriate permissions
+2. **[Azure Developer CLI (azd)](https://learn.microsoft.com/azure/developer/azure-developer-cli/)** installed
+
+### Development Environment
+- [Java 21 or higher](https://learn.microsoft.com/java/openjdk/download)
+- [Maven 3.6 or higher](https://maven.apache.org/download.cgi)
+- [Git](https://git-scm.com/downloads) (for cloning the repository)
+- [Visual Studio Code](https://code.visualstudio.com/) (recommended) or another Java IDE
+
+## Setup Instructions
+
+### Clone and Setup Project
+
+```bash
+# Clone this repository
+git clone https://github.com/Azure-Samples/documentdb-samples
+```
+
+### Deploy Azure Resources
+
+This project uses Azure Developer CLI (azd) to deploy all required Azure resources from the existing infrastructure-as-code files.
+
+#### Install Azure Developer CLI
+
+If you haven't already, install the Azure Developer CLI:
+
+**Windows:**
+```powershell
+winget install microsoft.azd
+```
+
+**macOS:**
+```bash
+brew tap azure/azd && brew install azd
+```
+
+**Linux:**
+```bash
+curl -fsSL https://aka.ms/install-azd.sh | bash
+```
+
+#### Deploy Resources
+
+Navigate to the root of the repository and run:
+
+```bash
+# Login to Azure
+azd auth login
+
+# Provision Azure resources
+azd up
+```
+
+During provisioning, you'll be prompted for:
+- **Environment name**: A unique name for your deployment (e.g., "my-vector-search")
+- **Azure subscription**: Select your Azure subscription
+- **Location**: Choose from `eastus2` or `swedencentral` (required for OpenAI models)
+
+The `azd up` command will:
+- Create a resource group
+- Deploy Azure OpenAI with text-embedding-3-small model
+- Deploy Azure DocumentDB (MongoDB vCore) cluster
+- Create a managed identity for secure access
+- Configure all necessary permissions and networking
+- Generate a `.env` file with all connection information at the repository root
+
+### Compile the Project
+
+```bash
+# Move to Java vector search project
+cd ai/vector-search-java
+
+# Compile the project
+mvn clean compile
+```
+
+### Load Environment Variables
+
+After deployment completes, load the environment variables from the generated `.env` file. The `set -a` command ensures variables are exported to child processes (like the Maven JVM):
+
+```bash
+# From the ai/vector-search-java directory
+set -a && source ../../.env && set +a
+```
+
+You can verify the environment variables are set:
+
+```bash
+echo $MONGO_CLUSTER_NAME
+```
+
+## Usage
+
+The project includes several Java classes that demonstrate different aspects of vector search.
+
+### Sign in to Azure for passwordless connection
+
+```bash
+az login
+```
+
+### DiskANN Vector Search
+
+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
+- Good balance of speed and accuracy
+
+### HNSW Vector Search
+
+Run HNSW (Hierarchical Navigable Small World) search:
+
+```bash
+mvn exec:java -Dexec.mainClass="com.azure.documentdb.samples.HNSW"
+```
+
+HNSW provides:
+- Excellent search performance
+- High recall rates
+- Hierarchical graph structure
+- Good for real-time applications
+
+### IVF Vector Search
+
+Run IVF (Inverted File) search:
+
+```bash
+mvn exec:java -Dexec.mainClass="com.azure.documentdb.samples.IVF"
+```
+
+IVF features:
+- Clusters vectors by similarity
+- Fast search through cluster centroids
+- Configurable accuracy vs speed trade-offs
+- Efficient for large vector datasets
+
+## Further Resources
+
+- [Azure Developer CLI Documentation](https://learn.microsoft.com/azure/developer/azure-developer-cli/)
+- [Azure DocumentDB Documentation](https://learn.microsoft.com/azure/documentdb/)
+- [Azure OpenAI Service Documentation](https://learn.microsoft.com/azure/ai-services/openai/)
+- [Vector Search in DocumentDB](https://learn.microsoft.com/azure/documentdb/vector-search)
+- [MongoDB Java Driver Documentation](https://mongodb.github.io/mongo-java-driver/)
+- [Azure SDK for Java Documentation](https://learn.microsoft.com/java/api/overview/azure/)
+
+## Support
+
+If you encounter issues:
+1. Verify Java 21+ is installed: `java -version`
+2. Verify Maven is installed: `mvn -version`
+3. Ensure Azure CLI is logged in: `az login`
+4. Verify environment variables are exported: `echo $MONGO_CLUSTER_NAME`
+5. Check Azure service status and quotas

ai/vector-search-java/pom.xml

@@ -0,0 +1,43 @@
+<project xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"
+         xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd">
+    <modelVersion>4.0.0</modelVersion>
+
+    <groupId>com.azure.documentdb.samples</groupId>
+    <artifactId>vector-search-quickstart</artifactId>
+    <version>1.0-SNAPSHOT</version>
+
+    <properties>
+        <maven.compiler.release>21</maven.compiler.release>
+        <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+    </properties>
+
+    <dependencies>
+        <dependency>
+            <groupId>org.mongodb</groupId>
+            <artifactId>mongodb-driver-sync</artifactId>
+            <version>5.6.2</version>
+        </dependency>
+        <dependency>
+            <groupId>com.azure</groupId>
+            <artifactId>azure-identity</artifactId>
+            <version>1.18.1</version>
+        </dependency>
+        <dependency>
+            <groupId>com.azure</groupId>
+            <artifactId>azure-ai-openai</artifactId>
+            <version>1.0.0-beta.16</version>
+        </dependency>
+        <dependency>
+            <groupId>tools.jackson.core</groupId>
+            <artifactId>jackson-databind</artifactId>
+            <version>3.0.3</version>
+        </dependency>
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-nop</artifactId>
+            <version>2.0.17</version>
+            <scope>runtime</scope>
+        </dependency>
+    </dependencies>
+</project>