home / skills / sickn33 / antigravity-awesome-skills / azure-ai-projects-java
This skill helps Java developers manage Azure AI Foundry projects by coordinating connections, datasets, indexes, and evaluations across sub-clients.
npx playbooks add skill sickn33/antigravity-awesome-skills --skill azure-ai-projects-javaReview the files below or copy the command above to add this skill to your agents.
---
name: azure-ai-projects-java
description: |
Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.
Triggers: "AIProjectClient java", "azure ai projects java", "Foundry project java", "ConnectionsClient", "DatasetsClient", "IndexesClient".
package: com.azure:azure-ai-projects
---
# Azure AI Projects SDK for Java
High-level SDK for Azure AI Foundry project management with access to connections, datasets, indexes, and evaluations.
## Installation
```xml
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-projects</artifactId>
<version>1.0.0-beta.1</version>
</dependency>
```
## Environment Variables
```bash
PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
```
## Authentication
```java
import com.azure.ai.projects.AIProjectClientBuilder;
import com.azure.identity.DefaultAzureCredentialBuilder;
AIProjectClientBuilder builder = new AIProjectClientBuilder()
.endpoint(System.getenv("PROJECT_ENDPOINT"))
.credential(new DefaultAzureCredentialBuilder().build());
```
## Client Hierarchy
The SDK provides multiple sub-clients for different operations:
| Client | Purpose |
|--------|---------|
| `ConnectionsClient` | Enumerate connected Azure resources |
| `DatasetsClient` | Upload documents and manage datasets |
| `DeploymentsClient` | Enumerate AI model deployments |
| `IndexesClient` | Create and manage search indexes |
| `EvaluationsClient` | Run AI model evaluations |
| `EvaluatorsClient` | Manage evaluator configurations |
| `SchedulesClient` | Manage scheduled operations |
```java
// Build sub-clients from builder
ConnectionsClient connectionsClient = builder.buildConnectionsClient();
DatasetsClient datasetsClient = builder.buildDatasetsClient();
DeploymentsClient deploymentsClient = builder.buildDeploymentsClient();
IndexesClient indexesClient = builder.buildIndexesClient();
EvaluationsClient evaluationsClient = builder.buildEvaluationsClient();
```
## Core Operations
### List Connections
```java
import com.azure.ai.projects.models.Connection;
import com.azure.core.http.rest.PagedIterable;
PagedIterable<Connection> connections = connectionsClient.listConnections();
for (Connection connection : connections) {
System.out.println("Name: " + connection.getName());
System.out.println("Type: " + connection.getType());
System.out.println("Credential Type: " + connection.getCredentials().getType());
}
```
### List Indexes
```java
indexesClient.listLatest().forEach(index -> {
System.out.println("Index name: " + index.getName());
System.out.println("Version: " + index.getVersion());
System.out.println("Description: " + index.getDescription());
});
```
### Create or Update Index
```java
import com.azure.ai.projects.models.AzureAISearchIndex;
import com.azure.ai.projects.models.Index;
String indexName = "my-index";
String indexVersion = "1.0";
String searchConnectionName = System.getenv("AI_SEARCH_CONNECTION_NAME");
String searchIndexName = System.getenv("AI_SEARCH_INDEX_NAME");
Index index = indexesClient.createOrUpdate(
indexName,
indexVersion,
new AzureAISearchIndex()
.setConnectionName(searchConnectionName)
.setIndexName(searchIndexName)
);
System.out.println("Created index: " + index.getName());
```
### Access OpenAI Evaluations
The SDK exposes OpenAI's official SDK for evaluations:
```java
import com.openai.services.EvalService;
EvalService evalService = evaluationsClient.getOpenAIClient();
// Use OpenAI evaluation APIs directly
```
## Best Practices
1. **Use DefaultAzureCredential** for production authentication
2. **Reuse client builder** to create multiple sub-clients efficiently
3. **Handle pagination** when listing resources with `PagedIterable`
4. **Use environment variables** for connection names and configuration
5. **Check connection types** before accessing credentials
## Error Handling
```java
import com.azure.core.exception.HttpResponseException;
import com.azure.core.exception.ResourceNotFoundException;
try {
Index index = indexesClient.get(indexName, version);
} catch (ResourceNotFoundException e) {
System.err.println("Index not found: " + indexName);
} catch (HttpResponseException e) {
System.err.println("Error: " + e.getResponse().getStatusCode());
}
```
## Reference Links
| Resource | URL |
|----------|-----|
| Product Docs | https://learn.microsoft.com/azure/ai-studio/ |
| API Reference | https://learn.microsoft.com/rest/api/aifoundry/aiprojects/ |
| GitHub Source | https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-projects |
| Samples | https://github.com/Azure/azure-sdk-for-java/tree/main/sdk/ai/azure-ai-projects/src/samples |
This skill provides a concise guide to the Azure AI Projects SDK for Java, a high-level client for managing Azure AI Foundry projects. It covers authentication, sub-clients (connections, datasets, indexes, evaluations), core operations, and recommended practices for production use. The content is practical and focused on common workflows developers need to integrate project management into Java applications.
The SDK exposes a central AIProjectClientBuilder to configure endpoint and credentials, then builds focused sub-clients such as ConnectionsClient, DatasetsClient, IndexesClient, and EvaluationsClient. Each sub-client offers targeted APIs: enumerate connected resources, upload and manage datasets, create or update search indexes, and run or manage evaluations. The SDK also exposes the OpenAI evaluation client for direct use of evaluation APIs.
How do I authenticate when running locally and in production?
Use DefaultAzureCredential to support local developer flows and managed identities in production. Set PROJECT_ENDPOINT as an environment variable.
Can I access OpenAI evaluation APIs through this SDK?
Yes. The evaluations client exposes the OpenAI EvalService client so you can call OpenAI evaluation APIs directly.