Agent skill
azure-ai-projects-java
Azure AI Projects SDK for Java. High-level SDK for Azure AI Foundry project management including connections, datasets, indexes, and evaluations.
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Install this agent skill to your Project
npx add-skill https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/azure-ai-projects-java
SKILL.md
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
- Use DefaultAzureCredential for production authentication
- Reuse client builder to create multiple sub-clients efficiently
- Handle pagination when listing resources with
PagedIterable - Use environment variables for connection names and configuration
- 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
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
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