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. Triggers: "AIProjectClient java", "azure ai projects java", "Foundry project java", "ConnectionsClient", "DatasetsClient", "IndexesClient".
Best use case
azure-ai-projects-java is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. 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".
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".
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "azure-ai-projects-java" skill to help with this workflow task. Context: 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".
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/azure-ai-projects-java/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How azure-ai-projects-java Compares
| Feature / Agent | azure-ai-projects-java | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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 |Related Skills
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