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".
- License 许可
- MIT
- 最近更新
- 2026-11-11
- Stars
- ★ 1
- Forks
- 0
01 前言 Front Matter
- name
- azure-ai-projects-java
- license
- MIT
- metadata
- { "author": "Microsoft", "package": "com.azure:azure-ai-projects", "version": "1.0.0" }
- 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".
02 资源文件 2
- 参考文档references/acceptance-criteria.md11.8 KB
- 参考文档references/examples.md11.8 KB
03 SKILL.md 正文
Azure AI Projects SDK for Java
High-level SDK for Azure AI Foundry project management with access to connections, datasets, indexes, and evaluations.
Installation
<dependency>
<groupId>com.azure</groupId>
<artifactId>azure-ai-projects</artifactId>
<version>1.0.0-beta.1</version>
</dependency>
Environment Variables
PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
Authentication
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 |
// 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
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
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
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:
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
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
04 同仓库更多 Skills
webapp-testing
Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.
skill-creator
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
playwright-skill
Complete browser automation with Playwright. Auto-detects dev servers, writes reusable test scripts, and supports screenshots, responsive checks, UX validation, login flows, link checks, and arbitrary browser automation. Use when the user wants to test a website, automate browser interactions, validate web functionality, or perform browser-based testing.
web-artifacts-builder
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
frontend-design
Create distinctive, production-grade frontend interfaces with high design quality. Use this skill when the user asks to build web components, pages, artifacts, posters, or applications (examples include websites, landing pages, dashboards, React components, HTML/CSS layouts, or when styling/beautifying any web UI). Generates creative, polished code and UI design that avoids generic AI aesthetics.
zarr-python
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.