refactor: flatten Microsoft skills from nested to flat directory structure
Rewrote sync_microsoft_skills.py (v4) to use each SKILL.md's frontmatter 'name' field as the flat directory name under skills/, replacing the nested skills/official/microsoft/<lang>/<category>/<service>/ hierarchy. This fixes CI failures caused by the indexing, validation, and catalog scripts expecting skills/<id>/SKILL.md (depth 1). Changes: - Rewrite scripts/sync_microsoft_skills.py for flat output with collision detection - Update scripts/tests/inspect_microsoft_repo.py for flat name mapping - Update scripts/tests/test_comprehensive_coverage.py for name uniqueness checks - Delete skills/official/ nested directory - Add 129 Microsoft skills as flat directories (e.g. skills/azure-mgmt-botservice-dotnet/) - Move attribution files to docs/ (LICENSE-MICROSOFT, microsoft-skills-attribution.json) - Rebuild skills_index.json, CATALOG.md, README.md (845 total skills)
This commit is contained in:
Ahmed Rehan
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skills/azure-ai-projects-ts/SKILL.md
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289
skills/azure-ai-projects-ts/SKILL.md
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---
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name: azure-ai-projects-ts
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description: Build AI applications using Azure AI Projects SDK for JavaScript (@azure/ai-projects). Use when working with Foundry project clients, agents, connections, deployments, datasets, indexes, evaluations, or getting OpenAI clients.
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package: @azure/ai-projects
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---
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# Azure AI Projects SDK for TypeScript
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High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.
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## Installation
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```bash
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npm install @azure/ai-projects @azure/identity
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```
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For tracing:
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```bash
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npm install @azure/monitor-opentelemetry @opentelemetry/api
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```
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## Environment Variables
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```bash
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AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
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MODEL_DEPLOYMENT_NAME=gpt-4o
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```
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## Authentication
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```typescript
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import { AIProjectClient } from "@azure/ai-projects";
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import { DefaultAzureCredential } from "@azure/identity";
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const client = new AIProjectClient(
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process.env.AZURE_AI_PROJECT_ENDPOINT!,
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new DefaultAzureCredential()
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);
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```
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## Operation Groups
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| Group | Purpose |
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|-------|---------|
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| `client.agents` | Create and manage AI agents |
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| `client.connections` | List connected Azure resources |
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| `client.deployments` | List model deployments |
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| `client.datasets` | Upload and manage datasets |
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| `client.indexes` | Create and manage search indexes |
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| `client.evaluators` | Manage evaluation metrics |
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| `client.memoryStores` | Manage agent memory |
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## Getting OpenAI Client
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```typescript
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const openAIClient = await client.getOpenAIClient();
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// Use for responses
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const response = await openAIClient.responses.create({
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model: "gpt-4o",
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input: "What is the capital of France?"
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});
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// Use for conversations
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const conversation = await openAIClient.conversations.create({
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items: [{ type: "message", role: "user", content: "Hello!" }]
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});
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```
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## Agents
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### Create Agent
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```typescript
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const agent = await client.agents.createVersion("my-agent", {
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kind: "prompt",
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model: "gpt-4o",
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instructions: "You are a helpful assistant."
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});
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```
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### Agent with Tools
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```typescript
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// Code Interpreter
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const agent = await client.agents.createVersion("code-agent", {
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kind: "prompt",
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model: "gpt-4o",
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instructions: "You can execute code.",
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tools: [{ type: "code_interpreter", container: { type: "auto" } }]
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});
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// File Search
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const agent = await client.agents.createVersion("search-agent", {
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kind: "prompt",
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model: "gpt-4o",
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tools: [{ type: "file_search", vector_store_ids: [vectorStoreId] }]
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});
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// Web Search
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const agent = await client.agents.createVersion("web-agent", {
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kind: "prompt",
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model: "gpt-4o",
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tools: [{
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type: "web_search_preview",
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user_location: { type: "approximate", country: "US", city: "Seattle" }
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}]
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});
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// Azure AI Search
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const agent = await client.agents.createVersion("aisearch-agent", {
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kind: "prompt",
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model: "gpt-4o",
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tools: [{
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type: "azure_ai_search",
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azure_ai_search: {
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indexes: [{
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project_connection_id: connectionId,
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index_name: "my-index",
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query_type: "simple"
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}]
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}
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}]
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});
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// Function Tool
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const agent = await client.agents.createVersion("func-agent", {
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kind: "prompt",
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model: "gpt-4o",
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tools: [{
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type: "function",
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function: {
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name: "get_weather",
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description: "Get weather for a location",
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strict: true,
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parameters: {
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type: "object",
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properties: { location: { type: "string" } },
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required: ["location"]
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}
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}
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}]
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});
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// MCP Tool
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const agent = await client.agents.createVersion("mcp-agent", {
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kind: "prompt",
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model: "gpt-4o",
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tools: [{
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type: "mcp",
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server_label: "my-mcp",
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server_url: "https://mcp-server.example.com",
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require_approval: "always"
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}]
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});
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```
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### Run Agent
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```typescript
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const openAIClient = await client.getOpenAIClient();
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// Create conversation
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const conversation = await openAIClient.conversations.create({
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items: [{ type: "message", role: "user", content: "Hello!" }]
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});
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// Generate response using agent
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const response = await openAIClient.responses.create(
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{ conversation: conversation.id },
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{ body: { agent: { name: agent.name, type: "agent_reference" } } }
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);
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// Cleanup
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await openAIClient.conversations.delete(conversation.id);
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await client.agents.deleteVersion(agent.name, agent.version);
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```
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## Connections
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```typescript
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// List all connections
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for await (const conn of client.connections.list()) {
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console.log(conn.name, conn.type);
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}
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// Get connection by name
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const conn = await client.connections.get("my-connection");
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// Get connection with credentials
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const connWithCreds = await client.connections.getWithCredentials("my-connection");
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// Get default connection by type
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const defaultAzureOpenAI = await client.connections.getDefault("AzureOpenAI", true);
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```
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## Deployments
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```typescript
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// List all deployments
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for await (const deployment of client.deployments.list()) {
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if (deployment.type === "ModelDeployment") {
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console.log(deployment.name, deployment.modelName);
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}
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}
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// Filter by publisher
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for await (const d of client.deployments.list({ modelPublisher: "OpenAI" })) {
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console.log(d.name);
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}
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// Get specific deployment
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const deployment = await client.deployments.get("gpt-4o");
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```
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## Datasets
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```typescript
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// Upload single file
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const dataset = await client.datasets.uploadFile(
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"my-dataset",
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"1.0",
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"./data/training.jsonl"
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);
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// Upload folder
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const dataset = await client.datasets.uploadFolder(
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"my-dataset",
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"2.0",
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"./data/documents/"
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);
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// Get dataset
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const ds = await client.datasets.get("my-dataset", "1.0");
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// List versions
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for await (const version of client.datasets.listVersions("my-dataset")) {
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console.log(version);
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}
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// Delete
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await client.datasets.delete("my-dataset", "1.0");
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```
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## Indexes
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```typescript
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import { AzureAISearchIndex } from "@azure/ai-projects";
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const indexConfig: AzureAISearchIndex = {
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name: "my-index",
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type: "AzureSearch",
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version: "1",
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indexName: "my-index",
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connectionName: "search-connection"
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};
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// Create index
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const index = await client.indexes.createOrUpdate("my-index", "1", indexConfig);
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// List indexes
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for await (const idx of client.indexes.list()) {
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console.log(idx.name);
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}
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// Delete
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await client.indexes.delete("my-index", "1");
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```
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## Key Types
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```typescript
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import {
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AIProjectClient,
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AIProjectClientOptionalParams,
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Connection,
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ModelDeployment,
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DatasetVersionUnion,
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AzureAISearchIndex
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} from "@azure/ai-projects";
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```
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## Best Practices
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1. **Use getOpenAIClient()** - For responses, conversations, files, and vector stores
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2. **Version your agents** - Use `createVersion` for reproducible agent definitions
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3. **Clean up resources** - Delete agents, conversations when done
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4. **Use connections** - Get credentials from project connections, don't hardcode
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5. **Filter deployments** - Use `modelPublisher` filter to find specific models
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