azure-ai-projects-ts

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.

npx skills add https://github.com/microsoft/skills --skill azure-ai-projects-ts

Azure AI Projects SDK for TypeScript

High-level SDK for Azure AI Foundry projects with agents, connections, deployments, and evaluations.

Installation

npm install @azure/ai-projects @azure/identity

For tracing:

npm install @azure/monitor-opentelemetry @opentelemetry/api

Environment Variables

AZURE_AI_PROJECT_ENDPOINT=https://<resource>.services.ai.azure.com/api/projects/<project>
MODEL_DEPLOYMENT_NAME=gpt-4o
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication

import { AIProjectClient } from "@azure/ai-projects";
import { DefaultAzureCredential, ManagedIdentityCredential } from "@azure/identity";

// Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
const credential = new DefaultAzureCredential({requiredEnvVars: ["AZURE_TOKEN_CREDENTIALS"]});
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/javascript/api/overview/azure/identity-readme?view=azure-node-latest#credential-classes
// const credential = new ManagedIdentityCredential();

const client = new AIProjectClient(
  process.env.AZURE_AI_PROJECT_ENDPOINT!,
  credential
);

Operation Groups

GroupPurpose
client.agentsCreate and manage AI agents
client.connectionsList connected Azure resources
client.deploymentsList model deployments
client.datasetsUpload and manage datasets
client.indexesCreate and manage search indexes
client.evaluatorsManage evaluation metrics
client.memoryStoresManage agent memory

Getting OpenAI Client

const openAIClient = await client.getOpenAIClient();

// Use for responses
const response = await openAIClient.responses.create({
  model: "gpt-4o",
  input: "What is the capital of France?"
});

// Use for conversations
const conversation = await openAIClient.conversations.create({
  items: [{ type: "message", role: "user", content: "Hello!" }]
});

Agents

Create Agent

const agent = await client.agents.createVersion("my-agent", {
  kind: "prompt",
  model: "gpt-4o",
  instructions: "You are a helpful assistant."
});

Agent with Tools

// Code Interpreter
const agent = await client.agents.createVersion("code-agent", {
  kind: "prompt",
  model: "gpt-4o",
  instructions: "You can execute code.",
  tools: [{ type: "code_interpreter", container: { type: "auto" } }]
});

// File Search
const agent = await client.agents.createVersion("search-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{ type: "file_search", vector_store_ids: [vectorStoreId] }]
});

// Web Search
const agent = await client.agents.createVersion("web-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{
    type: "web_search_preview",
    user_location: { type: "approximate", country: "US", city: "Seattle" }
  }]
});

// Azure AI Search
const agent = await client.agents.createVersion("aisearch-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{
    type: "azure_ai_search",
    azure_ai_search: {
      indexes: [{
        project_connection_id: connectionId,
        index_name: "my-index",
        query_type: "simple"
      }]
    }
  }]
});

// Function Tool
const agent = await client.agents.createVersion("func-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{
    type: "function",
    function: {
      name: "get_weather",
      description: "Get weather for a location",
      strict: true,
      parameters: {
        type: "object",
        properties: { location: { type: "string" } },
        required: ["location"]
      }
    }
  }]
});

// MCP Tool
const agent = await client.agents.createVersion("mcp-agent", {
  kind: "prompt",
  model: "gpt-4o",
  tools: [{
    type: "mcp",
    server_label: "my-mcp",
    server_url: "https://mcp-server.example.com",
    require_approval: "always"
  }]
});

Run Agent

const openAIClient = await client.getOpenAIClient();

// Create conversation
const conversation = await openAIClient.conversations.create({
  items: [{ type: "message", role: "user", content: "Hello!" }]
});

// Generate response using agent
const response = await openAIClient.responses.create(
  { conversation: conversation.id },
  { body: { agent: { name: agent.name, type: "agent_reference" } } }
);

// Cleanup
await openAIClient.conversations.delete(conversation.id);
await client.agents.deleteVersion(agent.name, agent.version);

Connections

// List all connections
for await (const conn of client.connections.list()) {
  console.log(conn.name, conn.type);
}

// Get connection by name
const conn = await client.connections.get("my-connection");

// Get connection with credentials
const connWithCreds = await client.connections.getWithCredentials("my-connection");

// Get default connection by type
const defaultAzureOpenAI = await client.connections.getDefault("AzureOpenAI", true);

Deployments

// List all deployments
for await (const deployment of client.deployments.list()) {
  if (deployment.type === "ModelDeployment") {
    console.log(deployment.name, deployment.modelName);
  }
}

// Filter by publisher
for await (const d of client.deployments.list({ modelPublisher: "OpenAI" })) {
  console.log(d.name);
}

// Get specific deployment
const deployment = await client.deployments.get("gpt-4o");

Datasets

// Upload single file
const dataset = await client.datasets.uploadFile(
  "my-dataset",
  "1.0",
  "./data/training.jsonl"
);

// Upload folder
const dataset = await client.datasets.uploadFolder(
  "my-dataset",
  "2.0",
  "./data/documents/"
);

// Get dataset
const ds = await client.datasets.get("my-dataset", "1.0");

// List versions
for await (const version of client.datasets.listVersions("my-dataset")) {
  console.log(version);
}

// Delete
await client.datasets.delete("my-dataset", "1.0");

Indexes

import { AzureAISearchIndex } from "@azure/ai-projects";

const indexConfig: AzureAISearchIndex = {
  name: "my-index",
  type: "AzureSearch",
  version: "1",
  indexName: "my-index",
  connectionName: "search-connection"
};

// Create index
const index = await client.indexes.createOrUpdate("my-index", "1", indexConfig);

// List indexes
for await (const idx of client.indexes.list()) {
  console.log(idx.name);
}

// Delete
await client.indexes.delete("my-index", "1");

Key Types

import {
  AIProjectClient,
  AIProjectClientOptionalParams,
  Connection,
  ModelDeployment,
  DatasetVersionUnion,
  AzureAISearchIndex
} from "@azure/ai-projects";

Best Practices

  1. Use getOpenAIClient() - For responses, conversations, files, and vector stores
  2. Version your agents - Use createVersion for reproducible agent definitions
  3. Clean up resources - Delete agents, conversations when done
  4. Use connections - Get credentials from project connections, don't hardcode
  5. Filter deployments - Use modelPublisher filter to find specific models

Mais skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Crie agentes do Azure AI Foundry usando o SDK Python do Microsoft Agent Framework (agent-framework-azure-ai). Use ao criar agentes persistentes com AzureAIAgentsProvider, usando ferramentas hospedadas (interpretador de código, pesquisa de arquivos, pesquisa na web), integrando servidores MCP, gerenciando threads de conversa ou implementando respostas em streaming. Abrange ferramentas de função, saídas estruturadas e agentes com múltiplas ferramentas.
development
airunway-aks-setup
microsoft
Configure o AI Runway no AKS — do cluster vazio ao modelo em execução. Abrange verificação do cluster, instalação do controlador, avaliação de GPU, configuração do provedor e primeira implantação. QUANDO: "configurar AI Runway", "integrar cluster AKS", "instalar AI Runway", "configuração do airunway", "implantar modelo no AKS", "inferência GPU no AKS", "configuração KAITO no AKS", "executar LLM no AKS", "vLLM no AKS", "configurar serviço de modelo no AKS", "controlador AI Runway".
devops
appinsights-instrumentation
microsoft
Orientação para instrumentar aplicações web com Azure Application Insights. Fornece padrões de telemetria, configuração de SDK e referências de configuração. QUANDO: como instrumentar o app, SDK do App Insights, padrões de telemetria, o que é App Insights, orientação sobre Application Insights, exemplos de instrumentação, melhores práticas de APM.
devops
applicationinsights-web-ts
microsoft
Instrumente aplicativos de navegador/web com o SDK JavaScript do Application Insights (@microsoft/applicationinsights-web). Use para Real User Monitoring (RUM) — visualizações de página, cliques, dependências AJAX/fetch, exceções, eventos personalizados e rastreamentos de agentes GenAI no lado do navegador correlacionados a rastreamentos OpenTelemetry no backend. Abrange o Script de Carregamento do SDK e a configuração via npm, extensões de frameworks (React, React Native, Angular), Click Analytics, inicializadores de telemetria e convenções semânticas GenAI do OTel para spans de agente/ferramenta/modelo emitidos pelo navegador.
devops
azure-ai-anomalydetector-java
microsoft
Crie aplicativos de detecção de anomalias com o SDK do Azure AI Anomaly Detector para Java. Use ao implementar detecção de anomalias univariada/multivariada, análise de séries temporais ou monitoramento com IA.
development
azure-ai-language-conversations-py
microsoft
Implemente o reconhecimento de linguagem conversacional (CLU) usando o SDK Python azure-ai-language-conversations. Use ao trabalhar com ConversationAnalysisClient para analisar intenção e entidades de conversas, criar recursos de NLP ou integrar o reconhecimento de linguagem em aplicativos.
development
azure-ai-ml-py
microsoft
SDK v2 do Azure Machine Learning para Python. Use para workspaces de ML, jobs, modelos, conjuntos de dados, computação e pipelines. Gatilhos: "azure-ai-ml", "MLClient", "workspace", "registro de modelos", "jobs de treinamento", "conjuntos de dados".
development