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适用于 JavaScript 的 Azure AI 项目客户端库 - 版本 1.0.1

2025-10-15

AI Projects 客户端库提供对 Azure AI Foundry 项目中资源的轻松访问。使用它可执行以下操作：

使用客户端上的属性.agents。
使用该方法.inference.azureOpenAI。
使用作列举部署到 Foundry 项目的 .deployments。
使用作枚举 Foundry 项目中.connections。
上传文档并创建 Datasets 以使用 .datasets 作引用它们。
使用作.indexes。
使用该函数enable_telemetry。

产品文档 | 样品 | 包（npm） | API 参考文档 | SDK 源码

入门指南

先决条件

LTS 版本的 Node.js
一个 Azure 订阅。
Azure AI Foundry中的项目。

授权

需要 Entra ID 来验证客户端。应用程序需要实现 TokenCredential 接口的对象。此处的代码示例使用 DefaultAzureCredential。若要使该工作正常，需要：
- Contributor 角色。可以通过 Azure 门户中 Azure AI 项目资源的“访问控制（IAM）”选项卡完成分配的角色。在此处了解有关角色分配的更多信息。
- 已安装 Azure CLI。
- 通过运行 az login登录到 Azure 帐户。
- 请注意，如果有多个 Azure 订阅，则包含 Azure AI 项目资源的订阅必须是默认订阅。运行 az account list --output table 列出所有订阅，并查看默认订阅。运行 az account set --subscription "Your Subscription ID or Name" 以更改默认订阅。

安装软件包

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

重要概念

创建客户端并对其进行身份验证

要构造 AIProjectsClient，可以从 endpoint 获取。下面我们假设定义了环境变量 AZURE_AI_PROJECT_ENDPOINT_STRING 来保存此值：

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

const endpoint = process.env["AZURE_AI_PROJECT_ENDPOINT_STRING"] || "<project endpoint string>";
const client = new AIProjectClient(endpoint, new DefaultAzureCredential());

客户端使用 API 版本 v1，请参阅 API 文档以了解有关支持功能的更多信息。

例子

执行 Agent作

.agents上的 AIProjectClient 属性允许您访问包中经过身份验证AgentsClient的 azure-ai-agents 。下面我们将介绍如何创建代理并删除它。要查看您可以对创建的执行agent哪些作，请参阅与该包关联的azure-ai-agents。

const agent = await project.agents.createAgent("gpt-4o", {
  name: "my-agent",
  instructions: "You are a helpful agent",
});
console.log(`Created agent, agent ID : ${agent.id}`);

// Do something with your Agent!
// See samples here https://github.com/Azure/azure-sdk-for-js/tree/@azure/ai-projects_1.0.1/sdk/ai/ai-agents/samples
await project.agents.deleteAgent(agent.id);
console.log(`Deleted agent, agent ID: ${agent.id}`);

获取经过身份验证的 AzureOpenAI 客户端

您的 Azure AI Foundry 项目可能部署了一个或多个支持聊天完成的 OpenAI 模型。使用以下代码从 openai 包中获取经过身份验证的 AzureOpenAI，并执行聊天完成调用。

运行下面的代码。这里我们假设 deploymentName （str）已定义。它是 Foundry 项目中 AI 模型的部署名称。如“模型 + 端点”选项卡的“名称”列下所示。

api_version使用此表的 “Data plane - inference” 行中找到的值更新该值。

您还可以选择（未显示）在 AI Foundry 项目中显式指定 Azure OpenAI 连接名称，该方法 inference.azureOpenAI 将使用此名称获取推理终端节点和身份验证凭据。如果不存在，将使用默认的 Azure OpenAI 连接。

const client = await project.inference.azureOpenAI({
  // The API version should match the version of the Azure OpenAI resource.
  apiVersion: "2024-10-21",
});
const response = await client.chat.completions.create({
  model: deploymentName,
  messages: [{ role: "user", content: "How many feet are in a mile?" }],
});
console.log("response = ", JSON.stringify(response, null, 2));

有关其他示例，请参阅包示例中的“inference”文件夹。

部署作

下面的代码显示了一些 Deployments作，这些作允许您枚举部署到 AI Foundry 项目的 AI 模型。这些模型可以在 AI Foundry 项目的 “Models + endpoints” 选项卡中看到。完整的示例可以在包示例中的 “deployment” 文件夹下找到。

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

const modelPublisher = process.env["MODEL_PUBLISHER"] || "<model publisher>";
console.log("List all deployments:");
const deployments: ModelDeployment[] = [];
const properties: Array<Record<string, string>> = [];

for await (const deployment of project.deployments.list()) {
  // Check if this is a ModelDeployment (has the required properties)
  if (
    deployment.type === "ModelDeployment" &&
    "modelName" in deployment &&
    "modelPublisher" in deployment &&
    "modelVersion" in deployment
  ) {
    deployments.push(deployment);
    properties.push({
      name: deployment.name,
      modelPublisher: deployment.modelPublisher,
      modelName: deployment.modelName,
    });
  }
}
console.log(`Retrieved deployments: ${JSON.stringify(properties, null, 2)}`);

// List all deployments by a specific model publisher (assuming we have one from the list)
console.log(`List all deployments by the model publisher '${modelPublisher}':`);
const filteredDeployments: ModelDeployment[] = [];
for await (const deployment of project.deployments.list({
  modelPublisher,
})) {
  // Check if this is a ModelDeployment
  if (
    deployment.type === "ModelDeployment" &&
    "modelName" in deployment &&
    "modelPublisher" in deployment &&
    "modelVersion" in deployment
  ) {
    filteredDeployments.push(deployment);
  }
}
console.log(
  `Retrieved ${filteredDeployments.length} deployments from model publisher '${modelPublisher}'`,
);

// Get a single deployment by name
if (deployments.length > 0) {
  const deploymentName = deployments[0].name;
  console.log(`Get a single deployment named '${deploymentName}':`);
  const singleDeployment = await project.deployments.get(deploymentName);
  console.log(`Retrieved deployment: ${JSON.stringify(singleDeployment, null, 2)}`);
}

连接作

下面的代码显示了一些 Connection作，这些作允许您枚举连接到 AI Foundry 项目的 Azure 资源。这些连接可以在 AI Foundry 项目的“管理中心”的“Connected resources”选项卡中看到。可以在包示例的“connections”文件夹下找到完整示例。

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

// List the details of all the connections
const connections: Connection[] = [];
const connectionNames: string[] = [];
for await (const connection of project.connections.list()) {
  connections.push(connection);
  connectionNames.push(connection.name);
}
console.log(`Retrieved connections: ${connectionNames}`);

// Get the details of a connection, without credentials
const connectionName = connections[0].name;
const connection = await project.connections.get(connectionName);
console.log(`Retrieved connection ${JSON.stringify(connection, null, 2)}`);

const connectionWithCredentials = await project.connections.getWithCredentials(connectionName);
console.log(
  `Retrieved connection with credentials ${JSON.stringify(connectionWithCredentials, null, 2)}`,
);

// List all connections of a specific type
const azureAIConnections: Connection[] = [];
for await (const azureOpenAIConnection of project.connections.list({
  connectionType: "AzureOpenAI",
  defaultConnection: true,
})) {
  azureAIConnections.push(azureOpenAIConnection);
}
console.log(`Retrieved ${azureAIConnections.length} Azure OpenAI connections`);

// Get the details of a default connection
const defaultConnection = await project.connections.getDefault("AzureOpenAI", true);
console.log(`Retrieved default connection ${JSON.stringify(defaultConnection, null, 2)}`);

数据集作

下面的代码展示了一些 Dataset作。完整示例可以在包示例的“datasets”文件夹下找到。

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

const VERSION1 = "1.0";
const VERSION2 = "2.0";
const VERSION3 = "3.0";

// sample files to use in the demonstration
const sampleFolder = "sample_folder";
// Create a unique dataset name for this sample run
const datasetName = `sample-dataset-basic`;
console.log("Upload a single file and create a new Dataset to reference the file.");
console.log("Here we explicitly specify the dataset version.");

const dataset1 = await project.datasets.uploadFile(
  datasetName,
  VERSION1,
  path.join(__dirname, sampleFolder, "sample_file1.txt"),
);
console.log("Dataset1 created:", JSON.stringify(dataset1, null, 2));

const credential = project.datasets.getCredentials(dataset1.name, dataset1.version, {});
console.log("Credential for the dataset:", credential);
console.log(
  "Upload all files in a folder (including subfolders) to the existing Dataset to reference the folder.",
);
console.log("Here again we explicitly specify a new dataset version");
const dataset2 = await project.datasets.uploadFolder(
  datasetName,
  VERSION2,
  path.join(__dirname, sampleFolder),
);
console.log("Dataset2 created:", JSON.stringify(dataset2, null, 2));
console.log(
  "Upload a single file to the existing dataset, while letting the service increment the version",
);
const dataset3 = await project.datasets.uploadFile(
  datasetName,
  VERSION3,
  path.join(__dirname, sampleFolder, "sample_file2.txt"),
);
console.log("Dataset3 created:", JSON.stringify(dataset3, null, 2));

console.log("Get an existing Dataset version `1`:");
const datasetVersion1 = await project.datasets.get(datasetName, VERSION1);
console.log("Dataset version 1:", JSON.stringify(datasetVersion1, null, 2));
console.log(`Listing all versions of the Dataset named '${datasetName}':`);
const datasetVersions = await project.datasets.listVersions(datasetName);
for await (const version of datasetVersions) {
  console.log("List versions:", version);
}
console.log("List latest versions of all Datasets:");
const latestDatasets = project.datasets.list();
for await (const dataset of latestDatasets) {
  console.log("List datasets:", dataset);
}
// List the details of all the datasets
const datasets = project.datasets.listVersions(datasetName);
const allDatasets: DatasetVersionUnion[] = [];
for await (const dataset of datasets) {
  allDatasets.push(dataset);
}
console.log(`Retrieved ${allDatasets.length} datasets`);
console.log("Delete all Datasets created above:");
await project.datasets.delete(datasetName, VERSION1);
await project.datasets.delete(datasetName, VERSION2);
await project.datasets.delete(datasetName, dataset3.version);
console.log("All specified Datasets have been deleted.");

索引作

下面的代码显示了一些 Indexes作。完整的示例可以在包示例的 “indexes” 文件夹下找到。

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

const indexName = "sample-index";
const version = "1";
const azureAIConnectionConfig: AzureAISearchIndex = {
  name: indexName,
  type: "AzureSearch",
  version,
  indexName,
  connectionName: "sample-connection",
};

// Create a new Index
const newIndex = await project.indexes.createOrUpdate(indexName, version, azureAIConnectionConfig);
console.log("Created a new Index:", newIndex);
console.log(`Get an existing Index version '${version}':`);
const index = await project.indexes.get(indexName, version);
console.log(index);
console.log(`Listing all versions of the Index named '${indexName}':`);
const indexVersions = project.indexes.listVersions(indexName);
for await (const indexVersion of indexVersions) {
  console.log(indexVersion);
}
console.log("List all Indexes:");
const allIndexes = project.indexes.list();
for await (const i of allIndexes) {
  console.log("Index:", i);
}
console.log("Delete the Index versions created above:");
await project.indexes.delete(indexName, version);

故障排除

例外

发出服务调用的客户端方法会引发 RestError，以响应来自该服务的非成功 HTTP 状态代码响应。异常 code 将保留 HTTP 响应状态代码。异常 error.message 包含可能有助于诊断问题的详细消息：

import { isRestError } from "@azure/core-rest-pipeline";

try {
  const result = await project.connections.list();
} catch (e) {
  if (isRestError(e)) {
    console.log(`Status code: ${e.code}`);
    console.log(e.message);
  } else {
    console.error(e);
  }
}

例如，提供错误的凭据时：

Status code: 401 (Unauthorized)
Operation returned an invalid status 'Unauthorized'

报告问题

若要报告客户端库问题或请求其他功能，请在此处打开 GitHub 问题

后续步骤

查看包示例文件夹，其中包含完全可运行的代码。

贡献

此项目欢迎贡献和建议。大多数贡献要求你同意参与者许可协议（CLA），声明你有权（实际这样做）授予我们使用你的贡献的权利。有关详细信息，请访问 https://cla.microsoft.com。

提交拉取请求时，CLA 机器人会自动确定是否需要提供 CLA 并适当修饰 PR（例如标签、注释）。只需按照机器人提供的说明进行操作。只需使用 CLA 在所有存储库中执行此操作一次。

该项目已采用 Microsoft开源行为准则。有关详细信息，请参阅《行为准则常见问题解答》或联系 opencode@microsoft.com，了解任何其他问题或意见。

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