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azure-ai-contentsafety-java

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使用 Azure AI 内容安全 SDK(Java 版)分析文本和图像中的有害内容。支持仇恨言论、暴力、色情内容和自残行为的检测,并提供黑名单管理功能。

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更新时间 2026-09-14

Azure AI 内容安全 SDK(Java 版)

使用 Azure AI 内容安全 SDK(Java 版)构建内容审核应用程序。

安装


    com.azure
    azure-ai-contentsafety
    1.1.0-beta.1

客户端创建

使用 API 密钥

import com.azure.ai.contentsafety.ContentSafetyClient;
import com.azure.ai.contentsafety.ContentSafetyClientBuilder;
import com.azure.ai.contentsafety.BlocklistClient;
import com.azure.ai.contentsafety.BlocklistClientBuilder;
import com.azure.core.credential.KeyCredential;

String endpoint = System.getenv("CONTENT_SAFETY_ENDPOINT");
String key = System.getenv("CONTENT_SAFETY_KEY");

ContentSafetyClient contentSafetyClient = new ContentSafetyClientBuilder()
    .credential(new KeyCredential(key))
    .endpoint(endpoint)
    .buildClient();

BlocklistClient blocklistClient = new BlocklistClientBuilder()
    .credential(new KeyCredential(key))
    .endpoint(endpoint)
    .buildClient();

使用 DefaultAzureCredential

import com.azure.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;

TokenCredential credential = new DefaultAzureCredentialBuilder()
    .requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
    .build();
// 或者在生产环境中直接使用特定的凭据:
// 参见 https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();

ContentSafetyClient client = new ContentSafetyClientBuilder()
    .credential(credential)
    .endpoint(endpoint)
    .buildClient();

关键概念

危害类别

类别 描述
仇恨 基于身份群体的歧视性言论
性内容、性关系、性行为
暴力 身体伤害、武器、受伤
自残 自残、与自杀相关的内容

严重程度等级

  • 文本:0-7分制(默认输出0、2、4、6)
  • 图像:0、2、4、6(截断量表)

核心模式

分析文本

import com.azure.ai.contentsafety.models.*;

AnalyzeTextResult result = contentSafetyClient.analyzeText(
    new AnalyzeTextOptions("这是待分析的文本"));

for (TextCategoriesAnalysis category : result.getCategoriesAnalysis()) {
    System.out.printf("类别: %s, 严重程度: %d%n",
        category.getCategory(),
        category.getSeverity());
}

使用选项分析文本

AnalyzeTextOptions options = new AnalyzeTextOptions("待分析的文本")
    .setCategories(Arrays.asList(
        TextCategory.HATE,
        TextCategory.VIOLENCE))
    .setOutputType(AnalyzeTextOutputType.EIGHT_SEVERITY_LEVELS);

AnalyzeTextResult result = contentSafetyClient.analyzeText(options);

使用屏蔽列表分析文本

AnalyzeTextOptions options = new AnalyzeTextOptions("我 h*te 你,想 k*ll 你")
    .setBlocklistNames(Arrays.asList("my-blocklist"))
    .setHaltOnBlocklistHit(true);

AnalyzeTextResult result = contentSafetyClient.analyzeText(options);

if (result.getBlocklistsMatch() != null) {
    for (TextBlocklistMatch match : result.getBlocklistsMatch()) {
        System.out.printf("黑名单:%s,条目:%s,文本:%s%n",
            match.getBlocklistName(),
            match.getBlocklistItemId(),
            match.getBlocklistItemText());
    }
}

分析图像

import com.azure.ai.contentsafety.models.*;
import com.azure.core.util.BinaryData;
import java.nio.file.Files;
import java.nio.file.Paths;

// 从文件读取
byte[] imageBytes = Files.readAllBytes(Paths.get("image.png"));
ContentSafetyImageData imageData = new ContentSafetyImageData()
    .setContent(BinaryData.fromBytes(imageBytes));

AnalyzeImageResult result = contentSafetyClient.analyzeImage(
    new AnalyzeImageOptions(imageData));

for (ImageCategoriesAnalysis category : result.getCategoriesAnalysis()) {
    System.out.printf("类别: %s, 严重程度: %d%n",
        category.getCategory(),
        category.getSeverity());
}

分析 URL 中的图片

ContentSafetyImageData imageData = new ContentSafetyImageData()
    .setBlobUrl("https://example.com/image.jpg");

AnalyzeImageResult result = contentSafetyClient.analyzeImage(
    new AnalyzeImageOptions(imageData));

黑名单管理

创建或更新黑名单

import com.azure.core.http.rest.RequestOptions;
import com.azure.core.http.rest.Response;
import com.azure.core.util.BinaryData;
import java.util.Map;

Map description = Map.of("description", "自定义屏蔽列表");
BinaryData resource = BinaryData.fromObject(description);

Response response = blocklistClient.createOrUpdateTextBlocklistWithResponse(
    "my-blocklist", resource, new RequestOptions());

if (response.getStatusCode() == 201) {
    System.out.println("黑名单已创建");
} else if (response.getStatusCode() == 200) {
    System.out.println("黑名单已更新");
}

添加屏蔽项

import com.azure.ai.contentsafety.models.*;
import java.util.Arrays;

List items = Arrays.asList(
    new TextBlocklistItem("badword1").setDescription("冒犯性词汇"),
    new TextBlocklistItem("badword2").setDescription("另一个词汇")
);

AddOrUpdateTextBlocklistItemsResult result = blocklistClient.addOrUpdateBlocklistItems(
    "my-blocklist",
    new AddOrUpdateTextBlocklistItemsOptions(items));

for (TextBlocklistItem item : result.getBlocklistItems()) {
    System.out.printf("已添加:%s (ID:%s)%n",
        item.getText(),
        item.getBlocklistItemId());
}

列出黑名单

分页可迭代对象 blocklists = blocklistClient.listTextBlocklists();

for (TextBlocklist 黑名单 : 黑名单列表) {
    System.out.printf("黑名单: %s, 描述: %s%n",
        黑名单.getName(),
        黑名单.getDescription());
}

获取黑名单

TextBlocklist 黑名单 = 黑名单客户端.getTextBlocklist("my-blocklist");
System.out.println("名称: " + 黑名单.getName());

列出屏蔽列表中的项目

PagedIterable items = 
    blocklistClient.listTextBlocklistItems("my-blocklist");

for (TextBlocklistItem item : items) {
    System.out.printf("ID: %s, 文本: %s%n",
        item.getBlocklistItemId(),
        item.getText());
}

删除块列表项

List itemIds = Arrays.asList("item-id-1", "item-id-2");

blocklistClient.removeBlocklistItems(
    "my-blocklist",
    new RemoveTextBlocklistItemsOptions(itemIds));

删除屏蔽列表

blocklistClient.deleteTextBlocklist("my-blocklist");

错误处理

import com.azure.core.exception.HttpResponseException;

try {
    contentSafetyClient.analyzeText(new AnalyzeTextOptions("test"));
} catch (HttpResponseException e) {
    System.out.println("状态: " + e.getResponse().getStatusCode());
    System.out.println("错误: " + e.getMessage());
    // 常见代码:InvalidRequestBody、ResourceNotFound、TooManyRequests
}

环境变量

 CONTENT_SAFETY_ENDPOINT=https://.cognitiveservices.azure.com/ # 所有身份验证方法均需此项
CONTENT_SAFETY_ENDPOINT=https:// .cognitiveservices.azure.com/ # 所有身份验证方法均需此项
AZURE_TOKEN_CREDENTIALS=prod  # 仅当在生产环境中使用 DefaultAzureCredential 时才需此项

最佳实践

  1. 黑名单延迟:更改生效约需 5 分钟
  2. 类别选择:仅请求必要的类别以减少延迟
  3. 严重性阈值:通常将阻塞严重性设为 >= 4 以实现严格审核
  4. 批量处理:并行处理多个项目以提高吞吐量
  5. 缓存:在适当情况下缓存屏蔽列表结果

触发短语

  • “内容安全 Java”
  • “内容审核 Azure”
  • “分析文本安全性”
  • “Java 图片审核”
  • “黑名单管理”
  • "仇恨言论检测"
  • “有害内容过滤器”
在 GitHub 上查看
---
name: azure-ai-contentsafety-java
description: Analyze text and images for harmful content using Azure AI Content Safety SDK for Java. Supports hate, violence, sexual content, and self-harm detection with blocklist management.
license: MIT
---

# Azure AI Content Safety SDK for Java

Build content moderation applications using the Azure AI Content Safety SDK for Java.

## Installation

```xml
<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-contentsafety</artifactId>
    <version>1.1.0-beta.1</version>
</dependency>
```

## Client Creation

### With API Key

```java
import com.azure.ai.contentsafety.ContentSafetyClient;
import com.azure.ai.contentsafety.ContentSafetyClientBuilder;
import com.azure.ai.contentsafety.BlocklistClient;
import com.azure.ai.contentsafety.BlocklistClientBuilder;
import com.azure.core.credential.KeyCredential;

String endpoint = System.getenv("CONTENT_SAFETY_ENDPOINT");
String key = System.getenv("CONTENT_SAFETY_KEY");

ContentSafetyClient contentSafetyClient = new ContentSafetyClientBuilder()
    .credential(new KeyCredential(key))
    .endpoint(endpoint)
    .buildClient();

BlocklistClient blocklistClient = new BlocklistClientBuilder()
    .credential(new KeyCredential(key))
    .endpoint(endpoint)
    .buildClient();
```

### With DefaultAzureCredential

```java
import com.azure.core.credential.TokenCredential;
import com.azure.identity.AzureIdentityEnvVars;
import com.azure.identity.DefaultAzureCredentialBuilder;
import com.azure.identity.ManagedIdentityCredentialBuilder;

TokenCredential credential = new DefaultAzureCredentialBuilder()
    .requireEnvVars(AzureIdentityEnvVars.AZURE_TOKEN_CREDENTIALS)
    .build();
// Or use a specific credential directly in production:
// See https://learn.microsoft.com/java/api/overview/azure/identity-readme?view=azure-java-stable#credential-classes
// TokenCredential credential = new ManagedIdentityCredentialBuilder().build();

ContentSafetyClient client = new ContentSafetyClientBuilder()
    .credential(credential)
    .endpoint(endpoint)
    .buildClient();
```

## Key Concepts

### Harm Categories
| Category | Description |
|----------|-------------|
| Hate | Discriminatory language based on identity groups |
| Sexual | Sexual content, relationships, acts |
| Violence | Physical harm, weapons, injury |
| Self-harm | Self-injury, suicide-related content |

### Severity Levels
- Text: 0-7 scale (default outputs 0, 2, 4, 6)
- Image: 0, 2, 4, 6 (trimmed scale)

## Core Patterns

### Analyze Text

```java
import com.azure.ai.contentsafety.models.*;

AnalyzeTextResult result = contentSafetyClient.analyzeText(
    new AnalyzeTextOptions("This is text to analyze"));

for (TextCategoriesAnalysis category : result.getCategoriesAnalysis()) {
    System.out.printf("Category: %s, Severity: %d%n",
        category.getCategory(),
        category.getSeverity());
}
```

### Analyze Text with Options

```java
AnalyzeTextOptions options = new AnalyzeTextOptions("Text to analyze")
    .setCategories(Arrays.asList(
        TextCategory.HATE,
        TextCategory.VIOLENCE))
    .setOutputType(AnalyzeTextOutputType.EIGHT_SEVERITY_LEVELS);

AnalyzeTextResult result = contentSafetyClient.analyzeText(options);
```

### Analyze Text with Blocklist

```java
AnalyzeTextOptions options = new AnalyzeTextOptions("I h*te you and want to k*ll you")
    .setBlocklistNames(Arrays.asList("my-blocklist"))
    .setHaltOnBlocklistHit(true);

AnalyzeTextResult result = contentSafetyClient.analyzeText(options);

if (result.getBlocklistsMatch() != null) {
    for (TextBlocklistMatch match : result.getBlocklistsMatch()) {
        System.out.printf("Blocklist: %s, Item: %s, Text: %s%n",
            match.getBlocklistName(),
            match.getBlocklistItemId(),
            match.getBlocklistItemText());
    }
}
```

### Analyze Image

```java
import com.azure.ai.contentsafety.models.*;
import com.azure.core.util.BinaryData;
import java.nio.file.Files;
import java.nio.file.Paths;

// From file
byte[] imageBytes = Files.readAllBytes(Paths.get("image.png"));
ContentSafetyImageData imageData = new ContentSafetyImageData()
    .setContent(BinaryData.fromBytes(imageBytes));

AnalyzeImageResult result = contentSafetyClient.analyzeImage(
    new AnalyzeImageOptions(imageData));

for (ImageCategoriesAnalysis category : result.getCategoriesAnalysis()) {
    System.out.printf("Category: %s, Severity: %d%n",
        category.getCategory(),
        category.getSeverity());
}
```

### Analyze Image from URL

```java
ContentSafetyImageData imageData = new ContentSafetyImageData()
    .setBlobUrl("https://example.com/image.jpg");

AnalyzeImageResult result = contentSafetyClient.analyzeImage(
    new AnalyzeImageOptions(imageData));
```

## Blocklist Management

### Create or Update Blocklist

```java
import com.azure.core.http.rest.RequestOptions;
import com.azure.core.http.rest.Response;
import com.azure.core.util.BinaryData;
import java.util.Map;

Map<String, String> description = Map.of("description", "Custom blocklist");
BinaryData resource = BinaryData.fromObject(description);

Response<BinaryData> response = blocklistClient.createOrUpdateTextBlocklistWithResponse(
    "my-blocklist", resource, new RequestOptions());

if (response.getStatusCode() == 201) {
    System.out.println("Blocklist created");
} else if (response.getStatusCode() == 200) {
    System.out.println("Blocklist updated");
}
```

### Add Block Items

```java
import com.azure.ai.contentsafety.models.*;
import java.util.Arrays;

List<TextBlocklistItem> items = Arrays.asList(
    new TextBlocklistItem("badword1").setDescription("Offensive term"),
    new TextBlocklistItem("badword2").setDescription("Another term")
);

AddOrUpdateTextBlocklistItemsResult result = blocklistClient.addOrUpdateBlocklistItems(
    "my-blocklist",
    new AddOrUpdateTextBlocklistItemsOptions(items));

for (TextBlocklistItem item : result.getBlocklistItems()) {
    System.out.printf("Added: %s (ID: %s)%n",
        item.getText(),
        item.getBlocklistItemId());
}
```

### List Blocklists

```java
PagedIterable<TextBlocklist> blocklists = blocklistClient.listTextBlocklists();

for (TextBlocklist blocklist : blocklists) {
    System.out.printf("Blocklist: %s, Description: %s%n",
        blocklist.getName(),
        blocklist.getDescription());
}
```

### Get Blocklist

```java
TextBlocklist blocklist = blocklistClient.getTextBlocklist("my-blocklist");
System.out.println("Name: " + blocklist.getName());
```

### List Block Items

```java
PagedIterable<TextBlocklistItem> items = 
    blocklistClient.listTextBlocklistItems("my-blocklist");

for (TextBlocklistItem item : items) {
    System.out.printf("ID: %s, Text: %s%n",
        item.getBlocklistItemId(),
        item.getText());
}
```

### Remove Block Items

```java
List<String> itemIds = Arrays.asList("item-id-1", "item-id-2");

blocklistClient.removeBlocklistItems(
    "my-blocklist",
    new RemoveTextBlocklistItemsOptions(itemIds));
```

### Delete Blocklist

```java
blocklistClient.deleteTextBlocklist("my-blocklist");
```

## Error Handling

```java
import com.azure.core.exception.HttpResponseException;

try {
    contentSafetyClient.analyzeText(new AnalyzeTextOptions("test"));
} catch (HttpResponseException e) {
    System.out.println("Status: " + e.getResponse().getStatusCode());
    System.out.println("Error: " + e.getMessage());
    // Common codes: InvalidRequestBody, ResourceNotFound, TooManyRequests
}
```

## Environment Variables

```bash
CONTENT_SAFETY_ENDPOINT=https://<resource>.cognitiveservices.azure.com/ # Required for all auth methods
CONTENT_SAFETY_KEY=<your-api-key> # Only required for AzureKeyCredential auth
AZURE_TOKEN_CREDENTIALS=prod  # Required only if DefaultAzureCredential is used in production
```

## Best Practices

1. **Blocklist Delay**: Changes take ~5 minutes to take effect
2. **Category Selection**: Only request needed categories to reduce latency
3. **Severity Thresholds**: Typically block severity >= 4 for strict moderation
4. **Batch Processing**: Process multiple items in parallel for throughput
5. **Caching**: Cache blocklist results where appropriate

## Trigger Phrases

- "content safety Java"
- "content moderation Azure"
- "analyze text safety"
- "image moderation Java"
- "blocklist management"
- "hate speech detection"
- "harmful content filter"

所有文件

0 个文件

安装 azure-ai-contentsafety-java

下载技能文件并将其解压到 .claude/skills/ 目录中。

下载ZIP

克隆仓库并复制技能文件到您的项目中。

git clone https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-java/skills/azure-ai-contentsafety-java # Copy SKILL.md to your .claude/skills/ directory

复制 复制
快速设置: 将技能文件夹复制到 .claude/skills/ Claude 会自动检测并使用该技能

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