azure-ai-contentsafety-java

Azure AI Content Safety SDK for Java

使用 Azure AI Content Safety SDK for Java 构建内容审核应用。

安装

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

创建客户端

使用 API Key

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.identity.DefaultAzureCredentialBuilder;

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

核心概念

有害类别

类别	描述
仇恨	基于身份群体的歧视性语言
色情	色情内容、关系、行为
暴力	身体伤害、武器、受伤
自残	自我伤害、自杀相关内容

严重程度级别

• 文本：0-7 级（默认输出 0、2、4、6）
• 图像：0、2、4、6（精简级别）

核心模式

分析文本

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());
}

使用选项分析文本

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

AnalyzeTextResult result = contentSafetyClient.analyzeText(options);

使用阻止列表分析文本

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());
    }
}

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;

// 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());
}

分析 URL 中的图像

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

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

阻止列表管理

创建或更新阻止列表

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");
}

添加阻止项

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());
}

列出阻止列表

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

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

获取阻止列表

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

列出阻止项

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

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

移除阻止项

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

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

删除阻止列表

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("Status: " + e.getResponse().getStatusCode());
    System.out.println("Error: " + e.getMessage());
    // Common codes: InvalidRequestBody, ResourceNotFound, TooManyRequests
}

环境变量

CONTENT_SAFETY_ENDPOINT=https://<resource>.cognitiveservices.azure.com/
CONTENT_SAFETY_KEY=<your-api-key>

最佳实践

1. 阻止列表延迟：更改约 5 分钟后生效
2. 类别选择：仅请求所需类别，以降低延迟
3. 严重程度阈值：严格审核通常会阻止严重程度 >= 4 的内容
4. 批处理：并行处理多个项目以提高吞吐量
5. 缓存：在适当情况下缓存阻止列表结果

触发短语

• “content safety Java”
• “content moderation Azure”
• “analyze text safety”
• “image moderation Java”
• “blocklist management”
• “hate speech detection”
• “harmful content filter”

适用场景

当需要执行概述中所述的工作流或操作时，可使用此技能。

限制

• 仅当任务明确符合上述范围时，才使用此技能。
• 不要将输出视为环境特定验证、测试或专家审查的替代品。
• 如果缺少必要的输入、权限、安全边界或成功标准，请停止并请求澄清。

模型评分是概率性信号，并不能保证内容安全。缺失或格式错误的分析结果以及服务错误均表示未评估，而非严重程度为零；请为应用选择有文档说明的默认拒绝或升级处理策略。应先获得传输敏感文本/图像的授权，尽量减少数据保留，并限制批处理并发量。阻止列表更改会影响实时审核，因此必须确认目标资源/列表范围。