azure-ai-contentsafety-java
microsoft/skills
使用 Azure AI 內容安全 SDK(Java 版)分析文字和圖片中的有害內容。支援仇恨言論、暴力、色情內容及自殘行為的偵測,並具備黑名單管理功能。
...展開全部Azure AI 內容安全 SDK(Java 版)
使用 Azure AI 內容安全 SDK(Java 版)建立內容審查應用程式。
安裝
com.azure
azure-ai-contentsafety
1.1.0-beta.1
建立客戶端
使用 API 金鑰
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();
// 或者在生產環境中直接使用特定的憑證:
// 請參閱 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();
關鍵概念
危害類別
| 類別 | 描述 |
|---|---|
| 仇恨 | 基於身分群體的歧視性言論 |
| 性 | 性內容、關係、行為 |
| 暴力 | 身體傷害、武器、受傷 |
| 自殘 | 自殘、與自殺相關的內容 |
嚴重程度等級
- 文字:0-7 分級(預設輸出 0、2、4、6)
- 圖片:0、2、4、6(裁剪後量表)
核心模式
分析文字
import com.azure.ai.contentsafety.models.*;
AnalyzeTextResult result = contentSafetyClient.analyzeText(
new AnalyzeTextOptions("這是待分析的文字"));
for (TextCategoriesAnalysis category : result.getCategoriesAnalysis()) {
System.out.printf("類別:%s,嚴重程度:%d%n",
category.getCategory(),
category.getSeverity());
}
使用選項分析文字
AnalyzeTextOptions options = new AnalyzeTextOptions("待分析的文字")
.setCategories(Arrays.asList(
TextCategory.HATE,
TextCategory.VIOLENCE))
.setOutputType(AnalyzeTextOutputType.EIGHT_SEVERITY_LEVELS);
AnalyzeTextResult result = contentSafetyClient.analyzeText(options);
使用封鎖清單分析文字
AnalyzeTextOptions 選項 = new AnalyzeTextOptions("我 h*ate 你,還想 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("封鎖清單:%s,項目:%s,文字:%s%n",
match.getBlocklistName(),
match.getBlocklistItemId(),
match.getBlocklistItemText());
}
}
分析圖片
import com.azure.ai.contentsafety.models.*;
import com.azure.core.util.BinaryData;
import java.nio.file.Files;
import java.nio.file.Paths;
// 從檔案讀取
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("類別:%s,嚴重程度:%d%n",
category.getCategory(),
category.getSeverity());
}
從 URL 分析圖片
ContentSafetyImageData imageData = new ContentSafetyImageData()
.setBlobUrl("https://example.com/image.jpg");
AnalyzeImageResult result = contentSafetyClient.analyzeImage(
new AnalyzeImageOptions(imageData));
封鎖清單管理
建立或更新封鎖清單
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", "自訂封鎖清單");
BinaryData resource = BinaryData.fromObject(description);
Response response = blocklistClient.createOrUpdateTextBlocklistWithResponse(
"my-blocklist", resource, new RequestOptions());
if (response.getStatusCode() == 201) {
System.out.println("封鎖清單已建立");
} else if (response.getStatusCode() == 200) {
System.out.println("封鎖清單已更新");
}
新增封鎖項目
import com.azure.ai.contentsafety.models.*;
import java.util.Arrays;
List items = Arrays.asList(
new TextBlocklistItem("badword1").setDescription("冒犯性詞彙"),
new TextBlocklistItem("badword2").setDescription("另一個詞彙")
);
AddOrUpdateTextBlocklistItemsResult 結果 = blocklistClient.addOrUpdateBlocklistItems(
"my-blocklist",
new AddOrUpdateTextBlocklistItemsOptions(項目));
for (TextBlocklistItem item : result.getBlocklistItems()) {
System.out.printf("已新增:%s (ID:%s)%n",
item.getText(),
item.getBlocklistItemId());
}
列出封鎖清單
PagedIterable blocklists = blocklistClient.listTextBlocklists();
for (TextBlocklist 封鎖清單 : 封鎖清單) {
System.out.printf("封鎖清單:%s,說明:%s%n",
封鎖清單.getName(),
封鎖清單.getDescription());
}
取得封鎖清單
TextBlocklist 阻擋清單 = blocklistClient.getTextBlocklist("my-blocklist");
System.out.println("名稱: " + 阻擋清單.getName());
列出封鎖項目
PagedIterable items =
blocklistClient.listTextBlocklistItems("my-blocklist");
for (TextBlocklistItem item : items) {
System.out.printf("ID: %s, 內容: %s%n",
item.getBlocklistItemId(),
item.getText());
}
移除區塊項目
List itemIds = Arrays.asList("item-id-1", "item-id-2");
blocklistClient.removeBlocklistItems(
"my-blocklist",
new RemoveTextBlocklistItemsOptions(itemIds));
刪除封鎖清單
blocklistClient.deleteTextBlocklist("my-blocklist");
錯誤處理
import com.azure.core.exception.HttpResponseException;
try {
contentSafetyClient.analyzeText(new AnalyzeTextOptions("test"));
} catch (HttpResponseException e) {
System.out.println("狀態: " + e.getResponse().getStatusCode());
System.out.println("錯誤: " + e.getMessage());
// 常見錯誤代碼:InvalidRequestBody、ResourceNotFound、TooManyRequests
}
環境變數
CONTENT_SAFETY_ENDPOINT=https://.cognitiveservices.azure.com/ # 所有驗證方法皆需此參數
CONTENT_SAFETY_ENDPOINT=https:// .cognitiveservices.azure.com/ # 所有驗證方式皆需此變數
AZURE_TOKEN_CREDENTIALS=prod # 僅在生產環境中使用 DefaultAzureCredential 時才需此變數
最佳實務
- 黑名單延遲:變更約需 5 分鐘才會生效
- 類別選取:僅請求所需的類別以減少延遲
- 嚴重性閾值:通常應將嚴重性閾值設為 >= 4 以進行嚴格審核
- 批次處理:並行處理多個項目以提升吞吐量
- 快取:在適當情況下快取封鎖清單結果
觸發詞彙
- 「內容安全 Java」
- 「內容審核 Azure」
- 「分析文字安全性」
- 「Java 圖片審核」
- 「黑名單管理」
- 「仇恨言論偵測」
- 「有害內容過濾器」
---
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"
所有檔案
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