文本处理

文本摘要是AI的重要应用之一，Spring AI提供了强大的文本摘要功能。

6.1.1 基础文本摘要

@RestController
public class SummarizationController {

    private final ChatClient chatClient;

    public SummarizationController(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @PostMapping("/summarize")
    public String summarizeText(@RequestBody String text) {
        String prompt = "Please summarize the following text in 3 sentences:\n\n" + text;

        return chatClient.prompt()
                .user(prompt)
                .call()
                .content();
    }
}

6.1.2 不同长度的摘要

@GetMapping("/summarize/length")
public String summarizeWithLength(
        @RequestParam String text,
        @RequestParam(defaultValue = "short") String length) {

    String prompt;
    switch (length.toLowerCase()) {
        case "short":
            prompt = "Summarize the following text in one sentence:\n\n" + text;
            break;
        case "medium":
            prompt = "Summarize the following text in 3 sentences:\n\n" + text;
            break;
        case "long":
            prompt = "Summarize the following text in 5 sentences:\n\n" + text;
            break;
        default:
            prompt = "Summarize the following text:\n\n" + text;
    }

    return chatClient.prompt()
            .user(prompt)
            .call()
            .content();
}

6.1.3 关键点提取

@GetMapping("/summarize/key-points")
public List<String> extractKeyPoints(@RequestBody String text) {
    String prompt = "Extract the key points from the following text:\n\n" + text +
                   "\nReturn the key points as a bulleted list.";

    String response = chatClient.prompt()
            .user(prompt)
            .call()
            .content();

    // 解析响应为列表
    return parseKeyPoints(response);
}

private List<String> parseKeyPoints(String response) {
    // 实现解析逻辑
    return Arrays.asList(response.split("\n- "));
}

6.2 内容翻译

6.2.1 基础翻译

@RestController
public class TranslationController {

    private final ChatClient chatClient;

    public TranslationController(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @PostMapping("/translate")
    public String translate(
            @RequestParam String text,
            @RequestParam String targetLanguage) {

        String prompt = "Translate the following text to " + targetLanguage + ":\n\n" + text;

        return chatClient.prompt()
                .user(prompt)
                .call()
                .content();
    }
}

6.2.2 多语言翻译

@GetMapping("/translate/multi")
public Map<String, String> multiLanguageTranslate(
        @RequestParam String text,
        @RequestParam List<String> targetLanguages) {

    Map<String, String> translations = new HashMap<>();

    for (String language : targetLanguages) {
        String prompt = "Translate the following text to " + language + ":\n\n" + text;

        String translation = chatClient.prompt()
                .user(prompt)
                .call()
                .content();

        translations.put(language, translation);
    }

    return translations;
}

6.2.3 上下文翻译

@PostMapping("/translate/context")
public String contextTranslation(
        @RequestParam String text,
        @RequestParam String targetLanguage,
        @RequestParam String context) {

    String prompt = "Context: " + context + "\n\n" +
                   "Translate the following text to " + targetLanguage + " while maintaining the context:\n\n" + text;

    return chatClient.prompt()
            .user(prompt)
            .call()
            .content();
}

6.3 代码生成

6.3.1 基础代码生成

@RestController
public class CodeGenerationController {

    private final ChatClient chatClient;

    public CodeGenerationController(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @PostMapping("/generate-code")
    public String generateCode(
            @RequestParam String language,
            @RequestParam String requirement) {

        String prompt = "Generate " + language + " code for: " + requirement;

        return chatClient.prompt()
                .user(prompt)
                .call()
                .content();
    }
}

6.3.2 函数生成

@GetMapping("/generate/function")
public String generateFunction(
        @RequestParam String language,
        @RequestParam String functionName,
        @RequestParam String description) {

    String prompt = "Generate a " + language + " function named '" + functionName +
                   "' that " + description;

    return chatClient.prompt()
            .user(prompt)
            .call()
            .content();
}

6.3.3 类生成

@PostMapping("/generate/class")
public String generateClass(
        @RequestParam String language,
        @RequestParam String className,
        @RequestParam String description) {

    String prompt = "Generate a " + language + " class named '" + className +
                   "' with the following functionality: " + description;

    return chatClient.prompt()
            .user(prompt)
            .call()
            .content();
}

6.3.4 代码解释

@PostMapping("/explain-code")
public String explainCode(
        @RequestParam String language,
        @RequestParam String code) {

    String prompt = "Explain the following " + language + " code:\n\n" + code;

    return chatClient.prompt()
            .user(prompt)
            .call()
            .content();
}

6.4 文档分析

6.4.1 文档摘要

@RestController
public class DocumentAnalysisController {

    private final ChatClient chatClient;

    public DocumentAnalysisController(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @PostMapping("/analyze-document")
    public DocumentAnalysis analyzeDocument(@RequestBody String document) {
        String prompt = "Analyze the following document and provide:\n" +
                       "1. Summary\n" +
                       "2. Key points\n" +
                       "3. Main themes\n" +
                       "4. Action items\n\n" + document;

        return chatClient.prompt()
                .user(prompt)
                .call()
                .entity(DocumentAnalysis.class);
    }
}

public class DocumentAnalysis {
    private String summary;
    private List<String> keyPoints;
    private List<String> mainThemes;
    private List<String> actionItems;
    // getters and setters
}

6.4.2 代码文档生成

@PostMapping("/generate-docs")
public String generateDocumentation(
        @RequestParam String language,
        @RequestParam String code) {

    String prompt = "Generate comprehensive documentation for the following " + language + " code:\n\n" + code;

    return chatClient.prompt()
            .user(prompt)
            .call()
            .content();
}

6.4.3 API文档生成

@PostMapping("/generate-api-docs")
public String generateApiDocs(@RequestBody String apiDefinition) {
    String prompt = "Generate API documentation for the following API definition:\n\n" + apiDefinition;

    return chatClient.prompt()
            .user(prompt)
            .call()
            .content();
}

6.5 高级文本处理技术

6.5.1 情感分析

@GetMapping("/analyze/sentiment")
public SentimentAnalysis analyzeSentiment(@RequestBody String text) {
    String prompt = "Analyze the sentiment of the following text:\n\n" + text +
                   "\nReturn sentiment (positive/negative/neutral) and confidence score.";

    return chatClient.prompt()
            .user(prompt)
            .call()
            .entity(SentimentAnalysis.class);
}

public class SentimentAnalysis {
    private String sentiment;
    private double confidence;
    private String explanation;
    // getters and setters
}

6.5.2 文本分类

@GetMapping("/classify/text")
public TextClassification classifyText(
        @RequestParam String text,
        @RequestParam List<String> categories) {

    String prompt = "Classify the following text into one of these categories: " +
                   String.join(", ", categories) + "\n\n" + text;

    return chatClient.prompt()
            .user(prompt)
            .call()
            .entity(TextClassification.class);
}

public class TextClassification {
    private String category;
    private double confidence;
    private String reasoning;
    // getters and setters
}

6.5.3 问答系统

@PostMapping("/qa/system")
public QaResponse answerQuestion(
        @RequestParam String question,
        @RequestParam String context) {

    String prompt = "Based on the following context, answer the question:\n\n" +
                   "Context: " + context + "\n\n" +
                   "Question: " + question;

    return chatClient.prompt()
            .user(prompt)
            .call()
            .entity(QaResponse.class);
}

public class QaResponse {
    private String answer;
    private String source;
    private double confidence;
    // getters and setters
}

6.6 实际应用示例

6.6.1 内容生成器

@Service
public class ContentGenerator {

    private final ChatClient chatClient;

    public ContentGenerator(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    public Content generateContent(
            String topic,
            String format,
            int length,
            String style) {

        String prompt = "Generate " + format + " content about " + topic +
                       " with approximately " + length + " words in " + style + " style";

        String content = chatClient.prompt()
                .user(prompt)
                .call()
                .content();

        return new Content(topic, format, content);
    }
}

public class Content {
    private String topic;
    private String format;
    private String content;
    // getters and setters
}

6.6.2 代码审查工具

@Service
public class CodeReviewer {

    private final ChatClient chatClient;

    public CodeReviewer(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    public CodeReview reviewCode(String code, String language) {
        String prompt = "Review the following " + language + " code and provide:\n" +
                       "1. Code quality assessment\n" +
                       "2. Potential issues\n" +
                       "3. Improvement suggestions\n\n" + code;

        return chatClient.prompt()
                .user(prompt)
                .call()
                .entity(CodeReview.class);
    }
}

public class CodeReview {
    private String qualityAssessment;
    private List<String> issues;
    private List<String> suggestions;
    // getters and setters
}

6.7 性能优化

6.7.1 批量处理

@RestController
public class BatchProcessingController {

    private final ChatClient chatClient;

    public BatchProcessingController(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    @PostMapping("/process/batch")
    public List<String> batchProcess(@RequestBody List<String> texts) {
        return texts.stream()
                .map(text -> chatClient.prompt()
                        .user("Process: " + text)
                        .call()
                        .content())
                .collect(Collectors.toList());
    }
}

6.7.2 缓存策略

@Configuration
@EnableCaching
public class TextProcessingCacheConfig {

    @Bean
    public CacheManager cacheManager() {
        SimpleCacheManager cacheManager = new SimpleCacheManager();
        cacheManager.setCaches(Arrays.asList(
            new ConcurrentMapCache("text-summaries"),
            new ConcurrentMapCache("translations"),
            new ConcurrentMapCache("code-generations")
        ));
        return cacheManager;
    }
}

6.8 错误处理

6.8.1 文本处理异常

@RestControllerAdvice
public class TextProcessingExceptionHandler {

    @ExceptionHandler(TextProcessingException.class)
    public ResponseEntity<ErrorResponse> handleTextProcessingError(TextProcessingException ex) {
        ErrorResponse error = new ErrorResponse(
            "TEXT_PROCESSING_ERROR",
            "文本处理失败: " + ex.getMessage()
        );
        return new ResponseEntity<>(error, HttpStatus.BAD_REQUEST);
    }
}

6.8.2 超长文本处理

@Service
public class LargeTextProcessor {

    private final ChatClient chatClient;

    public LargeTextProcessor(ChatClient chatClient) {
        this.chatClient = chatClient;
    }

    public String processLargeText(String text) {
        if (text.length() > 4000) {
            // 分块处理
            return processInChunks(text);
        }
        return chatClient.prompt()
                .user("Process: " + text)
                .call()
                .content();
    }

    private String processInChunks(String text) {
        // 实现分块处理逻辑
        return "Processed large text";
    }
}

6.9 监控与日志

6.9.1 文本处理指标

@RestController
public class TextProcessingMetricsController {

    private final MeterRegistry meterRegistry;

    public TextProcessingMetricsController(MeterRegistry meterRegistry) {
        this.meterRegistry = meterRegistry;
    }

    @GetMapping("/text-processing/metrics")
    public Map<String, Object> getMetrics() {
        return Map.of(
            "totalProcessing", meterRegistry.counter("text.processing.total").count(),
            "averageProcessingTime", meterRegistry.timer("text.processing.time").mean(),
            "errorRate", meterRegistry.counter("text.processing.errors").count() /
                       (meterRegistry.counter("text.processing.total").count() + 1.0)
        );
    }
}

6.9.2 文本处理日志

@Configuration
public class TextProcessingLogging {

    @Bean
    public ChatClient loggingChatClient(ChatClient chatClient) {
        return new ChatClient() {
            @Override
            public ChatResponse call(Prompt prompt) {
                long startTime = System.currentTimeMillis();
                try {
                    ChatResponse response = chatClient.call(prompt);
                    long duration = System.currentTimeMillis() - startTime;
                    log.info("文本处理 - 耗时: {}ms, Prompt: {}", duration, prompt);
                    return response;
                } catch (Exception ex) {
                    log.error("文本处理失败", ex);
                    throw ex;
                }
            }

            // 实现其他方法...
        };
    }
}

6.10 小结

本章详细介绍了文本处理与生成的高级技术：

文本摘要：掌握不同长度和风格的摘要生成
内容翻译：学习基础翻译和多语言翻译
代码生成：了解函数、类和代码解释的生成
文档分析：掌握文档摘要和API文档生成
高级技术：学习情感分析、文本分类等高级功能
实际应用：了解内容生成器和代码审查工具
性能优化：掌握批量处理和缓存策略
错误处理：了解文本处理异常和超长文本处理
监控日志：掌握文本处理指标和日志记录

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文本处理

第六章：文本处理与生成

6.1 文本摘要

6.1.1 基础文本摘要

6.1.2 不同长度的摘要

6.1.3 关键点提取

6.2 内容翻译

6.2.1 基础翻译

6.2.2 多语言翻译

6.2.3 上下文翻译

6.3 代码生成

6.3.1 基础代码生成

6.3.2 函数生成

6.3.3 类生成

6.3.4 代码解释

6.4 文档分析

6.4.1 文档摘要

6.4.2 代码文档生成

6.4.3 API文档生成

6.5 高级文本处理技术

6.5.1 情感分析

6.5.2 文本分类

6.5.3 问答系统

6.6 实际应用示例

6.6.1 内容生成器

6.6.2 代码审查工具

6.7 性能优化

6.7.1 批量处理

6.7.2 缓存策略

6.8 错误处理

6.8.1 文本处理异常

6.8.2 超长文本处理

6.9 监控与日志

6.9.1 文本处理指标

6.9.2 文本处理日志

6.10 小结