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README
Apache-2.0

TorchV Unstructured

English | 中文

Maven Central License Java Version

一个强大且开发者友好的文档解析库,专为RAG(检索增强生成)应用优化。基于Apache Tika、Apache POI和PDFBox等业界标准Java库构建,TorchV Unstructured提供了增强的解析能力,具备智能表格结构识别和内容提取功能。

🚀 核心特性

  • 智能表格解析:先进的表格结构分析,支持复杂单元格合并检测
  • 多格式支持:无缝处理DOC、DOCX、PDF等多种文档格式
  • RAG优化输出:专为AI/ML管道设计的结构化内容提取
  • Markdown和HTML导出:灵活的输出格式,保持表格结构完整性
  • 图像提取:自动提取和处理嵌入式图像
  • 内存高效:优化的大文档处理,最小化内存占用

📦 安装

Maven

<dependency>
    <groupId>com.torchv.infra</groupId>
    <artifactId>torchv-unstructured</artifactId>
    <version>1.0.0</version>
</dependency>

Gradle

implementation 'com.torchv.infra:torchv-unstructured:1.0.0'

🔧 快速开始

基础文档解析

import com.torchv.infra.unstructured.UnstructuredParser;

// 解析文档为Markdown格式(推荐用于RAG)
String content = UnstructuredParser.toMarkdown("document.docx");
System.out.println(content);

// 解析文档为带HTML表格的Markdown格式(保持表格结构)
String contentWithTables = UnstructuredParser.toMarkdownWithHtmlTables("document.docx");
System.out.println(contentWithTables);

高级表格提取

import com.torchv.infra.unstructured.UnstructuredParser;

import java.io.File;
import java.util.List;

// 仅提取Word文档中的表格
List<String> tables = UnstructuredParser.extractTables("document.docx");
for (int i = 0; i < tables.size(); i++) {
    System.out.println("表格 " + (i + 1) + ":");
    System.out.println(tables.get(i));
}

// 获取结构化结果,提供更多控制
DocumentResult result = UnstructuredParser.toStructuredResult("document.docx");
if (result.isSuccess()) {
    System.out.println("内容: " + result.getContent());
    System.out.println("表格: " + result.getTables());
}

文件格式支持

import com.torchv.infra.unstructured.UnstructuredParser;
import com.torchv.infra.unstructured.util.UnstructuredUtils;

// 检查文件格式是否支持
if (UnstructuredUtils.isSupportedFormat("document.docx")) {
    String content = UnstructuredParser.toMarkdownWithHtmlTables("document.docx");
    System.out.println("解析成功!");
} else {
    System.out.println("不支持的文件格式");
}

// 获取所有支持的格式
List<String> supportedFormats = UnstructuredUtils.getSupportedFormats();
System.out.println("支持的格式: " + String.join(", ", supportedFormats));

🎯 核心组件

统一入口

  • UnstructuredParser:主要入口类,为所有文档解析操作提供简单统一的API

文档解析器

  • UnstructuredWord:通用Word文档解析器,支持自动检测
  • TikaAutoUtils:支持自动检测的通用文档解析器(底层实现)
  • WordTableParser:专业的Word文档表格解析器
  • DocxTableParser:高级DOCX表格结构分析器

内容处理器

  • ToMarkdownWithHtmlTableContentHandler:将文档转换为带HTML表格的Markdown
  • DocMarkdownWithHtmlTableContentHandler:专门的DOC格式处理器
  • DocXMarkdownWithHtmlTableContentHandler:专门的DOCX格式处理器

表格分析

  • TableStructureAnalyzer:智能表格结构识别
  • CellMergeAnalyzer:高级单元格合并检测
  • HtmlTableBuilder:清洁的HTML表格生成器

实用工具

  • FileMagicUtils:文件类型检测和验证
  • ImageExtractParse:嵌入式图像提取

🔍 高级用法

RAG应用集成

import com.torchv.infra.unstructured.UnstructuredParser;
import com.torchv.infra.unstructured.core.DocumentResult;

// 为RAG应用优化
public class RAGDocumentProcessor {

    public DocumentChunk processDocument(String filePath) {
        // 解析时保持表格结构以获得更好的上下文
        String content = UnstructuredParser.toMarkdownWithHtmlTables(filePath);

        // 单独提取表格用于结构化数据处理
        List<String> tables = UnstructuredParser.extractTables(filePath);

        return new DocumentChunk(content, tables);
    }
}

批量处理

import com.torchv.infra.unstructured.UnstructuredParser;
import com.torchv.infra.unstructured.util.UnstructuredUtils;

public class BatchProcessor {

    public void processBatch(List<String> filePaths) {
        filePaths.parallelStream()
                .filter(UnstructuredUtils::isSupportedFormat)
                .forEach(this::processFile);
    }

    private void processFile(String filePath) {
        try {
            String content = UnstructuredParser.toMarkdownWithHtmlTables(filePath);
            // 保存或进一步处理内容
            saveProcessedContent(filePath, content);
        } catch (Exception e) {
            log.error("处理文件失败: {}", filePath, e);
        }
    }
}

错误处理和验证

import com.torchv.infra.unstructured.UnstructuredParser;
import com.torchv.infra.unstructured.util.UnstructuredUtils;

public class DocumentValidator {
    
    public ProcessingResult validateAndProcess(String filePath) {
        // 检查文件格式
        if (!UnstructuredUtils.isSupportedFormat(filePath)) {
            return ProcessingResult.unsupportedFormat();
        }
        
        try {
            String content = UnstructuredParser.toMarkdownWithHtmlTables(filePath);
            List<String> tables = UnstructuredParser.extractTables(filePath);
            
            return ProcessingResult.success(content, tables);
        } catch (RuntimeException e) {
            return ProcessingResult.error(e.getMessage());
        }
    }
}

🌟 为什么选择TorchV Unstructured?

适用于RAG应用

  • 结构化输出:清洁、结构化的内容提取,完美适配嵌入向量生成
  • 表格保持:维护表格关系,对文档理解至关重要
  • 丰富元数据:提取全面的文档元数据,增强检索效果

适用于开发者

  • 简单API:直观的接口设计,合理的默认配置
  • 可扩展:基于插件的架构,支持自定义内容处理器
  • 生产就绪:经过实战验证,具备全面的错误处理机制

性能优化

  • 内存高效:大文档的流式处理
  • 快速处理:优化算法确保快速解析
  • 可扩展:专为高吞吐量文档处理而设计

📚 文档

🤝 贡献

我们欢迎贡献!请查看我们的贡献指南了解详情。

  1. Fork这个仓库
  2. 创建你的功能分支 (git checkout -b feature/amazing-feature)
  3. 提交你的更改 (git commit -m 'Add some amazing feature')
  4. 推送到分支 (git push origin feature/amazing-feature)
  5. 打开一个Pull Request

📄 许可证

本项目基于Apache License 2.0许可证 - 查看LICENSE文件了解详情。

🙏 致谢

📞 支持


TorchV团队 用 ❤️ 制作

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一个强大且开发者友好的文档解析库,专为RAG(检索增强生成)应用优化 展开 收起
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