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Opendataloader Pdf

Community
chujianyun
opendataloader-pdf

PDF 数据提取工具。当用户提到"PDF 提取"、"PDF 转 Markdown"、"PDF 解析"、"提取 PDF 内容"、"PDF 转 JSON"、"RAG PDF"时使用。OpenDataLoader PDF 是目前基准测试第一的 PDF 解析器,支持本地模式(快速、确定)和混合 AI 模式(复杂表格、扫描件、公式),输出 Markdown、JSON(带边界框)、HTML。适用于需要从 PDF 提取结构化数据用于 RAG/LLM pipeline,或需要批量处理 PDF 文档的场景。

Overview

Publisherchujianyun
Repositoryskills
Skill nameopendataloader-pdf
Stars
743
Forks
103
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by chujianyun on GitHub. Read the source before you install it.

Installation

Install the Opendataloader Pdf AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chujianyun/skills/tree/main/skills/opendataloader-pdf
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chujianyun/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/opendataloader-pdf .claude/skills/opendataloader-pdf
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Opendataloader Pdf in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Opendataloader Pdf on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Opendataloader Pdf is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

OpenDataLoader PDF

PDF 解析器 · 基准测试第一 · RAG/LLM 数据提取利器

功能定位

  • 核心能力:从任意 PDF 提取结构化数据(Markdown、JSON、HTML),带边界框坐标
  • 技术亮点:XY-Cut++ 读取顺序、Bounding Box 定位、AI 混合模式处理复杂页面
  • 基准成绩:综合 0.90(第一),表格 0.93,读取顺序 0.94(对标 Docling、Marker、MinerU 等)
  • 许可证:Apache 2.0(核心功能免费)

适用场景

  • 批量提取 PDF 为 Markdown / JSON / HTML 用于 RAG 或 LLM 训练
  • 需要边界框坐标做源码溯源(哪个段落来自 PDF 第几页哪个位置)
  • 复杂表格、扫描件、含公式的学术 PDF
  • PDF 无障碍化(Tagged PDF 生成,Q2 2026 免费开放)

安装

前提

  • Java 11+
  • Python 3.10+
bash
pip install -U opendataloader-pdf

混合 AI 模式(复杂表格 / OCR / 公式):

bash
pip install "opendataloader-pdf[hybrid]"

快速使用

CLI(适合单文件或批量)

bash
# 快速模式:输出 Markdown + JSON
opendataloader-pdf input.pdf output_dir/

# 指定格式
opendataloader-pdf input.pdf output_dir/ --format markdown,json,html

# 混合 AI 模式(复杂表格 / 扫描件)
opendataloader-pdf --hybrid docling-fast input.pdf output_dir/

# 混合模式 + OCR(扫描件)
opendataloader-pdf --hybrid docling-fast --force-ocr input.pdf output_dir/

# 混合模式 + 公式识别
opendataloader-pdf --hybrid docling-fast --hybrid-mode full input.pdf output_dir/

Python API

python
import opendataloader_pdf

# 批量处理(一次调用会启动 JVM,建议批量一次性传入)
opendataloader_pdf.convert(
    input_path=["file1.pdf", "file2.pdf", "folder/"],
    output_dir="output/",
    format="markdown,json"
)

模式选择指南

文档类型模式命令
标准数字 PDF快速(默认)opendataloader-pdf file.pdf out/
复杂/无线框表格混合opendataloader-pdf --hybrid docling-fast file.pdf out/
扫描件混合 + OCR同上 + --force-ocr
非英语扫描件混合 + OCR--force-ocr --ocr-lang "ko,en"
含数学公式混合 + 公式--hybrid docling-fast --hybrid-mode full
图表需要描述混合 + 图片描述--enrich-picture-description --hybrid-mode full

输出格式说明

Markdown

保留标题层级、表格结构、列表嵌套,适合直接用于 chunking。

JSON(带边界框)

json
{
  "pages": [{
    "page_number": 1,
    "elements": [{
      "type": "heading",
      "text": "...",
      "bbox": [x0, y0, x1, y1],
      "level": 1
    }, {
      "type": "table",
      "bbox": [x0, y0, x1, y1],
      "html": "..."
    }]
  }]
}

每个元素都有 bbox 坐标,方便做源码溯源。

HTML

保留布局结构,适合渲染或进一步处理。

Gotchas

  • 每次 convert() 调用会启动一个新的 JVM 进程,所以批量文件建议一次传入,而不是循环多次调用
  • 混合模式需要在后台启动服务器:opendataloader-pdf-hybrid --port 5002,然后客户端加 --hybrid docling-fast
  • --enrich-formula 或 --enrich-picture-description 必须在混合服务器和客户端都加 --hybrid-mode full,否则强化功能静默跳过
  • Java 选项修改后必须运行 npm run sync,它会重新生成 options.json 和所有 Python/Node.js 绑定

与其他工具的对比

引擎综合分表格速度(秒/页)
opendataloader(混合)0.900.930.43
docling0.860.890.73
marker0.830.8153.93
mineru0.820.875.96
pymupdf4llm0.570.400.09

引用信息

Frequently asked questions

What does the Opendataloader Pdf AI skill do?

PDF 数据提取工具。当用户提到"PDF 提取"、"PDF 转 Markdown"、"PDF 解析"、"提取 PDF 内容"、"PDF 转 JSON"、"RAG PDF"时使用。OpenDataLoader PDF 是目前基准测试第一的 PDF 解析器,支持本地模式(快速、确定)和混合 AI 模式(复杂表格、扫描件、公式),输出 Markdown、JSON(带边界框)、HTML。适用于需要从 PDF 提取结构化数据用于 RAG/LLM pipeline,或需要批量处理 PDF 文档的场景。

Why use Opendataloader Pdf on TypingMind?

Because you install it once and use it with any model. Opendataloader Pdf is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Opendataloader Pdf in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chujianyun/skills/tree/main/skills/opendataloader-pdf. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Opendataloader Pdf?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Opendataloader Pdf?

As many as you like. As long as a model supports skills, you can use Opendataloader Pdf with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Opendataloader Pdf AI skill free?

It is published on GitHub by chujianyun. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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