feat: 完成中文mkdocs的编写

c93457ce · Sidney233 · b2d11663 · c93457ce · c93457ce · c93457ce
Commit c93457ce authored Jul 07, 2025 by Sidney233
14 changed files
--- a/docs/FAQ_en_us.md
+++ b/docs/FAQ_en_us.md
--- a/docs/en/index.md
+++ b/docs/en/index.md
+# MinerU
+test
\ No newline at end of file
--- a/docs/output_file_en_us.md
+++ b/docs/output_file_en_us.md
@@ -5,13 +5,13 @@ After executing the `mineru` command, in addition to outputting files related to
 ### some_pdf_layout.pdf
 Each page's layout consists of one or more bounding boxes. The number in the top-right corner of each box indicates the reading order. Additionally, different content blocks are highlighted with distinct background colors within the layout.pdf.
-![layout example](images/layout_example.png)
+![layout example](../images/layout_example.png)
 ### some_pdf_spans.pdf(Applicable only to the pipeline backend)
 All spans on the page are drawn with different colored line frames according to the span type. This file can be used for quality control, allowing for quick identification of issues such as missing text or unrecognized inline formulas.
-![spans example](images/spans_example.png)
+![spans example](../images/spans_example.png)
 ### some_pdf_model.json(Applicable only to the pipeline backend)
@@ -61,7 +61,7 @@ inference_result: list[PageInferenceResults] = []
 ```
 The format of the poly coordinates is \[x0, y0, x1, y1, x2, y2, x3, y3\], representing the coordinates of the top-left, top-right, bottom-right, and bottom-left points respectively.
-![Poly Coordinate Diagram](images/poly.png)
+![Poly Coordinate Diagram](../images/poly.png)
 #### example

--- a/docs/en/quick_start/quick_start.md
+++ b/docs/en/quick_start/quick_start.md
+# Quick Start
+If you encounter any installation issues, please first consult the <a href="#faq">FAQ</a>. </br>
+If the parsing results are not as expected, refer to the <a href="#known-issues">Known Issues</a>. </br>
+There are three different ways to experience MinerU:
+- [Online Demo](#online-demo)
+- [Local Deployment](#local-deployment)
+> [!WARNING]
+> **Pre-installation Notice—Hardware and Software Environment Support**
+>
+> To ensure the stability and reliability of the project, we only optimize and test for specific hardware and software environments during development. This ensures that users deploying and running the project on recommended system configurations will get the best performance with the fewest compatibility issues.
+>
+> By focusing resources on the mainline environment, our team can more efficiently resolve potential bugs and develop new features.
+>
+> In non-mainline environments, due to the diversity of hardware and software configurations, as well as third-party dependency compatibility issues, we cannot guarantee 100% project availability. Therefore, for users who wish to use this project in non-recommended environments, we suggest carefully reading the documentation and FAQ first. Most issues already have corresponding solutions in the FAQ. We also encourage community feedback to help us gradually expand support.
+<table>
+    <tr>
+        <td>Parsing Backend</td>
+        <td>pipeline</td>
+        <td>vlm-transformers</td>
+        <td>vlm-sglang</td>
+    </tr>
+    <tr>
+        <td>Operating System</td>
+        <td>windows/linux/mac</td>
+        <td>windows/linux</td>
+        <td>windows(wsl2)/linux</td>
+    </tr>
+    <tr>
+        <td>CPU Inference Support</td>
+        <td>✅</td>
+        <td colspan="2">❌</td>
+    </tr>
+    <tr>
+        <td>GPU Requirements</td>
+        <td>Turing architecture or later, 6GB+ VRAM or Apple Silicon</td>
+        <td colspan="2">Ampere architecture or later, 8GB+ VRAM</td>
+    </tr>
+    <tr>
+        <td>Memory Requirements</td>
+        <td colspan="3">Minimum 16GB+, 32GB+ recommended</td>
+    </tr>
+    <tr>
+        <td>Disk Space Requirements</td>
+        <td colspan="3">20GB+, SSD recommended</td>
+    </tr>
+    <tr>
+        <td>Python Version</td>
+        <td colspan="3">3.10-3.13</td>
+    </tr>
+</table>
\ No newline at end of file
--- a/docs/FAQ_zh_cn.md
+++ b/docs/FAQ_zh_cn.md
 # 常见问题解答
-### 1.在WSL2的Ubuntu22.04中遇到报错`ImportError: libGL.so.1: cannot open shared object file: No such file or directory`
+## 1.在WSL2的Ubuntu22.04中遇到报错`ImportError: libGL.so.1: cannot open shared object file: No such file or directory`
 WSL2的Ubuntu22.04中缺少`libgl`库，可通过以下命令安装`libgl`库解决：
@@ -11,7 +11,7 @@ sudo apt-get install libgl1-mesa-glx
 参考：https://github.com/opendatalab/MinerU/issues/388
-### 2.在 CentOS 7 或 Ubuntu 18 系统安装MinerU时报错`ERROR: Failed building wheel for simsimd`
+## 2.在 CentOS 7 或 Ubuntu 18 系统安装MinerU时报错`ERROR: Failed building wheel for simsimd`
 新版本albumentations(1.4.21)引入了依赖simsimd,由于simsimd在linux的预编译包要求glibc的版本大于等于2.28，导致部分2019年之前发布的Linux发行版无法正常安装，可通过如下命令安装:
 ```

--- a/docs/zh/index.md
+++ b/docs/zh/index.md
+<div align="center" xmlns="http://www.w3.org/1999/html">
+<!-- logo -->
+<p align="center">
+  <img src="../images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
+</p>
+</div>
+<!-- icon -->
+[![stars](https://img.shields.io/github/stars/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
+[![forks](https://img.shields.io/github/forks/opendatalab/MinerU.svg)](https://github.com/opendatalab/MinerU)
+[![open issues](https://img.shields.io/github/issues-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)
+[![issue resolution](https://img.shields.io/github/issues-closed-raw/opendatalab/MinerU)](https://github.com/opendatalab/MinerU/issues)
+[![PyPI version](https://img.shields.io/pypi/v/mineru)](https://pypi.org/project/mineru/)
+[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/mineru)](https://pypi.org/project/mineru/)
+[![Downloads](https://static.pepy.tech/badge/mineru)](https://pepy.tech/project/mineru)
+[![Downloads](https://static.pepy.tech/badge/mineru/month)](https://pepy.tech/project/mineru)
+[![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
+[![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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&labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)
+[![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,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&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)
+[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/gist/myhloli/3b3a00a4a0a61577b6c30f989092d20d/mineru_demo.ipynb)
+[![arXiv](https://img.shields.io/badge/arXiv-2409.18839-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2409.18839)
+[![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/opendatalab/MinerU)
+<div align="center" xmlns="http://www.w3.org/1999/html">
+<a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
+<!-- hot link -->
+<p align="center">
+<a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit: 高质量PDF解析工具箱</a>🔥🔥🔥
+<br>
+<br>
+🚀<a href="https://mineru.net/?source=github">MinerU 官网入口→✅ 免装在线版 ✅ 全功能客户端 ✅ 开发者API在线调用，省去部署麻烦，多种产品形态一键get，速冲！</a>
+</p>
+<!-- join us -->
+<p align="center">
+    👋 join us on <a href="https://discord.gg/Tdedn9GTXq" target="_blank">Discord</a> and <a href="http://mineru.space/s/V85Yl" target="_blank">WeChat</a>
+</p>
+</div>
+## 项目简介
+MinerU是一款将PDF转化为机器可读格式的工具（如markdown、json），可以很方便地抽取为任意格式。
+MinerU诞生于[书生-浦语](https://github.com/InternLM/InternLM)的预训练过程中，我们将会集中精力解决科技文献中的符号转化问题，希望在大模型时代为科技发展做出贡献。
+相比国内外知名商用产品MinerU还很年轻，如果遇到问题或者结果不及预期请到[issue](https://github.com/opendatalab/MinerU/issues)提交问题，同时**附上相关PDF**。
+![type:video](https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c)
+## 主要功能
+- 删除页眉、页脚、脚注、页码等元素，确保语义连贯
+- 输出符合人类阅读顺序的文本，适用于单栏、多栏及复杂排版
+- 保留原文档的结构，包括标题、段落、列表等
+- 提取图像、图片描述、表格、表格标题及脚注
+- 自动识别并转换文档中的公式为LaTeX格式
+- 自动识别并转换文档中的表格为HTML格式
+- 自动检测扫描版PDF和乱码PDF，并启用OCR功能
+- OCR支持84种语言的检测与识别
+- 支持多种输出格式，如多模态与NLP的Markdown、按阅读顺序排序的JSON、含有丰富信息的中间格式等
+- 支持多种可视化结果，包括layout可视化、span可视化等，便于高效确认输出效果与质检
+- 支持纯CPU环境运行，并支持 GPU(CUDA)/NPU(CANN)/MPS 加速
+- 兼容Windows、Linux和Mac平台
\ No newline at end of file
--- a/docs/output_file_zh_cn.md
+++ b/docs/output_file_zh_cn.md
-## 概览
+# 概览
 `mineru` 命令执行后除了输出 markdown 文件以外，还可能会生成若干个和 markdown 无关的文件。现在将一一介绍这些文件
-### some_pdf_layout.pdf
+## some_pdf_layout.pdf
 每一页的 layout 均由一个或多个框组成。 每个框右上角的数字表明它们的阅读顺序。此外 layout.pdf 框内用不同的背景色块圈定不同的内容块。
-![layout 页面示例](images/layout_example.png)
+![layout 页面示例](../images/layout_example.png)
-### some_pdf_spans.pdf(仅适用于pipeline后端)
+## some_pdf_spans.pdf(仅适用于pipeline后端)
 根据 span 类型的不同，采用不同颜色线框绘制页面上所有 span。该文件可以用于质检，可以快速排查出文本丢失、行内公式未识别等问题。
-![span 页面示例](images/spans_example.png)
+![span 页面示例](../images/spans_example.png)
-### some_pdf_model.json(仅适用于pipeline后端)
+## some_pdf_model.json(仅适用于pipeline后端)
-#### 结构定义
+### 结构定义
 ```python
 from pydantic import BaseModel, Field
@@ -62,9 +62,9 @@ inference_result: list[PageInferenceResults] = []
 ```
 poly 坐标的格式 \[x0, y0, x1, y1, x2, y2, x3, y3\], 分别表示左上、右上、右下、左下四点的坐标
-![poly 坐标示意图](images/poly.png)
+![poly 坐标示意图](../images/poly.png)
-#### 示例数据
+### 示例数据
 ```json
 [
@@ -117,7 +117,7 @@ poly 坐标的格式 \[x0, y0, x1, y1, x2, y2, x3, y3\], 分别表示左上、
 ]
 ```
-### some_pdf_model_output.txt(仅适用于vlm后端)
+## some_pdf_model_output.txt(仅适用于vlm后端)
 该文件是vlm模型的输出结果，使用`----`分割每一页的输出结果。  
 每一页的输出结果一些以`<|box_start|>`开头，`<|md_end|>`结尾的文本块。  
@@ -143,7 +143,7 @@ poly 坐标的格式 \[x0, y0, x1, y1, x2, y2, x3, y3\], 分别表示左上、
    该字段是该block的markdown内容，如type为text，文本末尾可能存在`<|txt_contd|>`标记，表示该文本块可以后后续text块连接。
    如type为table，内容为`otsl`格式表示的表格内容，需要转换为html格式才能在markdown中渲染。
-### some_pdf_middle.json
+## some_pdf_middle.json
 | 字段名            | 解释                                        |
 |:---------------|:------------------------------------------|
@@ -251,7 +251,7 @@ para_blocks内存储的元素为区块信息
 一级block(如有)->二级block->line->span
-#### 示例数据
+### 示例数据
 ```json
 {
@@ -354,7 +354,7 @@ para_blocks内存储的元素为区块信息
 }
 ```
-### some_pdf_content_list.json
+## some_pdf_content_list.json
 该文件是一个json数组，每个元素是一个dict，按阅读顺序平铺存储文档中所有可阅读的内容块。  
 content_list可以看成简化后的middle.json，内容块的类型基本和middle.json一致，但不包含布局信息。  
@@ -370,7 +370,7 @@ content的类型有如下几种：
 需要注意的是，content_list中的title和text块统一使用text类型表示，通过`text_level`字段来区分文本块的层级，不含`text_level`字段或`text_level`为0的文本块表示正文文本，`text_level`为1的文本块表示一级标题，`text_level`为2的文本块表示二级标题，以此类推。  
 每个content包含`page_idx`字段，表示该内容块所在的页码，从0开始。
-#### 示例数据
+### 示例数据
 ```json
 [

--- a/docs/zh/quick_start/index.md
+++ b/docs/zh/quick_start/index.md
+# 快速开始
+如果遇到任何安装问题，请先查询 <a href="#faq">FAQ</a> </br>
+如果遇到解析效果不及预期，参考 <a href="#known-issues">Known Issues</a></br>
+有2种不同方式可以体验MinerU的效果：
+- [在线体验](#在线体验)
+- [本地部署](#本地部署)
+> [!WARNING]
+> **安装前必看——软硬件环境支持说明**
+> 
+> 为了确保项目的稳定性和可靠性，我们在开发过程中仅对特定的软硬件环境进行优化和测试。这样当用户在推荐的系统配置上部署和运行项目时，能够获得最佳的性能表现和最少的兼容性问题。
+>
+> 通过集中资源和精力于主线环境，我们团队能够更高效地解决潜在的BUG，及时开发新功能。
+>
+> 在非主线环境中，由于硬件、软件配置的多样性，以及第三方依赖项的兼容性问题，我们无法100%保证项目的完全可用性。因此，对于希望在非推荐环境中使用本项目的用户，我们建议先仔细阅读文档以及FAQ，大多数问题已经在FAQ中有对应的解决方案，除此之外我们鼓励社区反馈问题，以便我们能够逐步扩大支持范围。
+<table>
+    <tr>
+        <td>解析后端</td>
+        <td>pipeline</td>
+        <td>vlm-transformers</td>
+        <td>vlm-sglang</td>
+    </tr>
+    <tr>
+        <td>操作系统</td>
+        <td>windows/linux/mac</td>
+        <td>windows/linux</td>
+        <td>windows(wsl2)/linux</td>
+    </tr>
+    <tr>
+        <td>CPU推理支持</td>
+        <td>✅</td>
+        <td colspan="2">❌</td>
+    </tr>
+    <tr>
+        <td>GPU要求</td>
+        <td>Turing及以后架构，6G显存以上或Apple Silicon</td>
+        <td colspan="2">Ampere及以后架构，8G显存以上</td>
+    </tr>
+    <tr>
+        <td>内存要求</td>
+        <td colspan="3">最低16G以上，推荐32G以上</td>
+    </tr>
+    <tr>
+        <td>磁盘空间要求</td>
+        <td colspan="3">20G以上，推荐使用SSD</td>
+    </tr>
+    <tr>
+        <td>python版本</td>
+        <td colspan="3">3.10-3.13</td>
+    </tr>
+</table>
\ No newline at end of file
--- a/docs/zh/quick_start/local_deployment.md
+++ b/docs/zh/quick_start/local_deployment.md
+# 本地部署
+## 安装 MinerU
+### 使用 pip 或 uv 安装
+```bash
+pip install --upgrade pip -i https://mirrors.aliyun.com/pypi/simple
+pip install uv -i https://mirrors.aliyun.com/pypi/simple
+uv pip install -U "mineru[core]" -i https://mirrors.aliyun.com/pypi/simple 
+```
+### 源码安装
+```bash
+git clone https://github.com/opendatalab/MinerU.git
+cd MinerU
+uv pip install -e .[core] -i https://mirrors.aliyun.com/pypi/simple
+```
+> [!NOTE]
+> Linux和macOS系统安装后自动支持cuda/mps加速，Windows用户如需使用cuda加速，
+> 请前往 [Pytorch官网](https://pytorch.org/get-started/locally/) 选择合适的cuda版本安装pytorch。
+### 安装完整版（支持 sglang 加速）（需确保设备有Turing及以后架构，8G显存及以上显卡）
+如需使用 **sglang 加速 VLM 模型推理**，请选择合适的方式安装完整版本：
+- 使用uv或pip安装
+  ```bash
+  uv pip install -U "mineru[all]" -i https://mirrors.aliyun.com/pypi/simple
+  ```
+- 从源码安装：
+  ```bash
+  uv pip install -e .[all] -i https://mirrors.aliyun.com/pypi/simple
+  ```
+> [!TIP]
+> sglang安装过程中如发生异常，请参考[sglang官方文档](https://docs.sglang.ai/start/install.html)尝试解决或直接使用docker方式安装。
+- 使用 Dockerfile 构建镜像：
+  ```bash
+  wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/china/Dockerfile
+  docker build -t mineru-sglang:latest -f Dockerfile .
+  ```
+  启动 Docker 容器：
+  ```bash
+  docker run --gpus all \
+    --shm-size 32g \
+    -p 30000:30000 \
+    --ipc=host \
+    mineru-sglang:latest \
+    mineru-sglang-server --host 0.0.0.0 --port 30000
+  ```
+  或使用 Docker Compose 启动：
+  ```bash
+    wget https://gcore.jsdelivr.net/gh/opendatalab/MinerU@master/docker/compose.yaml
+    docker compose -f compose.yaml up -d
+  ```
+> [!TIP]
+> Dockerfile默认使用`lmsysorg/sglang:v0.4.8.post1-cu126`作为基础镜像，支持Turing/Ampere/Ada Lovelace/Hopper平台，
+> 如您使用较新的`Blackwell`平台，请将基础镜像修改为`lmsysorg/sglang:v0.4.8.post1-cu128-b200`。
+### 安装client（用于在仅需 CPU 和网络连接的边缘设备上连接 sglang-server）
+```bash
+uv pip install -U mineru -i https://mirrors.aliyun.com/pypi/simple
+mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://<host_ip>:<port>
+```
+---
\ No newline at end of file
--- a/docs/zh/quick_start/online_demo.md
+++ b/docs/zh/quick_start/online_demo.md
+# 在线体验
+[![OpenDataLab](https://img.shields.io/badge/Demo_on_OpenDataLab-blue?logo=data:image/svg+xml;base64,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&labelColor=white)](https://mineru.net/OpenSourceTools/Extractor?source=github)
+[![ModelScope](https://img.shields.io/badge/Demo_on_ModelScope-purple?logo=data:image/svg+xml;base64,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&labelColor=white)](https://www.modelscope.cn/studios/OpenDataLab/MinerU)
+[![HuggingFace](https://img.shields.io/badge/Demo_on_HuggingFace-yellow.svg?logo=data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAF8AAABYCAMAAACkl9t/AAAAk1BMVEVHcEz/nQv/nQv/nQr/nQv/nQr/nQv/nQv/nQr/wRf/txT/pg7/yRr/rBD/zRz/ngv/oAz/zhz/nwv/txT/ngv/0B3+zBz/nQv/0h7/wxn/vRb/thXkuiT/rxH/pxD/ogzcqyf/nQvTlSz/czCxky7/SjifdjT/Mj3+Mj3wMj15aTnDNz+DSD9RTUBsP0FRO0Q6O0WyIxEIAAAAGHRSTlMADB8zSWF3krDDw8TJ1NbX5efv8ff9/fxKDJ9uAAAGKklEQVR42u2Z63qjOAyGC4RwCOfB2JAGqrSb2WnTw/1f3UaWcSGYNKTdf/P+mOkTrE+yJBulvfvLT2A5ruenaVHyIks33npl/6C4s/ZLAM45SOi/1FtZPyFur1OYofBX3w7d54Bxm+E8db+nDr12ttmESZ4zludJEG5S7TO72YPlKZFyE+YCYUJTBZsMiNS5Sd7NlDmKM2Eg2JQg8awbglfqgbhArjxkS7dgp2RH6hc9AMLdZYUtZN5DJr4molC8BfKrEkPKEnEVjLbgW1fLy77ZVOJagoIcLIl+IxaQZGjiX597HopF5CkaXVMDO9Pyix3AFV3kw4lQLCbHuMovz8FallbcQIJ5Ta0vks9RnolbCK84BtjKRS5uA43hYoZcOBGIG2Epbv6CvFVQ8m8loh66WNySsnN7htL58LNp+NXT8/PhXiBXPMjLSxtwp8W9f/1AngRierBkA+kk/IpUSOeKByzn8y3kAAAfh//0oXgV4roHm/kz4E2z//zRc3/lgwBzbM2mJxQEa5pqgX7d1L0htrhx7LKxOZlKbwcAWyEOWqYSI8YPtgDQVjpB5nvaHaSnBaQSD6hweDi8PosxD6/PT09YY3xQA7LTCTKfYX+QHpA0GCcqmEHvr/cyfKQTEuwgbs2kPxJEB0iNjfJcCTPyocx+A0griHSmADiC91oNGVwJ69RudYe65vJmoqfpul0lrqXadW0jFKH5BKwAeCq+Den7s+3zfRJzA61/Uj/9H/VzLKTx9jFPPdXeeP+L7WEvDLAKAIoF8bPTKT0+TM7W8ePj3Rz/Yn3kOAp2f1Kf0Weony7pn/cPydvhQYV+eFOfmOu7VB/ViPe34/EN3RFHY/yRuT8ddCtMPH/McBAT5s+vRde/gf2c/sPsjLK+m5IBQF5tO+h2tTlBGnP6693JdsvofjOPnnEHkh2TnV/X1fBl9S5zrwuwF8NFrAVJVwCAPTe8gaJlomqlp0pv4Pjn98tJ/t/fL++6unpR1YGC2n/KCoa0tTLoKiEeUPDl94nj+5/Tv3/eT5vBQ60X1S0oZr+IWRR8Ldhu7AlLjPISlJcO9vrFotky9SpzDequlwEir5beYAc0R7D9KS1DXva0jhYRDXoExPdc6yw5GShkZXe9QdO/uOvHofxjrV/TNS6iMJS+4TcSTgk9n5agJdBQbB//IfF/HpvPt3Tbi7b6I6K0R72p6ajryEJrENW2bbeVUGjfgoals4L443c7BEE4mJO2SpbRngxQrAKRudRzGQ8jVOL2qDVjjI8K1gc3TIJ5KiFZ1q+gdsARPB4NQS4AjwVSt72DSoXNyOWUrU5mQ9nRYyjp89Xo7oRI6Bga9QNT1mQ/ptaJq5T/7WcgAZywR/XlPGAUDdet3LE+qS0TI+g+aJU8MIqjo0Kx8Ly+maxLjJmjQ18rA0YCkxLQbUZP1WqdmyQGJLUm7VnQFqodmXSqmRrdVpqdzk5LvmvgtEcW8PMGdaS23EOWyDVbACZzUJPaqMbjDxpA3Qrgl0AikimGDbqmyT8P8NOYiqrldF8rX+YN7TopX4UoHuSCYY7cgX4gHwclQKl1zhx0THf+tCAUValzjI7Wg9EhptrkIcfIJjA94evOn8B2eHaVzvBrnl2ig0So6hvPaz0IGcOvTHvUIlE2+prqAxLSQxZlU2stql1NqCCLdIiIN/i1DBEHUoElM9dBravbiAnKqgpi4IBkw+utSPIoBijDXJipSVV7MpOEJUAc5Qmm3BnUN+w3hteEieYKfRZSIUcXKMVf0u5wD4EwsUNVvZOtUT7A2GkffHjByWpHqvRBYrTV72a6j8zZ6W0DTE86Hn04bmyWX3Ri9WH7ZU6Q7h+ZHo0nHUAcsQvVhXRDZHChwiyi/hnPuOsSEF6Exk3o6Y9DT1eZ+6cASXk2Y9k+6EOQMDGm6WBK10wOQJCBwren86cPPWUcRAnTVjGcU1LBgs9FURiX/e6479yZcLwCBmTxiawEwrOcleuu12t3tbLv/N4RLYIBhYexm7Fcn4OJcn0+zc+s8/VfPeddZHAGN6TT8eGczHdR/Gts1/MzDkThr23zqrVfAMFT33Nx1RJsx1k5zuWILLnG/vsH+Fv5D4NTVcp1Gzo8AAAAAElFTkSuQmCC&labelColor=white)](https://huggingface.co/spaces/opendatalab/MinerU)
\ No newline at end of file
--- a/docs/zh/usage/api.md
+++ b/docs/zh/usage/api.md
+# API 调用 或 可视化调用
+1. 使用python api直接调用：[Python 调用示例](demo/demo.py)
+2. 使用fast api方式调用：
+    ```bash
+    mineru-api --host 127.0.0.1 --port 8000
+    ```
+    在浏览器中访问 http://127.0.0.1:8000/docs 查看API文档。
+3. 使用gradio webui 或 gradio api调用
+    ```bash
+    # 使用 pipeline/vlm-transformers/vlm-sglang-client 后端
+    mineru-gradio --server-name 127.0.0.1 --server-port 7860
+    # 或使用 vlm-sglang-engine/pipeline 后端
+    mineru-gradio --server-name 127.0.0.1 --server-port 7860 --enable-sglang-engine true
+    ```
+    在浏览器中访问 http://127.0.0.1:7860 使用 Gradio WebUI 或访问 http://127.0.0.1:7860/?view=api 使用 Gradio API。
+> [!TIP]
+> 以下是一些使用sglang加速模式的建议和注意事项：
+> - sglang加速模式目前支持在最低8G显存的Turing架构显卡上运行，但在显存<24G的显卡上可能会遇到显存不足的问题, 可以通过使用以下参数来优化显存使用：
+>   - 如果您使用单张显卡遇到显存不足的情况时，可能需要调低KV缓存大小，`--mem-fraction-static 0.5`，如仍出现显存不足问题，可尝试进一步降低到`0.4`或更低。
+>   - 如您有两张以上显卡，可尝试通过张量并行（TP）模式简单扩充可用显存：`--tp-size 2`
+> - 如果您已经可以正常使用sglang对vlm模型进行加速推理，但仍然希望进一步提升推理速度，可以尝试以下参数：
+>   - 如果您有超过多张显卡，可以使用sglang的多卡并行模式来增加吞吐量：`--dp-size 2`
+>   - 同时您可以启用`torch.compile`来将推理速度加速约15%：`--enable-torch-compile`
+> - 如果您想了解更多有关`sglang`的参数使用方法，请参考 [sglang官方文档](https://docs.sglang.ai/backend/server_arguments.html#common-launch-commands)
+> - 所有sglang官方支持的参数都可用通过命令行参数传递给 MinerU，包括以下命令:`mineru`、`mineru-sglang-server`、`mineru-gradio`、`mineru-api`
+> [!TIP]
+> - 任何情况下，您都可以通过在命令行的开头添加`CUDA_VISIBLE_DEVICES` 环境变量来指定可见的 GPU 设备。例如：
+>   ```bash
+>   CUDA_VISIBLE_DEVICES=1 mineru -p <input_path> -o <output_path>
+>   ```
+> - 这种指定方式对所有的命令行调用都有效，包括 `mineru`、`mineru-sglang-server`、`mineru-gradio` 和 `mineru-api`，且对`pipeline`、`vlm`后端均适用。
+> - 以下是一些常见的 `CUDA_VISIBLE_DEVICES` 设置示例：
+>   ```bash
+>   CUDA_VISIBLE_DEVICES=1 Only device 1 will be seen
+>   CUDA_VISIBLE_DEVICES=0,1 Devices 0 and 1 will be visible
+>   CUDA_VISIBLE_DEVICES=“0,1” Same as above, quotation marks are optional
+>   CUDA_VISIBLE_DEVICES=0,2,3 Devices 0, 2, 3 will be visible; device 1 is masked
+>   CUDA_VISIBLE_DEVICES="" No GPU will be visible
+>   ```
+> - 以下是一些可能的使用场景：
+>   - 如果您有多张显卡，需要指定卡0和卡1，并使用多卡并行来启动'sglang-server'，可以使用以下命令：
+>   ```bash
+>   CUDA_VISIBLE_DEVICES=0,1 mineru-sglang-server --port 30000 --dp-size 2
+>   ```
+>   - 如果您有多张显卡，需要在卡0和卡1上启动两个`fastapi`服务，并分别监听不同的端口，可以使用以下命令：
+>   ```bash
+>   # 在终端1中
+>   CUDA_VISIBLE_DEVICES=0 mineru-api --host 127.0.0.1 --port 8000
+>   # 在终端2中
+>   CUDA_VISIBLE_DEVICES=1 mineru-api --host 127.0.0.1 --port 8001
+>   ```
+---
--- a/docs/zh/usage/config.md
+++ b/docs/zh/usage/config.md
+# 基于配置文件扩展 MinerU 功能
+- MinerU 现已实现开箱即用，但也支持通过配置文件扩展功能。您可以在用户目录下创建 `mineru.json` 文件，添加自定义配置。
+- `mineru.json` 文件会在您使用内置模型下载命令 `mineru-models-download` 时自动生成，也可以通过将[配置模板文件](./mineru.template.json)复制到用户目录下并重命名为 `mineru.json` 来创建。
+- 以下是一些可用的配置选项：
+  - `latex-delimiter-config`：用于配置 LaTeX 公式的分隔符，默认为`$`符号，可根据需要修改为其他符号或字符串。
+  - `llm-aided-config`：用于配置 LLM 辅助标题分级的相关参数，兼容所有支持`openai协议`的 LLM 模型，默认使用`阿里云百炼`的`qwen2.5-32b-instruct`模型，您需要自行配置 API 密钥并将`enable`设置为`true`来启用此功能。
+  - `models-dir`：用于指定本地模型存储目录，请为`pipeline`和`vlm`后端分别指定模型目录，指定目录后您可通过配置环境变量`export MINERU_MODEL_SOURCE=local`来使用本地模型。
+---
\ No newline at end of file
--- a/docs/zh/usage/index.md
+++ b/docs/zh/usage/index.md
+# 使用 MinerU
+## 命令行使用方式
+### 基础用法
+最简单的命令行调用方式如下：
+```bash
+mineru -p <input_path> -o <output_path>
+```
+- `<input_path>`：本地 PDF/图片 文件或目录（支持 pdf/png/jpg/jpeg/webp/gif）
+- `<output_path>`：输出目录
+### 查看帮助信息
+获取所有可用参数说明：
+```bash
+mineru --help
+```
+### 参数详解
+```text
+Usage: mineru [OPTIONS]
+Options:
+  -v, --version                   显示版本并退出
+  -p, --path PATH                 输入文件路径或目录（必填）
+  -o, --output PATH               输出目录（必填）
+  -m, --method [auto|txt|ocr]     解析方法：auto（默认）、txt、ocr（仅用于 pipeline 后端）
+  -b, --backend [pipeline|vlm-transformers|vlm-sglang-engine|vlm-sglang-client]
+                                  解析后端（默认为 pipeline）
+  -l, --lang [ch|ch_server|ch_lite|en|korean|japan|chinese_cht|ta|te|ka|latin|arabic|east_slavic|cyrillic|devanagari]
+                                  指定文档语言（可提升 OCR 准确率，仅用于 pipeline 后端）
+  -u, --url TEXT                  当使用 sglang-client 时，需指定服务地址
+  -s, --start INTEGER             开始解析的页码（从 0 开始）
+  -e, --end INTEGER               结束解析的页码（从 0 开始）
+  -f, --formula BOOLEAN           是否启用公式解析（默认开启）
+  -t, --table BOOLEAN             是否启用表格解析（默认开启）
+  -d, --device TEXT               推理设备（如 cpu/cuda/cuda:0/npu/mps，仅 pipeline 后端）
+  --vram INTEGER                  单进程最大 GPU 显存占用(GB)（仅 pipeline 后端）
+  --source [huggingface|modelscope|local]
+                                  模型来源，默认 huggingface
+  --help                          显示帮助信息
+```
+---
+## 模型源配置
+MinerU 默认在首次运行时自动从 HuggingFace 下载所需模型。若无法访问 HuggingFace，可通过以下方式切换模型源：
+### 切换至 ModelScope 源
+```bash
+mineru -p <input_path> -o <output_path> --source modelscope
+```
+或设置环境变量：
+```bash
+export MINERU_MODEL_SOURCE=modelscope
+mineru -p <input_path> -o <output_path>
+```
+### 使用本地模型
+#### 1. 下载模型到本地
+```bash
+mineru-models-download --help
+```
+或使用交互式命令行工具选择模型下载：
+```bash
+mineru-models-download
+```
+下载完成后，模型路径会在当前终端窗口输出，并自动写入用户目录下的 `mineru.json`。
+#### 2. 使用本地模型进行解析
+```bash
+mineru -p <input_path> -o <output_path> --source local
+```
+或通过环境变量启用：
+```bash
+export MINERU_MODEL_SOURCE=local
+mineru -p <input_path> -o <output_path>
+```
+---
+## 使用 sglang 加速 VLM 模型推理
+### 通过 sglang-engine 模式
+```bash
+mineru -p <input_path> -o <output_path> -b vlm-sglang-engine
+```
+### 通过 sglang-server/client 模式
+1. 启动 Server：
+```bash
+mineru-sglang-server --port 30000
+```
+2. 在另一个终端中使用 Client 调用：
+```bash
+mineru -p <input_path> -o <output_path> -b vlm-sglang-client -u http://127.0.0.1:30000
+```
+> [!TIP]
+> 更多关于输出文件的信息，请参考 [输出文件说明](docs/output_file_zh_cn.md)
+---
--- a/mkdocs.yml
+++ b/mkdocs.yml
+site_name: MinerU
+site_url: https://sidney233.github.io/MinerU/
+theme:
+  name: material
+  palette:
+    # Palette toggle for automatic mode
+    - media: "(prefers-color-scheme)"
+      scheme: default
+      primary: black
+      toggle:
+        icon: material/brightness-auto
+        name: Switch to light mode
+    # Palette toggle for light mode
+    - media: "(prefers-color-scheme: light)"
+      scheme: default
+      primary: black
+      toggle:
+        icon: material/brightness-7
+        name: Switch to dark mode
+    # Palette toggle for dark mode
+    - media: "(prefers-color-scheme: dark)"
+      scheme: slate
+      primary: black
+      toggle:
+        icon: material/brightness-4
+        name: Switch to system preference
+  features:
+    - content.tabs.link
+    - content.code.annotate
+    - content.code.copy
+    - navigation.instant
+    - navigation.instant.progress
+    - navigation.tabs
+    - navigation.tabs.sticky
+    - navigation.sections
+    - navigation.path
+    - navigation.indexes
+nav:
+  - Home:
+    - "MinerU": index.md
+    - Quick Start:
+      - quick_start/index.md
+      - Online Demo: quick_start/online_demo.md
+      - Local Deployment: quick_start/local_deployment.md
+    - Usage:
+      - usage/index.md
+      - API Calls or Visual Invocation: usage/api.md
+      - Extending MinerU Functionality Through Configuration Files: usage/config.md
+  - FAQ: FAQ.md
+  - Output File Format: output_file.md
+plugins:
+  - i18n:
+      docs_structure: folder
+      languages:
+        - locale: en
+          default: true
+          name: English
+          build: true
+        - locale: zh
+          name: 中文
+          build: true
+          nav_translations:
+            Home: 主页
+            Quick Start: 快速开始
+            Online Demo: 在线体验
+            Local Deployment: 本地部署
+            Usage: 使用方法
+            API Calls or Visual Invocation: API 调用 或 可视化调用
+            Extending MinerU Functionality Through Configuration Files: 基于配置文件扩展 MinerU 功能
+            FAQ: 常见问题
+            Output File Format: 输出文件格式
+  - mkdocs-video
+markdown_extensions:
+  - gfm_admonition
+  - pymdownx.highlight:
+      use_pygments: true
+  - pymdownx.superfences
\ No newline at end of file