Merge pull request #6 from opendatalab/dev

Dev

.github/ISSUE_TEMPLATE/bug_report.yml

@@ -80,6 +80,7 @@ body:
         -
         - "0.6.x"
         - "0.7.x"
+        - "0.8.x"
     validations:
       required: true
 

.github/workflows/cli.yml

@@ -10,7 +10,6 @@ on:
     paths-ignore:
       - "cmds/**"
       - "**.md"
-      - "**.yml"
   pull_request:
     branches:
       - "master"
@@ -18,12 +17,11 @@ on:
     paths-ignore:
       - "cmds/**"
       - "**.md"
-      - "**.yml"
   workflow_dispatch:
 jobs:
   cli-test:
     runs-on: pdf
-    timeout-minutes: 120
+    timeout-minutes: 240
     strategy:
       fail-fast: true
 
@@ -33,16 +31,16 @@ jobs:
       with:
         fetch-depth: 2
 
-    - name: install
+    - name: install&test
       run: |
-        echo $GITHUB_WORKSPACE && sh tests/retry_env.sh
-    - name: unit test
-      run: |        
-        cd $GITHUB_WORKSPACE && export PYTHONPATH=. && coverage run -m  pytest  tests/test_unit.py --cov=magic_pdf/ --cov-report term-missing --cov-report html
+        source activate mineru
+        conda env list
+        pip show coverage
+        # cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
+        cd $GITHUB_WORKSPACE && python tests/clean_coverage.py      
+        cd $GITHUB_WORKSPACE && coverage run -m pytest tests/unittest/ --cov=magic_pdf/  --cov-report html --cov-report term-missing
         cd $GITHUB_WORKSPACE && python tests/get_coverage.py
-    - name: cli test
-      run: |
-        cd $GITHUB_WORKSPACE &&  pytest -s -v tests/test_cli/test_cli_sdk.py
+        cd $GITHUB_WORKSPACE && pytest -m P0 -s -v tests/test_cli/test_cli_sdk.py
 
   notify_to_feishu:
     if: ${{ always() && !cancelled() && contains(needs.*.result, 'failure') && (github.ref_name == 'master') }}

.github/workflows/daily.yml

+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: mineru
+on:
+  schedule:
+    - cron: '0 22 * * *'  # 每天晚上 10 点执行
+jobs:
+  cli-test:
+    runs-on: pdf
+    timeout-minutes: 240
+    strategy:
+      fail-fast: true
+
+    steps:
+    - name: PDF cli
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: install&test
+      run: |
+        source activate mineru
+        conda env list
+        pip show coverage
+        # cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
+        cd $GITHUB_WORKSPACE && python tests/clean_coverage.py      
+        cd $GITHUB_WORKSPACE && coverage run -m pytest tests/unittest/ --cov=magic_pdf/  --cov-report html --cov-report term-missing
+        cd $GITHUB_WORKSPACE && python tests/get_coverage.py
+        cd $GITHUB_WORKSPACE && pytest -s -v tests/test_cli/test_cli_sdk.py
+
+  notify_to_feishu:
+    if: ${{ always() && !cancelled() && contains(needs.*.result, 'failure') && (github.ref_name == 'master') }}
+    needs: cli-test
+    runs-on: pdf
+    steps:
+    - name: get_actor
+      run: |
+          metion_list="dt-yy"
+          echo $GITHUB_ACTOR
+          if [[ $GITHUB_ACTOR == "drunkpig" ]]; then
+            metion_list="xuchao"
+          elif [[ $GITHUB_ACTOR == "myhloli" ]]; then
+            metion_list="zhaoxiaomeng"
+          elif [[ $GITHUB_ACTOR == "icecraft" ]]; then
+            metion_list="xurui1"
+          fi
+          echo $metion_list
+          echo "METIONS=$metion_list" >> "$GITHUB_ENV"
+          echo ${{ env.METIONS }}
+
+    - name: notify
+      run: |
+        echo ${{ secrets.USER_ID }}
+        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'${{ secrets.USER_ID }}'"}]]}}}}'  ${{ secrets.WEBHOOK_URL }}

.github/workflows/huigui.yml

+# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
+# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
+
+name: mineru
+on:
+  push:
+    branches:
+      - "master"
+      - "dev"
+    paths-ignore:
+      - "cmds/**"
+      - "**.md"
+  workflow_dispatch:
+jobs:
+  cli-test:
+    runs-on: pdf
+    timeout-minutes: 240
+    strategy:
+      fail-fast: true
+
+    steps:
+    - name: PDF cli
+      uses: actions/checkout@v3
+      with:
+        fetch-depth: 2
+
+    - name: install&test
+      run: |
+        source activate mineru
+        conda env list
+        pip show coverage
+        # cd $GITHUB_WORKSPACE && sh tests/retry_env.sh
+        cd $GITHUB_WORKSPACE && python tests/clean_coverage.py      
+        cd $GITHUB_WORKSPACE && coverage run -m pytest tests/unittest/ --cov=magic_pdf/  --cov-report html --cov-report term-missing
+        cd $GITHUB_WORKSPACE && python tests/get_coverage.py
+        cd $GITHUB_WORKSPACE && pytest -s -v tests/test_cli/test_cli_sdk.py
+
+  notify_to_feishu:
+    if: ${{ always() && !cancelled() && contains(needs.*.result, 'failure') && (github.ref_name == 'master') }}
+    needs: cli-test
+    runs-on: pdf
+    steps:
+    - name: get_actor
+      run: |
+          metion_list="dt-yy"
+          echo $GITHUB_ACTOR
+          if [[ $GITHUB_ACTOR == "drunkpig" ]]; then
+            metion_list="xuchao"
+          elif [[ $GITHUB_ACTOR == "myhloli" ]]; then
+            metion_list="zhaoxiaomeng"
+          elif [[ $GITHUB_ACTOR == "icecraft" ]]; then
+            metion_list="xurui1"
+          fi
+          echo $metion_list
+          echo "METIONS=$metion_list" >> "$GITHUB_ENV"
+          echo ${{ env.METIONS }}
+
+    - name: notify
+      run: |
+        echo ${{ secrets.USER_ID }}
+        curl -X POST -H "Content-Type: application/json" -d '{"msg_type":"post","content":{"post":{"zh_cn":{"title":"'${{ github.repository }}' GitHubAction Failed","content":[[{"tag":"text","text":""},{"tag":"a","text":"Please click here for details ","href":"https://github.com/'${{ github.repository }}'/actions/runs/'${GITHUB_RUN_ID}'"},{"tag":"at","user_id":"'${{ secrets.USER_ID }}'"}]]}}}}'  ${{ secrets.WEBHOOK_URL }}

.github/workflows/update_base.yml

-# This workflow will install Python dependencies, run tests and lint with a variety of Python versions
-# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python
-
-name: update-base
-on:
-  push:
-    tags:
-      - '*released'
-  workflow_dispatch:
-jobs:
-  pdf-test:
-    runs-on: pdf
-    timeout-minutes: 40
-
-
-    steps:
-    - name: update-base
-      uses: actions/checkout@v3
-    - name: start-update
-      run: |
-        echo "start test"
-  

.gitignore

 *.tar
 *.tar.gz
+*.zip
 venv*/
 envs/
 slurm_logs/
@@ -31,9 +32,14 @@ tmp
 .vscode
 .vscode/
 ocr_demo
-
+.coveragerc
 /app/common/__init__.py
 /magic_pdf/config/__init__.py
 source.dev.env
 
 tmp
+
+projects/web/node_modules
+projects/web/dist
+
+projects/web_demo/web_demo/static/

.readthedocs.yaml

+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+formats:
+  - epub
+
+python:
+  install:
+    - requirements: docs/zh_cn/requirements.txt
+
+sphinx:
+  configuration: docs/zh_cn/conf.py

LICENSE.md

@@ -659,3 +659,4 @@ specific requirements.
 if any, to sign a "copyright disclaimer" for the program, if necessary.
 For more information on this, and how to apply and follow the GNU AGPL, see
 <https://www.gnu.org/licenses/>.
+

README.md.bak

-<div id="top">
-
-<p align="center">
-  <img src="docs/images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
-</p>
-
-</div>
-<div align="center">
-
-[![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://badge.fury.io/py/magic-pdf.svg)](https://badge.fury.io/py/magic-pdf)
-[![Downloads](https://static.pepy.tech/badge/magic-pdf)](https://pepy.tech/project/magic-pdf)
-[![Downloads](https://static.pepy.tech/badge/magic-pdf/month)](https://pepy.tech/project/magic-pdf)
-
-<a href="https://trendshift.io/repositories/11174" target="_blank"><img src="https://trendshift.io/api/badge/repositories/11174" alt="opendatalab%2FMinerU | Trendshift" style="width: 200px; height: 55px;"/></a>
-
-
-
-
-[English](README.md) | [简体中文](README_zh-CN.md) | [日本語](README_ja-JP.md)
-
-</div>
-
-<div align="center">
-<p align="center">
-<a href="https://github.com/opendatalab/MinerU">MinerU: An end-to-end PDF parsing tool based on PDF-Extract-Kit, supporting conversion from PDF to Markdown.</a>🚀🚀🚀<br>
-<a href="https://github.com/opendatalab/PDF-Extract-Kit">PDF-Extract-Kit: A Comprehensive Toolkit for High-Quality PDF Content Extraction</a>🔥🔥🔥
-</p>
-
-<p align="center">
-    👋 join us on <a href="https://discord.gg/gPxmVeGC" target="_blank">Discord</a> and <a href="https://cdn.vansin.top/internlm/mineru.jpg" target="_blank">WeChat</a>
-</p>
-</div>
-
-# MinerU 
-
-
-## Introduction
-
-MinerU is a one-stop, open-source, high-quality data extraction tool, includes the following primary features:
-
-- [Magic-PDF](#Magic-PDF)  PDF Document Extraction  
-- [Magic-Doc](#Magic-Doc)  Webpage & E-book Extraction
-
-
-# Magic-PDF
-
-
-## Introduction
-
-Magic-PDF is a tool designed to convert PDF documents into Markdown format, capable of processing files stored locally or on object storage supporting S3 protocol.
-
-Key features include:
-
-- Support for multiple front-end model inputs
-- Removal of headers, footers, footnotes, and page numbers
-- Human-readable layout formatting
-- Retains the original document's structure and formatting, including headings, paragraphs, lists, and more
-- Extraction and display of images and tables within markdown
-- Conversion of equations into LaTeX format
-- Automatic detection and conversion of garbled PDFs
-- Compatibility with CPU and GPU environments
-- Available for Windows, Linux and macOS platforms
-
-
-https://github.com/user-attachments/assets/4bea02c9-6d54-4cd6-97ed-dff14340982c
-
-
-
-## Project Panorama
-
-![Project Panorama](docs/images/project_panorama_en.png)
-
-
-## Flowchart
-
-![Flowchart](docs/images/flowchart_en.png)
-
-### Dependency repositorys
-
-- [PDF-Extract-Kit : A Comprehensive Toolkit for High-Quality PDF Content Extraction](https://github.com/opendatalab/PDF-Extract-Kit) 🚀🚀🚀
-
-## Getting Started
-
-### Requirements
-
-- Python >= 3.9
-
-Using a virtual environment is recommended to avoid potential dependency conflicts; both venv and conda are suitable. 
-For example:
-```bash
-conda create -n MinerU python=3.10
-conda activate MinerU
-```
-
-### Installation and Configuration
-
-#### 1. Install Magic-PDF
-
-**1.Install dependencies**
-
-The full-feature package depends on detectron2, which requires a compilation installation.   
-If you need to compile it yourself, please refer to https://github.com/facebookresearch/detectron2/issues/5114  
-Alternatively, you can directly use our precompiled whl package (limited to Python 3.10):
-
-```bash
-pip install detectron2 --extra-index-url https://wheels.myhloli.com
-```
-
-**2.Install the full-feature package with pip**
->Note: The pip-installed package supports CPU-only and is ideal for quick tests.
->
->For CUDA/MPS acceleration in production, see [Acceleration Using CUDA or MPS](#4-Acceleration-Using-CUDA-or-MPS).
-
-```bash
-pip install magic-pdf[full]==0.6.2b1
-```
-> ❗️❗️❗️
-> We have pre-released the 0.6.2 beta version, addressing numerous issues mentioned in our logs. However, this build has not undergone full QA testing and does not represent the final release quality. Should you encounter any problems, please promptly report them to us via issues or revert to using version 0.6.1.
-> ```bash
-> pip install magic-pdf[full-cpu]==0.6.1
-> ```
-
-
-
-#### 2. Downloading model weights files
-
-For detailed references, please see below [how_to_download_models](docs/how_to_download_models_en.md)
-
-After downloading the model weights, move the 'models' directory to a directory on a larger disk space, preferably an SSD.
-
-
-#### 3. Copy the Configuration File and Make Configurations
-You can get the [magic-pdf.template.json](magic-pdf.template.json) file in the repository root directory.
-```bash
-cp magic-pdf.template.json ~/magic-pdf.json
-```
-In magic-pdf.json, configure "models-dir" to point to the directory where the model weights files are located.
-
-```json
-{
-  "models-dir": "/tmp/models"
-}
-```
-
-
-#### 4. Acceleration Using CUDA or MPS
-If you have an available Nvidia GPU or are using a Mac with Apple Silicon, you can leverage acceleration with CUDA or MPS respectively.
-##### CUDA
-
-You need to install the corresponding PyTorch version according to your CUDA version.  
-This example installs the CUDA 11.8 version.More information https://pytorch.org/get-started/locally/
-```bash
-pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
-```
-> ❗ ️Make sure to specify version
-> ```bash
-> torch==2.3.1 torchvision==0.18.1
-> ```
->  in the command, as these are the highest versions we support. Failing to specify the versions may result in automatically installing higher versions which can cause the program to fail.
-
-Also, you need to modify the value of "device-mode" in the configuration file magic-pdf.json.  
-```json
-{
-  "device-mode":"cuda"
-}
-```
-
-##### MPS
-
-For macOS users with M-series chip devices, you can use MPS for inference acceleration.  
-You also need to modify the value of "device-mode" in the configuration file magic-pdf.json.  
-```json
-{
-  "device-mode":"mps"
-}
-```
-
-
-### Usage
-
-#### 1.Usage via Command Line
-
-###### simple
-
-```bash
-magic-pdf pdf-command --pdf "pdf_path" --inside_model true
-```
-After the program has finished, you can find the generated markdown files under the directory "/tmp/magic-pdf".  
-You can find the corresponding xxx_model.json file in the markdown directory.   
-If you intend to do secondary development on the post-processing pipeline, you can use the command:  
-```bash
-magic-pdf pdf-command --pdf "pdf_path" --model "model_json_path"
-```
-In this way, you won't need to re-run the model data, making debugging more convenient.
-
-
-###### more 
-
-```bash
-magic-pdf --help
-```
-
-
-#### 2. Usage via Api
-
-###### Local
-```python
-image_writer = DiskReaderWriter(local_image_dir)
-image_dir = str(os.path.basename(local_image_dir))
-jso_useful_key = {"_pdf_type": "", "model_list": []}
-pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
-pipe.pipe_classify()
-pipe.pipe_parse()
-md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
-```
-
-###### Object Storage
-```python
-s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
-image_dir = "s3://img_bucket/"
-s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
-pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
-jso_useful_key = {"_pdf_type": "", "model_list": []}
-pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
-pipe.pipe_classify()
-pipe.pipe_parse()
-md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
-```
-
-Demo can be referred to [demo.py](demo/demo.py)
-
-
-# Magic-Doc
-
-
-## Introduction
-
-Magic-Doc is a tool designed to convert web pages or multi-format e-books into markdown format.
-
-Key Features Include:
-
-- Web Page Extraction
-  - Cross-modal precise parsing of text, images, tables, and formula information.
-
-- E-Book Document Extraction
-  - Supports various document formats including epub, mobi, with full adaptation for text and images.
-
-- Language Type Identification
-  - Accurate recognition of 176 languages.
-
-https://github.com/opendatalab/MinerU/assets/11393164/a5a650e9-f4c0-463e-acc3-960967f1a1ca
-
-
-
-https://github.com/opendatalab/MinerU/assets/11393164/0f4a6fe9-6cca-4113-9fdc-a537749d764d
-
-
-
-https://github.com/opendatalab/MinerU/assets/11393164/20438a02-ce6c-4af8-9dde-d722a4e825b2
-
-
-
-
-## Project Repository
-
-- [Magic-Doc](https://github.com/InternLM/magic-doc)
-  Outstanding Webpage and E-book Extraction Tool
-
-
-# All Thanks To Our Contributors
-
-<a href="https://github.com/opendatalab/MinerU/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=opendatalab/MinerU" />
-</a>
-
-
-# License Information
-
-[LICENSE.md](LICENSE.md)
-
-The project currently leverages PyMuPDF to deliver advanced functionalities; however, its adherence to the AGPL license may impose limitations on certain use cases. In upcoming iterations, we intend to explore and transition to a more permissively licensed PDF processing library to enhance user-friendliness and flexibility.
-
-
-# Acknowledgments
-
-- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
-- [PyMuPDF](https://github.com/pymupdf/PyMuPDF)
-- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)
-- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)
-
-
-# Citation
-
-```bibtex
-@article{he2024opendatalab,
-  title={Opendatalab: Empowering general artificial intelligence with open datasets},
-  author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},
-  journal={arXiv preprint arXiv:2407.13773},
-  year={2024}
-}
-
-@misc{2024mineru,
-    title={MinerU: A One-stop, Open-source, High-quality Data Extraction Tool},
-    author={MinerU Contributors},
-    howpublished = {\url{https://github.com/opendatalab/MinerU}},
-    year={2024}
-}
-```
-
-
-# Star History
-
-<a>
- <picture>
-   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date&theme=dark" />
-   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
-   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
- </picture>
-</a>
-
-# Links
-- [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU)
-- [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)
-- [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit)

README_ja-JP.md

@@ -290,14 +290,23 @@ https://github.com/opendatalab/MinerU/assets/11393164/20438a02-ce6c-4af8-9dde-d7
 # 引用
 
 ```bibtex
-@misc{2024mineru,
-    title={MinerU: A One-stop, Open-source, High-quality Data Extraction Tool},
-    author={MinerU Contributors},
-    howpublished = {\url{https://github.com/opendatalab/MinerU}},
-    year={2024}
+@misc{wang2024mineruopensourcesolutionprecise,
+      title={MinerU: An Open-Source Solution for Precise Document Content Extraction}, 
+      author={Bin Wang and Chao Xu and Xiaomeng Zhao and Linke Ouyang and Fan Wu and Zhiyuan Zhao and Rui Xu and Kaiwen Liu and Yuan Qu and Fukai Shang and Bo Zhang and Liqun Wei and Zhihao Sui and Wei Li and Botian Shi and Yu Qiao and Dahua Lin and Conghui He},
+      year={2024},
+      eprint={2409.18839},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2409.18839}, 
 }
-```
 
+@article{he2024opendatalab,
+  title={Opendatalab: Empowering general artificial intelligence with open datasets},
+  author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},
+  journal={arXiv preprint arXiv:2407.13773},
+  year={2024}
+}
+```
 
 # スター履歴
 

README_zh-CN.md.bak

-<div align="center" xmlns="http://www.w3.org/1999/html">
-<!-- logo -->
-<p align="center">
-  <img src="docs/images/MinerU-logo.png" width="300px" style="vertical-align:middle;">
-</p>
-
-<!-- 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://badge.fury.io/py/magic-pdf.svg)](https://badge.fury.io/py/magic-pdf)
-[![Downloads](https://static.pepy.tech/badge/magic-pdf)](https://pepy.tech/project/magic-pdf)
-[![Downloads](https://static.pepy.tech/badge/magic-pdf/month)](https://pepy.tech/project/magic-pdf)
-<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>
-
-<!-- language -->
-
-[English](README.md) | [简体中文](README_zh-CN.md)
-
-<!-- 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>🔥🔥🔥
-</p>
-
-<!-- join us -->
-
-<p align="center">
-    👋 join us on <a href="https://discord.gg/Tdedn9GTXq" target="_blank">Discord</a> and <a href="https://cdn.vansin.top/internlm/mineru.jpg" target="_blank">WeChat</a>
-</p>
-
-</div>
-
-# 更新记录
-
-- 2024/08/09 0.7.0b1发布，简化安装步骤提升易用性，加入表格识别功能
-- 2024/08/01 0.6.2b1发布，优化了依赖冲突问题和安装文档
-- 2024/07/05 首次开源
-
-<!-- TABLE OF CONTENT -->
-
-<details open="open">
-  <summary><h2 style="display: inline-block">文档目录</h2></summary>
-  <ol>
-    <li>
-      <a href="#mineru">MinerU</a>
-      <ul>
-        <li><a href="#项目简介">项目简介</a></li>
-        <li><a href="#主要功能">主要功能</a></li>
-        <li><a href="#快速开始">快速开始</a>
-            <ul>
-            <li><a href="#在线体验">在线体验</a></li>
-            <li><a href="#使用CPU快速体验">使用CPU快速体验</a></li>
-            <li><a href="#使用GPU">使用GPU</a></li>
-            </ul>
-        </li>
-        <li><a href="#使用">使用方式</a>
-            <ul>
-            <li><a href="#命令行">命令行</a></li>
-            <li><a href="#api">API</a></li>
-            <li><a href="#二次开发">二次开发</a></li>
-            </ul>
-        </li>
-      </ul>
-    </li>
-    <li><a href="#todo">TODO</a></li>
-    <li><a href="#known-issues">Known Issues</a></li>
-    <li><a href="#faq">FAQ</a></li>
-    <li><a href="#all-thanks-to-our-contributors">Contributors</a></li>
-    <li><a href="#license-information">License Information</a></li>
-    <li><a href="#acknowledgments">Acknowledgements</a></li>
-    <li><a href="#citation">Citation</a></li>
-    <li><a href="#star-history">Star History</a></li>
-    <li><a href="#magic-doc">magic-doc快速提取PPT/DOC/PDF</a></li>
-    <li><a href="#magic-html">magic-html提取混合网页内容</a></li>
-    <li><a href="#links">Links</a></li>
-  </ol>
-</details>
-
-# MinerU
-
-## 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**。
-
-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，保持语义连贯
-- 对多栏输出符合人类阅读顺序的文本
-- 保留原文档的结构，包括标题、段落、列表等
-- 提取图像、图片标题、表格、表格标题
-- 自动识别文档中的公式并将公式转换成latex
-- 自动识别文档中的表格并将表格转换成latex
-- 乱码PDF自动检测并启用OCR
-- 支持CPU和GPU环境
-- 支持windows/linux/mac平台
-
-## 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3种不同方式可以体验MinerU的效果：
-
-- [在线体验(无需任何安装)](#在线体验)
-- [使用CPU快速体验（Windows，Linux，Mac）](#使用cpu快速体验)
-- [Linux/Windows + CUDA](#使用gpu)
-
-**⚠️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 colspan="3" rowspan="2">操作系统</td>
-    </tr>
-    <tr>
-        <td>Ubuntu 22.04 LTS</td>
-        <td>Windows 10 / 11</td>
-        <td>macOS 11+</td>
-    </tr>
-    <tr>
-        <td colspan="3">CPU</td>
-        <td>x86_64</td>
-        <td>x86_64</td>
-        <td>x86_64 / arm64</td>
-    </tr>
-    <tr>
-        <td colspan="3">内存</td>
-        <td colspan="3">大于等于16GB，推荐32G以上</td>
-    </tr>
-    <tr>
-        <td colspan="3">python版本</td>
-        <td colspan="3">3.10</td>
-    </tr>
-    <tr>
-        <td colspan="3">Nvidia Driver 版本</td>
-        <td>latest(专有驱动)</td>
-        <td>latest</td>
-        <td>None</td>
-    </tr>
-    <tr>
-        <td colspan="3">CUDA环境</td>
-        <td>自动安装[12.1(pytorch)+11.8(paddle)]</td>
-        <td>11.8(手动安装)+cuDNN v8.7.0(手动安装)</td>
-        <td>None</td>
-    </tr>
-    <tr>
-        <td rowspan="2">GPU硬件支持列表</td>
-        <td colspan="2">最低要求 8G+显存</td>
-        <td colspan="2">3060ti/3070/3080/3080ti/4060/4070/4070ti<br>
-        8G显存仅可开启lavout和公式识别加速</td>
-        <td rowspan="2">None</td>
-    </tr>
-    <tr>
-        <td colspan="2">推荐配置 16G+显存</td>
-        <td colspan="2">3090/3090ti/4070tisuper/4080/4090<br>
-        16G及以上可以同时开启layout，公式识别和ocr加速</td>
-    </tr>
-</table>
-
-### 在线体验
-
-[在线体验点击这里](https://opendatalab.com/OpenSourceTools/Extractor/PDF)
-
-### 使用CPU快速体验
-
-#### 1. 安装magic-pdf
-
-最新版本国内镜像源同步可能会有延迟，请耐心等待
-
-```bash
-conda create -n MinerU python=3.10
-conda activate MinerU
-pip install magic-pdf[full]==0.7.0b1 --extra-index-url https://wheels.myhloli.com -i https://pypi.tuna.tsinghua.edu.cn/simple
-```
-
-#### 2. 下载模型权重文件
-
-详细参考 [如何下载模型文件](docs/how_to_download_models_zh_cn.md)
-
-> ❗️模型下载后请务必检查模型文件是否下载完整
->
-> 请检查目录下的模型文件大小与网页上描述是否一致，如果可以的话，最好通过sha256校验模型是否下载完整
-
-#### 3. 拷贝配置文件并进行配置
-
-在仓库根目录可以获得 [magic-pdf.template.json](magic-pdf.template.json) 配置模版文件
-
-> ❗️务必执行以下命令将配置文件拷贝到【用户目录】下，否则程序将无法运行
->
-> windows的用户目录为 "C:\\Users\\用户名", linux用户目录为 "/home/用户名", macOS用户目录为 "/Users/用户名"
-
-```bash
-cp magic-pdf.template.json ~/magic-pdf.json
-```
-
-在用户目录中找到magic-pdf.json文件并配置"models-dir"为[2. 下载模型权重文件](#2-下载模型权重文件)中下载的模型权重文件所在目录
-
-> ❗️务必正确配置模型权重文件所在目录的【绝对路径】，否则会因为找不到模型文件而导致程序无法运行
->
-> windows系统中此路径应包含盘符，且需把路径中所有的`"\"`替换为`"/"`,否则会因为转义原因导致json文件语法错误。
-> 
-
-> 例如：模型放在D盘根目录的models目录，则model-dir的值应为"D:/models"
-
-```json
-{
-  // other config
-  "models-dir": "D:/models",
-  "table-config": {
-        "is_table_recog_enable": false, // 表格识别功能默认是关闭的，如果需要修改此处的值
-        "max_time": 400
-    }
-}
-```
-
-### 使用GPU
-
-如果您的设备支持CUDA，且满足主线环境中的显卡要求，则可以使用GPU加速，请根据自己的系统选择适合的教程：
-
-- [Ubuntu22.04LTS + GPU](docs/README_Ubuntu_CUDA_Acceleration_zh_CN.md)
-- [Windows10/11 + GPU](docs/README_Windows_CUDA_Acceleration_zh_CN.md)
-- 使用Docker快速部署
-    > Docker 需设备gpu显存大于等于16GB，默认开启所有加速功能
-  ```bash
-  wget https://github.com/opendatalab/MinerU/raw/master/Dockerfile
-  docker build -t mineru:0.7.0b1 .
-  docker run --rm -it --gpus=all mineru:0.7.0b1 /bin/bash
-  magic-pdf --help
-  ```
-    
-
-## 使用
-
-### 命令行
-
-```bash
-magic-pdf --help
-Usage: magic-pdf [OPTIONS]
-
-Options:
-  -v, --version                display the version and exit
-  -p, --path PATH              local pdf filepath or directory  [required]
-  -o, --output-dir TEXT        output local directory
-  -m, --method [ocr|txt|auto]  the method for parsing pdf.
-                               ocr: using ocr technique to extract information from pdf,
-                               txt: suitable for the text-based pdf only and outperform ocr,
-                               auto: automatically choose the best method for parsing pdf
-                                  from ocr and txt.
-                               without method specified, auto will be used by default.
-  --help                       Show this message and exit.
-
-
-## show version
-magic-pdf -v
-
-## command line example
-magic-pdf -p {some_pdf} -o {some_output_dir} -m auto
-```
-
-其中 `{some_pdf}` 可以是单个pdf文件，也可以是一个包含多个pdf文件的目录。
-运行完命令后输出的结果会保存在`{some_output_dir}`目录下, 输出的文件列表如下
-
-```text
-├── some_pdf.md                          # markdown 文件
-├── images                               # 存放图片目录
-├── some_pdf_layout.pdf                  # layout 绘图
-├── some_pdf_middle.json                 # minerU 中间处理结果
-├── some_pdf_model.json                  # 模型推理结果
-├── some_pdf_origin.pdf                  # 原 pdf 文件
-└── some_pdf_spans.pdf                   # 最小粒度的bbox位置信息绘图
-```
-
-更多有关输出文件的信息，请参考[输出文件说明](docs/output_file_zh_cn.md)
-
-### API
-
-处理本地磁盘上的文件
-
-```python
-image_writer = DiskReaderWriter(local_image_dir)
-image_dir = str(os.path.basename(local_image_dir))
-jso_useful_key = {"_pdf_type": "", "model_list": []}
-pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer)
-pipe.pipe_classify()
-pipe.pipe_analyze()
-pipe.pipe_parse()
-md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
-```
-
-处理对象存储上的文件
-
-```python
-s3pdf_cli = S3ReaderWriter(pdf_ak, pdf_sk, pdf_endpoint)
-image_dir = "s3://img_bucket/"
-s3image_cli = S3ReaderWriter(img_ak, img_sk, img_endpoint, parent_path=image_dir)
-pdf_bytes = s3pdf_cli.read(s3_pdf_path, mode=s3pdf_cli.MODE_BIN)
-jso_useful_key = {"_pdf_type": "", "model_list": []}
-pipe = UNIPipe(pdf_bytes, jso_useful_key, s3image_cli)
-pipe.pipe_classify()
-pipe.pipe_analyze()
-pipe.pipe_parse()
-md_content = pipe.pipe_mk_markdown(image_dir, drop_mode="none")
-```
-
-详细实现可参考
-
-- [demo.py 最简单的处理方式](demo/demo.py)
-- [magic_pdf_parse_main.py 能够更清晰看到处理流程](demo/magic_pdf_parse_main.py)
-
-### 二次开发
-
-TODO
-
-# TODO
-
-- [ ] 基于语义的阅读顺序
-- [ ] 正文中列表识别
-- [ ] 正文中代码块识别
-- [ ] 目录识别
-- [x] 表格识别
-- [ ] [化学式识别](docs/chemical_knowledge_introduction/introduction.pdf)
-- [ ] 几何图形识别
-
-# Known Issues
-
-- 阅读顺序基于规则的分割，在一些情况下会乱序
-- 不支持竖排文字
-- 列表、代码块、目录在layout模型里还没有支持
-- 漫画书、艺术图册、小学教材、习题尚不能很好解析
-- 在一些公式密集的PDF上强制启用OCR效果会更好
-- 如果您要处理包含大量公式的pdf,强烈建议开启OCR功能。使用pymuPDF提取文字的时候会出现文本行互相重叠的情况导致公式插入位置不准确。
-- **表格识别**目前处于测试阶段，识别速度较慢，识别准确度有待提升。以下是我们在Ubuntu 22.04 LTS + Intel(R) Xeon(R) Platinum 8352V CPU @ 2.10GHz + NVIDIA GeForce RTX 4090环境下的一些性能测试结果，可供参考。
-
-| 表格大小     | 解析耗时 |
-| ------------ | -------- |
-| 6\*5 55kb    | 37s      |
-| 16\*12 284kb | 3m18s    |
-| 44\*7 559kb  | 4m12s    |
-
-# FAQ
-
-
-[常见问题](docs/FAQ_zh_cn.md)
-
-<<<<<<< HEAD
-=======
-[FAQ](docs/FAQ_en_us.md)
->>>>>>> 7f0fe20004af7416db886f4b75c116bcc1c986b4
-
-[FAQ](docs/FAQ_en_us.md)
-
-# All Thanks To Our Contributors
-
-<a href="https://github.com/opendatalab/MinerU/graphs/contributors">
-  <img src="https://contrib.rocks/image?repo=opendatalab/MinerU" />
-</a>
-
-# License Information
-
-[LICENSE.md](LICENSE.md)
-
-本项目目前采用PyMuPDF以实现高级功能，但因其遵循AGPL协议，可能对某些使用场景构成限制。未来版本迭代中，我们计划探索并替换为许可条款更为宽松的PDF处理库，以提升用户友好度及灵活性。
-
-# Acknowledgments
-
-- [PDF-Extract-Kit](https://github.com/opendatalab/PDF-Extract-Kit)
-- [StructEqTable](https://github.com/UniModal4Reasoning/StructEqTable-Deploy)
-- [PaddleOCR](https://github.com/PaddlePaddle/PaddleOCR)
-- [PyMuPDF](https://github.com/pymupdf/PyMuPDF)
-- [fast-langdetect](https://github.com/LlmKira/fast-langdetect)
-- [pdfminer.six](https://github.com/pdfminer/pdfminer.six)
-
-# Citation
-
-```bibtex
-@article{he2024opendatalab,
-  title={Opendatalab: Empowering general artificial intelligence with open datasets},
-  author={He, Conghui and Li, Wei and Jin, Zhenjiang and Xu, Chao and Wang, Bin and Lin, Dahua},
-  journal={arXiv preprint arXiv:2407.13773},
-  year={2024}
-}
-
-@misc{2024mineru,
-    title={MinerU: A One-stop, Open-source, High-quality Data Extraction Tool},
-    author={MinerU Contributors},
-    howpublished = {\url{https://github.com/opendatalab/MinerU}},
-    year={2024}
-}
-```
-
-# Star History
-
-<a>
- <picture>
-   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date&theme=dark" />
-   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
-   <img alt="Star History Chart" src="https://api.star-history.com/svg?repos=opendatalab/MinerU&type=Date" />
- </picture>
-</a>
-
-# Magic-doc
-
-[Magic-Doc](https://github.com/InternLM/magic-doc) Fast speed ppt/pptx/doc/docx/pdf extraction tool
-
-# Magic-html
-
-[Magic-HTML](https://github.com/opendatalab/magic-html) Mixed web page extraction tool
-
-# Links
-
-- [LabelU (A Lightweight Multi-modal Data Annotation Tool)](https://github.com/opendatalab/labelU)
-- [LabelLLM (An Open-source LLM Dialogue Annotation Platform)](https://github.com/opendatalab/LabelLLM)
-- [PDF-Extract-Kit (A Comprehensive Toolkit for High-Quality PDF Content Extraction)](https://github.com/opendatalab/PDF-Extract-Kit)

docs/README_Ubuntu_CUDA_Acceleration_en_US.md

-
-# Ubuntu 22.04 LTS
-
-### 1. Check if NVIDIA Drivers Are Installed
-   ```sh
-   nvidia-smi
-   ```
-   If you see information similar to the following, it means that the NVIDIA drivers are already installed, and you can skip Step 2.
-   ```plaintext
-   +---------------------------------------------------------------------------------------+
-   | NVIDIA-SMI 537.34                 Driver Version: 537.34       CUDA Version: 12.2     |
-   |-----------------------------------------+----------------------+----------------------+
-   | GPU  Name                     TCC/WDDM  | Bus-Id        Disp.A | Volatile Uncorr. ECC |
-   | Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |
-   |                                         |                      |               MIG M. |
-   |=========================================+======================+======================|
-   |   0  NVIDIA GeForce RTX 3060 Ti   WDDM  | 00000000:01:00.0  On |                  N/A |
-   |  0%   51C    P8              12W / 200W |   1489MiB /  8192MiB |      5%      Default |
-   |                                         |                      |                  N/A |
-   +-----------------------------------------+----------------------+----------------------+
-   ```
-
-### 2. Install the Driver
-   If no driver is installed, use the following command:
-   ```sh
-   sudo apt-get update
-   sudo apt-get install nvidia-driver-545
-   ```
-   Install the proprietary driver and restart your computer after installation.
-   ```sh
-   reboot
-   ```
-
-### 3. Install Anaconda
-   If Anaconda is already installed, skip this step.
-   ```sh
-   wget https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Linux-x86_64.sh
-   bash Anaconda3-2024.06-1-Linux-x86_64.sh
-   ```
-   In the final step, enter `yes`, close the terminal, and reopen it.
-
-### 4. Create an Environment Using Conda
-   Specify Python version 3.10.
-   ```sh
-   conda create -n MinerU python=3.10
-   conda activate MinerU
-   ```
-
-### 5. Install Applications
-   ```sh
-   pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com
-   ```
-❗ After installation, make sure to check the version of `magic-pdf` using the following command:
-   ```sh
-   magic-pdf --version
-   ```
-   If the version number is less than 0.7.0, please report the issue.
-
-### 6. Download Models
-   Refer to detailed instructions on [how to download model files](how_to_download_models_en.md).  
-   After downloading, move the `models` directory to an SSD with more space.
-   
-❗ After downloading the models, ensure they are complete:
-   - Check that the file sizes match the description on the website.
-   - If possible, verify the integrity using SHA256.
-
-### 7. Configuration Before First Run
-   Obtain the configuration template file `magic-pdf.template.json` from the root directory of the repository.
-   
-❗ Execute the following command to copy the configuration file to your home directory, otherwise the program will not run:
-   ```sh
-   wget https://github.com/opendatalab/MinerU/raw/master/magic-pdf.template.json
-   cp magic-pdf.template.json ~/magic-pdf.json
-   ```
-   Find the `magic-pdf.json` file in your home directory and configure `"models-dir"` to be the directory where the model weights from Step 6 were downloaded.
-   
-❗ Correctly specify the absolute path of the directory containing the model weights; otherwise, the program will fail due to missing model files.
-   ```json
-   {
-     "models-dir": "/tmp/models"
-   }
-   ```
-
-### 8. First Run
-   Download a sample file from the repository and test it.
-   ```sh
-   wget https://github.com/opendatalab/MinerU/raw/master/demo/small_ocr.pdf
-   magic-pdf -p small_ocr.pdf
-   ```
-
-### 9. Test CUDA Acceleration
-
-If your graphics card has at least 8GB of VRAM, follow these steps to test CUDA acceleration:
-
-1. Modify the value of `"device-mode"` in the `magic-pdf.json` configuration file located in your home directory.
-   ```json
-   {
-     "device-mode": "cuda"
-   }
-   ```
-2. Test CUDA acceleration with the following command:
-   ```sh
-   magic-pdf -p small_ocr.pdf
-   ```
-
-### 10. Enable CUDA Acceleration for OCR
-
-❗ The following operations require a graphics card with at least 16GB of VRAM; otherwise, the program may crash or experience reduced performance.
-    
-1. Download `paddlepaddle-gpu`. Installation will automatically enable OCR acceleration.
-   ```sh
-   python -m pip install paddlepaddle-gpu==3.0.0b1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
-   ```
-2. Test OCR acceleration with the following command:
-   ```sh
-   magic-pdf -p small_ocr.pdf
-   ```

docs/README_Windows_CUDA_Acceleration_en_US.md

-# Windows 10/11
-
-### 1. Install CUDA and cuDNN
-Required versions: CUDA 11.8 + cuDNN 8.7.0
-   - CUDA 11.8: https://developer.nvidia.com/cuda-11-8-0-download-archive
-   - cuDNN v8.7.0 (November 28th, 2022), for CUDA 11.x: https://developer.nvidia.com/rdp/cudnn-archive
-   
-### 2. Install Anaconda
-   If Anaconda is already installed, you can skip this step.
-   
-Download link: https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Windows-x86_64.exe
-
-### 3. Create an Environment Using Conda
-   Python version must be 3.10.
-   ```
-   conda create -n MinerU python=3.10
-   conda activate MinerU
-   ```
-
-### 4. Install Applications
-   ```
-   pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com
-   ```
-   >❗️After installation, verify the version of `magic-pdf`:
-   >  ```bash
-   >  magic-pdf --version
-   >  ```
-   > If the version number is less than 0.7.0, please report it in the issues section.
-   
-### 5. Download Models
-   Refer to detailed instructions on [how to download model files](how_to_download_models_en.md).  
-   After downloading, move the `models` directory to an SSD with more space.
-   
-   >❗ After downloading the models, ensure they are complete:
-   >- Check that the file sizes match the description on the website.
-   >- If possible, verify the integrity using SHA256.
-
-### 6. Configuration Before the First Run
-   Obtain the configuration template file `magic-pdf.template.json` from the repository root directory.
-    
-   >❗️Execute the following command to copy the configuration file to your user directory, or the program will not run.
-   >   
-   > In Windows, user directory is "C:\Users\username"
-   
-   ```powershell
-     (New-Object System.Net.WebClient).DownloadFile('https://github.com/opendatalab/MinerU/raw/master/magic-pdf.template.json', 'magic-pdf.template.json')
-     cp magic-pdf.template.json ~/magic-pdf.json
-   ```
-
-   Find the `magic-pdf.json` file in your user directory and configure `"models-dir"` to point to the directory where the model weights from step 5 were downloaded.
-   
-   > ❗️Ensure the absolute path of the model weights directory is correctly configured, or the program will fail to run due to not finding the model files.
-   >    
-   > In Windows, this path should include the drive letter and replace all `"\"` to `"/"`.
-   >   
-   > Example: If the models are placed in the root directory of drive D, the value for `model-dir` should be `"D:/models"`.
-   
-   ```json
-   {
-     "models-dir": "/tmp/models"
-   }
-   ```
-
-### 7. First Run
-   Download a sample file from the repository and test it.
-   ```powershell
-     (New-Object System.Net.WebClient).DownloadFile('https://github.com/opendatalab/MinerU/raw/master/demo/small_ocr.pdf', 'small_ocr.pdf')
-     magic-pdf -p small_ocr.pdf
-   ```
-
-### 8. Test CUDA Acceleration
-   If your graphics card has at least 8GB of VRAM, follow these steps to test CUDA-accelerated parsing performance.
-   1. **Overwrite the installation of torch and torchvision** supporting CUDA.
-      ```
-      pip install --force-reinstall torch==2.3.1 torchvision==0.18.1 --index-url https://download.pytorch.org/whl/cu118
-      ```
-      >❗️Ensure the following versions are specified in the command:
-      >```
-      > torch==2.3.1 torchvision==0.18.1
-      >```
-      >These are the highest versions we support. Installing higher versions without specifying them will cause the program to fail.
-   2. **Modify the value of `"device-mode"`** in the `magic-pdf.json` configuration file located in your user directory.
-     
-      ```json
-      {
-        "device-mode": "cuda"
-      }
-      ```
-   3. **Run the following command to test CUDA acceleration**:
-
-      ```
-      magic-pdf -p small_ocr.pdf
-      ```
-
-### 9. Enable CUDA Acceleration for OCR
-   >❗️This operation requires at least 16GB of VRAM on your graphics card, otherwise it will cause the program to crash or slow down.
-   1. **Download paddlepaddle-gpu**, which will automatically enable OCR acceleration upon installation.
-      ```
-      pip install paddlepaddle-gpu==2.6.1
-      ```
-   2. **Run the following command to test OCR acceleration**:
-      ```
-      magic-pdf -p small_ocr.pdf
-      ```

docs/download_models.py

-# use modelscope sdk download models
-from modelscope import snapshot_download
-model_dir = snapshot_download('opendatalab/PDF-Extract-Kit')
-print(f"model dir is: {model_dir}/models")

docs/en/.readthedocs.yaml

+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+formats:
+  - epub
+
+python:
+  install:
+    - requirements: docs/requirements.txt
+
+sphinx:
+  configuration: docs/en/conf.py

docs/en/Makefile

+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

docs/en/conf.py

+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+
+import os
+import subprocess
+import sys
+
+from sphinx.ext import autodoc
+
+
+def install(package):
+    subprocess.check_call([sys.executable, '-m', 'pip', 'install', package])
+
+
+requirements_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'requirements.txt'))
+if os.path.exists(requirements_path):
+    with open(requirements_path) as f:
+        packages = f.readlines()
+    for package in packages:
+        install(package.strip())
+
+sys.path.insert(0, os.path.abspath('../..'))
+
+# -- Project information -----------------------------------------------------
+
+project = 'MinerU'
+copyright = '2024, MinerU Contributors'
+author = 'OpenDataLab'
+
+# The full version, including alpha/beta/rc tags
+version_file = '../../magic_pdf/libs/version.py'
+with open(version_file) as f:
+    exec(compile(f.read(), version_file, 'exec'))
+__version__ = locals()['__version__']
+# The short X.Y version
+version = __version__
+# The full version, including alpha/beta/rc tags
+release = __version__
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.napoleon',
+    'sphinx.ext.viewcode',
+    'sphinx.ext.intersphinx',
+    'sphinx_copybutton',
+    'sphinx.ext.autodoc',
+    'sphinx.ext.autosummary',
+    'myst_parser',
+    'sphinxarg.ext',
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+# Exclude the prompt "$" when copying code
+copybutton_prompt_text = r'\$ '
+copybutton_prompt_is_regexp = True
+
+language = 'en'
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+html_theme = 'sphinx_book_theme'
+html_logo = '_static/image/logo.png'
+html_theme_options = {
+    'path_to_docs': 'docs/en',
+    'repository_url': 'https://github.com/opendatalab/MinerU',
+    'use_repository_button': True,
+}
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+# html_static_path = ['_static']
+
+# Mock out external dependencies here.
+autodoc_mock_imports = [
+    'cpuinfo',
+    'torch',
+    'transformers',
+    'psutil',
+    'prometheus_client',
+    'sentencepiece',
+    'vllm.cuda_utils',
+    'vllm._C',
+    'numpy',
+    'tqdm',
+]
+
+
+class MockedClassDocumenter(autodoc.ClassDocumenter):
+    """Remove note about base class when a class is derived from object."""
+
+    def add_line(self, line: str, source: str, *lineno: int) -> None:
+        if line == '   Bases: :py:class:`object`':
+            return
+        super().add_line(line, source, *lineno)
+
+
+autodoc.ClassDocumenter = MockedClassDocumenter
+
+navigation_with_keys = False