feat: add more docs about data releated api

next_docs/en/user_guide/data.rst

+
+
+Data
+=========
+
+.. toctree::
+   :maxdepth: 2
+
+   data/dataset
+
+   data/read_api
+
+   data/data_reader_writer 
+
+   data/io
+
+
+
+

next_docs/en/user_guide/data/data_reader_writer.rst

+
+Data Reader Writer 
+====================
+
+Aims for read or write bytes from different media, You can implement new classes to meet the needs of your personal scenarios 
+if MinerU have not provide the suitable classes. It is easy to implement new classes, the only one requirement is to inherit from
+``DataReader`` or ``DataWriter``
+
+.. code:: python
+
+    class SomeReader(DataReader):
+        def read(self, path: str) -> bytes:
+            pass
+
+        def read_at(self, path: str, offset: int = 0, limit: int = -1) -> bytes:
+            pass
+
+
+    class SomeWriter(DataWriter):
+        def write(self, path: str, data: bytes) -> None:
+            pass
+
+        def write_string(self, path: str, data: str) -> None:
+            pass
+
+
+Reader may curious about the difference between :doc:`io` and this section. Those two sections look very similarity at first glance.
+:doc:`io` provides fundamental functions, while This section thinks more at application level. Customer can build they own classes to meet 
+their own applications need which may share same IO function. That is why we have :doc:`io`.
+
+
+Important Classes
+-----------------
+
+.. code:: python
+
+    class FileBasedDataReader(DataReader):
+        def __init__(self, parent_dir: str = ''):
+            pass
+
+
+    class FileBasedDataWriter(DataWriter):
+        def __init__(self, parent_dir: str = '') -> None:
+            pass
+
+Class ``FileBasedDataReader`` initialized with unary param ``parent_dir``, That means that every method ``FileBasedDataReader`` provided will have features as follow.
+
+Features:
+    #. read content from the absolute path file, ``parent_dir`` will be ignored.
+    #. read the relative path, file will first join with ``parent_dir``, then read content from the merged path
+
+
+.. note::
+
+    ``FileBasedDataWriter`` shares the same behavior with ``FileBaseDataReader``
+
+
+.. code:: python 
+
+    class MultiS3Mixin:
+        def __init__(self, default_prefix: str, s3_configs: list[S3Config]):
+            pass
+
+    class MultiBucketS3DataReader(DataReader, MultiS3Mixin):
+        pass
+
+All read-related method that class ``MultiBucketS3DataReader`` provided will have features as follow.
+
+Features:
+    #. read object with full s3-format path, for example ``s3://test_bucket/test_object``, ``default_prefix`` will be ignored.
+    #. read object with relative path, file will join ``default_prefix`` and trim the ``bucket_name`` firstly, then read the content. ``bucket_name`` is the first element of the result after split ``default_prefix`` with delimiter ``\`` 
+
+.. note::
+    ``MultiBucketS3DataWriter`` shares the same behavior with ``MultiBucketS3DataReader``
+
+
+.. code:: python
+
+    class S3DataReader(MultiBucketS3DataReader):
+        pass
+
+``S3DataReader`` is build on top of MultiBucketS3DataReader which only support for bucket. So is ``S3DataWriter``. 
+
+
+Read Examples
+------------
+
+.. code:: python
+
+    # file based related 
+    file_based_reader1 = FileBasedDataReader('')
+
+    ## will read file abc 
+    file_based_reader1.read('abc') 
+
+    file_based_reader2 = FileBasedDataReader('/tmp')
+
+    ## will read /tmp/abc
+    file_based_reader2.read('abc')
+
+    ## will read /var/logs/message.txt
+    file_based_reader2.read('/var/logs/message.txt')
+
+    # multi bucket s3 releated
+    multi_bucket_s3_reader1 = MultiBucketS3DataReader("test_bucket1/test_prefix", list[S3Config(
+            bucket_name=test_bucket1, access_key=ak, secret_key=sk, endpoint_url=endpoint_url
+        ),
+        S3Config(
+            bucket_name=test_bucket_2,
+            access_key=ak_2,
+            secret_key=sk_2,
+            endpoint_url=endpoint_url_2,
+        )])
+    
+    ## will read s3://test_bucket1/test_prefix/abc
+    multi_bucket_s3_reader1.read('abc')
+
+    ## will read s3://test_bucket1/efg
+    multi_bucket_s3_reader1.read('s3://test_bucket1/efg')
+
+    ## will read s3://test_bucket2/abc
+    multi_bucket_s3_reader1.read('s3://test_bucket2/abc')
+
+    # s3 related
+    s3_reader1 = S3DataReader(
+        default_prefix_without_bucket = "test_prefix"
+        bucket: "test_bucket",
+        ak: "ak",
+        sk: "sk",
+        endpoint_url: "localhost"
+    )
+
+    ## will read s3://test_bucket/test_prefix/abc 
+    s3_reader1.read('abc')
+   
+    ## will read s3://test_bucket/efg
+    s3_reader1.read('s3://test_bucket/efg')
+
+
+Write Examples
+---------------
+
+.. code:: python
+
+    # file based related 
+    file_based_writer1 = FileBasedDataWriter('')
+
+    ## will write 123 to abc
+    file_based_writer1.write('abc', '123'.encode()) 
+
+    ## will write 123 to abc
+    file_based_writer1.write_string('abc', '123') 
+
+    file_based_writer2 = FileBasedDataWriter('/tmp')
+
+    ## will write 123 to /tmp/abc
+    file_based_writer2.write_string('abc', '123')
+
+    ## will write 123 to /var/logs/message.txt
+    file_based_writer2.write_string('/var/logs/message.txt', '123')
+
+    # multi bucket s3 releated
+    multi_bucket_s3_writer1 = MultiBucketS3DataWriter("test_bucket1/test_prefix", list[S3Config(
+            bucket_name=test_bucket1, access_key=ak, secret_key=sk, endpoint_url=endpoint_url
+        ),
+        S3Config(
+            bucket_name=test_bucket_2,
+            access_key=ak_2,
+            secret_key=sk_2,
+            endpoint_url=endpoint_url_2,
+        )])
+    
+    ## will write 123 to s3://test_bucket1/test_prefix/abc
+    multi_bucket_s3_writer1.write_string('abc', '123')
+
+    ## will write 123 to s3://test_bucket1/test_prefix/abc
+    multi_bucket_s3_writer1.write('abc', '123'.encode())
+
+    ## will write 123 to s3://test_bucket1/efg
+    multi_bucket_s3_writer1.write('s3://test_bucket1/efg', '123'.encode())
+
+    ## will write 123 to s3://test_bucket2/abc
+    multi_bucket_s3_writer1.write('s3://test_bucket2/abc', '123'.encode())
+
+    # s3 related
+    s3_writer1 = S3DataWriter(
+        default_prefix_without_bucket = "test_prefix"
+        bucket: "test_bucket",
+        ak: "ak",
+        sk: "sk",
+        endpoint_url: "localhost"
+    )
+
+    ## will write 123 to s3://test_bucket/test_prefix/abc 
+    s3_writer1.write('abc', '123'.encode())
+
+    ## will write 123 to s3://test_bucket/test_prefix/abc 
+    s3_writer1.write_string('abc', '123')
+
+    ## will write 123 to s3://test_bucket/efg
+    s3_writer1.write('s3://test_bucket/efg', '123'.encode())
+
+
+Check :doc:`../../api/classes` for more intuitions or check :doc:`../../api/data_reader_writer` for more details

next_docs/en/user_guide/data/dataset.rst

+
+
+Dataset 
+===========
+
+
+Import Classes 
+-----------------
+
+Dataset 
+^^^^^^^^
+
+Each pdfs or image will form one ``Dataset``. As we all know, Pdf has two categories, :ref:`digital_method_section` or :ref:`ocr_method_section`.
+Will get ``ImageDataset`` which is subclass of ``Dataset`` with images and get ``PymuDocDataset`` from pdf files.
+The difference between ``ImageDataset`` and ``PymuDocDataset`` is that ``ImageDataset`` only support ``OCR`` parse method, 
+while ``PymuDocDataset`` support both ``OCR`` and ``TXT``
+
+.. note::
+
+    In fact some pdf may generated by images, that means it can not support ``TXT`` methods. Currently it is something the user needs to ensure does not happen
+
+
+
+Pdf Parse Methods
+------------------
+
+.. _ocr_method_section:
+OCR 
+^^^^
+Extract chars via ``Optical Character Recognition`` technical.
+
+.. _digital_method_section:
+TXT
+^^^^^^^^
+Extract chars via third-party library, currently we use ``pymupdf``. 
+
+
+
+Check :doc:`../../api/classes` for more intuitions or check :doc:`../../api/dataset` for more details
+

next_docs/en/user_guide/data/io.rst

+
+IO
+===
+
+Aims for read or write bytes from different media, Currently We provide ``S3Reader``, ``S3Writer`` for AWS S3 compatible media 
+and ``HttpReader``, ``HttpWriter`` for remote Http file. You can implement new classes to meet the needs of your personal scenarios 
+if MinerU have not provide the suitable classes. It is easy to implement new classes, the only one requirement is to inherit from
+``IOReader`` or ``IOWriter``
+
+.. code:: python
+
+    class SomeReader(IOReader):
+        def read(self, path: str) -> bytes:
+            pass
+
+        def read_at(self, path: str, offset: int = 0, limit: int = -1) -> bytes:
+            pass
+
+
+    class SomeWriter(IOWriter):
+        def write(self, path: str, data: bytes) -> None:
+            pass
+
+Check :doc:`../../api/classes` for more intuitions or check :doc:`../../api/io` for more details
+

next_docs/en/user_guide/data/read_api.rst

+
+read_api 
+==========
+
+Read the content from file or directory to create ``Dataset``, Currently we provided serval functions that cover some scenarios.
+if you have new scenarios that is common to most of the users, you can post it on the offical github issues with detail descriptions.
+Also it is easy to implement your own read-related funtions.
+
+
+Important Functions
+-------------------
+
+
+read_jsonl
+^^^^^^^^^^^^^^^^
+
+Read the contet from jsonl which may located on local machine or remote s3. if you want to know more about jsonl, please goto :doc:`../../additional_notes/glossary`
+
+.. code:: python
+
+    # read jsonl from local machine 
+    datasets = read_jsonl("tt.jsonl", None)
+
+    # read jsonl from remote s3
+    datasets = read_jsonl("s3://bucket_1/tt.jsonl", s3_reader)
+
+
+read_local_pdfs
+^^^^^^^^^^^^^^^^
+
+Read pdf from path or directory.
+
+
+.. code:: python
+
+    # read pdf path
+    datasets = read_local_pdfs("tt.pdf")
+
+    # read pdfs under directory
+    datasets = read_local_pdfs("pdfs/")
+
+
+read_local_images
+^^^^^^^^^^^^^^^^^^^
+
+Read images from path or directory
+
+.. code:: python 
+
+    # read from image path 
+    datasets = read_local_images("tt.png")
+
+
+    # read files from directory that endswith suffix in suffixes array 
+    datasets = read_local_images("images/", suffixes=["png", "jpg"])
+
+
+Check :doc:`../../api/read_api` for more details
\ No newline at end of file

next_docs/en/user_guide/install.rst

+
+Installation
+==============
+
+.. toctree::
+   :maxdepth: 1
+
+   install/install
+   install//boost_with_cuda
+   install/download_model_weight_files
+
+

next_docs/en/user_guide/install/boost_with_cuda.rst

+
+Boost With Cuda 
+================
+
+
+If your device supports CUDA and meets the GPU requirements of the
+mainline environment, you can use GPU acceleration. Please select the
+appropriate guide based on your system:
+
+-  :ref:`ubuntu_22_04_lts_section`
+-  :ref:`windows_10_or_11_section`
+
+-  Quick Deployment with Docker > Docker requires a GPU with at least
+   16GB of VRAM, and all acceleration features are enabled by default.
+
+.. note:: 
+
+   Before running this Docker, you can use the following command to
+   check if your device supports CUDA acceleration on Docker. 
+
+   bash  docker run --rm --gpus=all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
+
+.. code:: sh
+
+   wget https://github.com/opendatalab/MinerU/raw/master/Dockerfile
+   docker build -t mineru:latest .
+   docker run --rm -it --gpus=all mineru:latest /bin/bash
+   magic-pdf --help
+
+.. _ubuntu_22_04_lts_section:
+
+Ubuntu 22.04 LTS
+-----------------
+
+1. Check if NVIDIA Drivers Are Installed
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: 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.
+
+Notice:``CUDA Version`` should be >= 12.1, If the displayed version
+number is less than 12.1, please upgrade the driver.
+
+.. code:: text
+
+   +---------------------------------------------------------------------------------------+
+   | 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:
+
+.. code:: sh
+
+   sudo apt-get update
+   sudo apt-get install nvidia-driver-545
+
+Install the proprietary driver and restart your computer after
+installation.
+
+.. code:: sh
+
+   reboot
+
+3. Install Anaconda
+~~~~~~~~~~~~~~~~~~~
+
+If Anaconda is already installed, skip this step.
+
+.. code:: 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.
+
+.. code:: sh
+
+   conda create -n MinerU python=3.10
+   conda activate MinerU
+
+5. Install Applications
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: 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:
+
+.. code:: 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 :doc:`download_model_weight_files`
+
+7. Understand the Location of the Configuration File
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+After completing the `6. Download Models <#6-download-models>`__ step,
+the script will automatically generate a ``magic-pdf.json`` file in the
+user directory and configure the default model path. You can find the
+``magic-pdf.json`` file in your user directory.
+
+   The user directory for Linux is “/home/username”.
+
+8. First Run
+~~~~~~~~~~~~
+
+Download a sample file from the repository and test it.
+
+.. code:: 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:
+
+   ❗ Due to the extremely limited nature of 8GB VRAM for running this
+   application, you need to close all other programs using VRAM to
+   ensure that 8GB of VRAM is available when running this application.
+
+1. Modify the value of ``"device-mode"`` in the ``magic-pdf.json``
+   configuration file located in your home directory.
+
+   .. code:: json
+
+      {
+        "device-mode": "cuda"
+      }
+
+2. Test CUDA acceleration with the following command:
+
+   .. code:: sh
+
+      magic-pdf -p small_ocr.pdf
+
+10. Enable CUDA Acceleration for OCR
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+1. Download ``paddlepaddle-gpu``. Installation will automatically enable
+   OCR acceleration.
+
+   .. code:: 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:
+
+   .. code:: sh
+
+      magic-pdf -p small_ocr.pdf
+
+.. _windows_10_or_11_section:
+
+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``:
+
+   .. code:: 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 :doc:`download_model_weight_files`
+
+6. Understand the Location of the Configuration File
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+After completing the `5. Download Models <#5-download-models>`__ step,
+the script will automatically generate a ``magic-pdf.json`` file in the
+user directory and configure the default model path. You can find the
+``magic-pdf.json`` file in your 【user directory】 .
+
+   The user directory for Windows is “C:/Users/username”.
+
+7. First Run
+~~~~~~~~~~~~
+
+Download a sample file from the repository and test it.
+
+.. code:: powershell
+
+     wget https://github.com/opendatalab/MinerU/raw/master/demo/small_ocr.pdf -O 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.
+
+   ❗ Due to the extremely limited nature of 8GB VRAM for running this
+   application, you need to close all other programs using VRAM to
+   ensure that 8GB of VRAM is available when running this application.
+
+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.
+
+   .. code:: 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
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+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
+

next_docs/en/user_guide/install/download_model_weight_files.rst

+
+Download Model Weight Files
+==============================
+
+Model downloads are divided into initial downloads and updates to the
+model directory. Please refer to the corresponding documentation for
+instructions on how to proceed.
+
+Initial download of model files
+------------------------------
+
+1. Download the Model from Hugging Face
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Use a Python Script to Download Model Files from Hugging Face
+
+.. code:: bash
+
+   pip install huggingface_hub
+   wget https://github.com/opendatalab/MinerU/raw/master/docs/download_models_hf.py -O download_models_hf.py
+   python download_models_hf.py
+
+The Python script will automatically download the model files and
+configure the model directory in the configuration file.
+
+The configuration file can be found in the user directory, with the
+filename ``magic-pdf.json``.
+
+How to update models previously downloaded
+-----------------------------------------
+
+1. Models downloaded via Git LFS
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+   Due to feedback from some users that downloading model files using
+   git lfs was incomplete or resulted in corrupted model files, this
+   method is no longer recommended.
+
+If you previously downloaded model files via git lfs, you can navigate
+to the previous download directory and use the ``git pull`` command to
+update the model.
+
+2. Models downloaded via Hugging Face or Model Scope
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If you previously downloaded models via Hugging Face or Model Scope, you
+can rerun the Python script used for the initial download. This will
+automatically update the model directory to the latest version.

next_docs/en/user_guide/install/install.rst

+
+Install 
+===============================================================
+If you encounter any installation issues, please first consult the FAQ.
+If the parsing results are not as expected, refer to the Known Issues.
+There are three different ways to experience MinerU
+
+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.
+
+.. raw:: html
+
+   <style>
+      table, th, td {
+      border: 1px solid black;
+      border-collapse: collapse;
+      }
+   </style>
+   <table>
+    <tr>
+        <td colspan="3" rowspan="2">Operating System</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">Memory</td>
+        <td colspan="3">16GB or more, recommended 32GB+</td>
+    </tr>
+    <tr>
+        <td colspan="3">Python Version</td>
+        <td colspan="3">3.10</td>
+    </tr>
+    <tr>
+        <td colspan="3">Nvidia Driver Version</td>
+        <td>latest (Proprietary Driver)</td>
+        <td>latest</td>
+        <td>None</td>
+    </tr>
+    <tr>
+        <td colspan="3">CUDA Environment</td>
+        <td>Automatic installation [12.1 (pytorch) + 11.8 (paddle)]</td>
+        <td>11.8 (manual installation) + cuDNN v8.7.0 (manual installation)</td>
+        <td>None</td>
+    </tr>
+    <tr>
+        <td rowspan="2">GPU Hardware Support List</td>
+        <td colspan="2">Minimum Requirement 8G+ VRAM</td>
+        <td colspan="2">3060ti/3070/3080/3080ti/4060/4070/4070ti<br>
+        8G VRAM enables layout, formula recognition acceleration and OCR acceleration</td>
+        <td rowspan="2">None</td>
+    </tr>
+    <tr>
+        <td colspan="2">Recommended Configuration 16G+ VRAM</td>
+        <td colspan="2">3090/3090ti/4070ti super/4080/4090<br>
+        16G VRAM or more can enable layout, formula recognition, OCR acceleration and table recognition acceleration simultaneously
+        </td>
+    </tr>
+   </table>
+
+
+Create an environment
+~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: shell
+
+    conda create -n MinerU python=3.10
+    conda activate MinerU
+    pip install -U magic-pdf[full] --extra-index-url https://wheels.myhloli.com
+
+
+Download model weight files
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code-block:: shell
+
+    pip install huggingface_hub
+    wget https://github.com/opendatalab/MinerU/raw/master/docs/download_models_hf.py -O download_models_hf.py
+    python download_models_hf.py    
+
+
+The MinerU is installed, Check out :doc:`../quick_start` or reading :doc:`boost_with_cuda` for accelerate inference
\ No newline at end of file

next_docs/en/user_guide/quick_start.rst

+
+Quick Start 
+==============
+
+Eager to get started? This page gives a good introduction to MinerU. Follow Installation to set up a project and install MinerU first.
+
+
+.. toctree::
+    :maxdepth: 1
+
+    quick_start/command_line
+    quick_start/extract_text
+

next_docs/en/user_guide/quick_start/command_line.rst

+
+
+Command Line
+===================
+
+.. code:: 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 PATH        output local directory  [required]
+     -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.
+     -l, --lang TEXT              Input the languages in the pdf (if known) to
+                                  improve OCR accuracy.  Optional. You should
+                                  input "Abbreviation" with language form url: ht
+                                  tps://paddlepaddle.github.io/PaddleOCR/en/ppocr
+                                  /blog/multi_languages.html#5-support-languages-
+                                  and-abbreviations
+     -d, --debug BOOLEAN          Enables detailed debugging information during
+                                  the execution of the CLI commands.
+     -s, --start INTEGER          The starting page for PDF parsing, beginning
+                                  from 0.
+     -e, --end INTEGER            The ending page for PDF parsing, beginning from
+                                  0.
+     --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
+
+``{some_pdf}`` can be a single PDF file or a directory containing
+multiple PDFs. The results will be saved in the ``{some_output_dir}``
+directory. The output file list is as follows:
+
+.. code:: text
+
+   ├── some_pdf.md                          # markdown file
+   ├── images                               # directory for storing images
+   ├── some_pdf_layout.pdf                  # layout diagram
+   ├── some_pdf_middle.json                 # MinerU intermediate processing result
+   ├── some_pdf_model.json                  # model inference result
+   ├── some_pdf_origin.pdf                  # original PDF file
+   ├── some_pdf_spans.pdf                   # smallest granularity bbox position information diagram
+   └── some_pdf_content_list.json           # Rich text JSON arranged in reading order
+
+For more information about the output files, please refer to the `Output
+File Description <docs/output_file_en_us.md>`__.
+

next_docs/en/user_guide/quick_start/extract_text.rst

+
+
+Extract Content from Pdf
+========================
+
+.. code:: python
+
+    from magic_pdf.data.read_api import read_local_pdfs
+    from magic_pdf.pdf_parse_union_core_v2 import pdf_parse_union
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze

next_docs/en/user_guide/tutorial.rst

+
+Tutorial
+----------
+
+From the beginning to the end, Show how to using mineru via a minimal project

next_docs/requirements.txt

@@ -5,7 +5,8 @@ Pillow==8.4.0
 pydantic>=2.7.2,<2.8.0
 PyMuPDF>=1.24.9
 sphinx
-sphinx-argparse
-sphinx-book-theme
-sphinx-copybutton
-sphinx_rtd_theme
+sphinx-argparse>=0.5.2
+sphinx-book-theme>=1.1.3
+sphinx-copybutton>=0.5.2
+sphinx_rtd_theme>=3.0.1
+autodoc_pydantic>=2.2.0
\ No newline at end of file