Merge pull request #1261 from opendatalab/release-0.10.6

Release 0.10.6

magic_pdf/pdf_parse_union_core_v2.py

@@ -4,8 +4,8 @@ import statistics
 import time
 from typing import List
 
-import torch
 import fitz
+import torch
 from loguru import logger
 
 from magic_pdf.config.enums import SupportedPdfParseMethod
@@ -16,17 +16,13 @@ from magic_pdf.libs.clean_memory import clean_memory
 from magic_pdf.libs.config_reader import get_local_layoutreader_model_dir
 from magic_pdf.libs.convert_utils import dict_to_list
 from magic_pdf.libs.hash_utils import compute_md5
-
 from magic_pdf.libs.pdf_image_tools import cut_image_to_pil_image
 from magic_pdf.model.magic_model import MagicModel
 
-os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
-os.environ['YOLO_VERBOSE'] = 'False'  # disable yolo logger
-
 try:
     import torchtext
 
-    if torchtext.__version__ >= "0.18.0":
+    if torchtext.__version__ >= '0.18.0':
         torchtext.disable_torchtext_deprecation_warning()
 except ImportError:
     pass
@@ -39,6 +35,9 @@ from magic_pdf.pre_proc.ocr_detect_all_bboxes import ocr_prepare_bboxes_for_layo
 from magic_pdf.pre_proc.ocr_dict_merge import fill_spans_in_blocks, fix_block_spans_v2, fix_discarded_block
 from magic_pdf.pre_proc.ocr_span_list_modify import get_qa_need_list_v2, remove_overlaps_low_confidence_spans, remove_overlaps_min_spans
 
+os.environ['NO_ALBUMENTATIONS_UPDATE'] = '1'  # 禁止albumentations检查更新
+os.environ['YOLO_VERBOSE'] = 'False'  # disable yolo logger
+
 
 def __replace_STX_ETX(text_str: str):
     """Replace \u0002 and \u0003, as these characters become garbled when extracted using pymupdf. In fact, they were originally quotation marks.
@@ -233,7 +232,7 @@ def txt_spans_extract_v2(pdf_page, spans, all_bboxes, all_discarded_blocks, lang
         # 初始化ocr模型
         atom_model_manager = AtomModelSingleton()
         ocr_model = atom_model_manager.get_atom_model(
-            atom_model_name="ocr",
+            atom_model_name='ocr',
             ocr_show_log=False,
             det_db_box_thresh=0.3,
             lang=lang
@@ -241,7 +240,7 @@ def txt_spans_extract_v2(pdf_page, spans, all_bboxes, all_discarded_blocks, lang
 
         for span in empty_spans:
             # 对span的bbox截图再ocr
-            span_img = cut_image_to_pil_image(span['bbox'], pdf_page, mode="cv2")
+            span_img = cut_image_to_pil_image(span['bbox'], pdf_page, mode='cv2')
             ocr_res = ocr_model.ocr(span_img, det=False)
             if ocr_res and len(ocr_res) > 0:
                 if len(ocr_res[0]) > 0:
@@ -681,7 +680,7 @@ def parse_page_core(
     """根据parse_mode，构造spans，主要是文本类的字符填充"""
     if parse_mode == SupportedPdfParseMethod.TXT:
 
-        """使用新版本的混合ocr方案"""
+        """使用新版本的混合ocr方案."""
         spans = txt_spans_extract_v2(page_doc, spans, all_bboxes, all_discarded_blocks, lang)
 
     elif parse_mode == SupportedPdfParseMethod.OCR:
@@ -689,7 +688,6 @@ def parse_page_core(
     else:
         raise Exception('parse_mode must be txt or ocr')
 
-
     """先处理不需要排版的discarded_blocks"""
     discarded_block_with_spans, spans = fill_spans_in_blocks(
         all_discarded_blocks, spans, 0.4
@@ -762,8 +760,8 @@ def parse_page_core(
 
 
 def pdf_parse_union(
-    dataset: Dataset,
     model_list,
+    dataset: Dataset,
     imageWriter,
     parse_mode,
     start_page_id=0,
@@ -771,6 +769,7 @@ def pdf_parse_union(
     debug_mode=False,
     lang=None,
 ):
+
     pdf_bytes_md5 = compute_md5(dataset.data_bits())
 
     """初始化空的pdf_info_dict"""

magic_pdf/pipe/AbsPipe.py

@@ -3,6 +3,7 @@ from abc import ABC, abstractmethod
 from magic_pdf.config.drop_reason import DropReason
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.dict2md.ocr_mkcontent import union_make
 from magic_pdf.filter.pdf_classify_by_type import classify
 from magic_pdf.filter.pdf_meta_scan import pdf_meta_scan
@@ -14,9 +15,9 @@ class AbsPipe(ABC):
     PIP_OCR = 'ocr'
     PIP_TXT = 'txt'
 
-    def __init__(self, pdf_bytes: bytes, model_list: list, image_writer: DataWriter, is_debug: bool = False,
+    def __init__(self, dataset: Dataset, model_list: list, image_writer: DataWriter, is_debug: bool = False,
                  start_page_id=0, end_page_id=None, lang=None, layout_model=None, formula_enable=None, table_enable=None):
-        self.pdf_bytes = pdf_bytes
+        self.dataset = Dataset
         self.model_list = model_list
         self.image_writer = image_writer
         self.pdf_mid_data = None  # 未压缩

magic_pdf/pipe/OCRPipe.py

@@ -2,40 +2,79 @@ from loguru import logger
 
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 from magic_pdf.pipe.AbsPipe import AbsPipe
 from magic_pdf.user_api import parse_ocr_pdf
 
 
 class OCRPipe(AbsPipe):
-
-    def __init__(self, pdf_bytes: bytes, model_list: list, image_writer: DataWriter, is_debug: bool = False,
-                 start_page_id=0, end_page_id=None, lang=None,
-                 layout_model=None, formula_enable=None, table_enable=None):
-        super().__init__(pdf_bytes, model_list, image_writer, is_debug, start_page_id, end_page_id, lang,
-                         layout_model, formula_enable, table_enable)
+    def __init__(
+        self,
+        dataset: Dataset,
+        model_list: list,
+        image_writer: DataWriter,
+        is_debug: bool = False,
+        start_page_id=0,
+        end_page_id=None,
+        lang=None,
+        layout_model=None,
+        formula_enable=None,
+        table_enable=None,
+    ):
+        super().__init__(
+            dataset,
+            model_list,
+            image_writer,
+            is_debug,
+            start_page_id,
+            end_page_id,
+            lang,
+            layout_model,
+            formula_enable,
+            table_enable,
+        )
 
     def pipe_classify(self):
         pass
 
     def pipe_analyze(self):
-        self.model_list = doc_analyze(self.pdf_bytes, ocr=True,
-                                      start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                      lang=self.lang, layout_model=self.layout_model,
-                                      formula_enable=self.formula_enable, table_enable=self.table_enable)
+        self.infer_res = doc_analyze(
+            self.dataset,
+            ocr=True,
+            start_page_id=self.start_page_id,
+            end_page_id=self.end_page_id,
+            lang=self.lang,
+            layout_model=self.layout_model,
+            formula_enable=self.formula_enable,
+            table_enable=self.table_enable,
+        )
 
     def pipe_parse(self):
-        self.pdf_mid_data = parse_ocr_pdf(self.pdf_bytes, self.model_list, self.image_writer, is_debug=self.is_debug,
-                                          start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                          lang=self.lang, layout_model=self.layout_model,
-                                          formula_enable=self.formula_enable, table_enable=self.table_enable)
+        self.pdf_mid_data = parse_ocr_pdf(
+            self.dataset,
+            self.infer_res,
+            self.image_writer,
+            is_debug=self.is_debug,
+            start_page_id=self.start_page_id,
+            end_page_id=self.end_page_id,
+            lang=self.lang,
+            layout_model=self.layout_model,
+            formula_enable=self.formula_enable,
+            table_enable=self.table_enable,
+        )
 
     def pipe_mk_uni_format(self, img_parent_path: str, drop_mode=DropMode.WHOLE_PDF):
         result = super().pipe_mk_uni_format(img_parent_path, drop_mode)
         logger.info('ocr_pipe mk content list finished')
         return result
 
-    def pipe_mk_markdown(self, img_parent_path: str, drop_mode=DropMode.WHOLE_PDF, md_make_mode=MakeMode.MM_MD):
+    def pipe_mk_markdown(
+        self,
+        img_parent_path: str,
+        drop_mode=DropMode.WHOLE_PDF,
+        md_make_mode=MakeMode.MM_MD,
+    ):
         result = super().pipe_mk_markdown(img_parent_path, drop_mode, md_make_mode)
         logger.info(f'ocr_pipe mk {md_make_mode} finished')
         return result

magic_pdf/pipe/TXTPipe.py

@@ -2,6 +2,7 @@ from loguru import logger
 
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 from magic_pdf.pipe.AbsPipe import AbsPipe
 from magic_pdf.user_api import parse_txt_pdf
@@ -9,23 +10,23 @@ from magic_pdf.user_api import parse_txt_pdf
 
 class TXTPipe(AbsPipe):
 
-    def __init__(self, pdf_bytes: bytes, model_list: list, image_writer: DataWriter, is_debug: bool = False,
+    def __init__(self, dataset: Dataset, model_list: list, image_writer: DataWriter, is_debug: bool = False,
                  start_page_id=0, end_page_id=None, lang=None,
                  layout_model=None, formula_enable=None, table_enable=None):
-        super().__init__(pdf_bytes, model_list, image_writer, is_debug, start_page_id, end_page_id, lang,
+        super().__init__(dataset, model_list, image_writer, is_debug, start_page_id, end_page_id, lang,
                          layout_model, formula_enable, table_enable)
 
     def pipe_classify(self):
         pass
 
     def pipe_analyze(self):
-        self.model_list = doc_analyze(self.pdf_bytes, ocr=False,
+        self.model_list = doc_analyze(self.dataset, ocr=False,
                                       start_page_id=self.start_page_id, end_page_id=self.end_page_id,
                                       lang=self.lang, layout_model=self.layout_model,
                                       formula_enable=self.formula_enable, table_enable=self.table_enable)
 
     def pipe_parse(self):
-        self.pdf_mid_data = parse_txt_pdf(self.pdf_bytes, self.model_list, self.image_writer, is_debug=self.is_debug,
+        self.pdf_mid_data = parse_txt_pdf(self.dataset, self.model_list, self.image_writer, is_debug=self.is_debug,
                                           start_page_id=self.start_page_id, end_page_id=self.end_page_id,
                                           lang=self.lang, layout_model=self.layout_model,
                                           formula_enable=self.formula_enable, table_enable=self.table_enable)

magic_pdf/pipe/UNIPipe.py

@@ -4,6 +4,7 @@ from loguru import logger
 
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.libs.commons import join_path
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 from magic_pdf.pipe.AbsPipe import AbsPipe
@@ -12,12 +13,32 @@ from magic_pdf.user_api import parse_ocr_pdf, parse_union_pdf
 
 class UNIPipe(AbsPipe):
 
-    def __init__(self, pdf_bytes: bytes, jso_useful_key: dict, image_writer: DataWriter, is_debug: bool = False,
-                 start_page_id=0, end_page_id=None, lang=None,
-                 layout_model=None, formula_enable=None, table_enable=None):
+    def __init__(
+        self,
+        dataset: Dataset,
+        jso_useful_key: dict,
+        image_writer: DataWriter,
+        is_debug: bool = False,
+        start_page_id=0,
+        end_page_id=None,
+        lang=None,
+        layout_model=None,
+        formula_enable=None,
+        table_enable=None,
+    ):
         self.pdf_type = jso_useful_key['_pdf_type']
-        super().__init__(pdf_bytes, jso_useful_key['model_list'], image_writer, is_debug, start_page_id, end_page_id,
-                         lang, layout_model, formula_enable, table_enable)
+        super().__init__(
+            dataset,
+            jso_useful_key['model_list'],
+            image_writer,
+            is_debug,
+            start_page_id,
+            end_page_id,
+            lang,
+            layout_model,
+            formula_enable,
+            table_enable,
+        )
         if len(self.model_list) == 0:
             self.input_model_is_empty = True
         else:
@@ -28,35 +49,66 @@ class UNIPipe(AbsPipe):
 
     def pipe_analyze(self):
         if self.pdf_type == self.PIP_TXT:
-            self.model_list = doc_analyze(self.pdf_bytes, ocr=False,
-                                          start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                          lang=self.lang, layout_model=self.layout_model,
-                                          formula_enable=self.formula_enable, table_enable=self.table_enable)
+            self.model_list = doc_analyze(
+                self.dataset,
+                ocr=False,
+                start_page_id=self.start_page_id,
+                end_page_id=self.end_page_id,
+                lang=self.lang,
+                layout_model=self.layout_model,
+                formula_enable=self.formula_enable,
+                table_enable=self.table_enable,
+            )
         elif self.pdf_type == self.PIP_OCR:
-            self.model_list = doc_analyze(self.pdf_bytes, ocr=True,
-                                          start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                          lang=self.lang, layout_model=self.layout_model,
-                                          formula_enable=self.formula_enable, table_enable=self.table_enable)
+            self.model_list = doc_analyze(
+                self.dataset,
+                ocr=True,
+                start_page_id=self.start_page_id,
+                end_page_id=self.end_page_id,
+                lang=self.lang,
+                layout_model=self.layout_model,
+                formula_enable=self.formula_enable,
+                table_enable=self.table_enable,
+            )
 
     def pipe_parse(self):
         if self.pdf_type == self.PIP_TXT:
-            self.pdf_mid_data = parse_union_pdf(self.pdf_bytes, self.model_list, self.image_writer,
-                                                is_debug=self.is_debug, input_model_is_empty=self.input_model_is_empty,
-                                                start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                                lang=self.lang, layout_model=self.layout_model,
-                                                formula_enable=self.formula_enable, table_enable=self.table_enable)
+            self.pdf_mid_data = parse_union_pdf(
+                self.dataset,
+                self.model_list,
+                self.image_writer,
+                is_debug=self.is_debug,
+                start_page_id=self.start_page_id,
+                end_page_id=self.end_page_id,
+                lang=self.lang,
+                layout_model=self.layout_model,
+                formula_enable=self.formula_enable,
+                table_enable=self.table_enable,
+            )
         elif self.pdf_type == self.PIP_OCR:
-            self.pdf_mid_data = parse_ocr_pdf(self.pdf_bytes, self.model_list, self.image_writer,
-                                              is_debug=self.is_debug,
-                                              start_page_id=self.start_page_id, end_page_id=self.end_page_id,
-                                              lang=self.lang)
-
-    def pipe_mk_uni_format(self, img_parent_path: str, drop_mode=DropMode.NONE_WITH_REASON):
+            self.pdf_mid_data = parse_ocr_pdf(
+                self.dataset,
+                self.model_list,
+                self.image_writer,
+                is_debug=self.is_debug,
+                start_page_id=self.start_page_id,
+                end_page_id=self.end_page_id,
+                lang=self.lang,
+            )
+
+    def pipe_mk_uni_format(
+        self, img_parent_path: str, drop_mode=DropMode.NONE_WITH_REASON
+    ):
         result = super().pipe_mk_uni_format(img_parent_path, drop_mode)
         logger.info('uni_pipe mk content list finished')
         return result
 
-    def pipe_mk_markdown(self, img_parent_path: str, drop_mode=DropMode.WHOLE_PDF, md_make_mode=MakeMode.MM_MD):
+    def pipe_mk_markdown(
+        self,
+        img_parent_path: str,
+        drop_mode=DropMode.WHOLE_PDF,
+        md_make_mode=MakeMode.MM_MD,
+    ):
         result = super().pipe_mk_markdown(img_parent_path, drop_mode, md_make_mode)
         logger.info(f'uni_pipe mk {md_make_mode} finished')
         return result
@@ -65,6 +117,7 @@ class UNIPipe(AbsPipe):
 if __name__ == '__main__':
     # 测试
     from magic_pdf.data.data_reader_writer import DataReader
+
     drw = DataReader(r'D:/project/20231108code-clean')
 
     pdf_file_path = r'linshixuqiu\19983-00.pdf'
@@ -82,10 +135,7 @@ if __name__ == '__main__':
     #     "model_list": model_list
     # }
 
-    jso_useful_key = {
-        '_pdf_type': '',
-        'model_list': model_list
-    }
+    jso_useful_key = {'_pdf_type': '', 'model_list': model_list}
     pipe = UNIPipe(pdf_bytes, jso_useful_key, img_writer)
     pipe.pipe_classify()
     pipe.pipe_parse()
@@ -94,5 +144,7 @@ if __name__ == '__main__':
 
     md_writer = DataWriter(write_path)
     md_writer.write_string('19983-00.md', md_content)
-    md_writer.write_string('19983-00.json', json.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4))
+    md_writer.write_string(
+        '19983-00.json', json.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4)
+    )
     md_writer.write_string('19983-00.txt', str(content_list))

magic_pdf/pipe/operators.py

+import json
+import os
+from typing import Callable
+import copy
+
+from magic_pdf.config.make_content_config import DropMode, MakeMode
+from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
+from magic_pdf.dict2md.ocr_mkcontent import union_make
+from magic_pdf.libs.draw_bbox import (draw_layout_bbox, draw_line_sort_bbox,
+                                      draw_span_bbox)
+from magic_pdf.libs.json_compressor import JsonCompressor
+
+
+class PipeResult:
+    def __init__(self, pipe_res, dataset: Dataset):
+        """Initialized.
+
+        Args:
+            pipe_res (list[dict]): the pipeline processed result of model inference result
+            dataset (Dataset): the dataset associated with pipe_res
+        """
+        self._pipe_res = pipe_res
+        self._dataset = dataset
+
+    def dump_md(
+        self,
+        writer: DataWriter,
+        file_path: str,
+        img_dir_or_bucket_prefix: str,
+        drop_mode=DropMode.WHOLE_PDF,
+        md_make_mode=MakeMode.MM_MD,
+    ):
+        """Dump The Markdown.
+
+        Args:
+            writer (DataWriter): File writer handle
+            file_path (str): The file location of markdown
+            img_dir_or_bucket_prefix (str): The s3 bucket prefix or local file directory which used to store the figure
+            drop_mode (str, optional): Drop strategy when some page which is corrupted or inappropriate. Defaults to DropMode.WHOLE_PDF.
+            md_make_mode (str, optional): The content Type of Markdown be made. Defaults to MakeMode.MM_MD.
+        """
+        pdf_info_list = self._pipe_res['pdf_info']
+        md_content = union_make(
+            pdf_info_list, md_make_mode, drop_mode, img_dir_or_bucket_prefix
+        )
+        writer.write_string(file_path, md_content)
+
+    def dump_content_list(
+        self, writer: DataWriter, file_path: str, image_dir_or_bucket_prefix: str
+    ):
+        """Dump Content List.
+
+        Args:
+            writer (DataWriter): File writer handle
+            file_path (str): The file location of content list
+            image_dir_or_bucket_prefix (str): The s3 bucket prefix or local file directory which used to store the figure
+        """
+        pdf_info_list = self._pipe_res['pdf_info']
+        content_list = union_make(
+            pdf_info_list,
+            MakeMode.STANDARD_FORMAT,
+            DropMode.NONE,
+            image_dir_or_bucket_prefix,
+        )
+        writer.write_string(
+            file_path, json.dumps(content_list, ensure_ascii=False, indent=4)
+        )
+
+    def dump_middle_json(self, writer: DataWriter, file_path: str):
+        """Dump the result of pipeline.
+
+        Args:
+            writer (DataWriter): File writer handler
+            file_path (str): The file location of middle json
+        """
+        writer.write_string(
+            file_path, json.dumps(self._pipe_res, ensure_ascii=False, indent=4)
+        )
+
+    def draw_layout(self, file_path: str) -> None:
+        """Draw the layout.
+
+        Args:
+            file_path (str): The file location of layout result file
+        """
+        dir_name = os.path.dirname(file_path)
+        base_name = os.path.basename(file_path)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name, exist_ok=True)
+        pdf_info = self._pipe_res['pdf_info']
+        draw_layout_bbox(pdf_info, self._dataset.data_bits(), dir_name, base_name)
+
+    def draw_span(self, file_path: str):
+        """Draw the Span.
+
+        Args:
+            file_path (str): The file location of span result file
+        """
+        dir_name = os.path.dirname(file_path)
+        base_name = os.path.basename(file_path)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name, exist_ok=True)
+        pdf_info = self._pipe_res['pdf_info']
+        draw_span_bbox(pdf_info, self._dataset.data_bits(), dir_name, base_name)
+
+    def draw_line_sort(self, file_path: str):
+        """Draw line sort.
+
+        Args:
+            file_path (str): The file location of line sort result file
+        """
+        dir_name = os.path.dirname(file_path)
+        base_name = os.path.basename(file_path)
+        if not os.path.exists(dir_name):
+            os.makedirs(dir_name, exist_ok=True)
+        pdf_info = self._pipe_res['pdf_info']
+        draw_line_sort_bbox(pdf_info, self._dataset.data_bits(), dir_name, base_name)
+
+    def get_compress_pdf_mid_data(self):
+        """Compress the pipeline result.
+
+        Returns:
+            str: compress the pipeline result and return
+        """
+        return JsonCompressor.compress_json(self.pdf_mid_data)
+
+    def apply(self, proc: Callable, *args, **kwargs):
+        """Apply callable method which.
+
+        Args:
+            proc (Callable): invoke proc as follows:
+                proc(pipeline_result, *args, **kwargs)
+
+        Returns:
+            Any: return the result generated by proc
+        """
+        return proc(copy.deepcopy(self._pipe_res), *args, **kwargs)

magic_pdf/tools/common.py

-import copy
-import json as json_parse
 import os
 
 import click
@@ -7,13 +5,12 @@ import fitz
 from loguru import logger
 
 import magic_pdf.model as model_config
+from magic_pdf.config.enums import SupportedPdfParseMethod
 from magic_pdf.config.make_content_config import DropMode, MakeMode
 from magic_pdf.data.data_reader_writer import FileBasedDataWriter
-from magic_pdf.libs.draw_bbox import (draw_layout_bbox, draw_line_sort_bbox,
-                                      draw_model_bbox, draw_span_bbox)
-from magic_pdf.pipe.OCRPipe import OCRPipe
-from magic_pdf.pipe.TXTPipe import TXTPipe
-from magic_pdf.pipe.UNIPipe import UNIPipe
+from magic_pdf.data.dataset import PymuDocDataset
+from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
+from magic_pdf.model.operators import InferenceResult
 
 # from io import BytesIO
 # from pypdf import PdfReader, PdfWriter
@@ -56,7 +53,11 @@ def prepare_env(output_dir, pdf_file_name, method):
 def convert_pdf_bytes_to_bytes_by_pymupdf(pdf_bytes, start_page_id=0, end_page_id=None):
     document = fitz.open('pdf', pdf_bytes)
     output_document = fitz.open()
-    end_page_id = end_page_id if end_page_id is not None and end_page_id >= 0 else len(document) - 1
+    end_page_id = (
+        end_page_id
+        if end_page_id is not None and end_page_id >= 0
+        else len(document) - 1
+    )
     if end_page_id > len(document) - 1:
         logger.warning('end_page_id is out of range, use pdf_docs length')
         end_page_id = len(document) - 1
@@ -94,78 +95,126 @@ def do_parse(
         f_draw_model_bbox = True
         f_draw_line_sort_bbox = True
 
-    if lang == "":
+    if lang == '':
         lang = None
 
-    pdf_bytes = convert_pdf_bytes_to_bytes_by_pymupdf(pdf_bytes, start_page_id, end_page_id)
+    pdf_bytes = convert_pdf_bytes_to_bytes_by_pymupdf(
+        pdf_bytes, start_page_id, end_page_id
+    )
 
-    orig_model_list = copy.deepcopy(model_list)
-    local_image_dir, local_md_dir = prepare_env(output_dir, pdf_file_name,
-                                                parse_method)
+    local_image_dir, local_md_dir = prepare_env(output_dir, pdf_file_name, parse_method)
 
-    image_writer, md_writer = FileBasedDataWriter(
-        local_image_dir), FileBasedDataWriter(local_md_dir)
+    image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
+        local_md_dir
+    )
     image_dir = str(os.path.basename(local_image_dir))
 
-    if parse_method == 'auto':
-        jso_useful_key = {'_pdf_type': '', 'model_list': model_list}
-        pipe = UNIPipe(pdf_bytes, jso_useful_key, image_writer, is_debug=True,
-                       # start_page_id=start_page_id, end_page_id=end_page_id,
-                       lang=lang,
-                       layout_model=layout_model, formula_enable=formula_enable, table_enable=table_enable)
-    elif parse_method == 'txt':
-        pipe = TXTPipe(pdf_bytes, model_list, image_writer, is_debug=True,
-                       # start_page_id=start_page_id, end_page_id=end_page_id,
-                       lang=lang,
-                       layout_model=layout_model, formula_enable=formula_enable, table_enable=table_enable)
-    elif parse_method == 'ocr':
-        pipe = OCRPipe(pdf_bytes, model_list, image_writer, is_debug=True,
-                       # start_page_id=start_page_id, end_page_id=end_page_id,
-                       lang=lang,
-                       layout_model=layout_model, formula_enable=formula_enable, table_enable=table_enable)
-    else:
-        logger.error('unknown parse method')
-        exit(1)
-
-    pipe.pipe_classify()
+    ds = PymuDocDataset(pdf_bytes)
 
     if len(model_list) == 0:
         if model_config.__use_inside_model__:
-            pipe.pipe_analyze()
-            orig_model_list = copy.deepcopy(pipe.model_list)
+            if parse_method == 'auto':
+                if ds.classify() == SupportedPdfParseMethod.TXT:
+                    infer_result = ds.apply(
+                        doc_analyze,
+                        ocr=False,
+                        lang=lang,
+                        layout_model=layout_model,
+                        formula_enable=formula_enable,
+                        table_enable=table_enable,
+                    )
+                    pipe_result = infer_result.pipe_txt_mode(
+                        image_writer, debug_mode=True, lang=lang
+                    )
+                else:
+                    infer_result = ds.apply(
+                        doc_analyze,
+                        ocr=True,
+                        lang=lang,
+                        layout_model=layout_model,
+                        formula_enable=formula_enable,
+                        table_enable=table_enable,
+                    )
+                    pipe_result = infer_result.pipe_ocr_mode(
+                        image_writer, debug_mode=True, lang=lang
+                    )
+
+            elif parse_method == 'txt':
+                infer_result = ds.apply(
+                    doc_analyze,
+                    ocr=False,
+                    lang=lang,
+                    layout_model=layout_model,
+                    formula_enable=formula_enable,
+                    table_enable=table_enable,
+                )
+                pipe_result = infer_result.pipe_txt_mode(
+                    image_writer, debug_mode=True, lang=lang
+                )
+            elif parse_method == 'ocr':
+                infer_result = ds.apply(
+                    doc_analyze,
+                    ocr=True,
+                    lang=lang,
+                    layout_model=layout_model,
+                    formula_enable=formula_enable,
+                    table_enable=table_enable,
+                )
+                pipe_result = infer_result.pipe_ocr_mode(
+                    image_writer, debug_mode=True, lang=lang
+                )
+            else:
+                logger.error('unknown parse method')
+                exit(1)
         else:
             logger.error('need model list input')
             exit(2)
+    else:
+        infer_result = InferenceResult(model_list, ds)
+        if parse_method == 'ocr':
+            pipe_result = infer_result.pipe_ocr_mode(
+                image_writer, debug_mode=True, lang=lang
+            )
+        elif parse_method == 'txt':
+            pipe_result = infer_result.pipe_txt_mode(
+                image_writer, debug_mode=True, lang=lang
+            )
+        else:
+            pipe_result = infer_result.pipe_auto_mode(
+                image_writer, debug_mode=True, lang=lang
+            )
+
+    if f_draw_model_bbox:
+        infer_result.draw_model(
+            os.path.join(local_md_dir, f'{pdf_file_name}_model.pdf')
+        )
 
-    pipe.pipe_parse()
-    pdf_info = pipe.pdf_mid_data['pdf_info']
     if f_draw_layout_bbox:
-        draw_layout_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name)
+        pipe_result.draw_layout(
+            os.path.join(local_md_dir, f'{pdf_file_name}_layout.pdf')
+        )
     if f_draw_span_bbox:
-        draw_span_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name)
-    if f_draw_model_bbox:
-        draw_model_bbox(copy.deepcopy(orig_model_list), pdf_bytes, local_md_dir, pdf_file_name)
+        pipe_result.draw_span(os.path.join(local_md_dir, f'{pdf_file_name}_spans.pdf'))
+
     if f_draw_line_sort_bbox:
-        draw_line_sort_bbox(pdf_info, pdf_bytes, local_md_dir, pdf_file_name)
+        pipe_result.draw_line_sort(
+            os.path.join(local_md_dir, f'{pdf_file_name}_line_sort.pdf')
+        )
 
-    md_content = pipe.pipe_mk_markdown(image_dir, drop_mode=DropMode.NONE, md_make_mode=f_make_md_mode)
     if f_dump_md:
-        md_writer.write_string(
+        pipe_result.dump_md(
+            md_writer,
             f'{pdf_file_name}.md',
-            md_content
+            image_dir,
+            drop_mode=DropMode.NONE,
+            md_make_mode=f_make_md_mode,
         )
 
     if f_dump_middle_json:
-        md_writer.write_string(
-            f'{pdf_file_name}_middle.json',
-            json_parse.dumps(pipe.pdf_mid_data, ensure_ascii=False, indent=4)
-        )
+        pipe_result.dump_middle_json(md_writer, f'{pdf_file_name}_middle.json')
 
     if f_dump_model_json:
-        md_writer.write_string(
-            f'{pdf_file_name}_model.json',
-            json_parse.dumps(orig_model_list, ensure_ascii=False, indent=4)
-        )
+        infer_result.dump_model(md_writer, f'{pdf_file_name}_model.json')
 
     if f_dump_orig_pdf:
         md_writer.write(
@@ -173,11 +222,11 @@ def do_parse(
             pdf_bytes,
         )
 
-    content_list = pipe.pipe_mk_uni_format(image_dir, drop_mode=DropMode.NONE)
     if f_dump_content_list:
-        md_writer.write_string(
+        pipe_result.dump_content_list(
+            md_writer,
             f'{pdf_file_name}_content_list.json',
-            json_parse.dumps(content_list, ensure_ascii=False, indent=4)
+            image_dir
         )
 
     logger.info(f'local output dir is {local_md_dir}')

magic_pdf/user_api.py

@@ -10,22 +10,29 @@
 from loguru import logger
 
 from magic_pdf.data.data_reader_writer import DataWriter
+from magic_pdf.data.dataset import Dataset
 from magic_pdf.libs.version import __version__
 from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 from magic_pdf.pdf_parse_by_ocr import parse_pdf_by_ocr
 from magic_pdf.pdf_parse_by_txt import parse_pdf_by_txt
-
-PARSE_TYPE_TXT = 'txt'
-PARSE_TYPE_OCR = 'ocr'
-
-
-def parse_txt_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, is_debug=False,
-                  start_page_id=0, end_page_id=None, lang=None,
-                  *args, **kwargs):
+from magic_pdf.config.constants import PARSE_TYPE_TXT, PARSE_TYPE_OCR
+
+
+def parse_txt_pdf(
+    dataset: Dataset,
+    model_list: list,
+    imageWriter: DataWriter,
+    is_debug=False,
+    start_page_id=0,
+    end_page_id=None,
+    lang=None,
+    *args,
+    **kwargs
+):
     """解析文本类pdf."""
     pdf_info_dict = parse_pdf_by_txt(
-        pdf_bytes,
-        pdf_models,
+        dataset,
+        model_list,
         imageWriter,
         start_page_id=start_page_id,
         end_page_id=end_page_id,
@@ -43,13 +50,21 @@ def parse_txt_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, i
     return pdf_info_dict
 
 
-def parse_ocr_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, is_debug=False,
-                  start_page_id=0, end_page_id=None, lang=None,
-                  *args, **kwargs):
+def parse_ocr_pdf(
+    dataset: Dataset,
+    model_list: list,
+    imageWriter: DataWriter,
+    is_debug=False,
+    start_page_id=0,
+    end_page_id=None,
+    lang=None,
+    *args,
+    **kwargs
+):
     """解析ocr类pdf."""
     pdf_info_dict = parse_pdf_by_ocr(
-        pdf_bytes,
-        pdf_models,
+        dataset,
+        model_list,
         imageWriter,
         start_page_id=start_page_id,
         end_page_id=end_page_id,
@@ -67,17 +82,24 @@ def parse_ocr_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, i
     return pdf_info_dict
 
 
-def parse_union_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter, is_debug=False,
-                    input_model_is_empty: bool = False,
-                    start_page_id=0, end_page_id=None, lang=None,
-                    *args, **kwargs):
+def parse_union_pdf(
+    dataset: Dataset,
+    model_list: list,
+    imageWriter: DataWriter,
+    is_debug=False,
+    start_page_id=0,
+    end_page_id=None,
+    lang=None,
+    *args,
+    **kwargs
+):
     """ocr和文本混合的pdf，全部解析出来."""
 
     def parse_pdf(method):
         try:
             return method(
-                pdf_bytes,
-                pdf_models,
+                dataset,
+                model_list,
                 imageWriter,
                 start_page_id=start_page_id,
                 end_page_id=end_page_id,
@@ -91,12 +113,12 @@ def parse_union_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter,
     pdf_info_dict = parse_pdf(parse_pdf_by_txt)
     if pdf_info_dict is None or pdf_info_dict.get('_need_drop', False):
         logger.warning('parse_pdf_by_txt drop or error, switch to parse_pdf_by_ocr')
-        if input_model_is_empty:
+        if len(model_list) == 0:
             layout_model = kwargs.get('layout_model', None)
             formula_enable = kwargs.get('formula_enable', None)
             table_enable = kwargs.get('table_enable', None)
-            pdf_models = doc_analyze(
-                pdf_bytes,
+            infer_res = doc_analyze(
+                dataset,
                 ocr=True,
                 start_page_id=start_page_id,
                 end_page_id=end_page_id,
@@ -105,6 +127,7 @@ def parse_union_pdf(pdf_bytes: bytes, pdf_models: list, imageWriter: DataWriter,
                 formula_enable=formula_enable,
                 table_enable=table_enable,
             )
+            model_list = infer_res.get_infer_res()
         pdf_info_dict = parse_pdf(parse_pdf_by_ocr)
         if pdf_info_dict is None:
             raise Exception('Both parse_pdf_by_txt and parse_pdf_by_ocr failed.')

next_docs/en/api.rst

@@ -7,3 +7,5 @@
    api/read_api
    api/schemas
    api/io
+   api/pipe_operators
+   api/model_operators
\ No newline at end of file

next_docs/en/api/model_operators.rst

+
+Model Api
+==========
+
+.. autoclass:: magic_pdf.model.InferenceResultBase
+   :members:
+   :inherited-members:
+   :show-inheritance:

next_docs/en/api/pipe_operators.rst

+
+
+Pipeline Api
+=============
+
+.. autoclass:: magic_pdf.pipe.operators.PipeResult
+   :members:
+   :inherited-members:
+   :show-inheritance:
\ No newline at end of file

next_docs/en/conf.py

@@ -114,7 +114,7 @@ autodoc_mock_imports = [
     'sentencepiece',
     'vllm.cuda_utils',
     'vllm._C',
-    'numpy',
+    # 'numpy',
     'tqdm',
 ]
 

next_docs/en/user_guide/quick_start/to_markdown.rst

@@ -12,17 +12,17 @@ Local File Example
     import os
 
     from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
-    from magic_pdf.config.make_content_config import DropMode, MakeMode
-    from magic_pdf.pipe.OCRPipe import OCRPipe
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 
-
-    ## args
-    model_list = []
+    # args
     pdf_file_name = "abc.pdf"  # replace with the real pdf path
+    name_without_suff = pdf_file_name.split(".")[0]
 
-
-    ## prepare env
+    # prepare env
     local_image_dir, local_md_dir = "output/images", "output"
+    image_dir = str(os.path.basename(local_image_dir))
+
     os.makedirs(local_image_dir, exist_ok=True)
 
     image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
@@ -30,27 +30,31 @@ Local File Example
     )
     image_dir = str(os.path.basename(local_image_dir))
 
+    # read bytes
     reader1 = FileBasedDataReader("")
-    pdf_bytes = reader1.read(pdf_file_name)   # read the pdf content
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
 
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
 
-    pipe = OCRPipe(pdf_bytes, model_list, image_writer)
+    ## inference 
+    infer_result = ds.apply(doc_analyze, ocr=True)
 
-    pipe.pipe_classify()
-    pipe.pipe_analyze()
-    pipe.pipe_parse()
+    ### draw model result on each page
+    infer_result.draw_model(os.path.join(local_md_dir, f"{name_without_suff}_model.pdf"))
 
-    pdf_info = pipe.pdf_mid_data["pdf_info"]
+    ## pipeline
+    pipe_result = infer_result.pipe_ocr_mode(image_writer)
 
+    ### draw layout result on each page
+    pipe_result.draw_layout(os.path.join(local_md_dir, f"{name_without_suff}_layout.pdf"))
 
-    md_content = pipe.pipe_mk_markdown(
-        image_dir, drop_mode=DropMode.NONE, md_make_mode=MakeMode.MM_MD
-    )
+    ### draw spans result on each page
+    pipe_result.draw_span(os.path.join(local_md_dir, f"{name_without_suff}_spans.pdf"))
 
-    if isinstance(md_content, list):
-        md_writer.write_string(f"{pdf_file_name}.md", "\n".join(md_content))
-    else:
-        md_writer.write_string(f"{pdf_file_name}.md", md_content)
+    ### dump markdown
+    pipe_result.dump_md(md_writer, f"{name_without_suff}.md", image_dir)
 
 
 S3 File Example
@@ -61,8 +65,8 @@ S3 File Example
     import os
 
     from magic_pdf.data.data_reader_writer import S3DataReader, S3DataWriter
-    from magic_pdf.config.make_content_config import DropMode, MakeMode
-    from magic_pdf.pipe.OCRPipe import OCRPipe
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 
     bucket_name = "{Your S3 Bucket Name}"  # replace with real bucket name
     ak = "{Your S3 access key}"  # replace with real s3 access key
@@ -74,29 +78,39 @@ S3 File Example
     writer = S3DataWriter('unittest/tmp', bucket_name, ak, sk, endpoint_url)
     image_writer = S3DataWriter('unittest/tmp/images', bucket_name, ak, sk, endpoint_url)
 
-    ## args
-    model_list = []
-    pdf_file_name = f"s3://{bucket_name}/{fake pdf path}"  # replace with the real s3 path
+    # args
+    pdf_file_name = (
+        "s3://llm-pdf-text-1/unittest/tmp/bug5-11.pdf"  # replace with the real s3 path
+    )
+
+    # prepare env
+    local_dir = "output"
+    name_without_suff = os.path.basename(pdf_file_name).split(".")[0]
 
+    # read bytes
     pdf_bytes = reader.read(pdf_file_name)  # read the pdf content
 
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
 
-    pipe = OCRPipe(pdf_bytes, model_list, image_writer)
+    ## inference 
+    infer_result = ds.apply(doc_analyze, ocr=True)
 
-    pipe.pipe_classify()
-    pipe.pipe_analyze()
-    pipe.pipe_parse()
+    ### draw model result on each page
+    infer_result.draw_model(os.path.join(local_dir, f'{name_without_suff}_model.pdf'))  # dump to local
 
-    pdf_info = pipe.pdf_mid_data["pdf_info"]
+    ## pipeline
+    pipe_result = infer_result.pipe_ocr_mode(image_writer)
 
-    md_content = pipe.pipe_mk_markdown(
-        "unittest/tmp/images", drop_mode=DropMode.NONE, md_make_mode=MakeMode.MM_MD
-    )
+    ### draw layout result on each page
+    pipe_result.draw_layout(os.path.join(local_dir, f'{name_without_suff}_layout.pdf'))  # dump to local
+
+    ### draw spans result on each page
+    pipe_result.draw_span(os.path.join(local_dir, f'{name_without_suff}_spans.pdf'))   # dump to local 
 
-    if isinstance(md_content, list):
-        writer.write_string(f"{pdf_file_name}.md", "\n".join(md_content))
-    else:
-        writer.write_string(f"{pdf_file_name}.md", md_content)
+    ### dump markdown
+    pipe_result.dump_md(writer, f'{name_without_suff}.md', "unittest/tmp/images")    # dump to remote s3
 
 
-Check :doc:`../data/data_reader_writer` for more [reader | writer] examples
+Check :doc:`../data/data_reader_writer` for more [reader | writer] examples and check :doc:`../../api/pipe_operators` or :doc:`../../api/model_operators` for api details

next_docs/en/user_guide/tutorial.rst

@@ -7,4 +7,6 @@ From the beginning to the end, Show how to using mineru via a minimal project
 .. toctree::
     :maxdepth: 1
 
-    tutorial/output_file_description
\ No newline at end of file
+    tutorial/output_file_description
+    tutorial/pipeline
+

next_docs/en/user_guide/tutorial/pipeline.rst

+
+
+Pipeline
+==========
+
+
+Minimal Example 
+^^^^^^^^^^^^^^^^^
+
+.. code:: python
+
+    import os
+
+    from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
+
+    # args
+    pdf_file_name = "abc.pdf"  # replace with the real pdf path
+    name_without_suff = pdf_file_name.split(".")[0]
+
+    # prepare env
+    local_image_dir, local_md_dir = "output/images", "output"
+    image_dir = str(os.path.basename(local_image_dir))
+
+    os.makedirs(local_image_dir, exist_ok=True)
+
+    image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
+        local_md_dir
+    )
+    image_dir = str(os.path.basename(local_image_dir))
+
+    # read bytes
+    reader1 = FileBasedDataReader("")
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(md_writer, f"{name_without_suff}.md", image_dir)
+
+Running the above code will result in the following
+
+
+.. code:: bash 
+
+    output/
+    ├── abc.md
+    └── images
+
+
+Excluding the setup of the environment, such as creating directories and importing dependencies, the actual code snippet for converting pdf to markdown is as follows
+
+
+.. code:: python 
+
+    # read bytes
+    reader1 = FileBasedDataReader("")
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(md_writer, f"{name_without_suff}.md", image_dir)
+
+``ds.apply(doc_analyze, ocr=True)`` generates an ``InferenceResult`` object. The ``InferenceResult`` object, when executing the ``pipe_ocr_mode`` method, produces a ``PipeResult`` object.
+The ``PipeResult`` object, upon executing ``dump_md``, generates a ``markdown`` file at the specified location.
+
+
+The pipeline execution process is illustrated in the following diagram
+
+
+.. image:: ../../_static/image/pipeline.drawio.svg 
+
+.. raw:: html
+
+    <br> </br>
+
+Currently, the process is divided into three stages: data, inference, and processing, which correspond to the ``Dataset``, ``InferenceResult``, and ``PipeResult`` entities in the diagram.
+These stages are linked together through methods like ``apply``, ``doc_analyze``, or ``pipe_ocr_mode``
+
+
+.. admonition:: Tip
+    :class: tip
+
+    For more examples on how to use ``Dataset``, ``InferenceResult``, and ``PipeResult``, please refer to :doc:`../quick_start/to_markdown`
+
+    For more detailed information about ``Dataset``, ``InferenceResult``, and ``PipeResult``, please refer to :doc:`../../api/dataset`, :doc:`../../api/model_operators`, :doc:`../../api/pipe_operators`
+
+
+Pipeline Composition
+^^^^^^^^^^^^^^^^^^^^^
+
+.. code:: python 
+
+    class Dataset(ABC):
+        @abstractmethod
+        def apply(self, proc: Callable, *args, **kwargs):
+            """Apply callable method which.
+
+            Args:
+                proc (Callable): invoke proc as follows:
+                    proc(self, *args, **kwargs)
+
+            Returns:
+                Any: return the result generated by proc
+            """
+            pass
+
+    class InferenceResult(InferenceResultBase):
+
+        def apply(self, proc: Callable, *args, **kwargs):
+            """Apply callable method which.
+
+            Args:
+                proc (Callable): invoke proc as follows:
+                    proc(inference_result, *args, **kwargs)
+
+            Returns:
+                Any: return the result generated by proc
+            """
+            return proc(copy.deepcopy(self._infer_res), *args, **kwargs)
+
+        def pipe_ocr_mode(
+            self,
+            imageWriter: DataWriter,
+            start_page_id=0,
+            end_page_id=None,
+            debug_mode=False,
+            lang=None,
+            ) -> PipeResult:
+            pass
+
+    class PipeResult:
+        def apply(self, proc: Callable, *args, **kwargs):
+            """Apply callable method which.
+
+            Args:
+                proc (Callable): invoke proc as follows:
+                    proc(pipeline_result, *args, **kwargs)
+
+            Returns:
+                Any: return the result generated by proc
+            """
+            return proc(copy.deepcopy(self._pipe_res), *args, **kwargs)
+
+
+The ``Dataset``, ``InferenceResult``, and ``PipeResult`` classes all have an ``apply`` method, which can be used to chain different stages of the computation. 
+As shown below, ``MinerU`` provides a set of methods to compose these classes.
+
+
+.. code:: python 
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    ds.apply(doc_analyze, ocr=True).pipe_ocr_mode(image_writer).dump_md(md_writer, f"{name_without_suff}.md", image_dir)
+
+
+Users can implement their own functions for chaining as needed. For example, a user could use the ``apply`` method to create a function that counts the number of pages in a ``pdf`` file.
+
+
+.. code:: python
+
+    from magic_pdf.data.data_reader_writer import  FileBasedDataReader
+    from magic_pdf.data.dataset import PymuDocDataset
+
+    # args
+    pdf_file_name = "abc.pdf"  # replace with the real pdf path
+
+    # read bytes
+    reader1 = FileBasedDataReader("")
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
+
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
+
+    def count_page(ds)-> int:
+        return len(ds)
+
+    print("page number: ", ds.apply(count_page)) # will output the page count of `abc.pdf`

next_docs/requirements.txt

+numpy==1.26.4
+click==8.1.7
+fast-langdetect==0.2.2
+Brotli==1.1.0
 boto3>=1.28.43
 loguru>=0.6.0
 myst-parser
@@ -9,4 +13,4 @@ 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
+autodoc_pydantic>=2.2.0

next_docs/zh_cn/user_guide/quick_start/to_markdown.rst

 
-
 转换为 Markdown 文件
 ========================
 
-
 本地文件示例
-^^^^^^^^^^^
+^^^^^^^^^^^^^^^^^^
 
 .. code:: python
 
     import os
 
     from magic_pdf.data.data_reader_writer import FileBasedDataWriter, FileBasedDataReader
-    from magic_pdf.config.make_content_config import DropMode, MakeMode
-    from magic_pdf.pipe.OCRPipe import OCRPipe
-
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 
-    ## args
-    model_list = []
+    # args
     pdf_file_name = "abc.pdf"  # replace with the real pdf path
+    name_without_suff = pdf_file_name.split(".")[0]
 
-
-    ## prepare env
+    # prepare env
     local_image_dir, local_md_dir = "output/images", "output"
+    image_dir = str(os.path.basename(local_image_dir))
+
     os.makedirs(local_image_dir, exist_ok=True)
 
     image_writer, md_writer = FileBasedDataWriter(local_image_dir), FileBasedDataWriter(
@@ -30,39 +28,43 @@
     )
     image_dir = str(os.path.basename(local_image_dir))
 
+    # read bytes
     reader1 = FileBasedDataReader("")
-    pdf_bytes = reader1.read(pdf_file_name)   # read the pdf content
+    pdf_bytes = reader1.read(pdf_file_name)  # read the pdf content
 
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
 
-    pipe = OCRPipe(pdf_bytes, model_list, image_writer)
+    ## inference 
+    infer_result = ds.apply(doc_analyze, ocr=True)
 
-    pipe.pipe_classify()
-    pipe.pipe_analyze()
-    pipe.pipe_parse()
+    ### draw model result on each page
+    infer_result.draw_model(os.path.join(local_md_dir, f"{name_without_suff}_model.pdf"))
 
-    pdf_info = pipe.pdf_mid_data["pdf_info"]
+    ## pipeline
+    pipe_result = infer_result.pipe_ocr_mode(image_writer)
 
+    ### draw layout result on each page
+    pipe_result.draw_layout(os.path.join(local_md_dir, f"{name_without_suff}_layout.pdf"))
 
-    md_content = pipe.pipe_mk_markdown(
-        image_dir, drop_mode=DropMode.NONE, md_make_mode=MakeMode.MM_MD
-    )
+    ### draw spans result on each page
+    pipe_result.draw_span(os.path.join(local_md_dir, f"{name_without_suff}_spans.pdf"))
 
-    if isinstance(md_content, list):
-        md_writer.write_string(f"{pdf_file_name}.md", "\n".join(md_content))
-    else:
-        md_writer.write_string(f"{pdf_file_name}.md", md_content)
+    ### dump markdown
+    pipe_result.dump_md(md_writer, f"{name_without_suff}.md", image_dir)
 
 
-对象存储使用示例
-^^^^^^^^^^^^^^^
+对象存储文件示例
+^^^^^^^^^^^^^^^^
 
 .. code:: python
 
     import os
 
     from magic_pdf.data.data_reader_writer import S3DataReader, S3DataWriter
-    from magic_pdf.config.make_content_config import DropMode, MakeMode
-    from magic_pdf.pipe.OCRPipe import OCRPipe
+    from magic_pdf.data.dataset import PymuDocDataset
+    from magic_pdf.model.doc_analyze_by_custom_model import doc_analyze
 
     bucket_name = "{Your S3 Bucket Name}"  # replace with real bucket name
     ak = "{Your S3 access key}"  # replace with real s3 access key
@@ -74,30 +76,39 @@
     writer = S3DataWriter('unittest/tmp', bucket_name, ak, sk, endpoint_url)
     image_writer = S3DataWriter('unittest/tmp/images', bucket_name, ak, sk, endpoint_url)
 
-    ## args
-    model_list = []
-    pdf_file_name = f"s3://{bucket_name}/{fake pdf path}"  # replace with the real s3 path
+    # args
+    pdf_file_name = (
+        "s3://llm-pdf-text-1/unittest/tmp/bug5-11.pdf"  # replace with the real s3 path
+    )
 
+    # prepare env
+    local_dir = "output"
+    name_without_suff = os.path.basename(pdf_file_name).split(".")[0]
+
+    # read bytes
     pdf_bytes = reader.read(pdf_file_name)  # read the pdf content
 
+    # proc
+    ## Create Dataset Instance
+    ds = PymuDocDataset(pdf_bytes)
 
-    pipe = OCRPipe(pdf_bytes, model_list, image_writer)
+    ## inference 
+    infer_result = ds.apply(doc_analyze, ocr=True)
 
-    pipe.pipe_classify()
-    pipe.pipe_analyze()
-    pipe.pipe_parse()
+    ### draw model result on each page
+    infer_result.draw_model(os.path.join(local_dir, f'{name_without_suff}_model.pdf'))  # dump to local
 
-    pdf_info = pipe.pdf_mid_data["pdf_info"]
+    ## pipeline
+    pipe_result = infer_result.pipe_ocr_mode(image_writer)
 
-    md_content = pipe.pipe_mk_markdown(
-        "unittest/tmp/images", drop_mode=DropMode.NONE, md_make_mode=MakeMode.MM_MD
-    )
+    ### draw layout result on each page
+    pipe_result.draw_layout(os.path.join(local_dir, f'{name_without_suff}_layout.pdf'))  # dump to local
 
-    if isinstance(md_content, list):
-        writer.write_string(f"{pdf_file_name}.md", "\n".join(md_content))
-    else:
-        writer.write_string(f"{pdf_file_name}.md", md_content)
+    ### draw spans result on each page
+    pipe_result.draw_span(os.path.join(local_dir, f'{name_without_suff}_spans.pdf'))  # dump to local 
 
+    ### dump markdown
+    pipe_result.dump_md(writer, f'{name_without_suff}.md', "unittest/tmp/images")  # dump to remote s3
 
 
 前去 :doc:`../data/data_reader_writer` 获取更多有关 **读写** 示例

next_docs/zh_cn/user_guide/tutorial.rst

@@ -9,3 +9,5 @@
     :caption: 教程
 
     tutorial/output_file_description
+    tutorial/pipeline
+