diff --git a/.env.template b/.env.template index 87854c2..c1740c9 100644 --- a/.env.template +++ b/.env.template @@ -48,4 +48,7 @@ TASK_TIMEOUT=3600 # 调试模式 DEBUG=false # 日志级别 -LOG_LEVEL=INFO \ No newline at end of file +LOG_LEVEL=INFO + +# PDF解析 +MINERU_MODEL_SOURCE=local \ No newline at end of file diff --git a/.gitignore b/.gitignore index 3120f40..0ec76d2 100644 --- a/.gitignore +++ b/.gitignore @@ -174,4 +174,5 @@ cython_debug/ # PyPI configuration file .pypirc -examples/ \ No newline at end of file +examples/ +models/ \ No newline at end of file diff --git a/mineru.json b/mineru.json new file mode 100644 index 0000000..5612e57 --- /dev/null +++ b/mineru.json @@ -0,0 +1,37 @@ +{ + "bucket_info": { + "bucket-name-1": [ + "ak", + "sk", + "endpoint" + ], + "bucket-name-2": [ + "ak", + "sk", + "endpoint" + ] + }, + "latex-delimiter-config": { + "display": { + "left": "$$", + "right": "$$" + }, + "inline": { + "left": "$", + "right": "$" + } + }, + "llm-aided-config": { + "title_aided": { + "api_key": "your_api_key", + "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1", + "model": "qwen2.5-32b-instruct", + "enable": false + } + }, + "models-dir": { + "pipeline": "models", + "vlm": "" + }, + "config_version": "1.3.0" +} \ No newline at end of file diff --git a/parsers/base_models.py b/parsers/base_models.py index 3ff179b..8343a89 100644 --- a/parsers/base_models.py +++ b/parsers/base_models.py @@ -1,6 +1,7 @@ import logging from abc import ABC, abstractmethod from enum import Enum +from pathlib import Path from typing import Any from pydantic import BaseModel, Field @@ -19,15 +20,34 @@ class TableDataItem(BaseModel): """表格数据类""" rows: int # 行数 columns: int # 列数 + grid: list[list[str]] = Field(default_factory=list) # 网格数据 row_headers: list[Any] = Field(default_factory=list) # 行头 column_headers: list[Any] = Field(default_factory=list) # 列头 data: list[list[str]] = Field(default_factory=list) # 数据 + caption: list[str] = Field(default_factory=list) # 表格标题 + footnote: list[str] = Field(default_factory=list) # 表格注脚 + +class TextDataItem(BaseModel): + """文本数据类""" + text: str # 文本 + text_level: int|None = None # 文本级别 + +class ImageDataItem(BaseModel): + """图片数据类""" + uri: str|None = None # 图片 URI + caption: list[str] = Field(default_factory=list) # 图片标题 + footnote: list[str] = Field(default_factory=list) # 图片注脚 + +class FormulaDataItem(BaseModel): + """公式数据类""" + text: str # 公式 + text_format: str|None = None # 公式格式 class ChunkData(BaseModel): """块数据类""" type: ChunkType name: str|None = None - content: str|TableDataItem|None = None + content: TableDataItem|TextDataItem|ImageDataItem|FormulaDataItem description: str|None = None class DocumentData(BaseModel): @@ -45,6 +65,6 @@ class DocumentParser(ABC): """文档解析器基类""" @abstractmethod - async def parse(self, file_path: str) -> DocumentData: + async def parse(self, file_path: Path) -> DocumentData: """解析文档""" pass diff --git a/parsers/docx_parser.py b/parsers/docx_parser.py index ddb9cb1..a69994d 100644 --- a/parsers/docx_parser.py +++ b/parsers/docx_parser.py @@ -8,6 +8,7 @@ import asyncio import logging import time +from pathlib import Path from docling.datamodel.base_models import InputFormat from docling.document_converter import DocumentConverter, WordFormatOption @@ -30,7 +31,10 @@ ChunkType, DocumentData, DocumentParser, + FormulaDataItem, + ImageDataItem, TableDataItem, + TextDataItem, ) from parsers.parser_registry import register_parser @@ -54,7 +58,7 @@ def __init__(self) -> None: ) logger.debug("DocxDocumentParser initialized with SimplePipeline") - async def parse(self, file_path: str) -> DocumentData: + async def parse(self, file_path: Path) -> DocumentData: """异步解析DOCX文件 Args: @@ -70,41 +74,64 @@ async def parse(self, file_path: str) -> DocumentData: result = await loop.run_in_executor(None, self._converter.convert, file_path) doc_data = result.document - # 确保文档数据包含所有必要的属性 - if not hasattr(doc_data, 'name'): - doc_data.name = 'Unknown Document' - if not hasattr(doc_data, 'texts'): - doc_data.texts = [] - if not hasattr(doc_data, 'pictures'): - doc_data.pictures = [] - if not hasattr(doc_data, 'tables'): - doc_data.tables = [] - - title = self._extract_title(doc_data) - images = self._extract_images(doc_data.pictures) - tables = self._extract_tables(doc_data.tables) - texts = self._extract_texts(doc_data.texts) + # 并行处理不同类型的内容 + document_data = await self._process_content_parallel(doc_data) processing_time = time.time() - start_time + document_data.processing_time = processing_time logger.info(f"Successfully parsed DOCX: {file_path} (took {processing_time:.2f}s)") - return DocumentData( - title=title, - texts=texts, - tables=tables, - images=images, - processing_time=processing_time, - success=True - ) + return document_data except Exception as e: - processing_time = time.time() - start_time - error_msg = f"Failed to parse DOCX file {file_path}: {type(e).__name__}: {e}" - logger.exception(error_msg) # 记录完整堆栈 - return DocumentData( - success=False, - error_message=str(e), - processing_time=processing_time - ) + raise Exception(f"Failed to parse DOCX file {file_path}") from e + + async def _process_content_parallel(self, doc_data: DoclingDocument) -> DocumentData: + """并行处理文档内容""" + # 创建任务列表 + tasks = [] + + # 添加图片处理任务 + if doc_data.pictures: + tasks.append(self._extract_images_async(doc_data.pictures)) + + # 添加表格处理任务 + if doc_data.tables: + tasks.append(self._extract_tables_async(doc_data.tables)) + + # 添加文本处理任务 + if doc_data.texts: + tasks.append(self._extract_texts_async(doc_data.texts)) + + # 并行执行所有任务 + results = await asyncio.gather(*tasks, return_exceptions=True) + + # 处理结果 + images: list[ChunkData] = [] + tables: list[ChunkData] = [] + texts: list[ChunkData] = [] + + for i, result in enumerate(results): + if isinstance(result, Exception): + logger.error(f"Error processing content type {i}: {result}") + continue + if isinstance(result, list): + if result and result[0].type == ChunkType.IMAGE: + images = result + elif result and result[0].type == ChunkType.TABLE: + tables = result + elif result and result[0].type == ChunkType.TEXT: + texts = result + + # 提取标题 + title = self._extract_title(doc_data) + + return DocumentData( + title=title, + texts=texts, + tables=tables, + images=images, + success=True + ) def _extract_images(self, pictures: list[PictureItem]) -> list[ChunkData]: """提取文档中的图片 @@ -115,22 +142,22 @@ def _extract_images(self, pictures: list[PictureItem]) -> list[ChunkData]: Returns: List[ChunkData]: 图片列表 """ - image_items = [] + image_items: list[ChunkData] = [] for idx, picture in enumerate(pictures): - image_uri = "" - if hasattr(picture, 'image') and picture.image and hasattr(picture.image, 'uri'): - image_uri = str(picture.image.uri) - - caption = "" - if hasattr(picture, 'captions') and picture.captions: - caption = str(picture.captions[0]) - + if not picture.image: + continue + image_uri = str(picture.image.uri) + caption = [caption.cref for caption in picture.captions] + footnote = [footnote.cref for footnote in picture.footnotes] image_items.append( ChunkData( type=ChunkType.IMAGE, - name=getattr(picture, 'self_ref', None) or f"#/pictures/{idx}", - content=image_uri, - description=caption + name=f"#/pictures/{idx}", + content=ImageDataItem( + uri=image_uri, + caption=caption, + footnote=footnote + ) ) ) @@ -145,32 +172,22 @@ def _extract_tables(self, tables: list[TableItem]) -> list[ChunkData]: Returns: List[ChunkData]: 表格列表 """ - # 添加安全检查，确保 tables 参数存在且可迭代 - if not tables or not hasattr(tables, '__iter__'): - return [] - table_items: list[ChunkData] = [] for table in tables: - if not hasattr(table, 'data') or not hasattr(table.data, 'grid'): - continue - if len(table.data.grid) == 0: - continue - - table_cells = table.data.grid - row_headers = [cell.text for cell in table_cells[0] if cell.row_header] - column_headers = [cell.text for cell in table_cells[0] if cell.column_header] - data = [[cell.text for cell in row] for row in table_cells[1:]] + caption = [caption.cref for caption in table.captions] + footnote = [footnote.cref for footnote in table.footnotes] + grid = [[cell.text if cell.text else '' for cell in row] for row in table.data.grid] table_data = TableDataItem( rows=table.data.num_rows, columns=table.data.num_cols, - row_headers=row_headers, - column_headers=column_headers, - data=data + grid=grid, + caption=caption, + footnote=footnote ) table_items.append( ChunkData( type=ChunkType.TABLE, - name=getattr(table, 'self_ref', None) or f"table-{len(table_items)}", + name=f"#/tables/{len(table_items)}", content=table_data ) ) @@ -212,16 +229,35 @@ def _extract_texts(self, texts:list[TitleItem|SectionHeaderItem|ListItem|CodeIte text_items.append( ChunkData( type=ChunkType.FORMULA, - name=item.self_ref or f"formula-{len(text_items)}", - content=item.text + name=f"formula-{len(text_items)}", + content=FormulaDataItem( + text=item.text + ) ) ) case _: text_items.append( ChunkData( type=ChunkType.TEXT, - name=f"text-{len(text_items)}", - content=item.text + name=f"#/texts/{len(text_items)}", + content=TextDataItem( + text=item.text + ) ) ) return text_items + + async def _extract_images_async(self, pictures: list[PictureItem]) -> list[ChunkData]: + """异步提取文档中的图片""" + loop = asyncio.get_event_loop() + return await loop.run_in_executor(None, self._extract_images, pictures) + + async def _extract_tables_async(self, tables: list[TableItem]) -> list[ChunkData]: + """异步提取文档中的表格""" + loop = asyncio.get_event_loop() + return await loop.run_in_executor(None, self._extract_tables, tables) + + async def _extract_texts_async(self, texts: list[TitleItem|SectionHeaderItem|ListItem|CodeItem|FormulaItem|TextItem]) -> list[ChunkData]: + """异步提取文档中的文本""" + loop = asyncio.get_event_loop() + return await loop.run_in_executor(None, self._extract_texts, texts) diff --git a/parsers/excel_parser.py b/parsers/excel_parser.py index 160276b..872bbfb 100644 --- a/parsers/excel_parser.py +++ b/parsers/excel_parser.py @@ -5,8 +5,10 @@ 包括表格数据提取和图片处理。 """ +import asyncio import base64 import json +import logging import time import warnings from dataclasses import dataclass @@ -24,7 +26,9 @@ ChunkType, DocumentData, DocumentParser, + ImageDataItem, TableDataItem, + TextDataItem, ) from parsers.parser_registry import register_parser @@ -35,6 +39,7 @@ CellValue = str|int|float|bool|None|datetime|date TableData = list[list[CellValue]] +logger = logging.getLogger(__name__) @dataclass class ExcelParseConfig: @@ -64,7 +69,7 @@ def __init__(self, config: ExcelParseConfig | None = None): self.config: ExcelParseConfig = config or ExcelParseConfig() self.image_index: int = 0 - async def parse(self, file_path: str) -> DocumentData: + async def parse(self, file_path: Path) -> DocumentData: """ 将Excel文件转换为JSON格式 Args: @@ -77,55 +82,90 @@ async def parse(self, file_path: str) -> DocumentData: try: # 初始化内容列表和图片列表 - texts: list[ChunkData] = [] - tables: list[ChunkData] = [] - images: list[ChunkData] = [] # 加载工作簿 workbook = self._load_workbook(file_path) - # 处理每个工作表 - for sheet_index, sheet_name in enumerate(workbook.sheetnames): - sheet = workbook[sheet_name] - - # 添加工作表标题 - texts.append(ChunkData( - type=ChunkType.TEXT, - name=sheet_name, - content=f"工作表 {sheet_index + 1}: {sheet_name}", - description="工作表标题" - )) - - # 处理图片 - sheet_images = self._extract_sheet_images(sheet) - images.extend(sheet_images) - - # 处理表格数据 - table_content = self._extract_table_data(sheet) - tables.append(ChunkData( - type=ChunkType.TABLE, - name="表格", - content=table_content, - description="表格" - )) + # 并行处理每个工作表 + document_data = await self._process_sheets_parallel(workbook, file_path) + processing_time = time.time() - start_time - return DocumentData( - title=Path(file_path).stem, - texts=texts, - tables=tables, - images=images, - processing_time=processing_time, - success=True - ) + document_data.processing_time = processing_time + return document_data except Exception as e: - processing_time = time.time() - start_time - return DocumentData( - success=False, - error_message=str(e), - processing_time=processing_time - ) + raise Exception(f"Failed to parse Excel file {file_path}: {type(e).__name__}: {e}") from e + + async def _process_sheets_parallel(self, workbook: Workbook, file_path: Path) -> DocumentData: + """并行处理所有工作表""" + # 创建任务列表 + tasks = [] + + for sheet_index, sheet_name in enumerate(workbook.sheetnames): + sheet = workbook[sheet_name] + tasks.append(self._process_sheet_async(sheet, sheet_index, sheet_name)) + + # 并行执行所有工作表处理任务 + if tasks: + results = await asyncio.gather(*tasks) + + # 合并结果 + texts: list[ChunkData] = [] + tables: list[ChunkData] = [] + images: list[ChunkData] = [] - def _load_workbook(self, excel_path: str) -> Workbook: + for result in results: + if result: + texts.extend(result.get('texts', [])) + tables.extend(result.get('tables', [])) + images.extend(result.get('images', [])) + + return DocumentData( + title=Path(file_path).stem, + texts=texts, + tables=tables, + images=images, + success=True + ) + + async def _process_sheet_async(self, sheet: Worksheet, sheet_index: int, sheet_name: str) -> dict|None: + """异步处理单个工作表""" + try: + loop = asyncio.get_event_loop() + + # 并行处理图片和表格 + image_task = loop.run_in_executor(None, self._extract_sheet_images, sheet) + table_task = loop.run_in_executor(None, self._extract_table_data, sheet) + + # 等待两个任务完成 + sheet_images, table_content = await asyncio.gather(image_task, table_task) + + # 添加工作表标题 + texts = [ChunkData( + type=ChunkType.TEXT, + name=sheet_name, + content=TextDataItem( + text=f"工作表 {sheet_index + 1}: {sheet_name}", + ), + )] + + # 创建表格数据 + tables = [ChunkData( + type=ChunkType.TABLE, + name=f"#/tables/{sheet_index}", + content=table_content + )] if table_content else [] + + return { + 'texts': texts, + 'tables': tables, + 'images': sheet_images + } + + except Exception as e: + logger.error(f"Error processing sheet {sheet_name}: {e}") + return None + + def _load_workbook(self, excel_path: Path) -> Workbook: """ 加载Excel工作簿 Args: @@ -191,8 +231,9 @@ def _process_image_object(self, img_obj: Image) -> ChunkData | None: image_info = ChunkData( type=ChunkType.IMAGE, name=f"#/pictures/{self.image_index}", - content=uri, - description=self.config.image_description_placeholder + content=ImageDataItem( + uri=uri + ) ) self.image_index += 1 @@ -263,8 +304,7 @@ def _extract_table_data(self, sheet: Worksheet) -> TableDataItem: return TableDataItem( rows=len(all_rows), columns=max_col, - row_headers=all_rows[0] if all_rows else [], - data=all_rows[1:] if len(all_rows) > 1 else [] + grid=all_rows ) def _get_merged_cells(self, sheet: Worksheet) -> dict[tuple[int, int, int, int], str]: diff --git a/parsers/pdf_parser.py b/parsers/pdf_parser.py new file mode 100644 index 0000000..64141b2 --- /dev/null +++ b/parsers/pdf_parser.py @@ -0,0 +1,325 @@ +""" +PDF文档解析器模块 + +该模块提供使用Mineru库解析PDF文档并提取结构化内容的功能。 +支持标题、段落、列表、表格和图片的识别与输出。 +""" + +import asyncio +import base64 +import os +import re +import shutil +import time +from pathlib import Path +from typing import Any + +import aiofiles # type: ignore +from bs4 import BeautifulSoup +from loguru import logger +from mineru.backend.pipeline.model_json_to_middle_json import ( # type: ignore + result_to_middle_json as pipeline_result_to_middle_json, +) +from mineru.backend.pipeline.pipeline_analyze import ( # type: ignore + doc_analyze as pipeline_doc_analyze, +) +from mineru.backend.pipeline.pipeline_middle_json_mkcontent import ( # type: ignore + union_make as pipeline_union_make, +) +from mineru.cli.common import prepare_env, read_fn # type: ignore +from mineru.data.data_reader_writer import FileBasedDataWriter # type: ignore +from mineru.utils.enum_class import MakeMode # type: ignore + +from parsers.base_models import ( + ChunkData, + ChunkType, + DocumentData, + DocumentParser, + FormulaDataItem, + ImageDataItem, + TableDataItem, + TextDataItem, +) +from parsers.parser_registry import register_parser + + +@register_parser(['.pdf']) +class PdfDocumentParser(DocumentParser): + """PDF文档解析器 + + 使用Mineru的现代解析管道提取PDF文档的结构化内容。 + 支持异步解析接口，符合DocumentParser抽象基类。 + """ + + def __init__(self) -> None: + """初始化解析器""" + super().__init__() + self.output_dir = Path(__file__).parent.parent / "output" + self.lang = "ch" + self.parse_method = "auto" + self.formula_enable = True + self.table_enable = True + + async def parse(self, file_path: Path) -> DocumentData: + start_time = time.time() + try: + # 执行同步转换（在异步中运行） + pdf_file_name = file_path.stem + local_image_dir, _ = prepare_env(self.output_dir, pdf_file_name, self.parse_method) + loop = asyncio.get_event_loop() + content_list = await loop.run_in_executor( + None, + self._parse_pdf_to_content_list, + file_path, local_image_dir, self.lang, self.parse_method, self.formula_enable, self.table_enable + ) + + # 执行并行处理 + document_data = await self._process_content_parallel(file_path, content_list) + + shutil.rmtree(local_image_dir, ignore_errors=True) + processing_time = time.time() - start_time + document_data.processing_time = processing_time + logger.info(f"Successfully parsed DOCX: {file_path} (took {processing_time:.2f}s)") + return document_data + + except Exception as e: + raise Exception(f"Failed to parse PDF file {file_path}: {type(e).__name__}: {e}") from e + + async def _process_content_parallel(self, file_path: Path, content_list: list[dict[str, Any]]) -> DocumentData: + # 创建任务列表 + tasks = [] + title = file_path.stem + texts_chunks: list[ChunkData] = [] + tables_chunks: list[ChunkData] = [] + images_chunks: list[ChunkData] = [] + formulas_chunks: list[ChunkData] = [] + + for idx, item in enumerate(content_list): + if item["type"] == "image": + tasks.append(self._process_image(idx, item)) + elif item["type"] == "table": + tasks.append(self._process_table_async(idx, item)) + elif item["type"] == "equation": + tasks.append(self._process_formula_async(idx, item)) + elif item["type"] == "text": + if item.get("text_level") == 1: + title = item.get("text", "") + continue + tasks.append(self._process_text_async(idx, item)) + + # 并行执行所有任务 + if tasks: + results = await asyncio.gather(*tasks) + + # 处理结果，过滤掉 None 和异常 + for result in results: + if result is None: + continue + + if result.type == ChunkType.IMAGE: + images_chunks.append(result) + elif result.type == ChunkType.TABLE: + tables_chunks.append(result) + elif result.type == ChunkType.FORMULA: + formulas_chunks.append(result) + elif result.type == ChunkType.TEXT: + texts_chunks.append(result) + return DocumentData( + title=title, + texts=texts_chunks, + tables=tables_chunks, + images=images_chunks, + formulas=formulas_chunks, + success=True + ) + + def _parse_pdf_to_content_list( + self, + file_path: Path, + local_image_dir: Path, + lang: str = "ch", + parse_method: str = "auto", + formula_enable: bool = True, + table_enable: bool = True, + ) -> list[dict[str, Any]]: + + # 1. 读取 PDF bytes + try: + pdf_bytes = read_fn(file_path) + except Exception as e: + logger.error(f"Failed to read PDF file {file_path}: {e}") + raise + + image_writer = FileBasedDataWriter(local_image_dir) + + # 4. 执行 pipeline 解析 + try: + infer_results, all_image_lists, all_pdf_docs, lang_list, ocr_enabled_list = pipeline_doc_analyze( + [pdf_bytes], [lang], parse_method=parse_method, + formula_enable=formula_enable, table_enable=table_enable + ) + model_list = infer_results[0] + images_list = all_image_lists[0] + pdf_doc = all_pdf_docs[0] + _lang = lang_list[0] + _ocr_enable = ocr_enabled_list[0] + except Exception as e: + logger.error(f"Failed in pipeline_doc_analyze: {e}") + raise + + # 5. 生成 middle_json + try: + middle_json = pipeline_result_to_middle_json( + model_list, images_list, pdf_doc, image_writer, _lang, _ocr_enable, formula_enable + ) + pdf_info = middle_json["pdf_info"] + except Exception as e: + logger.error(f"Failed in pipeline_result_to_middle_json: {e}") + raise + + # 6. 生成 content_list（不写入文件） + try: + content_list = pipeline_union_make(pdf_info, MakeMode.CONTENT_LIST, str(local_image_dir)) + except Exception as e: + logger.error(f"Failed in pipeline_union_make: {e}") + raise + return list(content_list) + + async def _process_image(self, idx:int,image:dict[str, Any]) -> ChunkData|None: + if not image.get("img_path") or not os.path.exists(str(image.get("img_path"))): + return None + image_path = Path(str(image.get("img_path"))) + + async with aiofiles.open(image_path, 'rb') as img_file: + img_data = await img_file.read() + + base64_data = base64.b64encode(img_data).decode("utf-8") + ext = os.path.splitext(image_path.name)[1].lower() + mime_type = "image/jpeg" + if ext == ".png": + mime_type = "image/png" + elif ext == ".gif": + mime_type = "image/gif" + + return ChunkData( + type=ChunkType.IMAGE, + name=f"#/pictures/{idx}", + content=ImageDataItem( + uri=f"data:{mime_type};base64,{base64_data}", + caption=image.get("img_caption", []), + footnote=image.get("img_footnote", []) + ) + ) + + + async def _process_table_async(self, idx:int, table:dict[str, Any]) -> ChunkData|None: + """异步处理表格（在线程池中执行）""" + loop = asyncio.get_event_loop() + return await loop.run_in_executor(None, self._process_table, idx, table) + + async def _process_formula_async(self, idx:int, formula:dict[str, Any]) -> ChunkData|None: + """异步处理公式（在线程池中执行）""" + loop = asyncio.get_event_loop() + return await loop.run_in_executor(None, self._process_formula, idx, formula) + + async def _process_text_async(self, idx:int, text:dict[str, Any]) -> ChunkData|None: + """异步处理文本（在线程池中执行）""" + loop = asyncio.get_event_loop() + return await loop.run_in_executor(None, self._process_text, idx, text) + + def _process_table(self, idx:int,table:dict[str, Any]) -> ChunkData|None: + """同步处理表格""" + html_str = table.get("table_body", "") + soup = BeautifulSoup(html_str, 'html.parser') + table_body = soup.find('table') + if not table_body: + return None + # 使用网格处理 rowspan 和 colspan + grid: list[list[str]] = [] + max_col = 0 + + for row_idx, tr in enumerate(table_body.find_all('tr')): # type: ignore + while len(grid) <= row_idx: + grid.append([]) + current_row: list[str] = grid[row_idx] + col_idx = 0 + + # 跳过被 rowspan 占据的位置 + while col_idx < len(current_row) and current_row[col_idx] is not None: + col_idx += 1 + + for cell in tr.find_all(['td', 'th']): + text = ''.join(cell.stripped_strings) + text = re.sub(r'\s+', ' ', text).strip() + if not text: + text = "" + + rowspan = int(cell.get('rowspan', 1)) + colspan = int(cell.get('colspan', 1)) + + # 找到下一个空位 + while col_idx < len(current_row) and current_row[col_idx] is not None: + col_idx += 1 + + # 扩展行 + while len(current_row) < col_idx + colspan: + current_row.append("") + + # 填入内容 + for r in range(rowspan): + actual_row_idx = row_idx + r + while len(grid) <= actual_row_idx: + grid.append([]) + actual_row = grid[actual_row_idx] + while len(actual_row) < col_idx + colspan: + actual_row.append("") + for c in range(colspan): + actual_row[col_idx + c] = text + + col_idx += colspan + + max_col = max(max_col, len(current_row)) + + # 确保所有行长度一致 + for row in grid: + while len(row) < max_col: + row.append("") + # 5. 创建并返回 TableDataItem 实例 + table_data = TableDataItem( + rows=len(grid), + columns=max_col, + grid=grid, + caption=table.get("table_caption", []), + footnote=table.get("table_footnote", []) + ) + return ChunkData( + type=ChunkType.TABLE, + name=f"#/tables/{idx}", + content=table_data + ) + + def _process_formula(self, idx:int,formula:dict[str, Any]) -> ChunkData|None: + """同步处理公式""" + if not formula.get("text") or formula.get("text") == "": + return None + return ChunkData( + type=ChunkType.FORMULA, + name=f"#/formulas/{idx}", + content=FormulaDataItem( + text=str(formula.get("text")), + text_format=formula.get("text_format") + ) + ) + + def _process_text(self, idx:int,text:dict[str, Any]) -> ChunkData|None: + """同步处理文本""" + if not text.get("text") or text.get("text") == "": + return None + return ChunkData( + type=ChunkType.TEXT, + name=f"#/texts/{idx}", + content=TextDataItem( + text=str(text.get("text")), + text_level=int(text.get("text_level", 0)) + ) + ) diff --git a/pyproject.toml b/pyproject.toml index 56d7d32..f69e8f5 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,6 +16,8 @@ dependencies = [ "openpyxl>=3.1.5", "pydantic>=2.11.7", "docling>=2.45.0", + "mineru[core]>=2.1.11", + "beautifulsoup4>=4.13.4", ] [dependency-groups] diff --git a/tests/test_async_parsers.py b/tests/test_async_parsers.py new file mode 100644 index 0000000..25d6c7e --- /dev/null +++ b/tests/test_async_parsers.py @@ -0,0 +1,262 @@ +""" +异步解析器测试模块 + +测试所有解析器的异步并行处理功能 +""" + +import pytest +import asyncio +from unittest.mock import Mock, patch, mock_open +from pathlib import Path + +from parsers.docx_parser import DocxDocumentParser +from parsers.excel_parser import ExcelParser +from parsers.pdf_parser import PdfDocumentParser +from parsers.base_models import ChunkData, ChunkType, ImageDataItem, TableDataItem, TextDataItem + + +class TestAsyncParsers: + """测试异步解析器""" + + @pytest.fixture + def docx_parser(self): + return DocxDocumentParser() + + @pytest.fixture + def excel_parser(self): + return ExcelParser() + + @pytest.fixture + def pdf_parser(self): + return PdfDocumentParser() + + def create_mock_chunk_data(self, chunk_type: ChunkType, **kwargs): + """创建正确的 Mock ChunkData 对象""" + mock_chunk = Mock(spec=ChunkData) + mock_chunk.type = chunk_type + mock_chunk.name = kwargs.get('name', f'mock_{chunk_type.value}') + mock_chunk.content = kwargs.get('content', Mock()) + return mock_chunk + + @pytest.mark.asyncio + async def test_docx_parallel_processing(self, docx_parser): + """测试DOCX解析器的并行处理""" + file_path = "/path/to/test.docx" + + # Mock 文档数据 + mock_doc = Mock() + mock_doc.name = "测试文档.docx" + mock_doc.pictures = [Mock(), Mock()] # 2张图片 + mock_doc.tables = [Mock(), Mock(), Mock()] # 3个表格 + mock_doc.texts = [Mock(), Mock(), Mock(), Mock()] # 4个文本 + + # Mock 转换器结果 + mock_result = Mock() + mock_result.document = mock_doc + + with patch.object(docx_parser, '_converter') as mock_converter: + mock_converter.convert.return_value = mock_result + + # Mock 各种提取方法，返回正确的 ChunkData 对象 + with patch.object(docx_parser, '_extract_images_async') as mock_images: + with patch.object(docx_parser, '_extract_tables_async') as mock_tables: + with patch.object(docx_parser, '_extract_texts_async') as mock_texts: + + # 设置返回值 - 使用正确的 Mock 对象 + mock_images.return_value = [ + self.create_mock_chunk_data(ChunkType.IMAGE) + ] + mock_tables.return_value = [ + self.create_mock_chunk_data(ChunkType.TABLE) + ] + mock_texts.return_value = [ + self.create_mock_chunk_data(ChunkType.TEXT) + ] + + result = await docx_parser.parse(Path(file_path)) + + # 验证并行处理被调用 + assert mock_images.called + assert mock_tables.called + assert mock_texts.called + + # 验证结果 + assert result.success is True + assert result.title == "测试文档.docx" + + @pytest.mark.asyncio + async def test_excel_parallel_processing(self, excel_parser): + """测试Excel解析器的并行处理""" + file_path = Path("/path/to/test.xlsx") + + # Mock 工作簿和工作表 + mock_sheet1 = Mock() + mock_sheet2 = Mock() + + mock_workbook = Mock() + mock_workbook.sheetnames = ["Sheet1", "Sheet2"] + # 让 workbook 可以像字典一样访问 - 使用正确的方式 + mock_workbook.__getitem__ = Mock(side_effect=lambda x: mock_sheet1 if x == "Sheet1" else mock_sheet2) + + with patch.object(excel_parser, '_load_workbook') as mock_load: + mock_load.return_value = mock_workbook + + # Mock 工作表处理 + with patch.object(excel_parser, '_process_sheet_async') as mock_sheet_process: + mock_sheet_process.side_effect = [ + { + 'texts': [self.create_mock_chunk_data(ChunkType.TEXT)], + 'tables': [self.create_mock_chunk_data(ChunkType.TABLE)], + 'images': [self.create_mock_chunk_data(ChunkType.IMAGE)] + }, + { + 'texts': [self.create_mock_chunk_data(ChunkType.TEXT)], + 'tables': [self.create_mock_chunk_data(ChunkType.TABLE)], + 'images': [] + } + ] + + result = await excel_parser.parse(file_path) + + # 验证并行处理被调用 + assert mock_sheet_process.call_count == 2 + + # 验证结果 + assert result.success is True + assert len(result.texts) == 2 + assert len(result.tables) == 2 + assert len(result.images) == 1 + + def create_async_mock_file(self, read_data: bytes): + """创建支持异步上下文管理器的 mock 文件对象""" + mock_file = Mock() + + # 创建异步 read 方法 + async def async_read(): + return read_data + + mock_file.read = async_read + + # 创建异步上下文管理器 + async def async_enter(self): + return mock_file + + async def async_exit(self, exc_type, exc_val, exc_tb): + pass + + mock_file.__aenter__ = async_enter + mock_file.__aexit__ = async_exit + + return mock_file + + @pytest.mark.asyncio + async def test_pdf_parallel_processing(self, pdf_parser): + """测试PDF解析器的并行处理""" + file_path = Path("/path/to/test.pdf") + + # Mock 内容列表 + mock_content = [ + {"type": "image", "img_path": "/path/to/img.jpg", "img_caption": [], "img_footnote": []}, + {"type": "table", "table_body": "
数据
", "table_caption": [], "table_footnote": []}, + {"type": "text", "text": "测试文本", "text_level": 2}, + {"type": "equation", "text": "x + y = z", "text_format": "latex"} + ] + + with patch.object(pdf_parser, '_parse_pdf_to_content_list') as mock_parse: + mock_parse.return_value = mock_content + + # Mock 图片文件读取和文件存在检查 + with patch('builtins.open', mock_open(read_data=b'fake_image_data')): + with patch('os.path.exists', return_value=True): + # 创建支持异步的 mock 文件 + async_mock_file = self.create_async_mock_file(b'fake_image_data') + with patch('aiofiles.open', return_value=async_mock_file): + + result = await pdf_parser.parse(file_path) + + # 验证结果 + assert result.success is True + assert len(result.images) == 1 + assert len(result.tables) == 1 + assert len(result.texts) == 1 + assert len(result.formulas) == 1 + + @pytest.mark.asyncio + async def test_parallel_processing_performance(self, docx_parser): + """测试并行处理的性能优势""" + import time + + file_path = "/path/to/large.docx" + + # 创建大量测试数据 + mock_doc = Mock() + mock_doc.name = "大型文档.docx" + mock_doc.pictures = [Mock() for _ in range(50)] # 50张图片 + mock_doc.tables = [Mock() for _ in range(100)] # 100个表格 + mock_doc.texts = [Mock() for _ in range(200)] # 200个文本 + + mock_result = Mock() + mock_result.document = mock_doc + + with patch.object(docx_parser, '_converter') as mock_converter: + mock_converter.convert.return_value = mock_result + + # Mock 提取方法，模拟处理时间 + async def mock_extract_images(pictures): + await asyncio.sleep(0.1) # 模拟100ms处理时间 + return [self.create_mock_chunk_data(ChunkType.IMAGE)] + + async def mock_extract_tables(tables): + await asyncio.sleep(0.2) # 模拟200ms处理时间 + return [self.create_mock_chunk_data(ChunkType.TABLE)] + + async def mock_extract_texts(texts): + await asyncio.sleep(0.15) # 模拟150ms处理时间 + return [self.create_mock_chunk_data(ChunkType.TEXT)] + + with patch.object(docx_parser, '_extract_images_async', side_effect=mock_extract_images): + with patch.object(docx_parser, '_extract_tables_async', side_effect=mock_extract_tables): + with patch.object(docx_parser, '_extract_texts_async', side_effect=mock_extract_texts): + + start_time = time.time() + result = await docx_parser.parse(Path(file_path)) + processing_time = time.time() - start_time + + # 验证结果 + assert result.success is True + + # 并行处理时间应该接近最慢的任务的时间（200ms） + # 而不是所有任务时间的总和（450ms） + print(f"并行处理时间: {processing_time:.3f}秒") + # 考虑到测试环境的开销，放宽时间限制 + assert processing_time < 0.6 # 应该小于600ms + + @pytest.mark.asyncio + async def test_error_handling_in_parallel(self, docx_parser): + """测试并行处理中的错误处理""" + file_path = "/path/to/error.docx" + + mock_doc = Mock() + mock_doc.name = "错误文档.docx" + mock_doc.pictures = [Mock()] + mock_doc.tables = [Mock()] + mock_doc.texts = [Mock()] + + mock_result = Mock() + mock_result.document = mock_doc + + with patch.object(docx_parser, '_converter') as mock_converter: + mock_converter.convert.return_value = mock_result + + # 模拟某些处理失败 + with patch.object(docx_parser, '_extract_images_async', side_effect=Exception("图片处理失败")): + with patch.object(docx_parser, '_extract_tables_async', return_value=[self.create_mock_chunk_data(ChunkType.TABLE)]): + with patch.object(docx_parser, '_extract_texts_async', return_value=[self.create_mock_chunk_data(ChunkType.TEXT)]): + + result = await docx_parser.parse(Path(file_path)) + + # 即使图片处理失败，其他内容仍应正常处理 + assert result.success is True + assert len(result.tables) == 1 + assert len(result.texts) == 1 + assert len(result.images) == 0 # 图片处理失败，返回空列表 diff --git a/tests/test_docx_parser.py b/tests/test_docx_parser.py index e077e2a..75f07c5 100644 --- a/tests/test_docx_parser.py +++ b/tests/test_docx_parser.py @@ -29,15 +29,15 @@ def mock_doc_data(self): # 模拟图片数据 mock_picture = Mock() - mock_picture.self_ref = "pic1" mock_picture.image.uri = "/path/to/image.jpg" - mock_picture.captions = ["图片说明"] + mock_picture.captions = [Mock(cref="图片说明")] + mock_picture.footnotes = [] mock_doc.pictures = [mock_picture] # 模拟表格数据 mock_table = Mock() - mock_table.self_ref = "table1" - mock_table.captions = ["表格标题"] + mock_table.captions = [Mock(cref="表格标题")] + mock_table.footnotes = [] mock_table.data.num_rows = 2 mock_table.data.num_cols = 3 mock_table.data.grid = [ @@ -57,12 +57,10 @@ def mock_doc_data(self): mock_text = Mock() mock_text.text = "这是正文内容" - mock_text.self_ref = "text1" mock_text.label = "text" mock_formula = Mock() mock_formula.text = "E = mc²" - mock_formula.self_ref = "formula1" mock_formula.label = "formula" mock_doc.texts = [mock_title, mock_text, mock_formula] @@ -94,11 +92,11 @@ async def test_parse_success(self, parser, mock_converter_result): # 验证文本内容 assert len(result.texts) == 3 # 标题不算在texts中 assert result.texts[0].type == ChunkType.TEXT - assert result.texts[0].content == "文档标题" + assert result.texts[0].content.text == "文档标题" assert result.texts[1].type == ChunkType.TEXT - assert result.texts[1].content == "这是正文内容" + assert result.texts[1].content.text == "这是正文内容" assert result.texts[2].type == ChunkType.FORMULA - assert result.texts[2].content == "E = mc²" + assert result.texts[2].content.text == "E = mc²" # 验证表格 assert len(result.tables) == 1 @@ -110,8 +108,9 @@ async def test_parse_success(self, parser, mock_converter_result): # 验证图片 assert len(result.images) == 1 assert result.images[0].type == ChunkType.IMAGE - assert result.images[0].content == "/path/to/image.jpg" - assert result.images[0].description == "图片说明" + assert result.images[0].content.uri == "/path/to/image.jpg" + assert result.images[0].content.caption == ["图片说明"] + assert result.images[0].content.footnote == [] @pytest.mark.asyncio async def test_parse_without_title(self, parser): @@ -126,7 +125,6 @@ async def test_parse_without_title(self, parser): mock_text = Mock() mock_text.text = "只有正文内容" - mock_text.self_ref = "text1" mock_text.label = "text" mock_doc.texts = [mock_text] @@ -178,15 +176,9 @@ async def test_parse_converter_error(self, parser): with patch.object(parser, '_converter') as mock_converter: mock_converter.convert.side_effect = Exception("转换失败") - result = await parser.parse(file_path) + with pytest.raises(Exception, match="Failed to parse DOCX file /path/to/invalid.docx"): + await parser.parse(Path(file_path)) - assert result.success is False - assert result.error_message == "转换失败" - assert result.processing_time > 0 - assert result.title is None - assert len(result.texts) == 0 - assert len(result.tables) == 0 - assert len(result.images) == 0 @pytest.mark.asyncio async def test_parse_with_complex_table(self, parser): @@ -200,8 +192,8 @@ async def test_parse_with_complex_table(self, parser): # 创建复杂表格 mock_table = Mock() - mock_table.self_ref = "complex_table" - mock_table.captions = ["复杂表格"] + mock_table.captions = [Mock(cref="复杂表格")] + mock_table.footnotes = [] mock_table.data.num_rows = 3 mock_table.data.num_cols = 4 @@ -236,9 +228,9 @@ async def test_parse_with_complex_table(self, parser): table = result.tables[0].content assert table.rows == 3 assert table.columns == 4 - assert table.column_headers == ["姓名", "年龄", "职业", "薪资"] - assert table.data[0] == ["张三", "25", "工程师", "15000"] - assert table.data[1] == ["李四", "30", "设计师", "18000"] + assert table.grid == [["姓名", "年龄", "职业", "薪资"], ["张三", "25", "工程师", "15000"], ["李四", "30", "设计师", "18000"]] + assert table.caption == ["复杂表格"] + assert table.footnote == [] @pytest.mark.asyncio async def test_parse_with_multiple_images(self, parser): @@ -254,9 +246,9 @@ async def test_parse_with_multiple_images(self, parser): mock_pictures = [] for i in range(3): mock_pic = Mock() - mock_pic.self_ref = f"pic{i+1}" mock_pic.image.uri = f"/path/to/image{i+1}.jpg" - mock_pic.captions = [f"图片{i+1}说明"] + mock_pic.captions = [Mock(cref=f"图片{i+1}说明")] + mock_pic.footnotes = [] mock_pictures.append(mock_pic) mock_doc.pictures = mock_pictures @@ -274,23 +266,9 @@ async def test_parse_with_multiple_images(self, parser): for i, img in enumerate(result.images): assert img.type == ChunkType.IMAGE - assert img.content == f"/path/to/image{i+1}.jpg" - assert img.description == f"图片{i+1}说明" - - @pytest.mark.asyncio - async def test_parse_processing_time_accuracy(self, parser, mock_converter_result): - """测试处理时间计算的准确性""" - file_path = "/path/to/test.docx" - - with patch.object(parser, '_converter') as mock_converter: - mock_converter.convert.return_value = mock_converter_result - - result = await parser.parse(file_path) - - assert result.success is True - assert result.processing_time >= 0 - # 处理时间应该是一个合理的正数 - assert result.processing_time < 10 # 假设不会超过10秒 + assert img.content.uri == f"/path/to/image{i+1}.jpg" + assert img.content.caption == [f"图片{i+1}说明"] + assert img.content.footnote == [] @pytest.mark.asyncio async def test_parse_with_section_headers(self, parser): @@ -304,8 +282,7 @@ async def test_parse_with_section_headers(self, parser): # 创建章节标题 mock_section = Mock() - mock_section.text = "第一章 引言" - mock_section.self_ref = "section1" + mock_section.text = "第一章 引言" mock_section.label = "section_header" mock_doc.texts = [mock_section] @@ -321,4 +298,4 @@ async def test_parse_with_section_headers(self, parser): assert result.success is True assert len(result.texts) == 1 assert result.texts[0].type == ChunkType.TEXT - assert result.texts[0].content == "第一章 引言" + assert result.texts[0].content.text == "第一章 引言" diff --git a/tests/test_excel_parser.py b/tests/test_excel_parser.py index 7a72ba8..197d84d 100644 --- a/tests/test_excel_parser.py +++ b/tests/test_excel_parser.py @@ -1,7 +1,7 @@ import base64 import os import tempfile - +from pathlib import Path import pytest from openpyxl import Workbook from openpyxl.drawing.image import Image as XLImage @@ -40,6 +40,7 @@ async def test_parse_real_basic_and_image(): ws2["A2"] = "Single Data" xlsx_fd, xlsx_path = tempfile.mkstemp(suffix=".xlsx") + xlsx_path = Path(xlsx_path) os.close(xlsx_fd) wb.save(xlsx_path) @@ -59,7 +60,7 @@ async def test_parse_real_basic_and_image(): assert result.texts[0].type == "text" and result.texts[0].name == "Sheet1" assert result.images[0].type == "image" assert result.images[0].name == "#/pictures/0" - assert result.images[0].content.startswith("data:image/") + assert result.images[0].content.uri.startswith("data:image/") assert result.tables[0].type == "table" assert result.texts[1].type == "text" and result.texts[1].name == "Sheet2" @@ -85,6 +86,7 @@ async def test_parse_real_merged_cells(): ws["B2"] = "Value2" xlsx_fd, xlsx_path = tempfile.mkstemp(suffix=".xlsx") + xlsx_path = Path(xlsx_path) os.close(xlsx_fd) wb.save(xlsx_path) @@ -102,8 +104,7 @@ async def test_parse_real_merged_cells(): assert table_chunk.type == "table" payload = table_chunk.content - assert payload.row_headers == ["Merged Header", "Merged Header"] - assert payload.data == [["Value1", "Value2"]] + assert payload.grid == [["Merged Header", "Merged Header"], ["Value1", "Value2"]] assert payload.rows == 2 assert payload.columns == 2 finally: diff --git a/tests/test_pdf_parser.py b/tests/test_pdf_parser.py new file mode 100644 index 0000000..e810dc9 --- /dev/null +++ b/tests/test_pdf_parser.py @@ -0,0 +1,234 @@ +""" +PDF解析器测试模块 + +测试PdfDocumentParser的parse函数的各种场景 +""" + +import pytest +from unittest.mock import Mock, patch +from pathlib import Path + +from parsers.pdf_parser import PdfDocumentParser +from parsers.base_models import ChunkType + + +class TestPdfDocumentParserParse: + """测试PdfDocumentParser的parse函数""" + + @pytest.fixture + def parser(self): + """创建解析器实例""" + return PdfDocumentParser() + + @pytest.fixture + def mock_content_list(self): + """模拟内容列表""" + return [ + { + "type": "text", + "text": "文档标题", + "text_level": 1 + }, + { + "type": "text", + "text": "文档内容", + "text_level": 0 + }, + { + "type": "table", + "table_body": "
姓名年龄
", + "table_caption": ["人员信息表"], + "table_footnote": [] + }, + { + "type": "image", + "img_path": "/path/to/image.jpg", + "img_caption": ["示例图片"], + "img_footnote": ["示例图片注脚"] + } + ] + + def create_async_mock_file(self, read_data: bytes): + """创建支持异步上下文管理器的 mock 文件对象""" + mock_file = Mock() + + # 创建异步 read 方法 + async def async_read(): + return read_data + + mock_file.read = async_read + + # 创建异步上下文管理器 + async def async_enter(self): + return mock_file + + async def async_exit(self, exc_type, exc_val, exc_tb): + pass + + mock_file.__aenter__ = async_enter + mock_file.__aexit__ = async_exit + + return mock_file + + @pytest.mark.asyncio + async def test_parse_success(self, parser, mock_content_list): + """测试成功解析PDF文件""" + file_path = Path("/path/to/test.pdf") + + with patch.object(parser, '_parse_pdf_to_content_list') as mock_parse: + mock_parse.return_value = mock_content_list + # 使用异步 mock 模拟图片文件的读取 + async_mock_file = self.create_async_mock_file(b'test_image_content') + with patch('aiofiles.open', return_value=async_mock_file): + with patch('os.path.exists', return_value=True): + result = await parser.parse(file_path) + + # 验证返回结果 + assert result.success is True + assert result.title == "文档标题" + assert result.processing_time > 0 + assert result.error_message is None + + # 验证内容数量 + assert len(result.texts) == 1 # 只有 text_level != 1 的文本会被处理 + assert len(result.tables) == 1 + assert len(result.images) == 1 + + @pytest.mark.asyncio + async def test_parse_error_handling(self, parser): + """测试PDF解析错误处理""" + file_path = Path("/path/to/invalid.pdf") + + with patch.object(parser, '_parse_pdf_to_content_list') as mock_parse: + mock_parse.side_effect = Exception("解析失败") + with pytest.raises(Exception, match="解析失败"): + await parser.parse(Path(file_path)) + + @pytest.mark.asyncio + async def test_parse_empty_document(self, parser): + """测试空PDF文档解析""" + file_path = Path("/path/to/empty.pdf") + + with patch.object(parser, '_parse_pdf_to_content_list') as mock_parse: + mock_parse.return_value = [] + + result = await parser.parse(file_path) + + assert result.success is True + assert result.title == "empty" + assert len(result.texts) == 0 + assert len(result.tables) == 0 + assert len(result.images) == 0 + + @pytest.mark.asyncio + async def test_parse_with_image_processing(self, parser): + """测试图片处理功能""" + file_path = Path("/path/to/image.pdf") + + mock_content = [ + { + "type": "image", + "img_path": "/path/to/test_image.jpg", + "img_caption": ["测试图片"], + "img_footnote": [] + } + ] + + with patch.object(parser, '_parse_pdf_to_content_list') as mock_parse: + mock_parse.return_value = mock_content + # 使用异步 mock 模拟图片文件，并确保文件路径存在 + async_mock_file = self.create_async_mock_file(b'fake_jpeg_data') + with patch('aiofiles.open', return_value=async_mock_file): + with patch('os.path.exists', return_value=True): + result = await parser.parse(Path(file_path)) + + assert result.success is True + assert len(result.images) == 1 + + # 验证图片内容 + image = result.images[0] + assert image.type == ChunkType.IMAGE + assert "data:image/jpeg;base64," in image.content.uri + assert image.content.caption == ["测试图片"] + + @pytest.mark.asyncio + async def test_parse_with_table_processing(self, parser): + """测试表格处理功能""" + file_path = Path("/path/to/table.pdf") + + mock_content = [ + { + "type": "table", + "table_body": "
列1列2
数据1数据2
", + "table_caption": ["测试表格"], + "table_footnote": [] + } + ] + + with patch.object(parser, '_parse_pdf_to_content_list') as mock_parse: + mock_parse.return_value = mock_content + + result = await parser.parse(Path(file_path)) + + assert result.success is True + assert len(result.tables) == 1 + + # 验证表格内容 + table = result.tables[0] + assert table.type == ChunkType.TABLE + assert table.content.rows == 2 # 标题行 + 数据行 + assert table.content.columns == 2 + assert table.content.caption == ["测试表格"] + + @pytest.mark.asyncio + async def test_parse_with_formula_processing(self, parser): + """测试公式处理功能""" + file_path = Path("/path/to/formula.pdf") + + mock_content = [ + { + "type": "equation", + "text": "E = mc²", + "text_format": "latex" + } + ] + + with patch.object(parser, '_parse_pdf_to_content_list') as mock_parse: + mock_parse.return_value = mock_content + + result = await parser.parse(Path(file_path)) + + assert result.success is True + assert len(result.formulas) == 1 + + # 验证公式内容 + formula = result.formulas[0] + assert formula.type == ChunkType.FORMULA + assert formula.content.text == "E = mc²" + assert formula.content.text_format == "latex" + + @pytest.mark.asyncio + async def test_parse_with_mixed_content(self, parser): + """测试混合内容处理功能""" + file_path = Path("/path/to/mixed.pdf") + + mock_content = [ + {"type": "text", "text": "标题", "text_level": 1}, + {"type": "image", "img_path": "/path/to/img.jpg", "img_caption": [], "img_footnote": []}, + {"type": "table", "table_body": "
数据
", "table_caption": [], "table_footnote": []}, + {"type": "equation", "text": "x + y = z", "text_format": "latex"} + ] + + with patch.object(parser, '_parse_pdf_to_content_list') as mock_parse: + mock_parse.return_value = mock_content + # 使用异步 mock 模拟图片文件，并确保文件路径存在 + async_mock_file = self.create_async_mock_file(b'mixed_content_image') + with patch('aiofiles.open', return_value=async_mock_file): + with patch('os.path.exists', return_value=True): + result = await parser.parse(Path(file_path)) + + assert result.success is True + assert len(result.texts) == 0 # text_level=1 的文本被用作标题，不进入 texts 列表 + assert len(result.images) == 1 + assert len(result.tables) == 1 + assert len(result.formulas) == 1 diff --git a/uv.lock b/uv.lock index deea53b..2361566 100644 --- a/uv.lock +++ b/uv.lock @@ -2,9 +2,14 @@ version = 1 revision = 3 requires-python = ">=3.12" resolution-markers = [ - "sys_platform == 'darwin'", - "platform_machine == 'aarch64' and sys_platform == 'linux'", - "(platform_machine != 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