diff --git a/enhancers/__init__.py b/enhancers/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/enhancers/information_enhancer.py b/enhancers/information_enhancer.py new file mode 100644 index 0000000..aeb8758 --- /dev/null +++ b/enhancers/information_enhancer.py @@ -0,0 +1,62 @@ +from abc import ABC, abstractmethod + +from parsers.base_models import ChunkData, ChunkType + + +class InformationEnhancer(ABC): + """信息增强器基类""" + @abstractmethod + async def enhance(self, information: ChunkData) -> ChunkData: + """增强信息""" + pass + +class TableInformationEnhancer(InformationEnhancer): + """表格信息增强器""" + + async def enhance(self, information: ChunkData) -> ChunkData: + """增强信息""" + return information + +class FormulasInformationEnhancer(InformationEnhancer): + """公式信息增强器""" + + async def enhance(self, information: ChunkData) -> ChunkData: + """增强信息""" + return information + +class ImageInformationEnhancer(InformationEnhancer): + """图片信息增强器""" + + async def enhance(self, information: ChunkData) -> ChunkData: + """增强信息""" + return information + +class InformationEnhancerFactory: + """信息增强器工厂""" + + def __init__(self) -> None: + self.enhancers = [ + TableInformationEnhancer(), + FormulasInformationEnhancer(), + ImageInformationEnhancer() + ] + + def get_enhancer(self, information: ChunkData) -> InformationEnhancer|None: + """获取信息增强器""" + match information.type: + case ChunkType.TABLE: + return TableInformationEnhancer() + case ChunkType.FORMULA: + return FormulasInformationEnhancer() + case ChunkType.IMAGE: + return ImageInformationEnhancer() + case _: + return None + + async def enhance_information(self, information: ChunkData) -> ChunkData: + """增强信息""" + enhancer = self.get_enhancer(information) + if not enhancer: + raise ValueError(f"不支持的模态类型: {information.type}") + return await enhancer.enhance(information) + diff --git a/parsers/base_models.py b/parsers/base_models.py new file mode 100644 index 0000000..6b6b310 --- /dev/null +++ b/parsers/base_models.py @@ -0,0 +1,58 @@ +import logging +from abc import ABC, abstractmethod +from enum import Enum +from typing import Any + +from pydantic import BaseModel, Field + +logger = logging.getLogger(__name__) + + +class ChunkType(str, Enum): + """块类型""" + TEXT = "text" + IMAGE = "image" + TABLE = "table" + FORMULA = "formula" + +class TableDataItem(BaseModel): + """表格数据类""" + rows: int # 行数 + columns: int # 列数 + 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 + +class ChunkData(BaseModel): + """块数据类""" + type: ChunkType + name: str + content: str|TableDataItem = "" + description: str = "" + +class DocumentData(BaseModel): + """解析结果类""" + title: str = "" + texts: list[ChunkData] = Field(default_factory=list) + tables: list[ChunkData] = Field(default_factory=list) + images: list[ChunkData] = Field(default_factory=list) + formulas: list[ChunkData] = Field(default_factory=list) + processing_time: float = 0 + success: bool + error_message: str | None = None + +class DocumentParser(ABC): + """文档解析器基类""" + + def __init__(self) -> None: + self.supported_formats: list[str] = Field(default_factory=list) + + @abstractmethod + async def parse(self, file_path: str) -> DocumentData: + """解析文档""" + pass + + @abstractmethod + def can_parse(self, file_path: str) -> bool: + """检查是否可以解析该文件""" + return any(file_path.lower().endswith(fmt) for fmt in self.supported_formats) diff --git a/parsers/document_parser.py b/parsers/document_parser.py index b203da3..782d019 100644 --- a/parsers/document_parser.py +++ b/parsers/document_parser.py @@ -1,114 +1,16 @@ import logging -from abc import ABC, abstractmethod -from typing import Any -logger = logging.getLogger(__name__) - -class DocumentParser(ABC): - """文档解析器基类""" - - def __init__(self) -> None: - self.supported_formats: list[str] = [] - - @abstractmethod - async def parse(self, file_path: str, file_content: bytes) -> dict[str, Any]: - """解析文档""" - pass - - @abstractmethod - def can_parse(self, file_path: str) -> bool: - """检查是否可以解析该文件""" - pass - -class PDFParser(DocumentParser): - """PDF文档解析器""" - - def __init__(self) -> None: - super().__init__() - self.supported_formats = ['.pdf'] - - def can_parse(self, file_path: str) -> bool: - return any(file_path.lower().endswith(fmt) for fmt in self.supported_formats) +from parsers.base_models import DocumentData, DocumentParser +from parsers.excel_parser import ExcelParser - async def parse(self, file_path: str, file_content: bytes) -> dict[str, Any]: - """解析PDF文档""" - try: - # 这里应该使用mineru库 - # 暂时返回模拟数据 - return { - "type": "pdf", - "text": f"PDF文档内容: {file_path}", - "pages": 1, - "images": [], - "tables": [], - "formulas": [] - } - except Exception as e: - logger.error(f"解析PDF失败: {e}") - raise - -class DOCXParser(DocumentParser): - """DOCX文档解析器""" - - def __init__(self) -> None: - super().__init__() - self.supported_formats = ['.docx','.doc'] - - def can_parse(self, file_path: str) -> bool: - return any(file_path.lower().endswith(fmt) for fmt in self.supported_formats) - - async def parse(self, file_path: str, file_content: bytes) -> dict[str, Any]: - """解析DOCX文档""" - try: - # 这里应该使用docling库 - # 暂时返回模拟数据 - return { - "type": "docx", - "text": f"DOCX文档内容: {file_path}", - "pages": 1, - "images": [], - "tables": [], - "formulas": [] - } - except Exception as e: - logger.error(f"解析DOCX失败: {e}") - raise - -class XLSXParser(DocumentParser): - """XLSX文档解析器""" - - def __init__(self) -> None: - super().__init__() - self.supported_formats = ['.xlsx'] - - def can_parse(self, file_path: str) -> bool: - return any(file_path.lower().endswith(fmt) for fmt in self.supported_formats) - - async def parse(self, file_path: str, file_content: bytes) -> dict[str, Any]: - """解析XLSX文档""" - try: - # 这里应该使用docling库 - # 暂时返回模拟数据 - return { - "type": "xlsx", - "text": f"XLSX文档内容: {file_path}", - "pages": 1, - "images": [], - "tables": [], - "formulas": [] - } - except Exception as e: - logger.error(f"解析XLSX失败: {e}") - raise +logger = logging.getLogger(__name__) class DocumentParserFactory: """文档解析器工厂""" def __init__(self) -> None: - self.parsers = [ - PDFParser(), - DOCXParser(), - XLSXParser() + self.parsers: list[DocumentParser] = [ + ExcelParser() ] def get_parser(self, file_path: str) -> DocumentParser | None: @@ -118,10 +20,10 @@ def get_parser(self, file_path: str) -> DocumentParser | None: return parser return None - async def parse_document(self, file_path: str, file_content: bytes) -> dict[str, Any]: + async def parse_document(self, file_path: str) -> DocumentData: """解析文档""" parser = self.get_parser(file_path) if not parser: raise ValueError(f"不支持的文件格式: {file_path}") - return await parser.parse(file_path, file_content) + return await parser.parse(file_path) diff --git a/parsers/excel_parser.py b/parsers/excel_parser.py new file mode 100644 index 0000000..e333faa --- /dev/null +++ b/parsers/excel_parser.py @@ -0,0 +1,354 @@ +""" +Excel文件解析器模块 + +该模块提供将Excel文件转换为结构化JSON格式的功能， +包括表格数据提取和图片处理。 +""" + +import base64 +import json +import time +import warnings +from dataclasses import dataclass +from datetime import date, datetime +from pathlib import Path +from typing import Any + +from openpyxl import load_workbook # type: ignore +from openpyxl.drawing.image import Image # type: ignore +from openpyxl.workbook.workbook import Workbook # type: ignore +from openpyxl.worksheet.worksheet import Worksheet # type: ignore + +from parsers.base_models import ( + ChunkData, + ChunkType, + DocumentData, + DocumentParser, + TableDataItem, +) + +# 忽略 openpyxl 的特定警告 +warnings.filterwarnings('ignore', category=UserWarning, module='openpyxl') + +# 类型别名 +CellValue = str|int|float|bool|None|datetime|date +TableData = list[list[CellValue]] + + +@dataclass +class ExcelParseConfig: + """Excel解析配置类""" + data_only: bool = True + keep_vba: bool = False + default_image_format: str = 'png' + image_description_placeholder: str = "[待生成]" + + +class ExcelParseError(Exception): + """Excel解析异常类""" + pass + + +class ExcelParser(DocumentParser): + """Excel文件解析器类""" + + def __init__(self, config: ExcelParseConfig | None = None): + """ + 初始化Excel解析器 + Args: + config: 解析配置，如果为None则使用默认配置 + """ + super().__init__() + self.config: ExcelParseConfig = config or ExcelParseConfig() + self.image_index: int = 0 + self.supported_formats: list[str] = ['.xlsx', '.xls'] + + def can_parse(self, file_path: str) -> bool: + """ + 验证输入文件 + Args: + file_path: 文件路径 + Returns: + bool: 是否支持解析 + """ + return any(file_path.lower().endswith(fmt) for fmt in self.supported_formats) + + async def parse(self, excel_path: str) -> DocumentData: + """ + 将Excel文件转换为JSON格式 + Args: + excel_path: Excel文件路径 + Returns: + DocumentData: 文档数据 + """ + # 获取文件名作为标题（不带扩展名） + start_time = time.time() + + 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(excel_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" + )) + processing_time = time.time() - start_time + return DocumentData( + title=Path(excel_path).stem, + texts=texts, + tables=tables, + images=images, + processing_time=processing_time, + success=True + ) + except Exception as e: + processing_time = time.time() - start_time + return DocumentData( + success=False, + error_message=str(e), + processing_time=processing_time + ) + + def _load_workbook(self, excel_path: str) -> Workbook: + """ + 加载Excel工作簿 + Args: + excel_path: Excel文件路径 + Returns: + Workbook: 加载的工作簿对象 + """ + return load_workbook( + excel_path, + data_only=self.config.data_only, + keep_vba=self.config.keep_vba + ) + + def _extract_sheet_images(self, sheet: Worksheet) -> list[ChunkData]: + """ + 提取工作表中的图片 + Args: + sheet: 工作表对象 + Returns: + List[DocumentData]: 图片信息列表 + """ + sheet_images: list[ChunkData] = [] + + images = getattr(sheet, '_images', None) + if not images or not isinstance(images, list|tuple): + return sheet_images + + # 收集图片信息 + for img_obj in images: + if not isinstance(img_obj, Image): + continue + + try: + image_info = self._process_image_object(img_obj) + if image_info: + sheet_images.append(image_info) + except Exception as e: + print(f"处理图片失败: {str(e)}") + continue + + return sheet_images + + def _process_image_object(self, img_obj: Image) -> ChunkData | None: + """ + 处理单个图片对象 + Args: + img_obj: 图片对象 + Returns: + Optional[DocumentData]: 图片信息，处理失败时返回None + """ + try: + # 获取图片数据 + img_data = img_obj._data() + + # 获取图片格式 + img_format = self._get_image_format(img_obj) + + # 生成Base64编码 + base64_encoded = base64.b64encode(img_data).decode('utf-8') + uri = f"data:image/{img_format};base64,{base64_encoded}" + + # 创建图片信息 + image_info = ChunkData( + type=ChunkType.IMAGE, + name=f"#/pictures/{self.image_index}", + content=uri, + description=self.config.image_description_placeholder + ) + + self.image_index += 1 + return image_info + + except Exception as e: + print(f"处理图片对象失败: {str(e)}") + return None + + def _get_image_format(self, img_obj: Image) -> str: + """ + 获取图片格式 + Args: + img_obj: 图片对象 + Returns: + str: 图片格式 + """ + fmt = getattr(img_obj, 'format', None) + if isinstance(fmt, str) and fmt: + img_format: str = fmt.lower() + # 处理JPEG格式的别名 + if img_format == 'jpeg': + img_format = 'jpg' + return img_format + return self.config.default_image_format + + def _process_cell_value(self, cell_value: Any) -> str: + """ + 预处理单元格值，将datetime对象转换为字符串 + Args: + cell_value: 原始单元格值 + Returns: + str: 处理后的单元格值 + """ + if cell_value is None: + return "" + + # 处理datetime对象，转换为ISO格式字符串 + if isinstance(cell_value, datetime): + return cell_value.strftime("%Y-%m-%d %H:%M:%S") + + # 处理date对象，转换为日期字符串 + if isinstance(cell_value, date): + return cell_value.strftime("%Y-%m-%d") + + # 对于其他类型，转换为字符串 + return str(cell_value) + + def _extract_table_data(self, sheet: Worksheet) -> TableDataItem: + """ + 提取表格数据 + Args: + sheet: 工作表对象 + Returns: + Dict[str, Any]: 表格数据 + """ + # 获取合并单元格信息 + merged_ranges = self._get_merged_cells(sheet) + merged_map = self._create_merged_cell_map(merged_ranges, sheet) + + # 计算表格维度 + max_row = sheet.max_row + max_col = sheet.max_column + + # 提取所有数据 + all_rows = self._extract_all_rows(sheet, max_row, max_col, merged_map) + + 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 [] + ) + + def _get_merged_cells(self, sheet: Worksheet) -> dict[tuple[int, int, int, int], str]: + """ + 获取合并单元格信息 + Args: + sheet: 工作表对象 + Returns: + Dict: 合并单元格映射 + """ + merged_ranges = {} + if sheet.merged_cells: + for merged_range in sheet.merged_cells.ranges: + min_row, min_col, max_row, max_col = ( + merged_range.min_row, merged_range.min_col, + merged_range.max_row, merged_range.max_col + ) + merged_value = sheet.cell(row=min_row, column=min_col).value + merged_ranges[(min_row, min_col, max_row, max_col)] = merged_value + return merged_ranges + + def _create_merged_cell_map(self, merged_ranges: dict, sheet: Worksheet) -> dict[tuple[int, int], str]: + """ + 创建合并单元格映射 + Args: + merged_ranges: 合并单元格范围 + sheet: 工作表对象 + Returns: + Dict: 合并单元格映射 + """ + merged_map = {} + for (min_row, min_col, max_row, max_col), value in merged_ranges.items(): + # 预处理合并单元格的值 + processed_value = self._process_cell_value(value) + for row_idx in range(min_row, max_row + 1): + for col_idx in range(min_col, max_col + 1): + merged_map[(row_idx, col_idx)] = processed_value + return merged_map + + def _extract_all_rows(self, sheet: Worksheet, max_row: int, max_col: int, + merged_map: dict[tuple[int, int], str]) -> list[list[str]]: + """ + 提取所有行数据 + Args: + sheet: 工作表对象 + max_row: 最大行数 + max_col: 最大列数 + merged_map: 合并单元格映射 + Returns: + TableData: 所有行数据 + """ + all_rows = [] + for row_idx in range(1, max_row + 1): + row_data = [] + for col_idx in range(1, max_col + 1): + # 检查是否是合并单元格 + if (row_idx, col_idx) in merged_map: + cell_value = merged_map[(row_idx, col_idx)] + else: + cell = sheet.cell(row=row_idx, column=col_idx) + cell_value = cell.value + + # 预处理单元格值 + processed_value = self._process_cell_value(cell_value) + row_data.append(processed_value) + all_rows.append(row_data) + + return all_rows + + def _save_json(self, data: Any, file_path: Path) -> None: + """ + 保存JSON数据到文件 + Args: + data: 要保存的数据 + file_path: 文件路径 + """ + with open(file_path, 'w', encoding='utf-8') as f: + json.dump(data, f, ensure_ascii=False, indent=4) diff --git a/pyproject.toml b/pyproject.toml index f6b4cbd..23446a4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,6 +13,8 @@ dependencies = [ "dotenv>=0.9.9", "aiobotocore>=2.24.0", "redis>=6.4.0", + "openpyxl>=3.1.5", + "pydantic>=2.11.7", ] [dependency-groups] @@ -25,7 +27,8 @@ all-dev = [ dev = [ "pytest>=8.4.1", "pytest-cov>=6.2.1", - "pytest-asyncio>=0.23.0", # 添加异步测试支持 + "pytest-asyncio>=0.23.0", # 添加异步测试支持 + "pillow>=11.3.0", ] lint = [ "bandit>=1.8.6", diff --git a/storage/s3_client.py b/storage/s3_client.py index da19e34..7cd41ba 100644 --- a/storage/s3_client.py +++ b/storage/s3_client.py @@ -40,6 +40,10 @@ def __init__(self) -> None: super().__init__("S3 client not initialized") +class S3Error(Exception): + """S3操作异常""" + pass + class AsyncS3Client: def __init__(self, endpoint_url: str | None, diff --git a/tests/test_excel_parser.py b/tests/test_excel_parser.py new file mode 100644 index 0000000..7a72ba8 --- /dev/null +++ b/tests/test_excel_parser.py @@ -0,0 +1,112 @@ +import base64 +import os +import tempfile + +import pytest +from openpyxl import Workbook +from openpyxl.drawing.image import Image as XLImage + +from parsers.excel_parser import ExcelParser +from parsers.base_models import ChunkData + + +@pytest.mark.asyncio +async def test_parse_real_basic_and_image(): + # 准备临时PNG图片（1x1透明像素） + one_px_png_b64 = ( + b"iVBORw0KGgoAAAANSUhEUgAAAAEAAAABCAQAAAC1HAwCAAAAC0lEQVR42mP8/x8AAusB9Y2oU5wAAAAASUVORK5CYII=" + ) + png_fd, png_path = tempfile.mkstemp(suffix=".png") + try: + with os.fdopen(png_fd, "wb") as f: + f.write(base64.b64decode(one_px_png_b64)) + + # 构建包含图片与两个工作表的真实Excel文件 + wb = Workbook() + ws1 = wb.active + ws1.title = "Sheet1" + # 表头与数据 + ws1["A1"] = "Header1" + ws1["B1"] = "Header2" + ws1["A2"] = "Data1" + ws1["B2"] = "Data2" + # 插入图片 + img = XLImage(png_path) + ws1.add_image(img, "A5") + + # 第二个工作表 + ws2 = wb.create_sheet("Sheet2") + ws2["A1"] = "Single Header" + ws2["A2"] = "Single Data" + + xlsx_fd, xlsx_path = tempfile.mkstemp(suffix=".xlsx") + os.close(xlsx_fd) + wb.save(xlsx_path) + + try: + parser = ExcelParser() + result = await parser.parse(xlsx_path) + + assert result.success is True + # 内容：Sheet1标题、Sheet1图片、Sheet1表格、Sheet2标题、Sheet2表格 + content = result.tables + assert len(content) == 2 + + assert len(result.images) == 1 + assert len(result.texts) == 2 + + # 校验顺序与关键字段 + 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.tables[0].type == "table" + assert result.texts[1].type == "text" and result.texts[1].name == "Sheet2" + assert result.tables[1].type == "table" + finally: + os.remove(xlsx_path) + finally: + os.remove(png_path) + + +@pytest.mark.asyncio +async def test_parse_real_merged_cells(): + # 构建包含合并单元格的真实Excel文件 + wb = Workbook() + ws = wb.active + ws.title = "Sheet1" + + # 合并 A1:B1 并设置值 + ws.merge_cells(start_row=1, start_column=1, end_row=1, end_column=2) + ws["A1"] = "Merged Header" + # 填充下一行数据 + ws["A2"] = "Value1" + ws["B2"] = "Value2" + + xlsx_fd, xlsx_path = tempfile.mkstemp(suffix=".xlsx") + os.close(xlsx_fd) + wb.save(xlsx_path) + + try: + parser = ExcelParser() + result = await parser.parse(xlsx_path) + + assert result.success is True + # 结构：标题、表格 + assert len(result.tables) == 1 + assert len(result.texts) == 1 + + # 表格在索引1 + table_chunk: ChunkData = result.tables[0] + assert table_chunk.type == "table" + + payload = table_chunk.content + assert payload.row_headers == ["Merged Header", "Merged Header"] + assert payload.data == [["Value1", "Value2"]] + assert payload.rows == 2 + assert payload.columns == 2 + finally: + os.remove(xlsx_path) + + diff --git a/tests/test_integration.py b/tests/test_integration.py index 973a2fa..020c90d 100644 --- a/tests/test_integration.py +++ b/tests/test_integration.py @@ -19,7 +19,7 @@ class TestRedisIntegration: async def redis_client(self): """获取真实的Redis客户端""" try: - client = await get_redis_client("redis://localhost:6379") + client = await get_redis_client(os.getenv("REDIS_URL")) # 清理测试数据 await client.flushdb() yield client @@ -223,7 +223,7 @@ async def system_components(self): """获取所有系统组件""" try: # Redis客户端 - redis_client = await get_redis_client("redis://localhost:6379") + redis_client = await get_redis_client(os.getenv("REDIS_URL")) await redis_client.flushdb() # 任务管理器 @@ -286,12 +286,12 @@ async def test_document_processing_workflow(self, system_components): assert retrieved_task["task_id"] == "workflow-test-123" # 6. 模拟文档解析结果 - parsing_result = { + parsing_result = [{ "text": "Chemical document content with formulas and structures.", "formulas": ["H2O", "CO2", "CH4"], "structures": ["molecular_structure_1.png"], "confidence": 0.92 - } + }] # 7. 更新任务状态和结果 update_success = await task_manager.update_task_status( @@ -314,6 +314,7 @@ async def test_document_processing_workflow(self, system_components): # 环境检查装饰器 def requires_redis(func): """需要Redis服务的装饰器""" + print(os.getenv("REDIS_URL")) return pytest.mark.skipif( not os.getenv("REDIS_URL") and not os.getenv("REDIS_ENABLED", "false").lower() == "true", reason="需要Redis服务" diff --git a/uv.lock b/uv.lock index d15f45a..6ee4cdd 100644 --- a/uv.lock +++ b/uv.lock @@ -111,6 +111,15 @@ wheels = [ { url = "https://files.pythonhosted.org/packages/fb/76/641ae371508676492379f16e2fa48f4e2c11741bd63c48be4b12a6b09cba/aiosignal-1.4.0-py3-none-any.whl", hash = "sha256:053243f8b92b990551949e63930a839ff0cf0b0ebbe0597b0f3fb19e1a0fe82e", size = 7490, upload-time = "2025-07-03T22:54:42.156Z" }, ] +[[package]] +name = "annotated-types" +version = "0.7.0" +source = { registry = "https://pypi.org/simple" } +sdist = { url = "https://files.pythonhosted.org/packages/ee/67/531ea369ba64dcff5ec9c3402f9f51bf748cec26dde048a2f973a4eea7f5/annotated_types-0.7.0.tar.gz", hash = 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enhancer_factory.enhance_information(chunk) + + # 并发增强每个信息 + enhanced_chunk_list = await asyncio.gather( + *(enhance_with_semaphore(chunk, semaphore) for chunk in chunk_list) + ) + parse_result.texts = enhanced_chunk_list[:len(parse_result.texts)] + parse_result.tables = enhanced_chunk_list[len(parse_result.texts):len(parse_result.texts) + len(parse_result.tables)] + parse_result.images = enhanced_chunk_list[len(parse_result.texts) + len(parse_result.tables):len(parse_result.texts) + len(parse_result.tables) + len(parse_result.images)] + parse_result.formulas = enhanced_chunk_list[len(parse_result.texts) + len(parse_result.tables) + len(parse_result.images):] + return parse_result.model_dump(mode="json")