import sys import os import concurrent.futures from concurrent.futures import ThreadPoolExecutor # 添加项目根目录到Python路径 sys.path.append(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) from langgraph.graph import StateGraph, START, END from langgraph.graph.message import add_messages from typing import List, Dict, Any, Annotated from pydantic import BaseModel, Field, ConfigDict from services.pdf_parser.pdf_splitter import PDFSplitter from services.model.qwen_vl import QWenVLParser from services.ragflow.ragflow_service import RAGFlowService from services.utils.vector_db import VectorDBFactory from services.model.multimodal_embedding import MultimodalEmbedding from conf.config import ModelConfig # 定义工作流状态类 class PDFParsingState(BaseModel): """PDF解析工作流状态""" model_config = ConfigDict(arbitrary_types_allowed=True) pdf_path: str = Field(..., description="PDF文件路径") dataset_id: str = Field(..., description="数据集ID") ragflow_service: RAGFlowService = Field(default_factory=RAGFlowService, description="RAGFLOW服务") vector_db: Any = Field(default_factory=VectorDBFactory.get_vector_db, description="向量数据库实例") embedding_model: MultimodalEmbedding = Field(default_factory=MultimodalEmbedding, description="多模态嵌入模型实例") document_id: str = Field(default="", description="上传后的文档ID") split_pages: List[Dict[str, Any]] = Field(default_factory=list, description="拆分后的页面列表") current_page: Dict[str, Any] = Field(default_factory=dict, description="当前处理的页面") parsed_results: List[Dict[str, Any]] = Field(default_factory=list, description="解析结果列表") vectorized_results: List[Dict[str, Any]] = Field(default_factory=list, description="向量化结果列表") processed_pages: int = Field(default=0, description="已处理的页面数量") vectorized_pages: int = Field(default=0, description="已向量化的页面数量") is_complete: bool = Field(default=False, description="是否处理完成") # 创建工作流构建器 class PDFParsingWorkflow: """PDF扫描件拆分解析工作流""" def __init__(self, model_name: str = "Qwen/Qwen3-VL-8B-Instruct"): """ 初始化PDF解析工作流 Args: model_name: QWEN VL模型名称 """ self.model_name = model_name self.workflow = self._build_workflow() def _build_workflow(self): """构建langgraph工作流，实现基于条件路由的并行处理""" # 创建状态图 graph = StateGraph(PDFParsingState) # 添加上传文档节点 graph.add_node("upload_document", self._upload_document_node) # 添加解析文档节点 graph.add_node("parse_document", self._parse_document_node) # 添加拆分PDF节点 graph.add_node("split_pdf", self._split_pdf_node) # 添加解析图像节点 graph.add_node("parse_image", self._parse_image_node) # 添加向量化入库节点 graph.add_node("vectorize_store", self._vectorize_store_node) # 添加完成节点 graph.add_node("complete", self._complete_node) # 定义边 graph.add_edge(START, "upload_document") # 添加解析文档边 graph.add_edge("upload_document", "parse_document") graph.add_edge("parse_document", "split_pdf") graph.add_edge("split_pdf", "parse_image") # 添加条件边：判断是否继续解析 graph.add_conditional_edges( "parse_image", self._should_continue_parsing, { "continue": "parse_image", "complete": "vectorize_store" } ) # 添加向量化入库边 graph.add_edge("vectorize_store", "complete") graph.add_edge("complete", END) # 编译工作流 return graph.compile() def _upload_document_node(self, state: PDFParsingState) -> Dict[str, Any]: """RAGFLOW上传文档节点""" print(f"开始上传文档到数据集 {state.dataset_id}: {state.pdf_path}") try: # 上传文档 document_info_list = state.ragflow_service.upload_document( dataset_id=state.dataset_id, file_path=state.pdf_path ) # 检查响应 if document_info_list and len(document_info_list) > 0: document_id = document_info_list[0].id print(f"文档上传成功，文档ID: {document_id}") return { "document_id": document_id } else: print("文档上传失败: 未返回有效的文档信息") raise Exception("文档上传失败: 未返回有效的文档信息") except Exception as e: print(f"上传文档时出错: {str(e)}") raise def _parse_document_node(self, state: PDFParsingState) -> Dict[str, Any]: """RAGFLOW文档解析节点""" print(f"开始解析文档 {state.dataset_id}: {state.document_id}") try: # 解析文档 parsed_results = state.ragflow_service.parse_document( dataset_id=state.dataset_id, document_ids=[state.document_id] ) # 检查响应parsed_results为bool if parsed_results: print(f"文档解析成功，文档ID: {state.document_id}") return { "parsed_results": parsed_results } else: print("文档解析失败: 未返回有效的解析结果") raise Exception("文档解析失败: 未返回有效的解析结果") except Exception as e: print(f"解析文档时出错: {str(e)}") raise def _split_pdf_node(self, state: PDFParsingState) -> Dict[str, Any]: """拆分PDF节点""" print(f"开始拆分PDF: {state.pdf_path}") # 拆分PDF splitter = PDFSplitter() split_pages = splitter.split_pdf(state.pdf_path) print(f"PDF拆分完成，共 {len(split_pages)} 页") return { "split_pages": split_pages, "parsed_results": [], "processed_pages": 0, "is_complete": False } def _parse_single_page(self, page: Dict[str, Any], model_name: str) -> Dict[str, Any]: """解析单个页面（用于并行处理）""" prompt = """ 你是一个文本提取助手，你的任务是从图像中提取出文本内容。 注意：不要修改任何文本原始内容与标点符号，只进行提取。 """ page_number = page["page_number"] image = page["image"] print(f"开始解析第 {page_number} 页") # 使用QWEN VL模型解析图像 parser = QWenVLParser(model_name) result = parser.parse_image(image, page_number, prompt) print(f"第 {page_number} 页解析完成") return result def _parse_image_node(self, state: PDFParsingState) -> Dict[str, Any]: """解析图像节点，使用并行处理""" if not state.split_pages: return state.dict() print(f"开始并行解析 {len(state.split_pages)} 页") parsed_results = [] # 使用ThreadPoolExecutor实现并行处理 with ThreadPoolExecutor(max_workers=10) as executor: # 提交所有页面解析任务 future_to_page = { executor.submit(self._parse_single_page, page, self.model_name): page for page in state.split_pages } # 收集解析结果 for future in concurrent.futures.as_completed(future_to_page): try: result = future.result() parsed_results.append(result) except Exception as e: page = future_to_page[future] print(f"解析第 {page['page_number']} 页时出错: {str(e)}") # 按页码排序结果 parsed_results.sort(key=lambda x: x["page_number"]) print(f"所有页面解析完成，共解析 {len(parsed_results)} 页") return { "split_pages": state.split_pages, # 保留split_pages，以便后续访问图片 "parsed_results": parsed_results, "processed_pages": len(parsed_results), "is_complete": True } def _should_continue_parsing(self, state: PDFParsingState) -> str: """判断是否继续解析""" # 由于我们使用了并行处理，parse_image_node会一次性处理所有页面 # 所以这里总是返回"complete" return "complete" def _vectorize_store_node(self, state: PDFParsingState) -> Dict[str, Any]: """向量化入库节点""" print(f"开始向量化入库，共 {len(state.parsed_results)} 页") # 创建索引（如果不存在） index_name = f"pdf_documents_{state.dataset_id}" state.vector_db.create_index(index_name) # 准备要入库的文档列表 documents_to_store = [] # 获取文件名和总页数 file_name = os.path.basename(state.pdf_path) file_page_count = len(state.split_pages) # 遍历所有解析结果，生成向量化文档 for i, parsed_result in enumerate(state.parsed_results): try: page_number = parsed_result.get("page_number") text = parsed_result.get("text", "") image = parsed_result.get("image") # 生成图片地址（假设图片已保存） image_path = parsed_result.get("image_path", f"temp/{file_name}_{page_number}.png") # 获取多模态嵌入向量 print(f"正在生成第 {page_number} 页的多模态嵌入...") embedding = state.embedding_model.get_multimodal_embedding(text, image) # 生成1024维稠密向量（如果嵌入向量维度不是1024，这里需要处理） dense_vector_1024 = embedding[:1024] # 取前1024维 # 创建文档 document = { "file_name": file_name, "file_page_count": file_page_count, "page_number": page_number, "text": text, "image_path": image_path, "sparse_vector": [], # 稀疏向量，暂时为空 "dense_vector_1024": dense_vector_1024, "dataset_id": state.dataset_id, "document_id": state.document_id } documents_to_store.append(document) print(f"第 {page_number} 页向量化完成") except Exception as e: print(f"第 {i+1} 页向量化失败: {str(e)}") # 批量入库 if documents_to_store: print(f"开始批量入库，共 {len(documents_to_store)} 个文档") result = state.vector_db.bulk_insert(index_name, documents_to_store) print(f"批量入库结果: {result}") return { "vectorized_results": documents_to_store, "vectorized_pages": len(documents_to_store), "is_complete": True } def _complete_node(self, state: PDFParsingState) -> Dict[str, Any]: """完成节点""" print(f"PDF解析工作流完成，共解析 {len(state.parsed_results)} 页，向量化 {state.vectorized_pages} 页") return { "is_complete": True } def run(self, pdf_path: str, dataset_id: str, ragflow_api_url: str, rag_flow_api_key: str) -> Dict[str, Any]: """ 运行PDF解析工作流 Args: pdf_path: PDF文件路径 dataset_id: 数据集ID ragflow_api_url: RAGFLOW API URL rag_flow_api_key: RAGFLOW API密钥 Returns: Dict: 包含最终状态的字典 """ initial_state = PDFParsingState( pdf_path=pdf_path, dataset_id=dataset_id, ragflow_service=RAGFlowService(base_url=ragflow_api_url, api_key=rag_flow_api_key) ) result = self.workflow.invoke(initial_state) # 检查结果类型，如果是字典直接返回，否则调用dict()方法 if isinstance(result, dict): return result else: return result.dict()