RagFlow_Plugs/services/pdf_parser/workflow.py

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()