# 导入 backend/__init__.py 中的所有内容
from .. import *
# # 模型
from backend.models import Knowledgebase, DocumentKbm, File2document, File, Task, TaskSublist, KbmDocumentType
from django.db.models import Count, Case, When, IntegerField, Q, Max
from django.core.paginator import Paginator, PageNotAnInteger, EmptyPage
from django.shortcuts import get_object_or_404
from django.db import transaction
from django.core.exceptions import ObjectDoesNotExist


from backend.Service.MinioService import MinioService
from tabulate import tabulate
from scipy.spatial.distance import cosine
from minio import Minio
from django.conf import settings
import pytesseract


from DCbackend.utils.common import success, fail
from DCbackend.settings import MILVUS_HOST, MILVUS_PORT, VECTOR_DIMENSION, IPINFO, MILVUS_USER, MILVUS_PASSWORD,MINIO_ENDPOINT,MINIO_ACCESS_KEY,MINIO_SECRET_KEY,MINIO_SECURE

# 导入pymilvus库中的连接、集合、字段模式、集合模式、数据类型和实用工具
from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType, utility
from bs4 import BeautifulSoup

# 从service_utils模块中导入rabbitmq和pdf_utils
from  .service_utils import rabbitmq as rabbitmq_Process , pdf_utils,ocr_utils


import chardet
import threading  # 基于线程的并行处理，用于多线程编程

os.environ['HF_ENDPOINT'] = 'https://hf-mirror.com'

class DocumentQueue:
    QUEUE_KEY = "document_process_queue"


minio_client = Minio(
    MINIO_ENDPOINT,
    access_key=MINIO_ACCESS_KEY,
    secret_key=MINIO_SECRET_KEY,
    secure=MINIO_SECURE  # 根据您的 Minio 配置选择 True 或 False
)

if os.name == 'nt':  # Windows
    pytesseract.pytesseract.tesseract_cmd = r'D:\Program Files\OCR\tesseract.exe'
else:  # macOS 或 Linux
    pytesseract.pytesseract.tesseract_cmd = r'/usr/bin/tesseract'


class KbmService:
    NLP_ZH =  spacy.load('zh_core_web_sm')

    bert_model = None
    bert_tokenizer = None
    # 大模型地址
    API_URL = f"http://{IPINFO}:11434/api/embeddings"
    semaphore = threading.Semaphore(4)
   

    # 通用rabbitmq 放入队列
    def send_to_rabbitmq(queue_name, message):
        """
        将消息发送到指定的RabbitMQ队列

        Args:
            queue_name (str): 目标RabbitMQ队列的名称
            message (dict): 需要发送的消息

        Returns:
            bool: 消息发送成功返回True，失败返回False
        """
        return rabbitmq_Process.send_to_rabbitmq(queue_name, message)

    @staticmethod
    def selectBucketInfo(request):
        """
        从请求中获取知识库信息，包括文档数量统计

        Args:
            request (HttpRequest): 请求对象，包含查询条件

        Returns:
            dict: 成功时返回包含所有知识库信息和文档计数的字典，失败时返回包含错误信息的字典
        """
        #user_id = request.POST.get("user_id")
        knowledgebases = Knowledgebase.objects.filter().exclude(status=4).order_by('-create_time').values('id', 'create_time', 'name', 'doc_num', 'description')

        result = []  # 初始化结果列表
        for kb in knowledgebases:
            # 对每个知识库，统计不同类型文档的数量
            counts = DocumentKbm.objects.filter(kb_id=kb['id']).aggregate(
                word_count=Count(Case(When(type__in=['doc', 'docx'], then=1), output_field=IntegerField())),
                pdf_count=Count(Case(When(type='pdf', then=1), output_field=IntegerField())),
                excel_count=Count(Case(When(type__in=['xls', 'xlsx'], then=1), output_field=IntegerField()))
            )

            # 创建知识库数据的副本，以避免修改原始数据
            kb_data = kb.copy()
            # 将统计的计数更新到知识库数据副本中
            kb_data.update(counts)
            # 将更新后的知识库数据添加到结果列表中
            result.append(kb_data)

        # 返回包含所有知识库信息和文档计数的成功响应
        return success(result)
    
    @staticmethod
    def getFileInfo(request):
        """
        获取文件信息

        从请求的POST数据中获取bucket_id、page、pageSize、object_name、run、type和doc_type_id，用于查询和分页文档信息。
        查询条件包括知识库ID、文档名称、运行标识、文档类型和文档类型ID。结果包括文档信息和分页信息。

        Args:
            request (HttpRequest): 请求对象，包含查询条件。

        Returns:
            dict: 成功时返回包含文档信息和分页信息的字典，失败时返回包含错误信息的字典。
        """
        try:
            bucket_id = request.POST.get("bucket_id")
            page = request.POST.get("page", 1)
            per_page = request.POST.get("pageSize", 10)
            object_name = request.POST.get("object_name", "")
            run = request.POST.get("run", "")
            type = request.POST.get("type", "")
            doc_type_id = request.POST.get("doc_type_id")
            if not bucket_id:
                return fail("bucket_id为空")


            # 确保 page 和 per_page 是整数
            page = int(page)
            per_page = int(per_page)

            # # 查询文档并排序
            # documents = DocumentKbm.objects.filter(
            #     Q(kb_id=bucket_id) &
            #     Q(name__icontains=object_name)&
            #     Q(run__icontains=run)&
            #     Q(type__icontains=type)&
            #     Q(doc_type_id=doc_type_id)&
            #     ~Q(status=4)
            # ).order_by('-create_time')

            # 构建基本查询
            query = Q(kb_id=bucket_id) & Q(name__icontains=object_name) & Q(run__icontains=run) & Q(type__icontains=type) & ~Q(status=4)

            # 如果 doc_type_id 有值，则添加到查询条件
            if doc_type_id:
                query &= Q(doc_type_id=doc_type_id)

            # 查询文档并排序
            documents = DocumentKbm.objects.filter(query).order_by('-create_time')

            # 创建分页器
            paginator = Paginator(documents, per_page)

            try:
                # 获取指定页的结果
                documents_page = paginator.page(page)
            except PageNotAnInteger:
                # 如果页码不是整数，返回第一页
                documents_page = paginator.page(1)
            except EmptyPage:
                # 如果页码超出范围，返回最后一页
                documents_page = paginator.page(paginator.num_pages)

            # 将查询结果转换为列表
            result = list(documents_page.object_list.values())
            
            # 循环整体会导致mysql多连反应，（后期应该改成数据载加到内存环境后一步塞入数据库中）
            # 发现人  qman

            for info in result:
                document_id = info['id']
                max_page = TaskSublist.objects.filter(doc_id=document_id).aggregate(Max('page_number'))['page_number__max']
                info['max_page'] = max_page if max_page is not None else 0

            pagination_info = {
                'total_count': paginator.count,
                'total_pages': paginator.num_pages,
                'total_size': per_page,
                'current_page': documents_page.number,
                'has_next': documents_page.has_next(),
                'has_previous': documents_page.has_previous()
            }
            data = {
                'pagination': pagination_info,
                'documents': result
            }

            return success(data)
        except Exception as e:
            return fail("获取信息失败")
        
    @staticmethod
    @transaction.atomic
    def updateName(request):
        """
        更新文档的名称

        从请求的POST数据中获取新名称和文档ID，更新对应的DocumentKbm对象和关联的File对象的名称。
        如果新名称不包含扩展名，则使用原始文件的扩展名。

        Args:
            request (HttpRequest): 请求对象，包含新名称和文档ID。

        Returns:
            dict: 成功时返回包含成功信息的字典，失败时返回包含错误信息的字典。
        """
        try:
            new_name = request.POST.get("new_name")
            document_id = request.POST.get("document_id")

            if not new_name or not document_id:
                return fail("新名称和文件ID不能为空")

            # 获取 DocumentKbm 实例并更新
            document = get_object_or_404(DocumentKbm, id=document_id)
            location = document.location

            # 获取原始文件的扩展名
            _, original_extension = os.path.splitext(location)

            # 检查新名称是否包含扩展名，如果没有则添加原始扩展名
            _, new_extension = os.path.splitext(new_name)
            if not new_extension:
                new_name = f"{new_name}{original_extension}"

            document.name = new_name
            document.save()

            # 获取关联的 File2document 和 File
            file2doc = File2document.objects.filter(document_id=document_id).first()
            if file2doc:
                file = get_object_or_404(File, id=file2doc.file_id)
                file.name = new_name
                file.save()
            else:
                # 记录一个警告，因为没有找到关联的 File
                logger.error(f"Warning: No associated File found for DocumentKbm with id {document_id}")

            return success("文件名更新成功")
        except ObjectDoesNotExist:
            return fail("指定的文件或关联文件不存在")
        except Exception as e:
            return fail(f"更新文件名失败: {str(e)}") 
                
    @staticmethod
    @transaction.atomic
    def deleteDocument(request):
        """
        删除文档

        从请求的POST数据中获取文档ID，更新文档和关联文件的状态为已删除。然后，更新知识库的文档数量。
        最后，尝试清理Milvus中的相关数据，并断开Milvus连接。

        Args:
            request (HttpRequest): 请求对象，包含文档ID。

        Returns:
            dict: 成功时返回包含成功信息的字典，失败时返回包含错误信息的字典。
        """
        document_id = request.POST.get("document_id")

        document = get_object_or_404(DocumentKbm, id=document_id)
        document.status = 4
        document.save()

        file2doc = File2document.objects.filter(document_id=document_id).first()
        if file2doc:
            file = get_object_or_404(File, id=file2doc.file_id)
            file.status = 4
            file.save()

        kb_id = document.kb_id
        new_count = DocumentKbm.objects.filter(kb_id=kb_id).exclude(status=4).count()
        Knowledgebase.objects.filter(id=kb_id).update(doc_num=new_count)
        try:
            # 需要 拆列方法 石峰
            # 1 、 删除 minio
            # 2、 删除 milvus
            # 清理milvus
            # 连接到 Milvus
            connections.connect("default", host=MILVUS_HOST, port=MILVUS_PORT ,user=MILVUS_USER,password=MILVUS_PASSWORD)

            kmb = Knowledgebase.objects.filter(id=kb_id).first()
            
            tasks = TaskSublist.objects.filter(doc_id=document.id)
            KbmService.clearPreviousData(document_id,document.name)
            # collection = Collection(kmb.location)


            # tasks = TaskSublist.objects.filter(doc_id=document.id)
            # for task in tasks:
            #     logger.info(f'删除{task.milvus_id}')
            #     expr = f'id in [{task.milvus_id}]'
            #     collection.delete(expr)
                # minio_client.remove_object(kmb.location, tasks.name)

            # files =  File.objects.filter(id=file2doc.file_id).first()
            # minio_client.remove_object(kmb.location, files.location)
            

                
            return success("删除成功")
        except Exception as e:
            return fail(f"删除milvus集合时发生错误: {str(e)}")

        finally:
            # 断开 Milvus 连接
            connections.disconnect("default")
    
    @staticmethod
    def getUrl(request):
        """
        根据请求获取文档的URL。

        从请求的POST数据中获取文档ID，通过文档ID获取对应的DocumentKbm对象和关联的Knowledgebase对象。
        然后，使用MinioService.geturl方法获取文档的URL。

        Args:
            request (HttpRequest): 请求对象，包含文档ID。

        Returns:
            dict: 成功时返回包含文档URL的字典，失败时返回包含错误信息的字典。
        """
        try:
            document_id = request.POST.get("document_id")
            if not document_id:
                return fail("文档ID不能为空")

            # 获取 DocumentKbm 对象
            document = get_object_or_404(DocumentKbm, id=document_id)

            object_name = document.location

            # 获取对应的 Knowledgebase 对象
            knowledgebase = get_object_or_404(Knowledgebase, id=document.kb_id)
            bucket_name = knowledgebase.location

            return MinioService.geturl(object_name, bucket_name)

        except ObjectDoesNotExist:
            return fail("指定的文档或知识库不存在")
        except Exception as e:
            return fail(f"获取URL失败: {str(e)}")
        
    @staticmethod
    def get_embedding_excel(text, target_dim=768):
        """
        根据输入文本获取其对应的嵌入向量。

        Args:
            text (str): 输入文本。
            target_dim (int, optional): 目标维度。默认为768。

        Returns:
            list: 嵌入向量的列表表示。
        """
        try:
            if not text or not text.strip():
                logger.warning("Empty text provided for embedding. Returning zero vector.")
                return np.zeros(target_dim).tolist()

            # 确保文本被正确编码
            encoded_text = text.encode('utf-8').decode('utf-8')

            payload = {
                "model": "nomic-embed-text:latest",
                "prompt": encoded_text
            }
            headers = {"Content-Type": "application/json"}
            # 
            response = requests.post(KbmService.API_URL, json=payload, headers=headers)
            logger.info(f"response::::{response}")
            response.raise_for_status()
            embedding_data = response.json()

            if 'embedding' not in embedding_data:
                raise ValueError(f"API 响应中没有找到嵌入向量. 响应内容: {embedding_data}")

            embedding = embedding_data['embedding']
            original_embedding = np.array(embedding)

            if len(original_embedding) == target_dim:
                return original_embedding.tolist()

            # 如果原始维度不等于目标维度，进行插值
            original_indices = np.arange(len(original_embedding))
            new_indices = np.linspace(0, len(original_embedding) - 1, target_dim)
            f = interpolate.interp1d(original_indices, original_embedding)
            extended_embedding = f(new_indices)

            return extended_embedding.tolist()

        except requests.exceptions.RequestException as e:
            logger.error(f"API 请求错误: {str(e)}")
            raise
        except ValueError as e:
            logger.error(f"值错误: {str(e)}")
            raise
        except Exception as e:
            logger.error(f"获取文本嵌入时发生意外错误: {str(e)}")
            raise

    #新rabbitmq队列
    @staticmethod
    def analysis(request):
        """
        分析请求并处理RabbitMQ队列中的消息。
        Args:
            request (object): 需要分析的请求对象。

        Returns:
            None
        """
        return rabbitmq_Process.analysis(request, KbmService)

    @staticmethod
    def check_and_process_queue():
        """
        检查并处理RabbitMQ队列中的消息，启用多线程 。

        Returns:
            None
        """
        KbmService.should_stop = False
        while not KbmService.should_stop:
            try:
                if KbmService.queue_has_messages():
                    KbmService.process_queue()
                else:
                    # logger.info("队列为空，等待下一次检查...")
                    time.sleep(60)  # 等待60秒后再次检查
            except Exception as e:
                logger.error(f"检查队列时发生错误: {str(e)}")
                time.sleep(60)  # 发生错误时，等待60秒后重试

    @staticmethod
    def queue_has_messages():
        """
        检查RabbitMQ队列中是否有消息。

        Returns:
            bool: 如果队列中有消息返回True，否则返回False。
        """
        try:
            connection = KbmService.create_connection()
            channel = connection.channel()
            queue = channel.queue_declare(queue=settings.RABBITMQ_QUEUE_NAME, passive=True)
            message_count = queue.method.message_count
            connection.close()
            return message_count > 0
        except Exception as e:
            logger.error(f"检查队列消息数量时发生错误: {str(e)}")
            return False
    
    @staticmethod
    def create_connection():
        """
        创建到RabbitMQ服务器的连接。

        Returns:
            pika.BlockingConnection: 连接对象。
        """
        return pika.BlockingConnection(pika.ConnectionParameters(
            host=settings.RABBITMQ_HOST,
            port=settings.RABBITMQ_PORT,
            credentials=pika.PlainCredentials(
                settings.RABBITMQ_USER,
                settings.RABBITMQ_PASSWORD
            )
        ))
    
    @staticmethod
    def process_queue():
        logger.info("队列中有消息，开始处理...")
        KbmService.connection = KbmService.create_connection()
        KbmService.channel = KbmService.connection.channel()
        KbmService.channel.queue_declare(queue=settings.RABBITMQ_QUEUE_NAME, durable=True)
        KbmService.channel.basic_qos(prefetch_count=4)
        KbmService.channel.basic_consume(queue=settings.RABBITMQ_QUEUE_NAME, on_message_callback=KbmService.callback)
        try:
            KbmService.channel.start_consuming()
        except KeyboardInterrupt:
            KbmService.should_stop = True
        finally:
            KbmService.close_connection()


    @staticmethod
    def callback(ch, method, properties, body):
        """
        处理RabbitMQ队列中的消息。

        Args:
            ch (pika.channel.Channel): RabbitMQ频道对象。
            method (pika.spec.Basic.Deliver): 消息投递信息。
            properties (pika.spec.BasicProperties): 消息属性。
            body (bytes): 消息体。

        Raises:
            Exception: 处理消息时发生的任何异常。
        """
        with KbmService.semaphore:
            try:
                job = json.loads(body)
                document_id = job['document_id']
                start_page = job['start_page']
                end_page = job['end_page']
                max_tokens = job['max_tokens']

                logger.info(f"开始执行解析文档 {document_id}")
                KbmService.async_analysis(document_id, start_page, end_page, max_tokens)
                ch.basic_ack(delivery_tag=method.delivery_tag)
            except Exception as e:
                logger.error(f"处理队列消息时发生错误: {str(e)}")
                ch.basic_nack(delivery_tag=method.delivery_tag, requeue=True)

            # 检查是否还有更多消息
            if not KbmService.queue_has_messages():
                logger.info("队列处理完毕，停止消费...")
                ch.stop_consuming()

    @staticmethod
    def close_connection():
        if KbmService.channel:
            try:
                KbmService.channel.close()
            except Exception:
                pass
        if KbmService.connection:
            try:
                KbmService.connection.close()
            except Exception:
                pass
        KbmService.channel = None
        KbmService.connection = None

    @staticmethod
    def stop_service():
        KbmService.should_stop = True
        if KbmService.channel:
            KbmService.channel.stop_consuming()
        KbmService.close_connection()

    @staticmethod
    def get_embedding_excel(text, target_dim=768):
        """
        获取文本的嵌入向量

        从API获取文本的嵌入向量，并根据需要调整到目标维度。

        Args:
            text (str): 需要获取嵌入向量的文本。
            target_dim (int, optional): 目标维度。默认为768。

        Returns:
            list: 文本的嵌入向量列表，长度为target_dim。

        Raises:
            Exception: 处理文本嵌入时发生的任何异常。
        """
        try:
            if not text or not text.strip():
                logger.warning("Empty text provided for embedding. Returning zero vector.")
                return np.zeros(target_dim).tolist()

            # 确保文本被正确编码
            encoded_text = text.encode('utf-8').decode('utf-8')

            payload = {
                "model": "nomic-embed-text:latest",
                "prompt": encoded_text
            }
            headers = {"Content-Type": "application/json"}

            response = requests.post(KbmService.API_URL, json=payload, headers=headers)
            logger.info(f"response::::{response}")
            response.raise_for_status()
            embedding_data = response.json()

            if 'embedding' not in embedding_data:
                raise ValueError(f"API 响应中没有找到嵌入向量. 响应内容: {embedding_data}")

            embedding = embedding_data['embedding']
            original_embedding = np.array(embedding)

            if len(original_embedding) == target_dim:
                return original_embedding.tolist()

            # 如果原始维度不等于目标维度，进行插值
            original_indices = np.arange(len(original_embedding))
            new_indices = np.linspace(0, len(original_embedding) - 1, target_dim)
            f = interpolate.interp1d(original_indices, original_embedding)
            extended_embedding = f(new_indices)

            return extended_embedding.tolist()

        except requests.exceptions.RequestException as e:
            logger.error(f"API 请求错误: {str(e)}")
            raise
        except ValueError as e:
            logger.error(f"值错误: {str(e)}")
            raise
        except Exception as e:
            logger.error(f"获取文本嵌入时发生意外错误: {str(e)}")
            raise

    @classmethod
    def get_embedding_pdf(cls, text, target_dim=768, max_retries=3, backoff_factor=0.3):
        """
        获取文本的嵌入向量，并填充或截断到目标维度，包含重试机制

        Args:
            text (str): 需要嵌入的文本。
            target_dim (int, optional): 目标维度。默认为 768。
            max_retries (int, optional): 最大重试次数。默认为 3。
            backoff_factor (float, optional): 每次重试的退避因子。默认为 0.3。

        Returns:
            np.ndarray: 填充或截断后的嵌入向量。
        """
        # 清楚  一些 垃圾 字符
        text =  KbmService.post_process_text(text)

        for attempt in range(max_retries):
            try:
                payload = {
                    "model": "nomic-embed-text:latest",
                    "prompt": text
                }
                headers = {
                    "Content-Type": "application/json"
                }

                response = requests.post(cls.API_URL, json=payload, headers=headers, timeout=30)
                sleep(0.3)
                response.raise_for_status()
                result = response.json()

                embedding = result.get('embedding')
                

                if embedding is None:
                    raise ValueError("API 响应中没有找到嵌入向量")

                embedding_array = np.array(embedding)
                current_dim = embedding_array.shape[0]

                if current_dim < target_dim:
                    padded_embedding = np.pad(embedding_array, (0, target_dim - current_dim), 'constant')
                    logger.info(f"向量已从 {current_dim} 维填充到 {target_dim} 维")
                    return padded_embedding
                elif current_dim > target_dim:
                    truncated_embedding = embedding_array[:target_dim]
                    logger.info(f"向量已从 {current_dim} 维截断到 {target_dim} 维")
                    return truncated_embedding
                else:
                    return embedding_array

            except RequestException as e:
                logger.error(f"API 请求错误 (尝试 {attempt + 1}/{max_retries}): {e}")
                if attempt == max_retries - 1:
                    raise
                time.sleep(backoff_factor * (2 ** attempt))
            except ValueError as e:
                logger.error(f"解析响应错误: {e}")
                raise
            except Exception as e:
                logger.error(f"获取文本嵌入时发生未知错误: {e}")
                raise

        raise Exception("达到最大重试次数，无法获取嵌入")

    @staticmethod
    def split_text_by_semantic(sentences, max_tokens, bucket_name, similarity_threshold=0.5, batch_size=1000):
        logger.info("开始分割文本并保存到向量数据库")
        chunks = []
        if len(sentences)<= 0:
            return chunks

        try:
            #object1   object1为后续可能添加的字段  因为无法直接修改名称  备用
            source = "知识库"
            object1 ="some_object1"
            object2 ="some_object2"
            # 连接到Milvus
            connections.connect("default", host=MILVUS_HOST, port=MILVUS_PORT,user=MILVUS_USER,password=MILVUS_PASSWORD)
            collection_name = f"{bucket_name}"
            collection = KbmService._get_or_create_collection(collection_name)
           

            
            current_chunk = sentences[0]
            current_embedding = KbmService.get_embedding_pdf(current_chunk, target_dim=VECTOR_DIMENSION)
            batch_data = []

            for sentence in sentences[1:]:
                if  len(sentence) > 10:
                    sentence_embedding = KbmService.get_embedding_pdf(sentence, target_dim=VECTOR_DIMENSION)
                    similarity = 1 - cosine(current_embedding, sentence_embedding)

                    if len(current_chunk) + len(sentence) <= max_tokens and similarity >= similarity_threshold:
                        current_chunk += sentence
                        current_embedding = (current_embedding + sentence_embedding) / 2
                    else:
                        batch_data.append((current_chunk, current_embedding))
                        if len(batch_data) >= batch_size:
                            ids = KbmService._insert_batch(collection, batch_data,source,object1,object2)
                            sleep(1)
                            logger.info("减少milvus压力睡眠1秒")
                            if ids is not None:
                                chunks.extend([{'content': chunk, 'milvus_id': id} for (chunk, _), id in zip(batch_data, ids)])
                            else:
                                logger.error("向 Milvus 插入批量数据失败，这批数据将被跳过")
                            batch_data = []
                        current_chunk = sentence
                        current_embedding = sentence_embedding

            # 处理最后一个chunk和剩余的batch数据
            if current_chunk:
                batch_data.append((current_chunk, current_embedding))
            if batch_data:
                ids = KbmService._insert_batch(collection, batch_data,source,object1,object2)
                sleep(1)
                logger.info("减少milvus压力睡眠1秒")
                if ids is not None:
                    chunks.extend([{'content': chunk, 'milvus_id': id} for (chunk, _), id in zip(batch_data, ids)])
                else:
                    logger.error("向 Milvus 插入批量数据失败，这批数据将被跳过")

            KbmService._create_index_and_load(collection)

            logger.info(f"成功将{len(chunks)}个文本块分割并保存到Milvus")
            return chunks

        except Exception as e:
            logger.error(f"处理文本时发生错误: {str(e)}")
            raise
        finally:
            connections.disconnect("default")

    @staticmethod
    def _get_or_create_collection(collection_name):
        if not utility.has_collection(collection_name):
            fields = [
                FieldSchema(name="id", dtype=DataType.INT64, is_primary=True, auto_id=True),
                FieldSchema(name="source", dtype=DataType.VARCHAR, max_length=65000),
                FieldSchema(name="object1", dtype=DataType.VARCHAR, max_length=65000),
                FieldSchema(name="object2", dtype=DataType.VARCHAR, max_length=65000),
                FieldSchema(name="content", dtype=DataType.VARCHAR, max_length=65000),
                FieldSchema(name="embedding", dtype=DataType.FLOAT_VECTOR, dim=VECTOR_DIMENSION)
            ]
            schema = CollectionSchema(fields, "Semantic text chunks collection")
            return Collection(name=collection_name, schema=schema)
        return Collection(name=collection_name)

    @staticmethod
    def _split_sentences(text):
        sentences = re.split('([。！？])', text)
        sentences = [''.join(i) for i in zip(sentences[0::2], sentences[1::2] + [''])]
        return [s.strip() for s in sentences if s.strip()]



        
    @staticmethod
    def _insert_batch(collection, batch_data, source, object1, object2):
        try:
            entities = [
                [source] * len(batch_data),  # source
                [object1]* len(batch_data),  # object1
                [object2]* len(batch_data),  # object2
                [chunk for chunk, _ in batch_data],
                [embedding.tolist() for _, embedding in batch_data]
            ]
            # 插入数据并获取插入操作的结果
            insert_result = collection.insert(entities)

            # 获取插入的 ID
            inserted_ids = insert_result.primary_keys
            return inserted_ids

            logger.info(f"成功插入{len(batch_data)}个文本块到Milvus")
        except Exception as e:
            logger.error(f"批量插入数据时发生错误: {str(e)}")

    @staticmethod
    def _create_index_and_load(collection):
        index_params = {
            "index_type": "IVF_FLAT",
            "metric_type": "L2",
            "params": {"nlist": 768}
        }
        collection.create_index("embedding", index_params)
        collection.load()


    @staticmethod
    def async_analysis(document_id, start_page, end_page, max_tokens):
        """
        异步分析文档内容。

        :param document_id: 文档的唯一标识符。
        :param start_page: 分析开始的页面号。
        :param end_page: 分析结束的页面号。
        :param max_tokens: 分析的最大令牌数。
        1  已解析、 2 未知： 、 3： 成功、 4： 失败、5： 待 处理
        """
        start_time = time.time()
        excel_status = 1
        result = []
        logger.info(f"开始处理文档 {document_id}")

        try:
            DocumentKbm.objects.filter(id=document_id).update(run=1)  # 更新文档状态为处理中
            document = get_object_or_404(DocumentKbm, id=document_id)
            object_name = document.location
            file_extension = object_name.split('.')[-1].lower()
            
            knowledgebase = get_object_or_404(Knowledgebase, id=document.kb_id)
            bucket_name = knowledgebase.location
            
            logger.info(f"文档信息: object_name:{object_name}, file_extension:{file_extension}, bucket_name:{bucket_name}")
            
            
            KbmService.clearPreviousData(document_id, bucket_name)
            
            response = minio_client.get_object(bucket_name, object_name)
            if not response:
                raise ValueError(f"MinIO响应为空。行号：{sys._getframe().f_lineno}")
            
            response_content = response.read()
            if not response_content:
                raise ValueError(f"从MinIO读取的文件内容为空。行号：{sys._getframe().f_lineno}")
            
            file_content = BytesIO(response_content)
            
            if file_content.getbuffer().nbytes == 0:
                raise ValueError(f"文件内容为空。行号：{sys._getframe().f_lineno}")
            
            if file_extension in ['xls', 'xlsx']:
                logger.info(f"开始处理Excel文件: {document_id}")
                result, excel_status = KbmService.process_excel(file_content, document_id, max_tokens, bucket_name)
            elif file_extension == 'pdf':
                logger.info(f"开始处理PDF文件: {document_id}")
                result = KbmService.process_pdf(file_content, document_id, max_tokens, bucket_name)
            elif file_extension == 'md':
                logger.info(f"开始处理MD文件: {document_id}")
                result = KbmService.process_markdown(file_content, document_id, max_tokens, bucket_name)
            elif file_extension in ['doc', 'docx']:
                logger.info(f"开始将DOC/DOCX文件转换为PDF: {document_id}")
                pdf_content = KbmService.convert_doc_to_pdf(file_content)
                result = KbmService.process_pdf(pdf_content, document_id, max_tokens, bucket_name)
                     
            if not result:
                raise ValueError("处理结果为空。")
            
            

            KbmService.saveTask(document_id, len(result))
            end_time = time.time()
            execution_time = round(end_time - start_time, 2)
            KbmService.updateDocument(max_tokens, len(result), document_id, execution_time)
            
            if excel_status == 6:
                DocumentKbm.objects.filter(id=document_id).update(run=6)  # Excel 特殊情况
                logger.info(f"文档 {document_id} 更新完成，状态设置为6（Excel特殊情况）")
            else:
                DocumentKbm.objects.filter(id=document_id).update(run=3)  # 假设3表示成功状态
                logger.info(f"文档 {document_id} 更新完成，状态设置为3（成功）")
            861
            logger.info(f"文档 {document_id} 处理完成")
            
        except Exception as e:
            # logger.error(f"处理文档 {document_id} 时发生错误: {str(e)}")
            logger.error(f"异常堆栈跟踪:{str(e)}")
            traceback.print_exc()
            DocumentKbm.objects.filter(id=document_id).update(run=4)  # 更新文档状态为失败

        


    @staticmethod
    def convert_doc_to_pdf(file_content):
        try:
            # 创建临时文件
            with tempfile.NamedTemporaryFile(delete=False, suffix='.docx') as temp_input:
                temp_input.write(file_content.getvalue())
                temp_input_path = temp_input.name

            temp_output_dir = tempfile.mkdtemp()

            # 查找 LibreOffice 路径
            libreoffice_path = KbmService.get_libreoffice_path()
            if not libreoffice_path:
                raise FileNotFoundError("找不到 LibreOffice 可执行文件")

            # 转换为 PDF
            pdf_path = KbmService.run_libreoffice_conversion(libreoffice_path, temp_input_path, temp_output_dir)

            # 读取 PDF 内容
            with open(pdf_path, 'rb') as pdf_file:
                pdf_content = pdf_file.read()


            # 读取并返回 PDF 内容
            with open(pdf_path, 'rb') as pdf_file:
                return BytesIO(pdf_file.read())

        except Exception as e:
            logger.error(f"将文档转换为 PDF 时出错: {str(e)}", exc_info=True)
            return BytesIO()

        finally:
            KbmService.cleanup_temp_files(temp_input_path, temp_output_dir)

    @staticmethod
    def get_libreoffice_path():
        system = platform.system()
        if system == "Windows":
            libreoffice_paths = [r"E:\tool\LibreOffice\program\soffice.exe"]
            return next((path for path in libreoffice_paths if os.path.exists(path)), None)
        else:  # Linux 或 macOS
            for path in ['/usr/bin/libreoffice', '/usr/bin/soffice', '/opt/libreoffice/program/soffice']:
                if os.path.exists(path):
                    return path
            return shutil.which('libreoffice') or shutil.which('soffice')

    @staticmethod
    def run_libreoffice_conversion(libreoffice_path, input_path, output_dir):
        cmd = [
            libreoffice_path,
            '--headless',
            '--convert-to', 'pdf:writer_pdf_Export:{"PageSize":{"Width":21000,"Height":29700}}',
            '--outdir', output_dir,
            input_path
        ]
        try:
            env = os.environ.copy()
            env['HOME'] = '/mnt/ql_api/tmp'  # 设置一个临时的 HOME 目录
            env['LC_ALL'] = 'C'  # 设置一个标准的语言环境
            sleep(0.3)
            result = subprocess.run(cmd, check=True, capture_output=True, text=True, timeout=60, env=env)
            logger.info(f"LibreOffice 转换输出: {result.stdout}")

            pdf_filename = os.path.splitext(os.path.basename(input_path))[0] + '.pdf'
            pdf_path = os.path.join(output_dir, pdf_filename)

            if not os.path.exists(pdf_path):
                raise FileNotFoundError(f"PDF 文件未生成。输出目录内容: {os.listdir(output_dir)}")

            # 使用 PyPDF2 检查页数
            with open(pdf_path, 'rb') as pdf_file:
                pdf_reader = PyPDF2.PdfReader(pdf_file)
                page_count = len(pdf_reader.pages)
                logger.info(f"生成的 PDF 文件页数: {page_count}")

            pdf_size = os.path.getsize(pdf_path)
            if pdf_size < 1000:
                logger.warning(f"生成的 PDF 文件大小异常小: {pdf_size} bytes")

            return pdf_path
        except subprocess.TimeoutExpired:
            raise TimeoutError("LibreOffice 转换超时")
        except subprocess.CalledProcessError as e:
            raise RuntimeError(f"LibreOffice 转换失败: {e.output}")

    @staticmethod
    def cleanup_temp_files(temp_input_path, temp_output_dir):
        if os.path.exists(temp_input_path):
            os.remove(temp_input_path)
        if os.path.exists(temp_output_dir):
            shutil.rmtree(temp_output_dir)
    @staticmethod
    def process_excel(file_content, document_id, max_tokens, bucket_name):
        result = []
        collection_name = f"{bucket_name}"
        status=1
        # object1   object1为后续可能添加的字段  因为无法直接修改名称  备用
        source = "知识库"
        object1 = "some_object1"
        object2 = "some_object2"

        def warning_catcher(message, category, filename, lineno, file=None, line=None):
            nonlocal status
            if category == UserWarning:
                if "File contains an invalid specification for 0" in str(message) or \
                        "Defined names for sheet index 0 cannot be located" in str(message):
                    status = 6
            logger.error(f"Warning: {message}")

        warnings.showwarning = warning_catcher
        try:
            excel_file = pd.ExcelFile(file_content)
        except Exception as e:
            logger.error(f"Error reading Excel file: {str(e)}")
            return result


        try:
            connections.connect("default", host=MILVUS_HOST, port=MILVUS_PORT,user=MILVUS_USER,password=MILVUS_PASSWORD)
            collection = KbmService._get_or_create_collection(collection_name)

            for sheet_name in excel_file.sheet_names:
                df = pd.read_excel(excel_file, sheet_name=sheet_name)
                if df.empty:
                    logger.warning(f"Sheet '{sheet_name}' is empty. Skipping.")
                    continue

                logger.info(f"Processing sheet '{sheet_name}' with shape {df.shape}")

                markdown_content = KbmService._excel_to_markdown(df, sheet_name)
                chunks = KbmService._split_markdown(markdown_content, max_tokens)

                for chunk_number, chunk_content in enumerate(chunks, start=1):
                    try:
                        if not chunk_content.strip():
                            logger.warning(f"Empty chunk {chunk_number} in sheet '{sheet_name}'. Skipping.")
                            continue

                        embedding = KbmService.get_embedding_excel(chunk_content, target_dim=VECTOR_DIMENSION)

                        if isinstance(embedding, (list, np.ndarray)) and len(embedding) == VECTOR_DIMENSION:
                            sleep(1)
                            logger.info("减少milvus压力睡眠1秒")
                            milvus_id = KbmService._insert_data(collection, chunk_content, embedding,source,object1,object2)
                        else:
                            logger.error(f"Invalid embedding format for chunk {chunk_number} of sheet {sheet_name}.")
                            continue

                        KbmService.saveTaskSublist(
                            document_id=document_id,
                            name=f"sheet_{sheet_name}",
                            page_number=1,
                            chunk_number=chunk_number,
                            content=chunk_content,
                            milvus_id=milvus_id
                        )

                        result.append({
                            'page_number': 1,
                            'chunk_number': chunk_number,
                        })

                    except Exception as e:
                        logger.error(f"Error processing chunk {chunk_number} of sheet {sheet_name}: {str(e)}")
                        logger.error(f"Chunk content: {chunk_content}")
                        logger.exception("Detailed error information:")

        except Exception as e:
            logger.error(f"Error processing Excel file: {str(e)}")
            logger.error("Detailed error information:")
            logger.error(traceback.format_exc())
            status = 6  # 设置状态为6，表示处理出错
            raise

        finally:
            connections.disconnect("default")

        return result, status
    #excel转markdown
    @staticmethod
    def _excel_to_markdown(df, sheet_name):
        if df.empty:
            return f"# {sheet_name}\n\n表格为空"

        headers = df.columns.tolist()
        data = df.values.tolist()

        # 将所有数据转换为字符串
        data = [[str(cell) for cell in row] for row in data]

        markdown = f"# {sheet_name}\n\n"
        markdown += tabulate(data, headers=headers, tablefmt="pipe", showindex=False)
        return markdown
    #excel分割策略
    @staticmethod
    def _split_json(json_str, max_tokens):
        # 简单的分割策略，可以根据需要优化
        data = json.loads(json_str)
        chunks = []
        current_chunk = []
        current_size = 0

        for item in data:
            item_str = json.dumps(item)
            item_size = len(item_str)
            if current_size + item_size > max_tokens and current_chunk:
                chunks.append(json.dumps(current_chunk))
                current_chunk = []
                current_size = 0
            current_chunk.append(item)
            current_size += item_size

        if current_chunk:
            chunks.append(json.dumps(current_chunk))

        return chunks

    @staticmethod
    def _split_markdown(markdown_content, max_tokens):
        chunks = []
        current_chunk = ""
        lines = markdown_content.split('\n')

        for line in lines:
            if len(current_chunk) + len(line) + 1 > max_tokens:
                if current_chunk:
                    chunks.append(current_chunk.strip())
                current_chunk = line
            else:
                current_chunk += '\n' + line if current_chunk else line

        if current_chunk:
            chunks.append(current_chunk.strip())

        return chunks
    #milvus excel插入格式
    @staticmethod
    def _insert_data(collection, content, embedding,source,object1,object2):
        try:
            data = [
                [source],  # content field
                [object1],  # content field
                [object2],  # content field
                [content],  # content field
                [embedding]  # embedding field
            ]
            insert_result = collection.insert(data)
            logger.info(f"Inserted 1 record into Milvus")
            return insert_result.primary_keys[0]  # 返回插入的 ID
        except Exception as e:
            logger.error(f"Error inserting data into Milvus: {str(e)}")
            raise
    #创建milvus索引
    @staticmethod
    def _create_index_if_not_exists(collection):
        if not collection.has_index():
            index_params = {
                "index_type": "IVF_FLAT",
                "metric_type": "L2",
                "params": {"nlist": 768}
            }
            collection.create_index("embedding", index_params)

    @staticmethod
    def process_pdf(file_content, document_id, max_tokens, bucket_name):
        """
        解析PDF文件并处理每一页的内容。

        Args:
            file_content (BytesIO): PDF文件的内容。
            document_id (str): 文档的ID。
            max_tokens (int): 最大令牌数。
            bucket_name (str): 存储桶的名称。

        Returns:
            list: 处理后的页面列表。
        """
        logger.info(f"开始处理 PDF，document_id: {document_id}, max_tokens: {max_tokens}")
        text_chunks = []
        try:
            sentence_chunks, page_images = pdf_utils.PDFProcessor(1).get_results(file_content, document_id, max_tokens, bucket_name, KbmService)
            #  一张页面 ， 一张页面的完这
            for  sentence_chunk in sentence_chunks:
                text_chunks.append( KbmService.split_text_by_semantic(sentence_chunk, max_tokens, bucket_name))
                sleep(.5)

            logger.info("分割完成")
            result = []
            
            page_inder = 0
            for i,  chunks in enumerate(text_chunks,1):
                inder = 1
                image_name = page_images[i-1][0]
                for j, chunk in enumerate(chunks,1):
                    KbmService.saveTaskSublist(
                        document_id=document_id,
                        name=image_name,
                        page_number=i,
                        chunk_number=j,
                        content=chunk['content'],
                        milvus_id=chunk['milvus_id']
                    )
                    inder +=1
                    result.append({
                        'page_number': i,
                        'chunk_number': page_inder,
                    })
                page_inder += inder
                logger.info(page_inder)

            logger.info("解析结束")
            return result
        
        except Exception as e:
            logger.error(f'错误 {str(e)}')
            # raise ValueError(f'{str(e)} 行号：{sys._getframe().f_lineno}')
            return result


    #解析markdown
    @staticmethod
    def process_markdown(file_content, document_id, max_tokens, bucket_name):
        logger.info(f"开始解析 Markdown，document_id: {document_id}, max_tokens: {max_tokens}")
        try:
            # 检测文件编码
            raw_content = file_content.read()
            detected = chardet.detect(raw_content)
            encoding = detected['encoding']
            logger.info(f"检测到的文件编码: {encoding}")

            # 解码文件内容
            text = raw_content.decode(encoding)
            logger.info(f"Markdown 文件总字符数: {len(text)}")
            logger.debug(f"Markdown 文件前100个字符: {text[:100]}")

            # 将 Markdown 转换为 HTML
            html = markdown.markdown(text)

            # 使用 BeautifulSoup 提取纯文本
            soup = BeautifulSoup(html, 'html.parser')
            plain_text = soup.get_text()

            logger.info(f"提取的纯文本总字符数: {len(plain_text)}")
            logger.debug(f"提取的纯文本前100个字符: {plain_text[:100]}")

            if not plain_text.strip():
                logger.warning("Markdown 文件内容为空")
                return []

            sentence_chunks = pdf_utils.PDFProcessor(1).split_sentences(plain_text)
            # 使用 split_text_by_semantic 方法分割文本
            text_chunks = KbmService.split_text_by_semantic(sentence_chunks, max_tokens, bucket_name)
            logger.info(f"分割后的文本块数: {len(text_chunks)}")

            result = []
            for i, chunk in enumerate(text_chunks, 1):
                KbmService.saveTaskSublist(
                    document_id=document_id,
                    name="markdown_content",
                    page_number=1,
                    chunk_number=i,
                    content=chunk['content'],
                    milvus_id=chunk['milvus_id']
                )
                result.append({
                    'page_number': 1,
                    'chunk_number': i,
                })

            logger.info(f"Markdown 处理完成，总共生成 {len(result)} 个文本块")
            return result

        except Exception as e:
            logger.error(f"处理 Markdown 时发生错误: {str(e)}")
            logger.exception("详细错误信息:")
            return []

    @staticmethod
    def split_text(text, max_tokens):
        words = text.split()
        chunks = []
        current_chunk = []
        current_token_count = 0

        for word in words:
            word_tokens = KbmService.estimate_tokens(word)
            if current_token_count + word_tokens > max_tokens and current_chunk:
                chunks.append(' '.join(current_chunk))
                current_chunk = []
                current_token_count = 0
            current_chunk.append(word)
            current_token_count += word_tokens

        if current_chunk:
            chunks.append(' '.join(current_chunk))

        return chunks

    @staticmethod
    def estimate_tokens(text):
        return len(re.findall(r'\w+', text)) * 1.3
    
    @staticmethod
    def extract_text_from_image(image_data):
        try:
            if isinstance(image_data, BytesIO):
                image_data = image_data.getvalue()

            # 读取图像
            nparr = np.frombuffer(image_data, np.uint8)
            image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)

            # 图像预处理
            preprocessed = KbmService.preprocess_image(image)

            # 使用进行识别
            # result = Paddle_OCR.ocr(preprocessed, cls=True)
            result = ocr_utils.OCRProcesso().ocr(preprocessed)

            # 后处理
            text = KbmService.post_process_text(result)

            if text and len(text) > 2:  # 假设有意义的文本至少有3个字符
                logger.info(f"提取的文本长度: {len(text)} {text}")
                return text
            else:
                logger.info("提取的内容似乎是图像，而不是文本")
                return "图片"

        except Exception as e:
            logger.error(f"从图像提取文本时出错: {str(e)} {sys._getframe().f_lineno}")
            return "图片"

    @staticmethod
    def preprocess_image(image):
        # 转换为灰度图像
        gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

        # 自适应阈值处理
        binary = cv2.adaptiveThreshold(gray, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 2)

        # 对二值图像进行膨胀操作，使文字更粗
        kernel = np.ones((2, 2), np.uint8)
        dilated = cv2.dilate(binary, kernel, iterations=1)

        return dilated

    def post_process_text(text):
        if text:
            # 将连续的冒号或点替换为空格
            text = re.sub(r'[:.]+', ' ', text)

            # 保留中文字符、英文字母、数字、常用标点
            text = re.sub(r'[^\u4e00-\u9fff\u3000-\u303fa-zA-Z0-9.,!?;:()"\'\s]', '', text)

            # 删除连续的数字（3个或更多）
            text = re.sub(r'\d{3,}', '', text)

            # 处理多余的空白字符
            text = re.sub(r'\s+', ' ', text).strip()

            # 删除单独的数字，但保留章节编号和有意义的数字
            text = re.sub(r'\b(?<![第章])\d+(?!\d)\b', '', text)

            # 清理多余的空格
            text = re.sub(r'\s+', ' ', text).strip()

        return text


    @staticmethod
    def render_page_to_image(page, scale=2):
        # logger.info(page)
        try:
            pix = page.get_pixmap(matrix=fitz.Matrix(scale, scale))
            img = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
            buffered = BytesIO()
            img.save(buffered, format="PNG")
            # image_name = rf"E:/page_image_{uuid.uuid4()}.png"
            # img.save(image_name)
            buffered.seek(0)
            return buffered
        except Exception as e:
            logger.error('文件数据不正确')
            raise ValueError("文件数据不正确")
       

    @staticmethod
    def save_image_to_minio(image_data, bucket_name):
        image_name = f"page_image_{uuid.uuid4()}.png"
        minio_client.put_object(bucket_name, image_name, image_data, length=image_data.getbuffer().nbytes)
        return image_name
    
    @staticmethod
    @transaction.atomic
    def saveTask(document_id, total_chunks):
        Task.objects.update_or_create(
            doc_id=document_id,  # 查找条件
            defaults={'to_page': total_chunks}  # 要更新或创建的字段
        )

    @staticmethod
    @transaction.atomic
    def saveTaskSublist(document_id, name, page_number=None, chunk_number=None, content=None,milvus_id=None):
        try:
            # 确保 content 是 Unicode 字符串
            if content is not None:
                if isinstance(content, bytes):
                    content = content.decode('utf-8')
                else:
                    content = str(content)

            TaskSublist.objects.create(
                doc_id=document_id,
                name=name,
                page_number=str(page_number) if page_number is not None else '0',
                chunk_number=str(chunk_number) if chunk_number is not None else '0',
                content=content,
                milvus_id=milvus_id
            )
            logger.info(f"Successfully saved TaskSublist for document {document_id}, chunk {chunk_number}")
        except Exception as e:
            logger.error(f"Error saving TaskSublist: {str(e)}")
            # 可以选择在这里重新抛出异常，或者进行其他错误处理

    @staticmethod
    @transaction.atomic
    def clearPreviousData(document_id,bucket_name):
        try:
            # 获取与文档相关的所有 TaskSublist,milvus 记录
            task_sublists = TaskSublist.objects.filter(doc_id=document_id)
            # 获取集合对象
            connections.connect("default", host=MILVUS_HOST, port=MILVUS_PORT,user=MILVUS_USER,password=MILVUS_PASSWORD)

            milvus_collection_exists = utility.has_collection(bucket_name)
            if milvus_collection_exists:
                collection = Collection(bucket_name)
            # 连接到 Milvus

            # 从 MinIO 中删除相关的图片
            for task in task_sublists:
                try:
                    # 执行删除minio
                    minio_client.remove_object(bucket_name, task.name)

                    # 执行删除milvus
                    if milvus_collection_exists and task.milvus_id:
                        expr = f'id in [{task.milvus_id}]'
                        collection.delete(expr)
                except Exception as e:
                    logger.error(f"Error deleting object from MinIO: {e}")

            # 从数据库中删除 TaskSublist 记录
            task_sublists.delete()
        except Exception as e:
            logger.error(f"Error deleting object from MinIO: {str(e)} {sys._getframe().f_lineno}")
        finally:
            connections.disconnect("default")



    @staticmethod
    @transaction.atomic
    def updateDocument(max_tokens, total_chunks, document_id,execution_time):
        try:
            # 检查是否存在相关的 TaskSublist
            count = TaskSublist.objects.filter(doc_id=document_id).count()

            # 根据 TaskSublist 的存在与否设置运行状态
            progress_status = 1 if count > 0 else -1

            # 更新 DocumentKbm 对象
            # updated = DocumentKbm.objects.filter(id=document_id).update(
            #     token_num=max_tokens,
            #     chunk_num=total_chunks,
            #     progress=progress_status,
            #     process_begin_at=timezone.now(),
            #     process_duation= execution_time
            # )
            document = DocumentKbm.objects.select_for_update().get(id=document_id)
            try:
                logger.info(f'document_id { document_id}')
                logger.info(f'max_tokens { max_tokens}')
                logger.info(f'total_chunks {total_chunks}')
                logger.info(f'progress {progress_status}')
                # logger.info(f'process_begin_at {timezone.now()}')
                logger.info(f'process_duation {execution_time}')
            except Exception as e:
                logger.error(e)

            # 更新 DocumentKbm 对象
            document.token_num = max_tokens
            document.chunk_num = total_chunks
            document.progress = progress_status
            # document.process_begin_at = timezone.now()
            document.process_duation = execution_time
            document.save()

            if document:
                return True, f"Document {document_id} updated successfully."
            else:
                return False, f"Document {document_id} not found."

        except Exception as e:
            # 如果发生任何错误，事务会自动回滚
            logger.error(str(e))
            return False, f"Error updating document: {str(e)}"
    #异步调用


    @staticmethod
    def searchTaskInfo(request):
        document_id = request.POST.get("document_id")
        page = request.POST.get('page', 1)
        page_size = request.POST.get('page_size', 10)  # 每页显示的项目数，默认为10

        taskSublists = TaskSublist.objects.filter(doc_id=document_id).order_by('id')

        document = get_object_or_404(DocumentKbm, id=document_id)
        location = document.location

        knowledgebase = get_object_or_404(Knowledgebase, id=document.kb_id)
        bucket_name = knowledgebase.location
        documentUrl = minio_client.presigned_get_object(
            bucket_name=bucket_name,
            object_name=location,
            expires=timedelta(days=1)  # URL有效期为1天
        )

        # 创建分页器
        paginator = Paginator(taskSublists, page_size)

        try:
            tasks_page = paginator.page(page)
        except PageNotAnInteger:
            # 如果页码不是整数，返回第一页
            tasks_page = paginator.page(1)
        except EmptyPage:
            # 如果页码超出范围，返回最后一页
            tasks_page = paginator.page(paginator.num_pages)

        task_results = []
        for task in tasks_page:
            try:
                # 生成MinIO对象的预签名URL
                url = minio_client.presigned_get_object(
                    bucket_name=bucket_name,
                    object_name=task.name,
                    expires=timedelta(days=1)  # URL有效期为1天
                )

                task_results.append({
                    'id': task.id,
                    'doc_id': task.doc_id,
                    'name': task.name,
                    'page_number': task.page_number,
                    'chunk_number': task.chunk_number,
                    'content': task.content,
                    'url': url
                })
            except Exception as e:
                logger.error(f"Error generating URL for object {task.name}: {str(e)}")
                # 如果生成URL失败，我们仍然添加其他信息，但URL为None
                task_results.append({
                    'id': task.id,
                    'doc_id': task.doc_id,
                    'name': task.name,
                    'page_number': task.page_number,
                    'chunk_number': task.chunk_number,
                    'content': task.content,
                    'url': None
                })

        # 创建包含 documentUrl 和分页信息的最终结果
        result = {
            'documentUrl': documentUrl,
            'tasks': task_results,
            'pagination': {
                'current_page': tasks_page.number,
                'num_pages': paginator.num_pages,
                'per_page': page_size,
                'total_count': paginator.count,
                'has_next': tasks_page.has_next(),
                'has_previous': tasks_page.has_previous(),
            }
        }

        return success(result)

    @staticmethod
    @transaction.atomic
    def deleteBucket(request):
        bucket_id = request.POST.get("bucket_id")

        if not bucket_id:
            return fail("Bucket ID 为空")

        try:
            # 获取知识库对象
            try:
                knowledgebase = Knowledgebase.objects.get(id=bucket_id)
            except ObjectDoesNotExist:
                return fail("指定的知识库不存在")

            # 检查是否存在文档
            active_docs_count = DocumentKbm.objects.filter(kb_id=bucket_id).count()

            if active_docs_count > 0:
                return fail(f"无法删除知识库，还有 {active_docs_count} 个文档")

            # 删除相关的 File 记录
            File.objects.filter(
                name=knowledgebase.name,
                source_type='knowledgebase',
                type='folder'
            ).delete()

            # 删除所有相关的 DocumentKbm 记录
            DocumentKbm.objects.filter(kb_id=bucket_id).delete()

            # 删除知识库
            knowledgebase.delete()

            # 可选：如果使用 MinIO 存储文件，删除 MinIO 中的文件
            try:
                minio_client.remove_bucket(bucket_id)
            except Exception as e:
                logger.warning(f"Failed to delete MinIO bucket {bucket_id}: {str(e)}")

            return success("知识库已成功删除")

        except Exception as e:
            logger.error(f"删除知识库时发生错误: {str(e)}", exc_info=True)
            return fail(f"删除知识库时发生错误: {str(e)}")
       

    @staticmethod
    def getRunStatus(request):
        document_id = request.POST.get("document_id")
        run = DocumentKbm.objects.filter(id = document_id).values("run").first()
        
        return success(run)

    @staticmethod
    def batchAnalysis(request):
        ids_str = request.POST.get("ids")
        start_page = int(request.POST.get('start_page', 1))
        end_page = int(request.POST.get('end_page', -1))
        max_tokens = int(request.POST.get('max_tokens', 2048))
        try:
            # 尝试将字符串解析为 JSON 列表
            ids = json.loads(ids_str)

            if not isinstance(ids, list):
                return fail("无效输入:'ids'应该是一个列表")
            results = []
            for document_id in ids:
                sleep(0.1)
                logger.info("缓解压力沉睡0.1秒")
                # 为每个 document_id 创建一个新的请求对象
                analysis_request = type('AnalysisRequest', (), {})()
                analysis_request.POST = {
                    'document_id': document_id,
                    'start_page': start_page,
                    'end_page': end_page,
                    'max_tokens': max_tokens
                }

                # 调用 analysis 方法
                response = KbmService.analysis(analysis_request)
                message = response.get('message')

            return success(message,"已添加到队列")

        except Exception as e:

            return fail(f"An error occurred: {str(e)}")

    # 假设这是您支持的文件后缀名列表
    SUPPORTED_SUFFIXES = [
        'txt', 'pdf', 'doc', 'docx', 'xls', 'xlsx', 'md'
    ]

    @staticmethod
    def getSuffixName(request):
        try:
            # 获取数据库中的所有不重复的 type 值
            db_types = DocumentKbm.objects.values_list('type', flat=True).distinct()

            # 将数据库中的类型转换为集合
            db_types_set = set(db_types)

            # 将 SUPPORTED_SUFFIXES 转换为集合
            supported_set = set(KbmService.SUPPORTED_SUFFIXES)

            # 合并两个集合，自动去除重复项
            combined_set = supported_set.union(db_types_set)

            # 将结果转换回列表
            combined_suffixes = list(combined_set)

            # 对结果进行排序（可选）
            combined_suffixes.sort()

            return success(combined_suffixes)

        except Exception as e:
            return fail(f"获取文件后缀名时发生错误: {str(e)}")

    @staticmethod
    @transaction.atomic
    def batchMove(request):
        ids = json.loads(request.POST.get("ids"))
        doc_type_id = request.POST.get("doc_type_id")
        if not doc_type_id:
            return fail("分类id为空")
        if  not ids:
            return fail("未传出文件id")

        type = KbmDocumentType.objects.filter(id=doc_type_id).exclude(status=4).first()
        if not type:
            return fail("当前分类不存在")
        DocumentKbm.objects.filter(id__in=ids).update(doc_type_id=doc_type_id)

        return success("批量移动成功")

    @staticmethod
    @transaction.atomic
    def moveDocument(doc_id, doc_type_id):
        # 这个方法可以保持不变，因为它已经是单次更新操作
        return DocumentKbm.objects.filter(id=doc_id).update(doc_type_id=doc_type_id)

   
    @staticmethod
    @transaction.atomic
    def updateKbm(request):
        """
        更新知识库信息

        Args:
            request (HttpRequest): 请求对象

        Returns:
            dict: 成功或失败的响应字典
        """
        id = request.POST.get("id")
        if not id:
            return fail("id为空")
        kmb = Knowledgebase.objects.filter(id=id).first()
        name = request.POST.get("name")
        if not name:
            return fail("名称不能为空")
        if kmb:
            kmb.name = name
            kmb.description = request.POST.get("description","")
            kmb.save()
            return success("修改成功")
        else:
            return fail("修改失败")