ragflow_api_test/src/add_chunk_cli_pdf_img.py

from ragflow_sdk import RAGFlow
import os
import re
# 在文件顶部添加新依赖
import requests
#from urllib.parse import urlparse
import tempfile

from elasticsearch import Elasticsearch
from minio import Minio
from minio.error import S3Error


from dotenv import load_dotenv  # 新增
# 加载 .env 文件中的环境变量
load_dotenv()


# 从环境变量初始化配置
api_key = os.getenv("RAGFLOW_API_KEY")
base_url = os.getenv("RAGFLOW_BASE_URL")
elastic_tenant_id = os.getenv("ELASTIC_TENANT_ID")

# 初始化 RAGFlow
rag_object = RAGFlow(api_key=api_key, base_url=base_url)

# 初始化 Elasticsearch
es = Elasticsearch(
    [{
        'host': os.getenv("ELASTIC_HOST"),
        'port': int(os.getenv("ELASTIC_PORT")),
        'scheme': 'http'
    }],
    basic_auth=(
        os.getenv("ELASTIC_USERNAME"),
        os.getenv("ELASTIC_PASSWORD")
    )
)

# MinIO 配置
MINIO_CONFIG = {
    "endpoint": f"{os.getenv('MINIO_HOST')}:{os.getenv('MINIO_PORT')}",
    "access_key": os.getenv("MINIO_USER"),
    "secret_key": os.getenv("MINIO_PASSWORD"),
    "secure": False
}

def update_img_id_in_elasticsearch(tenant_id, doc_id, chunk_id, new_img_id):
    """
    在 Elasticsearch 中更新指定文档块的 img_id。
    
    :param tenant_id: 租户 ID
    :param doc_id: 文档 ID
    :param chunk_id: 文档块 ID
    :param new_img_id: 新的 img_id
    :return: 更新结果
    """
    try:
        # 构建索引名称
        index_name = f"ragflow_{tenant_id}"

        # 构建查询条件
        query = {
            "bool": {
                "must": [
                    {"term": {"doc_id": doc_id}},
                    {"term": {"_id": chunk_id}}
                ]
            }
        }

        # 搜索目标文档
        result = es.search(index=index_name, body={"query": query})

        # 检查是否找到目标文档
        if result['hits']['total']['value'] == 0:
            print(f"在 Elasticsearch 中未找到文档: index={index_name}, doc_id={doc_id}, chunk_id={chunk_id}")
            return {"code": 102, "message": f"Can't find this chunk {chunk_id}"}

        # 获取目标文档的 ID
        hit = result['hits']['hits'][0]
        doc_id_in_es = hit['_id']

        # 构建更新请求
        update_body = {
            "doc": {
                "img_id": new_img_id
            }
        }

        # 更新文档
        update_result = es.update(
            index=index_name, 
            id=doc_id_in_es, 
            body=update_body,
            refresh=True  # 确保更新立即可见
        )
        
        print(f"Elasticsearch 更新结果: index={index_name}, id={doc_id_in_es}, result={update_result}")

        # 验证更新
        verify_doc = es.get(index=index_name, id=doc_id_in_es)
        if verify_doc['_source'].get('img_id') == new_img_id:
            print(f"成功更新 img_id 为: {new_img_id}")
            return {"code": 0, "message": ""}
        else:
            print(f"更新验证失败，当前 img_id: {verify_doc['_source'].get('img_id')}")
            return {"code": 100, "message": "Failed to verify img_id update"}

    except Exception as e:
        print(f"更新 Elasticsearch 时发生错误: {str(e)}")
        return {"code": 101, "message": f"Error updating img_id: {str(e)}"}



def get_minio_client():
    """创建MinIO客户端"""
    return Minio(
        endpoint=MINIO_CONFIG["endpoint"],
        access_key=MINIO_CONFIG["access_key"],
        secret_key=MINIO_CONFIG["secret_key"],
        secure=MINIO_CONFIG["secure"]
    )


def upload_file2minio(bucket_name, object_name, file_path):
    """上传文件到MinIO
    # 通过fput_object上传时：

    # 如果object_name为image\image.jpg，则上传后的名字就是image\image.jpg；

    # 如果object_name为image/image.jpg，则上传后image为文件夹，文件名为image.jpg；
    
    """

    minio_client= get_minio_client()

    try:
        # 检查存储桶是否存在，如果不存在则创建（可选）
        if not minio_client.bucket_exists(bucket_name):
            minio_client.make_bucket(bucket_name)
            print(f"Bucket '{bucket_name}' created")
        
        # 上传文件
        minio_client.fput_object(
            bucket_name=bucket_name,
            object_name=object_name,
            file_path=file_path
        )

        # 获取文件的预签名URL（可选）
        #res = minio_client.get_presigned_url("GET", bucket_name, object_name, expires=timedelta(days=7))

        #res = "http://127.0.0.1:9000" + "/"+bucket_name+"/"  + object_name

        
        #print(res)
        print(f"文件 '{file_path}' 成功上传到存储桶 '{bucket_name}' 为 '{object_name}'")
        return True
        
    except S3Error as exc:
        print("MinIO错误:", exc)
        return False
    except Exception as e:
        print("发生错误:", e)
        return False





def choose_from_list(options, prompt):
    for idx, item in enumerate(options):
        print(f"{idx + 1}. {item}")
    while True:
        choice = input(prompt)
        if choice.isdigit() and 1 <= int(choice) <= len(options):
            return options[int(choice) - 1]
        else:
            print("输入无效，请重新输入编号。")


def select_files(file_path, file_type="pdf"):
    """
    选择file_path中的所有指定类型文件（默认pdf），
    返回文件路径列表
    """
    file_list = []
    for root, dirs, files in os.walk(file_path):
        for file in files:
            if file.lower().endswith(f".{file_type.lower()}"):
                file_list.append(os.path.join(root, file))
    return file_list


def pair_pdf_and_txt(pdf_path, txt_path):
    """
    将pdf和txt文件对齐，
    返回对齐pdf_dict和txt_dict，
    pdf_dict和txt_dict的key为文件名（不含后缀），value为文件路径
    txt_dict仅收入与pdf_dict中存在的文件，
    如果pdf_dict中有文件名没有对应的txt文件，则不收入txt_dict
    """
    pdf_files = select_files(pdf_path, "pdf")
    txt_files = select_files(txt_path, "txt")

    # 构建文件名到路径的映射
    pdf_dict = {os.path.splitext(os.path.basename(f))[0]: f for f in pdf_files}
    txt_dict_all = {os.path.splitext(os.path.basename(f))[0]: f for f in txt_files}

    # 只保留有对应txt的pdf
    pdf_dict_aligned = {}
    txt_dict_aligned = {}
    for name in pdf_dict:
        if name in txt_dict_all:
            pdf_dict_aligned[name] = pdf_dict[name]
            txt_dict_aligned[name] = txt_dict_all[name]

    return pdf_dict_aligned, txt_dict_aligned


def select_dataset(rag_object):
    """选择可用数据集"""
    datasets = rag_object.list_datasets()
    if not datasets:
        print("没有可用的数据集。")
        return None
    
    dataset_names = [ds.name for ds in datasets]
    dataset_name = choose_from_list(dataset_names, "请选择数据集编号：")
    return [ds for ds in datasets if ds.name == dataset_name][0]

def upload_or_get_document(dataset, pdf_path, display_name):
    """上传或获取已存在的文档"""
    try:
        document = dataset.list_documents(name=display_name)[0]
        print(f"文档已存在: {display_name}，跳过上传。")
        return document
    except Exception:
        try:
            with open(pdf_path, "rb") as f:
                blob = f.read()
            dataset.upload_documents([{"display_name": display_name, "blob": blob}])
            return dataset.list_documents(name=display_name)[0]
        except Exception as e:
            print(f"上传PDF失败: {pdf_path}，错误: {e}")
            return None




def divid_txt_chunk_img(txt_chunk):
    """分离文本块中的图片链接和纯文本内容
    
    输入格式示例: 
        "这是文本内容![image](路径/IMAGE1.png)更多文本![image](路径/IMAGE2.png)"
    
    返回:
        clean_text: 移除所有图片链接后的纯文本内容
        image_paths: 提取到的图片路径列表
    """
    # 正则表达式匹配Markdown图片格式: ![alt_text](path)
    pattern = r'!\[.*?\]\((.*?)\)'
    
    # 提取所有图片路径
    image_paths = re.findall(pattern, txt_chunk)
    
    # 移除所有图片标记
    clean_text = re.sub(pattern, '', txt_chunk)
    
    # 移除多余空行并清理首尾空白
    clean_text = re.sub(r'\n\s*\n', '\n\n', clean_text).strip()
    
    return clean_text, image_paths

def extract_images_from_chunk( content):
    """从chunk内容中提取图片链接"""
    img_pattern = r'!\[.*?\]\((.*?)\)'
    return re.findall(img_pattern, content)

def remove_images_from_content( content):
    """从内容中移除图片链接"""
    # 移除markdown图片语法 ![alt](url)
    content = re.sub(r'!\[.*?\]\(.*?\)', '', content)
    # 清理多余的空行
    content = re.sub(r'\n\s*\n\s*\n', '\n\n', content)
    return content.strip()
    




# 修改 process_txt_chunks 函数中的图片处理逻辑
def process_txt_chunks(dataset_id, document, txt_path):
    try:
        with open(txt_path, 'r', encoding='utf-8') as file:
            file_content = file.read()
        img_chunk_ids = []
        for num, txt_chunk in enumerate(file_content.split('\n\n')):
            if txt_chunk.strip():
                print(f"处理文本块: {txt_chunk[:30]}...")
                img_urls = extract_images_from_chunk(txt_chunk)
                img_url = img_urls[0] if img_urls else None
                
                if img_url:
                    print(f"检测到图片链接: {img_url}")
                    clean_chunk = remove_images_from_content(txt_chunk)
                    chunk = document.add_chunk(content=clean_chunk)
                    
                    # 判断是否为网络图片 (新增逻辑)
                    if img_url.startswith(('http://', 'https://')):
                        # 下载网络图片到临时文件
                        try:
                            response = requests.get(img_url)
                            response.raise_for_status()
                            
                            # 创建临时文件
                            with tempfile.NamedTemporaryFile(delete=False, suffix='.jpg') as tmp_file:
                                tmp_file.write(response.content)
                                tmp_path = tmp_file.name
                            
                            # 上传临时文件
                            if upload_file2minio(dataset_id, chunk.id, tmp_path):
                                img_chunk_ids.append(chunk.id)
                                # new_img_id = f"{dataset_id}-{chunk.id}"
                                # print(f"网络图片 {img_url} 已下载并上传，新的 img_id: {new_img_id}")
                                # update_img_id_in_elasticsearch(elastic_tenant_id, document.id, chunk.id, new_img_id)
                            
                            # 删除临时文件
                            os.unlink(tmp_path)
                        except Exception as e:
                            print(f"下载网络图片失败: {e}")
                    else:
                        # 处理本地图片 (原逻辑)
                        if not os.path.isabs(img_url):
                            img_abs_path = os.path.join(os.path.dirname(txt_path), img_url)
                        else:
                            img_abs_path = img_url
                        
                        print(f"图片绝对路径: {img_abs_path}")
                        if os.path.exists(img_abs_path):
                            if upload_file2minio(dataset_id, chunk.id, img_abs_path):
                                img_chunk_ids.append(chunk.id)
                                # new_img_id = f"{dataset_id}-{chunk.id}"
                                # print(f"图片 {img_abs_path} 已上传，新的 img_id: {new_img_id}")
                                # update_img_id_in_elasticsearch(elastic_tenant_id, document.id, chunk.id, new_img_id)
                        else:
                            print(f"图片未找到: {img_abs_path}，跳过。")
                else:
                    print("未检测到图片链接，直接添加文本块。")
                    chunk = document.add_chunk(content=txt_chunk)
                print(f"第{num+1} Chunk添加成功! ID: {chunk.id}")
        for img_chunk_id in img_chunk_ids:
            update_img_id_in_elasticsearch(elastic_tenant_id, document.id, img_chunk_id, f"{dataset_id}-{img_chunk_id}")
                
    except Exception as e:
        print(f"处理文本文件时出错: {txt_path}，错误: {e}")





def process_pdf_txt_pairs(pdf_dict, txt_dict, dataset):
    """处理PDF-TXT文件对"""
    for name, pdf_path in pdf_dict.items():
        display_name = os.path.basename(pdf_path)
        document = upload_or_get_document(dataset, pdf_path, display_name)
        print(f"选择的文档: {document.name}，ID: {document.id}")
        if not document:
            continue
            
        txt_path = txt_dict.get(name)
        if txt_path:
            process_txt_chunks(dataset.id,document, txt_path)

def main():

    """主函数，处理PDF和TXT文件对
    
    dataset.id = bucket_name
    chunk_id = object_name
    """
    file_path = "F:\\Synology_nas\\SynologyDrive\\大模型\\厦门市城市道路开口设置指引DB3502T 141-2024\\"
    pdf_dict, txt_dict = pair_pdf_and_txt(file_path, file_path)
    
    if not pdf_dict:
        print("未选择任何文件。")
        return
        
    dataset = select_dataset(rag_object)
    print(f"选择的数据集: {dataset.name}")
    print(f"选择的数据集id: {dataset.id}")
    if not dataset:
        print("未选择数据集。")
        return
        
    process_pdf_txt_pairs(pdf_dict, txt_dict, dataset)










if __name__ == "__main__":
    main()