716 lines
23 KiB
Python
716 lines
23 KiB
Python
import asyncio
|
||
import base64
|
||
import os
|
||
import re
|
||
import time
|
||
import zipfile
|
||
from pathlib import Path
|
||
|
||
import aiofiles
|
||
from docling.datamodel.base_models import InputFormat
|
||
from docling.document_converter import DocumentConverter
|
||
from langchain_community.document_loaders import (
|
||
CSVLoader,
|
||
JSONLoader,
|
||
PyPDFLoader,
|
||
TextLoader,
|
||
UnstructuredHTMLLoader,
|
||
UnstructuredMarkdownLoader,
|
||
UnstructuredWordDocumentLoader,
|
||
)
|
||
from langchain_text_splitters import RecursiveCharacterTextSplitter
|
||
|
||
from src.knowledge.utils import calculate_content_hash
|
||
from src.storage.minio import get_minio_client
|
||
from src.utils import hashstr, logger
|
||
|
||
SUPPORTED_FILE_EXTENSIONS: tuple[str, ...] = (
|
||
".txt",
|
||
".md",
|
||
".docx",
|
||
".html",
|
||
".htm",
|
||
".json",
|
||
".csv",
|
||
".xls",
|
||
".xlsx",
|
||
".pdf",
|
||
".pptx",
|
||
".jpg",
|
||
".jpeg",
|
||
".png",
|
||
".bmp",
|
||
".tiff",
|
||
".tif",
|
||
".zip",
|
||
)
|
||
|
||
|
||
def is_supported_file_extension(file_name: str | os.PathLike[str]) -> bool:
|
||
"""Check whether the given file path has a supported extension."""
|
||
return Path(file_name).suffix.lower() in SUPPORTED_FILE_EXTENSIONS
|
||
|
||
|
||
# Docling 文档转换器（单例模式）
|
||
_docling_converter: DocumentConverter | None = None
|
||
|
||
|
||
def _get_docling_converter() -> DocumentConverter:
|
||
"""获取 Docling 文档转换器单例"""
|
||
global _docling_converter
|
||
if _docling_converter is None:
|
||
_docling_converter = DocumentConverter(
|
||
format_options={
|
||
InputFormat.DOCX: None,
|
||
InputFormat.XLSX: None,
|
||
InputFormat.PPTX: None,
|
||
}
|
||
)
|
||
return _docling_converter
|
||
|
||
|
||
def _upload_image_to_minio(image_data: bytes, filename: str, db_id: str) -> str:
|
||
"""上传图片到 MinIO，返回 URL"""
|
||
minio_client = get_minio_client()
|
||
minio_client.ensure_bucket_exists("kb-images")
|
||
file_id = hashstr(filename, length=16)
|
||
timestamp = int(time.time() * 1000000)
|
||
suffix = Path(filename).suffix.lower()
|
||
object_name = f"{db_id}/{file_id}/images/{timestamp}_{Path(filename).name}"
|
||
content_type_map = {
|
||
".jpg": "image/jpeg",
|
||
".jpeg": "image/jpeg",
|
||
".png": "image/png",
|
||
".gif": "image/gif",
|
||
".webp": "image/webp",
|
||
".bmp": "image/bmp",
|
||
".tif": "image/tiff",
|
||
".tiff": "image/tiff",
|
||
}
|
||
content_type = content_type_map.get(suffix, "image/jpeg")
|
||
|
||
result = minio_client.upload_file(
|
||
bucket_name="kb-images",
|
||
object_name=object_name,
|
||
data=image_data,
|
||
content_type=content_type,
|
||
)
|
||
return result.url
|
||
|
||
|
||
def _parse_data_uri(data_uri: str) -> tuple[bytes, str]:
|
||
"""解析 data URI，返回 (image_data, mime_type)"""
|
||
header, base64_data = data_uri.split(",", 1)
|
||
mime_type = header.split(":")[1].split(";")[0]
|
||
image_data = base64.b64decode(base64_data)
|
||
return image_data, mime_type
|
||
|
||
|
||
def _convert_with_docling(file_path: Path, params: dict | None = None) -> str:
|
||
"""
|
||
使用 Docling 将 docx/xlsx/pptx 转换为 Markdown
|
||
|
||
Args:
|
||
file_path: 文件路径
|
||
params: 参数，包含 db_id 用于图片上传
|
||
|
||
Returns:
|
||
Markdown 字符串
|
||
"""
|
||
params = params or {}
|
||
db_id = params.get("db_id") or "docling-docs"
|
||
|
||
converter = _get_docling_converter()
|
||
result = converter.convert(file_path)
|
||
|
||
if result.status.name != "SUCCESS":
|
||
raise RuntimeError(f"Docling 转换失败: {result.status}")
|
||
|
||
doc = result.document
|
||
|
||
# 提取图片并上传到 MinIO
|
||
if hasattr(doc, "pictures") and doc.pictures:
|
||
image_refs: list[tuple[str, bytes]] = []
|
||
|
||
for pic in doc.pictures:
|
||
if hasattr(pic, "image") and hasattr(pic.image, "uri"):
|
||
uri = str(pic.image.uri)
|
||
if uri.startswith("data:"):
|
||
image_data, mime_type = _parse_data_uri(uri)
|
||
timestamp = int(time.time() * 1000000) # 微秒级时间戳
|
||
filename = f"image_{timestamp}.{mime_type.split('/')[-1]}"
|
||
image_refs.append((filename, image_data))
|
||
|
||
# 上传图片并收集 URL
|
||
image_urls: list[str] = []
|
||
for filename, image_data in image_refs:
|
||
try:
|
||
url = _upload_image_to_minio(image_data, filename, db_id)
|
||
image_urls.append(f"")
|
||
except Exception as e:
|
||
logger.error(f"上传图片失败 {filename}: {e}")
|
||
image_urls.append(f"[图片: {filename}]")
|
||
|
||
# 导出 Markdown
|
||
markdown = doc.export_to_markdown()
|
||
|
||
# 替换 <!-- image --> 占位符为图片 URL
|
||
# Docling 使用 <!-- image --> 作为占位符
|
||
for url in reversed(image_urls):
|
||
markdown = re.sub(r"<!--\s*image\s*-->", url, markdown, count=1)
|
||
|
||
return markdown
|
||
|
||
# 无图片时直接导出
|
||
return doc.export_to_markdown()
|
||
|
||
|
||
def chunk_with_parser(file_path, params=None):
|
||
"""
|
||
使用文件解析器将文件切分成固定大小的块
|
||
|
||
Args:
|
||
file_path: 文件路径
|
||
params: 参数
|
||
"""
|
||
params = params or {}
|
||
chunk_size = int(params.get("chunk_size", 500))
|
||
chunk_overlap = int(params.get("chunk_overlap", 100))
|
||
|
||
file_type = Path(file_path).suffix.lower()
|
||
|
||
# 选择合适的加载器
|
||
if file_type in [".txt"]:
|
||
loader = TextLoader(file_path)
|
||
|
||
elif file_type in [".md"]:
|
||
loader = UnstructuredMarkdownLoader(file_path)
|
||
|
||
elif file_type in [".docx", ".doc"]:
|
||
loader = UnstructuredWordDocumentLoader(file_path)
|
||
|
||
elif file_type in [".html", ".htm"]:
|
||
loader = UnstructuredHTMLLoader(file_path)
|
||
|
||
elif file_type in [".json"]:
|
||
loader = JSONLoader(file_path, jq_schema=".")
|
||
|
||
elif file_type in [".csv"]:
|
||
loader = CSVLoader(file_path)
|
||
|
||
else:
|
||
raise ValueError(f"不支持的文件类型: {file_type}")
|
||
|
||
# 加载文档
|
||
docs = loader.load()
|
||
|
||
# 创建文本分割器
|
||
text_splitter = RecursiveCharacterTextSplitter(
|
||
chunk_size=chunk_size,
|
||
chunk_overlap=chunk_overlap,
|
||
separators=["\n\n", "\n", ".", " ", ""],
|
||
)
|
||
|
||
# 分割文档
|
||
nodes = text_splitter.split_documents(docs)
|
||
|
||
# 添加序号信息到metadata
|
||
for i, node in enumerate(nodes):
|
||
if node.metadata is None:
|
||
node.metadata = {}
|
||
node.metadata["chunk_idx"] = i
|
||
|
||
return nodes
|
||
|
||
|
||
def chunk_text(text, params=None):
|
||
"""
|
||
将文本切分成固定大小的块
|
||
"""
|
||
params = params or {}
|
||
chunk_size = int(params.get("chunk_size", 500))
|
||
chunk_overlap = int(params.get("chunk_overlap", 100))
|
||
|
||
# 创建文本分割器
|
||
text_splitter = RecursiveCharacterTextSplitter(
|
||
chunk_size=chunk_size, chunk_overlap=chunk_overlap, separators=["\n\n", "\n", ".", " ", ""]
|
||
)
|
||
|
||
# 分割文档
|
||
nodes = text_splitter.split_text(text)
|
||
|
||
# 添加序号信息到metadata
|
||
nodes = [{"text": node, "metadata": {"chunk_idx": i}} for i, node in enumerate(nodes)]
|
||
return nodes
|
||
|
||
|
||
def chunk(text_or_path, params=None):
|
||
raise NotImplementedError("chunk is deprecated, use chunk_with_parser or chunk_text instead")
|
||
|
||
|
||
def pdfreader(file_path, params=None):
|
||
"""读取PDF文件并返回text文本"""
|
||
if isinstance(file_path, str):
|
||
file_path = Path(file_path)
|
||
|
||
assert file_path.exists(), "File not found"
|
||
assert file_path.suffix.lower() == ".pdf", "File format not supported"
|
||
|
||
# 使用LangChain的PDF加载器
|
||
loader = PyPDFLoader(str(file_path))
|
||
docs = loader.load()
|
||
|
||
# 简单的拼接起来之后返回纯文本
|
||
text = "\n\n".join([d.page_content for d in docs])
|
||
return text
|
||
|
||
|
||
def plainreader(file_path):
|
||
"""读取普通文本文件并返回text文本"""
|
||
assert os.path.exists(file_path), "File not found"
|
||
|
||
# 使用LangChain的文本加载器
|
||
loader = TextLoader(str(file_path))
|
||
docs = loader.load()
|
||
text = "\n\n".join([d.page_content for d in docs])
|
||
return text
|
||
|
||
|
||
def parse_pdf(file, params=None):
|
||
"""
|
||
解析PDF文件，支持多种OCR方式
|
||
|
||
Args:
|
||
file: PDF文件路径
|
||
params: 参数字典，包含enable_ocr设置
|
||
|
||
Returns:
|
||
str: 解析得到的文本
|
||
|
||
Raises:
|
||
DocumentProcessorException: 处理失败时抛出
|
||
"""
|
||
from src.plugins.document_processor_base import DocumentProcessorException
|
||
from src.plugins.document_processor_factory import DocumentProcessorFactory
|
||
|
||
params = params or {}
|
||
opt_ocr = params.get("enable_ocr", "disable")
|
||
|
||
if opt_ocr == "disable":
|
||
return pdfreader(file, params=params)
|
||
|
||
try:
|
||
return DocumentProcessorFactory.process_file(opt_ocr, file, params)
|
||
|
||
except DocumentProcessorException as e:
|
||
logger.error(f"文档处理失败: {e.service_name} - {str(e)}")
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"PDF 解析失败: {str(e)}")
|
||
raise DocumentProcessorException(f"PDF解析失败: {str(e)}", opt_ocr, "parsing_failed")
|
||
|
||
|
||
def parse_image(file, params=None):
|
||
"""
|
||
解析图像文件，支持多种OCR方式
|
||
|
||
Args:
|
||
file: 图像文件路径
|
||
params: 参数字典，包含enable_ocr设置
|
||
|
||
Returns:
|
||
str: 解析得到的文本
|
||
|
||
Raises:
|
||
DocumentProcessorException: 处理失败时抛出
|
||
ValueError: 图像文件禁用OCR时抛出
|
||
"""
|
||
from src.plugins.document_processor_base import DocumentProcessorException
|
||
from src.plugins.document_processor_factory import DocumentProcessorFactory
|
||
|
||
params = params or {}
|
||
opt_ocr = params.get("enable_ocr", "disable")
|
||
|
||
# 图像文件必须使用 OCR,不能禁用
|
||
if opt_ocr == "disable":
|
||
raise ValueError(
|
||
"图像文件必须启用OCR才能提取文本内容。"
|
||
"请选择OCR方式 (onnx_rapid_ocr/mineru_ocr/mineru_official/paddlex_ocr) 或移除该文件。"
|
||
)
|
||
|
||
try:
|
||
return DocumentProcessorFactory.process_file(opt_ocr, file, params)
|
||
|
||
except DocumentProcessorException as e:
|
||
logger.error(f"图像处理失败: {e.service_name} - {str(e)}")
|
||
raise
|
||
except Exception as e:
|
||
logger.error(f"图像解析失败: {str(e)}")
|
||
raise DocumentProcessorException(f"图像解析失败: {str(e)}", opt_ocr, "parsing_failed")
|
||
|
||
|
||
async def parse_pdf_async(file, params=None):
|
||
return await asyncio.to_thread(parse_pdf, file, params=params)
|
||
|
||
|
||
async def parse_image_async(file, params=None):
|
||
return await asyncio.to_thread(parse_image, file, params=params)
|
||
|
||
|
||
async def process_file_to_markdown(file_path: str, params: dict | None = None) -> str:
|
||
"""
|
||
将不同类型的文件转换为markdown格式 - 支持本地文件和MinIO文件
|
||
|
||
Args:
|
||
file_path: 文件路径或MinIO URL
|
||
params: 处理参数，对于ZIP文件需要包含 db_id
|
||
|
||
Returns:
|
||
markdown格式内容
|
||
|
||
Note:
|
||
对于ZIP文件，会在params中保存处理结果供调用方使用：
|
||
- params['_zip_images_info']: 图片信息列表
|
||
- params['_zip_content_hash']: 内容哈希值
|
||
"""
|
||
import os
|
||
import tempfile
|
||
|
||
# 检测是否是MinIO URL
|
||
from src.knowledge.utils.kb_utils import is_minio_url
|
||
|
||
if is_minio_url(file_path):
|
||
# 从MinIO下载文件到临时位置
|
||
logger.debug(f"Downloading file from MinIO: {file_path}")
|
||
|
||
# 从MinIO URL中提取文件名
|
||
if "?" in file_path:
|
||
file_path_clean = file_path.split("?")[0]
|
||
else:
|
||
file_path_clean = file_path
|
||
|
||
original_filename = file_path_clean.split("/")[-1]
|
||
|
||
# 创建临时文件
|
||
with tempfile.NamedTemporaryFile(delete=False, suffix=Path(original_filename).suffix) as temp_file:
|
||
temp_path = temp_file.name
|
||
|
||
try:
|
||
# 使用通用函数解析MinIO URL并下载文件
|
||
from src.knowledge.utils.kb_utils import parse_minio_url
|
||
from src.storage.minio.client import get_minio_client
|
||
|
||
# 解析MinIO URL获取bucket_name和object_name
|
||
bucket_name, object_name = parse_minio_url(file_path)
|
||
|
||
# 获取MinIO客户端并下载文件
|
||
minio_client = get_minio_client()
|
||
file_content = await minio_client.adownload_file(bucket_name, object_name)
|
||
|
||
# 写入临时文件
|
||
async with aiofiles.open(temp_path, "wb") as f:
|
||
await f.write(file_content)
|
||
|
||
logger.debug(f"File downloaded to temp path: {temp_path}")
|
||
|
||
# 使用临时文件路径
|
||
actual_file_path = temp_path
|
||
|
||
except Exception as e:
|
||
# 清理临时文件
|
||
if os.path.exists(temp_path):
|
||
os.unlink(temp_path)
|
||
logger.error(f"Failed to download file from MinIO: {e}")
|
||
raise ValueError(f"无法从MinIO下载文件: {e}")
|
||
else:
|
||
# 本地文件
|
||
actual_file_path = file_path
|
||
|
||
try:
|
||
file_path_obj = Path(actual_file_path)
|
||
file_ext = file_path_obj.suffix.lower()
|
||
original_filename = file_path_obj.name
|
||
|
||
if file_ext == ".pdf":
|
||
# 使用 OCR 处理 PDF
|
||
text = await parse_pdf_async(str(file_path_obj), params=params)
|
||
result = f"{text}"
|
||
|
||
elif file_ext in [".txt", ".md"]:
|
||
# 直接读取文本文件
|
||
with open(file_path_obj, encoding="utf-8") as f:
|
||
content = f.read()
|
||
result = f"{content}"
|
||
|
||
elif file_ext in [".docx", ".pptx"]:
|
||
# 使用 Docling 处理 docx 和 pptx
|
||
result = _convert_with_docling(file_path_obj, params=params)
|
||
|
||
elif file_ext == ".doc":
|
||
# 旧版 .doc 文件仍使用原有解析方式
|
||
from langchain_community.document_loaders import UnstructuredWordDocumentLoader
|
||
|
||
loader = UnstructuredWordDocumentLoader(str(file_path_obj))
|
||
docs = loader.load()
|
||
result = "\n".join(doc.page_content for doc in docs).strip()
|
||
|
||
elif file_ext in [".jpg", ".jpeg", ".png", ".bmp", ".tiff", ".tif"]:
|
||
# 使用 OCR 处理图片
|
||
text = await parse_image_async(str(file_path_obj), params=params)
|
||
result = f"{text}"
|
||
|
||
elif file_ext in [".html", ".htm"]:
|
||
# 使用 BeautifulSoup 处理 HTML 文件
|
||
from markdownify import markdownify as md
|
||
|
||
with open(file_path_obj, encoding="utf-8") as f:
|
||
content = f.read()
|
||
text = md(content, heading_style="ATX")
|
||
result = f"{text}"
|
||
|
||
elif file_ext == ".csv":
|
||
# 处理 CSV 文件
|
||
import pandas as pd
|
||
|
||
df = pd.read_csv(file_path_obj)
|
||
# 将每一行数据与表头组合成独立的表格
|
||
markdown_content = ""
|
||
|
||
for index, row in df.iterrows():
|
||
# 创建包含表头和当前行的小表格
|
||
row_df = pd.DataFrame([row], columns=df.columns)
|
||
markdown_table = row_df.to_markdown(index=False)
|
||
markdown_content += f"{markdown_table}\n\n"
|
||
|
||
result = markdown_content.strip()
|
||
|
||
elif file_ext in [".xls", ".xlsx"]:
|
||
# 使用 Docling 处理 Excel 文件
|
||
result = _convert_with_docling(file_path_obj, params=params)
|
||
|
||
elif file_ext == ".json":
|
||
# 处理 JSON 文件
|
||
import json
|
||
|
||
async with aiofiles.open(file_path_obj, encoding="utf-8") as f:
|
||
content = await f.read()
|
||
data = json.loads(content)
|
||
# 将 JSON 数据格式化为 markdown 代码块
|
||
json_str = json.dumps(data, ensure_ascii=False, indent=2)
|
||
result = f"```json\n{json_str}\n```"
|
||
|
||
elif file_ext == ".zip":
|
||
if not params or "db_id" not in params:
|
||
raise ValueError("ZIP文件处理需要在params中提供db_id参数")
|
||
|
||
zip_result = await _process_zip_file(str(file_path_obj), params["db_id"])
|
||
|
||
# 将处理结果保存到params中供调用方使用
|
||
params["_zip_images_info"] = zip_result["images_info"]
|
||
params["_zip_content_hash"] = zip_result["content_hash"]
|
||
|
||
result = zip_result["markdown_content"]
|
||
|
||
else:
|
||
# 尝试作为文本文件读取
|
||
raise ValueError(f"Unsupported file type: {file_ext}")
|
||
|
||
except Exception:
|
||
# 清理临时文件
|
||
if is_minio_url(file_path) and os.path.exists(actual_file_path):
|
||
try:
|
||
os.unlink(actual_file_path)
|
||
logger.debug(f"Cleaned up temp file: {actual_file_path}")
|
||
except Exception as cleanup_e:
|
||
logger.warning(f"Failed to clean up temp file {actual_file_path}: {cleanup_e}")
|
||
raise
|
||
|
||
finally:
|
||
# 清理临时文件
|
||
if is_minio_url(file_path) and os.path.exists(actual_file_path):
|
||
try:
|
||
os.unlink(actual_file_path)
|
||
logger.debug(f"Cleaned up temp file: {actual_file_path}")
|
||
except Exception as e:
|
||
logger.warning(f"Failed to clean up temp file {actual_file_path}: {e}")
|
||
|
||
return result
|
||
|
||
|
||
async def _process_zip_file(zip_path: str, db_id: str) -> dict:
|
||
"""
|
||
处理ZIP文件，提取markdown内容和图片（内部函数）
|
||
|
||
Args:
|
||
zip_path: ZIP文件路径
|
||
db_id: 数据库ID
|
||
|
||
Returns:
|
||
dict: {
|
||
"markdown_content": str, # markdown内容
|
||
"content_hash": str, # 内容哈希值
|
||
"images_info": list[dict] # 图片信息列表
|
||
}
|
||
|
||
Raises:
|
||
FileNotFoundError: ZIP文件不存在
|
||
ValueError: ZIP文件格式错误或内容不符合要求
|
||
"""
|
||
# 1. 安全检查
|
||
if not os.path.exists(zip_path):
|
||
raise FileNotFoundError(f"ZIP 文件不存在: {zip_path}")
|
||
|
||
# 2. 解压ZIP并提取内容
|
||
with zipfile.ZipFile(zip_path, "r") as zf:
|
||
# 安全检查：防止路径遍历攻击
|
||
for name in zf.namelist():
|
||
if name.startswith("/") or name.startswith("\\"):
|
||
raise ValueError(f"ZIP 包含不安全路径: {name}")
|
||
if ".." in Path(name).parts:
|
||
raise ValueError(f"ZIP 路径包含上级引用: {name}")
|
||
|
||
# 查找markdown文件
|
||
md_files = [n for n in zf.namelist() if n.lower().endswith(".md")]
|
||
if not md_files:
|
||
raise ValueError("压缩包中未找到 .md 文件")
|
||
|
||
# 优先使用 full.md，否则使用第一个md文件
|
||
md_file = next((n for n in md_files if Path(n).name == "full.md"), md_files[0])
|
||
|
||
# 读取markdown内容
|
||
with zf.open(md_file) as f:
|
||
markdown_content = f.read().decode("utf-8")
|
||
|
||
# 3. 处理图片
|
||
images_info = []
|
||
images_dir = _find_images_directory(zf, md_file)
|
||
|
||
if images_dir:
|
||
images_info = await _process_images(zf, images_dir, db_id, md_file)
|
||
markdown_content = _replace_image_links(markdown_content, images_info)
|
||
|
||
# 4. 生成结果
|
||
content_hash = await calculate_content_hash(markdown_content.encode("utf-8"))
|
||
|
||
return {
|
||
"markdown_content": markdown_content,
|
||
"content_hash": content_hash,
|
||
"images_info": images_info,
|
||
}
|
||
|
||
|
||
def _find_images_directory(zip_file: zipfile.ZipFile, md_file_path: str) -> str | None:
|
||
"""查找images目录"""
|
||
md_parent = Path(md_file_path).parent
|
||
|
||
# 候选目录
|
||
candidates = []
|
||
if str(md_parent) != ".":
|
||
candidates.extend([str(md_parent / "images"), str(md_parent.parent / "images")])
|
||
candidates.append("images")
|
||
|
||
# 查找存在的目录
|
||
for cand in candidates:
|
||
cand_clean = cand.rstrip("/")
|
||
if any(n.startswith(cand_clean + "/") for n in zip_file.namelist()):
|
||
return cand_clean
|
||
|
||
return None
|
||
|
||
|
||
async def _process_images(zip_file: zipfile.ZipFile, images_dir: str, db_id: str, md_file_path: str) -> list[dict]:
|
||
"""处理图片：上传到MinIO并返回信息"""
|
||
# 支持的图片格式
|
||
SUPPORTED_EXTENSIONS = {".jpg", ".jpeg", ".png", ".gif", ".webp", ".bmp"}
|
||
CONTENT_TYPE_MAP = {
|
||
".jpg": "image/jpeg",
|
||
".jpeg": "image/jpeg",
|
||
".png": "image/png",
|
||
".gif": "image/gif",
|
||
".webp": "image/webp",
|
||
".bmp": "image/bmp",
|
||
}
|
||
|
||
images = []
|
||
image_names = [n for n in zip_file.namelist() if n.startswith(images_dir + "/")]
|
||
|
||
# 上传图片到MinIO
|
||
minio_client = get_minio_client()
|
||
bucket_name = "kb-images"
|
||
await asyncio.to_thread(minio_client.ensure_bucket_exists, bucket_name)
|
||
|
||
file_id = hashstr(Path(md_file_path).name, length=16)
|
||
|
||
for img_name in image_names:
|
||
suffix = Path(img_name).suffix.lower()
|
||
if suffix not in SUPPORTED_EXTENSIONS:
|
||
continue
|
||
|
||
try:
|
||
# 读取图片数据
|
||
with zip_file.open(img_name) as f:
|
||
data = f.read()
|
||
|
||
# 上传到MinIO
|
||
timestamp = int(time.time() * 1000000)
|
||
object_name = f"{db_id}/{file_id}/images/{timestamp}_{Path(img_name).name}"
|
||
content_type = CONTENT_TYPE_MAP.get(suffix, "image/jpeg")
|
||
|
||
result = await minio_client.aupload_file(
|
||
bucket_name=bucket_name,
|
||
object_name=object_name,
|
||
data=data,
|
||
content_type=content_type,
|
||
)
|
||
|
||
# 记录图片信息
|
||
img_info = {"name": Path(img_name).name, "url": result.url, "path": f"images/{Path(img_name).name}"}
|
||
images.append(img_info)
|
||
|
||
logger.debug(f"图片上传成功: {Path(img_name).name} -> {result.url}")
|
||
|
||
except Exception as e:
|
||
logger.error(f"上传图片失败 {Path(img_name).name}: {e}")
|
||
continue
|
||
|
||
return images
|
||
|
||
|
||
def _replace_image_links(markdown_content: str, images: list[dict]) -> str:
|
||
"""替换markdown中的图片链接为MinIO URL"""
|
||
if not images:
|
||
return markdown_content
|
||
|
||
# 构建路径映射
|
||
image_map = {}
|
||
for img in images:
|
||
path = img["path"]
|
||
url = img["url"]
|
||
image_map[path] = url
|
||
image_map[f"/{path}"] = url
|
||
image_map[img["name"]] = url
|
||
|
||
def replace_link(match):
|
||
alt_text = match.group(1) or ""
|
||
img_path = match.group(2)
|
||
|
||
# 尝试匹配各种路径格式
|
||
for pattern, url in image_map.items():
|
||
if img_path.endswith(pattern) or img_path == pattern:
|
||
return f""
|
||
|
||
# 尝试文件名匹配
|
||
filename = os.path.basename(img_path)
|
||
if filename in image_map:
|
||
return f""
|
||
|
||
return match.group(0)
|
||
|
||
# 使用正则表达式替换图片链接
|
||
pattern = r"!\[([^\]]*)\]\(([^)]+)\)"
|
||
return re.sub(pattern, replace_link, markdown_content)
|
||
|
||
|
||
async def process_url_to_markdown(url: str, params: dict | None = None) -> str:
|
||
raise NotImplementedError("URL 解析功能已禁用")
|